Deep Segmentation Feature-Based Radiomics Improves Recurrence Prediction of Hepatocellular Carcinoma

Abstract

Objective and Impact Statement. This study developed and validated a deep semantic segmentation feature-based radiomics (DSFR) model based on preoperative contrast-enhanced computed tomography (CECT) combined with clinical information to predict early recurrence (ER) of single hepatocellular carcinoma (HCC) after curative resection. ER prediction is of great significance to the therapeutic decision-making and surveillance strategy of HCC. Introduction. ER prediction is important for HCC. However, it cannot currently be adequately determined. Methods. Totally, 208 patients with single HCC after curative resection were retrospectively recruited into a model-development cohort (n=180) and an independent validation cohort (n=28). DSFR models based on different CT phases were developed. The optimal DSFR model was incorporated with clinical information to establish a DSFR-C model. An integrated nomogram based on the Cox regression was established. The DSFR signature was used to stratify high- and low-risk ER groups. Results. A portal phase-based DSFR model was selected as the optimal model (area under receiver operating characteristic curve (AUC): development cohort, 0.740; validation cohort, 0.717). The DSFR-C model achieved AUCs of 0.782 and 0.744 in the development and validation cohorts, respectively. In the development and validation cohorts, the integrated nomogram achieved C-index of 0.748 and 0.741 and time-dependent AUCs of 0.823 and 0.822, respectively, for recurrence-free survival (RFS) prediction. The RFS difference between the risk groups was statistically significant (P<0.0001 and P=0.045 in the development and validation cohorts, respectively). Conclusion. CECT-based DSFR can predict ER in single HCC after curative resection, and its combination with clinical information further improved the performance for ER prediction.

A multi-modal vision-language pipeline strategy for contour quality assurance and adaptive optimization

“…First, as indicated in tables 2 and 3, while CQA-AO excels in the contour QA tasks, its performance slightly reduces in the unacceptable contour categories analysis tasks. Combining CQA-AO with techniques such as weakly-and semi-supervised strategy (Hung et al 2022), spatial-graph convolution (You et al 2022), and radiomics feature (Wang et al 2022b), may enhance the model's adaptability in such scenarios. Second, it is worth noting that this study is focused only on 2D images, and there is an interest in addressing 3D structures in future research.…”

Machine Learning Based Diagnostic Paradigm in Viral and Non-Viral Hepatocellular Carcinoma

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Research advances in immunotherapy combined with DNA damage response inhibitors for liver cancer therapy

“…HAIC for HCC [29] implemented 5-fluorouracil and oxaliplatin-based chemotherapeutic protocols for localized HCC perfusion chemotherapy, leading to a significant enhancement in the tumor response rate and patient survival while mitigating the hematologic suppression and gastrointestinal reaction side effects associated with systemic chemotherapy. The combination of TACE with HAIC (TACE-HAIC) [30] , leveraging the synergistic attributes of both modalities, has emerged as one of the most efficacious approaches in liver cancer treatment, representing a notable translational therapy for local ablation strategies in managing HCC [31,32] . This therapeutic technique directly destroys tumors using physical or chemical methods under imaging guidance, and it is another therapeutic technique with the potential for a local cure for HCC after surgical resection.…”