The purpose of this study is to compare the predictive performance of a CT-based deep learning model for pure-solid nodules classification and compared with the tumor maximum standardized uptake value on PET in a multicenter prospective cohort.
Study Type
OBSERVATIONAL
Enrollment
260
CT-based deep learning model for pure-solid nodules classifications
Shanghai Pulmonary Hospital
Yangpu, Shanghai Municipality, China
RECRUITINGLanzhou
China, Gansu, China
RECRUITINGZunyi
China, Guizhou, China
RECRUITINGAUC
Area under the curve of the receiver operating characteristic
Time frame: 2022.01-2023.12
Accuracy
Ratio of the number of correctly classified samples to the total number of samples
Time frame: 2022.01-2023.12
sensitivity
The probability of detecting a positive test in the population with the gold standard for disease (positive)
Time frame: 2022.01-2023.12
Specificity
Odds of detecting a negative test in a population judged disease-free (negative) by the gold standard
Time frame: 2022.01-2023.12
PPV
Positive predictive value
Time frame: 2022.01-2023.12
NPV
Negative predictive value
Time frame: 2022.01-2023.12
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Nanchang
China, Jiangxi, China
RECRUITINGNingbo
China, Zhejiang, China
RECRUITING