Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Objectives: Parenchymal-sparing hepatectomy (PSH) is recommended in patients with colorectal liver metastases (CRLM). Based on the principle of PSH, to investigate the impact of anatomical resection (AR) and non-anatomic resection (NAR) on the outcome of CRLM and to evaluate the potential prognostic impact of three peritumoral imaging features.
Methods: Fifty-six patients who had abdominal gadoxetic acid-enhanced magnetic resonance imaging (MRI) before CRLM surgery were included in this retrospective research. Peritumoral early enhancement, peritumoral hypointensity on hepatobiliary phase (HBP), and biliary dilatation to the CRLM at MRI were evaluated. Survival estimates were calculated using the Kaplan-Meier method, and multivariate analysis was conducted to identify independent predictors of liver recurrence-free survival (LRFS), recurrence-free survival (RFS) and overall survival (OS).
Results: NAR had a lower 3-year LRFS compared with AR (36.6% vs. 78.6%, p = 0.012). No significant differences were found in 3-year RFS (34.1% vs. 41.7%) and OS (61.7% vs. 81.3%) (p > 0.05). In NAR group, peritumoral early enhancement was associated with poor LRFS (p = < 0.001, hazard ratio [HR] = 6.260; 95% confidence interval [CI], 2.322,16.876]) and poor RFS (p = 0.035, HR =2.516; 95% CI, 1.069,5.919). No independent predictors of CRLM were identified in the AR group.
Conclusions: In patients with CRLM, peritumoral early enhancement was a predictor of LRFS and RFS after NAR according to the principle of PSH.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1007/s00261-023-04044-3 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Deep Learning-Based Multimodal Prediction of NAC Response in LARC by Integrating MRI and Proteomics.
Cancer Res Treat
September 2025
Department of Biostatistics, Harbin Medical University, Harbin, China.
Purpose: Locally advanced rectal cancer (LARC) exhibits significant heterogeneity in response to neoadjuvant chemotherapy (NAC), with poor responders facing delayed treatment and unnecessary toxicity. Although MRI provides spatial pathophysiological information and proteomics reveals molecular mechanisms, current single-modal approaches cannot integrate these complementary perspectives, resulting in limited predictive accuracy and biological insight.
Materials And Methods: This retrospective study developed a multimodal deep learning framework using a cohort of 274 LARC patients treated with NAC (2012-2021).
MRI-based radiomics predicts complete responses to initial transcatheter arterial chemoembolization in virus-associated early and intermediate stage HCC.
Magn Reson Imaging
August 2025
The First Affiliated Hospital of China Medical University, PR China. Electronic address:
To evaluate the value of a multiparametric MRI-based nomogram on predicting response to transcatheter arterial chemoembolization (TACE) in virus-associated hepatocellular carcinoma (HCC) patients; METHODS: This study enrolled 235 and 51 patients from Center 1 and 2, respectively. All patients underwent baseline MRI scans before treatment. The least absolute shrinkage and selection operator (LASSO) regression method was used to screen radiomics features from intra- and peri-tumor areas to establish the radiomics signatures (RS).
View Article and Find Full Text PDFDensity and Maturity of Tertiary Lymphoid Structures Predict Clinical Outcome in Invasive Lung Adenocarcinoma.
Cancer Control
August 2025
Cellular and Molecular Biology Laboratory, Zhoushan Hospital, Wenzhou Medical University, Zhoushan, Zhejiang, China.
IntroductionTertiary lymphoid structures (TLSs) have been associated with the prognosis of various solid tumors. However, the association between TLSs and the prognosis of invasive lung adenocarcinoma (IAC) remains unclear in terms of location, density, and maturity.MethodsWe retrospectively reviewed the clinicopathological characteristics of 750 patients with IAC.
View Article and Find Full Text PDFRadiomics-Based Model Using Tumor and Peritumoral Features with Semi-Supervised and Privileged Learning for Metastatic Risk Prediction in Lung Cancer: A Multi-Site Study.
Comput Methods Programs Biomed
November 2025
Laboratory of Computing, Medical Informatics and Biomedical Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Greece. Electronic address:
Background: Lung cancer is the leading cause of cancer-related mortality globally. Early detection of high-risk patients for local or distant metastasis is challenging for better monitoring and treatment planning. Machine learning models have been proposed for diagnosis and prediction of metastasis risk.
View Article and Find Full Text PDFA Priori Prediction of Breast Cancer Response to Neoadjuvant Chemotherapy Using CT Radiomics.
Cancers (Basel)
August 2025
Physical Sciences, Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada.
(1) Background: Response to neoadjuvant chemotherapy (NAC) is a key prognostic indicator in breast cancer. However, current response evaluation methods rely on histopathological assessment after surgery, delaying opportunities for early treatment adaptation. This study aimed to develop a machine learning model by integrating radiomic features extracted from pre-treatment, contrast-enhanced computed tomography (CT) images with baseline clinical variables to predict NAC response before therapy initiation.
View Article and Find Full Text PDF