Artificial neural network-augmented dosiomic integration for predicting distant recurrence in NSCLC patients treated with SBRT.
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| Title: | Artificial neural network-augmented dosiomic integration for predicting distant recurrence in NSCLC patients treated with SBRT. |
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| Authors: | Halder K; Department of Radiation Oncology, SUNY Upstate Medical University, Syracuse, NY, United States.; Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, India., Alden R; Department of Radiation Oncology, SUNY Upstate Medical University, Syracuse, NY, United States., Podder R; Department of Radiation Oncology, University of Florida, Gainesville, FL, United States., Orlando MF; Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, India., Mix MD; Department of Radiation Oncology, SUNY Upstate Medical University, Syracuse, NY, United States., Biswas T; Department of Radiation Oncology, University of Florida, Gainesville, FL, United States., Bogart JB; Department of Radiation Oncology, SUNY Upstate Medical University, Syracuse, NY, United States., Podder TK; Department of Radiation Oncology, SUNY Upstate Medical University, Syracuse, NY, United States. |
| Source: | Frontiers in oncology [Front Oncol] 2025 Sep 25; Vol. 15, pp. 1669954. Date of Electronic Publication: 2025 Sep 25 (Print Publication: 2025). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Frontiers Research Foundation] Country of Publication: Switzerland NLM ID: 101568867 Publication Model: eCollection Cited Medium: Print ISSN: 2234-943X (Print) Linking ISSN: 2234943X NLM ISO Abbreviation: Front Oncol Subsets: PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 2234-943X |
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| DOI: | 10.3389/fonc.2025.1669954 |
