ArticleRadiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology2018
Unsupervised machine learning of radiomic features for predicting treatment response and overall survival of early stage non-small cell lung cancer patients treated with stereotactic body radiation therapy.
Article in Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers.
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Who cites it
60 citing papers in PubMed.
- Unsupervised clustering analysis unravels the role of systemic inflammatory indices in the prognosis of patients with locally advanced cervical cancer treated with chemoradiation.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2026Article
- Radiomics-based explainable artificial intelligence to predict treatment response following lung stereotactic body radiation therapy.The British journal of radiology · 2025Article
- Learning discrete structures for cancer radiomics.APL bioengineering · 2025Article
- Unsupervised Clustering Successfully Predicts Prognosis in NSCLC Brain Metastasis Cohorts.Diagnostics (Basel, Switzerland) · 2025Article
- Current and Future Applications of PET Radiomics in Radiation Oncology.PET clinics · 2025Review
- Evaluation of the performance of both machine learning models using PET and CT radiomics for predicting recurrence following lung stereotactic body radiation therapy: A single-institutional study.Journal of applied clinical medical physics · 2024Article
- Radiomics in stereotactic body radiotherapy for non-small cell lung cancer: a systematic review and radiomic quality score study.Radiation oncology journal · 2024Article
- Multicentric development and evaluation of [European journal of nuclear medicine and molecular imaging · 2024Article
- Developing a clinical-radiomic prediction model for 3-year cancer-specific survival in lung cancer patients treated with stereotactic body radiation therapy.Journal of cancer research and clinical oncology · 2024Article
- A dual-radiomics model for overall survival prediction in early-stage NSCLC patient using pre-treatment CT images.Frontiers in oncology · 2024Article
- Using KMeans Clustering to Evaluate and Alert for Deviations of Linac Photon Beam Parameters.Asian Pacific journal of cancer prevention : APJCP · 2024Article
- Review of the use of radiomics to assess the risk of recurrence in early-stage non-small cell lung cancer.Translational lung cancer research · 2023Review
- CT-based radiomics prediction of complete response after stereotactic body radiation therapy for patients with lung metastases.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2023Article
- Deep Clustering Survival Machines with Interpretable Expert Distributions.Proceedings. IEEE International Symposium on Biomedical Imaging · 2023Article
- Risk stratification and prognostic value of multi-modal MRI-based radiomics for extranodal nasal-type NK/T-cell lymphoma.BMC cancer · 2023Article
- Development of a multi-feature-combined model: proof-of-concept with application to local failure prediction of post-SBRT or surgery early-stage NSCLC patients.Frontiers in oncology · 2023Article
- A Multimodal Ensemble Driven by Multiobjective Optimisation to Predict Overall Survival in Non-Small-Cell Lung Cancer.Journal of imaging · 2022Article
- Introducing AI to the molecular tumor board: one direction toward the establishment of precision medicine using large-scale cancer clinical and biological information.Experimental hematology & oncology · 2022Review
- Prognostic value of patient-reported outcome measures (PROMs) in adults with non-small cell Lung Cancer: a scoping review.BMC cancer · 2022Article
- Radiomic and Volumetric Measurements as Clinical Trial Endpoints-A Comprehensive Review.Cancers · 2022Review
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Authors and funding
5 authors.
Funding
Abstract
background and purposeTo predict treatment response and survival of NSCLC patients receiving stereotactic body radiation therapy (SBRT), we develop an unsupervised machine learning method for stratifying patients and extracting meta-features simultaneously based on imaging data. MATERIAL AND
methodsThis study was performed based on an
resultsDifferences were found between 2 groups of patients when the patients were clustered into 3 groups in terms of both survival (p = 0.003) and freedom from nodal failure (p = 0.038). Average concordance measures for predicting survival and nodal failure were 0.640±0.029 and 0.664±0.063 respectively, better than those obtained by prediction models built upon clinical variables (p < 0.04).
conclusionsThe evaluation results demonstrate that our method allows us to stratify patients and predict survival and freedom from nodal failure with better performance than current alternative methods.
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