Artificial neural network-augmented dosiomic integration for predicting distant recurrence in NSCLC patients treated with SBRT
This study introduces an ANN-based model for predicting distant recurrence in NSCLC patients followed by SBRT. This study also demonstrates the impactful dosimetric and clinical features for the designed predictive model, highlighting its potential as an assistive tool for informed and individualized treatment planning in clinical practice.
Authors
- Tithi Biswas (ORCID: https://orcid.org/0000-0002-9074-733X)
- Tarun K. Podder (ORCID: https://orcid.org/0000-0002-2925-1431)
- Kaushik Halder (ORCID: https://orcid.org/0000-0002-4184-9698)
- Michael Mix (ORCID: https://orcid.org/0000-0002-9106-2519)
- Rihan Podder
- M. Felix Orlando (ORCID: https://orcid.org/0000-0003-1089-4098)
- Ryan S. Alden (ORCID: https://orcid.org/0000-0003-4040-1864)
- J.A. Bogart
Institutions
- Indian Institute of Technology Roorkee (IN)
- SUNY Upstate Medical University (US)
- University of Florida Health (US)
- University of Florida (US)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-09-25
- DOI
- https://doi.org/10.3389/fonc.2025.1669954
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00