Deep learning and digital pathology powers prediction of HCC development in steatotic liver disease.

BACKGROUND AND AIMS: Identifying patients with steatotic liver disease who are at a high risk of developing HCC remains challenging. We present a deep learning (DL) model to predict HCC development using hematoxylin and eosin-stained whole-slide images of biopsy-proven steatotic liver disease. APPROACH AND RESULTS: We included 639 patients who did not develop HCC for ≥7 years after biopsy (non-HCC class) and 46 patients who developed HCC <7 years after biopsy (HCC class). Paired cases of the HCC and non-HCC classes matched by biopsy date and institution were used for training, and the remaining nonpaired cases were used for validation. The DL model was trained using deep convolutional neural networks with 28,000 image tiles cropped from whole-slide images of the paired cases, with an accuracy of 81.0% and an AUC of 0.80 for predicting HCC development. Validation using the nonpaired cases also demonstrated a good accuracy of 82.3% and an AUC of 0.84. These results were comparable to the predictive ability of logistic regression model using fibrosis stage. Notably, the DL model also detected the cases of HCC development in patients with mild fibrosis. The saliency maps generated by the DL model highlighted various pathological features associated with HCC development, including nuclear atypia, hepatocytes with a high nuclear-cytoplasmic ratio, immune cell infiltration, fibrosis, and a lack of large fat droplets. CONCLUSIONS: The ability of the DL model to capture subtle pathological features beyond fibrosis suggests its potential for identifying early signs of hepatocarcinogenesis in patients with steatotic liver disease.

Diagnostic performance of two-dimensional shear wave elastography and attenuation imaging for fibrosis and steatosis assessment in chronic liver disease.

PURPOSE: We investigated the diagnostic performance of two-dimensional shear wave elastography (2D-SWE) and attenuation imaging (ATI) in detecting fibrosis and steatosis in patients with chronic liver disease (CLD), comparing them with established methods. METHODS: In 190 patients with CLD, 2D-SWE and vibration-controlled transient elastography (VCTE) were used for liver stiffness measurement (LSM), and ATI and controlled attenuation parameter (CAP) were used for steatosis quantification. The correlations between these new and established methods were analyzed. RESULTS: Significant correlations were found between 2D-SWE and VCTE (r = 0.78, P < 0.001), and between ATI and CAP (r = 0.70, P < 0.001). Liver stiffness tended to be lower with 2D-SWE compared with that with VCTE, especially in cases with higher LSM, and ATI was less influenced by skin-capsular distance than CAP. Area under the receiver-operating characteristics curves (AUCs) and optimal cut-offs of 2D-SWE for diagnosing liver fibrosis stages F2, F3, and F4 were 0.73 (8.7 kPa), 0.79 (9.1 kPa), and 0.88 (11.6 kPa), respectively. The AUCs and optimal cut-offs of ATI for diagnosing hepatic steatosis grades S1, S2, and S3 were 0.91 (0.66 dB/cm/MHz), 0.80 (0.79 dB/cm/MHz), and 0.88 (0.86 dB/cm/MHz), respectively. A subgroup analysis of 86 patients with metabolic dysfunction-associated steatotic liver disease also demonstrated good performance for 2D-SWE and ATI. CONCLUSION: 2D-SWE and ATI performed comparably with conventional VCTE and CAP in evaluating CLD, offering reliable alternatives for diagnosing liver fibrosis and steatosis.

The impact of COVID-19 on the diagnosis and treatment of HCC: analysis of a nationwide registry for advanced liver diseases (REAL).

The number of cancer cases diagnosed during the coronavirus disease 2019 (COVID-19) pandemic has decreased. This study investigated the impact of the pandemic on the clinical practice of hepatocellular carcinoma (HCC) using a novel nationwide REgistry for Advanced Liver diseases (REAL) in Japan. We retrieved data of patients initially diagnosed with HCC between January 2018 and December 2021. We adopted tumor size as the primary outcome measure and compared it between the pre-COVID-19 (2018 and 2019) and COVID-19 eras (2020 and 2021). We analyzed 13,777 patients initially diagnosed with HCC (8074 in the pre-COVID-19 era and 5703 in the COVID-19 era). The size of the maximal intrahepatic tumor did not change between the two periods (mean [SD] = 4.3 [3.6] cm and 4.4 [3.6] cm), whereas the proportion of patients with a single tumor increased slightly from 72.0 to 74.3%. HCC was diagnosed at a similar Barcelona Clinic Liver Cancer stage. However, the proportion of patients treated with systemic therapy has increased from 5.4 to 8.9%. The proportion of patients with a non-viral etiology significantly increased from 55.3 to 60.4%. Although the tumor size was significantly different among the etiologies, the subgroup analysis showed that the tumor size did not change after stratification by etiology. In conclusion, the characteristics of initially diagnosed HCC remained unchanged during the COVID-19 pandemic in Japan, regardless of differences in etiology. A robust surveillance system should be established particularly for non-B, non-C etiology to detect HCC in earlier stages.

Machine Learning-Based Personalized Prediction of Hepatocellular Carcinoma Recurrence After Radiofrequency Ablation.

Background and Aims: Radiofrequency ablation (RFA) is a widely accepted, minimally invasive treatment for hepatocellular carcinoma (HCC). This study aimed to develop a machine learning (ML) model to predict the risk of HCC recurrence after RFA treatment for individual patients. Methods: We included a total of 1778 patients with treatment-naïve HCC who underwent RFA. The cumulative probability of overall recurrence after the initial RFA treatment was 78.9% and 88.0% at 5 and 10 years, respectively. We developed a conventional Cox proportional hazard model and 6 ML models-including the deep learning-based DeepSurv model. Model performance was evaluated using Harrel's c-index and was validated externally using the split-sample method. Results: The gradient boosting decision tree (GBDT) model achieved the best performance with a c-index of 0.67 from external validation, and it showed a high discriminative ability in stratifying the external validation sample into 2, 3, and 4 different risk groups (P < .001 among all risk groups). The c-index of DeepSurv was 0.64. In order of significance, the tumor number, serum albumin level, and des-gamma-carboxyprothrombin level were the most important variables for the prediction of HCC recurrence in the GBDT model. Also, the current GBDT model enabled the output of a personalized cumulative recurrence prediction curve for each patient. Conclusion: We developed a novel ML model for the personalized risk prediction of HCC recurrence after RFA treatment. The current model may lead to the personalization of effective follow-up strategies after RFA treatment according to the risk stratification of HCC recurrence.