Evidence of recombination among early-vaccination era measles virus strains

Mikkel H Schierup email@example.com
Bioinformatics Research Center (BiRC)
University of Aarhus
Hoegh Guldbergs Gade 10, Building 090, DK-8000 Aarhus CDenmark

Carl H Mordhorst email@example.com
Department of Virology, Statens Serum Institut
CopenhagenDenmark

Claude P Muller
Institute of Immunology
Laboratoire National de Santé
PO Box 1102L-1011Luxembourg

Laurids S Christensen email@example.com
Department of Clinical Microbiology, Rigshospitalet
CopenhagenDenmark

BMC Evolutionary Biology

Evidence of recombination among early-vaccination era measles virus strains
10.1186/1471-2148-5-52BioMed Central Page 1 of 8 (page number not for citation purposes) Research article * Corresponding author
Background:The advent of live-attenuated vaccines against measles virus during the 1960'ies changed the circulation dynamics of the virus. Earlier the virus was indigenous to countries worldwide, but now it is mediated by a limited number of evolutionary lineages causing sporadic outbreaks/epidemics of measles or circulating in geographically restricted endemic areas of Africa, Asia and Europe. We expect that the evolutionary dynamics of measles virus has changed from a situation where a variety of genomic variants co-circulates in an epidemic with relatively high probabilities of co-infection of the individual to a situation where a co-infection with strains from evolutionary different lineages is unlikely.Results:We performed an analysis of the partial sequences of the hemagglutinin gene of 18 measles virus strains collected in Denmark between 1965 and 1983 where vaccination was first initiated in 1987. The results were compared with those obtained with strains collected from other parts of the world after the initiation of vaccination in the given place. Intergenomic recombination among pre-/early-vaccination strains is suggested by 1) estimations of linkage disequilibrium between informative sites, 2) the decay of linkage disequilibrium with distance between informative sites and 3) a comparison of the expected number of homoplasies to the number of apparent homoplasies in the most parsimonious tree. No significant evidence of recombination could be demonstrated among strains circulating at present.Conclusion:We provide evidence that recombination can occur in measles virus and that it has had a detectable impact on sequence evolution of pre-vaccination samples. We were not able to detect recombination from present-day sequence surveys. We believe that the decreased rate of visible recombination may be explained by changed dynamics, since divergent strains do not meet very often in current epidemics that are often spawned by a single sequence type. Signs of prevaccination recombination events in the present-day sequences are not strong enough to be detectable.

Background

Measles virus (MV) has a genome with negative polarity, consisting of non-segmented single-stranded RNA of approximately 15.9 kb. MV belongs to the Paramyxoviridae family in the order of Mononegavirales and only a single serotype is known. It is among the most infectious viruses known for humans, and no other host species has been identified. Only human populations of a considerable size are able to sustain circulation [1]. Global vaccination programs have resulted in a dramatic decline in measles cases and the documented discontinuation of indigenous circulation in a number of countries [2][3][4] has encouraged authorities to accomplish global control of measles. However, measles still cause a large number of deaths every year, mainly in developing countries, where endemic circulation of MV is still ongoing [5,6] as a result of poor vaccination coverage.

Intergenomic recombination has been documented in a vast number of virus species within most families of RNA and DNA viruses. Recombination can allow variants of a population to escape from the current fitness peak (escape from Muller's ratchet) and re-appear with a new phenotypic make-up and/or even re-establish in a new host-relationship [7,8]. However, recombination has never convincingly been documented in species of the Mononegavirales, and it is speculated whether this reflects an inability of these viruses to recombine. Yet, the order of Mononegavirales comprises groups of viruses with an apparently large evolutionary potential with frequent shifts of host species. Over the past decades, a considerable number of Mononegavirales members causing diseases in host species in which they had not previously been recognized have been identified. Examples are phocid distemper virus [9], Hendra virus [10], Menangele virus [11], Nipah virus [12] -not to forget the re-emerging divergent members of the Filoviridae (Marburg -and Ebola-like viruses) [see [13,14]]. The origins of these emerging viruses have not been identified and the mechanism(s) of their ability to explore new niches remains enigmatic.

Co-infection of host cells with phylogenetically distinct virus strains is required for recombination events to be detectable in sequence surveys. As a result of the global vaccination against measles a situation of endemic co-circulation of multiple strains [5,6,15] shifted to a situation with a limited number of strains being re-introduced to susceptible subpopulations in major geographical regions [2][3][4]16]. By sequencing part of the hemagglutinin gene [15], we recently characterized 18 MV strains collected during the pre-/early-vaccination era in Denmark. In the present study, the partial sequence of the hemagglutinin coding region of those older strains is subjected to further analysis and compared with strains sampled after vaccination (generally more recently identified) using various approaches to test for the presence of intergenomic recombination.

