Online citations, reference lists, and bibliographies.
Please confirm you are human
(Sign Up for free to never see this)
← Back to Search

Methodological Framework For Radiomics Applications In Hodgkin’s Lymphoma

M. Sollini, M. Kirienko, Lara Cavinato, F. Ricci, Matteo Biroli, Francesca Ieva, L. Calderoni, E. Tabacchi, C. Nanni, P. Zinzani, S. Fanti, A. Guidetti, A. Alessi, P. Corradini, E. Seregni, Carmelo Carlo-Stella, A. Chiti
Published 2020 · Medicine

Save to my Library
Download PDF
Analyze on Scholarcy
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 ( ) was used for [ 18 F]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.
This paper references
Prediction of disease-free survival by the PET/CT radiomic signature in non-small cell lung cancer patients undergoing surgery
M. Kirienko (2017)
CT-based texture analysis potentially provides prognostic information complementary to interim fdg-pet for patients with hodgkin’s and aggressive non-hodgkin’s lymphomas
B. Ganeshan (2016)
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations
Sollini (2020)
Quantitative radiomics: impact of stochastic effects on textural feature analysis implies the need for standards
Matthew Nyflot (2015)
Advances in the pathophysiology and treatment of relapsed/refractory Hodgkin’s lymphoma with an emphasis on targeted therapies and transplantation strategies
T. Karantanos (2017)
Circulating tumor DNA reveals genetics, clonal evolution, and residual disease in classical Hodgkin lymphoma.
V. Spina (2018)
Quality of science and reporting of radiomics in oncologic studies: room for improvement according to radiomics quality score and TRIPOD statement
J. Park (2019)
PET Radiomics in NSCLC: state of the art and a proposal for harmonization of methodology
M. Sollini (2017)
A Postreconstruction Harmonization Method for Multicenter Radiomic Studies in PET
F. Orlhac (2018)
FDG‐PET/CT in the management of lymphomas: current status and future directions
T. El-Galaly (2018)
Radiomic features of glucose metabolism enable prediction of outcome in mantle cell lymphoma
M. Mayerhoefer (2019)
Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics
M. Sollini (2019)
Circulating tumor DNA reveals genetics, clonal evolution and residual disease in classical Hodgkin lymphoma, Blood
S. Annunziata (2018)
Recent Advances in the Pathobiology of Hodgkin's Lymphoma: Potential Impact on Diagnostic, Predictive, and Therapeutic Strategies
Diponkar Banerjee (2011)
Circulating tumor DNA reveals genetics, clonal evolution and residual disease in classical Hodgkin lymphoma, Blood. blood-2017-11-812073
V Spina (2018)
PET/CT radiomics in breast cancer: mind the step.
M. Sollini (2020)
RUSBoost: A Hybrid Approach to Alleviating Class Imbalance
C. Seiffert (2010)
Publisher's Note
H. V. D. Togt (2003)
Association between textural and morphological tumor indices on baseline PET‐CT and early metabolic response on interim PET‐CT in bulky malignant lymphomas
F. Ben Bouallègue (2017)
18F-FDG PET/CT metabolic tumor parameters and radiomics features in aggressive non-Hodgkin’s lymphoma as predictors of treatment outcome and survival
Aatif Parvez (2018)
18F-FDG PET Uptake Characterization Through Texture Analysis: Investigating the Complementary Nature of Heterogeneity and Functional Tumor Volume in a Multi–Cancer Site Patient Cohort
M. Hatt (2015)
Treating Hodgkin lymphoma in the new millennium: Relapsed and refractory disease
A. LaCasce (2019)
Prognostic Value of Pretreatment Radiomic Features of 18F-FDG PET in Patients With Hodgkin Lymphoma.
Kun-Han Lue (2019)
Relationship between FDG uptake and expressions of glucose transporter type 1, type 3, and hexokinase-II in Reed-Sternberg cells of Hodgkin lymphoma.
Hye Kyung Shim (2009)
When should FDG‐PET be used in the modern management of lymphoma?
S. Barrington (2014)
Three-dimensional texture analysis of contrast enhanced CT images for treatment response assessment in Hodgkin lymphoma: comparison with F-18-FDG PET.
T. Knogler (2014)
A PET Radiomics Model to Predict Refractory Mediastinal Hodgkin Lymphoma
S. Milgrom (2019)
The Elementary Statistics of Majority Voting
L. Penrose (1946)
Hodgkin lymphoma: 2018 update on diagnosis, risk‐stratification, and management
S. Ansell (2018)
Biology of classical Hodgkin lymphoma: implications for prognosis and novel therapies.
A. Mottok (2018)
Quantitative imaging biomarkers in nuclear medicine: from SUV to image mining studies. Highlights from annals of nuclear medicine 2018
M. Sollini (2019)
Volumetric and texture analysis on FDG PET in evaluating and predicting treatment response and recurrence after chemotherapy in follicular lymphoma
M. Tatsumi (2019)
The Art of Data Augmentation
David A. van Dyk (2001)
LIFEx: A Freeware for Radiomic Feature Calculation in Multimodality Imaging to Accelerate Advances in the Characterization of Tumor Heterogeneity.
C. Nioche (2018)
Evaluation of PET texture features with heterogeneous phantoms: complementarity and effect of motion and segmentation method.
M. Carles (2017)
Immune and Inflammatory Cells of the Tumor Microenvironment Represent Novel Therapeutic Targets in Classical Hodgkin Lymphoma
Eleonora Calabretta (2019)
PET positivity – the agony of choice: response assessment and interpretation of increased FDG uptake of residual mediastinal tissue after frontline therapy in Hodgkin lymphoma
S. Gillessen (2020)

This paper is referenced by
Semantic Scholar Logo Some data provided by SemanticScholar