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Using Radiomics and Machine Learning Applied to MRI to Predict Response to Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer
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 presen...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10572442/ https://www.ncbi.nlm.nih.gov/pubmed/37835882 http://dx.doi.org/10.3390/diagnostics13193139 |
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author | Chiappa, Valentina Bogani, Giorgio Interlenghi, Matteo Vittori Antisari, Giulia Salvatore, Christian Zanchi, Lucia Ludovisi, Manuela Leone Roberti Maggiore, Umberto Calareso, Giuseppina Haeusler, Edward Raspagliesi, Francesco Castiglioni, Isabella |
author_facet | Chiappa, Valentina Bogani, Giorgio Interlenghi, Matteo Vittori Antisari, Giulia Salvatore, Christian Zanchi, Lucia Ludovisi, Manuela Leone Roberti Maggiore, Umberto Calareso, Giuseppina Haeusler, Edward Raspagliesi, Francesco Castiglioni, Isabella |
author_sort | Chiappa, Valentina |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10572442 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-105724422023-10-14 Using Radiomics and Machine Learning Applied to MRI to Predict Response to Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer Chiappa, Valentina Bogani, Giorgio Interlenghi, Matteo Vittori Antisari, Giulia Salvatore, Christian Zanchi, Lucia Ludovisi, Manuela Leone Roberti Maggiore, Umberto Calareso, Giuseppina Haeusler, Edward Raspagliesi, Francesco Castiglioni, Isabella Diagnostics (Basel) Article 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. MDPI 2023-10-06 /pmc/articles/PMC10572442/ /pubmed/37835882 http://dx.doi.org/10.3390/diagnostics13193139 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Chiappa, Valentina Bogani, Giorgio Interlenghi, Matteo Vittori Antisari, Giulia Salvatore, Christian Zanchi, Lucia Ludovisi, Manuela Leone Roberti Maggiore, Umberto Calareso, Giuseppina Haeusler, Edward Raspagliesi, Francesco Castiglioni, Isabella Using Radiomics and Machine Learning Applied to MRI to Predict Response to Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer |
title | Using Radiomics and Machine Learning Applied to MRI to Predict Response to Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer |
title_full | Using Radiomics and Machine Learning Applied to MRI to Predict Response to Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer |
title_fullStr | Using Radiomics and Machine Learning Applied to MRI to Predict Response to Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer |
title_full_unstemmed | Using Radiomics and Machine Learning Applied to MRI to Predict Response to Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer |
title_short | Using Radiomics and Machine Learning Applied to MRI to Predict Response to Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer |
title_sort | using radiomics and machine learning applied to mri to predict response to neoadjuvant chemotherapy in locally advanced cervical cancer |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10572442/ https://www.ncbi.nlm.nih.gov/pubmed/37835882 http://dx.doi.org/10.3390/diagnostics13193139 |
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