Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
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
_version_ 1785120236154912768
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
work_keys_str_mv AT chiappavalentina usingradiomicsandmachinelearningappliedtomritopredictresponsetoneoadjuvantchemotherapyinlocallyadvancedcervicalcancer
AT boganigiorgio usingradiomicsandmachinelearningappliedtomritopredictresponsetoneoadjuvantchemotherapyinlocallyadvancedcervicalcancer
AT interlenghimatteo usingradiomicsandmachinelearningappliedtomritopredictresponsetoneoadjuvantchemotherapyinlocallyadvancedcervicalcancer
AT vittoriantisarigiulia usingradiomicsandmachinelearningappliedtomritopredictresponsetoneoadjuvantchemotherapyinlocallyadvancedcervicalcancer
AT salvatorechristian usingradiomicsandmachinelearningappliedtomritopredictresponsetoneoadjuvantchemotherapyinlocallyadvancedcervicalcancer
AT zanchilucia usingradiomicsandmachinelearningappliedtomritopredictresponsetoneoadjuvantchemotherapyinlocallyadvancedcervicalcancer
AT ludovisimanuela usingradiomicsandmachinelearningappliedtomritopredictresponsetoneoadjuvantchemotherapyinlocallyadvancedcervicalcancer
AT leonerobertimaggioreumberto usingradiomicsandmachinelearningappliedtomritopredictresponsetoneoadjuvantchemotherapyinlocallyadvancedcervicalcancer
AT calaresogiuseppina usingradiomicsandmachinelearningappliedtomritopredictresponsetoneoadjuvantchemotherapyinlocallyadvancedcervicalcancer
AT haeusleredward usingradiomicsandmachinelearningappliedtomritopredictresponsetoneoadjuvantchemotherapyinlocallyadvancedcervicalcancer
AT raspagliesifrancesco usingradiomicsandmachinelearningappliedtomritopredictresponsetoneoadjuvantchemotherapyinlocallyadvancedcervicalcancer
AT castiglioniisabella usingradiomicsandmachinelearningappliedtomritopredictresponsetoneoadjuvantchemotherapyinlocallyadvancedcervicalcancer