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TMTV-Net: fully automated total metabolic tumor volume segmentation in lymphoma PET/CT images — a multi-center generalizability analysis

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Abstract

Purpose

Total metabolic tumor volume (TMTV) segmentation has significant value enabling quantitative imaging biomarkers for lymphoma management. In this work, we tackle the challenging task of automated tumor delineation in lymphoma from PET/CT scans using a cascaded approach.

Methods

Our study included 1418 2-[18F]FDG PET/CT scans from four different centers. The dataset was divided into 900 scans for development/validation/testing phases and 518 for multi-center external testing. The former consisted of 450 lymphoma, lung cancer, and melanoma scans, along with 450 negative scans, while the latter consisted of lymphoma patients from different centers with diffuse large B cell, primary mediastinal large B cell, and classic Hodgkin lymphoma cases. Our approach involves resampling PET/CT images into different voxel sizes in the first step, followed by training multi-resolution 3D U-Nets on each resampled dataset using a fivefold cross-validation scheme. The models trained on different data splits were ensemble. After applying soft voting to the predicted masks, in the second step, we input the probability-averaged predictions, along with the input imaging data, into another 3D U-Net. Models were trained with semi-supervised loss. We additionally considered the effectiveness of using test time augmentation (TTA) to improve the segmentation performance after training. In addition to quantitative analysis including Dice score (DSC) and TMTV comparisons, the qualitative evaluation was also conducted by nuclear medicine physicians.

Results

Our cascaded soft-voting guided approach resulted in performance with an average DSC of 0.68 ± 0.12 for the internal test data from developmental dataset, and an average DSC of 0.66 ± 0.18 on the multi-site external data (n = 518), significantly outperforming (p < 0.001) state-of-the-art (SOTA) approaches including nnU-Net and SWIN UNETR. While TTA yielded enhanced performance gains for some of the comparator methods, its impact on our cascaded approach was found to be negligible (DSC: 0.66 ± 0.16). Our approach reliably quantified TMTV, with a correlation of 0.89 with the ground truth (p < 0.001). Furthermore, in terms of visual assessment, concordance between quantitative evaluations and clinician feedback was observed in the majority of cases. The average relative error (ARE) and the absolute error (AE) in TMTV prediction on external multi-centric dataset were ARE = 0.43 ± 0.54 and AE = 157.32 ± 378.12 (mL) for all the external test data (n = 518), and ARE = 0.30 ± 0.22 and AE = 82.05 ± 99.78 (mL) when the 10% outliers (n = 53) were excluded.

Conclusion

TMTV-Net demonstrates strong performance and generalizability in TMTV segmentation across multi-site external datasets, encompassing various lymphoma subtypes. A negligible reduction of 2% in overall performance during testing on external data highlights robust model generalizability across different centers and cancer types, likely attributable to its training with resampled inputs. Our model is publicly available, allowing easy multi-site evaluation and generalizability analysis on datasets from different institutions.

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Data Availability

The data that was used for model development (autoPET) is publicly available. To protect study participant privacy, we cannot share the testing data from BC Cancer, University of Wisconsin and Seoul St. Mary’s Hospital.

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Funding

This research was in part supported by the Canadian Institutes of Health Research (CIHR) Project Grant PJT-173231, Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant RGPIN-2019–06467, GE Healthcare, and through computational resources and services provided by Microsoft for Health.

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Authors and Affiliations

  1. Department of Integrative Oncology, BC Cancer Research Institute, 675 West 10Th Avenue, Vancouver, BC, V5Z 1L3, Canada

    Fereshteh Yousefirizi, Ivan S. Klyuzhin, Carlos F. Uribe & Arman Rahmim

  2. College of Medicine, Seoul St. Mary’s Hospital, The Catholic University of Korea, Seoul, Republic of Korea

    Joo Hyun O

  3. BC Cancer, Vancouver, BC, Canada

    Sara Harsini, François Bénard, Laurie H. Sehn, Kerry J. Savage, Christian Steidl & Carlos F. Uribe

  4. Department of Radiology, University of WI–Madison, Madison, WI, USA

    Xin Tie, Muheon Shin, Changhee Lee, Steve Y. Cho & Tyler J. Bradshaw

  5. Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland

    Isaac Shiri & Habib Zaidi

  6. University Medical Center Groningen, University of Groningen, Groningen, Netherlands

    Habib Zaidi

  7. Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark

    Habib Zaidi

  8. University Research and Innovation Center, Óbuda University, Budapest, Hungary

    Habib Zaidi

  9. Department of Radiology, University of British Columbia, Vancouver, BC, Canada

    François Bénard, Carlos F. Uribe & Arman Rahmim

  10. Centre for Lymphoid Cancer, BC Cancer, Vancouver, Canada

    Laurie H. Sehn & Kerry J. Savage

  11. Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, Canada

    Christian Steidl

  12. Departments of Physics and Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada

    Arman Rahmim

  13. Department of Biomedical Engineering, University of British Columbia, Vancouver, Canada

    Arman Rahmim

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  1. Fereshteh Yousefirizi
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Correspondence to Fereshteh Yousefirizi.

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Ethics approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The retrospective study was conducted with the following ethics numbers: H19-01611 for the PMBCL study and H19-01866 for the DLBCL study and H19-001611 for Hodgkin study at BC Cancer, as well as KC11EISI0293 for the DLBCL study at Seoul St. Mary’s Hospital and UW2016-0418 for DLBCL and Hodgkin’s cases at University of Wisconsin.

Conflict of interest

Ivan Klyuzhin, Carlos Uribe, and Arman Rahmim are co-founders of Ascinta Technologies Inc.

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Yousefirizi, F., Klyuzhin, I.S., O, J.H. et al. TMTV-Net: fully automated total metabolic tumor volume segmentation in lymphoma PET/CT images — a multi-center generalizability analysis. Eur J Nucl Med Mol Imaging (2024). https://doi.org/10.1007/s00259-024-06616-x

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