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Machine learning-based analysis of 68Ga-PSMA-11 PET/CT images for estimation of prostate tumor grade

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Abstract

Early diagnosis of prostate cancer, the most common malignancy in men, can improve patient outcomes. Since the tissue sampling procedures are invasive and sometimes inconclusive, an alternative image-based method can prevent possible complications and facilitate treatment management. We aim to propose a machine-learning model for tumor grade estimation based on 68 Ga-PSMA-11 PET/CT images in prostate cancer patients. This study included 90 eligible participants out of 244 biopsy-proven prostate cancer patients who underwent staging 68Ga-PSMA-11 PET/CT imaging. The patients were divided into high and low-intermediate groups based on their Gleason scores. The PET-only images were manually segmented, both lesion-based and whole prostate, by two experienced nuclear medicine physicians. Four feature selection algorithms and five classifiers were applied to Combat-harmonized and non-harmonized datasets. To evaluate the model's generalizability across different institutions, we performed leave-one-center-out cross-validation (LOOCV). The metrics derived from the receiver operating characteristic curve were used to assess model performance. In the whole prostate segmentation, combining the ANOVA algorithm as the feature selector with Random Forest (RF) and Extra Trees (ET) classifiers resulted in the highest performance among the models, with an AUC of 0.78 and 083, respectively. In the lesion-based segmentation, the highest AUC was achieved by MRMR feature selector + Linear Discriminant Analysis (LDA) and Logistic Regression (LR) classifiers (0.76 and 0.79, respectively). The LOOCV results revealed that both the RF_ANOVA and ET_ANOVA models showed high levels of accuracy and generalizability across different centers, with an average AUC value of 0.87 for the ET_ANOVA combination. Machine learning-based analysis of radiomics features extracted from 68Ga-PSMA-11 PET/CT scans can accurately classify prostate tumors into low-risk and intermediate- to high-risk groups.

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Abbreviations

ACC:

Accuracy

ANOVA :

Analysis of Variance

AUC:

Area Under the Curve

CAD:

Computer-Aided Diagnosis

ET:

Extra Trees

FWHM:

Full Width at Half Maximum

GLCM:

Gray-Level Co-Occurrence Matrix

GLRLM:

Gray-Level Run Length Matrix

GLZLM:

Gray-Level Zone-Length Matrix

GS:

Gleason Score

IBSI:

Image Biomarker Standardization Initiative

KNN:

K-Nearest Neighbors

KW:

Kruskal–Wallis test

LDA:

Linear Discriminant Analysis

LP:

Lesion of Prostate

LR:

Logistic Regression

ML :

Machine Learning

MRMR:

Maximum Relevance Minimum Redundancy

mpMRI:

Multiparametric Magnetic Resonance Imaging

NGLDM:

Neighboring Gray-Level Dependence Matrix

PCa:

Prostate Cancer

PET/CT:

Positron Emission Tomography/Computed Tomography

PR:

Precision

PSA:

Prostate-Specific Antigen

PSMA:

Prostate-Specific Membrane Antigen

RF:

Random Forest

ROC:

Receiver Operator Characteristic

SUV:

Standardized Uptake Values

TLG:

Total Lesion Glycolysis

VOI:

Volume of Interest

WP:

Whole Prostate

References

  1. Plym A, Zhang Y, Stopsack KH, Delcoigne B, Wiklund F, Haiman C et al (2022) A healthy lifestyle in men at increased genetic risk for prostate cancer. Eur Urol 83:343

    Article  PubMed  PubMed Central  Google Scholar 

  2. Chung LW, Isaacs WB, Simons JW (2007) Prostate cancer: biology, genetics, and the new therapeutics. Springer Science & Business Media, Totowa

    Book  Google Scholar 

  3. Salam M. Principles and practice of Urology: JP Medical Ltd; 2013.

  4. Eder M, Schäfer M, Bauder-Wüst U, Hull W-E, Wängler C, Mier W et al (2012) 68Ga-complex lipophilicity and the targeting property of a urea-based PSMA inhibitor for PET imaging. Bioconjug Chem 23(4):688–697

    Article  CAS  PubMed  Google Scholar 

  5. Beheshti M, Langsteger W, Rezaee A. PET/CT in cancer: an interdisciplinary approach to individualized imaging: Elsevier Health Sciences; 2017.

