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A Graph-Based Approach to Identify Factors Contributing to Postoperative Lung Cancer Recurrence among Patients with Non-Small-Cell Lung Cancer
SIMPLE SUMMARY: This study aimed to model the causal relationship between radiographical features derived from CT scans and postoperative lung cancer recurrence and recurrence-free survival. A cohort of 363 lung cancer patients was retrospectively identified, and a novel causal graphical model was u...
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/PMC10340686/ https://www.ncbi.nlm.nih.gov/pubmed/37444581 http://dx.doi.org/10.3390/cancers15133472 |
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author | Iyer, Kartik Ren, Shangsi Pu, Lucy Mazur, Summer Zhao, Xiaoyan Dhupar, Rajeev Pu, Jiantao |
author_facet | Iyer, Kartik Ren, Shangsi Pu, Lucy Mazur, Summer Zhao, Xiaoyan Dhupar, Rajeev Pu, Jiantao |
author_sort | Iyer, Kartik |
collection | PubMed |
description | SIMPLE SUMMARY: This study aimed to model the causal relationship between radiographical features derived from CT scans and postoperative lung cancer recurrence and recurrence-free survival. A cohort of 363 lung cancer patients was retrospectively identified, and a novel causal graphical model was used to identify and visualize the causal relationship between these factors. Body composition, particularly with respect to adipose tissue distribution, was found to have a significant and causal impact on both recurrence and recurrence-free survival. ABSTRACT: The accurate identification of the preoperative factors impacting postoperative cancer recurrence is crucial for optimizing neoadjuvant and adjuvant therapies and guiding follow-up treatment plans. We modeled the causal relationship between radiographical features derived from CT scans and the clinicopathologic factors associated with postoperative lung cancer recurrence and recurrence-free survival. A retrospective cohort of 363 non-small-cell lung cancer (NSCLC) patients who underwent lung resections with a minimum 5-year follow-up was analyzed. Body composition tissues and tumor features were quantified based on preoperative whole-body CT scans (acquired as a component of PET-CT scans) and chest CT scans, respectively. A novel causal graphical model was used to visualize the causal relationship between these factors. Variables were assessed using the intervention do-calculus adjustment (IDA) score. Direct predictors for recurrence-free survival included smoking history, T-stage, height, and intramuscular fat mass. Subcutaneous fat mass, visceral fat volume, and bone mass exerted the greatest influence on the model. For recurrence, the most significant variables were visceral fat volume, subcutaneous fat volume, and bone mass. Pathologic variables contributed to the recurrence model, with bone mass, TNM stage, and weight being the most important. Body composition, particularly adipose tissue distribution, significantly and causally impacted both recurrence and recurrence-free survival through interconnected relationships with other variables. |
format | Online Article Text |
id | pubmed-10340686 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103406862023-07-14 A Graph-Based Approach to Identify Factors Contributing to Postoperative Lung Cancer Recurrence among Patients with Non-Small-Cell Lung Cancer Iyer, Kartik Ren, Shangsi Pu, Lucy Mazur, Summer Zhao, Xiaoyan Dhupar, Rajeev Pu, Jiantao Cancers (Basel) Article SIMPLE SUMMARY: This study aimed to model the causal relationship between radiographical features derived from CT scans and postoperative lung cancer recurrence and recurrence-free survival. A cohort of 363 lung cancer patients was retrospectively identified, and a novel causal graphical model was used to identify and visualize the causal relationship between these factors. Body composition, particularly with respect to adipose tissue distribution, was found to have a significant and causal impact on both recurrence and recurrence-free survival. ABSTRACT: The accurate identification of the preoperative factors impacting postoperative cancer recurrence is crucial for optimizing neoadjuvant and adjuvant therapies and guiding follow-up treatment plans. We modeled the causal relationship between radiographical features derived from CT scans and the clinicopathologic factors associated with postoperative lung cancer recurrence and recurrence-free survival. A retrospective cohort of 363 non-small-cell lung cancer (NSCLC) patients who underwent lung resections with a minimum 5-year follow-up was analyzed. Body composition tissues and tumor features were quantified based on preoperative whole-body CT scans (acquired as a component of PET-CT scans) and chest CT scans, respectively. A novel causal graphical model was used to visualize the causal relationship between these factors. Variables were assessed using the intervention do-calculus adjustment (IDA) score. Direct predictors for recurrence-free survival included smoking history, T-stage, height, and intramuscular fat mass. Subcutaneous fat mass, visceral fat volume, and bone mass exerted the greatest influence on the model. For recurrence, the most significant variables were visceral fat volume, subcutaneous fat volume, and bone mass. Pathologic variables contributed to the recurrence model, with bone mass, TNM stage, and weight being the most important. Body composition, particularly adipose tissue distribution, significantly and causally impacted both recurrence and recurrence-free survival through interconnected relationships with other variables. MDPI 2023-07-03 /pmc/articles/PMC10340686/ /pubmed/37444581 http://dx.doi.org/10.3390/cancers15133472 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 Iyer, Kartik Ren, Shangsi Pu, Lucy Mazur, Summer Zhao, Xiaoyan Dhupar, Rajeev Pu, Jiantao A Graph-Based Approach to Identify Factors Contributing to Postoperative Lung Cancer Recurrence among Patients with Non-Small-Cell Lung Cancer |
title | A Graph-Based Approach to Identify Factors Contributing to Postoperative Lung Cancer Recurrence among Patients with Non-Small-Cell Lung Cancer |
title_full | A Graph-Based Approach to Identify Factors Contributing to Postoperative Lung Cancer Recurrence among Patients with Non-Small-Cell Lung Cancer |
title_fullStr | A Graph-Based Approach to Identify Factors Contributing to Postoperative Lung Cancer Recurrence among Patients with Non-Small-Cell Lung Cancer |
title_full_unstemmed | A Graph-Based Approach to Identify Factors Contributing to Postoperative Lung Cancer Recurrence among Patients with Non-Small-Cell Lung Cancer |
title_short | A Graph-Based Approach to Identify Factors Contributing to Postoperative Lung Cancer Recurrence among Patients with Non-Small-Cell Lung Cancer |
title_sort | graph-based approach to identify factors contributing to postoperative lung cancer recurrence among patients with non-small-cell lung cancer |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10340686/ https://www.ncbi.nlm.nih.gov/pubmed/37444581 http://dx.doi.org/10.3390/cancers15133472 |
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