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 c...
| Publicado en: | Abdominal Radiology Vol. 47; no. 8; pp. 2770 - 2783 |
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| Autores principales: | , , , , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Aug2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=158081232&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 158081232 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Aug2022 vid: 47 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 158081232 157477046 10.1007/s00261-022-03572-8 158081232 ppf: 2770 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Combined artificial intelligence and radiologist model for predicting rectal cancer treatment response from magnetic resonance imaging: an external validation study. aug: au: 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 affil: Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, 10065, New York, NY, USA sug: ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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