  6. Emmett L, Willowson K, Violet J, Shin J, Blanksby A, Lee J (2017) Lutetium 177 PSMA radionuclide therapy for men with prostate cancer: a review of the current literature and discussion of practical aspects of therapy. Journal of medical radiation sciences 64(1):52–60

    Article  PubMed  PubMed Central  Google Scholar 

  7. Donato P, Morton A, Yaxley J, Ranasinghe S, Teloken PE, Kyle S et al (2020) 68 Ga-PSMA PET/CT better characterises localised prostate cancer after MRI and transperineal prostate biopsy: Is 68 Ga-PSMA PET/CT guided biopsy the future? Eur J Nucl Med Mol Imaging 47:1843–1851

    Article  PubMed  Google Scholar 

  8. Schmuck S, Nordlohne S, von Klot C-A, Henkenberens C, Sohns JM, Christiansen H et al (2017) Comparison of standard and delayed imaging to improve the detection rate of [68 Ga] PSMA I&T PET/CT in patients with biochemical recurrence or prostate-specific antigen persistence after primary therapy for prostate cancer. Eur J Nucl Med Mol Imaging 44:960–968

    Article  CAS  PubMed  Google Scholar 

  9. Hatt M, Krizsan A, Rahmim A, Bradshaw T, Costa P, Forgacs A et al (2023) Joint EANM/SNMMI guideline on radiomics in nuclear medicine: Jointly supported by the EANM Physics Committee and the SNMMI Physics, Instrumentation and Data Sciences Council. Eur J Nucl Med Mol Imaging 50(2):352–375

    Article  CAS  PubMed  Google Scholar 

  10. Hajianfar G, Haddadi Avval A, Hosseini SA, Nazari M, Oveisi M, Shiri I, Zaidi H (2023) Time-to-event overall survival prediction in glioblastoma multiforme patients using magnetic resonance imaging radiomics. Radiol Med (Torino) 128:1521

    Article  PubMed  Google Scholar 

  11. Benoit-Cattin H. Texture analysis for magnetic resonance imaging: Texture Analysis Magn Resona; 2006.

  12. Fusco R, Granata V, Grazzini G, Pradella S, Borgheresi A, Bruno A et al (2022) Radiomics in medical imaging: pitfalls and challenges in clinical management. Jpn J Radiol 40(9):919–929

    Article  PubMed  Google Scholar 

  13. Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, Van Stiphout RG, Granton P et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48(4):441–446

    Article  PubMed  PubMed Central  Google Scholar 

  14. Hajianfar G, Kalayinia S, Hosseinzadeh M, Samanian S, Maleki M, Sossi V et al (2023) Prediction of Parkinson’s disease pathogenic variants using hybrid Machine learning systems and radiomic features. Physica Med 113:102647

    Article  Google Scholar 

  15. Alongi P, Stefano A, Comelli A, Laudicella R, Scalisi S, Arnone G et al (2021) Radiomics analysis of 18F-Choline PET/CT in the prediction of disease outcome in high-risk prostate cancer: An explorative study on machine learning feature classification in 94 patients. Eur Radiol 31:4595–4605

    Article  CAS  PubMed  Google Scholar 

  16. Nai Y-H, Cheong DLH, Roy S, Kok T, Stephenson MC, Schaefferkoetter J et al (2023) Comparison of quantitative parameters and radiomic features as inputs into machine learning models to predict the Gleason score of prostate cancer lesions. Magn Reson Imaging 100:64–72

    Article  CAS  PubMed  Google Scholar 

  17. Zamboglou C, Carles M, Fechter T, Kiefer S, Reichel K, Fassbender TF et al (2019) 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 9(9):2595

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Zwanenburg A, Vallières M, Abdalah MA, Aerts HJ, Andrearczyk V, Apte A et al (2020) The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 295(2):328–338

    Article  PubMed  Google Scholar 

  19. Nioche C, Orlhac F, Boughdad S, Reuzé S, Goya-Outi J, Robert C et al (2018) LIFEx: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Can Res 78(16):4786–4789

    Article  CAS  Google Scholar 

  20. Shiri I, Amini M, Nazari M, Hajianfar G, Avval AH, Abdollahi H et al (2022) Impact of feature harmonization on radiogenomics analysis: Prediction of EGFR and KRAS mutations from non-small cell lung cancer PET/CT images. Comput Biol Med 142:105230

    Article  CAS  PubMed  Google Scholar 

  21. Hajianfar G, Avval AH, Sabouri M, Khateri M, Jenabi E, Geramifar P, et al., editors. ComBat Harmonization of Image Reconstruction Parameters to Improve the Repeatability of Radiomics Features. 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC); 2021: IEEE.

