ArticleAmerican journal of nuclear medicine and molecular imaging2021
Computer-aided detection of mantle cell lymphoma on
Article in American journal of nuclear medicine and molecular imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 14 citations in OpenAlex.
- Artificial intelligence performance in detecting lymphoma from medical imaging: a systematic review and meta-analysis.BMC medical informatics and decision making · 2024Pooled it
- Deep learning applications in visual data for benign and malignant hematologic conditions: a systematic review and visual glossary.Haematologica · 2023Pooled it
- A multimodal explainable ai framework to assist in the differential diagnosis of head and neck reactive follicular hyperplasia and follicular lymphoma: an international multicentre study.Virchows Archiv : an international journal of pathology · 2026Article
- CNN-based detection of pediatric lymphoma on whole body [American journal of nuclear medicine and molecular imaging · 2026Article
- Deep learning [Clinical and translational radiation oncology · 2026Article
- Review
- Review
- Deep learning for [The Lancet. Digital health · 2024Article
- Recent advances in imaging and artificial intelligence (AI) for quantitative assessment of multiple myeloma.American journal of nuclear medicine and molecular imaging · 2024Review
- Deep learning-based diagnosis of disease activity in patients with Graves' orbitopathy using orbital SPECT/CT.European journal of nuclear medicine and molecular imaging · 2023Article
- Deep Neural Networks and Machine Learning Radiomics Modelling for Prediction of Relapse in Mantle Cell Lymphoma.Cancers · 2022Article
- Artificial Intelligence in Lymphoma PET Imaging:: A Scoping Review (Current Trends and Future Directions).PET clinics · 2022Article
- Deep learning convolutional neural network (DLCNN): unleashing the potential ofAmerican journal of nuclear medicine and molecular imaging · 2021Article
- Recent advances in deep learning for lymphoma segmentation: Clinical applications and challenges.Digital healthReview
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 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
PubMed holds no abstract for this paper.
Indexed as
Identifiers
34513279PMC8414404W3199713133What 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.