Evidence map›Paper›PMID 42824644›Full record

ArticleNuclear medicine and molecular imaging2026

Enhanced Lymphoma Subtype Classification and Prognosis Using Machine Learning with

Setareh Hasanabadi, Seyed Mahmud Reza Aghamiri, Ahmad Ali Abin, Habibeh Vosoughi, Farshad Emami, Mehrdad Bakhshayesh Karam, Marzieh Nejabat, Abtin Dorudinia, Hossein Arabi, Habib Zaidi

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Article in Nuclear medicine and molecular imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Setareh HasanabadiDepartment of Medical Radiation Engineering, Shahid Beheshti University, Tehran, Iran.
Seyed Mahmud Reza AghamiriDepartment of Medical Radiation Engineering, Shahid Beheshti University, Tehran, Iran.
Ahmad Ali AbinFaculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran.
Habibeh VosoughiResearch Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran.
Farshad EmamiRazavi Cancer Research Center, Razavi Hospital, Imam Reza International University, Mashhad, Iran.
Mehrdad Bakhshayesh KaramRadiology Department, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Marzieh NejabatDepartment of Radiology and Nuclear Medicine, Medical University of Vienna, Vienna, Austria.
Abtin DorudiniaKeck School of Medicine, University of Southern California, Los Angeles, CA USA.
Hossein ArabiDivision of Nuclear Medicine & Molecular Imaging, Geneva University Hospital, Geneva, CH-1211 Switzerland.
Habib ZaidiDivision of Nuclear Medicine & Molecular Imaging, Geneva University Hospital, Geneva, CH-1211 Switzerland.ORCID 0000-0001-7559-5297

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: This study explored a machine learning approach using Methods: In this cohort study, baseline Results: A total of 156 lymphoma patients were analyzed, with 2,076 lesions segmented and 200 radiomic features extracted. For subtype classification, AdaBoost achieved the highest AUC for Diffuse Large B-cell (DLBCL) (0.863, accuracy 0.742), while XGBoost performed best for High-Grade Non-Hodgkin lymphoma (NHL) (AUC 0.825, accuracy 0.735) and Nodular Sclerosis Hodgkin Lymphoma (NS-HL) (AUC 0.827, accuracy 0.832). Logistic regression showed the best results for Classical Hodgkin Lymphoma (C-HL) (AUC 0.849, accuracy 0.775). The SUV Conclusion: Radiomic features combined with machine learning significantly improve lymphoma subtype classification over SUV Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1007/s13139-026-01017-4.

Indexed as

18F-FDG PET/CTClassificationExtra-nodal differentiationLymphomaMachine learningRadiomicsSurvival analysis

Identifiers

PMID42824644
PMCPMC13627636

What Socratic holds

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Registered trials

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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 Socratic graph.