Results and discussion

The term pre-/early-vaccination era isolates used for isolates collected in Denmark during the period of 1965-83 is meant to reflect that these isolates are from a period when vaccination against measles was not practiced in Denmark but was gradually becoming common practice in many other countries in the World. Thus, it cannot be excluded that vaccination in other countries influenced measles virus strains circulating in Denmark at the time, but it is anticipated that these isolates still bear valuable information on the nature of strains circulating before an influence of vaccination was imposed. Table 1 compares differences in basic population genetics of the data sets of the pre-/early-and post-vaccination eras. As might be expected, the global post-vaccination data set shows more sequence variability than the Danish pre-/early-vaccination era data set, whereas the latter shows relatively more singletons, i.e. variants present only in a single sequence. Some of these singletons might be sequencing errors in the pre-/early-vaccination sequences caused by partial degradation as also observed in other ancient DNA studies [33]. Sequencing errors may also explain the higher proportions of transversion substitutions (changes between purine and pyremidine base pairs) and the higher dn/ds ratio in the pre-/early-vaccination data set. For these reasons, singletons are not considered in any of the recombination analyses. Informative sites (i.e. non-singletons) are considered unlikely to be artefacts of the sequencing procedure, since one would not expect the same error to occur more than once, and all informative sites in the pre-/early-vaccination sample are also informative sites in the post-vaccination sample. Codon usage bias measured as the effective number of codons (ENC) is low in both data sets. Figure 1a shows a minimum evolution tree of the 40 postvaccination era isolates. The tree is topologically almost identical to the one of Christensen et al. [15] and bootstrap values support many of the genome types identified at present even though the tree is based on only 800 base pairs of the hemagglutinin gene for direct comparison with the pre-/early-vaccination sample [17][18][19]. Figure 1b shows a tree with the same sequences as in Figure 1a, but adding the 18 pre-/early-vaccination era isolates from Denmark. As shown previously [15], the Danish isolates cluster with genome types A, C2 and E. It is also clear that the inclusion of pre-/early-vaccination era samples makes the distinction of these three genome types less obvious since the similarity distances between post-vaccination era representatives of the genome types A, C2 and E are broken down to the sum of minor differences between the pre-/early-vaccination era isolates. This might reflect that the pre-/early-vaccination era sequences are generally older than the rest and thus at the basis of division of genome types as are also the strains used for vaccine development (e.g. Edmonston). Alternatively, frequent recombination among pre-/early-vaccination era genome types at that time would lead to a poorly resolved tree. Less recombination among different surviving strains after vaccination would then lead to the more differentiated genome types seen today. The subsequent recombination analysis addresses this possibility. Figure 2 shows all the segregating sites for the pre-/earlyvaccination era sequences with positions marked by base pair, and whether a substitution is synonymous or nonsynonymous or both (for multiple hits and complex codons). It is immediately clear that many incompatibilities (by the four-gamete test [27]) exist, but also that apparent groups of physically close sites are incompatible with other such groups. We identified by eye five such groups of informative sites and marked them with different colours. Comparing these five groups by the four gamete test demonstrates that some pairs of blocks are incompatible with the same evolutionary tree (Fig. 2b). Even though the identification of these groups is subjective, the presence of such groups suggests either that they have evolved on different evolutionary trees (i.e. recombination) or, alternatively, each of the sites for a given group has mutated at least twice, i.e. by parallel mutations, at approximately the same time (since there is strong linkage disequilibrium within all groups except block 1). Note that the incompatibilities within e.g. block 1 do not weaken this conclusion. A similar table was prepared for the post-vaccination era data set. Here we were not able to visually identify groups of sites failing to satisfy the fourgamete test (results not shown), and the blocks identified in Figure 2 are not incompatible with one another in the post-vaccination data set even though it contains all of the segregating sites. Thus, if the patterns observed in Table 1 were due to convergent evolution of groups of functionally important sites in different lineages, such convergent evolution does not play an important a role in present-day evolution of measles virus. The proportion of pairs of informative sites which is incompatible by the four gamete test is 43% in the pre-/early-vaccination data set as compared to only 23% in the post-vaccination data set even though the variability in general is lower among the pre-/early-vaccination sequences.

Analysis of recombination

One way to distinguish whether incompatibilities are caused by parallel mutations (i.e. true homoplasies) or by recombination is to correlate linkage disequilibrium (LD) between sets of informative sites to distance. Recombination causes a decrease in linkage disequilibrium and more recombination is expected between sites that are further apart from each other. The block structure of close sites is visualised in a different way in Figure 3, which shows all the significant cases of linkage disequilibrium. Visual inspection of the Figure suggests that physically close sites appear to be more likely to be in strong LD than sites far apart. This observation is tested more formally by correlating LD with distance (Table 2 and Figure 4). Table 2 shows the correlation of two commonly used measures of LD for both data sets. A significant negative correlation is observed by both measures of linkage disequilibrium in the pre-/early-vaccination data set, whereas the post-vaccination data set does not show any significant decay of linkage disequilibrium with distance. The decrease in linkage disequilibrium with distance for the pre-/earlyvaccination sequences is shown graphically in Figure 4.

An analysis of expected number of homoplasies caused by parallel mutations and the number of apparent homoplasies in the most parsimonious tree was performed on the pre-/early-vaccination data set [see [32,35]]. The basic idea is to investigate whether the number of apparent homoplasies in the most parsimonious tree is likely to be due to parallel (recurrent) mutations. If this is not the case, it is a strong indication of recombination since recombination easily creates "incompatible pairs of sites", i.e. pairs of sites that do not fit into the same phylogenetic tree, and thus will appear as homoplasies if assuming no recombination and a single phylogenetic tree. There are 800 sites. Of these, 267 are third position sites, and among these we observe 20 transition mutations. If we assume equal mutation rates at all third position sites, then the number of transition mutations we expect have happened while observing 20 different ones is 20.8+/-0.9

(calculated as the sum of geometric distributions). In the most parsimonious tree found using PAUP*, there are 26 transition mutations. In other words, given the observed number of mutation events of the transition type, we expect 0.8+/-0.9 parallel mutations/homoplasies under the assumption of no recombination (and no codon usage bias). We observe, however, 6 parallel mutations in the tree. This suggests that some of the apparent homoplasies resulted from recombination rather than from recurrent mutations. We also calculated the effective sites number [35] from the observed codon usage bias. However, since the codon usage bias is low, the effect is minor, and only 1. homoplasies. A large amount of mutation rate heterogeneity at the synonymous sites offers an alternative explanation of the excess homoplasies. While this explanation cannot be ruled out, analysis of the post-vaccination data set does not support large rate heterogeneity at silent sites in the evolution of present-day measles virus, since the number of homoplasies in this data set can be explained by recurrent mutations (results not shown).

Given these different lines of evidence for recombination, it is of interest to try to estimate the rate of recombination needed to explain the data. The most appropriate method is the finite site, composite likelihood approach implemented in LDhat [31]. The result ( Table 2) is that a significantly positive recombination rate is found in the pre-/ early-vaccination sequences, much reflecting the results of the similar R 2 test. An estimated rate of ρ = 15.8 corresponds to that an expected 45 recombination events have happened in the ancestral history of the 18 pre-/early-vaccination sequences. The estimated recombination rate appears smaller than rates reported for HIV and other viruses [31]. The estimated recombination rate in the

.65 --------------------------T---T---CG------G--G----T--T--------DEN/7.67 --------T-----------------T------GN-A-----G--G----T--T--------DEN/9.73 A----------------------AC-T------G--A-----G--G--G-T--T--------DEN/8.77 --------------A-----------T------G--A-------------T-----G-----

Linkage disequilibrium triangle plot for informative sites in the pre/early-vaccination sample Figure 3 Linkage disequilibrium triangle plot for informative sites in the pre/early-vaccination sample. Significant linkage disequilibrium is indicated my shading, grey shading is P < 0.05, black shading, P < 0.001 by Fisher's exact test .   24 42 67 248 357 369 439 489 498 553 564 573 574 597 692 730 779 780 781 791  24  42  67  248  357  369  439  489  498  553  564  573  574  597  692  730  779  780  781  791 post-vaccination era data sets also shows a positive rate of ρ, but it not significantly different from zero by the permutation test ( Table 2). The lack of evidence of recombi-nation in present day sequences of MV strains is consistent with what was also observed by [31].

