Impact of treatment modality on long-term survival of stage IA small-cell lung cancer patients: a cohort study of the U.S. SEER database

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Abstract

Background

The optimal treatment modality for patients with stage IA (T1N0M0) small-cell lung cancer (SCLC) is still unclear.

Methods

Patients who received surgical resection or chemo-radiotherapy (CRT) between January 2004 and December 2014 were identified from The Surveillance, Epidemiology and End Results (SEER) database. Surgical resection included lobectomy, wedge resection, segmentectomy with lymphadenectomy [examined lymph node (ELN) ≥1]. Propensity score match analysis was utilized to balance the baseline characteristics.

Results

A total of 686 stage IA SCLC cases were included: 337 patients underwent surgery and 349 patients were treated by CRT alone. Surgery achieved a better outcome than CRT alone, with an adjusted hazard ratio (HR) of 0.495. Patients who underwent lobectomy demonstrated a longer overall survival (OS), compared to those who received sublobectomy (crude cohort, median OS, 69 vs. 38 months; match cohort, median OS, 67 vs. 38 months). Patients with ELN >7 presented with longer OS than those with ELN ≤7 (crude cohort, median OS, 91 vs. 49 months; matched cohort, median OS, 91 vs. 54 months). The additional efficacy of chemotherapy or radiotherapy in patients receiving lobectomy was observed. The best prognosis was achieved in the lobectomy plus CRT cohort, with a 5-year survival rate of 73.5%.

Conclusions

The prolonged survival associated with lobectomy and chemotherapy or radiotherapy presents a viable treatment option in the management of patients with stage IA SCLC.

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Most cited references 36

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Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries

Ahmedin Jemal, Lindsey A Torre, Rebecca Siegel … (2018)

This article provides a status report on the global burden of cancer worldwide using the GLOBOCAN 2018 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer, with a focus on geographic variability across 20 world regions. There will be an estimated 18.1 million new cancer cases (17.0 million excluding nonmelanoma skin cancer) and 9.6 million cancer deaths (9.5 million excluding nonmelanoma skin cancer) in 2018. In both sexes combined, lung cancer is the most commonly diagnosed cancer (11.6% of the total cases) and the leading cause of cancer death (18.4% of the total cancer deaths), closely followed by female breast cancer (11.6%), prostate cancer (7.1%), and colorectal cancer (6.1%) for incidence and colorectal cancer (9.2%), stomach cancer (8.2%), and liver cancer (8.2%) for mortality. Lung cancer is the most frequent cancer and the leading cause of cancer death among males, followed by prostate and colorectal cancer (for incidence) and liver and stomach cancer (for mortality). Among females, breast cancer is the most commonly diagnosed cancer and the leading cause of cancer death, followed by colorectal and lung cancer (for incidence), and vice versa (for mortality); cervical cancer ranks fourth for both incidence and mortality. The most frequently diagnosed cancer and the leading cause of cancer death, however, substantially vary across countries and within each country depending on the degree of economic development and associated social and life style factors. It is noteworthy that high-quality cancer registry data, the basis for planning and implementing evidence-based cancer control programs, are not available in most low- and middle-income countries. The Global Initiative for Cancer Registry Development is an international partnership that supports better estimation, as well as the collection and use of local data, to prioritize and evaluate national cancer control efforts. CA: A Cancer Journal for Clinicians 2018;0:1-31. © 2018 American Cancer Society.

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Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group

Ralph D'Agostino (1998)

In observational studies, investigators have no control over the treatment assignment. The treated and non-treated (that is, control) groups may have large differences on their observed covariates, and these differences can lead to biased estimates of treatment effects. Even traditional covariance analysis adjustments may be inadequate to eliminate this bias. The propensity score, defined as the conditional probability of being treated given the covariates, can be used to balance the covariates in the two groups, and therefore reduce this bias. In order to estimate the propensity score, one must model the distribution of the treatment indicator variable given the observed covariates. Once estimated the propensity score can be used to reduce bias through matching, stratification (subclassification), regression adjustment, or some combination of all three. In this tutorial we discuss the uses of propensity score methods for bias reduction, give references to the literature and illustrate the uses through applied examples.

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Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score

Paul R. Rosenbaum, Donald B Rubin (1985)

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Author and article information

Journal

Journal ID (nlm-ta): Ann Transl Med

Journal ID (iso-abbrev): Ann Transl Med

Journal ID (publisher-id): ATM

Title: Annals of Translational Medicine

Publisher: AME Publishing Company

ISSN (Print): 2305-5839

ISSN (Electronic): 2305-5847

Publication date (Print and electronic): October 2020

Publication date (Print): October 2020

Volume: 8

Issue: 20

Electronic Location Identifier: 1292

Affiliations

[1 ]Department of Thoracic Surgery, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital& Fujian Provincial Key Laboratory of Tumor Biotherapy , Fuzhou, China;

[2 ]Department of Thoracic Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University , Guangzhou, China;

[3 ]Department of Thoracic Surgery, Peking University Shenzhen Hospital , Shenzhen, China;

[4 ]Department of Thoracic Oncology, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital , Fuzhou, China;

[5 ]Department of Surgery, Cleveland Clinic , Cleveland, Ohio, USA;

[6 ]S. Arthur Localio Laboratory, Department of Surgery, New York University School of Medicine , New York, NY, USA;

[7 ]Department of Thoracic Surgery, Shanghai First People’s Hospital, Shanghai Jiao Tong University , Shanghai, China;

[8 ]Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiao Tong University , Shanghai, China;

[9 ]Department of Ultrasonography, Guangzhou First People’s Hospital, Guangzhou Medical University , Guangzhou, China;

[10 ]Office of Research Service, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangzhou, China;

[11 ]Department of Thoracic Surgery, Guangdong Provincial People’s Hospital , Guangzhou, China;

[12 ]Department of Thoracic Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation , Guangzhou, China;

[13 ]Department of Thoracic Surgery, First Affiliated Hospital of Nanchang University , Nanchang, China

Author notes

Contributions: (I) Conception and design: SF Lin, XQ Li, YZ Zheng, KS Zhu, BT Yu; (II) Administrative support: KS Zhu, BT Yu; (III) Provision of study materials or patients: HP Xu, JJ Wang, W Wang, QY Huang; (IV) Collection and assembly of data: D Wu, CX Zhong, SS Fu, LX Yuan; (V) Data analysis and interpretation: SF Lin, YZ Zheng, SC Wang, RX Luo, WY Zhai; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

[#]

These authors contributed equally to this study.

Correspondence to: Professor Kun-Shou Zhu, MD. Department of Thoracic Oncology, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou, China. Email: zhuks@ 123456fjzlhospital.com ; Professor Ben-Tong Yu. Department of Thoracic Surgery, First Affiliated Hospital of Nanchang University, Nanchang, China. Email: yubentongcdyfy@ 123456163.com .

Article

Publisher ID: atm-08-20-1292

DOI: 10.21037/atm-20-5525

PMC ID: 7661878

PubMed ID: 33209872

SO-VID: c7eb850f-2015-4d0b-ab70-a4ad52605953

License:

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0.

History

Date received : 08 June 2020

Date accepted : 19 October 2020

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Impact of treatment modality on long-term survival of stage IA small-cell lung cancer patients: a cohort study of the U.S. SEER database

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Abstract

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Methods

Results

Conclusions

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Most cited references 36

Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries

Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group

Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score

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