دورية أكاديمية
أعرض في EDS

A novel assessment of whole-mount Gleason grading in prostate cancer to identify candidates for radical prostatectomy: a machine learning-based multiomics study.

التفاصيل البيبلوغرافية
العنوان: A novel assessment of whole-mount Gleason grading in prostate cancer to identify candidates for radical prostatectomy: a machine learning-based multiomics study.
المؤلفون: Ning J; Christian Doppler Laboratory for Applied Metabolomics, 1090 Vienna, Austria.; Clinical Institute of Pathology, Department for Experimental and Laboratory Animal Pathology, Medical University of Vienna, Vienna, Austria.; Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria., Spielvogel CP; Christian Doppler Laboratory for Applied Metabolomics, 1090 Vienna, Austria.; Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria., Haberl D; Christian Doppler Laboratory for Applied Metabolomics, 1090 Vienna, Austria.; Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria., Trachtova K; Christian Doppler Laboratory for Applied Metabolomics, 1090 Vienna, Austria.; Central European Institute of Technology, Masaryk University, Brno 62500, Czech Republic., Stoiber S; Christian Doppler Laboratory for Applied Metabolomics, 1090 Vienna, Austria.; Clinical Institute of Pathology, Department for Experimental and Laboratory Animal Pathology, Medical University of Vienna, Vienna, Austria., Rasul S; Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria., Bystry V; Central European Institute of Technology, Masaryk University, Brno 62500, Czech Republic., Wasinger G; Clinical Institute of Pathology, Department for Experimental and Laboratory Animal Pathology, Medical University of Vienna, Vienna, Austria., Baltzer P; Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, 1090 Vienna, Austria., Gurnhofer E; Clinical Institute of Pathology, Department for Experimental and Laboratory Animal Pathology, Medical University of Vienna, Vienna, Austria., Timelthaler G; Center for Cancer Research, Medical University of Vienna, 1090 Vienna, Austria., Schlederer M; Clinical Institute of Pathology, Department for Experimental and Laboratory Animal Pathology, Medical University of Vienna, Vienna, Austria., Papp L; Center for Medical Physics and Biomedical Engineering, Vienna, Austria., Schachner H; Clinical Institute of Pathology, Department for Experimental and Laboratory Animal Pathology, Medical University of Vienna, Vienna, Austria., Helbich T; Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, 1090 Vienna, Austria., Hartenbach M; Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria., Grubmüller B; Department of Urology, Medical University of Vienna, Vienna, Austria.; Working Group of Diagnostic Imaging in Urology, Austrian Society of Urology, Vienna, Austria., Shariat SF; Department of Urology, Medical University of Vienna, Vienna, Austria.; Karl Landsteiner Institute of Urology and Andrology, Vienna, Austria.; Department of Urology, University of Texas Southwestern, Dallas, Texas.; Division of Medical Oncology, Department of Urology, Weill Medical College of Cornell University, New York, New York.; Department of Urology, Second Faculty of Medicine, Charles University, Prague, Czech Republic.; Institute for Urology and Reproductive Health, I.M. Sechenov First Moscow State Medical University, Moscow, Russia., Hacker M; Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria., Haug A; Christian Doppler Laboratory for Applied Metabolomics, 1090 Vienna, Austria.; Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria., Kenner L; Christian Doppler Laboratory for Applied Metabolomics, 1090 Vienna, Austria.; Clinical Institute of Pathology, Department for Experimental and Laboratory Animal Pathology, Medical University of Vienna, Vienna, Austria.; Unit of Laboratory Animal Pathology, University of Veterinary Medicine Vienna, 1210 Vienna, Austria.; Comprehensive Cancer Center, Medical University Vienna, Vienna, Austria.; Center for Biomarker Research in Medicine (CBmed), Graz, Styria, Austria.
المصدر: Theranostics [Theranostics] 2024 Aug 01; Vol. 14 (12), pp. 4570-4581. Date of Electronic Publication: 2024 Aug 01 (Print Publication: 2024).
