Frontiers in oncology · 2025
Identification and visualization of fusion gene subtypes in APL using spatial attention mechanisms in vision models.
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Yan Peirou, Pu Guo, Wu Ping, Wen Lijun, Chen Suning, Xue Song
Аннотация
INTRODUCTION: Acute promyelocytic leukemia (APL) features leukemic cell differentiation arrest at the promyelocytic stage, mainly due to the t (15;17), (q24; q21) translocation that forms the PML-RARA fusion protein. Variant RARα translocations, with distinct biological traits and all-trans retinoic acid (ATRA) responses, often cause misdiagnosis and lengthy genetic testing.
METHODS: To solve these problems, we propose a spatial attention mechanism-enhanced convolutional neural network integrating ResNet Blocks and a spatial attention module (CNN with spatial attention), which can achieve high-precision identification of APL fusion gene subtypes and pixel-level visualization of key areas. Data collected from two hospitals and Kaggle, including bone marrow smear images of PML-RARA, TTMV-RARA, NPM1-RARA, STAT5B-RARA, and NUP98-RARG subtypes, were preprocessed to form a five-class dataset.
RESULTS: The model achieves an overall accuracy of 98.04% in five - class classification, with good performance in each category. The attention maps also enhance the model's interpretability.
DISCUSSION: Such a novel and rapid diagnostic approach for APL subtypes, which achieves high - precision identification and pixel - level visualization, holds significant clinical value.
METHODS: To solve these problems, we propose a spatial attention mechanism-enhanced convolutional neural network integrating ResNet Blocks and a spatial attention module (CNN with spatial attention), which can achieve high-precision identification of APL fusion gene subtypes and pixel-level visualization of key areas. Data collected from two hospitals and Kaggle, including bone marrow smear images of PML-RARA, TTMV-RARA, NPM1-RARA, STAT5B-RARA, and NUP98-RARG subtypes, were preprocessed to form a five-class dataset.
RESULTS: The model achieves an overall accuracy of 98.04% in five - class classification, with good performance in each category. The attention maps also enhance the model's interpretability.
DISCUSSION: Such a novel and rapid diagnostic approach for APL subtypes, which achieves high - precision identification and pixel - level visualization, holds significant clinical value.