A Deep Learning–Enabled Workflow to Estimate Real-World Progression-Free Survival in Patients With Metastatic Breast Cancer: Study Using Deidentified Electronic Health Records
This workflow enables rapid and reliable determination of rwPFS in patients with mBC receiving combination therapy. Further validation across more diverse external datasets and other cancer types is needed to ensure broader applicability and generalizability.
Authors
- Patrick J. Lenehan (ORCID: https://orcid.org/0000-0002-1950-9179)
- Scott A. Soefje (ORCID: https://orcid.org/0000-0002-5486-7748)
- Shashank Jaiswal
- C Subash
- V.A. Kurien
- Avinash Aman
- Viswanathan Thiagarajan
- Praveen Kumar (ORCID: https://orcid.org/0000-0002-9318-1167)
- Venky Soundararajan (ORCID: https://orcid.org/0000-0001-7434-9211)
- Rohit K. Yenukoti
- Bandlamudi Sai Ashrit
- Gowtham Varma
- Akash Anand
- Sunil Kumar Ravi
- K Purushotham
- Mithun Manoharan (ORCID: https://orcid.org/0009-0006-4094-9220)
- Rakesh Barve
Institutions
- Mayo Clinic (US)
- WinnMed (US)
- Murphy Oil Corporation (United States) (US)
- University of Cross River State (NG)
- Mayo Clinic in Arizona (US)
- Nference (India) (IN)
- Mayo Clinic in Florida (US)
- Nference (United States) (US)
Publication Details
- Journal
- JMIR Cancer
- Published
- 2025-04-09
- DOI
- https://doi.org/10.2196/64697
- Citations
- 5
- Primary Topic
- Advanced Breast Cancer Therapies
- Type
- article
- Field-Weighted Citation Impact
- 7.59