KEYWORDS: Digital breast tomosynthesis, Cancer, Cancer detection, Breast cancer, Education and training, Mammography, Breast, Diagnostics, Architectural distortion, Breast density
Introduction: Breast cancer is the most common cancer among women in China and early detection is key to reducing mortality. This study aimed to understand diagnostic performances of Chinese radiologists between FFDM (full-field digital mammography) and DBT (digital breast tomosynthesis) images in terms of lesion features and reader characteristics.
Methods: 32 Chinese radiologists read two mammogram test sets to identify cancer cases and to detect lesions. The first set was of FFDM images (60 cases, 21 cancers) and the second was of DBT images (35 cases, 15 cancers). The accuracy in cancer case detection and lesion detection of radiologists in each test set were analysed. Comparison of diagnostic performances of radiologists with different working experiences were also undertaken. Results were compared using the Wilcoxon Sign Rank and Mann-Whitney U tests.
Results: Chinese radiologists recorded higher diagnostic accuracy with FFDM than DBT for detecting certain lesion types (calcifications, architectural distortion, mixed types) and lesions ≤ 10 mm. There was no significant difference in the accuracy for cancer case detection between FFDM and DBT. Radiologists who had more than eight years working experience, read more than 60 cases per week or had no DBT training had significantly higher lesion accuracy with FFDM than DBT.
Conclusion: Chinese radiologists had higher lesion accuracy with FFDM in certain lesion types and sizes than DBT. This may be related to the lack of appropriate DBT training for radiologists in China.
KEYWORDS: Digital breast tomosynthesis, Breast density, Mammography, Breast, Cancer, Education and training, Diagnostics, Breast cancer, Cancer detection, Radiology
PurposeThis study aims to investigate the diagnostic performances of Australian and Shanghai-based Chinese radiologists in reading full-field digital mammogram (FFDM) and digital breast tomosynthesis (DBT) with different levels of breast density.ApproachEighty-two Australian radiologists interpreted a 60-case FFDM set, and 29 radiologists also reported a 35-case DBT set. Sixty Shanghai radiologists read the same FFDM set, and 32 radiologists read the DBT set. The diagnostic performances of Australian and Shanghai radiologists were assessed using truth data (cancer cases were biopsy proven) and compared overall in specificity, case sensitivity, lesion sensitivity, receiver operating characteristics (ROC) area under the curve, and jack-knife free-response receiver operating characteristics (JAFROC) figure of merit, and they were stratified by case characteristics using the Mann–Whitney U test. The Spearman rank test was used to explore the association between radiologists’ performances and their work experience in mammogram interpretation.ResultsThere were significantly higher performances of Australian radiologists compared with Shanghai radiologists in low breast density for case sensitivity, lesion sensitivity, ROC, and JAFROC in the FFDM set (P < 0.0001); in high breast density, Shanghai radiologists’ performances in lesion sensitivity and JAFROC were also lower than Australian radiologists (P < 0.0001). In the DBT test set, Australian radiologists performed better than Shanghai radiologists in cancer detection in both low and high breast density. The work experience of Australian radiologists was positively linked to their diagnostic performances, whereas this association was not statistically significant in Shanghai radiologists.ConclusionThere were significant variations in reading performances between Australian and Shanghai radiologists in FFDM and DBT across different levels of breast density, lesion types, and lesion sizes. An effective training initiative tailored to suit local readers is essential to enhancing the diagnostic accuracy of Shanghai radiologists.
Considering the rapid rise in breast cancer incidence in China and lack of calibrated breast cancer prediction models for the Chinese female population, developing a breast cancer model targeting the Chinese women is necessary. This study aimed at generating a breast cancer risk prediction model for Chinese women. A total of 1079 (85 images contralateral to a cancer and 994 cases without breast cancer) women were recruited from Fudan University Shanghai Cancer Centre. For each case, we collected sixteen demographic variables such as age, BMI, number of children, family history of breast cancer, and age at menarche. Moreover, the dense tissue was automatically segmented by AutoDensity. A set of quantitative features were extracted from the dense area. Using the 80th percentile of intensity values in the dense area, the segmented area was thresholded again and the second set of computer-extracted features was calculated. The features, i.e. the demographic variables, and texture features extracted from the mammographically dense areas of the image, have been fed into an ensemble of 250 decision trees, whose results were combined using RUSBoost. The classifier achieved an AUC of 0.88 (CI: 0.84 - 0.91) for identifying high-risk images. Therefore, adopting such model might lead to the augmentation of discriminatory power of currently-used risk prediction models. However, it should be noted that the cancer cases were retrieved from the diagnostic environment (not screening) and further validation on a dataset from a screening set-up will be required.
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