Deep-learning-based 3D super-resolution MRI radiomics model: superior predictive performance in preoperative T-staging of rectal cancer

Min Hou; Long Zhou; Jihong Sun

doi:10.1007/s00330-022-08952-8

Deep-learning-based 3D super-resolution MRI radiomics model: superior predictive performance in preoperative T-staging of rectal cancer

Eur Radiol. 2023 Jan;33(1):1-10. doi: 10.1007/s00330-022-08952-8. Epub 2022 Jun 21.

Authors

Min Hou¹, Long Zhou², Jihong Sun³

Affiliations

¹ Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, No. 3 East Qingchun Road, Hangzhou, 310016, Zhejiang Province, China.
² Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, No. 3 East Qingchun Road, Hangzhou, 310016, Zhejiang Province, China. zhoulong21@zju.edu.cn.
³ Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, No. 3 East Qingchun Road, Hangzhou, 310016, Zhejiang Province, China. sunjihong@zju.edu.cn.

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 model_HRT2 and model_SRT2 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: Model_SRT2 outperformed model_HRT2 (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: Model_SRT2 yielded superior predictive performance in preoperative RC T-staging by comparison with model_HRT2 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.

Keywords: Magnetic resonance imaging; Preoperative T-staging; Radiomics; Rectal cancer; Super-resolution.

MeSH terms

Deep Learning*
Humans
Magnetic Resonance Imaging / methods
ROC Curve
Rectal Neoplasms* / diagnostic imaging
Rectal Neoplasms* / surgery
Retrospective Studies

Abstract

MeSH terms

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