AI and Cancer Research ── Liver ── 2026-10-11
Liver cancer: treatment, trials and AI
- Abstract
Predicting transarterial chemoembolization (TACE) refractoriness is critical for optimizing treatment strategies in hepatocellular carcinoma (HCC); however, accurate risk assessment remains challenging. The initial therapeutic response, captured through imaging and clinical indicators, reflects intrinsic treatment sensitivity and holds substantial predictive value for long-term refractoriness. This study evaluated treatment effect heterogeneity following the initial TACE to develop a multimodal nomogram for predicting TACE refractoriness after repeated sessions. Using multiparametric MRI spatial habitat radiomics, we identified three imaging-defined habitats with signal profiles resembling liquefactive necrosis, residual viable tumor, and coagulative necrosis. By integrating habitat and conventional radiomic features, an interpretable radiomic score was constructed utilizing eight machine learning algorithms. This score was subsequently combined with longitudinal dynamic clinical parameters to build a joint clinical-radiomic nomogram. The nomogram achieved areas under the curve (AUCs) of 0.924, 0.860, and 0.864 in the training, internal validation, and external validation cohorts, respectively. These findings suggest that integrating habitat radiomics with dynamic clinical features may help stratify the risk of subsequent TACE refractoriness after the initial TACE session and before repeated treatment, potentially supporting early treatment-response assessment and individualized clinical decision-making.
Journal IF-equivalent: 5.2 (OpenAlex 2-year mean citedness, value as of 2026-10-04, retrieved 2026-10-05; not the official Clarivate IF)Reference: Liu JK, Gu YQ, Sun QA, Chang JH, Zhao J, Liu YB, et al. Multiparametric MRI habitat radiomics and longitudinal dynamic clinical parameters predict TACE refractoriness in HCC. Sci Rep. 2026 Oct 9 [Epub ahead of print]. doi:10.1038/s41598-026-74555-8.Checked: Abstract only - Abstract
Anatomical liver resection for hepatocellular carcinoma (HCC) relies heavily on accurate intraoperative identification of portal venous anatomy. Although intraoperative ultrasound (IOUS) is regarded as the gold standard for real-time vascular navigation during liver surgery, identification of segment-specific portal vein branches remains technically demanding and highly dependent on surgical experience. Artificial intelligence (AI)-assisted ultrasound interpretation may improve the efficiency and consistency of intraoperative vessel recognition; however, most previous studies have been limited to static image analysis without real-time surgical application. This study aimed to develop and evaluate a YOLOv5-based deep-learning pipeline for automatic recognition of selected portal vein branches during laparoscopic intraoperative ultrasound and to investigate the feasibility of real-time intraoperative deployment during anatomical liver resection. A total of 3254 laparoscopic intraoperative ultrasound images obtained from 100 consecutive patients undergoing anatomical liver resection for HCC were retrospectively collected and manually annotated by experienced hepatobiliary surgeons. The annotated dataset was divided into independent training, validation, and testing cohorts (80%, 15%, and 5%, respectively) at the patient level. The final YOLOv5 model was trained using five predefined right-sided portal vein classes: P58, P67, P6, P7, and P8d. The same five-class model was used without retraining or class modification during prospective real-time validation in 20 patients. The model achieved a mean average precision ([email protected]) of 0.941 on the independent testing dataset. The maximum F1-score was 0.58 at a confidence threshold of 0.202, while precision reached 1.00 at a confidence threshold of 0.764. Branch-specific frame-level accuracy was 0.81 for P58, 0.81 for P67, 0.80 for P8d, 0.79 for P7, and 0.78 for P6. During prospective intraoperative validation, the system performed continuous real-time recognition with a processing latency of less than 50 ms per frame. The proposed pipeline demonstrated the technical feasibility of real-time recognition of five selected right-sided portal vein classes during laparoscopic intraoperative ultrasound. Further multicenter studies with broader anatomical coverage and larger prospective cohorts are required to establish generalizability and clinical utility.
Journal IF-equivalent: 3.5 (OpenAlex 2-year mean citedness, value as of 2026-10-10, retrieved 2026-10-11; not the official Clarivate IF)Reference: Xu J, Zhang X, Li S, Wei Y, Huang S, Cao L, et al. A deep-learning pipeline to assist portal vein identification during laparoscopic ultrasound scanning for anatomical liver resection in real time. Eur J Med Res. 2026 Oct 9 [Epub ahead of print]. doi:10.1186/s40001-026-05291-y.Checked: Abstract only