2022
DOI: 10.2147/ijgm.s361179
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Establishment and Validation of a Predictive Nomogram for Postoperative Survival of Stage I Non-Small Cell Lung Cancer

Abstract: Background Surgical procedure is the preferred option for people with early-stage non-small cell lung cancer (NSCLC), while nearly 30% of patients experienced metastatic or recurrent tumor after operation. The primary intention of this context is to summarize high-risk prognostic factors and set up a novel nomogram to predict the overall survival of individuals with stage I NSCLC after resection. Methods Research objects, 10,218 patients with stage I NSCLC after operati… Show more

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Cited by 7 publications
(9 citation statements)
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“…It has been demonstrated that male gender is a distinct, unfavorable prognostic factor for NSCLC survival ( 19 , 20 ). The patient’s age is an important prognostic factor that affects lung cancer survival, in which elderly individuals have a worse OS ( 21 , 22 ).…”
Section: Discussionmentioning
confidence: 99%
“…It has been demonstrated that male gender is a distinct, unfavorable prognostic factor for NSCLC survival ( 19 , 20 ). The patient’s age is an important prognostic factor that affects lung cancer survival, in which elderly individuals have a worse OS ( 21 , 22 ).…”
Section: Discussionmentioning
confidence: 99%
“…Therefore, the AJCC staging system cannot accurately predict the prognosis for certain specific groups with lung cancer. 6,7 The nomogram is a novel prognostic predictive model that is well in line with each lung cancer subtype group, which incorporates a wide range of common clinical data. The nomogram can be intuitively and easily applied so as to assist both doctors and patients in making appropriate clinical decisions, and it has been widely used for a wide range of solid tumors.…”
Section: Introductionmentioning
confidence: 99%
“…Therefore, numerous studies have sought to identify reliable prognostic predictors and establish clinical risk models. Research employing tumor microenvironment, genomics-pathology correlation, and deep learning models has successfully predicted patient responses (5)(6)(7). However, the limited availability of these diagnostic tests in routine clinical settings hampers their widespread adoption.…”
Section: Introductionmentioning
confidence: 99%