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.
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.
Authors: Carlo N De Cecco; Maria Ciolina; Damiano Caruso; Marco Rengo; Balaji Ganeshan; Felix G Meinel; Daniela Musio; Francesca De Felice; Vincenzo Tombolini; Andrea Laghi Journal: Abdom Radiol (NY) Date: 2016-09
Authors: M R S Siddiqui; K L Gormly; J Bhoday; S Balyansikova; N J Battersby; M Chand; S Rao; P Tekkis; A M Abulafi; G Brown Journal: Clin Radiol Date: 2016-07-02 Impact factor: 2.350
Authors: Davide Cusumano; Nicola Dinapoli; Luca Boldrini; Giuditta Chiloiro; Roberto Gatta; Carlotta Masciocchi; Jacopo Lenkowicz; Calogero Casà; Andrea Damiani; Luigi Azario; Johan Van Soest; Andre Dekker; Philippe Lambin; Marco De Spirito; Vincenzo Valentini Journal: Radiol Med Date: 2017-12-11 Impact factor: 3.469
Authors: Uday Bharat Patel; Gina Brown; Harm Rutten; Nicholas West; David Sebag-Montefiore; Robert Glynne-Jones; Eric Rullier; Marc Peeters; Eric Van Cutsem; Sergio Ricci; Cornelius Van de Velde; Pennert Kjell; Philip Quirke Journal: Ann Surg Oncol Date: 2012-04-24 Impact factor: 5.344
Authors: Carlo N De Cecco; Balaji Ganeshan; Maria Ciolina; Marco Rengo; Felix G Meinel; Daniela Musio; Francesca De Felice; Nicola Raffetto; Vincenzo Tombolini; Andrea Laghi Journal: Invest Radiol Date: 2015-04 Impact factor: 6.016