ArticleStrahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]2018
Treatment-related features improve machine learning prediction of prognosis in soft tissue sarcoma patients.
Article in Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al], 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence applied to musculoskeletal oncology: a systematic review.Skeletal radiology · 2022Pooled it
- 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.Acta orthopaedica · 2026Article
- Automated detection of primary soft tissue sarcomas of the extremities using artificial intelligence and ChatGPT.Frontiers in oncology · 2026Article
- Diagnostic utility of MRI-based convolutional neural networks in soft tissue sarcomas: a mini-review.Frontiers in oncology · 2025Review
- Development and validation of a machine learning-based model to predict postoperative overall survival in patients with soft tissue sarcoma: a retrospective cohort study.American journal of cancer research · 2024Article
- The importance of planning CT-based imaging features for machine learning-based prediction of pain response.Scientific reports · 2023Article
- Applications of machine learning for imaging-driven diagnosis of musculoskeletal malignancies-a scoping review.European radiology · 2022Article
- Prognostic Assessment in High-Grade Soft-Tissue Sarcoma Patients: A Comparison of Semantic Image Analysis and Radiomics.Cancers · 2021Article
- Impact of preoperative treatment on the CINSARC prognostic signature: translational research results from a phase 1 trial of the German Interdisciplinary Sarcoma Group (GISG 03).Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2020Article
- Tumor grading of soft tissue sarcomas using MRI-based radiomics.EBioMedicine · 2019Article
- Using Image-guided Intensity-modulated Radiotherapy on Patients With Head and Neck Soft-tissue Sarcoma.In vivo (Athens, Greece)Article
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
background and purposeCurrent prognostic models for soft tissue sarcoma (STS) patients are solely based on staging information. Treatment-related data have not been included to date. Including such information, however, could help to improve these models. MATERIALS AND
methodsA single-center retrospective cohort of 136 STS patients treated with radiotherapy (RT) was analyzed for patients' characteristics, staging information, and treatment-related data. Therapeutic imaging studies and pathology reports of neoadjuvantly treated patients were analyzed for signs of response. Random forest machine learning-based models were used to predict patients' death and disease progression at 2 years. Pre-treatment and treatment models were compared.
resultsThe prognostic models achieved high performances. Using treatment features improved the overall performance for all three classification types: prediction of death, and of local and systemic progression (area under the receiver operatoring characteristic curve (AUC) of 0.87, 0.88, and 0.84, respectively). Overall, RT-related features, such as the planning target volume and total dose, had preeminent importance for prognostic performance. Therapy response features were selected for prediction of disease progression.
conclusionsA machine learning-based prognostic model combining known prognostic factors with treatment- and response-related information showed high accuracy for individualized risk assessment. This model could be used for adjustments of follow-up procedures.
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