{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T00:08:34Z","timestamp":1789171714961,"version":"build-2803163510"},"reference-count":38,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T00:00:00Z","timestamp":1776470400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Background: To explore the correlation between spatial dose distribution and post-implant urinary toxicity, aiming to assist decision making in low-dose-rate (LDR) treatment planning, thereby improving patient outcomes. Methods: Eighty-five prostate LDR patients with &gt;12-month follow-up were included. Patient-reported urinary toxicity was collected prospectively using the International Prostate Symptom Score (IPSS) questionnaire, from before implant (baseline) to post-implant follow-up. Patients were then grouped into those whose symptom scores returned to \u22642 points above baseline by 12 months (no long-term toxicity) vs. those who did not (long-term toxicity). A total of 106 features were extracted for each patient, including principal components of dose-volume histograms (DVHs) from multiple prostate subzones, the whole prostate and urethra, along with baseline IPSS, implantation characteristics, and additional DVH indicators for the prostate and the urethra. A machine learning (ML) model incorporating backward feature selection algorithm was developed to predict long-term toxicity status, using a shuffle-and-split validation strategy for model evaluation during feature selection. A univariate statistical analysis was conducted on the model\u2019s selected features. Results: Out of 85 patients, 41 (48%) had long-term urinary toxicity. Seven features were selected during model training, including baseline IPSS and six dosimetric features from several prostate subzones primarily located in the posterior prostate. The model achieved a high mean area under the receiver operating characteristic curve (AUC) of 0.81, with a balanced sensitivity and specificity of 0.78 by adjusting the probability threshold. In univariate analysis, only baseline IPSS and one selected dose feature were significantly correlated with long-term toxicity with AUC &lt; 0.71. Conclusions: The proposed ML model, integrating baseline IPSS and spatial dosimetric features, effectively predicts long-term urinary toxicity after prostate LDR. This approach offers a practical method for risk stratification, allowing clinicians to identify patients at elevated risk and prioritize them for targeted preventative measures and closer follow-up.<\/jats:p>","DOI":"10.3390\/cancers18081287","type":"journal-article","created":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T07:15:08Z","timestamp":1776669308000},"page":"1287","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Spatial Dosimetric-Based Prediction of Long-Term Urinary Toxicity After Permanent Prostate Brachytherapy"],"prefix":"10.3390","volume":"18","author":[{"given":"Chaoqiong","family":"Ma","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology, University of Kansas Medical Center, Kansas City, KS 66160, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Hou","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, University of Kansas Medical Center, Kansas City, KS 66160, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rajeev","family":"Badkul","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, University of Kansas Medical Center, Kansas City, KS 66160, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jufri","family":"Setianegara","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, University of Kansas Medical Center, Kansas City, KS 66160, USA"},{"name":"Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA 19104, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0337-860X","authenticated-orcid":false,"given":"Xinglei","family":"Shen","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, University of Kansas Medical Center, Kansas City, KS 66160, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jay","family":"Shiao","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, University of Kansas Medical Center, Kansas City, KS 66160, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harold","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, University of Kansas Medical Center, Kansas City, KS 66160, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ronald C.","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, University of Kansas Medical Center, Kansas City, KS 66160, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1111\/j.1464-410X.2011.10827.x","article-title":"Comparative Analysis of Prostate-Specific Antigen Free Survival Outcomes for Patients with Low, Intermediate and High Risk Prostate Cancer Treatment by Radical Therapy. 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