  22. Johnson WE, Li C, Rabinovic A (2007) Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8(1):118–127

    Article  PubMed  Google Scholar 

  23. Fortin J-P, Parker D, Tunç B, Watanabe T, Elliott MA, Ruparel K et al (2017) Harmonization of multi-site diffusion tensor imaging data. Neuroimage 161:149–170

    Article  PubMed  Google Scholar 

  24. Fortin J-P, Cullen N, Sheline YI, Taylor WD, Aselcioglu I, Cook PA et al (2018) Harmonization of cortical thickness measurements across scanners and sites. Neuroimage 167:104–120

    Article  PubMed  Google Scholar 

  25. Varghese B, Chen F, Hwang D, Palmer SL, De Castro Abreu AL, Ukimura O, et al., editors. Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images. Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics; 2020.

  26. Du D, Shiri I, Yousefirizi F, Salmanpour MR, Lv J, Wu H et al (2023) Impact of harmonization and oversampling methods on radiomics analysis of multi-center imbalanced datasets: Application to PET-based prediction of lung cancer subtypes. 71:209

    Google Scholar 

  27. DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837–845

    Article  CAS  PubMed  Google Scholar 

  28. Fendler WP, Eiber M, Beheshti M, Bomanji J, Calais J, Ceci F et al (2023) PSMA PET/CT: joint EANM procedure guideline/SNMMI procedure standard for prostate cancer imaging 2.0. Eur J Nucl Med Mol Imaging 50(5):1466–1486

    Article  PubMed  PubMed Central  Google Scholar 

  29. Dehm SM, Tindall DJ (2020) Prostate Cancer: Cellular and Genetic Mechanisms of Disease Development and Progression. Springer Nature, Cham

    Google Scholar 

  30. Yu AC, Eng J (2020) One algorithm may not fit all: how selection bias affects machine learning performance. Radiographics 40(7):1932–1937

    Article  PubMed  Google Scholar 

  31. Shiri I, Salimi Y, Pakbin M, Hajianfar G, Avval AH, Sanaat A et al (2022) COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients. Comput Biol Med 145:105467

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Orlhac F, Eertink JJ, Cottereau A-S, Zijlstra JM, Thieblemont C, Meignan M et al (2022) A guide to ComBat harmonization of imaging biomarkers in multicenter studies. J Nucl Med 63(2):172–179

    Article  PubMed  PubMed Central  Google Scholar 

  33. Nieri A, Manco L, Bauckneht M, Urso L, Caracciolo M, Donegani MI et al (2023) [18F] FDG PET-TC radiomics and machine learning in the evaluation of prostate incidental uptake. Expert Rev Med Devices 20(12):1183–1191

    Article  CAS  PubMed  Google Scholar 

  34. Yaxley JW, Raveenthiran S, Nouhaud F-X, Samartunga H, Yaxley AJ, Coughlin G et al (2019) Outcomes of primary lymph node staging of intermediate and high risk prostate cancer with 68Ga-PSMA positron emission tomography/computerized tomography compared to histological correlation of pelvic lymph node pathology. J Urol 201(4):815–820

    Article  PubMed  Google Scholar 

  35. Leung KH, Rowe SP, Leal JP, Ashrafinia S, Sadaghiani MS, Chung HW et al (2022) Deep learning and radiomics framework for PSMA-RADS classification of prostate cancer on PSMA PET. EJNMMI Res 12(1):1–15

    Article  Google Scholar 

  36. Japkowicz N, Stephen S (2002) The class imbalance problem: A systematic study. Intelligent data analysis 6(5):429–449

    Article  Google Scholar 

  37. Papp L, Spielvogel C, Grubmüller B, Grahovac M, Krajnc D, Ecsedi B et al (2021) Supervised machine learning enables non-invasive lesion characterization in primary prostate cancer with [68 Ga] Ga-PSMA-11 PET/MRI. Eur J Nucl Med Mol Imaging 48:1795–1805

    Article  CAS  PubMed  Google Scholar 

  38. Cysouw MC, Jansen BH, van de Brug T, Oprea-Lager DE, Pfaehler E, de Vries BM et al (2021) Machine learning-based analysis of [18 F] DCFPyL PET radiomics for risk stratification in primary prostate cancer. Eur J Nucl Med Mol Imaging 48:340–349

