Magma (New York, N.Y.) · 2025
Lymphoma classification with multi-parametric texture analysis of DWI and PET imaging in Hodgkin and non-Hodgkin lymphoma: a pilot study.
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Malagi Archana Vadiraj, Baidya Kayal Esha, Kandasamy Devasenathipathy, Pushpam Deepam, Khare Kedar, Sharma Raju, Kumar Rakesh, Bakhshi Sameer, Mehndiratta Amit
Аннотация
OBJECTIVE: Texture 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).
METHODS: A 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/mm2) at 1.5 T and whole-body FDG-PET/CT. IVIM-DKI parameters were estimated using IVIM-DKI model with total-variation (TV) spatial-regularization method (IDTV). Apparent diffusion coefficient (ADC) and standard uptake value (SUV) maps were also calculated. Total 31 of 3D texture features using global and second-order textures were extracted from imaging parameters in the volume-of-interest of malignant and benign lymph nodes. Machine learning linear classifier model was developed for distinguishing between malignant from benign lymph nodes and HL from NHL using textural features and area under receiver operating curve (AUROC) that were evaluated to assess diagnostic accuracy.
RESULTS: Texture 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.
CONCLUSION: Texture 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.
METHODS: A 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/mm2) at 1.5 T and whole-body FDG-PET/CT. IVIM-DKI parameters were estimated using IVIM-DKI model with total-variation (TV) spatial-regularization method (IDTV). Apparent diffusion coefficient (ADC) and standard uptake value (SUV) maps were also calculated. Total 31 of 3D texture features using global and second-order textures were extracted from imaging parameters in the volume-of-interest of malignant and benign lymph nodes. Machine learning linear classifier model was developed for distinguishing between malignant from benign lymph nodes and HL from NHL using textural features and area under receiver operating curve (AUROC) that were evaluated to assess diagnostic accuracy.
RESULTS: Texture 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.
CONCLUSION: Texture 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.
Предметные рубрики MeSH
HumansHodgkin DiseaseLymphoma, Non-HodgkinDiffusion Magnetic Resonance ImagingMaleMiddle AgedFemalePilot ProjectsAdultProspective StudiesAgedROC CurvePositron-Emission TomographyLymph NodesPositron Emission Tomography Computed TomographyMachine LearningImage Interpretation, Computer-AssistedFluorodeoxyglucose F18Young AdultImage Processing, Computer-AssistedDiagnosis, DifferentialArea Under Curve