Deep Learning Model for Pure Solid Nodules Classification

Deep Learning Model Supplementary PET-CT as a More Effectively Diagnostic Method for Pure Solid Nodules Classification: a Multicenter Observational Study

Status

UNKNOWN

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT05542992

Enrollment

260

Registered

2022-09-16

Start date

2022-01-01

Completion date

2023-12-31

Last updated

2022-09-16

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Lung Cancer

Keywords

deep learning model, pure-solid nodules, PET-CT

Brief summary

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.

Interventions

DIAGNOSTIC_TESTCT-based deep learning model

CT-based deep learning model for pure-solid nodules classifications

Study design

Observational model

COHORT

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to 75 Years

Healthy volunteers

Inclusion criteria

* Participants scheduled for surgery for radiological finding of pulmonary pure-solid lesions from the preoperative thin-section CT scans; * The maximum short-axis diameter of lymph nodes less than 3 cm on CT scan; * Age ranging from 18-75 years; * definied pathological examination report available; * Obtained written informed consent.

Exclusion criteria

* Multiple lung lesions; * Poor quality of CT images; * Participants with incomplete clinical information; * Participants who have received neoadjuvant therapy before initial CT evaluation.

Design outcomes

Primary

Measure	Time frame	Description
AUC	2022.01-2023.12	Area under the curve of the receiver operating characteristic

Secondary

Measure	Time frame	Description
Accuracy	2022.01-2023.12	Ratio of the number of correctly classified samples to the total number of samples
sensitivity	2022.01-2023.12	The probability of detecting a positive test in the population with the gold standard for disease (positive)
Specificity	2022.01-2023.12	Odds of detecting a negative test in a population judged disease-free (negative) by the gold standard
PPV	2022.01-2023.12	Positive predictive value
NPV	2022.01-2023.12	Negative predictive value

Countries

China

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026