Annotation-free deep learning for predicting gene mutations from whole slide images of acute myeloid leukemia - Bo-Han Wei; Xavier Cheng-Hong Tsai; Kuo-Jui Sun; Min-Yen Lo; Sheng-Yu Hung; Wen-Chien Chou; Hwei-Fang Tien; Hsin-An Hou; Chien-Yu Chen (npj Precision Oncology)
Abstract The rapid development of deep learning has revolutionized medical image processing, including analyzing whole slide images (WSIs). Despite the demonstrated potential for characterizing gene mutations directly from WSIs in certain cancers, challenges remain due to image resolution and reliance on manual annotations for acute myeloid leukemia (AML). We, therefore, propose a deep learning model based on multiple instance learning (MIL) with ensemble techniques to predict gene mutations from AML WSIs. Our model predicts NPM1 mutations and FLT3-ITD without requiring patch-level or cell-level annotations. Using a dataset of 572 WSIs, the largest database with both WSI and genetic mutation information, our model achieved an AUC of 0.90 ± 0.08 for NPM1 and 0.80 ± 0.10 for FLT3-ITD in the testing cohort. Additionally, we found that blasts are pivotal indicators for gene mutation predictions, with their proportions varying between mutated and standard WSIs, highlighting the clinical potential of AML WSI analysis.