文章：

Delta放射组学能否改善免疫检查点抑制剂治疗黑色素瘤患者最佳总体缓解、无进展生存期和总生存期的预测？

Can Delta Radiomics Improve the Prediction of Best Overall Response, Progression-Free Survival, and Overall Survival of Melanoma Patients Treated with Immune Checkpoint Inhibitors?

原文发布日期：26 July 2024

DOI: 10.3390/cancers16152669

类型: Article

开放获取: 是

英文摘要：

Background: The prevalence of metastatic melanoma is increasing, necessitating the identification of patients who do not benefit from immunotherapy. This study aimed to develop a radiomic biomarker based on the segmentation of all metastases at baseline and the first follow-up CT for the endpoints best overall response (BOR), progression-free survival (PFS), and overall survival (OS), encompassing various immunotherapies. Additionally, this study investigated whether reducing the number of segmented metastases per patient affects predictive capacity. Methods: The total tumour load, excluding cerebral metastases, from 146 baseline and 146 first follow-up CTs of melanoma patients treated with first-line immunotherapy was volumetrically segmented. Twenty-one random forest models were trained and compared for the endpoints BOR; PFS at 6, 9, and 12 months; and OS at 6, 9, and 12 months, using as input either only clinical parameters, whole-tumour-load delta radiomics plus clinical parameters, or delta radiomics from the largest ten metastases plus clinical parameters. Results: The whole-tumour-load delta radiomics model performed best for BOR (AUC 0.81); PFS at 6, 9, and 12 months (AUC 0.82, 0.80, and 0.77); and OS at 6 months (AUC 0.74). The model using delta radiomics from the largest ten metastases performed best for OS at 9 and 12 months (AUC 0.71 and 0.75). Although the radiomic models were numerically superior to the clinical model, statistical significance was not reached. Conclusions: The findings indicate that delta radiomics may offer additional value for predicting BOR, PFS, and OS in metastatic melanoma patients undergoing first-line immunotherapy. Despite its complexity, volumetric whole-tumour-load segmentation could be advantageous.

摘要翻译： 

背景：转移性黑色素瘤的发病率不断上升，亟需识别无法从免疫治疗中获益的患者。本研究旨在基于基线期和首次随访CT中所有转移灶的分割，开发一种用于评估最佳总体缓解（BOR）、无进展生存期（PFS）和总生存期（OS）的影像组学生物标志物，该标志物涵盖多种免疫治疗方案。同时，本研究探讨了减少每位患者分割转移灶数量是否会影响预测能力。方法：对146例接受一线免疫治疗的黑色素瘤患者的基线期和首次随访CT图像进行全肿瘤负荷（排除脑转移灶）的体积分割。针对BOR终点，以及6、9、12个月的PFS和OS终点，共训练并比较了21个随机森林模型。模型输入参数包括：仅临床参数、全肿瘤负荷Δ影像组学特征结合临床参数、或基于前十大转移灶的Δ影像组学特征结合临床参数。结果：全肿瘤负荷Δ影像组学模型在BOR（AUC 0.81）、6/9/12个月PFS（AUC 0.82/0.80/0.77）及6个月OS（AUC 0.74）预测中表现最优。基于前十大转移灶的Δ影像组学模型在9个月和12个月OS预测中表现最佳（AUC 0.71和0.75）。尽管影像组学模型在数值上优于临床模型，但未达到统计学显著性。结论：研究结果表明，Δ影像组学可能为预测接受一线免疫治疗的转移性黑色素瘤患者的BOR、PFS和OS提供附加价值。尽管全肿瘤负荷体积分割较为复杂，但其可能具有临床应用优势。

原文链接：

Can Delta Radiomics Improve the Prediction of Best Overall Response, Progression-Free Survival, and Overall Survival of Melanoma Patients Treated with Immune Checkpoint Inhibitors?