Background Due to the complex nature of digital breast tomosynthesis (DBT) in imaging techniques, reading times are longer than 2D mammograms. A robust computer‐aided diagnosis system in DBT could help radiologists reduce their workload and reading times. Purpose The purpose of this study was to develop algorithms for detecting biopsy‐proven breast lesions on DBT using multi‐depth level convolutional models and leveraging non‐biopsied samples. As biopsied positive samples in a lesion dataset are limited, we hypothesized that false positive (FP) findings by detection algorithms from non‐biopsied benign lesions could improve detection algorithms by using them as data augmentation. Approach We first extracted 2D slices from DBT volumes with biopsy‐proven breast lesions (cancer and benign), with non‐biopsied benign lesions (actionable), and for controls. Then, to provide lesion continuity along the z‐direction, we combined a lesion slice with its immediate adjacent slices to synthesize 2.5‐dimensional (2.5D) images of the lesion by assigning them into R, G, and B color channels. We used 224 biopsy‐proven lesions from 39 cancer and 62 benign patients from a DBTex challenge dataset of 1000 scans. We included the 2.5D images of immediate neighboring slices from the lesion's center to increase the number of training samples. For lesion detection, we used the YOLOv5 algorithm as our base network. We trained a baseline algorithm (medium‐depth level) using biopsied samples to detect actionable FPs in non‐biopsied images. Afterward, we fine‐tuned the baseline model on the augmented image set (actionable FPs added). For lesion inferencing, we processed the DBT volume slice‐by‐slice to estimate bounding boxes in each slice, and then combined them by connecting bounding boxes along the depth via volumetric morphological closing. We trained an additional model (large) with deeper‐depth levels by repeating the above process. Finally, we developed an ensemble algorithm by combining the medium and large detection models. We used the free‐response operating characteristic curve to evaluate our algorithms. We reported mean sensitivity per FPs per DBT volume only for biopsied views and sensitivity at 2‐false positives per image (2FPI) for all views. However, due to the limited accessibility to the truth of the challenge validation and test datasets, we used sensitivity at 2FPI for statistical evaluation. Results For the DBTex independent validation set, the medium baseline model achieved a mean sensitivity of 0.627 FPs per DBT volume, and a sensitivity of 0.640 at 2FPI. After adding actionable FP lesions, the model had an improved 2FPI of 0.769 over the baseline (p‐value = 0.013). Our ensemble algorithm with multi‐depth levels (medium + large) achieved a mean sensitivity of 0.815 FPs per DBT volume and an improved sensitivity at 2FPI of 0.80 over the baseline (p‐value < 0.001) on the validation set. Finally, our ensemble model achieved a mean sensitivity of 0.786 FPs per DBT volume and a sensitivity of 0.743 at 2FPI on the DB...

ImportanceAn accurate and robust artificial intelligence (AI) algorithm for detecting cancer in digital breast tomosynthesis (DBT) could significantly improve detection accuracy and reduce health care costs worldwide.ObjectivesTo make training and evaluation data for the development of AI algorithms for DBT analysis available, to develop well-defined benchmarks, and to create publicly available code for existing methods.Design, Setting, and ParticipantsThis diagnostic study is based on a multi-institutional international grand challenge in which research teams developed algorithms to detect lesions in DBT. A data set of 22 032 reconstructed DBT volumes was made available to research teams. Phase 1, in which teams were provided 700 scans from the training set, 120 from the validation set, and 180 from the test set, took place from December 2020 to January 2021, and phase 2, in which teams were given the full data set, took place from May to July 2021.Main Outcomes and MeasuresThe overall performance was evaluated by mean sensitivity for biopsied lesions using only DBT volumes with biopsied lesions; ties were broken by including all DBT volumes.ResultsA total of 8 teams participated in the challenge. The team with the highest mean sensitivity for biopsied lesions was the NYU B-Team, with 0.957 (95% CI, 0.924-0.984), and the second-place team, ZeDuS, had a mean sensitivity of 0.926 (95% CI, 0.881-0.964). When the results were aggregated, the mean sensitivity for all submitted algorithms was 0.879; for only those who participated in phase 2, it was 0.926.Conclusions and RelevanceIn this diagnostic study, an international competition produced algorithms with high sensitivity for using AI to detect lesions on DBT images. A standardized performance benchmark for the detection task using publicly available clinical imaging data was released, with detailed descriptions and analyses of submitted algorithms accompanied by a public release of their predictions and code for selected methods. These resources will serve as a foundation for future research on computer-assisted diagnosis methods for DBT, significantly lowering the barrier of entry for new researchers.

The purpose of this study was to develop a loss function that can drive a given CNN to achieve high sensitivity (or recall) for identifying women with a high-risk of having breast cancer. The cross-entropy (CE) loss function is widely used to optimize a CNN for natural scene classification due to its stability. However, CE loss treats each class equally, thus, it may not be suitable to train the CNN to have high sensitivity performance. Therefore, we hypothesized that a loss function based on the F β -measure, the weighted harmonic mean of precision and recall, can improve the sensitivity of the resulting CNN model by giving more weight to the recall metric. To do so, we combined CE loss with the F β -measure to implement a task-oriented loss function for achieving high sensitivity performance. In this preliminary work, we used a screening mammogram dataset of 2000 scans (1000 recalled lesions;1000 normal). We extracted recalled lesion patches using radiologists' annotations and normal patches from the center of the breast. We fine-tuned the DenseNet121 network using the image patch dataset with a data split ratio of 0.8:0.1:0.1 for training, validation, and testing. We conducted ROC analysis to evaluate the performance of our proposed model. In the test set, the model with the task-oriented loss function achieved an AUC of 0.90 compared to CE loss (AUC=0.88) alone. The ROC curve of the proposed loss function achieved (a sensitivity of 53% at 98% specificity level) higher sensitivity than the CE loss alone (a sensitivity of 41% at 98% specificity level) for a high specificity area.