Open AccessMachine Learning Radiomics Model for Early Identification of Small-Cell Lung Cancer on Computed Tomography Scans
Author(s) -
Rajesh P. Shah,
Heather M. Selby,
Pritam Mukherjee,
Shefali S. Verma,
Peiyi Xie,
Qiantong Xu,
Millie Das,
Sachin B. Malik,
Olivier Gevaert,
Sandy Napel
Publication year - 2021
Publication title -
jco clinical cancer informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.188
H-Index - 12
ISSN - 2473-4276
DOI - 10.1200/cci.21.00021
Subject(s) - overfitting , receiver operating characteristic , artificial intelligence , random forest , radiomics , logistic regression , feature selection , lung cancer , support vector machine , computer science , medicine , pattern recognition (psychology) , radiology , machine learning , nuclear medicine , pathology , artificial neural network
Small-cell lung cancer (SCLC) is the deadliest form of lung cancer, partly because of its short doubling time. Delays in imaging identification and diagnosis of nodules create a risk for stage migration. The purpose of our study was to determine if a machine learning radiomics model can detect SCLC on computed tomography (CT) among all nodules at least 1 cm in size.