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Novel magnetic resonance imaging (MRI)-based radiomics for predicting perineural invasion in rectal cancer: a
1Department of Radiology, Tongde Hospital of Zhejiang Province, No.234, Gucui Road, Hangzhou, Zhejiang Province, 310012, China.
Aim:
This study investigates the use of multiparametric magnetic resonance imaging (mp-MRI)-based radiomics for assessing perineural invasion (PNI) in rectal cancer.
Materials And Methods:
A retrospective analysis was performed on clinical and MRI data from 423 rectal cancer patients with confirmed surgical pathology, gathered from two centres. Of these, 343 patients from centre 1 were divided into a training set and an internal validation (in-vad) set in an 8:2 ratio, while 80 patients from centre 2 were used for independent external validation (ex-vad). Univariate and multivariate analyses were conducted on clinical features to build a clinical model. A combined model integrating both clinical and radiomic features was developed.
Results:
Among all patients, 131 cases (31.0 %) were PNI-positive. A multivariate analysis revealed MRI-reported T (mrT) stage (odds ratio [OR] = 1.66, P=.010) and MRI-reported N (mrN) stage (OR = 1.91, P=.002) as independent predictors of PNI, forming the clinical model. After selecting radiomic features, 30 features were used to construct the radiomics model. The area under the curve (AUC) values for the clinical model in the training, in-vad, and ex-vad sets were 0.719, 0.631, and 0.760, respectively. The AUC values for the radiomics model in the training, in-vad, and ex-vad sets were 0.841, 0.815, and 0.916, respectively, while the AUC values for the combined model in the training, in-vad, and ex-vad sets showed AUC values of 0.899, 0.826, and 0.914, respectively.
Conclusion:
The mp-MRI-based radiomics model demonstrates high accuracy in predicting PNI status in rectal cancer, offering a noninvasive and reliable tool for preoperative assessment.
Insights
Multiparametric magnetic resonance imaging (mp-MRI)-based radiomics accurately predicts perineural invasion (PNI) in rectal cancer. This noninvasive approach offers a reliable tool for preoperative assessment, improving patient management.
Area of Science:
- Oncology
- Radiology
- Medical Imaging
Background:
- Perineural invasion (PNI) is a critical prognostic factor in rectal cancer.
- Accurate preoperative assessment of PNI is essential for treatment planning and patient outcomes.
- Current methods for PNI detection have limitations in sensitivity and specificity.
Purpose of the Study:
- To investigate the efficacy of multiparametric magnetic resonance imaging (mp-MRI)-based radiomics for assessing perineural invasion (PNI) in rectal cancer.
- To develop and validate a radiomics model for noninvasive PNI prediction.
- To compare the performance of radiomics and clinical models in PNI detection.
Main Methods:
- Retrospective analysis of clinical and mp-MRI data from 423 rectal cancer patients.
- Development of a clinical model based on MRI-reported T and N stages.
- Construction of a radiomics model using selected radiomic features from mp-MRI.
- Validation of models using internal and independent external datasets.
Main Results:
- The radiomics model achieved high AUC values (0.841-0.916) across training and validation sets.
- A combined model integrating clinical and radiomic features demonstrated superior performance (AUC 0.826-0.899).
- MRI-reported T and N stages were identified as independent predictors of PNI in the clinical model.
Conclusions:
- mp-MRI-based radiomics is a highly accurate and noninvasive tool for predicting PNI in rectal cancer.
- The developed radiomics model can aid in preoperative assessment and treatment stratification.
- This approach has the potential to improve patient management and outcomes in rectal cancer.
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