ArticleMolecules (Basel, Switzerland)2019
Predicting Ewing Sarcoma Treatment Outcome Using Infrared Spectroscopy and Machine Learning.
Article in Molecules (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence applied to musculoskeletal oncology: a systematic review.Skeletal radiology · 2022Pooled it
- Applications of Artificial Intelligence in Pediatric Oncology: A Systematic Review.JCO clinical cancer informatics · 2021Pooled it
- Artificial Intelligence in Primary Malignant Bone Tumor Imaging: A Narrative Review.Diagnostics (Basel, Switzerland) · 2025Review
- Identification of novel diagnostic and prognostic microRNAs in sarcoma on TCGA dataset: bioinformatics and machine learning approach.Scientific reports · 2025Article
- Review
- Revolutionizing precision oncology: the role of artificial intelligence in personalized pediatric cancer care.Frontiers in medicine · 2025Review
- Review
- A Preliminary Study of FTIR Spectroscopy as a Potential Non-Invasive Screening Tool for Pediatric Precursor B Lymphoblastic Leukemia.Molecules (Basel, Switzerland) · 2021Article
- Advances in Near-Infrared Spectroscopy and Related Computational Methods.Molecules (Basel, Switzerland) · 2019Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundImproved outcome prediction is vital for the delivery of risk-adjusted, appropriate and effective care to paediatric patients with Ewing sarcoma-the second most common paediatric malignant bone tumour. Fourier transform infrared (FTIR) spectroscopy of tissues allows the bulk biochemical content of a biological sample to be probed and makes possible the study and diagnosis of disease.
methodsIn this retrospective study, FTIR spectra of sections of biopsy-obtained bone tissue were recorded. Twenty-seven patients (between 5 and 20 years of age) with newly diagnosed Ewing sarcoma of bone were included in this study. The prognostic value of FTIR spectra obtained from Ewing sarcoma (ES) tumours before and after neoadjuvant chemotherapy were analysed in combination with various data-reduction and machine learning approaches.
resultsRandom forest and linear discriminant analysis supervised learning models were able to correctly predict patient mortality in 92% of cases using leave-one-out cross-validation. The best performing model for predicting patient relapse was a linear Support Vector Machine trained on the observed spectral changes as a result of chemotherapy treatment, which achieved 92% accuracy.
conclusionFTIR spectra of tumour biopsy samples may predict treatment outcome in paediatric Ewing sarcoma patients with greater than 92% accuracy.
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