Deep Learning Signature for Predicting Occult Nodal Metas... | NCT05425134

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

NCT05425134

Sponsor

Shanghai Pulmonary Hospital, Shanghai, China

Status

Unknown status

Last Update Posted

Feb 9, 2023Actual

Enrollment

5,000Estimated

Phase

Not provided

Conditions

Non-small Cell Lung Cancer

Interventions

PET/CT-based Deep Learning Signature

Countries

China

Protocol Section

Identification Module Status Module Sponsor/Collaborators Module Oversight Module Description Module Conditions Module Design Module Arms/Interventions Module Outcomes Module Eligibility Module Contacts/Locations Module References Module IPD Sharing Statement Module

Results Section

Participant Flow Module Baseline Characteristics Module Outcome Measures Module Adverse Events Module More Info Module

Annotation Section

Annotation Module

Document Section

Large Document Module

Derived Section

Miscellaneous Info Module Condition Browse Module Intervention Browse Module

Protocol Section

Identification Module

NCT ID

Results Section

No data available

No data is available for this block.

Annotation Section

No data available

No data is available for this block.

Document Section

No data available

No data is available for this block.

Derived Section

Miscellaneous Info Module

Version Holder

Status Module

Record Verification Date

Feb 2023

Overall Recruitment Status or Expanded Access Status

Unknown status

Last Known Status

Recruiting

Delayed Posting

Not provided

Why Stopped

Not provided

Expanded Access Info

Start Date

Jan 1, 2022Actual

Primary Completion Date

Dec 31, 2023Estimated

Completion Date

Dec 31, 2023Estimated

First Submitted Date

Jun 15, 2022

First Submission Date that Met QC Criteria

Jun 15, 2022

First Posted Date

Jun 21, 2022Actual

Results Waived

Not provided

Results First Submitted Date

Not provided

Results First Submitted that Met QC Criteria

Not provided

Results First Posted Date

Not provided

Certification/Extension (aka Delayed Results) First Submitted Date

Not provided

Certification/Extension First Submitted that Passed QC Review

Not provided

Certification/Extension First Posted Date

Not provided

Last Update Submitted Date

Feb 7, 2023

Last Update Posted Date

Feb 9, 2023Actual

Sponsor/Collaborators Module

Responsible Party, by Official Title

Chang Chen, Professor, Shanghai Pulmonary Hospital, Shanghai, ChinaPrincipal Investigator

Lead Sponsor

Shanghai Pulmonary Hospital, Shanghai, ChinaOTHER

Collaborators

Name	Class
Ningbo No.2 Hospital	OTHER
Zunyi Medical College	OTHER
The First Affiliated Hospital of Nanchang University	OTHER

Oversight Module

Has Data Monitoring Committee (DMC)

Not provided

Is FDA Regulated Drug

Is FDA Regulated Device

Is Unapproved Device

Not provided

Pediatric Postmarket Surveillance of a Device Product

Not provided

Product Exported from US

Not provided

FDAAA801 Violation

Not provided

Description Module

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.

Detailed Description

Not provided

Design Module

Study Type

Observational

Number of References to an Expanded Access Study

Not provided

Expanded Access Types

Not provided

Patient Registry

Target Follow-Up Duration

Not provided

Phases

Not provided

Interventional Study Design

Allocation

Not provided

Intervention Model

Biospecimen

No data available

No data is available for this block.

Enrollment

5,000Estimated

Arms/Interventions Module

Arm Groups

Not provided

Interventions

Name	Type	Description	Arm Group Labels	Other Names
PET/CT-based Deep Learning Signature	Diagnostic Test	Deep Learning Signature Based on PET-CT for Predicting Occult Nodal Metastasis of Clinical N0 Non-small Cell Lung Cancer

Outcomes Module

Primary Outcomes

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

Secondary Outcomes

Measure	Description	Time Frame
Sensitivity Sensitivity	Sensitivity	2022.1-2023.12
Specificity	Specificity	2022.1-2023.12

Other Outcomes

Not provided

Eligibility Module

Eligibility Criteria

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.

Accepts Healthy Volunteers

Not provided

Sex

All

Sex/Gender Based

Not provided

Sex/Gender Description

Not provided

Minimum Age

20 Years

Maximum Age

75 Years

Standard Ages

AdultOlder Adult

Study Population

Clinica N0 Non-small Cell Lung Cancer

Sampling Method

Non-Probability Sample

Contacts/Locations Module

Central Contacts

Name	Role	Phone	Extension	Email
Chang Chen, MD, PhD	Contact	+86-021-65115006	2074	chenthoracic@163.com

Overall Officials

Not provided

Locations

Facility	Status	City	State	ZIP	Country	Contacts
Affiliated Hospital of Zunyi Medical University	Recruiting	Zunyi	Guizhou		China

IPD Sharing Statement Module

Plan to Share IPD

Description

Not provided

Types

Not provided

Time Frame

Not provided

Access Criteria

Not provided

URL

Not provided

Condition Browse Module

MeSH Terms

ID	Term
D002289	Carcinoma, Non-Small-Cell Lung
D008175	Lung Neoplasms
D008207	Lymphatic Metastasis

Ancestor Terms

ID	Term
D002283	Carcinoma, Bronchogenic
D001984	Bronchial Neoplasms
D012142	Respiratory Tract Neoplasms
D013899	Thoracic Neoplasms

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Browse Branches

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