학술논문
Development and Validation of a Nomogram Based on DCE-MRI Radiomics for Predicting Hypoxia-Inducible Factor 1α Expression in Locally Advanced Rectal Cancer.
Document Type
Academic Journal
Author
Li Z; Department of Radiology, Shaoxing People's Hospital, Shaoxing, China.; Huang H; Department of Radiology, Shaoxing People's Hospital, Shaoxing, China.; Zhao Z; Department of Radiology, Shaoxing People's Hospital, Shaoxing, China.; Ma W; Department of Radiology, Shaoxing People's Hospital, Shaoxing, China.; Mao H; Department of Radiology, Shaoxing People's Hospital, Shaoxing, China.; Liu F; Department of Pathology, Shaoxing People's Hospital, Shaoxing, China.; Yang Y; Department of Pathology, Shaoxing People's Hospital, Shaoxing, China.; Wang D; Department of Radiology, Shaoxing People's Hospital, Shaoxing, China.; Lu Z; Department of Radiology, Shaoxing People's Hospital, Shaoxing, China. Electronic address: luzx777@163.com.
Source
Publisher: Association Of University Radiologists Country of Publication: United States NLM ID: 9440159 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1878-4046 (Electronic) Linking ISSN: 10766332 NLM ISO Abbreviation: Acad Radiol Subsets: MEDLINE
Subject
Language
English
Abstract
Rationale and Objectives: The expression levels of hypoxia-inducible factor 1 alpha (HIF-1α) have been identified as a pivotal marker, correlating with treatment response in patients with locally advanced rectal cancer (LARC). This study aimed to develop and validate a nomogram based on dynamic contrast-enhanced MRI (DCE-MRI) radiomics and clinical features for predicting the expression of HIF-1α in patients with LARC.
Materials and Methods: A total of 102 patients diagnosed with locally advanced rectal cancer were divided into training (n = 71) and validation (n = 31) cohorts. The expression statuses of HIF-1α were histopathologically classified, categorizing patients into high and low expression groups. The intraclass correlation coefficient (ICC), minimum redundancy maximum relevance (mRMR), and the least absolute shrinkage and selection operator (LASSO) were employed for feature selection to construct a radiomics signature and calculate the radiomics score (Rad-score). Univariate and multivariate analyses of clinical features and Rad-score were applied, and the clinical model and the nomogram were constructed. The predictive performance of the nomogram incorporating clinical features and Rad-score was assessed using Receiver Operating Characteristics (ROC) curves, decision curve analysis (DCA), and calibration curves.
Results: Seven radiomics features from DCE-MRI were used to build the radiomics signature. The nomogram incorporating CEA, Ki-67 and Rad-score had the highest AUC values in the training cohort and in the validation cohort (AUC: 0.918 and 0.920). Decision curve analysis showed that the nomogram outperformed the clinical model and radiomics signature in terms of clinical utility. In addition, the calibration curve for the nomogram demonstrated good agreement between prediction and actual observation.
Conclusion: The nomogram based on DCE-MRI radiomics and clinical features showed favorable predictive efficacy and might be useful for preoperatively discriminating the expression of HIF-1α.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)
Materials and Methods: A total of 102 patients diagnosed with locally advanced rectal cancer were divided into training (n = 71) and validation (n = 31) cohorts. The expression statuses of HIF-1α were histopathologically classified, categorizing patients into high and low expression groups. The intraclass correlation coefficient (ICC), minimum redundancy maximum relevance (mRMR), and the least absolute shrinkage and selection operator (LASSO) were employed for feature selection to construct a radiomics signature and calculate the radiomics score (Rad-score). Univariate and multivariate analyses of clinical features and Rad-score were applied, and the clinical model and the nomogram were constructed. The predictive performance of the nomogram incorporating clinical features and Rad-score was assessed using Receiver Operating Characteristics (ROC) curves, decision curve analysis (DCA), and calibration curves.
Results: Seven radiomics features from DCE-MRI were used to build the radiomics signature. The nomogram incorporating CEA, Ki-67 and Rad-score had the highest AUC values in the training cohort and in the validation cohort (AUC: 0.918 and 0.920). Decision curve analysis showed that the nomogram outperformed the clinical model and radiomics signature in terms of clinical utility. In addition, the calibration curve for the nomogram demonstrated good agreement between prediction and actual observation.
Conclusion: The nomogram based on DCE-MRI radiomics and clinical features showed favorable predictive efficacy and might be useful for preoperatively discriminating the expression of HIF-1α.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)