Evidence map›Paper›PMID 39996091›Full record

ArticleJTO clinical and research reports2025

Temporal Trends in the Utilization and Survival Outcomes of Lobar, Segmental, and Wedge Resection for Early-Stage NSCLC, 2004 to 2020.

Eden Z Deng, Xiaofei Wang, Jianrong Zhang, Thomas E Stinchcombe, Chi-Fu Jeffrey Yang, Nasser Altorki

Abstract read
In one paragraph

Article in JTO clinical and research reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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0cells of the map it votes in
2citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Eden Z DengDepartment of Statistical Science, Duke University, Durham, North Carolina.
Xiaofei WangDepartment of Biostatistics & Bioinformatics, Duke University Medical Center, Durham, North Carolina.
Jianrong ZhangCentre for Cancer Research & Department of General Practice and Primary Care, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Victoria, Australia.
Thomas E StinchcombeDuke Cancer Institute, Duke University Medical Center, Durham, North Carolina.
Chi-Fu Jeffrey YangDepartment of Surgery, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts.
Nasser AltorkiDepartment of Cardiothoracic Surgery, Weill Cornell Medicine, New York-Presbyterian Hospital, New York, New York.

Funding

Transforming Clinical Trials for Elderly and Rare Cancers through Intelligent and Robust Information BorrowingR01AG066883 · NIA · NORTH CAROLINA STATE UNIVERSITY RALEIGH · PI Xiaofei Wang, Shu Yang · 2020 to 2026
$2.0M
NIA NIH HHS R01 AG066883
6 · The paper itself

Abstract

Introduction: Although lobectomy has long been the standard of surgical treatment for early-stage NSCLC, segmental and wedge resections have become another option often used over the past two decades. Methods: To examine the trends over time in the utilization, quality, and overall survival (OS) differences of lobectomy, segmentectomy, and wedge resection, we performed an observational, population-level study of 76,466 patients with T1 or T2 N0M0 NSCLC tumors 2 cm or less in size in the National Cancer Database, from 2004 to 2020. To compare the OS of the three treatments, we used inverse probability of treatment weighting to analyze a subgroup of cases with nodal examination and minimal comorbidity burden. Results: From 2004 to 2020, the use of lobectomy decreased from 75.2% to 67.6% of resections, wedge remained relatively stable (20.5%-22.8%), and segmentectomy increased from 4.3% to 9.7%. The likelihood of nodal assessments and negative margins has increased for all treatments. Younger patients, patients with low comorbidity burden, and patients with smaller tumors have become increasingly likely to receive segmental and wedge resections. Five-year OS of segmentectomy (80.6%, 95% confidence interval [CI]: 78.1%-83.2%) remained noninferior to lobectomy (83.6%, 95% CI: 83.1%-84.1%]), whereas wedge resection was inferior until 2016 to 2019 (five-y OS = 79.9%, 95% CI: 75.9%-83.8%). Conclusions: Sublobar resections, particularly segmentectomies, have increased in frequency and quality. The growing use of sublobar resections for younger and healthier patients highlights the need for additional clinical evidence demonstrating whether these trends do indeed lead to better outcomes.

Indexed as

Lung neoplasmsNational Cancer DatabaseSegmentectomySublobar resectionTemporal trendsWedge resection

Identifiers

PMID39996091
PMCPMC11849078

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.