In conclusion, the analysis of pre-/early-vaccination era MV sequences shows evidence of recombination at rates important to the evolution of MV. The five different tests of recombination should not be considered independent tests and some of the results might be explained by alternative mechanisms such as convergent evolution of functionally important sites and rate heterogeneity of synonymous variation. However, all tests agree and provide evidence of recombination both through an excess of apparent homoplasies compared to the expected frequency of parallel mutations, and through a decrease in LD with distance, which is difficult to explain by any other hypothesis than recombination. Furthermore, it is difficult to imagine a mechanism other than recombination by which apparent homoplasies could occur pairwise or in triplets in distant parts of the sequence considering also that such patterns are not seen in the post-vaccination data set.

The evidence of recombination among pre-/early-vaccination era MV strains and the lack of detection of recombination among post-vaccination era MV strains are consistent with the shift in epidemiology from a situation of co-circulation of strains in populations to a bottleneck situation with incidental introduction of a single strain to a susceptible sub-population of a geographical region. Given that intergenomic recombination and co-infection of individuals are common phenomena of MV it might be assumed that the lineages surviving till today did emerge from a pool of recombining pre-vaccination era strains. A lower level or absence of recombination due to changed epidemiology since then has erased our ability to detect recombination in a global sample of present day MV despite its high level of variability. It is possible that the Danish pre-/early-vaccination era strains are representatives of the very pool in which recombination took place while present-day MV strains are representatives of temporally and/or geographically separated lineages. Analysing informative sites in other parts of the MV genome of present-day lineages render it unlikely that these lineages

numerical analysis of recombination in the samples of the pre-vaccination and post-vaccination eras A negative correlation implies that LD decays with distance, the P-values are obtained by a permutation test (see Methods).

Early-vaccination sample

Post-vaccination sample Correlation between linkage disequilibrium and distance for the early-vaccination era data set using the R 2 and D' meas-ures of LD, respectively Figure 4 Correlation between linkage disequilibrium and distance for the early-vaccination era data set using the R 2 and D' measures of LD, respectively. The template for replication in members of the Paramyxovirinae is a nucleocapsid complex in which each nucleocapsid monomer (N) is predicted to be associated by hydrophobic bonds with 6 nucleotides in such a way as to resist non-ionic detergent and high salt and to protect the RNA from RNase digestion [36]. This tight association of RNA and protein has been dubbed "the rule of six" and excludes the intracellular presence of naked viral RNA molecules. It raises the question of the mechanism of RNA polymerase template recognition and has provided an explanation of the hypothesis that recombination possibly cannot occur in this group of viruses. Our data suggests that a mechanism of partial unwinding of the nucleocapsid structure exists to allow homologous intergenomic recombination or RNA polymerase template shift during replication.

Conclusion

Measles virus appear to possess the ability to recombine but the present-day epidemiology of the virus where different sequence types rarely or never meet make the impact of recombination on the distribution of sequence diversity negligible. However, in the prevaccination area, endemic MV allowed more divergent strains to meet and recombine. The present-day strains are thus descendants of recombined sequences but the signal of the early recombination is lost in present-day sequences.

Methods

Sequence data

The Danish pre-/early-vaccination sequences consist of an 800 base-pair region (nt. 659 to 1458) of the hemagglutinin coding region of 18 strains collected in Denmark, Greenland and the Faeroe Islands between 1965 and 1983 (GenBank accession numbers AJ417850-AJ417867) [15]. Post-vaccination sequences of 40 strains, representative of the 22 phylogenetic clusters identified [17][18][19], were trimmed to match the region of the pre-/early-vaccination era sequences. GenBank accession numbers of 33 of these sequences can be found in [15].  [23].

Sequence analysis

The alignment of the 18 pre-/early-vaccination and 40 post-vaccination sequences does not contain any gaps. The computer program DnaSP 3.99 [24] was used for estimation of standard parameters of population genetics. Segregating sites were classified as synonymous or nonsynonymous, except for complex codons where a site may be classified as both. Phylogenetic trees from the post-vaccination data set and the two data sets combined were built using MEGA 2.1 [25] and the minimum evolution criterion. The HKY substitution model [26] was assumed. Bootstrap values were estimated from 2000 re-samples.

Recombination was examined using five different, but complementary, approaches. (i) A graphical method by which sets of sites in strong linkage disequilibrium (LD) were visually identified and marked with different colourings (Fig. 2). It was then investigated whether different sets of blocks were incompatible by the four-gamete test [27]. If blocks are incompatible, one will infer either recombination or recurrent mutation of the sites in a block at about the same time. (ii) A plot of significant pairwise linkage disequilibria was constructed using DnaSP. (iii) Decay of linkage disequilibrium with distance (measured as either the squared correlation coefficient R 2 or the standardized measure D') was investigated following [28] and estimated using the R2-program [29]. Analysis was restricted to informative sites. (iv) Estimation of the scaled population recombination rate ρ by a composite maximum likelihood approach [30], using the LDhat program of [31]. This program also allows a test of the null hypothesis of no recombination (ρ = 0) by a permutation test. This analysis was also restricted to informative sites. (v) Comparison of the expected number of homoplasies to the number of apparent homoplasies in the most parsimonious phylogenetic tree, closely following the approach of [32], using PAUP* [32].

0+/-1.0 homoplasies are expected under this model, again significantly smaller than the six apparent Phylogenetic trees reconstructed using the HKY substitution model and the minimum evolution criterion as implemented in MEGA 2.1 [25] Figure 1Phylogenetic trees reconstructed using the HKY substitution model and the minimum evolution criterion as implemented in MEGA 2.1[25]. Bootstrap values >80% are shown. a) Tree based on the global post-vaccination era sample with classification of types marked, b) the same sequences as in a) plus the 18 early-vaccination era sequences, marked with a red circle.

