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A CT-based transfer learning approach to predict NSCLC recurrence: The added-value of peritumoral region
Non-small cell lung cancer (NSCLC) represents 85% of all new lung cancer diagnoses and presents a high recurrence rate after surgery. Thus, an accurate prediction of recurrence risk in NSCLC patients at diagnosis could be essential to designate risk patients to more aggressive medical treatments. In...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10153708/ https://www.ncbi.nlm.nih.gov/pubmed/37130116 http://dx.doi.org/10.1371/journal.pone.0285188 |
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author | Bove, Samantha Fanizzi, Annarita Fadda, Federico Comes, Maria Colomba Catino, Annamaria Cirillo, Angelo Cristofaro, Cristian Montrone, Michele Nardone, Annalisa Pizzutilo, Pamela Tufaro, Antonio Galetta, Domenico Massafra, Raffaella |
author_facet | Bove, Samantha Fanizzi, Annarita Fadda, Federico Comes, Maria Colomba Catino, Annamaria Cirillo, Angelo Cristofaro, Cristian Montrone, Michele Nardone, Annalisa Pizzutilo, Pamela Tufaro, Antonio Galetta, Domenico Massafra, Raffaella |
author_sort | Bove, Samantha |
collection | PubMed |
description | Non-small cell lung cancer (NSCLC) represents 85% of all new lung cancer diagnoses and presents a high recurrence rate after surgery. Thus, an accurate prediction of recurrence risk in NSCLC patients at diagnosis could be essential to designate risk patients to more aggressive medical treatments. In this manuscript, we apply a transfer learning approach to predict recurrence in NSCLC patients, exploiting only data acquired during its screening phase. Particularly, we used a public radiogenomic dataset of NSCLC patients having a primary tumor CT image and clinical information. Starting from the CT slice containing the tumor with maximum area, we considered three different dilatation sizes to identify three Regions of Interest (ROIs): CROP (without dilation), CROP 10 and CROP 20. Then, from each ROI, we extracted radiomic features by means of different pre-trained CNNs. The latter have been combined with clinical information; thus, we trained a Support Vector Machine classifier to predict the NSCLC recurrence. The classification performances of the devised models were finally evaluated on both the hold-out training and hold-out test sets, in which the original sample has been previously divided. The experimental results showed that the model obtained analyzing CROP 20 images, which are the ROIs containing more peritumoral area, achieved the best performances on both the hold-out training set, with an AUC of 0.73, an Accuracy of 0.61, a Sensitivity of 0.63, and a Specificity of 0.60, and on the hold-out test set, with an AUC value of 0.83, an Accuracy value of 0.79, a Sensitivity value of 0.80, and a Specificity value of 0.78. The proposed model represents a promising procedure for early predicting recurrence risk in NSCLC patients. |
format | Online Article Text |
id | pubmed-10153708 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-101537082023-05-03 A CT-based transfer learning approach to predict NSCLC recurrence: The added-value of peritumoral region Bove, Samantha Fanizzi, Annarita Fadda, Federico Comes, Maria Colomba Catino, Annamaria Cirillo, Angelo Cristofaro, Cristian Montrone, Michele Nardone, Annalisa Pizzutilo, Pamela Tufaro, Antonio Galetta, Domenico Massafra, Raffaella PLoS One Research Article Non-small cell lung cancer (NSCLC) represents 85% of all new lung cancer diagnoses and presents a high recurrence rate after surgery. Thus, an accurate prediction of recurrence risk in NSCLC patients at diagnosis could be essential to designate risk patients to more aggressive medical treatments. In this manuscript, we apply a transfer learning approach to predict recurrence in NSCLC patients, exploiting only data acquired during its screening phase. Particularly, we used a public radiogenomic dataset of NSCLC patients having a primary tumor CT image and clinical information. Starting from the CT slice containing the tumor with maximum area, we considered three different dilatation sizes to identify three Regions of Interest (ROIs): CROP (without dilation), CROP 10 and CROP 20. Then, from each ROI, we extracted radiomic features by means of different pre-trained CNNs. The latter have been combined with clinical information; thus, we trained a Support Vector Machine classifier to predict the NSCLC recurrence. The classification performances of the devised models were finally evaluated on both the hold-out training and hold-out test sets, in which the original sample has been previously divided. The experimental results showed that the model obtained analyzing CROP 20 images, which are the ROIs containing more peritumoral area, achieved the best performances on both the hold-out training set, with an AUC of 0.73, an Accuracy of 0.61, a Sensitivity of 0.63, and a Specificity of 0.60, and on the hold-out test set, with an AUC value of 0.83, an Accuracy value of 0.79, a Sensitivity value of 0.80, and a Specificity value of 0.78. The proposed model represents a promising procedure for early predicting recurrence risk in NSCLC patients. Public Library of Science 2023-05-02 /pmc/articles/PMC10153708/ /pubmed/37130116 http://dx.doi.org/10.1371/journal.pone.0285188 Text en © 2023 Bove et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Bove, Samantha Fanizzi, Annarita Fadda, Federico Comes, Maria Colomba Catino, Annamaria Cirillo, Angelo Cristofaro, Cristian Montrone, Michele Nardone, Annalisa Pizzutilo, Pamela Tufaro, Antonio Galetta, Domenico Massafra, Raffaella A CT-based transfer learning approach to predict NSCLC recurrence: The added-value of peritumoral region |
title | A CT-based transfer learning approach to predict NSCLC recurrence: The added-value of peritumoral region |
title_full | A CT-based transfer learning approach to predict NSCLC recurrence: The added-value of peritumoral region |
title_fullStr | A CT-based transfer learning approach to predict NSCLC recurrence: The added-value of peritumoral region |
title_full_unstemmed | A CT-based transfer learning approach to predict NSCLC recurrence: The added-value of peritumoral region |
title_short | A CT-based transfer learning approach to predict NSCLC recurrence: The added-value of peritumoral region |
title_sort | ct-based transfer learning approach to predict nsclc recurrence: the added-value of peritumoral region |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10153708/ https://www.ncbi.nlm.nih.gov/pubmed/37130116 http://dx.doi.org/10.1371/journal.pone.0285188 |
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