Validation of artificial intelligence contrast mammography in diagnosis of breast cancer: Relationship to histopathological results

Authors:
raw_author_name: Maha Helal
raw_affiliation_string: Radiology Department, National Cancer Institute, Cairo University, Cairo 11796, Egypt
raw_author_name: Rana Khaled
raw_affiliation_string: Radiology Department, National Cancer Institute, Cairo University, Cairo 11796, Egypt
raw_author_name: Omar Alfarghaly
raw_affiliation_string: Computer Science Department, Computers and Artificial Intelligence, Cairo University, Cairo 12613, Egypt
raw_author_name: Omnia Mokhtar
raw_affiliation_string: Radiology Department, National Cancer Institute, Cairo University, Cairo 11796, Egypt
raw_author_name: Abeer Elkorany
raw_affiliation_string: Computer Science Department, Computers and Artificial Intelligence, Cairo University, Cairo 12613, Egypt
raw_author_name: Aly Fahmy
raw_affiliation_string: Computer Science Department, Computers and Artificial Intelligence, Cairo University, Cairo 12613, Egypt
raw_author_name: Hebatalla El Kassas
raw_affiliation_string: Radiology Department, National Cancer Institute, Cairo University, Cairo 11796, Egypt

Abstract: Contrast-enhanced mammography (CEM) is used for characterization of breast lesions with increased diagnostic accuracy compared to digital mammography (DM). Artificial intelligence (AI) approaches are emerging with accuracies equal to an average radiologist. However, most studies trained deep learning (DL) models on DM images and there is a paucity in literature for discovering the application of AI using CEM.To develop and test a DL model that classifies CEM images and produces corresponding highlights of lesions detected.Fully annotated 2006 images of 326 females available from the previously published Categorized Digital Database for Contrast Enhanced Mammography images (CDD-CESM) were used for training. We developed a DL multiview contrast mammography model (MVCM) for classification of CEM low energy and recombined images. An external test set of 288 images of 37 females not included in the training was used for validation. Correlation with histopathological results and follow-up was considered the standard reference. The study protocol was approved by the Institutional Review Board and patient informed consent was obtained.Assessment was done on an external test set of 37 females (mean age, 51.31 years ± 11.07 [SD]) with AUC-ROC for AI performance 0.936; (95 % CI: 0.898, 0.973; p < 0.001) and the best cut off value for prediction of malignancy using AI score = 0.28. Findings were then correlated with histopathological results and follow up which revealed a sensitivity of 75 %, specificity 96.3 %, total accuracy of 90.1 %, positive predictive value (PPV) 87.1 %, and negative predictive value (NPV) 92 %, p-value (<0.001). Diagnostic indices of radiologists were sensitivity 88.9 %, specificity 92.6 %, total accuracy 91.7 %, PPV 80 %, and NPV 96.2 %, p-value (<0.001).A deep learning multiview CEM model was developed and evaluated in a cohort of female participants and showed promising results in detecting breast cancer. This warrants further studies, external training, and validation.