Background: Radiology subspecialisation is well-established in much of Europe, North America, and Australasia. It is a natural evolution of the radiology speciality catalysed by multiple factors.Objectives: The aim of this article is to analyse and provide an overview of the current status of radiology subspecialisation in African countries.Methods: We reviewed English-language articles, reports, and other documents on radiology specialisation and subspecialisation in Africa.Results: There are 54 sovereign countries in Africa (discounting disputed territories). Eighteen African countries with well-established radiology residency training were assessed for the availability of formal subspecialisation training locally. Eight (Egypt, Ethiopia, Kenya, Morocco, Nigeria, South Africa, Tanzania, and Tunisia) out of the 18 countries have local subspecialist training programmes. Data and/or information on subspecialisation were unavailable for three (Algeria, Libya, and Senegal) of the 18 countries. Paediatric Radiology (Ethiopia, Nigeria, South Africa, Tunisia) and Interventional Radiology (Egypt, Kenya, South Africa, Tanzania) were the most frequently available subspecialist training programmes. Except Tanzania, all the countries with subspecialisation training programmes have ≥ 100 radiologists in their workforce.Conclusion: There is limited availability of subspecialist radiology training programmes in African countries. Alternative models of subspecialist radiology training are suggested to address this deficit.

Background: Personalized breast cancer (BC) screening adjusts the imaging modality and frequency of exams according to a woman's risk of developing BC. This can lower cost and false positives by reducing unnecessary exams and has the potential to find more cancers at a curable stage. Deep learning (DL) is a class of artificial intelligence algorithms that progressively extracts higher-level representations from raw input. A critical challenge to applying DL for BC risk prediction is that images are needed from exams performed before a possible cancer diagnosis. Large longitudinal datasets with cancer labeling are relatively scarce. Recently, new self-supervised methods have been developed which do not require labeling. Instead, they learn to recognize higher-level features by comparing two augmented images and determining if they are derived from the same original image. Methods: We developed Self-supervised AI for CAncer Risk Assessment (SAICARA), a mammography-based DL model to predict BC risk. We trained SAICARA on mammograms from the Chicago Multiethnic Epidemiologic Cohort (ChiMEC). We used the momentum contrast method in pretraining to train an encoder that produces compact representations of input mammography views. We initialized the encoders with weights obtained from training on the ImageNet dataset. We continued pretraining with 223,415 chest radiographs from the CheXpert database. Finally, we used mammograms from ChiMEC without any requirements on the exam date. We used augmentations from two different mammography views to provide better positive pairs for self-supervised learning. For fine-tuning, we trained with exams from women who were known to be cancer-free with at least 100 days of follow-up, and patients diagnosed with BC at least 30 days following the exam. Optimization was performed using a negative-log likelihood loss function which was discretized by considering quantiles of the event-time distribution. Hyperparameters were tuned using a Bayesian optimization strategy implemented by Weights and Biases. We computed the concordance index and the area under the receiver-operating characteristic curve (AUC) at two years to evaluate the discriminating capacity of the predicted risk of BC. We evaluated our model using 10-fold cross-validation. Results: In the final phase of pretraining, we used 13,194 mammography exams from 2,835 women. For fine-tuning, we used 4,849 exams from 1,418 women who were known to be cancer-free at their last follow-up, and 1,760 exams from 744 women who had exams that were followed by a BC diagnosis. SAICARA achieved a mean concordance index of 0.62 (standard deviation, SD = 0.11) and a mean AUC of 0.61 (SD = 0.09). Conclusion: Self-supervised DL holds promise as a technique for improving the performance of image-based BC risk prediction models. Citation Format: Anna Woodard, Olasubomi J. Omoleye, Rachna Gupta, Fangyuan Zhao, Aarthi Koripelly, Ian Foster, Kyle Chard, Toshio F. Yoshimatsu, Yonglan Zheng, Dezheng Huo, Olufunmilayo I. Olopade. Self-supervised deep learning to assess breast cancer risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5047.

