基于铂类的新辅助化疗 (NACT) 随后进行根治性子宫切除术已被提议作为 Ib2-IIb 期宫颈癌 (CC) 的替代治疗方法，这些患者强烈希望接受手术治疗。我们的研究旨在通过使用放射组学和深度学习开发基于多模态 MRI 的模型来预测接受新辅助放化疗 (NACRT) 治疗的 CC 患者的治疗反应。 2009年8月至2013年6月，在福建省肿瘤医院接受NACRT的Ib2-IIb期（FIGO 2008）CC患者纳入我们的研究。分别收集临床信息、对比增强T1加权成像（CE-T1WI）和T2加权成像（T2WI）数据。分别使用放射组学和深度学习模型从图像中提取放射组学特征和深度抽象特征。然后，采用ElasticNet和SVM-RFE进行特征选择，构建四个单序列特征集。对两个多序列特征集和一个混合特征集进行早期融合，然后使用四个机器学习分类器进行分类预测。随后，通过将患者分为训练集和验证集来评估模型在预测 NACRT 反应方面的性能。此外，使用 Kaplan-Meier 生存曲线评估总生存期 (OS) 和无病生存期 (DFS)。在四种机器学习模型中，SVM 表现出最好的预测性能（AUC=0.86）。在七个特征集中，混合特征集在验证集中实现了 AUC（0.86）、ACC（0.75）、Recall（0.75）、Precision（0.81）和 F1-score（0.75）的最高值，优于其他特征套。此外，该模型的预测结果与患者 OS 和 DFS 密切相关（p = 0.0044；p = 0.0039）。基于 MRI 图像的模型具有多个序列和不同方法的特征，可以精确预测 CC 患者对 NACRT 的反应。该模型可以帮助临床医生制定个性化治疗计划并预测患者生存结果。© 2024。作者。

Platinum-based neoadjuvant chemotherapy (NACT) followed by radical hysterectomy has been proposed as an alternative treatment approach for cervical cancer (CC) in stage Ib2-IIb, who had a strong desire to be treated with surgery. Our study aims to develop a model based on multimodal MRI by using radiomics and deep learning to predict the treatment response in CC patients treated with neoadjuvant chemoradiotherapy (NACRT). From August 2009 to June 2013, CC patients in stage Ib2-IIb (FIGO 2008) who received NACRT at Fujian Cancer Hospital were enrolled in our study. Clinical information, contrast-enhanced T1-weighted imaging (CE-T1WI), and T2-weighted imaging (T2WI) data were respectively collected. Radiomic features and deep abstract features were extracted from the images using radiomics and deep learning models, respectively. Then, ElasticNet and SVM-RFE were employed for feature selection to construct four single-sequence feature sets. Early fusion of two multi-sequence feature sets and one hybrid feature set were performed, followed by classification prediction using four machine learning classifiers. Subsequently, the performance of the models in predicting the response to NACRT was evaluated by separating patients into training and validation sets. Additionally, overall survival (OS) and disease-free survival (DFS) were assessed using Kaplan-Meier survival curves. Among the four machine learning models, SVM exhibited the best predictive performance (AUC=0.86). Among the seven feature sets, the hybrid feature set achieved the highest values for AUC (0.86), ACC (0.75), Recall (0.75), Precision (0.81), and F1-score (0.75) in the validation set, outperforming other feature sets. Furthermore, the predicted outcomes of the model were closely associated with patient OS and DFS (p = 0.0044; p = 0.0039). A model based on MRI images with features from multiple sequences and different methods could precisely predict the response to NACRT in CC patients. This model could assist clinicians in devising personalized treatment plans and predicting patient survival outcomes.© 2024. The Author(s).