Radiomics of locally advanced rectal cancer: machine learning-based prediction of response to neoadjuvant chemoradiotherapy using pre-treatment sagittal T2-weighted MRI

Yardimci A. H., Kocak B., Sel I., Bulut H., Bektas C. T., Cin M., ...Daha Fazla

Japanese Journal of Radiology, cilt.41, sa.1, ss.71-82, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 41 Sayı: 1
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1007/s11604-022-01325-7
  • Dergi Adı: Japanese Journal of Radiology
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Biotechnology Research Abstracts, CINAHL, EMBASE, MEDLINE
  • Sayfa Sayıları: ss.71-82
  • Anahtar Kelimeler: Machine learning, Neoadjuvant chemoradiotherapy, Radiomics, Rectal cancer, Texture analysis
  • İstanbul Gelişim Üniversitesi Adresli: Evet

Özet

© 2022, The Author(s) under exclusive licence to Japan Radiological Society.Purpose: Variable response to neoadjuvant chemoradiotherapy (nCRT) is observed among individuals with locally advanced rectal cancer (LARC), having a significant impact on patient management. In this work, we aimed to investigate the potential value of machine learning (ML)-based magnetic resonance imaging (MRI) radiomics in predicting therapeutic response to nCRT in patients with LARC. Materials and methods: Seventy-six patients with LARC were included in this retrospective study. Radiomic features were extracted from pre-treatment sagittal T2-weighted MRI images, with 3D segmentation. Dimension reduction was performed with a reliability analysis, pair-wise correlation analysis, analysis of variance, recursive feature elimination, Kruskal–Wallis, and Relief methods. Models were created using four different algorithms. In addition to radiomic models, clinical only and different combined models were developed and compared. The reference standard was tumor regression grade (TRG) based on the Modified Ryan Scheme (TRG 0 vs TRG 1–3). Models were compared based on net reclassification index (NRI). Clinical utility was assessed with decision curve analysis (DCA). Results: Number of features with excellent reliability is 106. The best result was achieved with radiomic only model using eight features. The area under the curve (AUC), accuracy, sensitivity, and specificity for validation were 0.753 (standard deviation [SD], 0.082), 81.1%, 83.8%, and 75.0%; for testing, 0.705 (SD, 0.145), 73.9%, 81.2%, and 57.1%, respectively. Based on the clinical only model as reference, NRI for radiomic only model was the best. DCA also showed better clinical utility for radiomic only model. Conclusions: ML-based T2-weighted MRI radiomics might have a potential in predicting response to nCRT in patients with LARC.