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Ivyspring International Publisher Country of Publication: Australia NLM ID: 101552395 Publication Model: eCollection Cited Medium: Internet ISSN: 1838-7640 (Electronic) Linking ISSN: 18387640 NLM ISO Abbreviation: Theranostics Subsets: MEDLINE
أسماء مطبوعة: Original Publication: Wyoming, N.S.W. : Ivyspring International Publisher, 2011-
مواضيع طبية MeSH: Prostatic Neoplasms*/surgery , Prostatic Neoplasms*/pathology , Prostatic Neoplasms*/genetics , Prostatic Neoplasms*/diagnostic imaging , Machine Learning* , Prostatectomy*/methods , Neoplasm Grading*, Humans ; Male ; Aged ; Middle Aged ; Retrospective Studies ; Prospective Studies ; Pilot Projects ; Positron-Emission Tomography/methods ; Magnetic Resonance Imaging/methods ; Genomics/methods ; Multiomics
مستخلص: Purpose : This study aims to assess whole-mount Gleason grading (GG) in prostate cancer (PCa) accurately using a multiomics machine learning (ML) model and to compare its performance with biopsy-proven GG (bxGG) assessment. Materials and Methods : A total of 146 patients with PCa recruited in a pilot study of a prospective clinical trial (NCT02659527) were retrospectively included in the side study, all of whom underwent 68 Ga-PSMA-11 integrated positron emission tomography (PET) / magnetic resonance (MR) before radical prostatectomy (RP) between May 2014 and April 2020. To establish a multiomics ML model, we quantified PET radiomics features, pathway-level genomics features from whole exome sequencing, and pathomics features derived from immunohistochemical staining of 11 biomarkers. Based on the multiomics dataset, five ML models were established and validated using 100-fold Monte Carlo cross-validation. Results : Among five ML models, the random forest (RF) model performed best in terms of the area under the curve (AUC). Compared to bxGG assessment alone, the RF model was superior in terms of AUC (0.87 vs 0.75), specificity (0.72 vs 0.61), positive predictive value (0.79 vs 0.75), and accuracy (0.78 vs 0.77) and showed slightly decreased sensitivity (0.83 vs 0.89) and negative predictive value (0.80 vs 0.81). Among the feature categories, bxGG was identified as the most important feature, followed by pathomics, clinical, radiomics and genomics features. The three important individual features were bxGG, PSA staining and one intensity-related radiomics feature. Conclusion : The findings demonstrate a superior assessment of the developed multiomics-based ML model in whole-mount GG compared to the current clinical baseline of bxGG. This enables personalized patient management by identifying high-risk PCa patients for RP.
Competing Interests: Competing Interests: The authors have declared that no competing interest exists.
(© The author(s).)
References: Clin Cancer Res. 2018 Dec 15;24(24):6300-6307. (PMID: 30139879)
Nat Commun. 2015 Jul 22;6:7736. (PMID: 26198641)
J Transl Med. 2020 Jan 7;18(1):10. (PMID: 31910880)
Future Oncol. 2018 Apr;14(8):709-718. (PMID: 29323532)
J Med Life. 2017 Jan-Mar;10(1):13-18. (PMID: 28255370)
J Urol. 2011 Nov;186(5):2107-14. (PMID: 21944124)
Am J Surg Pathol. 2016 Feb;40(2):244-52. (PMID: 26492179)
Eur Urol. 2016 Mar;69(3):428-35. (PMID: 26166626)
BMC Med Inform Decis Mak. 2016 Jul 25;16 Suppl 3:74. (PMID: 27454392)
Arch Toxicol. 2020 Feb;94(2):371-388. (PMID: 32034435)
Front Oncol. 2022 Jun 23;12:893424. (PMID: 35814412)
