Dataset related to article "A virtual biopsy of liver parenchyma to predict the outcome of liver resection"

This record contains raw data related to article “A virtual biopsy of liver parenchyma to predict the outcome of liver resection" Abstract The preoperative risk assessment of liver resections (LR) is still an open issue. Liver parenchyma characteristics influence the outcome but cannot be adequately evaluated in the preoperative setting. The present study aims to elucidate the contribution of the radiomic analysis of non-tumoral parenchyma to the prediction of complications after elective LR. All consecutive patients undergoing LR between 2017 and 2021 having a preoperative computed tomography (CT) were included. Patients with associated biliary/colorectal resection were excluded. Radiomic features were extracted from a virtual biopsy of non-tumoral liver parenchyma (a 2 mL cylinder) outlined in the portal phase of preoperative CT. Data were internally validated. Overall, 378 patients were analyzed (245 males/133 females-median age 67 years-39 cirrhotics). Radiomics increased the performances of the preoperative clinical models for both liver dysfunction (at internal validaton, AUC = 0.727 vs. 0.678) and bile leak (AUC = 0.744 vs. 0.614). The final predictive model combined clinical and radiomic variables: for bile leak, segment 1 resection, exposure of Glissonean pedicles, HU-related indices, NGLDM_Contrast, GLRLM indices, and GLZLM_ZLNU; for liver dysfunction, cirrhosis, liver function tests, major hepatectomy, segment 1 resection, and NGLDM_Contrast. The combined clinical-radiomic model for bile leak based on preoperative data performed even better than the model including the intraoperative data (AUC = 0.629). The textural features extracted from a virtual biopsy of non-tumoral liver parenchyma improved the prediction of postoperative liver dysfunction and bile leak, implementing information given by standard clinical data. Radiomics should become part of the preoperative assessment of candidates to LR.本数据集包含与论文《肝实质虚拟活检预测肝切除术结局》相关的原始数据。 摘要 肝切除术（liver resection, LR）的术前风险评估仍是待解决的临床难题。肝实质特征可影响手术结局，但在术前场景中无法对其进行充分评估。本研究旨在阐明非肿瘤肝实质的放射组学（radiomics）分析对预测择期肝切除术后并发症的价值。研究纳入2017至2021年间所有接受肝切除术且术前完成计算机断层扫描（computed tomography, CT）的连续入组患者，排除合并胆道或结直肠切除术的受试者。从术前CT门静脉期勾画的非肿瘤肝实质虚拟活检标本（体积为2 mL的圆柱体）中提取放射组学特征，并开展内部验证。最终共纳入378例患者进行分析（男性245例、女性133例，中位年龄67岁，其中肝硬化患者39例）。放射组学特征可显著提升术前临床模型的预测性能：在内部验证中，针对术后肝功能不全的预测AUC从0.678提升至0.727，针对胆漏的预测AUC从0.614提升至0.744。最终的联合预测模型整合了临床与放射组学变量：针对胆漏，变量包括第1肝段切除术、Glisson蒂暴露、HU相关指数、NGLDM_Contrast、GLRLM指数及GLZLM_ZLNU；针对肝功能不全，变量包括肝硬化、肝功能检测指标、大肝切除术、第1肝段切除术及NGLDM_Contrast。基于术前数据构建的胆漏临床-放射组学联合模型，其预测性能甚至优于纳入术中数据的模型（AUC=0.629）。从非肿瘤肝实质虚拟活检标本中提取的纹理特征，可补充标准临床数据所提供的信息，从而优化术后肝功能不全与胆漏的预测效能。放射组学应成为肝切除术候选者术前评估的常规组成部分。