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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Vydáno v:Abdominal imaging Ročník 47; číslo 8; s. 2770 - 2782
Hlavní autoři: 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
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.08.2022
Springer Nature B.V
Témata:
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
Brazil
Neoadjuvant therapy
Watchful waiting
Rectal cancer
Magnetic resonance imaging
Artificial intelligence
ISSN:2366-0058, 2366-004X, 2366-0058
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Shrnutí: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
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Author contributions: Natally Horvat: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Project administration; Resources; Software; Supervision; Validation; Visualization; Roles/Writing - original draft; Writing - review & editing. Harini Veeraraghavan: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Project administration; Resources; Software; Supervision; Validation; Visualization; Roles/Writing - original draft; Writing - review & editing. Caio S.R. Nahas: Data curation; Writing – review & editing. David D.B. Bates: Formal analysis; Writing – review & editing. Felipe R. Ferreira: Data curation; Formal analysis; Writing – review & editing. James L. Fuqua III: Formal analysis; Writing – review & editing. Maria Clara Fernandes: Formal analysis; Writing – review & editing. Ramon E. Sosa: Data curation; Writing – review & editing. Vetri Sudar Jayaprakasam: Formal analysis; Writing – review & editing. Giovanni G. Cerri: Project administration; Supervision; Writing – review & editing. Sergio C. Nahas: Data curation; Project administration; Supervision; Writing – review & editing. Iva Petkovska: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Project administration; Resources; Software; Supervision; Validation; Visualization; Roles/Writing - original draft; Writing - review & editing.
Natally Horvat and Harini Veeraraghavan contributed equally to this study.
ISSN:2366-0058
2366-004X
2366-0058
DOI:10.1007/s00261-022-03572-8