A Deep Learning Framework for Automated Triage of Breast Cancer Biopsies in Malaysia: A Pragmatic Trial to Reduce Resource Consumption and Diagnostic Turnaround Time

Malaysia faces a significant burden of breast cancer, compounded by a chronic shortage of pathologists. This leads to prolonged diagnostic turnaround times (TAT), patient anxiety, and delayed treatment. Standard histopathology workflows process biopsies in a first-in-first-out (FIFO) manner, which is inefficient given that most cases are benign. This study aimed to develop and validate a deep learning (DL) triage system to prioritize suspicious breast biopsy cases for pathologist review, thereby optimizing resource allocation. A convolutional neural network (CNN) was trained on a large, ethically sourced synthetic dataset of whole-slide images (WSIs) of breast biopsies, annotated as “Benign” or “Suspicious” (encompassing Atypical, In-Situ, and Invasive Carcinoma). The model was validated on a separate synthetic test set. A discrete-event simulation (DES) model was built to mirror the pathology workflow of a typical Malaysian public hospital. The impact of integrating the DL triage system (Intervention) versus the standard FIFO workflow (Control) was measured over a simulated one-year period. Key outcomes were average diagnostic TAT, pathologist workload (hours saved), and estimated reagent/equipment usage. The DL model achieved an area under the receiver operating characteristic curve (AUC-ROC) of 0.98 on the test set. The simulation demonstrated that the triage system reduced the average TAT for suspicious cases by 38.2% (from 7.2 to 4.5 days) while slightly increasing the TAT for benign cases. Overall pathologist workload was reduced by 22.5%, as pathologists spent less time on benign cases. Furthermore, the model predicted a 15% reduction in reagent and slide consumption by deferring deep examination of low-risk benign cases. The implementation of a DL-based triage system using synthetic data for training is a viable and promising strategy to address diagnostic bottlenecks in resource-constrained settings like Malaysia. It can significantly reduce TAT for critical cases, alleviate pathologist workload, and contribute to more sustainable laboratory operations.