Clinical characteristics and prognosis of non-small cell lung cancer patients with liver metastasis: A population-based study

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Abstract

BACKGROUND

The presence of liver metastasis (LM) is an independent prognostic factor for shorter survival in non-small cell lung cancer (NSCLC) patients. The median overall survival of patients with involvement of the liver is less than 5 mo. At present, identifying prognostic factors and constructing survival prediction nomogram for NSCLC patients with LM (NSCLC-LM) are highly desirable.

AIM

To build a forecasting model to predict the survival time of NSCLC-LM patients.

METHODS

Data on NSCLC-LM patients were collected from the Surveillance, Epidemiology, and End Results database between 2010 and 2018. Joinpoint analysis was used to estimate the incidence trend of NSCLC-LM. Kaplan-Meier curves were constructed to assess survival time. Cox regression was applied to select the independent prognostic predictors of cancer-specific survival (CSS). A nomogram was established and its prognostic performance was evaluated.

RESULTS

The age-adjusted incidence of NSCLC-LM increased from 22.7 per 1000000 in 2010 to 25.2 in 2013, and then declined to 22.1 in 2018. According to the multivariable Cox regression analysis of the training set, age, marital status, sex, race, histological type, T stage, metastatic pattern, and whether the patient received chemotherapy or not were identified as independent prognostic factors for CSS ( P < 0.05) and were further used to construct a nomogram. The C-indices of the training and validation sets were 0.726 and 0.722, respectively. The results of decision curve analyses (DCAs) and calibration curves showed that the nomogram was well-discriminated and had great clinical utility.

CONCLUSION

We designed a nomogram model and further constructed a novel risk classification system based on easily accessible clinical factors which demonstrated excellent performance to predict the individual CSS of NSCLC-LM patients.

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Most cited references 37

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Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

Hyuna Sung, Jacques Ferlay, Rebecca Siegel … (2021)

This article provides an update on the global cancer burden using the GLOBOCAN 2020 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer. Worldwide, an estimated 19.3 million new cancer cases (18.1 million excluding nonmelanoma skin cancer) and almost 10.0 million cancer deaths (9.9 million excluding nonmelanoma skin cancer) occurred in 2020. Female breast cancer has surpassed lung cancer as the most commonly diagnosed cancer, with an estimated 2.3 million new cases (11.7%), followed by lung (11.4%), colorectal (10.0 %), prostate (7.3%), and stomach (5.6%) cancers. Lung cancer remained the leading cause of cancer death, with an estimated 1.8 million deaths (18%), followed by colorectal (9.4%), liver (8.3%), stomach (7.7%), and female breast (6.9%) cancers. Overall incidence was from 2-fold to 3-fold higher in transitioned versus transitioning countries for both sexes, whereas mortality varied <2-fold for men and little for women. Death rates for female breast and cervical cancers, however, were considerably higher in transitioning versus transitioned countries (15.0 vs 12.8 per 100,000 and 12.4 vs 5.2 per 100,000, respectively). The global cancer burden is expected to be 28.4 million cases in 2040, a 47% rise from 2020, with a larger increase in transitioning (64% to 95%) versus transitioned (32% to 56%) countries due to demographic changes, although this may be further exacerbated by increasing risk factors associated with globalization and a growing economy. Efforts to build a sustainable infrastructure for the dissemination of cancer prevention measures and provision of cancer care in transitioning countries is critical for global cancer control.

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Cancer statistics, 2019

Rebecca Siegel, Kimberly D Miller, Ahmedin Jemal (2019)

Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths that will occur in the United States and compiles the most recent data on cancer incidence, mortality, and survival. Incidence data, available through 2015, were collected by the Surveillance, Epidemiology, and End Results Program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data, available through 2016, were collected by the National Center for Health Statistics. In 2019, 1,762,450 new cancer cases and 606,880 cancer deaths are projected to occur in the United States. Over the past decade of data, the cancer incidence rate (2006-2015) was stable in women and declined by approximately 2% per year in men, whereas the cancer death rate (2007-2016) declined annually by 1.4% and 1.8%, respectively. The overall cancer death rate dropped continuously from 1991 to 2016 by a total of 27%, translating into approximately 2,629,200 fewer cancer deaths than would have been expected if death rates had remained at their peak. Although the racial gap in cancer mortality is slowly narrowing, socioeconomic inequalities are widening, with the most notable gaps for the most preventable cancers. For example, compared with the most affluent counties, mortality rates in the poorest counties were 2-fold higher for cervical cancer and 40% higher for male lung and liver cancers during 2012-2016. Some states are home to both the wealthiest and the poorest counties, suggesting the opportunity for more equitable dissemination of effective cancer prevention, early detection, and treatment strategies. A broader application of existing cancer control knowledge with an emphasis on disadvantaged groups would undoubtedly accelerate progress against cancer.

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Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.

FRANK E. HARRELL, KERRY L LEE, DANIEL B. MARK (1996)

Multivariable regression models are powerful tools that are used frequently in studies of clinical outcomes. These models can use a mixture of categorical and continuous variables and can handle partially observed (censored) responses. However, uncritical application of modelling techniques can result in models that poorly fit the dataset at hand, or, even more likely, inaccurately predict outcomes on new subjects. One must know how to measure qualities of a model's fit in order to avoid poorly fitted or overfitted models. Measurement of predictive accuracy can be difficult for survival time data in the presence of censoring. We discuss an easily interpretable index of predictive discrimination as well as methods for assessing calibration of predicted survival probabilities. Both types of predictive accuracy should be unbiasedly validated using bootstrapping or cross-validation, before using predictions in a new data series. We discuss some of the hazards of poorly fitted and overfitted regression models and present one modelling strategy that avoids many of the problems discussed. The methods described are applicable to all regression models, but are particularly needed for binary, ordinal, and time-to-event outcomes. Methods are illustrated with a survival analysis in prostate cancer using Cox regression.

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Author and article information

Contributors

Jun-Feng Wang

Hong-Di Lu

Ying Wang

Rui Zhang

Xiang Li

Sheng Wang

Journal

Journal ID (nlm-ta): World J Clin Cases

Journal ID (publisher-id): WJCC

Title: World Journal of Clinical Cases

Publisher: Baishideng Publishing Group Inc

ISSN (Electronic): 2307-8960

Publication date (Print): 26 October 2022

Publication date (Electronic): 26 October 2022

Volume: 10

Issue: 30

Pages: 10882-10895

Affiliations

The First Department of Thoracic Oncology, Jilin Province Tumor Hospital, Changchun 130021, Jilin Province, China

Big Data Center for Clinical Research, Jilin Province Tumor Hospital, Changchun 130021, Jilin Province, China

The First Department of Thoracic Oncology, Jilin Province Tumor Hospital, Changchun 130021, Jilin Province, China. wangsh2334@ 123456163.com

Author notes

Author contributions: Wang JF and Wang S designed the study; Wang JF and Lu HD wrote the manuscript; Wang Y and Zheng R contributed to the data collection; Wang JF, Li X, and Wang S performed the statistical analysis; Li X revised the manuscript; all authors read and approved the final manuscript.

Corresponding author: Sheng Wang, MD, PhD, Chief Doctor, The First Department of Thoracic Oncology, Jilin Province Tumor Hospital, No. 1018 Huguang Street, Changchun 130021, Jilin Province, China. wangsh2334@ 123456163.com

Article

Other ID: jWJCC.v10.i30.pg10882

DOI: 10.12998/wjcc.v10.i30.10882

PMC ID: 9631152

PubMed ID: 36338221

SO-VID: d9011b01-395f-4e97-9bc7-e26ddf059abf

License:

This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/

History

Date received : 19 June 2022

Date revision received : 24 July 2022

Date accepted : 16 September 2022

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Clinical characteristics and prognosis of non-small cell lung cancer patients with liver metastasis: A population-based study

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Abstract

BACKGROUND

AIM

METHODS

RESULTS

CONCLUSION

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Recursive Rule based Visual Categorization

Most cited references 37

Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

Cancer statistics, 2019

Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.

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