Machine Learning Radiomics Model for Early Identification of Small-Cell Lung Cancer on Computed Tomography Scans | Zendy

Rajesh P. Shah | Zendy; Heather M. Selby | Zendy; Pritam Mukherjee | Zendy; Shefali S. Verma | Zendy; Peiyi Xie | Zendy; Qinmei Xu | Zendy; Millie Das | Zendy; Sachin B. Malik | Zendy; Olivier Gevaert | Zendy; Sandy Napel | Zendy

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Machine 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,

Qinmei 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.

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