Molecular Pharmacology, Department of Experimental Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milano, Italy; Sarcoma Service, Department of Surgery, Fondazione IRCCS Istituto Nazionale dei Tumori, Milano, Italy.
Unit of Biostatistics for Clinical Research, Department of Data Science, Fondazione IRCCS Istituto Nazionale dei Tumori, Milano, Italy.
Eur J Cancer. 2024 Dec;213:115120. doi: 10.1016/j.ejca.2024.115120. Epub 2024 Nov 9.
Risk-stratification of patients with retroperitoneal sarcomas (RPS) relies on validated nomograms, such as Sarculator. This retrospective study investigated whether radiomic features extracted from computed tomography (CT) imaging could i) enhance the performance of Sarculator and ii) identify G3 dedifferentiated liposarcoma (DDLPS) or leiomyosarcoma (LMS), which are currently consider in a randomized clinical trial testing neoadjuvant chemotherapy.
Patients with primary localized RPS treated with curative-intent surgery (2011-2015) and available pre-operative CT imaging were included. Regions of interest (ROIs) were manually annotated on both unenhanced and portal venous phase acquisitions. Top performing radiomic features were selected with outcome-specific random forest models, through generation of replicative experiments (contexts) where patients were split into training and testing sets. Endpoints were overall and disease-free survival (OS, DFS). Prognostic models for DFS and OS included the top five selected radiomic features and the Sarculator nomogram score. Models accuracy was assessed with Harrell's Concordance (C-)index.
The study included 112 patients, with a median follow-up of 77 months (IQR 65-92 months). Sarculator alone achieved a C-index of 0.622 and 0.686 for DFS and OS, respectively. Radiomic features only marginally enhanced the prediction accuracy of Sarculator for OS (C-index=0.726, C-index gain: 0.04) or DFS (C-index=0.639, C-index gain: 0.017). Finally, radiomic features identified patients with G3 DDLPS or LMS with an accuracy of 0.806.
Radiomic features marginally improved the performance of Sarculator in RPS. However, they accurately identified G3 DDLPS or LMS at diagnosis, potentially improving patients selection for neoadjuvant treatments.
腹膜后肉瘤（RPS）患者的风险分层依赖于经过验证的列线图，如 Sarculator。本回顾性研究旨在探讨从 CT 成像中提取的放射组学特征是否可以：i）提高 Sarculator 的性能；ii）识别 G3 去分化脂肪肉瘤（DDLPS）或平滑肌肉瘤（LMS），目前正在一项临床试验中测试新辅助化疗。
纳入 2011 年至 2015 年接受根治性手术治疗的原发性局限性 RPS 患者，并提供术前 CT 影像学资料。通过生成复制实验（上下文），将患者分为训练集和测试集，手动在未增强和门静脉期采集图像上标注感兴趣区域（ROI）。使用基于结果的随机森林模型选择表现最佳的放射组学特征。终点为总生存期（OS）和无病生存期（DFS）。DFS 和 OS 的预后模型包括前五个选定的放射组学特征和 Sarculator 列线图评分。模型准确性通过 Harrell 一致性（C-）指数评估。
本研究纳入 112 例患者，中位随访时间为 77 个月（IQR 65-92 个月）。Sarculator 单独用于预测 DFS 和 OS 的 C-指数分别为 0.622 和 0.686。放射组学特征仅略微提高了 Sarculator 对 OS（C-指数=0.726，C-指数增益：0.04）或 DFS（C-指数=0.639，C-指数增益：0.017）的预测准确性。最后，放射组学特征准确识别出 G3 DDLPS 或 LMS 患者，准确率为 0.806。
放射组学特征略微提高了 Sarculator 在 RPS 中的性能。然而，它们在诊断时准确识别出 G3 DDLPS 或 LMS，可能会改善患者对新辅助治疗的选择。
