Development and multicenter validation of a machine learning framework for predicting severe myelosuppression in nasopharyngeal carcinoma: Evidence from large-scale real-world data and prospective clinical trials.

6023 Background: Severe treatment-related myelosuppression (Tr-MS) in nasopharyngeal carcinoma (NPC) necessitates dose reductions, compromising survival. Current static models rely on baseline "snapshots," failing to capture dynamic bone marrow fluctuations. We developed a dynamic deep learning system using longitudinal real-world data to predict cycle-specific Tr-MS risk. Methods: We analyzed 103,919 longitudinal records from 12,065 NPC patients (Nanfang/SYSUCC). A rolling-window strategy incorporated dynamic pre-dose labs, prior-cycle nadirs, and cumulative exposure to predict Grade ≥3 Tr-MS (CTCAE v6.0). Models (Logistic/XGBoost/LightGBM/TabPFN) were trained (6:3:1 split) and validated in two independent external cohorts (n=197) and three prospective trials (NCT03919552, NCT06767488, NCT06017895; n=173). The model was deployed as an EHR-integrated tool for automated real-time risk stratification. Results: Dynamic models demonstrated robust discrimination across all validation hierarchies (Table). In internal validation, AUCs ranged from 0.84 to 0.96. Performance remained stable in external cohorts (AUC 0.75-0.87) and prospective trials (AUC 0.79-0.86). SHAP analysis identified cumulative chemotherapy dosage and prior-cycle nadirs as dominant risk factors. Notably, the HIS-integrated tool successfully automated data retrieval, eliminating manual entry burden. Conclusions: This first EHR-integrated, cycle-specific dynamic system for Tr-MS in NPC captures longitudinal marrow exhaustion, enabling a shift from reactive rescue to precise, proactive prevention. Performance of dynamic prediction models across validation cohorts. Endpoint Cohort AUC (95% CI) Sens Spec Anemia Internal (n=1,231) 0.96 (0.96-0.98) 0.91 0.91 Anemia External (n=197) 0.87 (0.85-0.89) 0.89 0.86 Anemia Prospective (n=173) 0.86 (0.82-0.89) 0.88 0.86 PLT Internal (n=1,231) 0.90 (0.87-0.92) 0.79 0.84 PLT External (n=197) 0.80 (0.77-0.81) 0.81 0.82 PLT Prospective (n=173) 0.81 (0.80-0.83) 0.80 0.83 WBC/Neut Internal (n=1,231) 0.84 (0.82-0.85) 0.71 0.79 WBC/Neut External (n=197) 0.75 (0.73-0.77) 0.69 0.77 WBC/Neut Prospective (n=173) 0.79 (0.77-0.81) 0.69 0.78 Abbreviations: AUC, area under the ROC curve; PLT, thrombocytopenia; WBC/Neut, leukopenia/neutropenia; Sens, sensitivity; Spec, specificity.

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Publication Details

Journal
Journal of Clinical Oncology
Published
2026-05-27
DOI
https://doi.org/10.1200/jco.2026.44.16_suppl.6023
Primary Topic
Head and Neck Cancer Studies
Type
article
Field-Weighted Citation Impact
0.00

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article

Development and multicenter validation of a machine learning framework for predicting severe myelosuppression in nasopharyngeal carcinoma: Evidence from large-scale real-world data and prospective clinical trials.

X G Wang, Tingxi Tang, Yutong Wang, Xiangjian Zhang et al.
Journal of Clinical Oncology
Head and Neck Cancer Studies
article

Development and multicenter validation of a machine learning framework for predicting severe myelosuppression in nasopharyngeal carcinoma: Evidence from large-scale real-world data and prospective clinical trials.

X G Wang, Tingxi Tang, Yutong Wang, Xiangjian Zhang, Ruiting Huang, Jian Guan
article en

Abstract

6023 Background: Severe treatment-related myelosuppression (Tr-MS) in nasopharyngeal carcinoma (NPC) necessitates dose reductions, compromising survival. Current static models rely on baseline "snapshots," failing to capture dynamic bone marrow fluctuations. We developed a dynamic deep learning system using longitudinal real-world data to predict cycle-specific Tr-MS risk. Methods: We analyzed 103,919 longitudinal records from 12,065 NPC patients (Nanfang/SYSUCC). A rolling-window strategy incorporated dynamic pre-dose labs, prior-cycle nadirs, and cumulative exposure to predict Grade ≥3 Tr-MS (CTCAE v6.0). Models (Logistic/XGBoost/LightGBM/TabPFN) were trained (6:3:1 split) and validated in two independent external cohorts (n=197) and three prospective trials (NCT03919552, NCT06767488, NCT06017895; n=173). The model was deployed as an EHR-integrated tool for automated real-time risk stratification. Results: Dynamic models demonstrated robust discrimination across all validation hierarchies (Table). In internal validation, AUCs ranged from 0.84 to 0.96. Performance remained stable in external cohorts (AUC 0.75-0.87) and prospective trials (AUC 0.79-0.86). SHAP analysis identified cumulative chemotherapy dosage and prior-cycle nadirs as dominant risk factors. Notably, the HIS-integrated tool successfully automated data retrieval, eliminating manual entry burden. Conclusions: This first EHR-integrated, cycle-specific dynamic system for Tr-MS in NPC captures longitudinal marrow exhaustion, enabling a shift from reactive rescue to precise, proactive prevention. Performance of dynamic prediction models across validation cohorts. Endpoint Cohort AUC (95% CI) Sens Spec Anemia Internal (n=1,231) 0.96 (0.96-0.98) 0.91 0.91 Anemia External (n=197) 0.87 (0.85-0.89) 0.89 0.86 Anemia Prospective (n=173) 0.86 (0.82-0.89) 0.88 0.86 PLT Internal (n=1,231) 0.90 (0.87-0.92) 0.79 0.84 PLT External (n=197) 0.80 (0.77-0.81) 0.81 0.82 PLT Prospective (n=173) 0.81 (0.80-0.83) 0.80 0.83 WBC/Neut Internal (n=1,231) 0.84 (0.82-0.85) 0.71 0.79 WBC/Neut External (n=197) 0.75 (0.73-0.77) 0.69 0.77 WBC/Neut Prospective (n=173) 0.79 (0.77-0.81) 0.69 0.78 Abbreviations: AUC, area under the ROC curve; PLT, thrombocytopenia; WBC/Neut, leukopenia/neutropenia; Sens, sensitivity; Spec, specificity.

Journal of Clinical OncologyVol. 44(16_suppl)
Nanfang Hospital (CN), Beijing Tsinghua Chang Gung Hospital (CN), Southern Medical University (CN)
National Natural Science Foundation of China, China Postdoctoral Science Foundation, Basic and Applied Basic Research Foundation of Guangdong Province
Peace, Justice and strong institutions
Openalex Percentile: Top 8%
Head and Neck Cancer Studies
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