ArticleMolecular imaging and biology2023
A Machine Learning Approach Using [
Article in Molecular imaging and biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis 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
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- Predicting histologic grades for pancreatic neuroendocrine tumors by radiologic image-based artificial intelligence: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- PET/CT contouring techniques in neuroendocrine neoplasms: a state-of-the-art scoping review.EJNMMI reports · 2026Review
- O-GlcNAcylation levels predict radiotherapy outcome in non-small cell lung cancer.Translational lung cancer research · 2025Article
- Molecular Imaging: Unveiling Metabolic Abnormalities in Pancreatic Cancer.International journal of molecular sciences · 2025Review
- Article
- Construction of feature selection and efficacy prediction model for transformation therapy of locally advanced pancreatic cancer based on CT,Cancer cell international · 2025Article
- Can CT Image Reconstruction Parameters Impact the Predictive Value of Radiomics Features in Grading Pancreatic Neuroendocrine Neoplasms?Bioengineering (Basel, Switzerland) · 2025Article
- The value of radiomics based on 2-[18 F]FDG PET/CT in predicting WHO/ISUP grade of clear cell renal cell carcinoma.EJNMMI research · 2024Article
- Artificial Intelligence in Pancreatic Image Analysis: A Review.Sensors (Basel, Switzerland) · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
purposeWe sought to develop and validate machine learning (ML) models for predicting tumor grade and prognosis using 2-[ PROCEDURES: A total of 58 patients with PNETs who underwent pretherapeutic [
resultsWe developed two separate ML models for predicting high-grade tumors (Grade 3) and tumors with poor prognosis (disease progression within two years). The integrated models consisting of clinical and radiomic features with NN algorithm showed the best performances than the other models (stand-alone clinical or radiomics models). The performance metrics of the integrated model by NN algorithm were AUROC of 0.864 in the tumor grade prediction model and AUROC of 0.830 in the prognosis prediction model. In addition, AUROC of the integrated clinico-radiomics model with NN was significantly higher than that of tumor maximum standardized uptake model in predicting prognosis (P < 0.001).
conclusionsIntegration of clinical features and [
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.