Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress 2023 EHA Congress 2023 ASCO Annual Meeting
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on sleep in brain health

LIVE: Thursday 2nd May 2024, 18:00-19:30 (CEST)

Quality sleep is essential for health. But what happens to our brains when sleep patterns are disturbed? Join our experts to explore the interplay between sleep disruption and neurological diseases, and the questions that you need to be asking your patients to help you prevent the harmful effects of sleep deprivation.
ADVANCED SEARCH
Log in
Top
European Journal of Nuclear Medicine and Molecular Imaging
Published in: European Journal of Nuclear Medicine and Molecular Imaging 3/2020

01-03-2020 | Prostate Cancer | Original Article

Deep neural network for automatic characterization of lesions on 68Ga-PSMA-11 PET/CT

Authors: Yu Zhao, Andrei Gafita, Bernd Vollnberg, Giles Tetteh, Fabian Haupt, Ali Afshar-Oromieh, Bjoern Menze, Matthias Eiber, Axel Rominger, Kuangyu Shi

Published in: European Journal of Nuclear Medicine and Molecular Imaging | Issue 3/2020

Login to get access

Abstract

Purpose

This study proposes an automated prostate cancer (PC) lesion characterization method based on the deep neural network to determine tumor burden on 68Ga-PSMA-11 PET/CT to potentially facilitate the optimization of PSMA-directed radionuclide therapy.

Methods

We collected 68Ga-PSMA-11 PET/CT images from 193 patients with metastatic PC at three medical centers. For proof-of-concept, we focused on the detection of pelvis bone and lymph node lesions. A deep neural network (triple-combining 2.5D U-Net) was developed for the automated characterization of these lesions. The proposed method simultaneously extracts features from axial, coronal, and sagittal planes, which mimics the workflow of physicians and reduces computational and memory requirements.

Results

Among all the labeled lesions, the network achieved 99% precision, 99% recall, and an F1 score of 99% on bone lesion detection and 94%, precision 89% recall, and an F1 score of 92% on lymph node lesion detection. The segmentation accuracy is lower than the detection. The performance of the network was correlated with the amount of training data.

