用CT和临床数据提前预测卵巢癌化疗反应，辅助术前决策。
Vision Transformers for Preoperative CT-Based Prediction of Histopathologic Chemotherapy Response Score in High-Grade Serous Ovarian Carcinoma
- 用视觉Transformer处理腹部CT图像，融合临床信息做多模态分析。
- 内部验证集准确率95%，外部验证集AUC达0.68，具备临床应用潜力。
- 适合肿瘤科、放射科医生在术前评估化疗效果时参考使用。
高分级浆液性卵巢癌（HGSOC）具有显著的生物学与空间异质性，常于晚期确诊。对于无法立即进行减瘤手术的患者，常采用新辅助化疗（NACT）后延迟原发手术。化疗反应评分（CRS）是经验证的组织病理学标志物，但仅可在术后获得。本研究探讨是否可利用术前计算机断层扫描（CT）影像与临床数据，预测CRS，作为多学科团队（MDT）讨论中预判治疗反应的辅助工具。我们提出一种2.5D多模态深度学习框架，采用预训练的视觉Transformer编码器处理病灶密集的网膜切片，并通过中间融合模块将视觉表征与临床变量结合以预测CRS。结果显示，该多模态模型在内部测试队列（IEO，n=41）上达到0.95的ROC-AUC，准确率95%，精确率80%；在外部测试集（OV04，n=70）上，实现0.68的ROC-AUC，准确率67%，精确率75%。初步结果表明，基于Transformer的深度学习方法可有效利用常规临床数据与CT影像，实现术前对HGSOC CRS的预测。作为一项探索性术前决策支持工具，该方法或可帮助多学科团队更早获取非侵入性治疗反应预估。
原文摘要 · Abstract (English)
Purpose. High-grade serous ovarian carcinoma (HGSOC) is characterized by pronounced biological and spatial heterogeneity and is frequently diagnosed at an advanced stage. Neoadjuvant chemotherapy (NACT) followed by delayed primary surgery is commonly employed in patients unsuitable for primary cytoreduction. The Chemotherapy Response Score (CRS) is a validated histopathological biomarker of response to NACT, but it is only available postoperatively. In this study, we investigate whether pre-treatment computed tomography (CT) imaging and clinical data can be used to predict CRS as an investigational decision-support adjunct to inform multidisciplinary team (MDT) discussions regarding expected treatment response. Methods. We proposed a 2.5D multimodal deep learning framework that processes lesion-dense omental slices using a pre-trained Vision Transformer encoder and integrates the resulting visual representations with clinical variables through an intermediate fusion module to predict CRS. Results. Our multimodal model, integrating imaging and clinical data, achieved a ROC-AUC of 0.95 alongside 95% accuracy and 80% precision on the internal test cohort (IEO, n=41 patients). On the external test set (OV04, n=70 patients), it achieved a ROC-AUC of 0.68, alongside 67% accuracy and 75% precision. Conclusion. These preliminary results demonstrate the feasibility of transformer-based deep learning for preoperative prediction of CRS in HGSOC using routine clinical data and CT imaging. As an investigational, pre-treatment decision-support tool, this approach may assist MDT discussions by providing early, non-invasive estimates of treatment response.
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