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Purpose: Radical surgery, the first‐line treatment for patients with hepatocellular cancer (HCC), faces the dilemma of high early recurrence rates and the inability to predict effectively. We aim to develop and validate a multimodal model combining clinical, radiomics, and pathomics features to predict the risk of early recurrence. Materials and Methods: We recruited HCC patients who underwent radical surgery and collected their preoperative clinical information, enhanced computed tomography (CT) images, and whole slide images (WSI) of hematoxylin and eosin (H & E) stained biopsy sections. After feature screening analysis, independent clinical, radiomics, and pathomics features closely associated with early recurrence were identified. Next, we built 16 models using four combination data composed of three type features, four machine learning algorithms, and 5‐fold cross‐validation to assess the performance and predictive power of the comparative models. Results: Between January 2016 and December 2020, we recruited 107 HCC patients, of whom 45.8% (49/107) experienced early recurrence. After analysis, we identified two clinical features, two radiomics features, and three pathomics features associated with early recurrence. Multimodal machine learning models showed better predictive performance than bimodal models. Moreover, the SVM algorithm showed the best prediction results among the multimodal models. The average area under the curve (AUC), accuracy (ACC), sensitivity, and specificity were 0.863, 0.784, 0.731, and 0.826, respectively. Finally, we constructed a comprehensive nomogram using clinical features, a radiomics score and a pathomics score to provide a reference for predicting the risk of early recurrence. Conclusions: The multimodal models can be used as a primary tool for oncologists to predict the risk of early recurrence after radical HCC surgery, which will help optimize and personalize treatment strategies. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the advantages and primary technical efficacy of an electromagnetic (EM) navigation system for computed tomography (CT)-guided thermal ablation of liver tumors. Material and methods: From August 2016 to January 2018, 40 patients scheduled for CT- guided thermal ablation were prospectively enrolled and divided into two groups. Twenty patients underwent CT-guided thermal ablation with an EM navigation system (navigation group), while the other 20 patients underwent conventional CT-guided thermal ablation (control group). Data on skin punctures, instrument adjustments, puncture time to target, CT scans, CT fluoroscopy time and dose-length-product (DLP) were compared between the two groups. Any postoperative complications were recorded and the primary technical efficacy was evaluated four to six weeks after the procedure. Results: All 20 patients in the navigation group successfully underwent EM navigation. Compared to the control group, there were fewer instrument adjustments (mean 2.40 vs. 4.95; p =.003), fewer CT scans (mean 7.10 vs. 10.30; p =.006), less CT fluoroscopy time (mean 40.47 vs. 59.98 s, p =.046), and less DLP (mean 807.39 vs. 1578.67 mGy × cm; p =.001). Although not statistically significant, EM navigation resulted in fewer skin punctures (mean 1.20 vs. 1.25; p =.803) and slightly longer puncture time to target (mean 16.50 vs. 15.20 min; p =.725). No patients experienced major complications and the primary efficacy rate was 90% and 84.21% in the navigation and control groups, respectively (p =.661). Conclusions: EM navigation system optimizes the thermal ablation process and reduces radiation exposure in patients. However, further studies are warranted to determine whether an EM navigation system can improve procedure time, complication rates, and primary technical efficiacy of thermal ablation. [ABSTRACT FROM AUTHOR]