术前超声放射组学预测胰腺切除术后临床相关的胰瘘。

Yun-Lin Huang, Xiao-Fan Tian, Yi-Jie Qiu, Wen-Hui Lou, Ernst-Michael Jung, Yi Dong, Han-Zhang Wang, Wen-Ping Wang
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引用次数: 0

摘要

目的:评估基于术前B型超声(BMUS)和剪切波弹性成像(SWE)的放射组学模型预测临床相关术后胰瘘(CR-POPF)发生的疗效。分析了POPF的危险因素(B级和C级)。术前BMUS图像、胰腺病变及周围实质的SWE值用于建立术前预测放射组学模型。使用最小冗余最大相关性(mRMR)算法和L1惩罚逻辑回归提取并构建放射特征。使用多元回归建立了一个组合模型,该模型结合了放射组学特征和临床数据。结果:从2020年1月到2021年11月,共有147名患者(85例远端胰切除术和62例胰十二指肠切除术)入选。在胰腺切除术后三周的随访中,B/C级POPF的发生率为28.6%(42/147)。在CR-POPF的术前无创预测中,由胰腺实质区BMUS构建的放射特征在受试者工作特征曲线下的面积(AUC)为0.75,准确率为68.7%,灵敏度为85.7%,特异性为61.9%。当panRS用于预测胰十二指肠切除术后CR-POPF时,放射组学模型的AUC增加到0.81。结论:基于超声图像的放射组学模型有可能用于预测CR-POPF。术后应密切监测有高危因素的患者。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Preoperative ultrasound radiomics for predicting clinically relevant postoperative pancreatic fistula after pancreatectomy.

Objectives: To evaluate the efficacy of the radiomics model based on preoperative B-mode ultrasound (BMUS) and shear wave elastography (SWE) for predicting the occurrence of clinically relevant-postoperative pancreatic fistula (CR-POPF).

Methods: Patients who were scheduled to undergo pancreatectomy were prospectively enrolled and received ultrasound assessment within one week before surgery. The risk factors of POPF (grades B and grades C) were analyzed. Preoperative BMUS images, SWE values of pancreatic lesions and surrounding parenchyma were used to build preoperative prediction radiomics models. Radiomic signatures were extracted and constructed using a minimal Redundancy Maximal Relevance (mRMR) algorithm and an L1 penalized logistic regression. A combined model was built using multivariate regression which incorporated radiomics signatures and clinical data.

Results: From January 2020 to November 2021, a total of 147 patients (85 distal pancreatectomies and 62 pancreaticoduodenectomies) were enrolled. During the three-week follow-up after pancreatectomy, the incidence rates of grade B/C POPF were 28.6% (42/147). Radiomic signatures constructed from BMUS of pancreas parenchymal regions (panRS) achieved an area under the receiver operating characteristic curve (AUC) of 0.75, accuracy of 68.7%, sensitivity of 85.7 %, and specificity of 61.9 % in preoperative noninvasive prediction of CR-POPF. The AUC of the radiomics model increased to 0.81 when panRS was used for the prediction of CR-POPF after pancreaticoduodenectomy.

Conclusions: Radiomics model based on ultrasound images was potentially useful for predicting CR-POPF. Patients with high-risk factors should be closely monitored when postoperation.

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