Evidence map›Paper›PMID 41252089›Full record

ArticleMagma (New York, N.Y.)2026

Lymphoma classification with multi-parametric texture analysis of DWI and PET imaging in Hodgkin and non-Hodgkin lymphoma: a pilot study.

Archana Vadiraj Malagi, Esha Baidya Kayal, Devasenathipathy Kandasamy, Deepam Pushpam, Kedar Khare, Raju Sharma, Rakesh Kumar, Sameer Bakhshi, Amit Mehndiratta

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Article in Magma (New York, N.Y.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Archana Vadiraj MalagiCentre for Biomedical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India.
Esha Baidya KayalCentre for Biomedical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India.
Devasenathipathy KandasamyDepartment of Radio Diagnosis, All India Institute of Medical Sciences, New Delhi, India.
Deepam PushpamDepartment of Medical Oncology, All India Institute of Medical Sciences, New Delhi, India.
Kedar KhareDepartment of Physics, Indian Institute of Technology Delhi, New Delhi, India.
Raju SharmaDepartment of Radio Diagnosis, All India Institute of Medical Sciences, New Delhi, India.
Rakesh KumarDepartment of Nuclear Medicine, All India Institute of Medical Sciences, New Delhi, India.
Sameer BakhshiDepartment of Medical Oncology, All India Institute of Medical Sciences, New Delhi, India.
Amit MehndirattaCentre for Biomedical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India. amit.mehndiratta@keble.oxon.org.ORCID http://orcid.org/0000-0001-6477-2462

Funding

Science and Engineering Research Board CRG/2021/005342
6 · The paper itself

Abstract

objectiveTexture analysis in quantitative IVIM-DKI parameters was investigated for characterizing malignant and benign lymph nodes and distinguishing lymphoma subtypes, specifically Hodgkin lymphoma (HL) and non-Hodgkin lymphoma (NHL).

methodsA prospective cohort of twenty-one patients (n = 21) diagnosed with biopsy-proven lymphoma (HL: 13 and NHL: 8) were analyzed. All patients underwent conventional MRI including DWI with 9 b-values (0-2000s/mm

resultsTexture parameters of neighborhood gray-tone difference matrix (NGTDM) in all IVIM-DKI parameters along with ADC demonstrated excellent diagnostic accuracy showing the highest AUROC of 0.99 (individual highest AUROC by ADC: 0.99; AUROC by all: 0.95-0.99) for distinguishing between malignant and benign lymph nodes. While gray-level co-occurrence matrix (GLCM) and gray-level run-length matrix (GLRLM) features in ADC, diffusion coefficient (D), perfusion coefficient (D*), and perfusion fraction (f) displayed the best AUROC of 0.98 (individual highest AUROC by D: 0.96; AUROC by all: 0.85-0.96) for distinguishing HL from NHL.

conclusionTexture analysis of IVIM-DKI parameters showed promising diagnostic performance in characterizing HL and NHL. Quantitative IVIM-DKI analysis with TV may have a wide range of applicability for the clinical evaluation of lymphomas.

Indexed as

Diffusion Magnetic Resonance ImagingHodgkin DiseaseLymphoma, Non-HodgkinPositron-Emission TomographyAdultAgedArea Under CurveDiagnosis, DifferentialFemaleFluorodeoxyglucose F18HumansImage Interpretation, Computer-AssistedImage Processing, Computer-AssistedLymph NodesMachine LearningMaleFluorodeoxyglucose F18IVIM-DKILymph node characterizationLymphomaLymphoma subtypesPETTexture analysis

Identifiers

PMID41252089

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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 OpenQuestion graph.