ArticleFrontiers in oncology2026
Prediction of myelosuppression risk in postoperative cervical cancer patients undergoing concurrent chemoradiotherapy using machine learning models.
Article in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: By integrating clinical features and pelvic bone marrow dose-volume parameters, this study developed and compared multiple machine learning models to predict the risk of bone marrow suppression (BMS) in postoperative cervical cancer patients receiving concurrent chemoradiotherapy (CCRT). Methods: A total of 415 cervical cancer patients who received postoperative CCRT at Siyang Hospital and Shanxian Central Hospital between March 2022 and April 2025 were retrospectively enrolled. The primary outcome was BMS. Candidate predictors included baseline clinical characteristics, laboratory indices, and pelvic bone marrow dose-volume parameters. Feature selection was performed using the Least Absolute Shrinkage and Selection Operator (LASSO). Logistic regression (LR), random forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), support vector machines (SVM), and artificial neural networks (ANN) were constructed. Model performance was evaluated in an independent test set using the area under the curve (AUC), calibration curves, and decision curve analysis, and model interpretability was assessed using SHAP. Results: Among the 415 patients, 220 (53.0%) developed BMS. Significant differences were observed between patients with and without BMS in lymph node boost irradiation, body mass index (BMI), aspartate aminotransferase (AST), serum albumin (ALB), and low-dose pelvic bone marrow irradiated volume parameters (V10, V20, and V30). LASSO retained BMI, AST, ALB, V10, V20, and lymph node boost irradiation as key predictors. Among the six models, the RF model achieved the best performance (AUC = 0.799). Calibration curves and decision curve analysis demonstrated good calibration and potential clinical net benefit of the RF model. Conclusion: Machine learning models integrating routine clinical indicators and pelvic bone marrow dose-volume parameters can effectively predict the risk of myelosuppression in postoperative cervical cancer patients receiving CCRT. The random forest model demonstrated optimal performance and may serve as a practical tool for individualised risk stratification and early intervention.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.