Trial reportScientific reports2020
Trial report in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed, 2 syntheses or guidelines pooled it, 44 citations in OpenAlex.
- Application of machine learning for lung cancer survival prognostication-A systematic review and meta-analysis.Frontiers in artificial intelligence · 2024Pooled it
- Construction and case study of a novel lung cancer risk index.BMC cancer · 2022Pooled it
- Differentiation between benign and malignant orbital tumors using deep transfer learning features and hand-crafted radiomics features from traditional CT imaging.Frontiers in oncology · 2026Article
- The Role of Artificial Intelligence in Theranostics.Journal of nuclear medicine technology · 2025Review
- Thorax-encompassing multi-modality PET/CT deep learning model for resected lung cancer prognostication: A retrospective, multicenter study.Medical physics · 2025Article
- Transparency and Representation in Clinical Research Utilizing Artificial Intelligence in Oncology: A Scoping Review.Cancer medicine · 2025Article
- Deep Learning Radiomics for Survival Prediction in Non-Small-Cell Lung Cancer Patients from CT Images.Journal of medical systems · 2025Article
- A Weighted Survival Regression Framework for Incorporating External Prediction Information.Journal of statistical theory and practice · 2025Article
- Prognostic value of combining clinical factors,Annals of nuclear medicine · 2024Article
- Comparing deep learning and handcrafted radiomics to predict chemoradiotherapy response for locally advanced cervical cancer using pretreatment MRI.Scientific reports · 2024Article
- Clinical application ofJapanese journal of radiology · 2024Review
- Methodological evaluation of original articles on radiomics and machine learning for outcome prediction based on positron emission tomography (PET).Nuklearmedizin. Nuclear medicine · 2023Review
- The transformational potential of molecular radiomics.Journal of medical radiation sciences · 2023Review
- Differentiation of acute and chronic vertebral compression fractures using conventional CT based on deep transfer learning features and hand-crafted radiomics features.BMC musculoskeletal disorders · 2023Article
- A CT-based transfer learning approach to predict NSCLC recurrence: The added-value of peritumoral region.PloS one · 2023Article
- Effect of EGFR-TKIs combined with craniocerebral radiotherapy on the prognosis ofFrontiers in oncology · 2023Article
- Predicting recurrence risks in lung cancer patients using multimodal radiomics and random survival forests.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- Radiomics in Oncological PET Imaging: A Systematic Review-Part 1, Supradiaphragmatic Cancers.Diagnostics (Basel, Switzerland) · 2022Review
- Causal Inference in Radiomics: Framework, Mechanisms, and Algorithms.Frontiers in neuroscience · 2022Article
- Structural and functional radiomics for lung cancer.European journal of nuclear medicine and molecular imaging · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hand-crafted radiomics has been used for developing models in order to predict time-to-event clinical outcomes in patients with lung cancer. Hand-crafted features, however, are pre-defined and extracted without taking the desired target into account. Furthermore, accurate segmentation of the tumor is required for development of a reliable predictive model, which may be objective and a time-consuming task. To address these drawbacks, we propose a deep learning-based radiomics model for the time-to-event outcome prediction, referred to as DRTOP that takes raw images as inputs, and calculates the image-based risk of death or recurrence, for each patient. Our experiments on an in-house dataset of 132 lung cancer patients show that the obtained image-based risks are significant predictors of the time-to-event outcomes. Computed Tomography (CT)-based features are predictors of the overall survival (OS), with the hazard ratio (HR) of 1.35, distant control (DC), with HR of 1.06, and local control (LC), with HR of 2.66. The Positron Emission Tomography (PET)-based features are predictors of OS and recurrence free survival (RFS), with hazard ratios of 1.67 and 1.18, respectively. The concordance indices of
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.