BACKGROUNDTransplantation of livers obtained from donors after circulatory death is associated with an increased risk of nonanastomotic biliary strictures. Hypothermic oxygenated machine perfusion of livers may reduce the incidence of biliary complications, but data from prospective, controlled studies are limited.

Background A small remnant liver volume is an important risk factor for posthepatectomy liver failure and can be predicted accurately by computed tomography (CT) volumetry using radiologic image analysis software. Unfortunately, this software is expensive and usually requires support by a radiologist. ImageJ is a freely downloadable image analysis software package developed by the National Institute of Health (NIH) and brings liver volumetry to the surgeon's desktop. We aimed to assess the accuracy of ImageJ for hepatic CT volumetry. Methods ImageJ was downloaded from http://www.rsb. info.nih.gov/ij/. Preoperative CT scans of 15 patients who underwent liver resection for colorectal cancer liver metastases were retrospectively analyzed. Scans were opened in ImageJ; and the liver, all metastases, and the intended parenchymal transection line were manually outlined on each slice. The area of each selected region, metastasis, resection specimen, and remnant liver was multiplied by the slice thickness to calculate volume. Volumes of virtual liver resection specimens measured with ImageJ were compared with specimen weights and calculated volumes obtained during pathology examination after resection. Results There was an excellent correlation between the volumes calculated with ImageJ and the actual measured weights of the resection specimens (r 2 = 0.98, p \ 0.0001). The weight/volume ratio amounted to 0.88 AE 0.04 (standard error) and was in agreement with our earlier findings using CT-linked radiologic software. Conclusion ImageJ can be used for accurate hepatic CT volumetry on a personal computer. This application brings CT volumetry to the surgeon's desktop at no expense and is particularly useful in cases of tertiary referred patients, who already have a proper CT scan on CD-ROM from the referring institution. Most likely the discrepancy between volume and weight results from exsanguination of the liver after resection.

Objectives Differentiating benign gallbladder diseases from gallbladder cancer (GBC) remains a radiological challenge because they can appear very similar on imaging. This study aimed at investigating whether CT-based radiomic features of suspicious gallbladder lesions analyzed by machine learning algorithms could adequately discriminate benign gallbladder disease from GBC. In addition, the added value of machine learning models to radiological visual CT-scan interpretation was assessed. Methods Patients were retrospectively selected based on confirmed histopathological diagnosis and available contrast-enhanced portal venous phase CT-scan. The radiomic features were extracted from the entire gallbladder, then further analyzed by machine learning classifiers based on Lasso regression, Ridge regression, and XG Boosting. The results of the best-performing classifier were combined with radiological visual CT diagnosis and then compared with radiological visual CT assessment alone. Results In total, 127 patients were included: 83 patients with benign gallbladder lesions and 44 patients with GBC. Among all machine learning classifiers, XG boosting achieved the best AUC of 0.81 (95% CI 0.72-0.91) and the highest accuracy rate of 73% (95% CI 65-80%). When combining radiological visual interpretation and predictions of the XG boosting classifier, the highest diagnostic performance was achieved with an AUC of 0.98 (95% CI 0.96-1.00), a sensitivity of 91% (95% CI 86-100%), a specificity of 93% (95% CI 90-100%), and an accuracy of 92% (95% CI 90-100%). Conclusions Machine learning analysis of CT-based radiomic features shows promising results in discriminating benign from malignant gallbladder disease. Combining CT-based radiomic analysis and radiological visual interpretation provided the most optimal strategy for GBC and benign gallbladder disease differentiation. Key Points& Radiomic-based machine learning algorithms are able to differentiate benign gallbladder disease from gallbladder cancer. & Combining machine learning algorithms with a radiological visual interpretation of gallbladder lesions at CT increases the specificity, compared to visual interpretation alone, from 73 to 93% and the accuracy from 85 to 92%. & Combined use of machine learning algorithms and radiological visual assessment seems the most optimal strategy for GBC and benign gallbladder disease differentiation.

To determine diagnostic performance of preoperative CT in differentiating between benign and malignant suspicious gallbladder lesions and to develop a preoperative risk score. Method: All patients referred between January 2007 and September 2018 for suspicion of gallbladder cancer (GBC) or incidentally found GBC were retrospectively analyzed. Patients were excluded when preoperative CT or histopathologic examination was lacking. Two radiologists, blinded to histopathology results, independently reviewed CT images to differentiate benign disease from GBC. Multivariable analysis and internal validation were used to develop a risk score for GBC. Model discrimination, calibration, and diagnostic performance were assessed. Results: In total, 118 patients with 39 malignant (33 %) and 79 benign (67 %) lesions were included. Sensitivity of CT for diagnosing GBC was 90 % (95 % confidence interval [CI]: 76-97). Specificity rates were 61 % (95 % CI: 49-72) and 59 % (95 % CI: 48-70). Three predictors of GBC (irregular lesion aspect, absence of fat stranding, and locoregional lymphadenopathy) were included in the risk score ranging from-1 to 4. Adequate performance was found (AUC: 0.79, calibration slope: 0.89). In patients allocated >0 points, the model showed higher performance in excluding GBC than the radiologists (sensitivity 92 % [95 % CI: 79-98]). Moreover, when allocated >3 points, the risk score was superior in diagnosing GBC (specificity 99 % [95 % CI: 93-100]). Conclusions: Sensitivity rates of CT for differentiation between benign and malignant gallbladder lesions are high, however specificity rates are relatively low. The proposed risk score may facilitate differentiation between benign and malignant suspicious gallbladder lesions.