Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

Background: Tumor regression grade (TRG) and histopathological growth pattern (HGP) reflect the response of colorectal liver metastases (CRLM) to neoadjuvant therapy (NAT) from the perspectives of the tumor and its microenvironment, respectively. Based on these two indicators, this study aimed to develop a prognostic index for CRLM undergoing surgery after NAT.

Materials And Methods: 237 patients who underwent curative-intent resection following NAT from 2012 to 2022 were selected. Correlations between HGP and TRG were assessed. Cox regression analyses were employed to determine the optimal cut-off point for constructing the Tumor-Boundary Response Index (TBRI). Kaplan-Meier analyses of overall survival (OS), disease-free survival (DFS) and hepatic relapse-free survival (hRFS) were used to evaluate the prognostic value. The predictive ability of TBRI, Fong's clinical risk score (CRS) and Genetic And Morphological Evaluation (GAME) score was compared by time-dependent receiver operating characteristic (ROC) analysis. Calibration plot was utilized to assess the goodness of fit.

Results: Desmoplastic HGP (dHGP) exhibited an inverse correlation with TRG in lesions. TBRI stratified patients into four tiers based on whether HGP is predominant desmoplastic (>50 %) and whether TRG is ≤ 3, showing significant prognostic value in OS, DFS and hRFS (median OS for TBRI 1-4: 78.6, 42.6, 27.8 and 22.5, p < 0.001; median DFS for TBRI 1-4: 22.4, 12.4, 10.9 and 6.5 months, p < 0.001; median hRFS for TBRI 1-4: 29.2, 12.9, 10.9, 6.8, p < 0.001). Additionally, TBRI surpassed CRS and GAME score with superior discriminatory power and displayed exceptional consistency.

Conclusions: TBRI demonstrated a promising ability to predict the postoperative survival of CRLM patients receiving NAT.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.ejso.2025.110291DOI Listing

Publication Analysis

Top Keywords

tumor-boundary response
8
colorectal liver
8
liver metastases
8
neoadjuvant therapy
8
response tbri-
4
tbri- promising
4
promising indicator
4
indicator predicting
4
predicting outcomes
4
outcomes colorectal
4

Similar Publications

Von Hippel-Lindau (VHL) disease describes a hereditary tumor predisposition syndrome, caused by germline mutations in the VHL tumor suppressor gene, resulting in the functional loss of the VHL protein (pVHL). pVHL loss translates into a pseudo-hypoxic state that drives clear cell renal cell carcinoma (ccRCC) development. ccRCC tumors frequently form a pseudocapsule (PC) at the tumor boundary.

View Article and Find Full Text PDF

Glioblastoma multiforme (GBM) is the most aggressive type of brain cancer, making effective treatments essential to improve patient survival. To advance the understanding of GBM and develop more effective therapies, preclinical studies commonly use mouse models due to their genetic and physiological similarities to humans. In particular, the GL261 mouse glioma model is employed for its reproducible tumor growth and ability to mimic key aspects of human gliomas.

View Article and Find Full Text PDF

Background/objectives: Cell-cell communication (CCC) is a critical process within the tumor microenvironment, governing regulatory interactions between cancer cells and other cellular subpopulations. Aiming to improve the accuracy and completeness of intercellular gene-regulatory network inference, we constructed a novel spatial-resolved gene-regulatory network framework (spGRN).

Methods: Firstly, the spatial multi-omics data of colorectal cancer (CRC) patients were analyzed.

View Article and Find Full Text PDF

Background: Tumor regression grade (TRG) and histopathological growth pattern (HGP) reflect the response of colorectal liver metastases (CRLM) to neoadjuvant therapy (NAT) from the perspectives of the tumor and its microenvironment, respectively. Based on these two indicators, this study aimed to develop a prognostic index for CRLM undergoing surgery after NAT.

Materials And Methods: 237 patients who underwent curative-intent resection following NAT from 2012 to 2022 were selected.

View Article and Find Full Text PDF

Explainable, federated deep learning model predicts disease progression risk of cutaneous squamous cell carcinoma.

NPJ Precis Oncol

June 2025

Center for Molecular Medicine Cologne (CMMC), Faculty of Medicine and University Hospital Cologne, University of Cologne, Köln, Germany.

Predicting cancer patient disease progression is a key step towards personalized medicine and secondary prevention. Risk stratification systems based on clinico-pathological criteria aim to identify high-risk patients, but accurate predictions remain challenging. Deep learning models present new opportunities for patient risk prediction, yet their interpretability has been largely unexplored.

View Article and Find Full Text PDF