Correlations between baseline  18 F-FDG PET tumour parameters and circulating DNA in diffuse large B cell lymphoma and Hodgkin lymphoma

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Background

¹⁸F-FDG PET/CT is a standard for many B cell malignancies, while blood DNA measurements are emerging tools. Our objective was to evaluate the correlations between baseline PET parameters and circulating DNA in diffuse large B cell lymphoma (DLBCL) and classical Hodgkin lymphoma (cHL).

Methods

Twenty-seven DLBCL and forty-eight cHL were prospectively included. Twelve PET parameters were analysed. Spearman’s correlations were used to compare PET parameters each other and to circulating cell-free DNA ([cfDNA]) and circulating tumour DNA ([ctDNA]). p values were controlled by Benjamini–Hochberg correction.

Results

Among the PET parameters, three different clusters for tumour burden, fragmentation/massiveness and dispersion parameters were observed. Some PET parameters were significantly correlated with blood DNA parameters, including the total metabolic tumour surface (TMTS) describing the tumour–host interface (e.g. ρ = 0.81 p < 0.001 for [ctDNA] of DLBLC), the tumour median distance between the periphery and the centroid (medPCD) describing the tumour’s massiveness (e.g. ρ = 0.81 p < 0.001 for [ctDNA] of DLBLC) and the volume of the bounding box including tumours (TumBB) describing the disease’s dispersion (e.g. ρ = 0.83 p < 0.001 for [ctDNA] of DLBLC).

Conclusions

Some PET parameters describing tumour burden, fragmentation/massiveness and dispersion are significantly correlated with circulating DNA parameters of DLBCL and cHL patients. These results could help to understand the pathophysiology of B cell malignancies.

Related collections

Most cited references 24

Record: found
Abstract: found
Article: not found

Radiomics: the bridge between medical imaging and personalized medicine

Seán Froudist Walsh, Joachim Wildberger, Felix Mottaghy … (2017)

Radiomics, the high-throughput mining of quantitative image features from standard-of-care medical imaging that enables data to be extracted and applied within clinical-decision support systems to improve diagnostic, prognostic, and predictive accuracy, is gaining importance in cancer research. Radiomic analysis exploits sophisticated image analysis tools and the rapid development and validation of medical imaging data that uses image-based signatures for precision diagnosis and treatment, providing a powerful tool in modern medicine. Herein, we describe the process of radiomics, its pitfalls, challenges, opportunities, and its capacity to improve clinical decision making, emphasizing the utility for patients with cancer. Currently, the field of radiomics lacks standardized evaluation of both the scientific integrity and the clinical relevance of the numerous published radiomics investigations resulting from the rapid growth of this area. Rigorous evaluation criteria and reporting guidelines need to be established in order for radiomics to mature as a discipline. Herein, we provide guidance for investigations to meet this urgent need in the field of radiomics.

0 comments Cited 1601 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping

Alex Zwanenburg, Martin Vallières, Mahmoud A. Abdalah … (2020)

Background Radiomic features may quantify characteristics present in medical imaging. However, the lack of standardized definitions and validated reference values have hampered clinical use. Purpose To standardize a set of 174 radiomic features. Materials and Methods Radiomic features were assessed in three phases. In phase I, 487 features were derived from the basic set of 174 features. Twenty-five research teams with unique radiomics software implementations computed feature values directly from a digital phantom, without any additional image processing. In phase II, 15 teams computed values for 1347 derived features using a CT image of a patient with lung cancer and predefined image processing configurations. In both phases, consensus among the teams on the validity of tentative reference values was measured through the frequency of the modal value and classified as follows: less than three matches, weak; three to five matches, moderate; six to nine matches, strong; 10 or more matches, very strong. In the final phase (phase III), a public data set of multimodality images (CT, fluorine 18 fluorodeoxyglucose PET, and T1-weighted MRI) from 51 patients with soft-tissue sarcoma was used to prospectively assess reproducibility of standardized features. Results Consensus on reference values was initially weak for 232 of 302 features (76.8%) at phase I and 703 of 1075 features (65.4%) at phase II. At the final iteration, weak consensus remained for only two of 487 features (0.4%) at phase I and 19 of 1347 features (1.4%) at phase II. Strong or better consensus was achieved for 463 of 487 features (95.1%) at phase I and 1220 of 1347 features (90.6%) at phase II. Overall, 169 of 174 features were standardized in the first two phases. In the final validation phase (phase III), most of the 169 standardized features could be excellently reproduced (166 with CT; 164 with PET; and 164 with MRI). Conclusion A set of 169 radiomics features was standardized, which enabled verification and calibration of different radiomics software. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Kuhl and Truhn in this issue.

0 comments Cited 1065 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: not found
Article: not found

Diffuse large B-cell lymphoma (DLBCL): ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up.

H. Tilly, M. Gomes da Silva, U Vitolo … (2015)

0 comments Cited 191 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Contributors

Pierre Decazes:

ORCID: http://orcid.org/0000-0001-5323-9910

pierre.decazes@chb.unicancer.fr

Journal

Journal ID (nlm-ta): EJNMMI Res

Journal ID (iso-abbrev): EJNMMI Res

Title: EJNMMI Research

Publisher: Springer Berlin Heidelberg (Berlin/Heidelberg )

ISSN (Electronic): 2191-219X

Publication date (Electronic): 7 October 2020

Publication date PMC-release: 7 October 2020

Publication date Collection: 2020

Volume: 10

Electronic Location Identifier: 120

Affiliations

[1 ]Department of Nuclear Medicine, Henri Becquerel Cancer Centre, Rouen, France

[2 ]GRID grid.10400.35, ISNI 0000 0001 2108 3034, QuantIF-LITIS-EA4108, , University of Rouen, ; Rouen, France

[3 ]Department of Haematology, Henri Becquerel Cancer Centre, Rouen, France

[4 ]GRID grid.10400.35, ISNI 0000 0001 2108 3034, INSERM U1245, , Henri Becquerel Cancer Centre and Rouen University, ; Rouen, France

Author information

Pierre Decazes http://orcid.org/0000-0001-5323-9910

Article

Publisher ID: 717

DOI: 10.1186/s13550-020-00717-y

PMC ID: 7541805

PubMed ID: 33029662

SO-VID: 4449452c-59cc-4220-ba01-998a2c370aab

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 30 July 2020

Date accepted : 24 September 2020

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100004099, Ligue Contre le Cancer;

Funded by: Canceropole Nord-Ouest

Custom metadata

ScienceOpen disciplines: Radiology & Imaging

Keywords: positron emission tomography,b cell malignancies,diffuse large b cell lymphoma,hodgkin lymphoma,circulating tumour dna,circulating free dna

Data availability:

ScienceOpen disciplines: Radiology & Imaging

Keywords: positron emission tomography, b cell malignancies, diffuse large b cell lymphoma, hodgkin lymphoma, circulating tumour dna, circulating free dna

Comments

Comment on this article

scite_

Cited by 7

Radiomics in PET Imaging
Authors: Fanny Orlhac, Christophe Nioche, Ivan Klyuzhin …
Clinical Application of Liquid Biopsy in Non-Hodgkin Lymphoma
Authors: Liwei Lv, Yuanbo Liu
Circulating tumor DNA in Hodgkin lymphoma
Authors: Maria Maco, Kristyna Kupcova, Vaclav Herman …

See all cited by

Most referenced authors 639

See all reference authors