Rectal cancer response to neoadjuvant chemoradiotherapy evaluated with MRI: Development and validation of a classification algorithm. Issue 147 (February 2022)

• Record Type: Journal Article
• Authors: Rengo, Marco; Landolfi, Federica; Picchia, Simona; Bellini, Davide; Losquadro, Chiara; Badia, Stefano; Caruso, Damiano; Iannicelli, Elsa; Osti, Mattia Falchetto; Tombolini, Vincenzo; Carbone, Iacopo; Giunta, Gaetano; Laghi, Andrea
• Is Part Of: European journal of radiology. Issue 147(2022)
• Publication Date: 2022-02
• Subjects: Rectal cancer -- Neoadjuvant Therapy -- Magnetic Resonance Imaging -- Data mining; MRI Magnetic Resonance Imaging -- LARC Local advanced rectal cancer -- CRT Neoadjuvant chemoradiotherapy -- TME Total Mesorectal Excision -- CR Complete responders -- NCR Non Complete Responders -- pCR Complete Response at histology -- pNCR Non Complete Responders at histology -- TSE Turbo Spin Echo -- DWI Diffusion Weighted Images -- ADC Apparent Diffusion Coefficient -- TV Tumor Volume -- SV Staging Volume -- RV Restaging Volume -- TVRR Tumor Volume Reduction Rate -- TRG Tumor Regression Grade -- MR-TRG Magnetic Resonance-Tumor Regression Grade -- ROI Region of Interest -- SI Signal Intensity -- DSI Signal Intensity Decrease -- SSI Staging Signal Intensity -- RSI Restaging Signal Intensity -- SIRR Signal Intensity Reduction Rate; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2021.110146
• Languages: English
• ISSNs: 0720-048X

Abstract

Abstract: Objective: The aim of this study was to develop and validate a decision support model using data mining algorithms, based on morphologic features derived from MRI images, to discriminate between complete responders (CR) and non-complete responders (NCR) patients after neoadjuvant chemoradiotherapy (CRT), in a population of patients with locally advanced rectal cancer (LARC). Methods: Two populations were retrospectively enrolled: group A (65 patients) was used to train a data mining decision tree algorithm whereas group B (30 patients) was used to validate it. All patients underwent surgery; according to the histology evaluation, patients were divided in CR and NCR. Staging and restaging MRI examinations were retrospectively analysed and seven parameters were considered for data mining classification. Five different classification methods were tested and evaluated in terms of sensitivity, specificity, accuracy and AUC in order to identify the classification model able to achieve the best performance. The best classification algorithm was subsequently applied to group B for validation: sensitivity, specificity, positive and negative predictive value, accuracy and ROC curve were calculated. Inter and intra-reader agreement were calculated. Results: Four features were selected for the development of the classification algorithm: MRI tumor regression grade (MR-TRG), staging volume (SV), tumor volume reduction rate (TVRR) and signal intensity reduction rate (SIRR). The decision tree J48 showed the highest efficiency: when applied to group B, all the CR and 18/21 NCR were correctly classified (sensitivity 85.71%, specificity 100%, PPV 100%, NPV 94.2%, accuracy 95.7%, AUC 0.833). Both inter- and intra-reader evaluation showed good agreement (κ > 0.6). Conclusions: The proposed decision support model may help in distinguishing between CR and NCR patients with LARC after CRT.