Cancer Today, https://gco.iarc.fr/today/en, last accessed 2024/02/22.

Ilic, D., Djulbegovic, M., Jung, J.H., Hwang, E.C., Zhou, Q., Cleves, A., Agoritsas, T., Dahm, P.: Prostate cancer screening with prostate-specific antigen (PSA) test: a systematic review and meta-analysis. BMJ. 362, (2018). https://doi.org/10.1136/BMJ.K3519.

Ahmed, H.U., El-Shater Bosaily, A., Brown, L.C., Gabe, R., Kaplan, R., Parmar, M.K., Collaco-Moraes, Y., Ward, K., Hindley, R.G., Freeman, A., Kirkham, A.P., Oldroyd, R., Parker, C., Emberton, M.: Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study. The Lancet. 389, 815–822 (2017). https://doi.org/10.1016/S0140-6736(16)32401-1.

Article  Google Scholar 

Mottet, N., van den Bergh, R.C.N., Briers, E., Van den Broeck, T., Cumberbatch, M.G., De Santis, M., Fanti, S., Fossati, N., Gandaglia, G., Gillessen, S., Grivas, N., Grummet, J., Henry, A.M., van der Kwast, T.H., Lam, T.B., Lardas, M., Liew, M., Mason, M.D., Moris, L., Oprea-Lager, D.E., van der Poel, H.G., Rouvière, O., Schoots, I.G., Tilki, D., Wiegel, T., Willemse, P.P.M., Cornford, P.: EAU-EANM-ESTRO-ESUR-SIOG Guidelines on Prostate Cancer—2020 Update. Part 1: Screening, Diagnosis, and Local Treatment with Curative Intent. Eur Urol. 79, 243–262 (2021). https://doi.org/10.1016/J.EURURO.2020.09.042.

Parker, C., Castro, E., Fizazi, K., Heidenreich, A., Ost, P., Procopio, G.: Prostate cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up† (2020). https://doi.org/10.1016/j.annonc.2020.06.011.

Ferraro, D.A., Becker, A.S., Kranzbühler, B., Mebert, I., Baltensperger, A., Zeimpekis, K.G., Grünig, H., Messerli, M., Rupp, N.J., Rueschoff, J.H., Mortezavi, A., Donati, O.F., Sapienza, M.T., Eberli, D., Burger, I.A.: Diagnostic performance of 68Ga-PSMA-11 PET/MRI-guided biopsy in patients with suspected prostate cancer: a prospective single-center study. Eur J Nucl Med Mol Imaging. 48, 3315–3324 (2021). https://doi.org/10.1007/S00259-021-05261-Y.

Article  PubMed  PubMed Central  Google Scholar 

Donato, P., Morton, A., Yaxley, J., Ranasinghe, S., Teloken, P.E., Kyle, S., Coughlin, G., Esler, R., Dunglison, N., Gardiner, R.A., Roberts, M.J.: 68Ga-PSMA PET/CT better characterises localised prostate cancer after MRI and transperineal prostate biopsy: Is 68Ga-PSMA PET/CT guided biopsy the future? Eur J Nucl Med Mol Imaging. 47, 1843–1851 (2020). https://doi.org/10.1007/S00259-019-04620-0.

Article  PubMed  Google Scholar 

Ross, J.S., Sheehan, C.E., Fisher, H.A.G., Kaufman, R.P., Kaur, P., Gray, K., Webb, I., Gray, G.S., Mosher, R., Kallakury, B.V.S.: Correlation of Primary Tumor Prostate-Specific Membrane Antigen Expression with Disease Recurrence in Prostate Cancer.

