ArticleTechnology in cancer research & treatment
Developing and Validating a Risk Model for Severe Bone Marrow Suppression in Esophageal Cancer Treated with Radiotherapy or Chemoradiotherapy: A Retrospective Cohort Study.
Article in Technology in cancer research & treatment. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
IntroductionSevere bone marrow suppression (BMS) is a frequent and dose-limiting toxicity in esophageal cancer treated with chemoradiotherapy. A simple pre-treatment tool is needed to identify high-risk patients.MethodsIn this single-center retrospective cohort study, we analyzed 404 patients receiving radiotherapy or chemoradiotherapy between February 2022 and July 2025. Severe BMS, defined per CTCAE v5.0, was predicted using variables selected by LASSO and multivariable logistic regression to construct a nomogram. Model performance was evaluated by discrimination (AUC/C-index), calibration, and decision-curve analysis in training and test cohorts.ResultsAmong 404 patients, 143 developed severe BMS (35.4%). Multivariable analysis identified sex, age, N stage, Pre-ALB, creatinine, and platelet count as independent predictors. Given their clinical relevance, radiotherapy regimen and overall treatment approach were retained in the final model. In this dataset, the nomogram showed strong discrimination, with an AUC of 0.972 (95% CI, 0.941-0.988) in the training set and 0.956 (95% CI, 0.932-0.983) in the internal test set. Calibration was close to the observed risk, with mean absolute errors of 0.011 in the training cohort and 0.037 in the testing cohort; the Hosmer-Lemeshow test showed no significant evidence of poor fit in either cohort (training cohort, P=0.512; testing cohort, P=0.738). Decision-curve analysis suggested potential net benefit within the internal validation framework across threshold probabilities of 0.10-0.80 in the training set and 0.10-0.70 in the testing set.ConclusionWe developed and internally validated a nomogram based on routine clinical and laboratory measures to estimate the risk of severe BMS before radiotherapy or chemoradiotherapy in esophageal cancer. This tool may help identify patients at higher risk before treatment and may support risk stratification, closer monitoring, and supportive care planning. Prospective, multicenter studies are warranted to further evaluate the generalizability of these findings.
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