Prognostic value of FDG-PET radiomics with machine learning in pancreatic cancer

Prognostic value of FDG-PET radiomics with machine learning in pancreatic cancer - Prognostic value of FDG-PET radiomics with machine learning in pancreatic cancer

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

JPRN

Registry ID

JPRN-UMIN000039293

Enrollment

1000

Registered

2020-01-29

Start date

2019-07-11

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

Pancreatic cancer

Interventions

None listed

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: We enrolled 314 consecutive patients with biopsy-confirmed pancreatic invasive ductal carcinoma who underwent FDG-PET/CT before treatment between April 2010 and March 2018. 20 years old or older

Exclusion criteria

Exclusion criteria: The exclusion criteria were as follows: 1) no significant solid mass on CT/MRI; 2) no significant FDG-uptake; 3) uncontrolled diabetes (<150 mg/dl); 4) multiple cancer; 5) unknown clinical course; 6) under best supportive care; 7) sudden death; 8) early death after surgery.

Design outcomes

Primary

Measure	Time frame
The study endpoint was overall survival (OS), defined as the time from pretreatment FDG-PET/CT scan to cancer-related death. Outcome data were collected from the medical records of each patient. Surviving patients were censored at the time of last clinical follow-up.	—

Countries

Japan

Contacts

Public ContactYoshitaka Toyama

Tohoku University Hospital Department of Diagnostic Radiology

ytoyama0818@gmail.com+81-22-717-7312

Outcome results