ArticleEuropean journal of nuclear medicine and molecular imaging2019
Discovery of pre-therapy 2-deoxy-2-
Article in European journal of nuclear medicine and molecular imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 3 of them syntheses that pooled it.
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The trial behind it
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
39 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- The application of radiomics in vestibular schwannomas.The Journal of laryngology and otology · 2025Pooled it
- The application of radiomics in laryngeal cancer.The British journal of radiology · 2021Pooled it
- Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics.European journal of nuclear medicine and molecular imaging · 2019Pooled it
- An explainable imaging-clinical biomarker for non-small cell lung cancer prognostication based on normalised hotspot to centroid distance and [European journal of nuclear medicine and molecular imaging · 2026Article
- A Comprehensive Review of Radiomics in Pulmonary Nodule Management: Clinical Applications and Standardization Dilemmas.Current medical imaging · 2026Review
- The Role of Artificial Intelligence in Theranostics.Journal of nuclear medicine technology · 2025Review
- Radiomics Features from Different Prostatic Zones onCancers · 2025Article
- Computed tomography-based radiomic markers are independent prognosticators of survival in advanced laryngeal cancer: a pilot study.The Journal of laryngology and otology · 2024Article
- Review
- Radiomics-based decision support tool assists radiologists in small lung nodule classification and improves lung cancer early diagnosis.British journal of cancer · 2023Article
- Methodological evaluation of original articles on radiomics and machine learning for outcome prediction based on positron emission tomography (PET).Nuklearmedizin. Nuclear medicine · 2023Review
- Novel tools for early diagnosis and precision treatment based on artificial intelligence.Chinese medical journal pulmonary and critical care medicine · 2023Review
- Tumor and local lymphoid tissue interaction determines prognosis in high-grade serous ovarian cancer.Cell reports. Medicine · 2023Article
- Evaluation of recurrence risk for patients with stage I invasive lung adenocarcinoma manifesting as solid nodules based onEJNMMI research · 2023Article
- The transformational potential of molecular radiomics.Journal of medical radiation sciences · 2023Review
- Application of radiomics in diagnosis and treatment of lung cancer.Frontiers in pharmacology · 2023Review
- Application of radiomics in adrenal incidentaloma: a literature review.Discover oncology · 2022Review
- Gross tumour volume radiomics for prognostication of recurrence & death following radical radiotherapy for NSCLC.NPJ precision oncology · 2022Article
- Radiomics in Oncological PET Imaging: A Systematic Review-Part 1, Supradiaphragmatic Cancers.Diagnostics (Basel, Switzerland) · 2022Review
- Article
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Authors and funding
17 authors.
Funding
Abstract
purposeThe aim of this multi-center study was to discover and validate radiomics classifiers as image-derived biomarkers for risk stratification of non-small-cell lung cancer (NSCLC). PATIENTS AND
methodsPre-therapy PET scans from a total of 358 Stage I-III NSCLC patients scheduled for radiotherapy/chemo-radiotherapy acquired between October 2008 and December 2013 were included in this seven-institution study. A semi-automatic threshold method was used to segment the primary tumors. Radiomics predictive classifiers were derived from a training set of 133 scans using TexLAB v2. Least absolute shrinkage and selection operator (LASSO) regression analysis was used for data dimension reduction and radiomics feature vector (FV) discovery. Multivariable analysis was performed to establish the relationship between FV, stage and overall survival (OS). Performance of the optimal FV was tested in an independent validation set of 204 patients, and a further independent set of 21 (TESTI) patients.
resultsOf 358 patients, 249 died within the follow-up period [median 22 (range 0-85) months]. From each primary tumor, 665 three-dimensional radiomics features from each of seven gray levels were extracted. The most predictive feature vector discovered (FVX) was independent of known prognostic factors, such as stage and tumor volume, and of interest to multi-center studies, invariant to the type of PET/CT manufacturer. Using the median cut-off, FVX predicted a 14-month survival difference in the validation cohort (N = 204, p = 0.00465; HR = 1.61, 95% CI 1.16-2.24). In the TESTI cohort, a smaller cohort that presented with unusually poor survival of stage I cancers, FVX correctly indicated a lack of survival difference (N = 21, p = 0.501). In contrast to the radiomics classifier, clinically routine PET variables including SUV
conclusionPET-based radiomics classifiers derived from routine pre-treatment imaging possess intrinsic prognostic information for risk stratification of NSCLC patients to radiotherapy/chemo-radiotherapy.
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