Sublobar resection is not always superior for early-stage lung cancer in high-risk patients

Author:

Fukui Mariko1ORCID,Matsunaga Takeshi1,Hattori Aritoshi1,Takamochi Kazuya1ORCID,Nojiri Shuko2,Suzuki Kenji1ORCID

Affiliation:

1. Department of General Thoracic Surgery and Juntendo University School of Medicine , Tokyo, Japan

2. Medical Technology Innovation Center, Juntendo University School of Medicine , Tokyo, Japan

Abstract

Abstract OBJECTIVES The phase III trial, Japan Clinical Oncology Group 0802, illustrated the superiority of sublobar resection for early-stage lung cancer in terms of overall survival, with more non-lung cancer-related deaths after a lobectomy. The advantages of sublobar resection may be more pronounced in high-risk patients. The goal of this study was to elucidate the prognoses of high-risk patients. METHODS Patients with a risk of being ineligible for Japan Clinical Oncology Group 0802 for general conditions were classified as the high-risk group, and those who were not at risk of being ineligible were classified as the normal-risk group. Overall survival and prognostic factors were analysed in the high-risk group. RESULTS There were 254 (19.4%) and 1054 patients in the high- and normal-risk groups, respectively. Five-year survival rates were 94.5% and 79.1% in the normal-risk and high-risk groups, respectively (P < 0.001). More patients in the high-risk group died of lung cancer (P < 0.001) and non-lung cancer deaths (P < 0.001) than patients in the normal-risk group. In the high-risk group, 151 lobectomies and 103 sublobar resections were performed. There was no significant difference in the numbers of lung cancer deaths and of non-lung cancer deaths between the procedures. Stratified survival analyses showed that the diffusing capacity of the lungs for carbon monoxide  < 40% tended to favour sublobar resection; being female and having a high carcinoembryonic antigen level tended to favour a lobectomy. CONCLUSIONS Sublobar resection is not always superior for early-stage lung cancer. Even in such cases, the surgical method should be determined by taking into consideration the patient's background and lung cancer surveillance.

Funder

JSPS KAKENHI

Publisher

Oxford University Press (OUP)

Subject

Cardiology and Cardiovascular Medicine,Pulmonary and Respiratory Medicine,General Medicine,Surgery

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1. Preoperatively predicting survival outcome for clinical stage IA pure-solid non–small cell lung cancer by radiomics-based machine learning;The Journal of Thoracic and Cardiovascular Surgery;2024-05

2. Neural Perspectives on Chest X-rays: Comparative Evaluation of Deep Learning Models for Thoracic Disorder Detection;2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI);2024-03-14

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