Clinical Report: Correction on Integration of MRI and Clinical Indicators

Overview

This correction addresses the omission of funding details in a study that integrates multiparametric MRI with clinical factors to predict outcomes in advanced hepatocellular carcinoma (HCC) patients undergoing immune-targeted therapy. The study emphasizes the importance of combining imaging and clinical data to enhance treatment response predictions.

Background

Hepatocellular carcinoma (HCC) is a highly malignant primary liver cancer with variable responses to treatment, particularly to immune-targeted therapies. Accurate prediction of treatment outcomes is crucial for optimizing patient management and improving survival rates. The integration of advanced imaging techniques, such as multiparametric MRI, with clinical indicators may provide valuable insights into patient responses to therapy.

Data Highlights

No numerical data presented in the correction.

Key Findings

• The correction highlights the omission of funding details in the original publication.
• Multiparametric MRI has potential as a noninvasive biomarker for predicting responses to immune-targeted therapy in HCC.
• Combining MRI data with clinical factors may enhance the accuracy of treatment outcome predictions.
• Related studies support the use of radiomics in assessing treatment responses in HCC.
• Current guidelines emphasize the role of combination immunotherapy as a first-line treatment for unresectable HCC.

Clinical Implications

Clinicians should consider the integration of multiparametric MRI with clinical factors when evaluating treatment options for patients with advanced HCC. This approach may lead to more personalized treatment strategies and improved patient outcomes.

Conclusion

The correction underscores the importance of transparency in research funding and highlights the potential of combining imaging and clinical data to enhance treatment predictions in advanced HCC.

Related Resources & Content

1. Han S, Meng F, Wang L, Gao P, Zhang H, Qu J, Frontiers in Oncology, 2026 -- Correction: Combining Multiparametric MRI with Clinical Factors to Forecast Immune-Targeted Therapy Outcomes in Advanced Hepatocellular Carcinoma Patients
2. Frontiers in Oncology — Habitat and peritumoral CT radiomics accurately predict early treatment response to hepatic arterial infusion chemotherapy combined with tyrosine kinase inhibitors and programmed death‑1 inhibitors in unresectable hepatocellular carcinoma
3. npj Digital Medicine — Personalized Treatment Approaches Utilizing Artificial Intelligence for Unresectable Hepatocellular Carcinoma: Incorporating HSP90α for Prognostic Evaluation and Survival Forecasting
4. Journal of Gastroenterology — Evaluating the Prognostic Value of Alpha-fetoprotein and Tumor Shape Irregularity in Patients with Advanced Hepatocellular Carcinoma Undergoing Immune-Checkpoint Inhibitor Therapy: A Multi-Center Retrospective Study
5. Frontiers in Medicine — Prediction of the efficacy after the first transarterial chemoembolization in hepatocellular carcinoma using CT radiomics combined with inflammatory composite indicators
6. Hepatocellular carcinoma: ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up
7. Noninvasive Tumor Profiling: Quantitative Contrast-Enhanced MRI Markers Predict PD-L1 and CTNNB1 Status in Hepatocellular Carcinoma