R Ferrari1, C Mancini-Terracciano2, C Voena3, M Rengo4, M Zerunian4, A Ciardiello5, S Grasso6, V Mare'7, R Paramatti5, A Russomando8, R Santacesaria2, A Satta9, E Solfaroli Camillocci10, R Faccini5, A Laghi11. 1. Az. Osp. San Camillo Forlanini, Department of Emergency Radiology, Viale Gianicolense 87, 00152, Rome, Italy. 2. Istituto Nazionale di Fisica Nucleare, Sezione di Roma, Piazzale A. Moro 2, 00185, Rome, Italy. 3. Istituto Nazionale di Fisica Nucleare, Sezione di Roma, Piazzale A. Moro 2, 00185, Rome, Italy. Electronic address: cecilia.voena@roma1.infn.it. 4. "Sapienza", University of Rome, Department of Radiological Science, Oncology and Pathology, Polo Pontino, Icot Hospital, via Franco Faggiana 1680, 04100, Latina, Italy. 5. Istituto Nazionale di Fisica Nucleare, Sezione di Roma, Piazzale A. Moro 2, 00185, Rome, Italy; "Sapienza", University of Rome, Department of Physics, Piazzale A. Moro 2, 00185, Rome, Italy. 6. "Sapienza", University of Rome, Department of Physics, Piazzale A. Moro 2, 00185, Rome, Italy. 7. "Sapienza", University of Rome, Department of Physics, Piazzale A. Moro 2, 00185, Rome, Italy; University "Cattolica del Sacro Cuore", Specialty School of Medical Physics, Largo Francesco Vito 1, 00198, Rome, Italy. 8. Centro Científico Tecnológico de Valparaíso-CCTVal, Universidad Técnica Federico Santa María, Avenida España 1680, Valparaiso, Chile. 9. Istituto Nazionale di Fisica Nucleare, Sezione di Roma Tor Vergata, Via della Ricerca Scientifica 1, 00133, Rome, Italy. 10. Istituto Nazionale di Fisica Nucleare, Sezione di Roma, Piazzale A. Moro 2, 00185, Rome, Italy; "Sapienza", University of Rome, Department of Physics, Piazzale A. Moro 2, 00185, Rome, Italy; "Sapienza", University of Rome, Specialty School of Medical Physics, Piazzale Aldo Moro 2, 00185, Rome, Italy. 11. "Sapienza", University of Rome, Department of Radiological Science, Oncology and Pathology, Sant'Andrea University hospital, via di Grottarossa 1035, 00189, Rome, Italy.
Abstract
PURPOSE: To develop and validate an Artificial Intelligence (AI) model based on texture analysis of high-resolution T2 weighted MR images able 1) to predict pathologic Complete Response (CR) and 2) to identify non-responders (NR) among patients with locally-advanced rectal cancer (LARC) after receiving neoadjuvant chemoradiotherapy (CRT). METHOD: Fifty-five consecutive patients with LARC were retrospectively enrolled in this study. Patients underwent 3 T Magnetic Resonance Imaging (MRI) acquiring T2-weighted images before, during and after CRT. All patients underwent complete surgical resection and histopathology was the gold standard. Textural features were automatically extracted using an open-source software. A sub-set of statistically significant textural features was selected and two AI models were built by training a Random Forest (RF) classifier on 28 patients (training cohort). Model performances were estimated on 27 patients (validation cohort) using a ROC curve and a decision curve analysis. RESULTS: Sixteen of 55 patients achieved CR. The AI model for CR classification showed good discrimination power with mean area under the receiver operating curve (AUC) of 0.86 (95% CI: 0.70, 0.94) in the validation cohort. The discriminatory power for the NR classification showed a mean AUC of 0.83 (95% CI: 0.71,0.92). Decision curve analysis confirmed higher net patient benefit when using AI models compared to standard-of-care. CONCLUSIONS: AI models based on textural features of MR images of patients with LARC may help to identify patients who will show CR at the end of treatment and those who will not respond to therapy (NR) at an early stage of the treatment.
PURPOSE: To develop and validate an Artificial Intelligence (AI) model based on texture analysis of high-resolution T2 weighted MR images able 1) to predict pathologic Complete Response (CR) and 2) to identify non-responders (NR) among patients with locally-advanced rectal cancer (LARC) after receiving neoadjuvant chemoradiotherapy (CRT). METHOD: Fifty-five consecutive patients with LARC were retrospectively enrolled in this study. Patients underwent 3 T Magnetic Resonance Imaging (MRI) acquiring T2-weighted images before, during and after CRT. All patients underwent complete surgical resection and histopathology was the gold standard. Textural features were automatically extracted using an open-source software. A sub-set of statistically significant textural features was selected and two AI models were built by training a Random Forest (RF) classifier on 28 patients (training cohort). Model performances were estimated on 27 patients (validation cohort) using a ROC curve and a decision curve analysis. RESULTS: Sixteen of 55 patients achieved CR. The AI model for CR classification showed good discrimination power with mean area under the receiver operating curve (AUC) of 0.86 (95% CI: 0.70, 0.94) in the validation cohort. The discriminatory power for the NR classification showed a mean AUC of 0.83 (95% CI: 0.71,0.92). Decision curve analysis confirmed higher net patient benefit when using AI models compared to standard-of-care. CONCLUSIONS: AI models based on textural features of MR images of patients with LARC may help to identify patients who will show CR at the end of treatment and those who will not respond to therapy (NR) at an early stage of the treatment.
Authors: Mustafa Bektaş; Jurriaan B Tuynman; Jaime Costa Pereira; George L Burchell; Donald L van der Peet Journal: World J Surg Date: 2022-09-15 Impact factor: 3.282
Authors: Iram Shahzadi; Alex Zwanenburg; Annika Lattermann; Annett Linge; Christian Baldus; Jan C Peeken; Stephanie E Combs; Markus Diefenhardt; Claus Rödel; Simon Kirste; Anca-Ligia Grosu; Michael Baumann; Mechthild Krause; Esther G C Troost; Steffen Löck Journal: Sci Rep Date: 2022-06-17 Impact factor: 4.996