Deep Learning Signature for Predicting Occult Nodal Metastasis of Clinical N0 Lung Cancer

PET/CT-based Deep Learning Signature for Predicting Occult Nodal Metastasis of Clinical Stage N0 Non-Small Cell Lung Cancer: A Multicenter Prospective Diagnostic Trial

Status

UNKNOWN

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT05425134

Enrollment

5000

Registered

2022-06-21

Start date

2022-01-01

Completion date

2023-12-31

Last updated

2023-02-09

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

Conditions

Non-small Cell Lung Cancer

Keywords

Lung cancer, Lymph node metastasis, Deep learning, PET-CT

Brief summary

The purpose of this study is to evaluate the performance of a PET/CT-based deep learning signature for predicting occult nodal metastasis of clinical stage N0 non-small cell lung cancer in a multicenter prospective cohort.

Interventions

DIAGNOSTIC_TESTPET/CT-based Deep Learning Signature

Deep Learning Signature Based on PET-CT for Predicting Occult Nodal Metastasis of Clinical N0 Non-small Cell Lung Cancer

Study design

Observational model

COHORT

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

20 Years to 75 Years

Inclusion criteria

(1) Participants scheduled for surgery for radiological finding of pulmonary lesions from the preoperative thin-section CT scans; (2) The maximum short-axis diameter of N1 and N2 lymph nodes less than 1 cm on CT scan; (3) The SUVmax of N1 and N2 lymph nodes less than 2.5; (4) Pathological confirmation of primary NSCLC; (5) Age ranging from 20-75 years; (6) Obtained written informed consent.

Exclusion criteria

(1) Multiple lung lesions; (2) Poor quality of PET-CT images; (3) Participants with incomplete clinical information; (4) Participants not receiving systematic lymph node dissection; (5) Participants who have received neoadjuvant therapy.

Design outcomes

Primary

Measure	Time frame	Description
Area under the receiver operating characteristic curve	2022.1-2023.12	Area under the receiver operating characteristic curve

Secondary

Measure	Time frame	Description
Sensitivity Sensitivity	2022.1-2023.12	Sensitivity
Specificity	2022.1-2023.12	Specificity
Positive predictive value	2022.1-2023.12	Positive predictive value
Negative predictive value	2022.1-2023.12	Negative predictive value
Accuracy	2022.1-2023.12	Accuracy

Countries

China

Contacts

Primary ContactChang Chen, MD, PhD

chenthoracic@163.com+86-021-65115006

Outcome results

None listed

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