Segregating sites in the pre/early-vaccination era sequences with indication of position and state (synonymous or nonsynonymous)Figure 2Segregating sites in the pre/early-vaccination era sequences with indication of position and state (synonymous or nonsynonymous). Different colours identify five different blocks of sites in strong LD.Table insertedindicates compatibility (+) or incompatibility (-) among blocks. Blocks 2 and 4 are only partly incompatible. Synonymous ---S-SS-S-S----SSSS---S--SS-S-SS--S-S-SS-SS--S--S--SSSS---S---informative sites Non-synon. NNNNN--NNN-NNNN----NNN-NN--N-N--NN-N-N--N--NNNNN-NN--16.83 ---C----T------A----------TA-----------------G------------T---DEN/34.80 ---C----T----------------------------------------------G-G-TGT DEN/4.70 ---C----T-----------------T------G-------AGT-G----T-TT--------DEN/40.81 -----A-C--T----------G--C---------C---CTC---A-CA-C-T----------DEN/12.82 -A-C--------------TA-G--CA------------------------------------DEN/16.72 ---------------------G--C-------------------------------------DEN/31.74 ---------------------G--C-------------------------------------DEN/8.75 --GA-----------------G--C-----------------G-----------C-------DEN/26.81 ---------------------G--C--------------------------------GTT--DEN/10.80 ---C--G--------------G----------------------------------------DEN/17.77 ---------C----A------G--------------AA------------T-----------DEN/13.83 -----------CCC--CG---GT---------------C-C-------------------G-DEN/20.67 --------T-----A-----------T------G--------GT-G----T--T--------DEN/30.69 ----C---------------C-----T-TT-CTGC------AGT-G----T--T--------DEN/5

Table 1 :
1Basic population genetics summary of the data sets of the pre-vaccination and post-vaccination eras.Danish early-vaccination era data set Global post-vaccination era data setLength of sequences
800
800
Number of sequences
18
40
Average number of differences
12.2
31.0
# synonymous substitutions
26
131
# non-synonymous substitutions
32
71
Average Pi (nucleotide diversity)
1. all sites
0.014
0.040
2. synonymous sites
0.030
0.109
3. non-synonymous sites
0.009
0.019
Transition/transversion bias
2.2
9.4
Codon usage bias (ENC)
53
55

Table 2 :
2Summary of

The remaining 7 HA sequences that complete the list of globally circulating genome types, identified at present, are those of strains Mvi/Gambia/93 (Type B3, Acc. No AF484955) [20], MVi/ Alberta.CAN/20.00/1 (Type D7, Acc. No. AF410986) (G.A. Tipples et al., submitted 14-Aug-2001), MVi/Montreal.CAN/19.98 (Type D8, Acc. No. AF410985) (G.A. Tipples et al., sumitted 14-Aug-2001), MVi/Vic.AU/12.99 (Type D9, Acc No. AY127853) [21], (Type G2, Acc. No. AF243851) [22], MVi/Gresik.INO/18.02 (Type G3, Acc. No. AY184218) (P.A. Rota and S.L. Liffick, submitted 19-Nov-2002), and China94-1 (Type H2, Acc. No. AF045203)
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Computer-aided detection (CAD) system for breast MRI in assessment of local tumor extent, nodal status, and multifocality of invasive breast cancers: preliminary study

Sung Eun Song
Bo Kyoung Seo
Kyu Ran Cho
Ok Hee Woo
Gil Soo Son
Chulhan Kim
Sung Bum Cho
Soon-Sun Kwon
Computer-aided detection (CAD) system for breast MRI in assessment of local tumor extent, nodal status, and multifocality of invasive breast cancers: preliminary study
10.1186/s40644-015-0036-2R E S E A R C H A R T I C L E Open Access
Background: We aimed to investigate the efficacy of computer-aided detection (CAD) for MRI in the assessment of tumor extent, lymph node status, and multifocality in invasive breast cancers in comparison with other breast imaging modalities.Methods: Two radiologists measured the maximum tumor size, as well as, analyzed lymph node status and multifocality in 86 patients with invasive breast cancers using mammography, ultrasound, CT, MRI with and without CAD, and 18-fludeoxyglucose positron emission tomography (FDG-PET). The assessed data were compared with pathology. Results: For tumor extent, there were no significant differences between pathological size and measured size using mammography, ultrasound, CT, or MRI with and without CAD (P > 0.05). For evaluation of lymph node status, ultrasound had the best kappa coefficients (0.522) for agreement between imaging and pathology, and diagnostic performance with 92.1% specificity and 90.0% positive predictive value. For multifocality, MRI with CAD had the highest area under the receiver operating characteristic curve (AUC = 0.888).Conclusions: CAD for MRI is feasible to assess tumor extent and multifocality in invasive breast cancer patients. However, CAD is not effective in evaluation of nodal status.

Background

Precise information regarding tumor extent, lymph node (LN) status, and multifocality is of great importance in breast cancer treatment and prognosis, as well as, recurrence prediction. In particular, negative margins in breast-conserving therapy significantly affect local tumor recurrence [1,2]. Axillary LN status is also a major prognostic indicator and acts as a guide, in terms of the need for adjuvant chemotherapy, in breast-cancer patients [3]. Multimodal breast imaging techniques for the preoperative assessment of breast cancer staging are available, especially mammography (MMG), ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI), and 18-fludeoxyglucose positron emission tomography (FDG-PET). There are several studies comparing the diagnostic performance of these modalities, with breast MRI reported to be the most accurate imaging modality for assessment of tumor extent and LN status [4][5][6][7].

Breast MRI, however, has various specificity, and high false positive rates, when detecting breast cancers and also tends to overestimate tumor extent [8][9][10][11]. Thus, physicians have not achieved a consensus on the utilization of MRI staging prior to surgery. Evaluation of breast cancer using MRI takes significant time for image processing and interpretation [12,13]. In addition, interand intra-observer variations are an additional drawback of breast MRI [14].

Various commercially available computer-aided detection (CAD) systems for breast MRI have been introduced to address these limitations. CAD automates and speeds up image processing and analysis functions [10], and detects breast lesions by using an enhancement threshold. The system provides both morphologic and kinetic features of the breast lesion, and also provides quantitative information, such as, lesion dimensions and maximum tumor volumes. Levrini et al. [15] reported that CAD is just as accurate as breast MRI for size assessment of breast lesions. In addition, Bhooshan et al. [6] demonstrated that CAD for MRI could differentiate between invasive and noninvasive breast lesions, and also, between invasive breast cancers with LN metastasis and invasive cancers without LN metastasis. There are few clinical reports, however, showing the utility of CAD for MRI in tumor staging [6,15].

The purpose of this preliminary study was to compare diagnostic accuracy for preoperative assessment of tumor extent, LN status, and multifocality in invasive breast cancers among multimodal breast imaging options, such as, MMG, US, CT, MRI, and FDG-PET. In addition, our specific goal was to investigate if CAD for MRI can be extended from cancer detection to cancer staging for the determination of treatment and prognosis in clinical settings.

