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An MRI-based Radiomics Approach to Improve Breast Cancer Histological Grading.
- Source :
- Academic Radiology; Sep2023, Vol. 30 Issue 9, p1794-1804, 11p
- Publication Year :
- 2023
-
Abstract
- Nottingham histological grade (NHG) 2 breast cancer has an intermediate risk of recurrence, which is not informative for therapeutic decision-making. We sought to develop and independently validate an MRI-based radiomics signature (Rad-Grade) to improve prognostic re-stratification of NHG 2 tumors. Nine hundred-eight subjects with invasive breast cancer and preoperative MRI scans were retrospectively obtained. The NHG 1 and 3 tumors were randomly split into training and independent test cohort, with the NHG 2 as the prognostic validation set. From MRI image features, a radiomics-based signature predictive of the histological grade was built by use of the LASSO logistic regression algorithm. The model was developed for identifying NHG 1 and 3 radiological patterns, followed with re-stratification of NHG 2 tumors into Rad-Grade (RG)2-low (NHG 1-like) and RG2-high (NHG 3-like) subtypes using the learned patterns, and the prognostic value was assessed in terms of recurrence-free survival (RFS). The Rad-Grade showed independent prognostic value for re-stratification of NHG 2 tumors, where RG2-high had an increased risk for recurrence (HR 2.20, 1.10â4.40, p = 0.026) compared with RG2-low after adjusting for established risk factors. RG2-low shared similar phenotypic characteristics and RFS outcomes with NHG 1, and RG2-high with NHG 3, revealing that the model captures radiomic features in NHG 2 that are associated with different aggressiveness. The prognostic value of Rad-Grade was further validated in the NHG2 ER+ (HR 2.53, 1.13â5.56, p = 0.023) and NHG 2 ER+LNâ (HR 5.72, 1.24â26.44, p = 0.025) subgroups, and in specific treatment contexts. The radiomics-based re-stratification of NHG 2 tumors offers a cost-effective promising alternative to gene expression profiling for tumor grading and thus may improve clinical decisions. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10766332
- Volume :
- 30
- Issue :
- 9
- Database :
- Supplemental Index
- Journal :
- Academic Radiology
- Publication Type :
- Academic Journal
- Accession number :
- 170047319
- Full Text :
- https://doi.org/10.1016/j.acra.2022.12.014