{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T17:46:14Z","timestamp":1761932774180,"version":"build-2065373602"},"reference-count":47,"publisher":"Public Library of Science (PLoS)","issue":"10","license":[{"start":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T00:00:00Z","timestamp":1761868800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Lung and Bronchus cancer is the most fatal type of cancer in the United States. According to the American Cancer Society, there were more than 127,000 deaths from lung cancer in 2023. Lung cancer care cost 23.8 billion dollars in 2020. In Texas, only 22.8% of lung cancer patients survived 5 years or more past diagnosis based on 2012\u20132018 data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>This study evaluates the survival length of lung and bronchus cancer patients in Texas using advanced statistical and machine learning methods applied to an 11-year cohort study from Surveillance, Epidemiology, and End Results Program. It also quantifies the causal effect of early (localized) versus late (distant) stage at diagnosis on survival time of those patients. Additionally, it explores the influence of demographic and available clinical factors to assess disparities in survival across different groups.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Methodology<\/jats:title>\n                    <jats:p>We performed classical survival analyses, followed by causal survival analysis to study the average years lost among different patient groups. Additionally, we performed survival random forest and survival neural network modeling. Finally, we conducted causal inference and causal survival random forest to estimate and predict the average treatment effect of early-stage diagnosis on lung cancer patient survival.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec004\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Stage and age are the two most important factors in predicting the survival of patients with lung and bronchus cancer. Lung cancer patients diagnosed with the regional stage have about twice the risk of dying as those in the localized stage at any time, and this risk increases as the stage advances. We also find that the average extended lifetime of the localized stage group was about 4 years compared to survivors diagnosed with the distant stage. It can also extend the probability of survival by up to 50%.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec005\">\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our study underscores the need for early screening, diagnosis and improving equity in lung cancer patients care, which could lead to improved outcomes and reduced mortality in this high-risk population.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec006\">\n                    <jats:title>Impact<\/jats:title>\n                    <jats:p>Understanding lung and bronchus cancer survival using advanced causal inference and predictive modeling techniques, highlights the critical importance of early-stage diagnosis, showing that patients diagnosed at localized stages have a substantially higher survival probability. This research underscores the necessity of promoting early screening and equitable cancer care to improve survival rates and healthcare outcomes for lung and bronchus cancer patients.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pone.0333477","type":"journal-article","created":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T17:35:58Z","timestamp":1761932158000},"page":"e0333477","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Causal predictive modeling of survival of lung and bronchus cancer patients diagnosed during 2010\u20132011 in Texas"],"prefix":"10.1371","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-5633-9008","authenticated-orcid":true,"given":"Zeinab","family":"Mohamed","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sidketa","family":"Fofana","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Everado","family":"Cobos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manish K.","family":"Tripathi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tamer","family":"Oraby","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2025,10,31]]},"reference":[{"key":"pone.0333477.ref001","unstructured":"National Cancer Institute S. 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