Imaging-based machine learning models are promising tools for breast cancer risk prediction. Validating these models across diverse cohorts is necessary to establish performance and spur clinical implementation. We conducted an independent, external validation study of Mirai, a mammography-based deep learning model, using the Chicago Multiethnic Epidemiologic Cohort (ChiMEC), comprising 1671 exams from 704 cases and 4947 exams from 1437 cancer-free controls. We preprocessed images by extracting metadata from mammograms and excluded non-screening exams. Only exams with the four standard mammographic views were included. Images were converted from DICOM to PNG format using the DCMTK library. We computed the area under the receiver-operating characteristic curve (AUC) to evaluate the model’s discriminating capacity for predicting breast cancer within 1-5 years. We analyzed the entire cohort and stratified by race and hormone-receptor (HR) status. Mirai performed well in our study, but the performance is lower than that in the originally published validation of Mirai model. The AUC of the 1-year-risk is 0.72 in our full cohort, which is higher than that of the 5-year-risk (0.65). The 1-year AUC is high in African Americans but decreases over time. In contrast, the model showed lower but time-consistent AUC values in White patients. Performance is slightly better for predicting HR + compared to HR - cancers. Our results suggest that Mirai has better accuracy for predicting short-term breast cancer risk than traditional risk factor-based models, such as the Gail and Tyrer-Cuzick models. This initial evaluation revealed some performance differences by race and HR status and underscores the need for more independent validations in diverse datasets to elucidate the generalizability of image-based deep learning for breast cancer risk prediction. Table 1. Evaluation of performance of Mirai in ChiMEC Cohort Subset Case exams Control exams Harrel's C-index 1-year AUC 2-year AUC 3-year AUC 4-year AUC 5-year AUC Full cohort (MGH) 588 25267 .75 (.72 .78) .84 (.80, .87) 78 (.75, .82) .77 (.74, .80) .76 (.73, .79) .76 (.73, .79) Full cohort (ChiMEC) 1656 4765 .64 (.62, .66) .72 (.68, .75) .67 (.65, .69) .65 (.63, .67) .65 (.64, .67) .65 (.64, .67) African American 829 2174 .64 (.61, .67) .78 (.74, .82) .69 (.65, .72) .66 (.63, .69) .66 (.64, .69) .66 (.63, .68) White 711 1808 .62 (.59, .65) .63 (.57, .68) .65 (.61, .68) .63 (.60, .66) .63 (.61, .66) .64 (.61, .67) Hispanic 20 164 .65 (.45, .86) .63 (.31, .96) .74 (.51, .97) .70 (.51, .89) .67 (.50, .83) .67 (.51, .83) Asian and Native American 80 178 .59 (.49, .70) .67 (.53, .81) .62 (.52, .73) .63 (.54, .72) .62 (.52, .71) .63 (.53, .72) Hormone receptor positive 1281 4765 .65 (.62, .68) .74 (.70, .78) .68 (.66, .71) .66 (.64, .68) .66 (.64, .68 .66 (.64, .68) Hormone receptor negative 300 4765 .62 (.58, .67) .68 (.61, .75) .65 (.60, .70) .63 (.59, .67) .63 (.59, .67) .64 (.60, .67) HER2 positive 139 4765 .62 (.54, .71) .74 (.61, .86) .64 (.56, .72) .63 (.56, .69) .64 (.58, .69) .64 (.58, .69) HER2 negative 1138 4765 .65 (.62, .67) .74 (.70, .78) .68 (.65, .71) .66 (.64, .68) .66 (.64, .68) .66 (.64, .68) Triple negative 207 4765 .61 (.55, .67) .64 (.54, .74) .63 (.57, .69) .62 (.57, .67) .62 (.57, .66) .62 (.58, .67) Citation Format: Olasubomi J. Omoleye, Anna Woodard, Fangyuan Zhao, Maksim Levental, Toshio F. Yoshimatsu, Yonglan Zheng, Olufunmilayo I. Olopade, Dezheng Huo. Independent evaluation and validation of mammography-based breast cancer risk models in a diverse patient cohort [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1933.

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