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A Deep Learning Framework with Explainability for the Prediction of Lateral Locoregional Recurrences in Rectal Cancer Patients with Suspicious Lateral Lymph Nodes

Malignant lateral lymph nodes (LLNs) in low, locally advanced rectal cancer can cause (ipsi-lateral) local recurrences ((L)LR). Accurate identification is, therefore, essential. This study explored LLN features to create an artificial intelligence prediction model, estimating the risk of (L)LR. This...

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Autores principales: Sluckin, Tania C., Hekhuis, Marije, Kol, Sabrine Q., Nederend, Joost, Horsthuis, Karin, Beets-Tan, Regina G. H., Beets, Geerard L., Burger, Jacobus W. A., Tuynman, Jurriaan B., Rutten, Harm J. T., Kusters, Miranda, Benson, Sean
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10572128/
https://www.ncbi.nlm.nih.gov/pubmed/37835842
http://dx.doi.org/10.3390/diagnostics13193099
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author Sluckin, Tania C.
Hekhuis, Marije
Kol, Sabrine Q.
Nederend, Joost
Horsthuis, Karin
Beets-Tan, Regina G. H.
Beets, Geerard L.
Burger, Jacobus W. A.
Tuynman, Jurriaan B.
Rutten, Harm J. T.
Kusters, Miranda
Benson, Sean
author_facet Sluckin, Tania C.
Hekhuis, Marije
Kol, Sabrine Q.
Nederend, Joost
Horsthuis, Karin
Beets-Tan, Regina G. H.
Beets, Geerard L.
Burger, Jacobus W. A.
Tuynman, Jurriaan B.
Rutten, Harm J. T.
Kusters, Miranda
Benson, Sean
author_sort Sluckin, Tania C.
collection PubMed
description Malignant lateral lymph nodes (LLNs) in low, locally advanced rectal cancer can cause (ipsi-lateral) local recurrences ((L)LR). Accurate identification is, therefore, essential. This study explored LLN features to create an artificial intelligence prediction model, estimating the risk of (L)LR. This retrospective multicentre cohort study examined 196 patients diagnosed with rectal cancer between 2008 and 2020 from three tertiary centres in the Netherlands. Primary and restaging T2W magnetic resonance imaging and clinical features were used. Visible LLNs were segmented and used for a multi-channel convolutional neural network. A deep learning model was developed and trained for the prediction of (L)LR according to malignant LLNs. Combined imaging and clinical features resulted in AUCs of 0.78 and 0.80 for LR and LLR, respectively. The sensitivity and specificity were 85.7% and 67.6%, respectively. Class activation map explainability methods were applied and consistently identified the same high-risk regions with structural similarity indices ranging from 0.772–0.930. This model resulted in good predictive value for (L)LR rates and can form the basis of future auto-segmentation programs to assist in the identification of high-risk patients and the development of risk stratification models.
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spelling pubmed-105721282023-10-14 A Deep Learning Framework with Explainability for the Prediction of Lateral Locoregional Recurrences in Rectal Cancer Patients with Suspicious Lateral Lymph Nodes Sluckin, Tania C. Hekhuis, Marije Kol, Sabrine Q. Nederend, Joost Horsthuis, Karin Beets-Tan, Regina G. H. Beets, Geerard L. Burger, Jacobus W. A. Tuynman, Jurriaan B. Rutten, Harm J. T. Kusters, Miranda Benson, Sean Diagnostics (Basel) Article Malignant lateral lymph nodes (LLNs) in low, locally advanced rectal cancer can cause (ipsi-lateral) local recurrences ((L)LR). Accurate identification is, therefore, essential. This study explored LLN features to create an artificial intelligence prediction model, estimating the risk of (L)LR. This retrospective multicentre cohort study examined 196 patients diagnosed with rectal cancer between 2008 and 2020 from three tertiary centres in the Netherlands. Primary and restaging T2W magnetic resonance imaging and clinical features were used. Visible LLNs were segmented and used for a multi-channel convolutional neural network. A deep learning model was developed and trained for the prediction of (L)LR according to malignant LLNs. Combined imaging and clinical features resulted in AUCs of 0.78 and 0.80 for LR and LLR, respectively. The sensitivity and specificity were 85.7% and 67.6%, respectively. Class activation map explainability methods were applied and consistently identified the same high-risk regions with structural similarity indices ranging from 0.772–0.930. This model resulted in good predictive value for (L)LR rates and can form the basis of future auto-segmentation programs to assist in the identification of high-risk patients and the development of risk stratification models. MDPI 2023-09-29 /pmc/articles/PMC10572128/ /pubmed/37835842 http://dx.doi.org/10.3390/diagnostics13193099 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
Sluckin, Tania C.
Hekhuis, Marije
Kol, Sabrine Q.
Nederend, Joost
Horsthuis, Karin
Beets-Tan, Regina G. H.
Beets, Geerard L.
Burger, Jacobus W. A.
Tuynman, Jurriaan B.
Rutten, Harm J. T.
Kusters, Miranda
Benson, Sean
A Deep Learning Framework with Explainability for the Prediction of Lateral Locoregional Recurrences in Rectal Cancer Patients with Suspicious Lateral Lymph Nodes
title A Deep Learning Framework with Explainability for the Prediction of Lateral Locoregional Recurrences in Rectal Cancer Patients with Suspicious Lateral Lymph Nodes
title_full A Deep Learning Framework with Explainability for the Prediction of Lateral Locoregional Recurrences in Rectal Cancer Patients with Suspicious Lateral Lymph Nodes
title_fullStr A Deep Learning Framework with Explainability for the Prediction of Lateral Locoregional Recurrences in Rectal Cancer Patients with Suspicious Lateral Lymph Nodes
title_full_unstemmed A Deep Learning Framework with Explainability for the Prediction of Lateral Locoregional Recurrences in Rectal Cancer Patients with Suspicious Lateral Lymph Nodes
title_short A Deep Learning Framework with Explainability for the Prediction of Lateral Locoregional Recurrences in Rectal Cancer Patients with Suspicious Lateral Lymph Nodes
title_sort deep learning framework with explainability for the prediction of lateral locoregional recurrences in rectal cancer patients with suspicious lateral lymph nodes
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10572128/
https://www.ncbi.nlm.nih.gov/pubmed/37835842
http://dx.doi.org/10.3390/diagnostics13193099
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