Background: According to published data, radiomics features differ between lesions of refractory/relapsing HL patients from those of long-term responders. However, several methodological aspects have not been elucidated yet. Purpose: The study aimed at setting up a methodological framework in radiomics applications in Hodgkin’s lymphoma (HL), especially at (a) developing a novel feature selection approach, (b) evaluating radiomic intra-patient lesions’ similarity, and (c) classifying relapsing refractory (R/R) vs non-(R/R) patients. Methods: We retrospectively included 85 patients (male:female = 52:33; median age 35 years, range 19–74). LIFEx (www.lifexsoft.org) was used for [18F]FDG-PET/CT segmentation and feature extraction. Features were a-priori selected if they were highly correlated or uncorrelated to the volume. Principal component analysis-transformed features were used to build the fingerprints that were tested to assess lesions’ similarity, using the silhouette. For intra-patient similarity analysis, we used patients having multiple lesions only. To classify patients as non-R/R and R/R, the fingerprint considering one single lesion (fingerprint_One) and all lesions (fingerprint_All) was tested using Random Undersampling Boosting of Tree Ensemble (RUBTE). Results: HL fingerprints included up to 15 features. Intra-patient lesion similarity analysis resulted in mean/median silhouette values below 0.5 (low similarity especially in the non-R/R group). In the test set, the fingerprint_One classification accuracy was 62% (78% sensitivity and 53% specificity); the classification by RUBTE using fingerprint_All resulted in 82% accuracy (70% sensitivity and 88% specificity). Conclusions: Lesion similarity analysis was developed, and it allowed to demonstrate that HL lesions were not homogeneous within patients in terms of radiomics signature. Therefore, a random target lesion selection should not be adopted for radiomics applications. Moreover, the classifier to predict R/R vs non-R/R performed the best when all the lesions were used.

Methodological framework for radiomics applications in Hodgkin’s lymphoma / Sollini M.; Kirienko M.; Cavinato L.; Ricci F.; Biroli M.; Ieva F.; Calderoni L.; Tabacchi E.; Nanni C.; Zinzani P.L.; Fanti S.; Guidetti A.; Alessi A.; Corradini P.; Seregni E.; Carlo-Stella C.; Chiti A.. - In: EUROPEAN JOURNAL OF HYBRID IMAGING. - ISSN 2510-3636. - STAMPA. - 4:1(2020), pp. 9-9. [10.1186/s41824-020-00078-8]

Methodological framework for radiomics applications in Hodgkin’s lymphoma

Tabacchi E.;Nanni C.;Zinzani P. L.;Fanti S.;
2020

Abstract

Background: According to published data, radiomics features differ between lesions of refractory/relapsing HL patients from those of long-term responders. However, several methodological aspects have not been elucidated yet. Purpose: The study aimed at setting up a methodological framework in radiomics applications in Hodgkin’s lymphoma (HL), especially at (a) developing a novel feature selection approach, (b) evaluating radiomic intra-patient lesions’ similarity, and (c) classifying relapsing refractory (R/R) vs non-(R/R) patients. Methods: We retrospectively included 85 patients (male:female = 52:33; median age 35 years, range 19–74). LIFEx (www.lifexsoft.org) was used for [18F]FDG-PET/CT segmentation and feature extraction. Features were a-priori selected if they were highly correlated or uncorrelated to the volume. Principal component analysis-transformed features were used to build the fingerprints that were tested to assess lesions’ similarity, using the silhouette. For intra-patient similarity analysis, we used patients having multiple lesions only. To classify patients as non-R/R and R/R, the fingerprint considering one single lesion (fingerprint_One) and all lesions (fingerprint_All) was tested using Random Undersampling Boosting of Tree Ensemble (RUBTE). Results: HL fingerprints included up to 15 features. Intra-patient lesion similarity analysis resulted in mean/median silhouette values below 0.5 (low similarity especially in the non-R/R group). In the test set, the fingerprint_One classification accuracy was 62% (78% sensitivity and 53% specificity); the classification by RUBTE using fingerprint_All resulted in 82% accuracy (70% sensitivity and 88% specificity). Conclusions: Lesion similarity analysis was developed, and it allowed to demonstrate that HL lesions were not homogeneous within patients in terms of radiomics signature. Therefore, a random target lesion selection should not be adopted for radiomics applications. Moreover, the classifier to predict R/R vs non-R/R performed the best when all the lesions were used.
2020
Methodological framework for radiomics applications in Hodgkin’s lymphoma / Sollini M.; Kirienko M.; Cavinato L.; Ricci F.; Biroli M.; Ieva F.; Calderoni L.; Tabacchi E.; Nanni C.; Zinzani P.L.; Fanti S.; Guidetti A.; Alessi A.; Corradini P.; Seregni E.; Carlo-Stella C.; Chiti A.. - In: EUROPEAN JOURNAL OF HYBRID IMAGING. - ISSN 2510-3636. - STAMPA. - 4:1(2020), pp. 9-9. [10.1186/s41824-020-00078-8]
Sollini M.; Kirienko M.; Cavinato L.; Ricci F.; Biroli M.; Ieva F.; Calderoni L.; Tabacchi E.; Nanni C.; Zinzani P.L.; Fanti S.; Guidetti A.; Alessi A.; Corradini P.; Seregni E.; Carlo-Stella C.; Chiti A.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/904811
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