A Machine-Learning Approach Using PET-Based Radiomics to Predict the Histological Subtypes of Lung Cancer.

Journal: Clinical nuclear medicine

Published Date: Dec 1, 2019

Abstract

PURPOSE: We sought to distinguish lung adenocarcinoma (ADC) from squamous cell carcinoma using a machine-learning algorithm with PET-based radiomic features.

Authors

Seung Hyup Hyun

From the Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul.
Mi Sun Ahn

Departments of Hematology-Oncology.
Young Wha Koh

Pathology.
Su Jin Lee

Nuclear Medicine, Ajou University School of Medicine, Suwon, Republic of Korea.

Keywords

Aged Area Under Curve Bayes Theorem Carcinoma, Squamous Cell Female Humans Image Processing, Computer-Assisted Lung Neoplasms Machine Learning Male Middle Aged Positron Emission Tomography Computed Tomography Retrospective Studies ROC Curve

External Resources

View on PubMed Access via DOI PubMed (31689276)

A Machine-Learning Approach Using PET-Based Radiomics to Predict the Histological Subtypes of Lung Cancer.

Abstract

Authors

Keywords

External Resources

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