ReviewCancers2024
Enhancing Lymphoma Diagnosis, Treatment, and Follow-Up Using
Review in Cancers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic Value ofTechnology in cancer research & treatmentPooled it
- Enhanced Lymphoma Subtype Classification and Prognosis Using Machine Learning withNuclear medicine and molecular imaging · 2026Article
- A convolutional neural network for fully automated total metabolic tumor volume delineation in patients with aggressive Non-Hodgkin lymphoma.European journal of nuclear medicine and molecular imaging · 2026Article
- AI-Powered MRI Radiomics and Deep Learning for Preoperative Prediction of Cavernous Sinus Invasion in Pituitary Adenomas: A Clinically Oriented Review of Current Evidence.Neuroimage. Reports · 2026Review
- ¹⁸F-FDG PET radiomics and machine learning for virtual biopsy and treatment decisions in lymphoma: a multicenter study.Physical and engineering sciences in medicine · 2026Article
- Automatic Lugano staging for risk stratification in lymphoma: a multicenter PET radiomics and machine learning study with survival analysis.Nuclear medicine communications · 2025Article
- Association of [Diagnostics (Basel, Switzerland) · 2025Article
- Utilizing Nanomaterials in Microfluidic Devices for Disease Detection and Treatment.Nanomaterials (Basel, Switzerland) · 2025Review
- Diagnostic and therapeutic pathways for lymphoma patients: expert consensus through Nominal Group Technique and Delphi methodology.Frontiers in oncology · 2025Article
- Mapping the landscape and research trend of imaging diagnosis in lymphoma: a bibliometric analysis from 1976 to 2024.Frontiers in medicine · 2025Article
- Review
- Research hotspots and trends of artificial intelligence in lymphoma: A bibliometric analysis from 2010 to 2024.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lymphoma, encompassing a wide spectrum of immune system malignancies, presents significant complexities in its early detection, management, and prognosis assessment since it can mimic post-infectious/inflammatory diseases. The heterogeneous nature of lymphoma makes it challenging to definitively pinpoint valuable biomarkers for predicting tumor biology and selecting the most effective treatment strategies. Although molecular imaging modalities, such as positron emission tomography/computed tomography (PET/CT), specifically
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.