ArticleEuropean journal of nuclear medicine and molecular imaging2019
Integrating manual diagnosis into radiomics for reducing the false positive rate of
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 27 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
27 citing papers in PubMed, 44 citations in OpenAlex.
- Can the BMI-based dose regimen be used to reduce injection activity and to obtain a constant image quality in oncological patients byEuropean journal of nuclear medicine and molecular imaging · 2021Trial
- The deep learning radiomics nomogram for risk stratification in multiple myeloma using automatic whole-body [European journal of nuclear medicine and molecular imaging · 2026Article
- Development and validation of a nomogram using interpretable machine learning to integrate CT radiomics and PET metabolic parameters for predicting benign-malignant differentiation of pulmonary space-occupying lesions.Frontiers in radiology · 2026Article
- The study of dual-phaseScientific reports · 2025Article
- Differentiating second primary lung cancer from pulmonary metastasis in patients of single solitary pulmonary lesion with extrapulmonary tumor using multiparametric analysis of FDG PET/CT.Annals of nuclear medicine · 2025Article
- The Value of PET/CT-Based Radiomics in Predicting Adrenal Metastases in Patients with Cancer.Diagnostics (Basel, Switzerland) · 2025Article
- Predicting Human Epidermal Growth Factor Receptor 2 Expression in Breast Cancer Based on Radiomics of MRI Habitat and US.Breast cancer (Dove Medical Press) · 2025Article
- PET/CT radiomics and deep learning in the diagnosis of benign and malignant pulmonary nodules: progress and challenges.Frontiers in oncology · 2024Review
- Different hydration protocols for the quantification of healthy tissue uptake of half-doseQuantitative imaging in medicine and surgery · 2023Article
- The role of artificial intelligence based on PET/CT radiomics in NSCLC: Disease management, opportunities, and challenges.Frontiers in oncology · 2023Review
- Current status and prospect of PET-related imaging radiomics in lung cancer.Frontiers in oncology · 2023Review
- Role of Machine Learning in Precision Oncology: Applications in Gastrointestinal Cancers.Cancers · 2022Review
- Intraoperative fluorescence molecular imaging accelerates the coming of precision surgery in China.European journal of nuclear medicine and molecular imaging · 2022Review
- Radiomics in Oncological PET Imaging: A Systematic Review-Part 1, Supradiaphragmatic Cancers.Diagnostics (Basel, Switzerland) · 2022Review
- The value of AI in the Diagnosis, Treatment, and Prognosis of Malignant Lung Cancer.Frontiers in radiology · 2022Review
- Establishment and Optimization of Radiomics Algorithms for Prediction of KRAS Gene Mutation by Integration of NSCLC Gene Mutation Mutual Exclusion Information.Frontiers in pharmacology · 2022Article
- PET/CT Based EGFR Mutation Status Classification of NSCLC Using Deep Learning Features and Radiomics Features.Frontiers in pharmacology · 2022Article
- The progress of multimodal imaging combination and subregion based radiomics research of cancers.International journal of biological sciences · 2022Review
- A Pilot Study of Radiomics Models Combining Multi-Probe and Multi-Modality Images ofFrontiers in oncology · 2022Article
- Frontiers and hotspots ofFrontiers in oncology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe high false positive rate (FPR) of
methodsAmong 3,947
resultsThe FPR of manual diagnosis was found to be 30.6%. Among the four RS, PET/CT RS exhibited the best performance. By integrating manual diagnosis, the hybrid nomogram integrating PET/CT RS and manual diagnosis demonstrated lowest FPR and highest area under curve (AUC) and Youden index (YI) in both training and test cohorts (FPR: 5.4% and 9.1%, AUC: 0.98 and 0.92, YI: 85.8% and 75.5%, respectively). This hybrid nomogram respectively corrected 78.6% and 37.5% among FPR cases produced by PET/CT RS, without significantly sacrificing its sensitivity. The net benefit of hybrid nomogram appeared highest at <85% threshold probability.
conclusionThe established hybrid nomogram integrating PET/CT RS and manual diagnosis can significantly reduce FPR, improve diagnostic accuracy and enhance clinical benefit compared to manual diagnosis. By integrating manual diagnosis, the performance of this hybrid nomogram is superior to PET/CT RS, indicating the importance of clinicians' judgement as an essential information source for improving radiomics diagnostic approaches.
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.