Methods

Patient population

Institutional review board approval was obtained from the Korea University Ansan Hospital, and the requirement for informed consent was waived for this retrospective review. We searched our institution's database for invasive breast cancer patients who underwent all imaging modalities from August 2008 to February 2012, including MMG, US, CT, MRI with CAD, and FDG-PET before breast cancer surgery. Of 211 invasive breast cancer patients, 147 patients, who underwent all imaging modalities, were initially included. Among these 147 patients, we excluded 34 patients who underwent neoadjuvant chemotherapy, or excisional biopsy, within 6 months because recent neoadjuvant chemotherapy, or excisional biopsy, may produce complications or changes in surrounding breast tissues, which could interfere with the pathologic-radiologic correlation. Further, of the 34 exclusions, twenty patients of the patients had no surgical confirmation, and seven patients did not receive sentinel LN biopsy or axillary LN dissection. Four patients who had no detectable lesions on CAD were included in our study to reflect possible false negative results of CAD. Therefore, a total of 86 patients (mean ages: 49.18, range: 28-82 years old) were included in this study. Pathological diagnoses of these patients included 77 invasive ductal carcinomas, four invasive micropapillary carcinomas, one invasive lobular carcinoma, one invasive apocrine carcinoma, one invasive mucinous carcinoma, one invasive cribriform carcinoma, and one adenoid cystic carcinoma. Forty-five patients underwent modified radical mastectomy, and the remaining 41 patients underwent breast conserving surgery. All patients underwent sentinel LN biopsy, and 56 patients underwent axillary LN dissection.

Imaging techniques

All 86 patients underwent multimodal breast imaging using a digital MMG unit (Selenia FFDM system, Hologic Cooperation, Denver, CO, USA), US units (LOGIQ 9 unit, GE Medical Systems, Milwaukee, Wis, USA and iU22, Philips Medical Systems, Bothell, WA, USA), a 64-channel multidetector CT scanner (Brilliance 64channel, Philips Medical Systems, Cleveland, OH, USA), a 3 T MRI system (Achieva 3.0 T TX, Philips Medical Systems, Best, the Netherlands), and a PET/CT scanner (Gemini TF/Brilliance 16, Philips Medical Systems, Best, the Netherlands). MMG captured both craniocaudal and mediolateral oblique views. US examination was performed in bilateral whole breasts, and axillae, with high-frequency linear transducers, also, survey scanning was performed in transverse and sagittal planes. CT examinations were performed by dynamic study after intravenous infusion of 130 ml of nonionic contrast media (Iopamiro 300, Bracco, Italy or Ultravist, Schering, Germany) at a rate of 2.0 ml/sec. Early phase post-contrast images were obtained 90 seconds after contrast media injection, and delayed phase post-contrast images at 300 seconds after injection. The CT scan was performed at a low-dose setting (120 kVp and 30 or 50 mAs).

For MRI, we obtained axial T2-weighted turbo spin echo images, dynamic sagittal T1-weighted gradient echo images with fat saturation, and axial and coronal 3D reconstruction images with maximum intensity projection. T2-weighted scan was performed; TE/TR 120/ 9022 ms, inversion delay spectral attenuated inversion recovery (SPAIR), 125 ms, flip angle 90°, field of view (FOV) 340 × 340 mm 2 , acquired voxel size 1.01 × 1.31 × 2.0 mm 3 , reconstructed voxel size 0.66 × 0.66 × 2.00 mm 3 . Fatsaturated T1-weighted images were acquired before and after contrast media injection with a total of six dynamic acquisitions; TE/TR 1.3/3.4 ms, flip angle 10°, FOV 320 × 320 mm 2 , acquired voxel size 0.91 × 0.91 × 2.00 mm 3 , reconstructed voxel size 0.83 × 0.83 × 1.00 mm 3 . Twenty ml of 0.5 mmol/ml Gadodiamide (Omniscan, Nycomed-Amersham, Princeton, NJ, USA) was injected intravenously followed by a 20-ml saline flush at the rate of 2.0 ml/sec. All collected MRI data were processed using a commercially available CAD system (CADstream software, version 18.104.22.1681, Merge Healthcare, Chicago, IL, USA).

For FDG-PET scan, CT images were obtained after FDG injection and then PET scans were performed. PET acquisition data were reconstructed using the 3D row action maximum likelihood algorithm. Reconstruction was performed to yield slices containing 144 × 144 pixels. We obtained 3D maximum intensity projection images, axial images with a slice thickness of 4 mm, and coronal and sagittal images with a slice thickness of 4.6 mm.

Image evaluation

Two radiologists with 3 or 13 years of experience in breast imaging interpreted all images using a picture archiving and communication system (PACS). One radiologist had 3 years of experience in CAD reading and the other had 5 years of experience. The radiologists used consensus double reading. Each reader independently evaluated images and the final decision was reached by discussion between the two readers. US images and mammograms were reviewed at the time of CT, MRI, or FDG-PET interpretation. The radiologists were blinded to the final pathological results. The Breast Imaging Reporting and Data System (BI-RADS) lexicon was used for diagnosis of malignant tumor on MMG, US, and MRI [16]. Morphology on MMG, US, and MRI, and kinetic features on MRI were evaluated. On breast CT, focally-enhancing lesions, after contrast injection, were evaluated [17]. On PET/CT, breast lesions were suspected to be malignant if they showed elevated tracer uptake when compared with the adjacent breast tissue.

For tumor extent, the longest diameter of a breast tumor was recorded using each modality ( Figure 1). On MMG, tumor sizes were measured with both craniocaudal and mediolateral oblique views, and then the maximum length was chosen. Tumor sizes were measured on multi-sections using US, CT, FDG-PET, and MRI, and then the longest length was selected. CAD system automatically provided maximum tumor extent if we clicked the enhancing tumor.

A lymph node was considered as metastasis if it had more than one of these characteristics: 1) the longitudinaltransverse axis ratio of the LN was less than two; 2) the cortex of the LN was concentrically or eccentrically thickened more than 3 mm; 3) fatty hilum was absent or displaced; 4) there was extranodal fat infiltration; 5) the LN showed early enhancement with a delayed washout pattern on dynamic CT and MRI; or 6) the LN showed increased standardized uptake value (SUV) on FDG-PET. If the axillary LN exhibited any of the findings mentioned above, it was defined as metastatic LN. LNs that exhibited none of the above mentioned criterion were defined as negative for axillary LN metastasis [18,19]. On MRI, we used fat-saturated T1-weighted imaging for assessment of LN status, and we often used precontrastT2-weighted images for evaluation of extranodal fat infiltration. We also evaluated presence of multifocality of breast carcinomas on all imaging modalities.

