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Despite tremendous advances in traditional imaging technology over the past few decades, the intraoperative identification of lesions is still based on naked eye observation or pre-operative image evaluation. However, these two-dimensional image data cannot objectively reflect the complex anatomical structure of the liver and the detailed morphological features of the lesion, which directly limits the clinical application value of these imaging data in surgery in that it cannot improve the curative efficacy of surgery and the prognosis of the patient. This traditional mode of diagnosis and treatment has been changed by digital medical imaging technology in the new era with its significant function of accurate and efficient diagnosis of diseases, selection of reasonable treatment schemes, improvement of radical resection rate and reduction of surgical risk. In this paper, we reviewed the latest application of digital intelligent diagnosis and treatment technology related to liver surgery in the hope that it may help to achieve accurate treatment of liver surgery diseases.

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Objective: This study aimed to build and evaluate a radiomics feature-based model for the preoperative prediction of microvascular invasion (MVI) in patients with hepatocellular carcinoma. Methods: A total of 145 patients were retrospectively included in the study pool, and the patients were divided randomly into two independent cohorts with a ratio of 7:3 (training cohort: n = 101, validation cohort: n = 44). For a pilot study of this predictive model another 18 patients were recruited into this study. A total of 1,231 computed tomography (CT) image features of the liver parenchyma without tumors were extracted from portalphase CT images. A least absolute shrinkage and selection operator (LASSO) logistic regression was applied to build a radiomics score (Rad-score) model. Afterwards, a nomogram, including Rad-score as well as other clinicopathological risk factors, was established with a multivariate logistic regression model. The discrimination efficacy, calibration efficacy, and clinical utility value of the nomogram were evaluated. Results: The Rad-score scoring model could predict MVI with the area under the curve (AUC) of 0.637 (95% CI, 0.516-0.758) in the training cohort as well as of 0.583 (95% CI, 0.395-0.770) in the validation cohort; however, the aforementioned discriminative approach could not completely outperform those existing predictors (alpha fetoprotein, neutrophilic granulocyte, and preoperative hemoglobin). The individual predictive nomogram which included the Rad-score, alpha fetoprotein, neutrophilic granulocyte, and preoperative hemoglobin showed a better discrimination efficacy with AUC of 0.865 (95% CI, 0.786-0.944), which was higher than the conventional methods' AUCs (nomogram vs Rad-score, alpha fetoprotein, neutrophilic granulocyte, and preoperative hemoglobin at P < 0.001, P = 0.025, P < 0.001, and P = 0.001, respectively). When applied to the validation cohort, the nomogram discrimination efficacy was still outbalanced those above mentioned three remaining methods (AUC: 0.705; 95% CI, 0.537-0.874). The calibration curves of this proposed method showed a satisfying

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