Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Iyer, Kartik, Ren, Shangsi, Pu, Lucy, Mazur, Summer, Zhao, Xiaoyan, Dhupar, Rajeev, Pu, Jiantao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
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
_version_ 1785072138789584896
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
work_keys_str_mv AT iyerkartik agraphbasedapproachtoidentifyfactorscontributingtopostoperativelungcancerrecurrenceamongpatientswithnonsmallcelllungcancer
AT renshangsi agraphbasedapproachtoidentifyfactorscontributingtopostoperativelungcancerrecurrenceamongpatientswithnonsmallcelllungcancer
AT pulucy agraphbasedapproachtoidentifyfactorscontributingtopostoperativelungcancerrecurrenceamongpatientswithnonsmallcelllungcancer
AT mazursummer agraphbasedapproachtoidentifyfactorscontributingtopostoperativelungcancerrecurrenceamongpatientswithnonsmallcelllungcancer
AT zhaoxiaoyan agraphbasedapproachtoidentifyfactorscontributingtopostoperativelungcancerrecurrenceamongpatientswithnonsmallcelllungcancer
AT dhuparrajeev agraphbasedapproachtoidentifyfactorscontributingtopostoperativelungcancerrecurrenceamongpatientswithnonsmallcelllungcancer
AT pujiantao agraphbasedapproachtoidentifyfactorscontributingtopostoperativelungcancerrecurrenceamongpatientswithnonsmallcelllungcancer
AT iyerkartik graphbasedapproachtoidentifyfactorscontributingtopostoperativelungcancerrecurrenceamongpatientswithnonsmallcelllungcancer
AT renshangsi graphbasedapproachtoidentifyfactorscontributingtopostoperativelungcancerrecurrenceamongpatientswithnonsmallcelllungcancer
AT pulucy graphbasedapproachtoidentifyfactorscontributingtopostoperativelungcancerrecurrenceamongpatientswithnonsmallcelllungcancer
AT mazursummer graphbasedapproachtoidentifyfactorscontributingtopostoperativelungcancerrecurrenceamongpatientswithnonsmallcelllungcancer
AT zhaoxiaoyan graphbasedapproachtoidentifyfactorscontributingtopostoperativelungcancerrecurrenceamongpatientswithnonsmallcelllungcancer
AT dhuparrajeev graphbasedapproachtoidentifyfactorscontributingtopostoperativelungcancerrecurrenceamongpatientswithnonsmallcelllungcancer
AT pujiantao graphbasedapproachtoidentifyfactorscontributingtopostoperativelungcancerrecurrenceamongpatientswithnonsmallcelllungcancer