Conclusion

We developed a deep neural network to characterize automatically the PC lesions on 68Ga-PSMA-11 PET/CT. The preliminary test within the pelvic area confirms the potential of deep learning methods. Increasing the amount of training data should further enhance the performance of the proposed method and may ultimately allow whole-body assessments.
Appendix
Available only for authorised users
Literature
1.
Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68:394–424.CrossRefPubMed
2.
Maurer T, Eiber M, Schwaiger M, Gschwend JE. Current use of PSMA–PET in prostate cancer management. Nat Rev Urol. 2016;13:226.CrossRefPubMed
3.
Howlader N, Noone A, Krapcho M, Neyman N, Aminou R, Altekruse S, et al. SEER cancer statistics review, 1975-2009 (vintage 2009 populations), National Cancer Institute. Bethesda 2012.
4.
Bernacki KD, Fields KL, Roh MH. The utility of PSMA and PSA immunohistochemistry in the cytologic diagnosis of metastatic prostate carcinoma. Diagn Cytopathol. 2014;42:570–5.CrossRefPubMed
5.
Fizazi K, Faivre L, Lesaunier F, Delva R, Gravis G, Rolland F, et al. Androgen deprivation therapy plus docetaxel and estramustine versus androgen deprivation therapy alone for high-risk localised prostate cancer (GETUG 12): a phase 3 randomised controlled trial. Lancet Oncol. 2015;16:787–94.CrossRefPubMed
6.
Attard G, Parker C, Eeles R, Schroffder F, Tomlins SA, Tannock I, et al. Prostate cancer. Lancet. 2016;387:70–82.CrossRefPubMed
7.
Weineisen M, Schottelius M, Simecek J, Baum RP, Yildiz A, Beykan S, et al. 68Ga-and 177Lu-labeled PSMA I&T: optimization of a PSMA-targeted theranostic concept and first proof-of-concept human studies. J Nucl Med. 2015;56:1169–76.CrossRefPubMed
8.
9.
Kratochwil C, Bruchertseifer F, Giesel FL, Weis M, Verburg FA, Mottaghy F, et al. 225Ac-PSMA-617 for PSMA-targeted a-radiation therapy of metastatic castration-resistant prostate cancer. J Nucl Med. 2016;57:1941–4.CrossRefPubMed
10.
Kratochwil C, Schmidt K, Afshar-Oromieh A, Bruchertseifer F, Rathke H, Morgenstern A, et al. Targeted alpha therapy of mCRPC: dosimetry estimate of 213Bismuth-PSMA-617. Eur Jo Nucl Med Mol Imaging. 2018;45:31–7.CrossRef
11.
Rahbar K, Schmidt M, Heinzel A, Eppard E, Bode A, Yordanova A, et al. Response and tolerability of a single dose of 177Lu-PSMA-617 in patients with metastatic castration-resistant prostate cancer: a multicenter retrospective analysis. J Nucl Med. 2016;57:1334–8.CrossRefPubMed
12.
Afshar-Oromieh A, Haberkorn U, Zechmann C, Armor T, Mier W, Spohn F, et al. Repeated PSMA-targeting radioligand therapy of metastatic prostate cancer with 131 I-MIP-1095. Eur J Nucl Med Mol Imaging. 2017;44:950–9.CrossRefPubMedPubMedCentral
13.
Hofman MS, Violet J, Hicks RJ, Ferdinandus J, Thang SP, Akhurst T, et al. [177Lu]-PSMA-617 radionuclide treatment in patients with metastatic castration-resistant prostate cancer (LuPSMA trial): a single-centre, single-arm, phase 2 study. Lancet Oncol. 2018;19:825–33.CrossRefPubMed
14.
Eiber M, Fendler WP, Rowe SP, Calais J, Hofman MS, Maurer T, et al. Prostate-specific membrane antigen ligands for imaging and therapy. J Nucl Med. 2017;58:67S–76S.CrossRefPubMed
15.
Bieth M, Krönke M, Tauber R, Dahlbender M, Retz M, Nekolla SG, et al. Exploring new multimodal quantitative imaging indices for the assessment of osseous tumor burden in prostate cancer using 68Ga-PSMA PET/CT. J Nucl Med. 2017;58:1632–7.CrossRefPubMed
16.
17.
18.
19.
20.
Ciresan D, Giusti A, Gambardella LM, Schmidhuber J. Deep neural networks segment neuronal membranes in electron microscopy images. Advances in neural information processing systems; 2012. p. 2843-51.
21.
Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition; 2015. p. 3431-40.
22.
Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical image computing and computer-assisted intervention: Springer; 2015. p. 234-41.
