Touraine Célia, Winter Audrey, Castan Florence, Azria David, Gourgou Sophie
Biometrics Unit, Cancer Institute of Montpellier (ICM), University Montpellier, 34090 Montpellier, France.
French National Platform Quality of Life and Cancer, 34090 Montpellier, France.
Cancers (Basel). 2023 Sep 22;15(19):4676. doi: 10.3390/cancers15194676.
Late fibrosis can occur in breast cancer patients treated with curative-intent radiotherapy. Predicting this toxicity is of clinical interest in order to adapt the irradiation dose delivered. Radiation-induced CD8 T-lymphocyte apoptosis (RILA) had been proven to be associated with less grade ≥2 late radiation-induced toxicities in patients with miscellaneous cancers. Tobacco smoking status and adjuvant hormonotherapy were also identified as potential factors related to late-breast-fibrosis-free survival. This article evaluates the predictive performance of the RILA using a ROC curve analysis that takes into account the dynamic nature of fibrosis occurrence. This time-dependent ROC curve approach is also applied to evaluate the ability of the RILA combined with the other previously identified factors. Our analysis includes a Monte Carlo cross-validation procedure and the calculation of an expected cost of misclassification, which provides more importance to patients who have no risk of late fibrosis in order to be able to treat them with the maximal irradiation dose. Performance evaluation was assessed at 12, 24, 36 and 50 months. At 36 months, our results were comparable to those obtained in a previous study, thus underlying the predictive power of the RILA. Based on specificity and cost, RILA alone seemed to be the most performant, while its association with the other factors had better negative predictive value results.
晚期纤维化可发生在接受根治性放疗的乳腺癌患者中。预测这种毒性对于调整放疗剂量具有临床意义。已证实辐射诱导的CD8 T淋巴细胞凋亡(RILA)与各类癌症患者中≥2级晚期辐射诱导毒性的减少有关。吸烟状况和辅助激素治疗也被确定为与晚期无乳腺纤维化生存相关的潜在因素。本文使用考虑纤维化发生动态性质的ROC曲线分析评估RILA的预测性能。这种时间依赖性ROC曲线方法也用于评估RILA与其他先前确定因素相结合的能力。我们的分析包括蒙特卡罗交叉验证程序和误分类预期成本的计算,这对无晚期纤维化风险的患者更为重要,以便能够用最大放疗剂量治疗他们。在12、24、36和50个月时进行性能评估。在36个月时,我们的结果与先前研究中获得的结果相当,从而突出了RILA的预测能力。基于特异性和成本,单独的RILA似乎表现最佳,而其与其他因素的联合具有更好的阴性预测值结果。