Scholarlink

Background <p>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.</p> Methods <p>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 (<i>n</i> = 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.</p> Results <p>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., <i>N_APL_Ratio_YZ</i>) drove predictions. Critically, the model requires no additional tests, making it deployable even in low-resource clinics.</p> Conclusions <p>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 &gt; 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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Development of a screening model for APL using cell population data and deep learning-extracted WBC scattergram features

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月7日

查看全文
  • Qi Cai,
  • Bo Ye,
  • Wenbo Zheng,
  • Shihong Zhang,
  • Jingxian Zhang,
  • Yimin Shen,
  • Donglan Yao,
  • Huihui Zhang,
  • Zhixi Huang,
  • Jian Hu,
  • Yushuai Ma,
  • Jianbiao Wang,
  • Yong Wang

摘要

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.