Impact of the number of dissected lymph nodes on machine learning-based prediction of postoperative lung cancer recurrence: a single-hospital retrospective cohort study.

Journal: BMJ open respiratory research

PMID: 39327061

Abstract

BACKGROUND: The optimal number of lymph nodes to be dissected during lung cancer surgery to minimise the postoperative recurrence risk remains undetermined. This study aimed to elucidate the impact of the number of dissected lymph nodes on the risk of postoperative recurrence of non-small cell lung cancer (NSCLC) using machine learning algorithms and statistical analyses.

Authors

Kensuke Kojima

Department of General Thoracic Surgery, NHO Kinki Chuo Chest Medical Center, Osaka, Japan k7kensuke@icloud.com.
Hironobu Samejima

Department of General Thoracic Surgery, NHO Kinki Chuo Chest Medical Center, Osaka, Japan.
Kyoichi Okishio

Clinical Research Center, NHO Kinki Chuo Chest Medical Center, Osaka, Japan.
Toshiteru Tokunaga

Department of General Thoracic Surgery, NHO Kinki Chuo Chest Medical Center, Osaka, Japan.
Hyungeun Yoon

Department of General Thoracic Surgery, NHO Kinki Chuo Chest Medical Center, Osaka, Japan.
Shinji Atagi

Department of Thoracic Oncology, National Hospital Organization Kinki-Chuo Chest Medical Center, Osaka, Japan.

Keywords

Aged Carcinoma, Non-Small-Cell Lung Female Humans Lung Neoplasms Lymph Node Excision Lymph Nodes Lymphatic Metastasis Machine Learning Male Middle Aged Neoplasm Recurrence, Local Pneumonectomy Retrospective Studies

External Resources

View on PubMed Access via DOI PubMed (39327061)

Impact of the number of dissected lymph nodes on machine learning-based prediction of postoperative lung cancer recurrence: a single-hospital retrospective cohort study.

Abstract

Authors

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