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

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 can...

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Bibliographic Details
Published in:Abdominal imaging 2022-08, Vol.47 (8), p.2770-2782
Main Authors: Horvat, Natally, Veeraraghavan, Harini, Nahas, Caio S. R., Bates, ‬David D. B., Ferreira, Felipe R., Zheng, Junting, Capanu, Marinela, Fuqua, James L., Fernandes, Maria Clara, Sosa, Ramon E., Jayaprakasam, Vetri Sudar, Cerri, Giovanni G., Nahas, Sergio C., Petkovska, Iva
Format: Article
Language:English
Subjects:
Artificial Intelligence
Brazil
Cancer
Cancer therapies
Chemoradiotherapy
Classifiers
Colorectal cancer
Datasets
Female
Females
Gastroenterology
Hepatology
Hollow Organ GI
Humans
Imaging
Magnetic Resonance Imaging
Magnetic Resonance Spectroscopy
Male
Medicine
Medicine & Public Health
Middle Aged
Patients
Radiologists
Radiology
Radiomics
Rectal Neoplasms - diagnostic imaging
Rectal Neoplasms - pathology
Rectal Neoplasms - therapy
Rectum
Retrospective Studies
Texture
Treatment Outcome
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Summary: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. Graphical abstract
ISSN:2366-0058
2366-004X
2366-0058
DOI:10.1007/s00261-022-03572-8