ArticleEJNMMI research2020
Value of
Article in EJNMMI research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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
12 citing papers in PubMed, 20 citations in OpenAlex.
- Integrating deep learning and radiomics in the differentiation of major histological subtypes of invasive non-mucinous lung adenocarcinoma using positron emission tomography and computed tomography.Translational lung cancer research · 2025Article
- Predicting malignant risk of ground-glass nodules using convolutional neural networks based on dual-time-pointCancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Predicting the invasiveness of pulmonary adenocarcinoma using intratumoral and peritumoral radiomics features.Frontiers in medicine · 2025Article
- Predicting solitary pulmonary lesions in breast cancer patients usingBMC pulmonary medicine · 2024Article
- Premalignant Progression in the Lung: Knowledge Gaps and Novel Opportunities for Interception of Non-Small Cell Lung Cancer. An Official American Thoracic Society Research Statement.American journal of respiratory and critical care medicine · 2024Review
- PreoperativeComputational and structural biotechnology journal · 2023Article
- Radiomics in Oncological PET Imaging: A Systematic Review-Part 1, Supradiaphragmatic Cancers.Diagnostics (Basel, Switzerland) · 2022Review
- A Pilot Study of Radiomics Models Combining Multi-Probe and Multi-Modality Images ofFrontiers in oncology · 2022Article
- Structural and functional radiomics for lung cancer.European journal of nuclear medicine and molecular imaging · 2021Review
- A Systematic Review of PET Textural Analysis and Radiomics in Cancer.Diagnostics (Basel, Switzerland) · 2021Review
- Radiomics Study for Discriminating Second Primary Lung Cancers From Pulmonary Metastases in Pulmonary Solid Lesions.Frontiers in oncology · 2021Article
- The value ofTzu chi medical journalReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
backgroundTo establish and validate
methodsNinety-three ground-glass nodules (GGNs) from 91 patients with stage I who underwent a preoperative
resultsGGNs were divided into lepidic group (n = 18) and acinar-papillary group (n = 75). Four radiomic features (2 for PET and 2 for CT) were selected to calculate the rad-score, and the area under the curve (AUC) of rad-score was 0.790, which was not significantly different as the attenuation value of the ground-glass opacity component on CT (CT
conclusionPET/CT-based radiomics model shows good performance in predicting intermediate-high risk growth patterns in early IAC. This model provides a useful method for risk stratification, clinical management, and personalized treatment.
Indexed as
Identifiers
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.