This paper is in the following e-collection/theme issue:

Cancer Prognosis Models and Machine Learning (67) Breast Cancer (290) Cancer Survivorship (334) Methods in Cancer Research (54) Electronic Health Records (1319) Natural Language Processing (917) Clinical Informatics (1325) Chemotherapy and Side Effects Management (110)

Published on in Vol 11 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/64697, first published .
A Deep Learning–Enabled Workflow to Estimate Real-World Progression-Free Survival in Patients With Metastatic Breast Cancer: Study Using Deidentified Electronic Health Records

A Deep Learning–Enabled Workflow to Estimate Real-World Progression-Free Survival in Patients With Metastatic Breast Cancer: Study Using Deidentified Electronic Health Records

A Deep Learning–Enabled Workflow to Estimate Real-World Progression-Free Survival in Patients With Metastatic Breast Cancer: Study Using Deidentified Electronic Health Records

Authors of this article:

Gowtham Varma1 Author Orcid Image ;   Rohit Kumar Yenukoti1 Author Orcid Image ;   Praveen Kumar M1 Author Orcid Image ;   Bandlamudi Sai Ashrit1 Author Orcid Image ;   K Purushotham1 Author Orcid Image ;   C Subash1 Author Orcid Image ;   Sunil Kumar Ravi1 Author Orcid Image ;   Verghese Kurien1 Author Orcid Image ;   Avinash Aman2 Author Orcid Image ;   Mithun Manoharan1 Author Orcid Image ;   Shashank Jaiswal2 Author Orcid Image ;   Akash Anand2 Author Orcid Image ;   Rakesh Barve2 Author Orcid Image ;   Viswanathan Thiagarajan2 Author Orcid Image ;   Patrick Lenehan3 Author Orcid Image ;   Scott A Soefje4 Author Orcid Image ;   Venky Soundararajan3 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Wang M, Chang W, Zhang Y. Artificial Intelligence for the Diagnosis and Management of Cancers: Potentials and Challenges. MedComm 2025;6(11) View
  2. May P, Greß J, Seidel C, Sommer S, Schuler M, Nokodian S, Schröder F, Jung J. Enabling Just-in-Time Clinical Oncology Analysis With Large Language Models: Feasibility and Validation Study Using Unstructured Synthetic Data. JMIR Medical Informatics 2025;13:e78332 View