For CAD analysis, a color overlay map was placed on all enhancing lesions at a 50% enhancement threshold level in a pixel by pixel comparison across a precontrast, early post-contrast, and delayed post-contrast series, and set threshold of 50% could not be altered during the analysis of individual lesions. Numerous prior studies have used higher enhancement thresholds, more than 50%, to improve specificity [13,20,21]. Levman et al. [22]. insisted that enhancement thresholds can limit a CAD test's ability to diagnose a lesion's full size and, as such, should not be raised above 60%. However, the signal enhancement ratio (SER) method exhibits a high rate of false positives at low enhancement thresholds. Therefore, they demonstrated that the most appropriate threshold would be 50%-60%. Based on these previous studies, we selected a 50% threshold in this study.

The initial phase determined by the signal change between the pre-contrast and early, post-contrast, series was classified as medium (50-100%), or rapid (>100%) enhancement. Delayed-phase enhancement type, after early post-contrast series, was classified as persistent, plateau, or washout. The washout type, displayed in red, represented pixel signal intensity with at least a 10% decrease in the delayed post-contrast series compared to the early post-contrast series. The persistent type, displayed in blue, represented pixel signal intensity with at least a 10% increase in the delayed post-contrast series compared to the early post-contrast series. The plateau type, displayed in green, represented pixel signal intensity with a less than 10% increase, and a less than 10% decrease, compared to the early post-contrast series. Morphologic evaluation was also performed based on the BI-RADS lexicon [16]. Although a lesion had morphologically non-malignant features, such as, a circumscribed margin and oval shape, we considered it as malignant if the lesion had malignant kinetic features or strong enhancement about the threshold on CAD. For measurement of tumor extent with CAD, an enhancing tumor above the threshold was selected with the color overlay map, and then the, CAD-generated, maximum tumor size was recorded. For evaluation of nodal status on CAD, LNs above threshold enhancement were individually selected by radiologists and the presence of significant enhancement, as indicated by the presence of a color overlay map on the LN, was recorded as positive LN. These findings were confirmed using same-level breast MRI images because CAD sometimes detected engorged axilla vessels as positive LN. We determined positive or negative LN based on morphologic and kinetic features on CAD.

Pathological examination

Pathologic specimens were obtained within 2 weeks after the acquisition of preoperative images. Specimens were prepared by making serial 5-mm slices of breastconserving surgical specimens, and 5-10-mm slices of mastectomy specimens, according to the technique reported by Egan [23]. For the correlation of pathological findings and breast imaging features, pathologists and radiologists reviewed all specimens together. For assessment of LN status, sentinel lymph node biopsy was performed and then evaluated with frozen microscopic examinations. If there was a positive sentinel lymph node, axillary node dissection was performed. For diagnosis of multifocality, an imaging-guided core biopsy, prior to surgery, or a needle-localization and excisional biopsy, during surgery, was performed on suspicious additional breast lesions. Pathologists reviewed all pathologic findings and reported tumor extent, nodal status, and multifocality of invasive breast carcinomas.

Analysis

Evaluated tumor extent, LN status, and multifocality of invasive breast carcinomas on breast-imaging modalities were compared with actual pathological results. For assessment of the accuracy of tumor extent on each modality, correlation coefficients were calculated using the Spearman rank correlation test in order to assess the strength of the linear association between the pathologic tumor size, and the recorded size, from each modality. The pathologic tumor size was considered to be the tumor extent on pathological examination. The Mann-Whitney test was used to investigate whether there were size differences between the pathologic tumor size and the recorded size on each imaging modality. Further, the Kruskal-Wallis test was used to study whether there were significant differences between various imaging modalities for the assessment of tumor extent.

Weighted kappa coefficients were used to find the agreement of LN status between multimodal breast imaging and actual pathological results. A kappa coefficient greater than 0.80 indicated excellent agreement, 0.61-0.80 good agreement, 0.41-0.60 moderate agreement, 0.21-0.40 fair agreement, and less than 0.20 poor agreement [24]. The chi-square test was used to compare the LN status on multimodality breast imaging and final pathological results. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated.

For comparison of the accuracies of diagnosis of multifocality on the various imaging modalities, standard receiver operating characteristic analysis was used to generate sensitivity, specificity, and area, under the ROC curve (AUC) of imaging modalities used for detecting multifocality in a breast cancer patients. AUC equal to 1 indicated a perfect test, 0.7-0.9 moderate accuracy, 0.5-0.7 less accuracy, and equal to 0.5 was non-informative. Statistical analyses were performed using SAS version 9.1 (SAS Institute, Cary, NC, USA). P values equal to or less than 0.05 were accepted as statistically significant. Table 1 demonstrates lesion characteristics of all 82 invasive carcinomas with measured tumor extent on multimodal breast imaging including CAD. Mean and median values of tumor extent, and correlation coefficients between pathologic tumor size and recorded size on multimodal breast imaging, are shown on Table 2. The median pathological tumor extent was 22 mm. All imaging modalities yielded statistically significant measurements with a range of correlation coefficients, 0.513-0.766 (P < 0.05). The correlation coefficient was highest in MRI without CAD, followed by CT, US, MMG, FDG-PET, and MRI with CAD. The Kruskal-Wallis test was used to investigate whether there were statistically significant size differences between all imaging modalities, and the result indicated there were no significant size differences between the six imaging types (P = 0.165). In addition, the Mann-Whitney test was used to investigate if there were differences between pathologic tumor size and recorded tumor size. There were no significant differences between pathologic tumor size and recorded size on US, CT, or MRI with or without CAD (P > 0.05), but there was a significant size difference between pathologic tumor size and recorded size on FDG-PET (P = 0.007). Table 3 demonstrates weighted kappa coefficients for agreement of LN status between multimodality breast imaging and actual pathological results. The coefficients ranged from 0.158 to 0.420. US and CT had moderate agreement. MMG, MRI without CAD, and FDG-PET had fair agreement. CAD for MRI showed the worst result; poor agreement. Table 4 shows diagnostic performance of LN status with multimodal breast imaging assessed using the Chisquare test. All imaging modalities had higher values of specificity and PPV than those of sensitivity and NPV. US was the best imaging modality, with higher values of sensitivity (61.4%), specificity (92.1%), PPV (90.0%), and NPV (67.3%). MMG and FDG-PET had high values of specificity (86.8%, 81.6% respectively) and PPV (76.2%, 75.0%), however, sensitivity (36.4%, 47.7%) and NPV (54.1%, 57.4%) were very low. CT and MRI without CAD had a range of 61.4%-78.9% in all diagnostic performance parameters. CAD for MRI did not improve diagnostic performance when compared with MRI without CAD.

