MRI的瘤内和瘤周栖息地放射组学可预测口腔鳞状细胞癌对新辅助化疗免疫治疗的病理完全缓解。

Intratumoral and peritumoral habitat radiomics of MRI predicts pathologic complete response to neoadjuvant chemoimmunotherapy in oral squamous cell carcinoma.

作者信息

Yuan Zilong, Ai Shuangquan, He Qing, Wu Kun, Yang Miao, Zheng Kaiyi, He Yaoyao, Tang Xiaojuan, Liu Yulin, Wu Zheng, Wu Yuan

机构信息

Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan. Hubei, China.

Department of radiation oncology, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University & Hunan Cancer Hospital.

出版信息

Int J Surg. 2025 Jun 20. doi: 10.1097/JS9.0000000000002715.

DOI:10.1097/JS9.0000000000002715

PMID:40540293

Abstract

BACKGROUND

Neoadjuvant chemoimmunotherapy (NACI) shows promise in oral squamous cell carcinoma (OSCC), but reliable noninvasive biomarkers for predicting pathologic complete response (pCR) remain scarce. Radiomics integrating intratumoral and peritumoral heterogeneity across multi-sequence MRI may offer novel insights into treatment response evaluation.

METHODS

The data of 212 patients with OSCC after NACI were retrospectively collected and analyzed. Among these patients, 56 (26.4%) achieved pCR after NACI. Intratumoral and peritumoral habitat imaging (HI)was achieved using the K-means clustering algorithm applied to T1-weighted imaging (T1WI), axial T2-weighted imaging with fat suppression (T2WI), and contrast-enhanced T1-weighted imaging with fat suppression (T1C). Moreover, intratumoral and peritumoral HI models were constructedand compared using the receiver operating characteristic curve(ROC). Five-fold cross-validation was performed to mitigate model overfitting.

RESULTS

Intratumoral HI models derived from different sequences and the Intratumoral Fusion model exhibited favorable predictive ability, with AUCs of 0.738-0.817 and 0.729-0.789 in the training and testing cohorts, respectively. Moreover, peritumoral HI models displayed marginally higher predictive abilities compared to intratumoral HI and Fusion models, with AUCs of 0.734-0.869 and 0.788-0.802 in the training and testing cohorts, respectively. Meanwhile, the decision model with peritumoral habitat features (PHF S ), intratumoral habitat features (IHF S ), and 3 clinical features displayed the highest performance, with average AUCs of 0.913 and 0.843 in both respective cohorts. Among the most important features screened by SHAP, three IHFs and one PHF could effectively distinguish between the lower and higher groups of programmed cell death ligand 1 (PD-L1) Combined Positive Score (t = 2.027-2.275, p < 0.05), whilst two PHFs were highly correlated with CD45 + white blood cell densities in the stroma (r = 0.958, - 0.920, p < 0.05), which wereassociated with pCR.

CONCLUSIONS

Integrated intratumoral and peritumoral HI derived from multi-sequence MRI offers a high predictive capacity for pCR following NACI in OSCC patients.

摘要

背景

新辅助化疗免疫疗法（NACI）在口腔鳞状细胞癌（OSCC）中显示出前景，但用于预测病理完全缓解（pCR）的可靠非侵入性生物标志物仍然稀缺。整合多序列MRI中肿瘤内和肿瘤周围异质性的放射组学可能为治疗反应评估提供新的见解。

方法

回顾性收集并分析212例接受NACI治疗的OSCC患者的数据。在这些患者中，56例（26.4%）在NACI治疗后实现了pCR。使用应用于T1加权成像（T1WI）、轴向脂肪抑制T2加权成像（T2WI）和脂肪抑制对比增强T1加权成像（T1C）的K均值聚类算法实现肿瘤内和肿瘤周围生境成像（HI）。此外，构建肿瘤内和肿瘤周围HI模型，并使用受试者操作特征曲线（ROC）进行比较。进行五折交叉验证以减轻模型过拟合。

结果

来自不同序列的肿瘤内HI模型和肿瘤内融合模型表现出良好的预测能力，在训练和测试队列中的AUC分别为0.738 - 0.817和0.729 - 0.789。此外，肿瘤周围HI模型的预测能力略高于肿瘤内HI和融合模型，在训练和测试队列中的AUC分别为0.734 - 0.869和0.788 - 0.802。同时，具有肿瘤周围生境特征（PHF S）、肿瘤内生境特征（IHF S）和3个临床特征的决策模型表现最佳，在各自队列中的平均AUC分别为0.913和0.843。在SHAP筛选出的最重要特征中，三个IHF和一个PHF可以有效区分程序性细胞死亡配体1（PD-L1）联合阳性评分的低分组和高分组（t = 2.027 - 2.275，p < 0.05），而两个PHF与基质中CD45 +白细胞密度高度相关（r = 0.958， - 0.920，p < 0.05），这与pCR相关。