Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore.
Sci Rep. 2022 Apr 13;12(1):6167. doi: 10.1038/s41598-022-10175-2.
A feasibility study was performed to determine if CT-based radiomics could play an augmentative role in predicting neoadjuvant rectal score (NAR), locoregional failure free survival (LRFFS), distant metastasis free survival (DMFS), disease free survival (DFS) and overall survival (OS) in locally advanced rectal cancer (LARC). The NAR score, which takes into account the pathological tumour and nodal stage as well as clinical tumour stage, is a validated surrogate endpoint used for early determination of treatment response whereby a low NAR score (< 8) has been correlated with better outcomes and high NAR score (> 16) has been correlated with poorer outcomes. CT images of 191 patients with LARC were used in this study. Primary tumour (GTV) and mesorectum (CTV) were contoured separately and radiomics features were extracted from both segments. Two NAR models (NAR > 16 and NAR < 8) models were constructed using Least Absolute Shrinkage and Selection Operator (LASSO) and the survival models were constructed using regularized Cox regressions. Area under curve (AUC) and time-dependent AUC were used to quantify the performance of the LASSO and Cox regression respectively, using ten folds cross validations. The NAR > 16 and NAR < 8 models have an average AUCs of 0.68 ± 0.13 and 0.59 ± 0.14 respectively. There are statistically significant differences between the clinical and combined model for LRFFS (from 0.68 ± 0.04 to 0.72 ± 0.04), DMFS (from 0.68 ± 0.05 to 0.70 ± 0.05) and OS (from 0.64 ± 0.06 to 0.66 ± 0.06). CTV radiomics features were also found to be more important than GTV features in the NAR prediction model. The most important clinical features are age and CEA for NAR > 16 and NAR < 8 models respectively, while the most significant clinical features are age, surgical margin and NAR score across all the four survival models.
本研究旨在评估 CT 影像组学是否可作为补充预测新辅助直肠评分（NAR）、局部无复发生存（LRFFS）、无远处转移生存（DMFS）、无疾病生存（DFS）和总生存（OS）的预测因子。NAR 评分综合了病理肿瘤和淋巴结分期以及临床肿瘤分期，是一种经过验证的替代终点，可用于早期判断治疗反应，低 NAR 评分（<8）与更好的预后相关，高 NAR 评分（>16）与更差的预后相关。本研究共纳入了 191 例局部晚期直肠癌（LARC）患者的 CT 图像。分别勾画原发肿瘤（GTV）和直肠系膜（CTV），并从两个节段提取影像组学特征。使用最小绝对值收缩和选择算子（LASSO）构建了两个 NAR 模型（NAR>16 和 NAR<8），使用正则化 Cox 回归构建了生存模型。使用 10 折交叉验证，分别使用曲线下面积（AUC）和时间依赖性 AUC 来量化 LASSO 和 Cox 回归的性能。NAR>16 和 NAR<8 模型的 AUC 平均值分别为 0.68±0.13 和 0.59±0.14。LRFFS（从 0.68±0.04 至 0.72±0.04）、DMFS（从 0.68±0.05 至 0.70±0.05）和 OS（从 0.64±0.06 至 0.66±0.06）的临床模型和联合模型之间存在统计学显著差异。CTV 影像组学特征在 NAR 预测模型中比 GTV 特征更为重要。对于 NAR>16 和 NAR<8 模型，最重要的临床特征分别是年龄和 CEA，而在所有四个生存模型中，最重要的临床特征是年龄、手术切缘和 NAR 评分。
