Your browser doesn't support javascript.
loading
MR-based artificial intelligence model to assess response to therapy in locally advanced rectal cancer.
Ferrari, R; Mancini-Terracciano, C; Voena, C; Rengo, M; Zerunian, M; Ciardiello, A; Grasso, S; Mare', V; Paramatti, R; Russomando, A; Santacesaria, R; Satta, A; Solfaroli Camillocci, E; Faccini, R; Laghi, A.
Afiliación
  • Ferrari R; Az. Osp. San Camillo Forlanini, Department of Emergency Radiology, Viale Gianicolense 87, 00152, Rome, Italy.
  • Mancini-Terracciano C; Istituto Nazionale di Fisica Nucleare, Sezione di Roma, Piazzale A. Moro 2, 00185, Rome, Italy.
  • Voena C; Istituto Nazionale di Fisica Nucleare, Sezione di Roma, Piazzale A. Moro 2, 00185, Rome, Italy. Electronic address: cecilia.voena@roma1.infn.it.
  • Rengo M; "Sapienza", University of Rome, Department of Radiological Science, Oncology and Pathology, Polo Pontino, Icot Hospital, via Franco Faggiana 1680, 04100, Latina, Italy.
  • Zerunian M; "Sapienza", University of Rome, Department of Radiological Science, Oncology and Pathology, Polo Pontino, Icot Hospital, via Franco Faggiana 1680, 04100, Latina, Italy.
  • Ciardiello A; 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.
  • Grasso S; "Sapienza", University of Rome, Department of Physics, Piazzale A. Moro 2, 00185, Rome, Italy.
  • Mare' V; "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.
  • Paramatti R; 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.
  • Russomando A; Centro Científico Tecnológico de Valparaíso-CCTVal, Universidad Técnica Federico Santa María, Avenida España 1680, Valparaiso, Chile.
  • Santacesaria R; Istituto Nazionale di Fisica Nucleare, Sezione di Roma, Piazzale A. Moro 2, 00185, Rome, Italy.
  • Satta A; Istituto Nazionale di Fisica Nucleare, Sezione di Roma Tor Vergata, Via della Ricerca Scientifica 1, 00133, Rome, Italy.
  • Solfaroli Camillocci E; 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
  • Faccini R; 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.
  • Laghi A; "Sapienza", University of Rome, Department of Radiological Science, Oncology and Pathology, Sant'Andrea University hospital, via di Grottarossa 1035, 00189, Rome, Italy.
Eur J Radiol ; 118: 1-9, 2019 Sep.
Article en En | MEDLINE | ID: mdl-31439226
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.
Asunto(s)
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias del Recto / Inteligencia Artificial / Imagen por Resonancia Magnética / Terapia Neoadyuvante / Quimioradioterapia Adyuvante Tipo de estudio: Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Aged / Aged80 / Female / Humans / Male / Middle aged Idioma: En Revista: Eur J Radiol Año: 2019 Tipo del documento: Article País de afiliación: Italia Pais de publicación: Irlanda
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias del Recto / Inteligencia Artificial / Imagen por Resonancia Magnética / Terapia Neoadyuvante / Quimioradioterapia Adyuvante Tipo de estudio: Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Aged / Aged80 / Female / Humans / Male / Middle aged Idioma: En Revista: Eur J Radiol Año: 2019 Tipo del documento: Article País de afiliación: Italia Pais de publicación: Irlanda