Top

International Journal of Computer Assisted Radiology and Surgery

Published in:

08-02-2024 | Original Article

Detection support of lesions in patients with prostate cancer using \({}_{{}}^{18} {\text{F}}\)-PSMA 1007 PET/CT

Authors: Naoki Tsuchiya, Koichiro Kimura, Ukihide Tateishi, Tadashi Watabe, Koji Hatano, Motohide Uemura, Norio Nonomura, Akinobu Shimizu

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 4/2024

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Purpose

This study proposes a detection support system for primary and metastatic lesions of prostate cancer using \({}_{{}}^{18} {\text{F}}\)-PSMA 1007 positron emission tomography/computed tomography (PET/CT) images with non-image information, including patient metadata and location information of an input slice image.

Methods

A convolutional neural network with condition generators and feature-wise linear modulation (FiLM) layers was employed to allow input of not only PET/CT images but also non-image information, namely, Gleason score, flag of pre- or post-prostatectomy, and normalized z-coordinate of an input slice. We explored the insertion position of the FiLM layers to optimize the conditioning of the network using non-image information.

Results

\({}_{{}}^{18} {\text{F}}\)-PSMA 1007 PET/CT images were collected from 163 patients with prostate cancer and applied to the proposed system in a threefold cross-validation manner to evaluate the performance. The proposed system achieved a Dice score of 0.5732 (per case) and sensitivity of 0.8200 (per lesion), which are 3.87 and 4.16 points higher than the network without non-image information.

Conclusion

This study demonstrated the effectiveness of the use of non-image information, including metadata of the patient and location information of the input slice image, in the detection of prostate cancer from \({}_{{}}^{18} {\text{F}}\)-PSMA 1007 PET/CT images. Improvement in the sensitivity of inactive and small lesions remains a future challenge.

previous article Regulation of AI algorithms for clinical decision support: a personal opinion

next article A semantic fidelity interpretable-assisted decision model for lung nodule classification

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

MaryBeth BC, Isabelle S, Jason AE, Freddie B, Ahmedin J (2020) Recent global patterns in prostate cancer incidence and mortality rates. Eur Urol 77:38–52. https://doi.org/10.1016/j.eururo.2019.08.005CrossRef

Siegel RL, Miller KD, Goding Sauer A, Fedewa SA, Butterly LF, Anderson JC, Cercek A, Smith RA, Jemal A (2020) Colorectal cancer statistics. CA: Cancer J Clin 70(3):145–164. https://doi.org/10.3322/caac.21601CrossRefPubMed

Mason BR, Eastham JA, Davis BJ, Mynderse LA, Pugh TJ, Lee RJ, Ippolito JE (2019) Current status of MRI and PET in the NCCN guidelines for prostate cancer. J Natl Compr Cancer Netw 17(5):506–513. https://doi.org/10.6004/jnccn.2019.7306CrossRef

Schmidkonz C, Cordes M, Goetz TI, Prante O, Kuwert T, Ritt P, Uder M, Wullich B, Goebell P, Bäuerle T (2019) ⁶⁸Ga-PSMA-11 PET/CT derived quantitative volumetric tumor parameters for classification and evaluation of therapeutic response of bone metastases in prostate cancer patients. Ann Nucl Med 33:766–775. https://doi.org/10.1007/s12149-019-01387-0CrossRefPubMed

Seifert R, Herrmann K, Kleesiek J, Schäfers M, Shah V, Xu Z, Chabin G, Grbic S, Spottiswoode B, Rahbar K (2020) Semiautomatically quantified tumor volume using ⁶⁸Ga-PSMA-11 PET as a biomarker for survival in patients with advanced prostate cancer. J Nucl Med 61(12):1786–1792. https://doi.org/10.2967/jnumed.120.242057CrossRefPubMed

