Exploratory integrated analysis to build a pathologic finding, recurrence, survival predicting algorithm using artificial intelligence from clinical factors and image finding on thin-section computed tomography in patients with clinical stage IA non-small cell lung cancer

Exploratory integrated analysis to build a pathologic finding, recurrence, survival predicting algorithm using artificial intelligence from clinical factors and image finding on thin-section computed tomography in patients with clinical stage IA non-small cell lung cancer - AI PREDICTION

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

JPRN

Registry ID

JPRN-UMIN000048869

Enrollment

3129

Registered

2022-09-07

Start date

2022-09-06

Completion date

Unknown

Last updated

2026-06-29

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

Interventions

None listed

Eligibility

Sex/Gender

Male

Inclusion criteria

Inclusion criteria: Cohort 1 Satisfy one of the following. 1) Registered and eligible in JCOG0802/WJOG4607L 2) Registered and eligible in JCOG0804/WJOG4507L 3) Registered and eligible in JCOG1211. Cohort 2 All of the following items are satisfied. 1) Non-small cell lung cancer (excluding low-grade malignant tumors) operated at the Department of Thoracic Surgery, National Cancer Center Hospital East from January 2003 to December 2014. 2) Performance status (PS) is 0 or 1 according to ECOG criteria. 3) Complete pathological resection. 4) 80 years old or younger at the time of surgery. 5) Clinical stage 0-IB. 6) Preoperative thin-section CT (with or without contrast enhancement, slice thickness: 10 mm or less) can be obtained by the research office. 7) Written informed consent has been obtained from the survivors to participate in this study.

Exclusion criteria

Exclusion criteria: Cohort 1 1) No thin-section image of the main tumor in CT images submitted for central image review Cohort 2 1) A history of other cancers at the time of surgery. 2) Multiple lung cancers were observed at the time of surgery. 3) Preoperative treatment was performed. 4) The purpose of the surgery was biopsy or sublobar resection for compromised patients. 5) Insufficient data in the medical record required for this ancillary study.

Design outcomes

Primary

Measure	Time frame
Diagnostic sensitivity and specificity of prediction of cancer recurrence by artificial intelligence prediction model.	—

Countries

Japan

Contacts

Public ContactKeiju Aokage

National cancer center hospital East Department of thoracic surgery

kaokage@east.ncc.go.jp0471331111

Outcome results