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. 2023 Nov;18(11):2083-2090.
doi: 10.1007/s11548-023-02968-1. Epub 2023 Jun 12.

Automated full body tumor segmentation in DOTATATE PET/CT for neuroendocrine cancer patients

Alice Santilli  1 , Prashanth Panyam  2 , Arthur Autz  2 , Rick Wray  2 , John Philip  3 , Pierre Elnajjar  2 , Nathaniel Swinburne #  2 , Marius Mayerhoefer #  2
Affiliations

Automated full body tumor segmentation in DOTATATE PET/CT for neuroendocrine cancer patients

Alice Santilli et al. Int J Comput Assist Radiol Surg. 2023 Nov.

Abstract

Purpose: Neuroendocrine tumors (NETs) are a rare form of cancer that can occur anywhere in the body and commonly metastasizes. The large variance in location and aggressiveness of the tumors makes it a difficult cancer to treat. Assessments of the whole-body tumor burden in a patient image allow for better tracking of disease progression and inform better treatment decisions. Currently, radiologists rely on qualitative assessments of this metric since manual segmentation is unfeasible within a typical busy clinical workflow.

Methods: We address these challenges by extending the application of the nnU-net pipeline to produce automatic NET segmentation models. We utilize the ideal imaging type of 68Ga-DOTATATE PET/CT to produce segmentation masks from which to calculate total tumor burden metrics. We provide a human-level baseline for the task and perform ablation experiments of model inputs, architectures, and loss functions.

Results: Our dataset is comprised of 915 PET/CT scans and is divided into a held-out test set (87 cases) and 5 training subsets to perform cross-validation. The proposed models achieve test Dice scores of 0.644, on par with our inter-annotator Dice score on a subset 6 patients of 0.682. If we apply our modified Dice score to the predictions, the test performance reaches a score of 0.80.

Conclusion: In this paper, we demonstrate the ability to automatically generate accurate NET segmentation masks given PET images through supervised learning. We publish the model for extended use and to support the treatment planning of this rare cancer.

Keywords: Automatic segmentation; DOTATATE; Neuroendocrine tumor; PET; Radiology; Tumor burden; nnUnet.

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Conflict of interest statement

Conflict of Interest: The authors declare no conflicts of interest. Pierre Elnajjar currently employed by Regeneron, Inc.

Figures

Fig. 1
Fig. 1
Overview of a radiology pipeline including the use of an automatic tumor segmentation model to inform radiologist of changes in NET tumor burden and lesion number over time.
Fig. 2
Fig. 2
Example predictions from three cases of both models (PET and PET+CT). The bolded scores refer to the modified Dice allowance calculation discussed in section 3.2.2. For visual reference, the middle images are the corresponding PET slices to the inner most predicted image in each row.
Fig. 3
Fig. 3
Example slice from an image that demonstrates what is calculated to be true using our modified Dice metric with allowance of one pixel from a true pixel
See this image and copyright information in PMC

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