Background:Pancreatic and periampullary cancers are common tumors of the digestive tract. As a radical surgical approach, laparoscopic pancreaticoduodenectomy requires crucial preoperative assessment of its surgical difficulty.Materials and methods:A retrospective cohort of 150 patients who underwent LPD between June 2019 and June 2023 was enrolled. The criteria for defining the difficult group were identified as unplanned conversion to open procedure, intraoperative blood loss, and operative time. Participants were randomly allocated to a training set (n= 105) or a testing set (n= 45) in a 7:3 ratio. Hand-crafted radiomics (HCR) features and deep learning-derived radiomics (DLR) features were extracted from portal venous phase CT images, focusing on gross tumor volume and gross peri-tumor volume. A hybrid prediction model was developed using a support vector machine algorithm, with performance evaluated through receiver operating characteristic analysis, calibration curves, and decision curve analysis (DCA).Results:The combined model demonstrated significantly superior discriminative ability, achieving an area under the curve (AUC) of 0.942 (95% CI: 0.893–0.992) in the training set and 0.848 (95% CI: 0.738–0.958) in the testing set. This performance exceeded both the standalone HCR model (testing AUC = 0.754) and the DLR model (testing AUC = 0.816). DCA further confirmed the clinical utility of the combined model, showing the highest net benefit across threshold probabilities exceeding 20%.Conclusions:The novel integrated model combining hand-crafted and deep learning-derived radiomics features enables effective prediction of surgical difficulty in laparoscopic pancreaticoduodenectomy.
背景：胰腺及壶腹周围癌是常见的消化道肿瘤。腹腔镜胰十二指肠切除术作为根治性手术方式，其术前手术难度评估至关重要。 材料与方法：本研究纳入2019年6月至2023年6月期间接受腹腔镜胰十二指肠切除术的150例患者回顾性队列。将非计划中转开腹、术中失血量及手术时间作为界定困难手术组的标准。按7:3比例将参与者随机分配至训练集（n=105）或测试集（n=45）。从门静脉期CT图像中提取手工放射组学特征和深度学习放射组学特征，重点关注肿瘤总体积及瘤周总体积。采用支持向量机算法构建混合预测模型，并通过受试者工作特征曲线分析、校准曲线及决策曲线分析评估模型性能。 结果：融合模型展现出显著优越的判别能力，在训练集中曲线下面积达0.942（95% CI：0.893-0.992），测试集中为0.848（95% CI：0.738-0.958）。该性能优于独立的手工放射组学模型（测试集AUC=0.754）和深度学习放射组学模型（测试集AUC=0.816）。决策曲线分析进一步证实融合模型的临床实用性，在阈值概率超过20%的范围内均显示最高净获益。 结论：融合手工与深度学习放射组学特征的新型集成模型，能够有效预测腹腔镜胰十二指肠切除术的手术难度。
肿瘤(癌症)患者之家