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Identifying predictors of treatment failure is crucial. Artificial intelligence (AI) models may improve prediction, but data on NSCLC BM are scarce.\u00a0A retrospective study analyzed NSCLC patients with single brain metastases treated with SRS (Elekta Gamma Knife) from 2010 to 2015, with up to 10 years of follow-up. Clinical, radiological, and histological data were collected. Kaplan-Meier and Cox proportional hazards models assessed survival. Decision tree and random forest (RF) models predicted treatment failure, with feature importance analyzed.\u00a0Among 133 patients (mean age 61.6, 56.4% male), most tumors were grade 1 (56.4%) and in the right hemisphere (60.2%). The mean tumor volume was 1.84\u00a0cm\u00b3. Decision trees identified metastasis volume and location as key predictors (AUC\u2009=\u20090.85). RF models improved prediction (AUC\u2009=\u20090.92). Tumor volume, diameter, and age were major predictors. AI models effectively identified patients at risk of treatment failure.\u00a0A random forest based artificial intelligence model presented an excellent predictive ability for stereotactic radiosurgery success\/failure in a population with NSCLC brain metastases, with an area under curve of 0.92. This predictive ability was superior to a decision tree or a simple diameter-to-volume ratio.<\/jats:p>","DOI":"10.1007\/s10143-025-04051-6","type":"journal-article","created":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T07:39:32Z","timestamp":1770449972000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Artificial intelligence improves survival prediction in patients with brain metastases submitted to radiosurgery"],"prefix":"10.1007","volume":"49","author":[{"given":"Eliseu Becco","family":"Neto","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jo\u00e3o Paulo","family":"Mota Telles","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Larissa Zaira Rafael","family":"Rolim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francisco","family":"de Assis de Souza Filho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vinicius Costa Becco","family":"de Souza","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Let\u00edcia Costa Becco","family":"de Souza","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Helv\u00e9cio Neves","family":"Feitosa Filho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rodrigo Becco","family":"de Souza","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dhiego Chaves","family":"de Almeida Bastos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sujit","family":"Prabhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eberval Gadelha","family":"Figueiredo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,2,7]]},"reference":[{"issue":"6\u20137","key":"4051_CR1","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1007\/s10585-017-9865-7","volume":"34","author":"A Thiagarajan","year":"2017","unstructured":"Thiagarajan A, Yamada Y (2017) Radiobiology and radiotherapy of brain metastases. 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