Evidence map›Paper›PMID 41805979›Full record

ArticleActa orthopaedica2026

Machine learning and logistic regression in estimating survival in patients with high-malignant deep-seated soft tissue sarcomas: development and analysis based on a population-based retrospective cohort.

Andrea Thorn, Jessica A Lavery, Thomas Baad-Hansen, Jonathan A Forsberg, Michael Mørk Petersen, Christina Enciso Holm

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Article in Acta orthopaedica, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Andrea ThornDepartment of Orthopaedic Surgery, Rigshospitalet - University of Copenhagen, Copenhagen, Denmark. andrea.thorn@regionh.dk.ORCID 0000-0003-3761-2791
Jessica A LaveryDepartment of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, USA.
Thomas Baad-HansenDepartment of Orthopaedic Surgery, Aarhus University Hospital, Aarhus, Denmark.ORCID 0000-0003-4826-8412
Jonathan A ForsbergDepartment of Orthopedic Surgery, Orthopaedic Service, Oncology, Memorial Sloan Kettering Cancer Center, New York, USA.
Michael Mørk PetersenDepartment of Orthopaedic Surgery, Rigshospitalet - University of Copenhagen, Copenhagen, Denmark.ORCID 0000-0002-2324-6420
Christina Enciso HolmDepartment of Orthopaedic Surgery, Rigshospitalet - University of Copenhagen, Copenhagen, Denmark.ORCID 0000-0002-5868-9125

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Hongbiao Carl Lekaye · 1985 to 2026
$347.4M
NCI NIH HHS P30 CA008748
6 · The paper itself

Abstract

background and purposeSoft tissue sarcomas are a heterogeneous group of malignant tumors with a high risk of metastasis, primarily to the lungs, making accurate survival prediction an essential part of long-term planning. No machine learning (ML) survival prediction models have been developed using a modern, population-based dataset from Scandinavia. We aimed to develop and compare ML models with logistic regression in predicting 5-year survival in soft tissue sarcoma patients and identify key predictive variables.

methodsThis retrospective cohort study included patients diagnosed with deep-seated, high-grade soft tissue sarcomas of the extremities and trunk wall in Denmark from 2000 to 2016. Logistic regression was compared with 4 developed ML models, including random forest. Performance was assessed using the area under the curve (AUC), sensitivity, specificity, and calibration metrics, with a 70:30 training-test split and 5-fold cross-validation to evaluate the models.

results516 patients were included, of whom 226 (44%) died within 5 years following surgery. Random forest demonstrated the best ML performance on the training set and was compared with logistic regression on the test set. Logistic regression achieved an AUC of 0.74 (95% confidence interval [CI] 0.66-0.82), outperforming random forest's AUC of 0.65 (CI 0.56-0.74). Logistic regression also had higher sensitivity (0.65 vs 0.59) and specificity (0.72 vs 0.69), while random forest had a lower Brier score (0.38 vs 0.41).

conclusionAlthough the developed random forest ML model performed well during training, logistic regression outperformed it after internal validation. Soft tissue sarcomas located in the trunk, grade 3 tumors, and chemotherapy within 3 months of surgery demonstrated the highest negative effect on survival, consistent with current treatment protocols in which patients with high-risk disease are managed with more aggressive multimodal therapy. Further external validation and assessment of clinical utility are required before potential clinical implementation.

Indexed as

Machine LearningSarcomaSoft Tissue NeoplasmsAdultAgedClassification AlgorithmsDenmarkFemaleHumansLogistic ModelsMaleMiddle AgedPrediction AlgorithmsPredictive Learning ModelsRandom ForestRetrospective Studies

Identifiers

PMID41805979
PMCPMC12976663

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.