Curr Opin Urol. 2020 Nov;30(6):823-831. (PMID: 32881725)
Int J Cancer. 2021 Feb 1;148(3):731-747. (PMID: 33034050)
Am J Clin Pathol. 2017 Mar 01;147(3):273-277. (PMID: 28395050)
Int J Radiat Oncol Biol Phys. 2018 Aug 1;101(5):1179-1187. (PMID: 29908785)
Am J Surg Pathol. 2020 Aug;44(8):e87-e99. (PMID: 32459716)
Am J Clin Pathol. 2021 Apr 26;155(5):711-717. (PMID: 33079976)
Ther Clin Risk Manag. 2023 Jan 15;19:43-56. (PMID: 36686217)
Nat Commun. 2022 May 20;13(1):2835. (PMID: 35595767)
Nature. 2021 Nov;599(7883):91-95. (PMID: 34707284)
CA Cancer J Clin. 2022 Jan;72(1):7-33. (PMID: 35020204)
Front Oncol. 2022 Apr 22;12:879089. (PMID: 35530334)
Oncotarget. 2019 Sep 10;10(52):5439-5453. (PMID: 31534629)
Radiology. 2019 Apr;291(1):53-59. (PMID: 30694160)
J Nucl Med. 2017 Dec;58(12):1962-1968. (PMID: 28522740)
J Nucl Med. 2022 Oct;63(10):1611-1616. (PMID: 35210300)
Nat Rev Urol. 2020 Mar;17(3):177-188. (PMID: 32086498)
J Med Internet Res. 2021 Oct 15;23(10):e29301. (PMID: 34652275)
Radiology. 2020 May;295(2):328-338. (PMID: 32154773)
J Clin Pathol. 1995 Sep;48(9):876-8. (PMID: 7490328)
Phys Med Biol. 2016 Jul 7;61(13):R150-66. (PMID: 27269645)
PLoS Comput Biol. 2016 Apr 21;12(4):e1004873. (PMID: 27100738)
Br J Cancer. 2014 Feb 4;110(3):565-72. (PMID: 24300977)
Cancer Res. 2017 Nov 1;77(21):e104-e107. (PMID: 29092951)
Adv Nutr. 2016 Mar 15;7(2):418-9. (PMID: 26980827)
Am J Cancer Res. 2022 Mar 15;12(3):1323-1336. (PMID: 35411226)
Tunis Med. 2018 Jul;96(7):430-436. (PMID: 30430487)
Rev Col Bras Cir. 2022 Jul 01;49:e20223200. (PMID: 35792806)
N Engl J Med. 2018 Dec 13;379(24):2319-2329. (PMID: 30575473)
Cancers (Basel). 2022 Jan 25;14(3):. (PMID: 35158864)
Nucleic Acids Res. 2019 Jan 8;47(D1):D886-D894. (PMID: 30371827)
Eur Urol. 2021 Feb;79(2):243-262. (PMID: 33172724)
Clin Cancer Res. 2022 Aug 15;28(16):3509-3525. (PMID: 35695870)
Eur Urol Open Sci. 2020 Jul;20:37-47. (PMID: 33000006)
Eur J Nucl Med Mol Imaging. 2021 Jun;48(6):1795-1805. (PMID: 33341915)
Turk Patoloji Derg. 2019;35(3):185-192. (PMID: 30859516)
Nat Commun. 2020 Aug 6;11(1):3905. (PMID: 32764609)
Methods Mol Biol. 2020;2120:47-70. (PMID: 32124311)
Appl Immunohistochem Mol Morphol. 2020 Apr;28(4):325-330. (PMID: 31033496)
J Med Internet Res. 2020 Nov 19;22(11):e22421. (PMID: 33211015)
Cancers (Basel). 2018 Jul 28;10(8):. (PMID: 30060575)
Int J Mol Sci. 2021 Sep 15;22(18):. (PMID: 34576134)
Curr Opin Urol. 2017 Sep;27(5):495-499. (PMID: 28661898)
Clin Epidemiol. 2022 Jan 18;14:59-70. (PMID: 35082531)
IEEE Trans Pattern Anal Mach Intell. 2005 Aug;27(8):1226-38. (PMID: 16119262)
Br J Cancer. 2022 Dec;127(12):2186-2197. (PMID: 36243890)
Curr Protoc Hum Genet. 2013 Jan;Chapter 7:Unit7.20. (PMID: 23315928)
Urol J. 2020 Aug 04;18(4):395-399. (PMID: 32798231)
Korean J Radiol. 2020 Sep;21(9):1087-1094. (PMID: 32691544)
Eur Urol. 2020 Sep;78(3):327-332. (PMID: 32461072)
Radiol Med. 2019 Jun;124(6):555-567. (PMID: 30607868)
Phys Med Biol. 2020 Dec 17;65(24):24TR02. (PMID: 32688357)
Bioinformatics. 2010 May 15;26(10):1340-7. (PMID: 20385727)
فهرسة مساهمة: Keywords: Gleason grading; PSMA; machine learning; multiomics; prostate cancer
تواريخ الأحداث: Date Created: 20240906 Date Completed: 20240906 Latest Revision: 20240910
رمز التحديث: 20240910
مُعرف محوري في PubMed: PMC11373617
DOI: 10.7150/thno.96921
PMID: 39239512
قاعدة البيانات: MEDLINE
الوصف
تدمد:1838-7640
DOI:10.7150/thno.96921