Predicting High-Grade Patterns in Stage I Solid Lung Adenocarcinoma: A Study of 371 Patients Using Refined Radiomics and Deep Learning-Guided CatBoost Classifier.

Journal: Technology in cancer research & treatment

PMID: 39692551

Abstract

INTRODUCTION: This study aimed to devise a diagnostic algorithm, termed the Refined Radiomics and Deep Learning Features-Guided CatBoost Classifier (RRDLC-Classifier), and evaluate its efficacy in predicting pathological high-grade patterns in patients diagnosed with clinical stage I solid lung adenocarcinoma (LADC).

Authors

Hong Zheng

School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou 325035, China.
Wei Chen

Department of Urology, Zigong Fourth People's Hospital, Sichuan, China.
Jun Liu

Department of Radiology, Second Xiangya Hospital, Changsha, Hunan, China.
Lian Jian

Department of Radiology, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, Hunan, China.
Tao Luo
Xiaoping Yu

Department of Radiology, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, Hunan, China.

Keywords

Adenocarcinoma of Lung Adult Aged Algorithms Deep Learning Female Humans Lung Neoplasms Male Middle Aged Neoplasm Grading Neoplasm Staging Prognosis Radiomics Retrospective Studies ROC Curve Tomography, X-Ray Computed

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View on PubMed Access via DOI PubMed (39692551)

Predicting High-Grade Patterns in Stage I Solid Lung Adenocarcinoma: A Study of 371 Patients Using Refined Radiomics and Deep Learning-Guided CatBoost Classifier.

Abstract

Authors

Keywords

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