BMC cancer · 2025
Development of a screening model for APL using cell population data and deep learning-extracted WBC scattergram features.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Cai Qi, Ye Bo, Zheng Wenbo, Zhang Shihong, Zhang Jingxian, Shen Yimin, Yao Donglan, Zhang Huihui, Huang Zhixi, Hu Jian, Ma Yushuai, Wang Jianbiao, Wang Yong
Аннотация
BACKGROUND: Acute promyelocytic leukemia (APL), a high-risk subtype of acute myeloid leukemia, necessitates rapid diagnosis upon hospital admission to mitigate early mortality. Current diagnosing approaches relying on time-consuming genetic testing or morphological expertise are particularly challenging in resource-limited settings. Herein, this study introduces a novel machine learning approach leveraging routine lab data to enable immediate APL suspicion, offering a new diagnostic possibility for under-resourced hospitals.
METHODS: We developed a two-stage machine learning model using multi-center retrospective data. The cohort included 94 confirmed APL patients (2020-2024) from three tertiary hospitals, with an external validation set (n = 541) from an independent center. Using four VGG-16 networks, we extracted APL-specific 3D scatterplot features from DIFF and WNB channels of routine blood tests. These features were then fed into an optimized random forest classifier-scatterplot (RFC-S) model, refined via recursive feature elimination and threshold tuning.
RESULTS: The RFC-S model achieved near-perfect discrimination, with an AUC of 0.9893 in the test set and 0.9979 in external validation. It maintained 98.15% sensitivity and 95.52% specificity-outperforming conventional methods. SHAP analysis confirmed that key scattergram-derived features (e.g., N_APL_Ratio_YZ) drove predictions. Critically, the model requires no additional tests, making it deployable even in low-resource clinics.
CONCLUSIONS: The RFC-S model represents an innovative approach to APL screening by combining deep learning-derived scattergram features with routine blood parameters. This two-stage methodology achieves high diagnostic accuracy (AUC > 0.98) while maintaining computational efficiency. Importantly, the model's ability to utilize existing laboratory data without requiring additional tests makes it particularly valuable for resource-constrained settings where access to genetic testing or hematological expertise may be limited. Our findings suggest this approach could serve as a practical tool for early APL identification, potentially reducing diagnostic delays in diverse clinical environments.
METHODS: We developed a two-stage machine learning model using multi-center retrospective data. The cohort included 94 confirmed APL patients (2020-2024) from three tertiary hospitals, with an external validation set (n = 541) from an independent center. Using four VGG-16 networks, we extracted APL-specific 3D scatterplot features from DIFF and WNB channels of routine blood tests. These features were then fed into an optimized random forest classifier-scatterplot (RFC-S) model, refined via recursive feature elimination and threshold tuning.
RESULTS: The RFC-S model achieved near-perfect discrimination, with an AUC of 0.9893 in the test set and 0.9979 in external validation. It maintained 98.15% sensitivity and 95.52% specificity-outperforming conventional methods. SHAP analysis confirmed that key scattergram-derived features (e.g., N_APL_Ratio_YZ) drove predictions. Critically, the model requires no additional tests, making it deployable even in low-resource clinics.
CONCLUSIONS: The RFC-S model represents an innovative approach to APL screening by combining deep learning-derived scattergram features with routine blood parameters. This two-stage methodology achieves high diagnostic accuracy (AUC > 0.98) while maintaining computational efficiency. Importantly, the model's ability to utilize existing laboratory data without requiring additional tests makes it particularly valuable for resource-constrained settings where access to genetic testing or hematological expertise may be limited. Our findings suggest this approach could serve as a practical tool for early APL identification, potentially reducing diagnostic delays in diverse clinical environments.