    Article  CAS  PubMed  Google Scholar 

  39. Mofrad FB, Zoroofi RA, Tehrani-Fard AA, Akhlaghpoor S, Sato Y (2014) Classification of normal and diseased liver shapes based on spherical harmonics coefficients. J Med Syst 38:1–9

    Article  Google Scholar 

  40. Davnall F, Yip CS, Ljungqvist G, Selmi M, Ng F, Sanghera B et al (2012) Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights Imaging 3:573–589

    Article  PubMed  PubMed Central  Google Scholar 

  41. Chen X, Oshima K, Schott D, Wu H, Hall W, Song Y et al (2017) Assessment of treatment response during chemoradiation therapy for pancreatic cancer based on quantitative radiomic analysis of daily CTs: An exploratory study. PLoS ONE 12(6):e0178961

    Article  PubMed  PubMed Central  Google Scholar 

  42. Qiu Q, Duan J, Yin Y (2020) Radiomics in radiotherapy: applications and future challenges. Precision Radiation Oncol 4(1):29–33

    Article  Google Scholar 

  43. Dong X, Sun X, Sun L, Maxim PG, Xing L, Huang Y et al (2016) Early change in metabolic tumor heterogeneity during chemoradiotherapy and its prognostic value for patients with locally advanced non-small cell lung cancer. PLoS ONE 11(6):e0157836

    Article  PubMed  PubMed Central  Google Scholar 

  44. Solari EL, Gafita A, Schachoff S, Bogdanović B, Villagrán Asiares A, Amiel T et al (2022) The added value of PSMA PET/MR radiomics for prostate cancer staging. Eur J Nucl Med Mol Imaging 49(2):527–538

    Article  PubMed  Google Scholar 

  45. Krajnc D, Spielvogel CP, Grahovac M, Ecsedi B, Rasul S, Poetsch N et al (2022) Automated data preparation for in vivo tumor characterization with machine learning. Front Oncol 12:1017911

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Hambarde P, Talbar S, Mahajan A, Chavan S, Thakur M, Sable N (2020) Prostate lesion segmentation in MR images using radiomics based deeply supervised U-Net. Biocybernetics Biomed Eng 40(4):1421–1435

    Article  Google Scholar 

  47. Gong L, Xu M, Fang M, He B, Li H, Fang X et al (2022) The potential of prostate gland radiomic features in identifying the Gleason score. Comput Biol Med 144:105318

    Article  CAS  PubMed  Google Scholar 

  48. Peng Y, Shen D, Liao S, Turkbey B, Rais-Bahrami S, Wood B et al (2015) MRI-based prostate volume-adjusted prostate-specific antigen in the diagnosis of prostate cancer. J Magn Reson Imaging 42(6):1733–1739

    Article  PubMed  PubMed Central  Google Scholar 

  49. Karademir I, Shen D, Peng Y, Liao S, Jiang Y, Yousuf A et al (2013) Prostate volumes derived from MRI and volume-adjusted serum prostate-specific antigen: correlation with Gleason score of prostate cancer. AJR Am J Roentgenol 201(5):1041

    Article  PubMed  PubMed Central  Google Scholar 

  50. Bai W, Fadil Y, Idrissi O, Dakir M, Debbagh A, Abouteib R (2021) The correlation between the gleason score of the biopsy and that of the prostatectomy patch. Annals Med Surg 63:102169

    Article  CAS  Google Scholar 

  51. Shiri I, Amini M, Yousefirizi F, Vafaei Sadr A, Hajianfar G, Salimi Y et al (2023) Information fusion for fully automated segmentation of head and neck tumors from PET and CT images. Med Phys 51:319

    Article  PubMed  Google Scholar 

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Authors and Affiliations

  1. Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Maziar Khateri & Farshid Babapour Mofrad

  2. Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran

    Parham Geramifar & Elnaz Jenabi

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  1. Maziar Khateri
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  2. Farshid Babapour Mofrad
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Correspondence to Farshid Babapour Mofrad.

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The present study has been approved by Research Ethics Committees of Islamic Azad University - Science and Research Branch with the ethics code: IR.IAU.SRB.REC.1402.070.

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Khateri, M., Babapour Mofrad, F., Geramifar, P. et al. Machine learning-based analysis of 68Ga-PSMA-11 PET/CT images for estimation of prostate tumor grade. Phys Eng Sci Med (2024). https://doi.org/10.1007/s13246-024-01402-3

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