Preoperative Maximum Standardized Uptake Value Emphasized in Explainable Machine Learning Model for Predicting the Risk of Recurrence in Resected Non-Small Cell Lung Cancer.

Journal: JCO clinical cancer informatics

PMID: 40043221

Abstract

PURPOSE: To comprehensively analyze the association between preoperative maximum standardized uptake value (SUV) on 18F-fluorodeoxyglucose positron emission tomography-computed tomography and postoperative recurrence in resected non-small cell lung cancer (NSCLC) using machine learning (ML) and statistical approaches.

Authors

Takafumi Iguchi

Department of General Thoracic Surgery, NHO Kinki Chuo Chest Medical Center, Osaka, Japan.
Kensuke Kojima

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

Department of General Thoracic Surgery, NHO Kinki Chuo Chest Medical Center, Osaka, Japan.
Toshiteru Tokunaga

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.
Hyungeun Yoon

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

Keywords

Aged Carcinoma, Non-Small-Cell Lung Female Fluorodeoxyglucose F18 Humans Lung Neoplasms Machine Learning Male Middle Aged Neoplasm Recurrence, Local Positron Emission Tomography Computed Tomography Preoperative Period Prognosis Retrospective Studies ROC Curve

External Resources

View on PubMed Access via DOI PubMed (40043221)

Preoperative Maximum Standardized Uptake Value Emphasized in Explainable Machine Learning Model for Predicting the Risk of Recurrence in Resected Non-Small Cell Lung Cancer.

Abstract

Authors

Keywords

External Resources

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