Reliability of rectal MRI radiomic features: Comparing rectal MRI radiomic features across reader expertise levels, image segmentation technique, and timing of rectal MRI in patients with locally advanced rectal cancer.

• Tác giả: Marinela Capanu, Jayasree Chakraborty, Charlotte Charbel, Shalini Chhabra, Sofia Danilova, Mohammad Ali El Amine, Maria El Homsi, Natalie Gangai, Natally Horvat, Henry C Kwok, Joao Miranda, Iva Petkovska, Rami S Vanguri, Junting Zheng
• Ngôn ngữ: eng
• Thông tin xuất bản: Ireland : European journal of radiology , 2025
• Bộ sưu tập: NCBI
• ID: 703223

Tóm tắt

 OBJECTIVES: To assess the reliability of rectal MRI radiomic features across reader expertise level, image segmentation technique, and timing of rectal MRI. MATERIAL AND METHODS: This retrospective single-institutional study included consecutive patients with rectal adenocarcinoma who underwent total neoadjuvant therapy from January 2018 to June 2018. Baseline and restaging rectal MRI T2-weighted images were segmented independently by six radiologists (two fellows, two non-rectal radiologists, and two rectal radiologists). Four segmentation strategies were used and varied by image segmentation technique and timing of rectal MRI: (a) baseline volume of interest (VOI), (b) baseline region of interest (ROI), (c) restaging VOI, and (d) restaging ROI. Inter-reader agreement on each extracted radiomic feature was evaluated using the intra-class correlation coefficient (ICC). RESULTS: Among 24 patients (16 men
  median age, 56 years [interquartile range: 49-62]), 1,595 radiomic features were extracted. Baseline VOI segmentation achieved the highest inter-reader agreement rate, with 68 % (1,079/1,595) of radiomic features having an ICC >
  0.7. Restaging ROI segmentation achieved the worst inter-reader agreement rate, with only 26 % (415/1,595) of radiomic features having an ICC >
  0.7. First-order statistics and Gray Level Co-occurrence Matrix (GLCM) feature subgroups showed high inter-reader agreement rates, and the application of 'Square Root' and 'LOG Sigma' filters resulted in improved inter-reader agreement rates relative to original images. The expertise level of radiologists performing the segmentations did not affect the distribution of inter-agreement rates according to image segmentation technique or timing of rectal MRI. CONCLUSIONS: Radiomic features were more reliable when extracted from baseline (vs. restaging) rectal MRIs and using 3D volume of interest (vs. 2D region of interest) segmentation, independent of the expertise level of the radiologists performing the segmentation. CLINICAL RELEVANCE STATEMENT: Radiomic studies on rectal MRI employ various segmentation strategies and few assess their impact on reproducibility. Establishing the optimal segmentation method enhances radiomics model generalizability, potentially bridging the gap in clinical translation and improving clinical management of patients.