Papp, L., Spielvogel, C.P., Grubmüller, B., Grahovac, M., Krajnc, D., Ecsedi, B., Sareshgi, R.A.M., Mohamad, D., Hamboeck, M., Rausch, I., Mitterhauser, M., Wadsak, W., Haug, A.R., Kenner, L., Mazal, P., Susani, M., Hartenbach, S., Baltzer, P., Helbich, T.H., Kramer, G., Shariat, S.F., Beyer, T., Hartenbach, M., Hacker, M.: Supervised machine learning enables non-invasive lesion characterization in primary prostate cancer with [68Ga]Ga-PSMA-11 PET/MRI. Eur J Nucl Med Mol Imaging. 48, 1795–1805 (2021). https://doi.org/10.1007/S00259-020-05140-Y.

Article  CAS  PubMed  Google Scholar 

Solari, E.L., Gafita, A., Schachoff, S., Bogdanović, B., Villagrán Asiares, A., Amiel, T., Hui, W., Rauscher, I., Visvikis, D., Maurer, T., Schwamborn, K., Mustafa, M., Weber, W., Navab, N., Eiber, M., Hatt, M., Nekolla, S.G.: The added value of PSMA PET/MR radiomics for prostate cancer staging. Eur J Nucl Med Mol Imaging. 49, 527–538 (2022). https://doi.org/10.1007/S00259-021-05430-Z.

Article  PubMed  Google Scholar 

Zang, S., Ai, S., Yang, R., Zhang, P., Wu, W., Zhao, Z., Ni, Y., Zhang, Q., Sun, H., Guo, H., Jia, R., Wang, F.: Development and validation of 68Ga-PSMA-11 PET/CT-based radiomics model to detect primary prostate cancer. EJNMMI Res. 12, 63 (2022). https://doi.org/10.1186/s13550-022-00936-5.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Lv, J., Yu, H., Yin, H., Shi, Y., Shi, H.: A single-center, multi-factor, retrospective study to improve the diagnostic accuracy of primary prostate cancer using [68Ga]Ga-PSMA-11 total-body PET/CT imaging. Eur J Nucl Med Mol Imaging. 51, 919–927 (2023). https://doi.org/10.1007/S00259-023-06464-1.

Article  PubMed  Google Scholar 

Luining, W.I., Oprea-Lager, D.E., Vis, A.N., van Moorselaar, R.J.A., Knol, R.J.J., Wondergem, M., Boellaard, R., Cysouw, M.C.F.: Optimization and validation of 18F-DCFPyL PET radiomics-based machine learning models in intermediate- to high-risk primary prostate cancer. PLoS One. 18, e0293672 (2023). https://doi.org/10.1371/JOURNAL.PONE.0293672.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Cysouw, M.C.F., Jansen, B.H.E., van de Brug, T., Oprea-Lager, D.E., Pfaehler, E., de Vries, B.M., van Moorselaar, R.J.A., Hoekstra, O.S., Vis, A.N., Boellaard, R.: Machine learning-based analysis of [18F]DCFPyL PET radiomics for risk stratification in primary prostate cancer. Eur J Nucl Med Mol Imaging. 48, 340–349 (2021). https://doi.org/10.1007/S00259-020-04971-Z.

Article  CAS  PubMed  Google Scholar 

Wang, Z., Li, Y., Zheng, A., Gao, J., Yuan, W., Shen, C., Bai, L., Duan, X.: Evaluation of a radiomics nomogram derived from Fluoride-18 PSMA-1007 PET/CT for risk stratification in newly diagnosed prostate cancer. Front Oncol. 12, 1018833 (2022). https://doi.org/10.3389/fonc.2022.1018833.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Yao, F., Bian, S., Zhu, D., Yuan, Y., Pan, K., Pan, Z., Feng, X., Tang, K., Yang, Y.: Machine learning-based radiomics for multiple primary prostate cancer biological characteristics prediction with 18F-PSMA-1007 PET: comparison among different volume segmentation thresholds. Radiologia Medica. 127, 1170–1178 (2022). https://doi.org/10.1007/S11547-022-01541-1.

