Contrast-enhanced ultrasound-based AI model for multi-classification of focal liver lesions

Micro-Nano Convergence-Driven Radiotheranostic Revolution in Hepatocellular Carcinoma

Radiotherapy, as an important means of treating hepatocellular carcinoma (HCC), has shown unique therapeutic advantages, especially in patients who are unable to undergo surgery or transplantation. It mainly includes external radiotherapy, transarterial radioembolization and intratumoral radioactive particle implantation. However, under the influence of factors such as the hypoxic characteristics of the liver tumor microenvironment and the radioresistance of tumor cells, the effect of radiotherapy may be unstable and may cause side effects, affecting the quality of life of patients. In recent years, with the development of nanotechnology, drug delivery systems based on micro-nanomaterials have provided new solutions for improving the effect of radiotherapy for HCC. Despite this, the application of micro-nano drug delivery systems in the treatment of HCC still faces some challenges, mainly including the in vivo safety and in vivo metabolism of micro-nano materials. This article reviews the latest progress of micro-nano materials in the treatment of HCC, especially their application in radiosensitization and their clinical translation potential. This article systematically analyzes the role of micro-nanomaterials in external or internal radiotherapy sensitization and radioimmunotherapy and explores the advantages of micro-nanomaterials in improving the treatment effect of HCC.

Deep Learning to Enhance Diagnosis and Management of Intrahepatic Cholangiocarcinoma

Intrahepatic cholangiocarcinoma (iCCA) is associated with a poor prognosis and necessitates a multimodal, multidisciplinary approach from diagnosis to treatment to achieve optimal outcomes. A noninvasive preoperative diagnosis using abdominal imaging techniques can represent a clinical challenge. Given the differential response of iCCA to localized and systemic therapies compared with hepatocellular carcinoma and secondary hepatic malignancies, an accurate diagnosis is crucial. Deep learning (DL) models for image analysis have emerged as a promising adjunct for the abdominal radiologist, potentially enhancing the accurate detection and diagnosis of iCCA. Over the last five years, several reports have proposed robust DL models, which demonstrate a diagnostic accuracy that is either comparable to or surpasses that of radiologists with varying levels of experience. Recent studies have expanded DL applications into other aspects of iCCA management, including histopathologic diagnosis, the prediction of histopathological features, the preoperative prediction of survival, and the pretreatment prediction of responses to systemic therapy. We herein critically evaluate the expanding body of research on DL applications in the diagnosis and management of iCCA, providing insights into the current progress and future research directions. We comprehensively synthesize the performance and limitations of DL models in iCCA research, identifying key challenges that serve as a translational reference for clinicians.