Predicting Radiation Esophagitis in Patients Undergoing Synchronous Boost Radiotherapy Post-Breast-Conserving Surgery
This study constructed a predictive model for occurrence of radiation esophagitis during breast-cancer radiotherapy. 308 breast-cancer patients were analyzed. Lasso regression identified crucial variables that were further integrated into a radiation esophagitis risk score, which was used to segrega...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-04-01
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| Series: | Dose-Response |
| Online Access: | https://doi.org/10.1177/15593258251335802 |
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| _version_ | 1849713223484309504 |
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| author | Huai-wen Zhang Yi-ren Wang Jingao Li Wei Huang Bin Xu Hao-wen Pang Chun-ling Jiang |
| author_facet | Huai-wen Zhang Yi-ren Wang Jingao Li Wei Huang Bin Xu Hao-wen Pang Chun-ling Jiang |
| author_sort | Huai-wen Zhang |
| collection | DOAJ |
| description | This study constructed a predictive model for occurrence of radiation esophagitis during breast-cancer radiotherapy. 308 breast-cancer patients were analyzed. Lasso regression identified crucial variables that were further integrated into a radiation esophagitis risk score, which was used to segregate patients into high- and low-risk groups. A nomogram model was designed for clinical applicability. Training and validations were performed to assess robustness and generalizability of proposed models, employing C-index, AUCs, calibration curves, and decision curves. SHAP algorithm was used for model interpretation, offering insights into the major contributory factors. Seven significant variables were identified by Lasso regression. C-indexes of nomograms of individual clinical variables and risk score were 0.795 and 0.784, respectively, exhibiting strong predictive ability. In internal validation, AUCs for risk score, nomogram, and logistic models were 0.784, 0.795, and 0.812, respectively. Calibration curves showed a close fit between predicted and observed outcomes across models. Decision curve analysis indicated logistic model’s superior clinical utility when the risk threshold was above 0.2. SHAP interpretation emphasized radiation dose, pruritus, molecular type, and hepatic dysfunction as top contributory factors for radiation esophagitis. Models based on interpretable machine learning offer an intuitive tool to assess risk of radiation esophagitis in breast-cancer radiotherapy. |
| format | Article |
| id | doaj-art-62c6531cb942423bbaf0aa061cef11be |
| institution | DOAJ |
| issn | 1559-3258 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Dose-Response |
| spelling | doaj-art-62c6531cb942423bbaf0aa061cef11be2025-08-20T03:14:01ZengSAGE PublishingDose-Response1559-32582025-04-012310.1177/15593258251335802Predicting Radiation Esophagitis in Patients Undergoing Synchronous Boost Radiotherapy Post-Breast-Conserving SurgeryHuai-wen ZhangYi-ren WangJingao LiWei HuangBin XuHao-wen PangChun-ling JiangThis study constructed a predictive model for occurrence of radiation esophagitis during breast-cancer radiotherapy. 308 breast-cancer patients were analyzed. Lasso regression identified crucial variables that were further integrated into a radiation esophagitis risk score, which was used to segregate patients into high- and low-risk groups. A nomogram model was designed for clinical applicability. Training and validations were performed to assess robustness and generalizability of proposed models, employing C-index, AUCs, calibration curves, and decision curves. SHAP algorithm was used for model interpretation, offering insights into the major contributory factors. Seven significant variables were identified by Lasso regression. C-indexes of nomograms of individual clinical variables and risk score were 0.795 and 0.784, respectively, exhibiting strong predictive ability. In internal validation, AUCs for risk score, nomogram, and logistic models were 0.784, 0.795, and 0.812, respectively. Calibration curves showed a close fit between predicted and observed outcomes across models. Decision curve analysis indicated logistic model’s superior clinical utility when the risk threshold was above 0.2. SHAP interpretation emphasized radiation dose, pruritus, molecular type, and hepatic dysfunction as top contributory factors for radiation esophagitis. Models based on interpretable machine learning offer an intuitive tool to assess risk of radiation esophagitis in breast-cancer radiotherapy.https://doi.org/10.1177/15593258251335802 |
| spellingShingle | Huai-wen Zhang Yi-ren Wang Jingao Li Wei Huang Bin Xu Hao-wen Pang Chun-ling Jiang Predicting Radiation Esophagitis in Patients Undergoing Synchronous Boost Radiotherapy Post-Breast-Conserving Surgery Dose-Response |
| title | Predicting Radiation Esophagitis in Patients Undergoing Synchronous Boost Radiotherapy Post-Breast-Conserving Surgery |
| title_full | Predicting Radiation Esophagitis in Patients Undergoing Synchronous Boost Radiotherapy Post-Breast-Conserving Surgery |
| title_fullStr | Predicting Radiation Esophagitis in Patients Undergoing Synchronous Boost Radiotherapy Post-Breast-Conserving Surgery |
| title_full_unstemmed | Predicting Radiation Esophagitis in Patients Undergoing Synchronous Boost Radiotherapy Post-Breast-Conserving Surgery |
| title_short | Predicting Radiation Esophagitis in Patients Undergoing Synchronous Boost Radiotherapy Post-Breast-Conserving Surgery |
| title_sort | predicting radiation esophagitis in patients undergoing synchronous boost radiotherapy post breast conserving surgery |
| url | https://doi.org/10.1177/15593258251335802 |
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