Article  PubMed  Google Scholar 

Roberts, M.J., Morton, A., Donato, P., Kyle, S., Pattison, D.A., Thomas, P., Coughlin, G., Esler, R., Dunglison, N., Gardiner, R.A., Doi, S.A., Emmett, L., Yaxley, J.: 68Ga-PSMA PET/CT tumour intensity pre-operatively predicts adverse pathological outcomes and progression-free survival in localised prostate cancer. Eur J Nucl Med Mol Imaging. 48, 477–482 (2021). https://doi.org/10.1007/S00259-020-04944-2.

Article  CAS  PubMed  Google Scholar 

Perera, M., Papa, N., Roberts, M., Williams, M., Udovicich, C., Vela, I., Christidis, D., Bolton, D., Hofman, M.S., Lawrentschuk, N., Murphy, D.G.: Gallium-68 Prostate-specific Membrane Antigen Positron Emission Tomography in Advanced Prostate Cancer—Updated Diagnostic Utility, Sensitivity, Specificity, and Distribution of Prostate-specific Membrane Antigen-avid Lesions: A Systematic Review and Meta-analysis. Eur Urol. 77, 403–417 (2020). https://doi.org/10.1016/J.EURURO.2019.01.049.

Article  PubMed  Google Scholar 

Benfante, V., Stefano, A., Ali, M., Laudicella, R., Arancio, W., Cucchiara, A., Caruso, F., Cammarata, F.P., Coronnello, C., Russo, G., Miele, M., Vieni, A., Tuttolomondo, A., Yezzi, A., Comelli, A.: An Overview of In Vitro Assays of 64Cu-, 68Ga-, 125I-, and 99mTc-Labelled Radiopharmaceuticals Using Radiometric Counters in the Era of Radiotheranostics. Diagnostics. 13, 1210 (2023). https://doi.org/10.3390/diagnostics13071210.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Giesel, F.L., Hadaschik, B., Cardinale, J., Radtke, J., Vinsensia, M., Lehnert, W., Kesch, C., Tolstov, Y., Singer, S., Grabe, N., Duensing, S., Schäfer, M., Neels, O.C., Mier, W., Haberkorn, U., Kopka, K., Kratochwil, C.: F-18 labelled PSMA-1007: biodistribution, radiation dosimetry and histopathological validation of tumor lesions in prostate cancer patients. Eur J Nucl Med Mol Imaging. 44, 678–688 (2017). https://doi.org/10.1007/s00259-016-3573-4.

Article  CAS  PubMed  Google Scholar 

Fanti, S., Minozzi, S., Morigi, J.J., Giesel, F., Ceci, F., Uprimny, C., Hofman, M.S., Eiber, M., Schwarzenbock, S., Castellucci, P., Bellisario, C., Chauvie, S., Bergesio, F., Emmett, L., Haberkorn, U., Virgolini, I., Schwaiger, M., Hicks, R.J., Krause, B.J., Chiti, A.: Development of standardized image interpretation for 68Ga-PSMA PET/CT to detect prostate cancer recurrent lesions. Eur J Nucl Med Mol Imaging. 44, 1622–1635 (2017). https://doi.org/10.1007/S00259-017-3725-1.

Article  CAS  PubMed  Google Scholar 

Kendrick, J., Francis, R.J., Hassan, G.M., Rowshanfarzad, P., Ong, J.S.L., Jeraj, R., Barry, N., Hagan, T., Ebert, M.A.: Prospective inter-and intra-tracer repeatability analysis of radiomics features in [68Ga]Ga-PSMA-11 and [18F]F-PSMA-1007 PET scans in metastatic prostate cancer. British Journal of Radiology. 96, (2023). https://doi.org/10.1259/bjr.20221178.

Alongi, P., Laudicella, R., Lanzafame, H., Farolfi, A., Mapelli, P., Picchio, M., Burger, I.A., Iagaru, A., Minutoli, F., Evangelista, L.: PSMA and Choline PET for the Assessment of Response to Therapy and Survival Outcomes in Prostate Cancer Patients: A Systematic Review from the Literature. Cancers (Basel). 14, (2022). https://doi.org/10.3390/CANCERS14071770.

