def METHOD_NAME(self, dict_dvh, path, csv_name, patient_id):
    """
    Export dvh data to csv file.
    Append to existing file
    :param dict_dvh: A dictionary of DVH {ROINumber: DVH}
    :param path: Target path of CSV export
    :param csv_name: CSV file name
    :param patient_id: Patient Identifier
    """
    # full path of the target csv file
    tar_path = path + csv_name
    create_header = not os.path.isfile(tar_path)
    dvh_csv_list = []
    csv_header = []
    csv_header.append('Patient ID')
    csv_header.append('ROI')
    csv_header.append('Volume (mL)')
    max_roi_dose = 0
    for i in dict_dvh:
        dvh_roi_list = []
        dvh = dict_dvh[i]
        name = dvh.name
        volume = dvh.volume
        dvh_roi_list.append(patient_id)
        dvh_roi_list.append(name)
        dvh_roi_list.append(volume)
        dose = dvh.relative_volume.counts
        for i in range(0, len(dose), 10):
            dvh_roi_list.append(dose[i])
            # Update the maximum dose value, if current dose
            # exceeds the current maximum dose
            if i > max_roi_dose:
                max_roi_dose = i
        dvh_csv_list.append(dvh_roi_list)
    for i in range(0, max_roi_dose + 1, 10):
        csv_header.append(str(i) + 'cGy')
    # Convert the list into pandas dataframe, with 2 digit rounding.
    pddf_csv = pd.DataFrame(dvh_csv_list, columns=csv_header).round(2)
    # Fill empty blocks with 0.0
    pddf_csv.fillna(0.0, inplace=True)
    pddf_csv.set_index('Patient ID', inplace=True)
    # Convert and export pandas dataframe to CSV file
    pddf_csv.to_csv(tar_path, mode='a', header=create_header)