Preoperative identification of microvascular invasion in hepatocellular carcinoma based on multi-modal and multi response convolutional neural network

Xin Dai; Sue Cao; Yongze Guo; Wenqi Shi; Lina Zhang; Mengsi Li; Jingbiao Chen; Jin Wang; Yao Lu

doi:10.1117/12.2680693

27 June 2023 Preoperative identification of microvascular invasion in hepatocellular carcinoma based on multi-modal and multi response convolutional neural network

Xin Dai, Sue Cao, Yongze Guo, Wenqi Shi, Lina Zhang, Mengsi Li, Jingbiao Chen, Jin Wang, Yao Lu

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Proceedings Volume 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022); 1270510 (2023) https://doi.org/10.1117/12.2680693
Event: Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 2022, Nanjing, China

Abstract

Microvascular invasion (MVI) is a reliable predictor of the survival of patients with hepatocellular carcinoma (HCC). Accurate preoperative MVI assessment is essential to determine the appropriate surgical approach and management strategy to decrease the HCC recurrence rate. In this study, a preoperative evaluation method was proposed based on a convolutional neural network (CNN) model. Using Computed Tomography (CT) volume data, the relationship between CT volume data and MVI can be explored based on a multi-modal and multi-response CNN via an end-to-end model. A total of 400 patients were included in this study. First, the arterial phase (AP) and venous phase (VP) volume data were used as the inputs of the model; The size of the input was arbitrary and the inputs was converted to the same size by spatial pyramid pooling (SPP) behind. Then, these features were merged by the multi-modal network. The features of the AP and VP were combined through the multi-modal fusion of decision-making layers. Of the 400 patients, 215 (53.75%) and 185 (46.25%) are MVI-positive and MVI-negative cases, respectively. The areas under the receiver operating characteristic curves of the three-dimensional (3D) CNN model corresponding to the training and testing sets were 0.904 and 0.893, respectively. In the test set, 88.89% of the MVI-negative cases (16/18) and 86.37% of the MVI-positive cases (19/22) were detected. The evaluation results indicated a considerable potential feature correlation between CT volume data and MVI. The proposed multi-modal and multi-response CNN model had positive effect on the preoperative evaluation of MVI.

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Xin Dai, Sue Cao, Yongze Guo, Wenqi Shi, Lina Zhang, Mengsi Li, Jingbiao Chen, Jin Wang, and Yao Lu "Preoperative identification of microvascular invasion in hepatocellular carcinoma based on multi-modal and multi response convolutional neural network", Proc. SPIE 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 1270510 (27 June 2023); https://doi.org/10.1117/12.2680693

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KEYWORDS

Feature extraction

Feature fusion

Tumors

Data modeling

Process modeling

3D modeling

Spiral phase plates

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