Pasini, G., Russo, G., Mantarro, C., Bini, F., Richiusa, S., Morgante, L., Comelli, A., Russo, G.I., Sabini, M.G., Cosentino, S., Ippolito, M., Stefano, A.: A Critical Analysis of the Robustness of Radiomics to Variations in Segmentation Methods in 18F-PSMA-1007 PET Images of Patients Affected by Prostate Cancer. Diagnostics. 13, (2023). https://doi.org/10.3390/diagnostics13243640.

Stefano, A.: Challenges and limitations in applying radiomics to PET imaging: Possible opportunities and avenues for research. Comput Biol Med. 179, 108827 (2024). https://doi.org/10.1016/j.compbiomed.2024.108827.

Article  PubMed  Google Scholar 

Fendler, W.P., Eiber, M., Beheshti, M., Bomanji, J., Ceci, F., Cho, S., Giesel, F., Haberkorn, U., Hope, T.A., Kopka, K., Krause, B.J., Mottaghy, F.M., Schöder, H., Sunderland, J., Wan, S., Wester, H.J., Fanti, S., Herrmann, K.: 68Ga-PSMA PET/CT: Joint EANM and SNMMI procedure guideline for prostate cancer imaging: version 1.0. Eur J Nucl Med Mol Imaging. 44, 1014–1024 (2017). https://doi.org/10.1007/S00259-017-3670-Z.

Prostate Cancer: Stages and Grades | Cancer.Net, https://www.cancer.net/cancer-types/prostate-cancer/stages-and-grades, last accessed 2024/02/15.

Pasini, G., Bini, F., Russo, G., Comelli, A., Marinozzi, F., Stefano, A.: matRadiomics: A Novel and Complete Radiomics Framework, from Image Visualization to Predictive Model. J Imaging. 8, (2022). https://doi.org/10.3390/jimaging8080221.

van Griethuysen, J.J.M., Fedorov, A., Parmar, C., Hosny, A., Aucoin, N., Narayan, V., Beets-Tan, R.G.H., Fillion-Robin, J.-C., Pieper, S., Aerts, H.J.W.L.: Computational Radiomics System to Decode the Radiographic Phenotype. Cancer Res. 77, e104–e107 (2017). https://doi.org/10.1158/0008-5472.CAN-17-0339.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Haralick, R.M., Dinstein, I., Shanmugam, K.: Textural Features for Image Classification. IEEE Trans Syst Man Cybern. SMC-3, 610–621 (1973). https://doi.org/10.1109/TSMC.1973.4309314.

Galloway, M.M.: Texture analysis using gray level run lengths. Computer Graphics and Image Processing. 4, 172–179 (1975). https://doi.org/10.1016/S0146-664X(75)80008-6.

Article  Google Scholar 

Thibault, G., Angulo, J., Meyer, F.: Advanced statistical matrices for texture characterization: Application to cell classification. IEEE Trans Biomed Eng. 61, 630–637 (2014). https://doi.org/10.1109/TBME.2013.2284600.

Article  PubMed  Google Scholar 

Amadasun, M., King, R.: Texural Features Corresponding to Texural Properties. IEEE Trans Syst Man Cybern. 19, 1264–1274 (1989). https://doi.org/10.1109/21.44046.

Article  Google Scholar 

Sun, C., Wee, W.G.: Neighboring gray level dependence matrix for texture classification. Comput Vis Graph Image Process. 23, 341–352 (1983). https://doi.org/10.1016/0734-189X(83)90032-4.

Article  Google Scholar 

MATLAB, https://uk.mathworks.com/products/matlab.html, last accessed 2024/03/01.

He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. In: 2015 IEEE International Conference on Computer Vision (ICCV). pp. 1026–1034 (2015).