Results

Tumor extent

LN status

Multifocality

Six of the total 86 patients had multiple carcinomas on pathologic examination. The number of multifocal lesions for each patient ranged from two to four. Five of these six (83.3%) patients underwent mastectomy and only one patient underwent breast conserving surgery. The surgical plan was changed in three (50%) patients due to multifocality. Table 5 and Figure 2 present the sensitivity, specificity and AUC of each imaging modality for detecting multifocality in breast cancer patients. Sensitivity was the best in MRI with or without CAD (100%, 6/6) compared with other imaging modalities. FDG-PET had the highest value of specificity (93.4%, 71/76), however, it had the lowest value of sensitivity (33.3%, 2/6). AUC ranged from 0.634 to 0.888. MRI with CAD had the highest AUC value. All imaging modalities except FDG-PET yielded statistically significant AUC and were moderately accurate (0.7 < AUC < 0.9) at detecting multifocality. Sensitivities of MRI with or without CAD for detecting multifocality of breast cancer were 100%. MRI with CAD had superior specificity (77.6%) when compared with that of MRI without CAD (61.8%).

Discussion

Breast cancers are dependent on angiogenesis for growth and development, because they need blood vessels to obtain nutrients [25]. Dynamic breast MRI is sensitive to variations in vascular permeability and blood volume, which can be associated with tumor angiogenesis [26]. Signal enhancement ratio, a quantitative method for characterizing angiogenesis in breast cancer, measures changes in contrast signal intensity over three time points and acts as a surrogate marker for contrast kinetics [27]. CAD uses the SER to compute the angiomap which refers to an image with color-coded tumors representing kinetic information and provide volume, maximal intensity projection, multi-planar reform, and the curve classification of enhancing lesions. As suggested by Hylton [25], SER can provide signs that correlate with angiogenesis. In detail, CAD incorporates unenhanced images and contrast-enhanced images and then compares pixel signal intensity values on the immediate-and delayed-contrast enhanced images to indicate washout, plateau, or persistent enhancement pattern based on pixel value increases above a user-specified minimum enhancement threshold [28]. Specific colors such as blue, green and red are assigned to each pixel for different types of tissue enhancement, such as persistent, plateau and washout, respectively [28]. CAD is a postprocessing software program that performs image analysis and integrates with MRI automatically. Its abilities to promptly analyze considerable numbers of images, facilitate reconstructions, aid in visual subtraction, and increase tumor detection rates are considerable advantages. CAD also improves the interpretation efficiency and diagnostic performance of radiologists by reducing reading time and maintaining the consistency of lesion detection [12]. CAD for MRI has been developed for breast cancer detection. Although the system provides automatic size and volume data and regional LN information, up to now there have been few reports that have demonstrated the utility of CAD for preoperative tumor staging [6,15]. In this study, we investigated the feasibility of CAD for assessment of tumor extent, LN status, and multifocality in invasive breast cancers by comparing CAD for MRI with other breast imaging modalities, including MMG, US, CT, MRI without CAD, and FDG-PET.

In terms of tumor extent, all imaging modalities tended to overestimate tumor size compared with actual pathological size, but there was no significant difference between recorded size and pathological size, except with FDG-PET. Small underestimation of actual pathological size can be explained by shrinkage of tissues during fixation and processing [29]. On FDG-PET, breast cancers with higher SUV can be overestimated due to the overflow effect and small cancers with lower SUV can be underestimated due to the partial volume effect. In addition, size measurement is affected by manipulation of scale bar. Uematsu et al. [7] and Heusner et al. [30] also reported that FDG-PET has significantly lower accuracy than MRI for measurement of tumor extent and, thus, is not to be recommended for this purpose. In our study, manual measurement on MRI was the best modality to assess tumor extent. US, CT, and MRI were more accurate than mammography, and thus, good imaging types to assess breast cancer staging. Although the correlation coefficient of MRI without CAD was higher than that of MRI with CAD, there was no statistically significant difference between automatically measured tumor size on CAD and manually measured tumor size on MRI in this study. However, a preliminary study by  Demartini et al. [31] with 15 patients demonstrated CAD tumor sizes are less accurate in predicting the size of residual malignancy than those measured by the radiologist. Therefore, further study in a large population would be necessary to evaluate utility of automatic tumor measurement of CAD.

In terms of LN status, MMG, US, CT, MRI, and FDG-PET all had fair-to-moderate agreement between breast imaging and actual pathological results, and all imaging modalities showed higher specificity values than sensitivity values in detecting metastatic LN. US demonstrated the best diagnostic performance in evaluating LN status, a finding that has been reported in previous studies [32][33][34]. Sensitivity and NPV were not satisfactory, however, despite US having the best results. Alvarez et al. [34] reported axillary US was moderately sensitive and quite specific in the diagnosis of axillary LN metastasis, and thus negative sonographic results do not exclude the presence of metastasis. US-guided biopsy, or aspiration of sonographically suspicious nodes, would be recommended to increase the specificity.

To our knowledge, the use of MRI with CAD for LN evaluation has not been reported. In this study, CAD showed limitations in identifying LN metastasis in invasive breast cancers. CAD had the worst correlation coefficient for agreement of determined LN status with pathological results among the breast imaging modalities studied. CAD also did not improve diagnostic performance values of breast MRI when compared with MRI alone in the evaluation of LN status. Therefore, CAD is useful in evaluating the breast itself, but may be limited in assessing LN status in breast cancer patients.

In terms of the detection of multifocality in invasive breast cancers, MMG, US, CT, and MRI had satisfactory results. FDG-PET had very low sensitivity (33.33%) even though it had high specificity (93.42%), and thus had an AUC value that was significantly lower than the other imaging modalities. Breast MRI had higher sensitivity (100%) and higher AUC value (0.809) than MMG, US, CT, or FDG-PET. CAD improved the specificity (77.63%) of breast MRI for detection of multiple breast cancers with preservation of the sensitivity of MRI, and thus had the best AUC value (0.888).

CAD for MRI has a number of advantages in detecting breast cancers. The system handles large images with speed and provides an angiomap and colored imaging that can correlate with kinetic features of a breast lesion. This enables easy detection of abnormal breast lesions. CAD also can improve lesion contrast and diagnostic performance, especially specificity [12]. These advantages may improve the ability to detect multifocality on MRI. Detection of mulfocality is essential to plan surgical methods and to reduce tumor recurrence. In this study, three patients who had multifocal cancers changed their surgical planning form conservation to mastectomy.

