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Current status and quality of radiomics studies in lymphoma: a systematic review

Abstract

Objectives

To perform a systematic review regarding the developments in the field of radiomics in lymphoma. To evaluate the quality of included articles by the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2), the phases classification criteria for image mining studies, and the radiomics quality scoring (RQS) tool.

Methods

We searched for eligible articles in the MEDLINE/PubMed and EMBASE databases using the terms “radiomics”, “texture” and “lymphoma”. The included studies were divided into two categories: diagnosis-, therapy response- and outcome-related studies. The diagnosis-related studies were evaluated using the QUADAS-2; all studies were evaluated using the phases classification criteria for image mining studies and the RQS tool by two reviewers.

Results

Forty-five studies were included; thirteen papers (28.9%) focused on the differential diagnosis of primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM). Thirty-two (71.1%) studies were classified as discovery science according to the phase classification criteria for image mining studies. The mean RQS score of all studies was 14.2% (ranging from 0.0 to 40.3%), and 23 studies (51.1%) were given a score of < 10%.

Conclusion

The radiomics features could serve as diagnostic and prognostic indicators in lymphoma. However, the current conclusions should be interpreted with caution due to the suboptimal quality of the studies. In order to introduce radiomics into lymphoma clinical settings, the lesion segmentation and selection, the influence of the pathological pattern and the extraction of multiple modalities and multiple time points features need to be further studied.

Key Points

• The radiomics approach may provide useful information for diagnosis, prediction of the therapy response, and outcome of lymphoma.

• The quality of published radiomics studies in lymphoma has been suboptimal to date.

• More studies are needed to examine lesion selection and segmentation, the influence of pathological patterns, and the extraction of multiple modalities and multiple time point features.

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Abbreviations

AUC:

Area under the curve

CT:

Computed tomography

DLBCL:

Diffuse large B cell lymphoma

GBM:

Glioblastoma

HL:

Hodgkin’s lymphoma

MRI:

Magnetic resonance imaging

NHL:

Non-Hodgkin’s lymphoma

PCNSL:

Primary central nervous system lymphoma

PET:

Positron emission tomography

QUADAS-2:

The Quality Assessment of Diagnostic Accuracy Studies-2

RQS:

Radiomics quality scoring

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Funding

This study has received funding from the Key Projects of the Ministry of Science and Technology (grant 2017YFC0113304).

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Correspondence to Xuelei Ma or Rong Tian.

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The scientific guarantor of this publication is Rong Tian, PhD.

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The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

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No complex statistical methods were necessary for this paper.

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Written informed consent was not required for this study because it is a systematic review.

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Institutional review board approval was not required because it is a systematic review.

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• Retrospective

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• Performed at one institution

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Wang, H., Zhou, Y., Li, L. et al. Current status and quality of radiomics studies in lymphoma: a systematic review. Eur Radiol 30, 6228–6240 (2020). https://doi.org/10.1007/s00330-020-06927-1

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Keywords

  • Lymphoma
  • Multidetector computed tomography
  • Magnetic resonance imaging
  • Positron emission tomography, computed tomography