Abstract
Abstract
Objectives
To investigate the feasibility and efficacy of a deep-learning (DL)-based three-dimensional (3D) super-resolution (SR) MRI radiomics model for preoperative T-staging prediction in rectal cancer (RC).
Methods
Seven hundred six eligible RC patients (T1/2 = 287, T3/4 = 419) were retrospectively enrolled in this study and chronologically allocated into a training cohort (n = 565) and a validation cohort (n = 141). We conducted a deep-transfer-learning network on high-resolution (HR) T2-weighted imaging (T2WI) to enhance the z-resolution of the images and acquired the preoperative SRT2WI. The radiomics models named modelHRT2 and modelSRT2 were respectively constructed with high-dimensional quantitative features extracted from manually segmented volume of interests of HRT2WI and SRT2WI through the Least Absolute Shrinkage and Selection Operator method. The performances of the models were evaluated by ROC, calibration, and decision curves.
Results
ModelSRT2 outperformed modelHRT2 (AUC 0.869, sensitivity 71.1%, specificity 93.1%, and accuracy 83.3% vs. AUC 0.810, sensitivity 89.5%, specificity 70.1%, and accuracy 77.3%) in distinguishing T1/2 and T3/4 RC with significant difference (p < 0.05). Both radiomics models achieved higher AUCs than the expert radiologists (0.685, 95% confidence interval 0.595–0.775, p < 0.05). The calibration curves confirmed high goodness of fit, and the decision curve analysis revealed the clinical value.
Conclusions
ModelSRT2 yielded superior predictive performance in preoperative RC T-staging by comparison with modelHRT2 and expert radiologists’ visual assessments.
Key Points
• For the first time, DL-based 3D SR images were applied in radiomics analysis for clinical utility.
• Compared with the visual assessment of expert radiologists and the conventional radiomics model based on HRT2WI, the SR radiomics model showed a more favorable capability in helping clinicians assess the invasion depth of RC preoperatively.
• This is the largest radiomics study for T-staging prediction in RC.
Funder
National Natural Science Foundation of China
Major Program Co-sponsored by Province and Ministry
Key Research and Development Program of Zhejiang Province
Publisher
Springer Science and Business Media LLC
Subject
Radiology, Nuclear Medicine and imaging,General Medicine
Cited by
13 articles.
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