{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T05:53:51Z","timestamp":1782280431218,"version":"3.54.5"},"reference-count":56,"publisher":"Oxford University Press (OUP)","funder":[{"DOI":"10.13039\/100030692","name":"Intramural Research Program","doi-asserted-by":"publisher","award":["ZIC BC 010,991"],"award-info":[{"award-number":["ZIC BC 010,991"]}],"id":[{"id":"10.13039\/100030692","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,21]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Traditional Kaplan\u2013Meier curves capture aggregate survival trends within broad patient subgroups but overlook the heterogeneity of individual patients. In contrast, single-patient survival risk models bridge this gap by incorporating each patient\u2019s unique clinical, genomic, and demographic characteristics, generating personalized survival curves. These individualized visualizations enhance patient\u2013clinician communication by translating complex statistics into intuitive, time-based visuals that are easier to interpret. However, the complexity, high dimensionality, and heterogeneity of multiomics data present significant challenges for analysis, interpretation, and model development. To address these challenges, we introduce the Cancer Patient Survival Model (CPSM), an R package designed to deliver individualized survival and risk predictions through a fully integrated, reproducible computational pipeline. CPSM includes 10 core functions organized into 4 key steps: (i) data preprocessing and normalization, (ii) feature selection, (iii) survival risk group prediction modeling, and (iv) visualization and nomogram construction. We demonstrate the utility of CPSM using publicly available\u00a0datasets from The Cancer Genome Atlas for 4 cancer types: glioblastoma multiforme (GBM), acute myeloid leukemia (LAML), pancreatic adenocarcinoma (PAAD), and breast invasive cancer (BRCA). CPSM efficiently handles high-dimensional datasets with over 60,000 RNA transcripts and diverse clinical variables, enabling robust and interpretable individualized survival predictions under varying data conditions. Model performance was evaluated using repeated cross-validation with uncertainty quantification, ensuring robust and reliable estimates in high-dimensional, small-sample settings. In summary, CPSM provides an efficient, user-friendly, end-to-end solution for integrating patient data and generating personalized survival and risk predictions. Its integrated visual tools enhance interpretability and support more informed clinical decision-making. The package is freely available on Bioconductor (https:\/\/bioconductor.org\/packages\/devel\/bioc\/html\/CPSM.html) and GitHub (https:\/\/github.com\/hks5august\/CPSM).<\/jats:p>","DOI":"10.1093\/gigascience\/giag067","type":"journal-article","created":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T11:32:55Z","timestamp":1780399975000},"source":"Crossref","is-referenced-by-count":1,"title":["CPSM: an R package for cancer patient survival risk model using transcriptomics and clinical data"],"prefix":"10.1093","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0421-8341","authenticated-orcid":false,"given":"Harpreet","family":"Kaur","sequence":"first","affiliation":[{"name":"Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health , Bethesda, MD 20892 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