一种用于增强卵巢癌症化疗结果预测的视觉转化体放射组学方法

摘要(中文译文)

卵巢癌患者化疗反应的早期预测对于实现个体化治疗策略和改善临床结局至关重要。然而，由于肿瘤生物学的高度异质性、患者个体因素以及治疗方案差异，这一预测仍具有挑战性。近年来，源自影像组学和先进深度学习方法衍生的影像生物标志物为表征肿瘤表型和预测治疗结局提供了有前景的工具。在这项回顾性研究中，分析了182例卵巢癌患者的治疗前CT扫描。提取了三类影像特征：手工设计的影像组学描述符、预训练视觉Transformer（ViT）的嵌入特征，以及MedSAM（一种适用于分割的医学基础模型）的嵌入特征。所有特征均经过标准化处理，并采用最小绝对收缩和选择算子（LASSO）回归进行特征选择。训练支持向量机（SVM）分类器以预测6个月无进展生存期（PFS）。采用交叉验证指标评估模型性能，包括受试者工作特征曲线下面积（AUC）和分类准确率。ViT与MedSAM嵌入特征的联合模型取得了最高AUC，为0.924 ± 0.032。整合全部三组特征（影像组学、ViT和MedSAM）获得了相当的AUC，为0.924 ± 0.037，以及最高的分类准确率，为0.831 ± 0.042。这些发现表明，整合互补的影像表征可增强化疗反应预测。基于Transformer的嵌入特征与影像组学特征的结合可从CT成像中提供丰富的、任务特异性的肿瘤表征，并支持精准肿瘤学决策工具的开发。

Abstract

Early prediction of chemotherapy response in ovarian cancer patients is essential for enabling personalized treatment strategies and improving clinical outcomes. However, this prediction remains challenging due to the high heterogeneity of tumor biology, patient-specific factors, and treatment regimens. Recent advances in imaging biomarkers derived from both radiomics and advanced deep learning methods offer promising tools for characterizing tumor phenotypes and predicting treatment outcomes. In this retrospective study, pre-treatment CT scans from 182 ovarian cancer patients were analyzed. Three categories of imaging features were extracted: handcrafted radiomics descriptors, embeddings from a pretrained Vision Transformer (ViT), and embeddings from MedSAM, a medical foundation model adapted for segmentation. All features were standardized and subjected to least absolute shrinkage and selection operator (LASSO) regression for feature selection. Support vector machine (SVM) classifiers were trained to predict 6-month progression-free survival (PFS). Model performance was evaluated using cross-validated metrics including area under the receiver operating characteristic curve (AUC) and classification accuracy. The combined ViT and MedSAM embedding model achieved the highest AUC of 0.924 ± 0.032. Integration of all three feature groups (radiomics, ViT, and MedSAM) yielded a comparable AUC of 0.924 ± 0.037 and the highest classification accuracy of 0.831 ± 0.042. These findings demonstrate that integrating complementary imaging representations enhances chemotherapy response prediction. The combination of transformer-based embeddings and radiomics features provides rich, task-specific tumor characterization from CT imaging and supports the development of precision oncology decision tools.

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