Hofman MS, Lawrentschuk N, Francis RJ, Tang C, Vela I, Thomas P, Rutherford N, Rutherford MJM, Frydenberg M, Shakher R, Wong LM, Taubman K, Lee ST, Hsiao E, Roach P, Nottage M, Kirkwood I, Hayne D, Link E, Marusic P, Matera A, Herschtal A, Iravani A, Hicks RJ, Williams S, Murphy DG, Taneja S (2020) Prostate-specific membrane antigen PET-CT in patients with high-risk prostate cancer before curative-intent surgery or radiotherapy (proPSMA): a prospective, randomised, multicentre study. The Lancet 395(10231):1208–1216. https://doi.org/10.1016/S0140-6736(20)30314-7CrossRef

Tateishi U (2020) Prostate-specific membrane antigen (PSMA)–ligand positron emission tomography and radioligand therapy (RLT) of prostate cancer. Jpn J Clin Oncol 50(4):349–356. https://doi.org/10.1093/jjco/hyaa004CrossRefPubMedPubMedCentral

Awenat S, Piccardo A, Carvoeiras P, Signore G, Giovanella L, Prior JO, Treglia G (2021) Diagnostic role of ¹⁸F-PSMA-1007 PET/CT in prostate cancer staging: a systematic review. Diagnostics 11(3):552. https://doi.org/10.3390/diagnostics11030552CrossRefPubMedPubMedCentral

Foley RW, Redman SL, Graham RN, Loughborough WW, Little D (2020) Fluorine-18 labelled prostate-specific membrane antigen (PSMA)-1007 positron-emission tomography-computed tomography: normal patterns, pearls, and pitfalls. Clin Radiol 75(12):903–913. https://doi.org/10.1016/j.crad.2020.06.031CrossRefPubMed

10.

Tateishi U, Kimura K, Tsuchiya J, Kano D, Watabe T, Nonomura N, Saito K, Yokoyama K, Yamagiwa K, Adachi T, Kojima Y, Yoshida S, Fujii Y (2023) Phase I/IIa trial of ¹⁸F-prostate specific membrane antigen (PSMA) 1007 PET/CT in healthy volunteers and prostate cancer patients. Jpn J Clin Oncol. https://doi.org/10.1093/jjco/hyad166

11.

Watabe T, Uemura M, Soeda F, Naka S, Ujike T, Hatano K, Sasaki H, Kamiya T, Shimosegawa E, Kato H, Cardinale J, Tateishi U, Nonomura N, Giesel FL (2021) High detection rate in ¹⁸F-PSMA-1007 PET: Interim results focusing on biochemical recurrence in prostate cancer patients. Ann Nucl Med 35:523–528. https://doi.org/10.1007/s12149-021-01602-xCrossRefPubMedPubMedCentral

12.

Zhao Y, Gafita A, Tetteh G, Haupt F, Afshar-Oromieh A, Menze B, Eiber M, Rominger A, Shi K (2019) Deep neural network for automatic characterization of lesions on ⁶⁸Ga-PSMA-11 PET/CT Images. In: 2019 41st annual international conference of the ieee engineering in medicine and biology society, pp 951–954. https://doi.org/10.1109/EMBC.2019.8857955

13.

Capobianco N, Sibille L, Chantadisai M, Gafita A, Langbein T, Platsch G, Solari EL, Shah V, Spottiswoode B, Eiber M, Weber WA, Navab N, Nekolla SG (2022) Whole-body uptake classification and prostate cancer staging in ⁶⁸Ga-PSMA-11 PET/CT using dual-tracer learning. Eur J Nucl Med Mol Imaging. https://doi.org/10.1007/s00259-021-05473-2

14.

Mei X, Lee HC, Diao KY, Huang M, Lin B, Liu C, Xie Z, Ma Y, Robson PM, Chung M, Bernheim A, Mani V, Calcagno C, Li K, Li S, Shan H, Lv J, Zhao T, Xia J, Long Q, Steinberger S, Jacobi A, Deyer T, Luksza M, Liu F, Little BP, Fayad ZA, Yang Y (2020) Artificial intelligence–enabled rapid diagnosis of patients with COVID-19. Nat Med 26(8):1224–1228. https://doi.org/10.1038/s41591-020-0931-3CrossRefPubMedPubMedCentral

15.

