Using Radiomics and Machine Learning Applied to MRI to Predict Response to Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer

Author:

Chiappa Valentina1,Bogani Giorgio1ORCID,Interlenghi Matteo2,Vittori Antisari Giulia3,Salvatore Christian24ORCID,Zanchi Lucia5,Ludovisi Manuela6ORCID,Leone Roberti Maggiore Umberto1,Calareso Giuseppina7,Haeusler Edward8,Raspagliesi Francesco1,Castiglioni Isabella9ORCID

Affiliation:

1. Gynecologic Oncology, Fondazione IRCCS Istituto Nazionale Tumori di Milano, 20133 Milan, Italy

2. DeepTrace Technologies S.R.L., 20126 Milan, Italy

3. Azienda Ospedaliero-Universitaria di Verona, University of Verona, 37134 Verona, Italy

4. Department of Science, Technology and Society, University School for Advanced Studies IUSS Pavia, 27100 Pavia, Italy

5. Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, Unit of Obstetrics and Gynaecology, University of Pavia, IRCCS San Matteo Hospital Foundation, 27100 Pavia, Italy

6. Department of Clinical Medicine, Life Health and Environmental Sciences, University of L’Aquila, 67100 L’Aquila, Italy

7. Radiology, Fondazione IRCCS Istituto Nazionale Tumori di Milano, 20133 Milan, Italy

8. Department of Anaesthesiology, Fondazione IRCCS Istituto Nazionale Tumori di Milano, 20133 Milan, Italy

9. Department of Physics G. Occhialini, University of Milan-Bicocca, 20133 Milan, Italy

Abstract

Neoadjuvant chemotherapy plus radical surgery could be a safe alternative to chemo-radiation in cervical cancer patients who are not willing to receive radiotherapy. The response to neoadjuvant chemotherapy is the main factor influencing the need for adjunctive treatments and survival. In the present paper we aim to develop a machine learning model based on cervix magnetic resonance imaging (MRI) images to stratify the single-subject risk of cervical cancer. We collected MRI images from 72 subjects. Among these subjects, 28 patients (38.9%) belonged to the “Not completely responding” class and 44 patients (61.1%) belonged to the ’Completely responding‘ class according to their response to treatment. This image set was used for the training and cross-validation of different machine learning models. A robust radiomic approach was applied, under the hypothesis that the radiomic features could be able to capture the disease heterogeneity among the two groups. Three models consisting of three ensembles of machine learning classifiers (random forests, support vector machines, and k-nearest neighbor classifiers) were developed for the binary classification task of interest (“Not completely responding” vs. “Completely responding”), based on supervised learning, using response to treatment as the reference standard. The best model showed an ROC-AUC (%) of 83 (majority vote), 82.3 (mean) [79.9–84.6], an accuracy (%) of 74, 74.1 [72.1–76.1], a sensitivity (%) of 71, 73.8 [68.7–78.9], and a specificity (%) of 75, 74.2 [71–77.5]. In conclusion, our preliminary data support the adoption of a radiomic-based approach to predict the response to neoadjuvant chemotherapy.

Publisher

MDPI AG

Subject

Clinical Biochemistry

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