Fiz Francesco, Rossi Noemi, Langella Serena, Conci Simone, Serenari Matteo, Ardito Francesco, Cucchetti Alessandro, Gallo Teresa, Zamboni Giulia A, Mosconi Cristina, Boldrini Luca, Mirarchi Mariateresa, Cirillo Stefano, Ruzzenente Andrea, Pecorella Ilaria, Russolillo Nadia, Borzi Martina, Vara Giulio, Mele Caterina, Ercolani Giorgio, Giuliante Felice, Cescon Matteo, Guglielmi Alfredo, Ferrero Alessandro, Sollini Martina, Chiti Arturo, Torzilli Guido, Ieva Francesca, Viganò Luca
Nuclear Medicine Unit, Department of Diagnostic Imaging, Ente Ospedaliero "Ospedali Galliera", Genoa, Italy.
Department of Nuclear Medicine and Clinical Molecular Imaging, University Hospital, Tübingen, Germany.
Ann Surg Oncol. 2024 Sep;31(9):5604-5614. doi: 10.1245/s10434-024-15457-9. Epub 2024 May 26.
For many tumors, radiomics provided a relevant prognostic contribution. This study tested whether the computed tomography (CT)-based textural features of intrahepatic cholangiocarcinoma (ICC) and peritumoral tissue improve the prediction of survival after resection compared with the standard clinical indices.
All consecutive patients affected by ICC who underwent hepatectomy at six high-volume centers (2009-2019) were considered for the study. The arterial and portal phases of CT performed fewer than 60 days before surgery were analyzed. A manual segmentation of the tumor was performed (Tumor-VOI). A 5-mm volume expansion then was applied to identify the peritumoral tissue (Margin-VOI).
The study enrolled 215 patients. After a median follow-up period of 28 months, the overall survival (OS) rate was 57.0%, and the progression-free survival (PFS) rate was 34.9% at 3 years. The clinical predictive model of OS had a C-index of 0.681. The addition of radiomic features led to a progressive improvement of performances (C-index of 0.71, including the portal Tumor-VOI, C-index of 0.752 including the portal Tumor- and Margin-VOI, C-index of 0.764, including all VOIs of the portal and arterial phases). The latter model combined clinical variables (CA19-9 and tumor pattern), tumor indices (density, homogeneity), margin data (kurtosis, compacity, shape), and GLRLM indices. The model had performance equivalent to that of the postoperative clinical model including the pathology data (C-index of 0.765). The same results were observed for PFS.
The radiomics of ICC and peritumoral tissue extracted from preoperative CT improves the prediction of survival. Both the portal and arterial phases should be considered. Radiomic and clinical data are complementary and achieve a preoperative estimation of prognosis equivalent to that achieved in the postoperative setting.
对于许多肿瘤，放射组学提供了相关的预后信息。本研究旨在测试与标准临床指标相比，基于计算机断层扫描（CT）的肝内胆管癌（ICC）及瘤周组织纹理特征是否能改善切除术后生存的预测。
本研究纳入了在六个大型中心（2009 - 2019年）接受肝切除术的所有连续性ICC患者。分析术前少于60天进行的CT动脉期和门静脉期图像。对肿瘤进行手动分割（肿瘤感兴趣区，Tumor-VOI）。然后进行5毫米的体积扩展以识别瘤周组织（边缘感兴趣区，Margin-VOI）。
本研究共纳入215例患者。中位随访期28个月后，3年总生存率（OS）为57.0%，无进展生存率（PFS）为34.9%。OS的临床预测模型C指数为0.681。添加放射组学特征后性能逐步改善（包括门静脉期Tumor-VOI时C指数为0.71，包括门静脉期Tumor-VOI和Margin-VOI时C指数为0.752，包括门静脉期和动脉期所有感兴趣区时C指数为0.764）。后一种模型结合了临床变量（CA19-9和肿瘤形态）、肿瘤指标（密度、均匀性）、边缘数据（峰度、紧密性、形状）和灰度游程长度矩阵（GLRLM）指标。该模型的性能与包含病理数据的术后临床模型相当（C指数为0.765）。PFS也观察到相同结果。
从术前CT中提取的ICC及瘤周组织的放射组学可改善生存预测。应同时考虑门静脉期和动脉期。放射组学和临床数据具有互补性，可实现与术后情况相当的术前预后评估。