23.
24.
25.
Corral JE, Hussein S, Kandel P, Bolan CW, Wallace MB, Bagci U. Su1337-deep learning to diagnose intraductal papillary mucinous neoplasms (IPMN) with MRI. Gastroenterology. 2018;154:S-524–S-5.CrossRef
26.
27.
28.
Sironi A, Türetken E, Lepetit V, Fua P. Multiscale centerline detection. IEEE Trans Pattern Anal Mach Intell. 2015;38:1327–41.CrossRef
29.
Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O. 3D U-Net: learning dense volumetric segmentation from sparse annotation. International Conference on Medical Image Computing and Computer-Assisted Intervention: Springer; 2016. p. 424-32.
30.
Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:150203167. 2015.
31.
32.
Milletari F, Navab N, Ahmadi S-A. V-net: Fully convolutional neural networks for volumetric medical image segmentation. 3D Vision (3DV), 2016 Fourth International Conference on: IEEE; 2016. p. 565-71.
33.
Bailey J, Piert M. Performance of 68Ga-PSMA PET/CT for prostate cancer management at initial staging and time of biochemical recurrence. Curr Urol Rep. 2017;18:84.CrossRefPubMed
34.
Hoffmann MA, Miederer M, Wieler HJ, Ruf C, Jakobs FM, Schreckenberger M. Diagnostic performance of 68Gallium-PSMA-11 PET/CT to detect significant prostate cancer and comparison with 18FEC PET/CT. Oncotarget. 2017;8:111073.PubMedPubMedCentral
35.
Isensee F, Kickingereder P, Wick W, Bendszus M, Maier-Hein KH. Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge. International MICCAI Brainlesion Workshop: Springer; 2017. p. 287-97.CrossRef
36.
Arlot S, Celisse A. A survey of cross-validation procedures for model selection. Stat Surv. 2010;4:40–79.CrossRef
37.
Kingma DP, Ba J. Adam: A method for stochastic optimization. arXiv preprint arXiv:14126980. 2014.
38.
39.
40.
Ghafoorian M, Mehrtash A, Kapur T, Karssemeijer N, Marchiori E, Pesteie M, et al. Transfer learning for domain adaptation in mri: Application in brain lesion segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention: Springer; 2017. p. 516-24.
41.
42.
Zamboglou C, Carles M, Fechter T, Kiefer S, Reichel K, Fassbender TF, et al. Radiomic features from PSMA PET for non-invasive intraprostatic tumor discrimination and characterization in patients with intermediate-and high-risk prostate cancer-a comparison study with histology reference. Theranostics. 2019;9:2595.CrossRefPubMedPubMedCentral
43.
Albarqouni S, Baur C, Achilles F, Belagiannis V, Demirci S, Navab N. Aggnet: deep learning from crowds for mitosis detection in breast cancer histology images. IEEE Trans Med Imaging. 2016;35:1313–21.CrossRefPubMed
44.
Afshar-Oromieh A, Sattler LP, Steiger K, Holland-Letz T, da Cunha ML, Mier W, et al. Tracer uptake in mediastinal and paraaortal thoracic lymph nodes as a potential pitfall in image interpretation of PSMA ligand PET/CT. Eur J Nucl Med Mol Imaging. 2018;45:1179–87.CrossRefPubMed
45.
Afshar-Oromieh A, Avtzi E, Giesel FL, Holland-Letz T, Linhart HG, Eder M, et al. The diagnostic value of PET/CT imaging with the 68 Ga-labelled PSMA ligand HBED-CC in the diagnosis of recurrent prostate cancer. Eur J Nucl Med Mol Imaging. 2015;42:197–209.CrossRefPubMed
46.
Eiber M, Maurer T, Souvatzoglou M, Beer AJ, Ruffani A, Haller B, et al. Evaluation of hybrid 68Ga-PSMA ligand PET/CT in 248 patients with biochemical recurrence after radical prostatectomy. J Nucl Med. 2015;56:668–74.CrossRefPubMed
47.
Sahlmann C-O, Meller B, Bouter C, Ritter CO, Ströbel P, Lotz J, et al. Biphasic 68 Ga-PSMA-HBED-CC-PET/CT in patients with recurrent and high-risk prostate carcinoma. Eur J Nucl Med Mol Imaging. 2016;43:898–905.CrossRefPubMed
48.
Herlemann A, Wenter V, Kretschmer A, Thierfelder KM, Bartenstein P, Faber C, et al. 68Ga-PSMA positron emission tomography/computed tomography provides accurate staging of lymph node regions prior to lymph node dissection in patients with prostate cancer. Eur Urol. 2016;70:553–7.CrossRefPubMed
