A convolutional neural network for total tumor segmentation in [64Cu]Cu-DOTATATE PET/CT of patients with neuroendocrine neoplasms
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A convolutional neural network for total tumor segmentation in [64Cu]Cu-DOTATATE PET/CT of patients with neuroendocrine neoplasms. / Carlsen, Esben Andreas; Lindholm, Kristian; Hindsholm, Amalie; Gæde, Mathias; Ladefoged, Claes Nøhr; Loft, Mathias; Johnbeck, Camilla Bardram; Langer, Seppo Wang; Oturai, Peter; Knigge, Ulrich; Kjaer, Andreas; Andersen, Flemming Littrup.
I: EJNMMI Research, Bind 12, Nr. 1, 30, 2022, s. 1-10.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - A convolutional neural network for total tumor segmentation in [64Cu]Cu-DOTATATE PET/CT of patients with neuroendocrine neoplasms
AU - Carlsen, Esben Andreas
AU - Lindholm, Kristian
AU - Hindsholm, Amalie
AU - Gæde, Mathias
AU - Ladefoged, Claes Nøhr
AU - Loft, Mathias
AU - Johnbeck, Camilla Bardram
AU - Langer, Seppo Wang
AU - Oturai, Peter
AU - Knigge, Ulrich
AU - Kjaer, Andreas
AU - Andersen, Flemming Littrup
N1 - Publisher Copyright: © 2022, The Author(s).
PY - 2022
Y1 - 2022
N2 - Background: Segmentation of neuroendocrine neoplasms (NENs) in [64Cu]Cu-DOTATATE positron emission tomography makes it possible to extract quantitative measures useable for prognostication of patients. However, manual tumor segmentation is cumbersome and time-consuming. Therefore, we aimed to implement and test an artificial intelligence (AI) network for tumor segmentation. Patients with gastroenteropancreatic or lung NEN with [64Cu]Cu-DOTATATE PET/CT performed were included in our training (n = 117) and test cohort (n = 41). Further, 10 patients with no signs of NEN were included as negative controls. Ground truth segmentations were obtained by a standardized semiautomatic method for tumor segmentation by a physician. The nnU-Net framework was used to set up a deep learning U-net architecture. Dice score, sensitivity and precision were used for selection of the final model. AI segmentations were implemented in a clinical imaging viewer where a physician evaluated performance and performed manual adjustments. Results: Cross-validation training was used to generate models and an ensemble model. The ensemble model performed best overall with a lesion-wise dice of 0.850 and pixel-wise dice, precision and sensitivity of 0.801, 0.786 and 0.872, respectively. Performance of the ensemble model was acceptable with some degree of manual adjustment in 35/41 (85%) patients. Final tumor segmentation could be obtained from the AI model with manual adjustments in 5 min versus 17 min for ground truth method, p < 0.01. Conclusion: We implemented and validated an AI model that achieved a high similarity with ground truth segmentation and resulted in faster tumor segmentation. With AI, total tumor segmentation may become feasible in the clinical routine.
AB - Background: Segmentation of neuroendocrine neoplasms (NENs) in [64Cu]Cu-DOTATATE positron emission tomography makes it possible to extract quantitative measures useable for prognostication of patients. However, manual tumor segmentation is cumbersome and time-consuming. Therefore, we aimed to implement and test an artificial intelligence (AI) network for tumor segmentation. Patients with gastroenteropancreatic or lung NEN with [64Cu]Cu-DOTATATE PET/CT performed were included in our training (n = 117) and test cohort (n = 41). Further, 10 patients with no signs of NEN were included as negative controls. Ground truth segmentations were obtained by a standardized semiautomatic method for tumor segmentation by a physician. The nnU-Net framework was used to set up a deep learning U-net architecture. Dice score, sensitivity and precision were used for selection of the final model. AI segmentations were implemented in a clinical imaging viewer where a physician evaluated performance and performed manual adjustments. Results: Cross-validation training was used to generate models and an ensemble model. The ensemble model performed best overall with a lesion-wise dice of 0.850 and pixel-wise dice, precision and sensitivity of 0.801, 0.786 and 0.872, respectively. Performance of the ensemble model was acceptable with some degree of manual adjustment in 35/41 (85%) patients. Final tumor segmentation could be obtained from the AI model with manual adjustments in 5 min versus 17 min for ground truth method, p < 0.01. Conclusion: We implemented and validated an AI model that achieved a high similarity with ground truth segmentation and resulted in faster tumor segmentation. With AI, total tumor segmentation may become feasible in the clinical routine.
KW - Artificial intelligence
KW - Neuroendocrine neoplasms
KW - Prognostication
KW - Tumor segmentation
KW - [Cu]Cu-DOTATATE PET
UR - http://www.scopus.com/inward/record.url?scp=85130936426&partnerID=8YFLogxK
U2 - 10.1186/s13550-022-00901-2
DO - 10.1186/s13550-022-00901-2
M3 - Journal article
C2 - 35633448
AN - SCOPUS:85130936426
VL - 12
SP - 1
EP - 10
JO - EJNMMI Research
JF - EJNMMI Research
SN - 2191-219X
IS - 1
M1 - 30
ER -
ID: 316874621