Yap J, Yolland W, Tschandl P (2018) Multimodal skin lesion classification using deep learning. Exp Dermatol 27(11):1261–1267. https://doi.org/10.1111/exd.13777CrossRefPubMed

16.

Perez E, Strub F, De Vries H, Dumoulin V Courville A (2018) Film: visual reasoning with a general conditioning layer. AAAI Confer Artif Intell. https://doi.org/10.1609/aaai.v32i1.11671

17.

Maryam H, Luca C, Éric P (2022) End-to-end brain-driven speech enhancement in multi-talker conditions. IEEE/ACM Trans Audio, Speech, Lang Process 30:1718–1733. https://doi.org/10.1109/TASLP.2022.3169629CrossRef

18.

Mengwei R, Neel D, James F, Guido G (2021) Segmentation-renormalized deep feature modulation for unpaired image harmonization. IEEE Trans Med Imaging 40(6):1519–1530. https://doi.org/10.1109/TMI.2021.3059726CrossRef

19.

Qin ZQ, Pan GJ, Xu Z, Wang H, Xu LW, Jia RP (2022) The performance of ¹⁸F-PSMA PET/CT in the detection of prostate cancer: a systematic review and meta-analysis. Asian J Androl 24(4):373. https://doi.org/10.4103/aja202162CrossRefPubMed

20.

Nioche C, Orlhac F, Boughdad S, Reuzé S, Goya-Outi J, Robert C, Pellot-Barakat C, Soussan M, Frouin F, and Buvat I (2018) LIFEx: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res 78(16):4786–4789. https://www.lifexsoft.org/. Accessed 12 May 2023

21.

Fu X, Bi L, Kumar A, Fulham M, Kim J (2021) Multimodal spatial attention module for targeting multimodal PET-CT lung tumor segmentation. IEEE J Biomed Health Inform 25(9):3507–3516. https://doi.org/10.1109/JBHI.2021.3059453CrossRefPubMed

22.

Thie JA (2009) Understanding the standardized uptake value, its methods, and implications for usage. J Nucl Med 45(9):1431–1434

23.

He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. IEEE Int Confer Comput Vis. https://doi.org/10.1109/ICCV.2015.123

24.

Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980

25.

Wang T, Lei Y, Fu Y, Wynne JF, Curran WJ, Liu T, Yang X (2021) A review on medical imaging synthesis using deep learning and its clinical applications. J Appl Clin Med Phys 22:11–36. https://doi.org/10.1002/acm2.13121CrossRefPubMed

26.

Sluis J, Jong J, Schaar J, Noordzij W, van Snick P, Dierckx R, Borra R, Willemsen A, Boellaard R (2019) Performance characteristics of the digital biograph vision PET/CT system. J Nucl Med 60(7):1031–1036. https://doi.org/10.2967/jnumed.118.215418CrossRefPubMed

Title: Detection support of lesions in patients with prostate cancer using -PSMA 1007 PET/CT
Authors: Naoki Tsuchiya
Koichiro Kimura
Ukihide Tateishi
Tadashi Watabe
Koji Hatano
Motohide Uemura
Norio Nonomura
Akinobu Shimizu
Publication date: 08-02-2024
Publisher: Springer International Publishing
Published in: International Journal of Computer Assisted Radiology and Surgery / Issue 4/2024
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-024-03067-5

Springer Professional

Abstract

Purpose

Methods

Results

Conclusion

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Other articles of this Issue 4/2024

Surgical phase and instrument recognition: how to identify appropriate dataset splits

Automatic image registration on intraoperative CBCT compared to Surface Matching registration on preoperative CT for spinal navigation: accuracy and workflow

Model guided medicine and the search for truth

Path tracking control of a steerable catheter in transcatheter cardiology interventions

Stereo matching of binocular laparoscopic images with improved densely connected neural architecture search

Regulation of AI algorithms for clinical decision support: a personal opinion

Premium Partner