Radiomics in Malignant Lymphomas

Chauvie, Stéphane; Ceriani, Luca; Zucca, Emanuele

doi:10.36255/exon-publications.lymphoma.2021.radiomics

Cited by 5 publications

(2 citation statements)

References 54 publications

(68 reference statements)

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“…Radiomics analysis has been emerging as an interesting way to extract information from imaging data, potentially reflecting disease biological features and providing additional promising prognostic biomarkers in several oncological conditions, such as lymphomas 24 . It is possible to get a wide variety of quantitative data in a short amount of time and a non‐invasive way.…”

Section: Discussionmentioning

confidence: 99%

The role of baseline 2‐[¹⁸F]‐FDG‐PET/CT metrics and radiomics features in predicting primary gastric lymphoma diagnosis

Albano,

Calabrò,

Dondi

et al. 2024

Hematological Oncology

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Diffuse Large B‐Cell Lymphomas (DLCBL) and mucosa‐associated lymphoid tissue (MALT) are the two most common primary gastric lymphomas (PGLs), but have strongly different features. DLBCL is more aggressive, is frequently diagnosed at an advanced stage and has a poorer prognosis. The aim of this retrospective study was to explore the role of fluorine‐18‐fluorodeoxyglucose positron emission tomography/computed tomography (2‐[18F]‐FDG‐PET/CT) and radiomics features (RFs) in predicting the final diagnosis of patients with PGLs. Ninety‐one patients with newly diagnosed PGLs who underwent pre‐treatment 2‐[18F]‐FDG‐PET/CT were included. PET images were qualitatively and semi‐quantitatively analyzed by deriving maximum standardized uptake value body weight (SUVbw), maximum standardized uptake value lean body mass (SUVlbm), maximum standardized uptake value body surface area (SUVbsa), lesion to liver SUVmax ratio (L‐L SUV R), lesion to blood‐pool SUVmax ratio (L‐BP SUV R), metabolic tumor volume (gMTV) and total lesion glycolysis of gastric lesion (gTLG), total MTV (tMTV), TLG, and first‐order RFs (histogram‐related and shape related). Receiver‐operating characteristic (ROC) curve analyses were performed to determine the differential diagnostic values of PET parameters. The final diagnosis was DLBCL in 54 (59%) cases and MALT in 37 cases (41%). PGLs showed FDG avidity in 83 cases (90%), 54/54 of DLBCL and 29/37 of MALT. All PET/CT metabolic features, such as stage of disease and tumor size, were significantly higher in DLBCL than MALT; while the presence of H. Pylori infection was more common in MALT. At univariate analysis, all PET/CT metrics were significantly higher in DLBCL than MALT lymphomas, while among RFs only Shape volume_vx and Shape sphericity showed a significant difference between the two groups. In conclusion we demonstrated that 2‐[18F]‐FDG‐PET/CT parameters can potentially discriminate between DLBCL and MALT lymphomas with high accuracy. Among first‐order RFs, only Shape volume_vx and Shape sphericity helped in the differential diagnosis.

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Section: Discussionmentioning

confidence: 99%

The role of baseline 2‐[¹⁸F]‐FDG‐PET/CT metrics and radiomics features in predicting primary gastric lymphoma diagnosis

Albano,

Calabrò,

Dondi

et al. 2024

Hematological Oncology

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“…8 More recently the application of the radiomic analysis has increased the number of achievable PET quantitative parameters and consequently the possibility to explore new predictive biomarkers. 9 The metabolic heterogeneity and the maximal distance between the two farthest lesions (D max ) have emerged as new promising predictors of Progression-Free (PFS) and Overall Survival (OS) in patients with lymphomas. 10,11 These two parameters have been proposed substantially as complementary biomarkers to MTV.…”

Section: Funding Informationmentioning

confidence: 99%

D_max: A simple and reliable PET/CT‐derived new biomarker of lymphoma outcome?

Ceriani

Zucca

2022

Hematological Oncology

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Comparison of radiomics models and dual-energy material decomposition to decipher abdominal lymphoma in contrast-enhanced CT

et al. 2023

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Purpose The radiologists’ workload is increasing, and computational imaging techniques may have the potential to identify visually unequivocal lesions, so that the radiologist can focus on equivocal and critical cases. The purpose of this study was to assess radiomics versus dual-energy CT (DECT) material decomposition to objectively distinguish visually unequivocal abdominal lymphoma and benign lymph nodes. Methods Retrospectively, 72 patients [m, 47; age, 63.5 (27–87) years] with nodal lymphoma (n = 27) or benign abdominal lymph nodes (n = 45) who had contrast-enhanced abdominal DECT between 06/2015 and 07/2019 were included. Three lymph nodes per patient were manually segmented to extract radiomics features and DECT material decomposition values. We used intra-class correlation analysis, Pearson correlation and LASSO to stratify a robust and non-redundant feature subset. Independent train and test data were applied on a pool of four machine learning models. Performance and permutation-based feature importance was assessed to increase the interpretability and allow for comparison of the models. Top performing models were compared by the DeLong test. Results About 38% (19/50) and 36% (8/22) of the train and test set patients had abdominal lymphoma. Clearer entity clusters were seen in t-SNE plots using a combination of DECT and radiomics features compared to DECT features only. Top model performances of AUC = 0.763 (CI = 0.435–0.923) were achieved for the DECT cohort and AUC = 1.000 (CI = 1.000–1.000) for the radiomics feature cohort to stratify visually unequivocal lymphomatous lymph nodes. The performance of the radiomics model was significantly (p = 0.011, DeLong) superior to the DECT model. Conclusions Radiomics may have the potential to objectively stratify visually unequivocal nodal lymphoma versus benign lymph nodes. Radiomics seems superior to spectral DECT material decomposition in this use case. Therefore, artificial intelligence methodologies may not be restricted to centers with DECT equipment.

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Radiomics in Malignant Lymphomas

Cited by 5 publications

References 54 publications

The role of baseline 2‐[¹⁸F]‐FDG‐PET/CT metrics and radiomics features in predicting primary gastric lymphoma diagnosis

The role of baseline 2‐[¹⁸F]‐FDG‐PET/CT metrics and radiomics features in predicting primary gastric lymphoma diagnosis

D_max: A simple and reliable PET/CT‐derived new biomarker of lymphoma outcome?

Comparison of radiomics models and dual-energy material decomposition to decipher abdominal lymphoma in contrast-enhanced CT

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Radiomics in Malignant Lymphomas

Cited by 5 publications

References 54 publications

The role of baseline 2‐[18F]‐FDG‐PET/CT metrics and radiomics features in predicting primary gastric lymphoma diagnosis

The role of baseline 2‐[18F]‐FDG‐PET/CT metrics and radiomics features in predicting primary gastric lymphoma diagnosis

Dmax: A simple and reliable PET/CT‐derived new biomarker of lymphoma outcome?

Comparison of radiomics models and dual-energy material decomposition to decipher abdominal lymphoma in contrast-enhanced CT

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The role of baseline 2‐[¹⁸F]‐FDG‐PET/CT metrics and radiomics features in predicting primary gastric lymphoma diagnosis

The role of baseline 2‐[¹⁸F]‐FDG‐PET/CT metrics and radiomics features in predicting primary gastric lymphoma diagnosis

D_max: A simple and reliable PET/CT‐derived new biomarker of lymphoma outcome?