49.
Maurer T, Weirich G, Schottelius M, Weineisen M, Frisch B, Okur A, et al. Prostate-specific membrane antigen–radioguided surgery for metastatic lymph nodes in prostate cancer. Eur Urol. 2015;68:530–4.CrossRefPubMed
50.
Pfister D, Porres D, Heidenreich A, Heidegger I, Knuechel R, Steib F, et al. Detection of recurrent prostate cancer lesions before salvage lymphadenectomy is more accurate with 68 Ga-PSMA-HBED-CC than with 18 F-Fluoroethylcholine PET/CT. Eur J Nucl Med Mol Imaging. 2016;43:1410–7.CrossRefPubMed
51.
Hijazi S, Meller B, Leitsmann C, Strauss A, Meller J, Ritter C, et al. Pelvic lymph node dissection for nodal oligometastatic prostate cancer detected by 68Ga-PSMA-positron emission tomography/computerized tomography. Prostate. 2015;75:1934–40.CrossRefPubMed
52.
Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, et al. The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging. 2014;34:1993–2024.CrossRefPubMedPubMedCentral
53.
Dolz J, Gopinath K, Yuan J, Lombaert H, Desrosiers C, Ayed IB. HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentation. IEEE Trans Med Imaging. 2018;38:1116–26.CrossRefPubMed
54.
Oktay O, Ferrante E, Kamnitsas K, Heinrich M, Bai W, Caballero J, et al. Anatomically constrained neural networks (ACNNs): application to cardiac image enhancement and segmentation. IEEE Trans Med Imaging. 2017;37:384–95.CrossRefPubMed
55.
Hussein S, Kandel P, Bolan CW, Wallace MB, Bagci U. Lung and pancreatic tumor characterization in the deep learning era: novel supervised and unsupervised learning approaches. IEEE Trans Med Imaging 2019.
56.
Leclerc S, Smistad E, Pedrosa J, Østvik A, Cervenansky F, Espinosa F, et al. Deep learning for segmentation using an open large-scale dataset in 2d echocardiography. IEEE Trans Med Imaging 2019.
57.
Gibson E, Giganti F, Hu Y, Bonmati E, Bandula S, Gurusamy K, et al. Automatic multi-organ segmentation on abdominal CT with dense v-networks. IEEE Trans Med Imaging. 2018;37:1822–34.CrossRefPubMedPubMedCentral
58.
Wang L, Nie D, Li G, Puybareau É, Dolz J, Zhang Q, et al. Benchmark on automatic 6-month-old infant brain segmentation algorithms: the iSeg-2017 challenge. IEEE Trans Med Imaging 2019.
Metadata
Title
Deep neural network for automatic characterization of lesions on 68Ga-PSMA-11 PET/CT
Authors
Yu Zhao
Andrei Gafita
Bernd Vollnberg
Giles Tetteh
Fabian Haupt
Ali Afshar-Oromieh
Bjoern Menze
Matthias Eiber
Axel Rominger
Kuangyu Shi
Publication date
01-03-2020
Publisher
Springer Berlin Heidelberg
Published in
European Journal of Nuclear Medicine and Molecular Imaging / Issue 3/2020
Print ISSN: 1619-7070
Electronic ISSN: 1619-7089
DOI
https://doi.org/10.1007/s00259-019-04606-y

Other articles of this Issue 3/2020

European Journal of Nuclear Medicine and Molecular Imaging 3/2020 Go to the issue

Original Article

Assessment of the impact of 2015 American Thyroid Association guidelines in management of differentiated thyroid cancer patients

Correction

Correction to: The 68Ga/177Lu-theragnostic concept in PSMA-targeting of metastatic castration–resistant prostate cancer: impact of post-therapeutic whole-body scintigraphy in the follow-up

Original Article

Impact of 68Ga-PSMA-11 PET staging on clinical decision-making in patients with intermediate or high-risk prostate cancer

Letter to the Editor

Letter to the Editor: 18F-NaF not cited in the EANM practice guideline for PET/CT imaging in medullary thyroid carcinoma

Image of the Month

Isolated involvement of thyroid gland by IgG4-related disease revealed by 18F-FDG PET/CT

Original Article

The role of additional late PSMA-ligand PET/CT in the differentiation between lymph node metastases and ganglia