Objective Hepatocellular carcinoma (HCC) is one of the most lethal malignancies worldwide with poor prognosis due to the high incidence of recurrence. For patients with advanced HCC, transcatheter arterial chemoembolization (TACE) is the preferred treatment option owing to the minimal invasive clinical treatment with optimum therapeutic outcomes. But, there is a paucity of studies on early detection of residual cancer and relapse that result in the bottleneck of long-term effects after TACE therapy. Patients and methods Using next-generation sequencing platform targeting a panel of 622 cancer-associated genes, we prospectively evaluated the predictive significance of plasma cell-free DNA (cfDNA) to detect minimal residual disease in plasma cfDNA in comparison with DNA obtained from tumor tissue and blood cells of three eligible cases with HCC following TACE therapy. Results The results indicated that the mutational spectrum from plasma cfDNA was consistent with tumor-derived DNA and potentially suggested disease progression. Next, we determined if the dynamic variation of plasma cfDNA could indicate treatment response, the findings suggested that the mutation burden of plasma cfDNA could reveal relapse before alterations in conventional computed tomography imaging and serum α-fetoprotein values. Conclusion The mutation burden in plasma cfDNA may serve as a novel prognostic biomarker by providing early evidence of residual disease and identifying high risk of recurrence in patients with HCC following TACE therapy.

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PurposeThe study aimed to construct and evaluate a CT-Based radiomics model for noninvasive detecting perineural invasion (PNI) of perihilar cholangiocarcinoma (pCCA) preoperatively.Materials and MethodsFrom February 2012 to October 2021, a total of 161 patients with pCCA who underwent resection were retrospectively enrolled in this study. Patients were allocated into the training cohort and the validation cohort according to the diagnostic time. Venous phase images of contrast-enhanced CT were used for radiomics analysis. The intraclass correlation efficient (ICC), the correlation analysis, and the least absolute shrinkage and selection operator (LASSO) regression were applied to select radiomics features and built radiomics signature. Logistic regression analyses were performed to establish a clinical model, a radiomics model, and a combined model. The performance of the predictive models was measured by area under the receiver operating characteristic curve (AUC), and pairwise ROC comparisons between models were tested using the Delong method. Finally, the model with the best performance was presented as a nomogram, and its calibration and clinical usefulness were assessed.ResultsFinally, 15 radiomics features were selected to build a radiomics signature, and three models were developed through logistic regression. In the training cohort, the combined model showed a higher predictive capability (AUC = 0.950) than the radiomics model and the clinical model (AUC: radiomics = 0.914, clinical = 0.756). However, in the validation cohort, the AUC of the radiomics model (AUC = 0.885) was significantly higher than the other two models (AUC: combined = 0.791, clinical = 0.567). After comprehensive consideration, the radiomics model was chosen to develop the nomogram. The calibration curve and decision curve analysis (DCA) suggested that the nomogram had a good consistency and clinical utility.ConclusionWe developed a CT-based radiomics model with good performance to noninvasively predict PNI of pCCA preoperatively.

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