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04.08.2021 | Imaging Informatics and Artificial Intelligence

Deep convolutional neural network for preoperative prediction of microvascular invasion and clinical outcomes in patients with HCCs

verfasst von: Xinming Li, Zhendong Qi, Haiyan Du, Zhijun Geng, Zhipeng Li, Shuping Qin, Xuhui Zhang, Jianye Liang, Xiao Zhang, Wen Liang, Wei Yang, Chuanmiao Xie, Xianyue Quan

Erschienen in: European Radiology | Ausgabe 2/2022

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Abstract

Objectives

We aimed to develop and validate a deep convolutional neural network (DCNN) model for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) and its clinical outcomes using contrast-enhanced computed tomography (CECT) in a large population of candidates for surgery.

Methods

This retrospective study included 1116 patients with HCC who had undergone preoperative CECT and curative hepatectomy. Radiological (R), DCNN, and combined nomograms were constructed in a training cohort (n = 892) respectively based on clinicoradiological factors, DCNN probabilities, and all factors; the performance of each model was confirmed in a validation cohort (n = 244). Accuracy and the AUC to predict MVI were calculated. Disease-free survival (DFS) and overall survival (OS) after surgery were recorded.

Results

The proportion of MVI-positive patients was respectively 38.8% (346/892) and 35.7 % (87/244) in the training and validation cohorts. The AUCs of the R, DCNN, and combined nomograms were respectively 0.809, 0.929, and 0.940 in the training cohorts and 0.837, 0.865, and 0.897 in the validation cohort. The combined nomogram outperformed the R nomogram in the training (p < 0.001) and validation (p = 0.009) cohorts. There was a significant difference in DFS and OS between the R, DCNN, and combined nomogram-predicted groups with and without MVI (p < 0.001).

Conclusions

The combined nomogram based on preoperative CECT performs well for preoperative prediction of MVI and outcome.

Key Points

• A combined nomogram based on clinical information, preoperative CECT, and DCNN can predict MVI and clinical outcomes of patients with HCC.

• DCNN provides added diagnostic ability to predict MVI.

• The AUCs of the combined nomogram are 0.940 and 0.897 in the training and validation cohorts, respectively.

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Titel: Deep convolutional neural network for preoperative prediction of microvascular invasion and clinical outcomes in patients with HCCs
verfasst von: Xinming Li
Zhendong Qi
Haiyan Du
Zhijun Geng
Zhipeng Li
Shuping Qin
Xuhui Zhang
Jianye Liang
Xiao Zhang
Wen Liang
Wei Yang
Chuanmiao Xie
Xianyue Quan
Publikationsdatum: 04.08.2021
Verlag: Springer Berlin Heidelberg
Erschienen in: European Radiology / Ausgabe 2/2022
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-021-08198-w

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Deep convolutional neural network for preoperative prediction of microvascular invasion and clinical outcomes in patients with HCCs