Trial reportPloS one2019
Challenges and caveats of a multi-center retrospective radiomics study: an example of early treatment response assessment for NSCLC patients using FDG-PET/CT radiomics.
Trial report in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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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
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for lung cancer: a systematic review of head‑to‑head CT, FDG PET/CT, and multimodal models across screening, staging, and prognosis.BMC medical imaging · 2026Pooled it
- A Pilot Study on Quantitative Accuracy and Radiomic Feature Stability of Deep Progressive Learning Reconstruction in NSCLC PET.Journal of imaging informatics in medicine · 2026Article
- Article
- Unveiling the creatinine-to-albumin ratio: a novel predictor of acute heart failure post-TBI.Frontiers in nutrition · 2026Article
- An exploratory assessment of early and delta PET radiomic features for outcome prediction in locally advanced cervical cancer.European journal of nuclear medicine and molecular imaging · 2025Article
- Multicentric study on the reproducibility and robustness of PET-based radiomics features with a realistic activity painting phantom.PloS one · 2024Article
- Methodological evaluation of original articles on radiomics and machine learning for outcome prediction based on positron emission tomography (PET).Nuklearmedizin. Nuclear medicine · 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
- Predictive value ofEuropean journal of nuclear medicine and molecular imaging · 2023Article
- Predicting response to immunotherapy in non-small cell lung cancer- from bench to bedside.Frontiers in oncology · 2023Article
- Application of radiomics in diagnosis and treatment of lung cancer.Frontiers in pharmacology · 2023Review
- A radiomic model to classify response to neoadjuvant chemotherapy in breast cancer.BMC medical imaging · 2022Article
- Synergistic impact of motion and acquisition/reconstruction parameters onMedical physics · 2022Article
- Exploratory ensemble interpretable model for predicting local failure in head and neck cancer: the additive benefit of CT and intra-treatment cone-beam computed tomography features.Quantitative imaging in medicine and surgery · 2021Article
- [European journal of nuclear medicine and molecular imaging · 2021Article
- Making Radiomics More Reproducible across Scanner and Imaging Protocol Variations: A Review of Harmonization Methods.Journal of personalized medicine · 2021Review
- Radiomics for prediction of radiation-induced lung injury and oncologic outcome after robotic stereotactic body radiotherapy of lung cancer: results from two independent institutions.Radiation oncology (London, England) · 2021Article
- A CT-Based Radiomics Approach to Predict Nivolumab Response in Advanced Non-Small-Cell Lung Cancer.Frontiers in oncology · 2021Article
- The application of a workflow integrating the variable reproducibility and harmonizability of radiomic features on a phantom dataset.PloS one · 2021Article
- A Role for FDG PET Radiomics in Personalized Medicine?Seminars in nuclear medicine · 2020Review
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPrognostic models based on individual patient characteristics can improve treatment decisions and outcome in the future. In many (radiomic) studies, small size and heterogeneity of datasets is a challenge that often limits performance and potential clinical applicability of these models. The current study is example of a retrospective multi-centric study with challenges and caveats. To highlight common issues and emphasize potential pitfalls, we aimed for an extensive analysis of these multi-center pre-treatment datasets, with an additional 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) scan acquired during treatment.
methodsThe dataset consisted of 138 stage II-IV non-small cell lung cancer (NSCLC) patients from four different cohorts acquired from three different institutes. The differences between the cohorts were compared in terms of clinical characteristics and using the so-called 'cohort differences model' approach. Moreover, the potential prognostic performances for overall survival of radiomic features extracted from CT or FDG-PET, or relative or absolute differences between the scans at the two time points, were assessed using the LASSO regression method. Furthermore, the performances of five different classifiers were evaluated for all image sets.
resultsThe individual cohorts substantially differed in terms of patient characteristics. Moreover, the cohort differences model indicated statistically significant differences between the cohorts. Neither LASSO nor any of the tested classifiers resulted in a clinical relevant prognostic model that could be validated on the available datasets.
conclusionThe results imply that the study might have been influenced by a limited sample size, heterogeneous patient characteristics, and inconsistent imaging parameters. No prognostic performance of FDG-PET or CT based radiomics models can be reported. This study highlights the necessity of extensive evaluations of cohorts and of validation datasets, especially in retrospective multi-centric datasets.
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