Combined artificial intelligence and radiologist model for predicting rectal cancer treatment response from magnetic resonance imaging: an external validation study.

Horvat, Natally; Veeraraghavan, Harini; Nahas, Caio S. R.; Bates, ‬David D. B.; Ferreira, Felipe R.; Zheng, Junting; Capanu, Marinela; Fuqua III, James L.; Fernandes, Maria Clara; Sosa, Ramon E.; Jayaprakasam, Vetri Sudar; Cerri, Giovanni G.; Nahas, Sergio C.; Petkovska, Iva

Purpose: To evaluate an MRI-based radiomic texture classifier alone and combined with radiologist qualitative assessment in predicting pathological complete response (pCR) using restaging MRI with internal training and external validation. Methods: Consecutive patients with locally advanced rectal cancer (LARC) who underwent neoadjuvant therapy followed by total mesorectal excision from March 2012 to February 2016 (Memorial Sloan Kettering Cancer Center/internal dataset, n = 114, 41% female, median age = 55) and July 2014 to October 2015 (Instituto do Câncer do Estado de São Paulo/external dataset, n = 50, 52% female, median age = 64.5) were retrospectively included. Two radiologists (R1, senior; R2, junior) independently evaluated restaging MRI, classifying patients (radiological complete response vs radiological partial response). Model A (n = 33 texture features), model B (n = 91 features including texture, shape, and edge features), and two combination models (model A + B + R1, model A + B + R2) were constructed. Pathology served as the reference standard for neoadjuvant treatment response. Comparison of the classifiers' AUCs on the external set was done using DeLong's test. Results: Models A and B had similar discriminative ability (P = 0.3; Model B AUC = 83%, 95% CI 70%–97%). Combined models increased inter-reader agreement compared with radiologist-only interpretation (κ = 0.82, 95% CI 0.70–0.89 vs k = 0.25, 95% CI 0.11–0.61). The combined model slightly increased junior radiologist specificity, positive predictive value, and negative predictive values (93% vs 90%, 57% vs 50%, and 91% vs 90%, respectively). Conclusion: We developed and externally validated a combined model using radiomics and radiologist qualitative assessment, which improved inter-reader agreement and slightly increased the diagnostic performance of the junior radiologist in predicting pCR after neoadjuvant treatment in patients with LARC.

SAO Paulo (Brazil : State); MEMORIAL Sloan-Kettering Cancer Center; MAGNETIC resonance imaging; RECTAL cancer; ARTIFICIAL intelligence; RADIOLOGISTS; CANCER treatment; HIGH dose rate brachytherapy

Abdominal Radiology, 2022, Vol 47, Issue 8, p2770

2366-004X

Academic Journal

10.1007/s00261-022-03572-8