Department of Radiology, the Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, No. 127 Dongming Road, Zhengzhou, 450008, Henan, China.
Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, 200062, China.
Eur Radiol. 2024 Jan;34(1):485-494. doi: 10.1007/s00330-023-10040-4. Epub 2023 Aug 4.
To investigate the MRI radiomics signatures in predicting pathologic response among patients with locally advanced esophageal squamous cell carcinoma (ESCC), who received neoadjuvant chemotherapy (NACT).
Patients who underwent NACT from March 2015 to October 2019 were prospectively included. Each patient underwent esophageal MR scanning within one week before NACT and within 2-3 weeks after completion of NACT, prior to surgery. Radiomics features extracted from T2-TSE-BLADE were randomly split into the training and validation sets at a ratio of 7:3. According to the progressive tumor regression grade (TRG), patients were stratified into two groups: good responders (GR, TRG 0 + 1) and poor responders (non-GR, TRG 2 + 3). We constructed the Pre/Post-NACT model (Pre/Post-model) and the Delta-NACT model (Delta-model). Kruskal-Wallis was used to select features, logistic regression was used to develop the final model.
A total of 108 ESCC patients were included, and 3/2/4 out of 107 radiomics features were selected for constructing the Pre/Post/Delta-model, respectively. The selected radiomics features were statistically different between GR and non-GR groups. The highest area under the curve (AUC) was for the Delta-model, which reached 0.851 in the training set and 0.831 in the validation set. Among the three models, Pre-model showed the poorest performance in the training and validation sets (AUC, 0.466 and 0.596), and the Post-model showed better performance than the Pre-model in the training and validation sets (AUC, 0.753 and 0.781).
MRI-based radiomics models can predict the pathological response after NACT in ESCC patients, with the Delta-model exhibiting optimal predictive efficacy.
MRI radiomics features could be used as a useful tool for predicting the efficacy of neoadjuvant chemotherapy in esophageal carcinoma patients, especially in selecting responders among those patients who may be candidates to benefit from neoadjuvant chemotherapy.
• The MRI radiomics features based on T2WI-TSE-BLADE could potentially predict the pathologic response to NACT among ESCC patients. • The Delta-model exhibited the best predictive ability for pathologic response, followed by the Post-model, which similarly had better predictive ability, while the Pre-model performed less well in predicting TRG.
探讨磁共振成像（MRI）放射组学特征在预测接受新辅助化疗（NACT）的局部晚期食管鳞状细胞癌（ESCC）患者病理反应中的作用。
前瞻性纳入 2015 年 3 月至 2019 年 10 月接受 NACT 的患者。每位患者在 NACT 前 1 周内和 NACT 完成后 2-3 周内行食管 MRI 扫描，然后进行手术。从 T2-TSE-BLADE 中提取放射组学特征，并以 7:3 的比例随机分为训练集和验证集。根据肿瘤渐进性消退分级（TRG），将患者分为两组：良好反应者（GR，TRG 0+1）和不良反应者（非 GR，TRG 2+3）。我们构建了新辅助化疗前后模型（Pre/Post-model）和新辅助化疗差值模型（Delta-NACT 模型，Delta-model）。Kruskal-Wallis 用于选择特征，逻辑回归用于建立最终模型。
共纳入 108 例 ESCC 患者，分别有 3/2/4 个放射组学特征被纳入构建 Pre/Post/Delta-model。在 GR 和非 GR 组之间，选定的放射组学特征具有统计学差异。Delta-model 的曲线下面积（AUC）最高，在训练集和验证集中分别达到 0.851 和 0.831。在这三个模型中，Pre-model 在训练集和验证集的表现最差（AUC 分别为 0.466 和 0.596），而 Post-model 的表现优于 Pre-model（AUC 分别为 0.753 和 0.781）。
基于 MRI 的放射组学模型可预测 ESCC 患者 NACT 后的病理反应，Delta-model 显示出最佳的预测效果。
MRI 放射组学特征可作为预测食管癌患者新辅助化疗疗效的有用工具，特别是在选择可能从新辅助化疗中获益的患者的反应者方面。
T2WI-TSE-BLADE 上的 MRI 放射组学特征可预测 ESCC 患者对 NACT 的病理反应。
Delta-model 对病理反应的预测能力最佳，其次是 Post-model，其预测能力也较好，而 Pre-model 在预测 TRG 方面表现较差。
