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.

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.. - In: EUROPEAN JOURNAL OF RADIOLOGY. - ISSN 0720-048X. - 118:(2019), pp. 1-9. [10.1016/j.ejrad.2019.06.013]

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.;Mare V.;Paramatti R.;Russomando A.;Solfaroli Camillocci E.;Faccini R.;Laghi A.
2019

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.
Artificial intelligence; Magnetic resonance imaging; Neoadjuvant chemoradiotherapy; Rectal cancer; Texture analysis; Aged; Aged, 80 and over; Chemoradiotherapy, Adjuvant; Cohort Studies; Female; Humans; Magnetic Resonance Imaging; Male; Middle Aged; Neoadjuvant Therapy; Prospective Studies; ROC Curve; Rectal Neoplasms; Rectum; Reproducibility of Results; Retrospective Studies; Treatment Outcome; Artificial Intelligence
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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.. - In: EUROPEAN JOURNAL OF RADIOLOGY. - ISSN 0720-048X. - 118:(2019), pp. 1-9. [10.1016/j.ejrad.2019.06.013]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1361070
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