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. 2022 Apr 18;17(4):e0267275.
doi: 10.1371/journal.pone.0267275. eCollection 2022.

Automatic classification of lymphoma lesions in FDG-PET-Differentiation between tumor and non-tumor uptake

Thomas W Georgi  1 , Axel Zieschank  2 , Kevin Kornrumpf  2 , Lars Kurch  1 , Osama Sabri  1 , Dieter Körholz  3 , Christine Mauz-Körholz  3 , Regine Kluge  1 , Stefan Posch  2
Affiliations

Automatic classification of lymphoma lesions in FDG-PET-Differentiation between tumor and non-tumor uptake

Thomas W Georgi et al. PLoS One. .

Abstract

Introduction: The automatic classification of lymphoma lesions in PET is a main topic of ongoing research. An automatic algorithm would enable the swift evaluation of PET parameters, like texture and heterogeneity markers, concerning their prognostic value for patients outcome in large datasets. Moreover, the determination of the metabolic tumor volume would be facilitated. The aim of our study was the development and evaluation of an automatic algorithm for segmentation and classification of lymphoma lesions in PET.

Methods: Pre-treatment PET scans from 60 Hodgkin lymphoma patients from the EuroNet-PHL-C1 trial were evaluated. A watershed algorithm was used for segmentation. For standardization of the scan length, an automatic cropping algorithm was developed. All segmented volumes were manually classified into one of 14 categories. The random forest method and a nested cross-validation was used for automatic classification and evaluation.

Results: Overall, 853 volumes were segmented and classified. 203/246 tumor lesions and 554/607 non-tumor volumes were classified correctly by the automatic algorithm, corresponding to a sensitivity, a specificity, a positive and a negative predictive value of 83%, 91%, 79% and 93%. In 44/60 (73%) patients, all tumor lesions were correctly classified. In ten out of the 16 patients with misclassified tumor lesions, only one false-negative tumor lesion occurred. The automatic classification of focal gastrointestinal uptake, brown fat tissue and composed volumes consisting of more than one tissue was challenging.

Conclusion: Our algorithm, trained on a small number of patients and on PET information only, showed a good performance and is suitable for automatic lymphoma classification.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Methodical workflow of the classification algorithm.
Fig 2
Fig 2. Patient from the EuroNet-PHL-C1 study with an optimal automatic classification.
Fig 3
Fig 3. Patient from the EuroNet-PHL-C1 study with a suboptimal automatic classification and incorrect cropping at the upper border.
See this image and copyright information in PMC

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