Although our preliminary results are promising, there were several limitations to the study. First, it is a retrospective review of nonconsecutive patients with invasive breast cancers. Second, the number of patients was small. Furthermore, we did not evaluate differences in the measurement of tumor extent or multifocality according to the tumor morphology on breast imaging. For example, mass or non-mass like enhancement on MRI may produce different results. Further validation using a larger study population is warranted. Third, tumor extents in each modality were not measured in the same orientation-e.g. we recorded tumor extent on CT computed tomography, MRI magnetic resonance imaging, CAD computer-aided detection system, FDG-PET 18-fludeoxyglucose positron emission tomography, PPV positive predictive value, NPV negative predictive value. CT computed tomography, MRI magnetic resonance imaging, CAD computer-aided detection system, FDG-PET 18-fludeoxyglucose positron emission tomography, PPV positive predictive value, NPV negative predictive value, AUC area under the ROC curve. mammography in either cranial or caudal orientation, on MRI in axial or sagittal orientation, on US in any orientation. Size measurement in these various orientations may affect the results, although we measured tumor extent in multiple planes and then recorded the longest diameter of a breast tumor in each modality. Fourth, the patients' order was randomized. but the images were not presented to the radiologists in a randomized order. The radiologists independently evaluated a series of images from each patient, similar to the real clinical practice. However, this may produce a bias of image interpretation. It is recommended that a clinical study in a large population, utilize completely randomized image order to assess CAD utility. Fifth, we did not perform node-to-node correlation, but rather, case-tocase correlation with imaging and pathological results. Sixth, various CAD systems are customized for various kinds of MRI. Therefore, it is not certain that our results will be precisely reproducible if we use another kind of CAD or MRI. Finally, MR images were assessed in the light of mammographic or US findings.

Conclusions

In conclusion, CAD for breast MRI can be a feasible method of evaluating tumor extent and multifocality in invasive breast cancer patients. Although manual measurement of tumor extent on MRI had a higher correlation coefficient with pathologic size than automatized measurement of CAD, there was no statistically significant difference. Detection of multifocality is important for surgical planning and prevention of residual tumors or recurrence. However, CAD is limited in evaluating LN status. US is the most accurate diagnostic tool for axillary LN metastasis and can help to overcome the limitations of CAD for MRI. Based on our preliminary results, utility of CAD may be extended from cancer detection to cancer staging, with particular regard to tumor extent and multifocality, for determination of treatment and prognosis in clinical settings.

Figure 1
1How to measure the tumor extent. The longest diameter of a breast tumor was recorded using each modality; MMG (A), US (B), CT (C), MRI without CAD (D), MRI with CAD (E), and FDG-PET (F). For measurement of tumor extent with CAD, an enhancing tumor above the threshold was selected with the color overlay map, and then CAD-generated maximum tumor size was recorded.

Figure 2
2Summary of ROC curve of each imaging modality for detecting multifocality in breast cancer patients. AUC was 0.781 for MMG, 0.772 for US, 0.728 for CT, 0.809 for MRI without CAD, 0.888 for MRI with CAD and 0.634 for FDG-PET.

Table 1
1Lesion characteristics of 86 invasive breast carcinomas using multimodal breast imagingImaging modality
Lesion characteristics

Mammography
Mass 29 (33.6%)

Calcifications 3 (3.5%)

Mass with calcifications 36 (41.9%)

Asymmetry 18 (21.0%)

Ultrasound
Mass 46 (53.5%)

Mass with calcifications 32 (37.2%)

Mass with abnormal ducts 8 (9.7%)

CT
Enhancing mass 86 (100.0%)

MRI
Mass 76 (88.4%)

Non-mass-like enhancement 10 (11.6%)

FDG-PET
Elevated tracer uptake with enhancing
mass 86 (100.0%)

CT computed tomography, MRI magnetic resonance imaging, FDG-PET
18-fludeoxyglucose positron emission tomography.

Table 2
2Tumor size and correlation coefficients representing the strength of a linear association between pathological tumor sizes and recorded sizes with multimodal breast imaging CT computed tomography, MRI magnetic resonance imaging, CAD computer-aided detection system, FDG-PET 18-fludeoxyglucose positron emission tomography.Pathology Mammography Ultrasound CT
MRI without CAD system MRI with CAD system FDG-PET

Mean tumor size
25.2 ± 14.8 30.7 ± 19.1
28.5 ± 17.7
27.6 ± 15.6 29.1 ± 16.3
28.3 ± 23.1
32.7 ± 21.5

Median tumor size
22
27
23
22
24
22
28

Size Range
(3-74)
(9-120)
(5-100)
(8-79)
(8-78)
(0.5-100)
(0-103)

Correlation coefficients
0.583
0.656
0.711
0.766
0.513
0.616

Table 3
3Weighted kappa coefficients for agreement of LN status between multimodality breast imaging and pathological resultsCT computed tomography, MRI magnetic resonance imaging, CAD computer-aided detection system, FDG-PET 18-fludeoxyglucose positron emission tomography.Mammography
Ultrasound
CT
MRI without CAD system
MRI with CAD system
FDG-PET

Weighted kappa coefficients
0.223
0.522
0.420
0.346
0.158
0.285

Table 4
4Diagnostic performance of the determination of LN status with multimodality breast imagingMammography
Ultrasound
CT
MRI without CAD system
MRI with CAD system
FDG-PET

Sensitivity (%)
36.4
61.4
63.6
61.4
47.7
47.7

Specificity (%)
86.8
92.1
78.9
73.7
68.4
81.6

PPV (%)
76.2
90.0
77.8
73.0
63.6
75.0

NPV (%)
54.1
67.3
65.2
62.2
53.1
57.4

Table 5
5Overall sensitivity, specificity, and AUC for evaluation of multifocality with multimodality breast imagingMammography
Ultrasound
CT
MRI without CAD system
MRI with CAD system
FDG-PET

Sensitivity (%)
66.7
83.3
66.7
100.0
100.0
33.3

Specificity (%)
89.5
71.1
79.0
61.8
77.6
93.4

AUC
0.781
0.772
0.728
0.809
0.888
0.634

P value
0.00
0.00
0.03
0.00
0.00
0.20

AcknowledgementsWe would like to thank In Young Choi and Duk Ki Hong in the breast examination team.Competing interestThe authors declare that they have no competing interests.Authors' contributions SES and BKS designed the research and wrote the primary manuscript. KRC and OHW evaluated radiological data. GSS and CK performed literature searches and collected the clinical data. GSS and SBC critically reviewed and revised the manuscript. SK participated in statistical analyses. All authors read and approved the final manuscript.
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