Liver failure is a key determinant influencing the natural history of hepatocellular carcinoma (HCC). In this large multi-centre study we externally validate a novel biomarker of liver functional reserve, the ALBI grade, across all the stages of HCC.

Intermediate stage hepatocellular carcinoma (HCC) is a very heterogeneous tumor in terms of tumor size (>3 cm ∼ over 10 cm), tumor number (4 ∼ over 20) and liver function (Child-Pugh score 5-9). However, transarterial chemoembolization is the only recommended treatment option according to the Barcelona Clinic Liver Cancer (BCLC) staging. Bolondi's subclassification of BCLC B stage is feasible; however, there are several weak points. Therefore, by modifying Bolondi's subclassification, we have proposed a more simplified subclassification, Kinki criteria. The Kinki criteria consist of 2 factors: liver function (Child-Pugh score 5-7 or 8, 9) and tumor status (Beyond Milan and within up-to-7 criteria; IN and OUT). The Kinki criteria classifies BCLC B stage from B1 (Child-Pugh score 5-7 and within up-to-7), B2 (Child-Pugh score 5-7 and beyond up-to-7) and B3 (Child-Pugh score 8, 9 and any tumor status). These criteria are simple and easy to apply to clinical practice. Therefore, these criteria will stratify the heterogeneous population of BCLC B group patient well and give the treatment indication according to each substage. These criteria should be further validated both retrospectively and prospectively.

Background and Aims: Patients with intermediate-stage hepatocellular carcinoma (HCC) refractory to transcatheter arterial chemoembolization (TACE) are considered to be candidates for sorafenib. The aim of this study was to evaluate the superiority of conversion of treatment to sorafenib on overall survival (OS) for cases refractory to TACE. Methods: This was a retrospective cohort study carried out on 497 patients with HCC who were treated with TACE therapy at our hospital between January 2008 and December 2013. Fifty-six patients were diagnosed as refractory to TACE during their clinical course and they were divided into two cohorts, (1) those who switched from TACE to sorafenib and (2) those who continued TACE. The overall survival (OS) after the time of being refractory to TACE was evaluated between the two groups. Results: After refractoriness to TACE therapy was confirmed, 24 patients continued with TACE (TACE-group) and 32 patients underwent treatment conversion to sorafenib (sorafenib-group). The median OS was 24.7 months in the sorafenib-group and 13.6 months in the TACE-group (p=0.002). Conclusions: Conversion to sorafenib significantly improves the OS in patients refractory to TACE therapy with intermediate-stage HCC. Administration of sorafenib is therefore recommended in such circumstances of TACE treatment failure.

Background and Aim: Computer-aided diagnosis (CAD) is becoming a next-generation tool for the diagnosis of human disease. CAD for colon polyps has been suggested as a particularly useful tool for trainee colonoscopists, as the use of a CAD system avoids the complications associated with endoscopic resections. In addition to conventional CAD, a convolutional neural network (CNN) system utilizing artificial intelligence (AI) has been developing rapidly over the past 5 years. We attempted to generate a unique CNN-CAD system with an AI function that studied endoscopic images extracted from movies obtained with colonoscopes used in routine examinations. Here, we report our preliminary results of this novel CNN-CAD system for the diagnosis of colon polyps. Methods: A total of 1,200 images from cases of colonoscopy performed between January 2010 and December 2016 at Kindai University Hospital were used. These images were extracted from the video of actual endoscopic examinations. Additional video images from 10 cases of unlearned processes were retrospectively assessed in a pilot study. They were simply diagnosed as either an adenomatous or nonadenomatous polyp. Results: The number of images used by AI to learn to distinguish adenomatous from nonadenomatous was 1,200:600. These images were extracted from the videos of actual endoscopic examinations. The size of each image was adjusted to 256 × 256 pixels. A 10-hold cross-validation was carried out. The accuracy of the 10-hold cross-validation is 0.751, where the accuracy is the ratio of the number of correct answers over the number of all the answers produced by the CNN. The decisions by the CNN were correct in 7 of 10 cases. Conclusion: A CNN-CAD system using routine colonoscopy might be useful for the rapid diagnosis of colorectal polyp classification. Further prospective studies in an in vivo setting are required to confirm the effectiveness of a CNN-CAD system in routine colonoscopy.

In a multicenter validation study, we developed a modified version of the HAP that predicts survival of patients with HCC treated with TACE in Europe and Asia. This system might be used to identify patients with HCC most likely to benefit from TACE in clinical practice.