Dataset Information

The Prognostic Impact of Tumor Differentiation on Recurrence and Survival after Resection of Hepatocellular Carcinoma Is Dependent on Tumor Size

ABSTRACT:

Introduction

The present study aimed to evaluate the effect of poor differentiation and tumor size on survival outcome after hepatic resection of hepatocellular carcinoma (HCC).

Methods

A total of 1,107 patients who underwent initial and curative hepatic resection for HCC without macroscopic vascular invasion participated in the study. Using the multivariable Cox proportional hazards regression model, we evaluated changes in hazard ratios (HRs) for the association between tumor differentiation and survival based on tumor size.

Results

In patients with poorly (Por) differentiated HCCs, the adjusted HRs of reduced overall survival (OS), recurrence-free survival (RFS), early RFS, and early extrahepatic RFS were 1.31 (95% confidence interval [CI]; 1.07–1.59), 1.07 (95% CI 0.89–1.28), 1.31 (95% CI 1.06–1.62), and 1.81 (95% CI 1.03–3.17), respectively. Moreover, based on an analysis of the effect modification of tumor differentiation according to tumor size, Por HCC was found to be associated with a reduced OS (p = 0.033). The HRs of Por HCCs sharply increased in patients with tumors measuring up to 5 cm. The adjusted HRs of reduced OS in patients with Por HCCs measuring <2, ≥2 and <5, and ≥5 cm were 1.22 (95% CI 0.69–2.14), 1.33 (95% CI 1.02–1.73), and 1.58 (95% CI 1.04–2.42), respectively. The corresponding adjusted HRs of reduced early RFS were 0.85 (95% CI 0.46–1.57), 1.34 (95% CI 1.01–1.8), and 1.57 (95% CI 1.03–2.39), respectively. The adjusted HRs of reduced early extrahepatic RFS were 1.89 (95% CI 0.83–4.3) in patients with tumors measuring ≥2 and <5 cm and 2.33 (95% CI 0.98–5.54) in those with tumors measuring ≥5 cm.

Conclusions

Por HCC measuring ≥2 cm was associated with early recurrence. Hence, it had negative effects on OS. After surgery, patients with Por HCC measuring ≥5 cm should be cautiously monitored for early extrahepatic recurrence. These findings will help physicians devise treatment strategies for patients with HCC.

SUBMITTER: Shinkawa H

PROVIDER: S-EPMC8527909 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:OBJECTIVE:To construct a prediction model based on peritumoral radiomics signatures from CT images and investigate its efficiency in predicting early recurrence (ER) of hepatocellular carcinoma (HCC) after curative treatment. MATERIALS AND METHODS:In total, 156 patients with primary HCC were randomly divided into the training cohort (109 patients) and the validation cohort (47 patients). From the pretreatment CT images, we extracted 3-phase two-dimensional images from the largest cross-sectional area of the tumor. A region of interest (ROI) was manually delineated around the lesion for tumoral radiomics (T-RO) feature extraction, and another ROI was outlined with an additional 2 cm peritumoral area for peritumoral radiomics (PT-RO) feature extraction. The least absolute shrinkage and selection operator (LASSO) logistic regression model was applied for feature selection and model construction. The T-RO and PT-RO models were constructed. In the validation cohort, the prediction efficiencies of the two models and peritumoral enhancement (PT-E) were evaluated qualitatively by receiver operating characteristic (ROC) curves, calibration curves and decision curves and quantitatively by area under the curve (AUC), the category-free net reclassification index (cfNRI) and integrated discrimination improvement values (IDI). RESULTS:By comparing AUC values, the prediction accuracy in the validation cohort was good for the PT-RO model (0.80 vs. 0.79, P = 0.47) but poor for the T-RO model (0.82 vs. 0.62, P < 0.01), which was significantly overfitted. In the validation cohort, the ROC curves, calibration curves and decision curves indicated that the PT-RO model had better calibration efficiency and provided greater clinical benefits. CfNRI indicated that the PT-RO model correctly reclassified 47% of ER patients and 32% of non-ER patients compared to the T-RO model (P < 0.01); additionally, the PT-RO model correctly reclassified 24% of ER patients and 41% of non-ER patients compared to PT-E (P = 0.02). IDI indicated that the PT-RO model could improve prediction accuracy by 0.22 (P < 0.01) compared to the T-RO model and by 0.20 (P = 0.01) compared to PT-E. CONCLUSION:The CT-based PT-RO model can effectively predict the ER of HCC and is more efficient than the T-RO model and the conventional imaging feature PT-E.