Application of support vector machine algorithm for early differential diagnosis of prostate cancer

Adams, 1853, The case of scirrhous of the prostate gland with corresponding affliction of the lymphatic glands in the lumbar region and in the pelvis, Lancet, 1, 393 Akinnuwesi, 2020, Decision support system for diagnosing rheumatic-musculoskeletal disease using fuzzy cognitive map technique, Inform. Med. Unlocked, 18, 1 Aldoj, 2020, Semi-automatic classification of prostate cancer on multi-parametric MR imaging using a multi-channel 3D convolutional neural network, Eur. Radiol., 30, 1243, 10.1007/s00330-019-06417-z Alexander, 2010, A review and meta-analysis of prospective studies of red and processed meat intake and prostate cancer, Nutr. J., 9, 9, 10.1186/1475-2891-9-50 Alkadi, 2019, A deep learning-based approach for the detection and localization of prostate cancer in T2 magnetic resonance images, J. Digit. Imag., 32, 793, 10.1007/s10278-018-0160-1 Alkhateeb, 2020, Machine learning methods for prostate cancer diagnosis. J, J. Cancer, 1, 70 Antonelli, 2019, Machine learning classifiers can predict Gleason pattern 4 prostate cancer with greater accuracy than experienced radiologists, Eur. Radiol., 29, 4754, 10.1007/s00330-019-06244-2 Barentsz, 2012, ESUR prostate MR guidelines 2012, Eur. Radiol., 22, 746, 10.1007/s00330-011-2377-y Bernatz, 2020, Comparison of machine learning algorithms to predict clinically significant prostate cancer of the peripheral zone with multiparametric MRI using clinical assessment categories and radiomic features, Eur. Radiol., 30, 6757, 10.1007/s00330-020-07064-5 Bonekamp, 2011, Advancements in MR imaging of the prostate: from diagnosis to interventions, Radiographics, 31, 677, 10.1148/rg.313105139 Brabletz, 2018, EMT in cancer, Nat. Rev. Cancer, 18, 128, 10.1038/nrc.2017.118 Bulten, 2020, Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study, Lancet Oncol., 21, 233, 10.1016/S1470-2045(19)30739-9 Bylsma, 2015, A review and meta-analysis of prospective studies of red and processed meat, meat cooking methods, heme iron, heterocyclic amines and prostate cancer, Nutr. J., 14, 545, 10.1186/s12937-015-0111-3 Caini, 2014, Sexually transmitted infections and prostate cancer risk: a systematic review and meta-analysis, Cancer Epidemiol., 38, 329, 10.1016/j.canep.2014.06.002 Cannon, 2012, Prevention of cancer and non-communicable diseases, Asian Pac. J. Cancer Prev., 13, 3 Cheng, 2017, Automatic MR prostate segmentation by deep learning with holistically-nested networks. Medical Imaging 2017, Image Processing Cheng, 2017, Automatic magnetic resonance prostate segmentation by deep learning with holistically nested networks, J. Med. Imag., 4 Clark, 2017, Fully automated segmentation of prostate whole gland and transition zone in diffusion-weighted MRI using convolutional neural networks, J. Med. Imag., 4, 1, 10.1117/1.JMI.4.4.041307 Cortes, 1995, Support-vector networks, Mach. Learn., 20, 273, 10.1007/BF00994018 Cosma, 2017, A survey on computational intelligence approaches for predictive modeling in prostate cancer, Expert Syst. Appl., 70, 1, 10.1016/j.eswa.2016.11.006 Cunha, 1987, The endocrinology and developmental biology of the prostate, Endocr. Rev., 8, 338, 10.1210/edrv-8-3-338 De Bono, 2011, Abiraterone and increased survival in metastatic prostate cancer, N. Engl. J. Med., 364, 1995, 10.1056/NEJMoa1014618 De Vincentis, 2018, Narrative medicine in metastatic prostate cancer reveals ways to improve patient awareness & quality of care, Future Oncol., 14, 2821, 10.2217/fon-2018-0318 Denmeade, 2002, A history of prostate cancer treatment, Nat. Rev. Cancer, 2, 389, 10.1038/nrc801 Federman, 2007, Differential Diagnosis in Internal medicine: from symptom to Diagnosis, JAMA, 298, 2070, 10.1001/jama.298.17.2072 Gann, 2002, Risk factors for prostate cancer, Rev. Urol., 4, S3 Gerlinger, 2012, Intratumor heterogeneity and branched evolution revealed by multiregion sequencing, N. Engl. J. Med., 366, 883, 10.1056/NEJMoa1113205 Goldenberg, 2019, A new era: artificial intelligence and machine learning in prostate cancer, Nat. Rev. Urol., 16, 391, 10.1038/s41585-019-0193-3 Hagiwara, 2018, Computer-aided diagnosis of atrial fibrillation based on ECG signals: a review, Inf. Sci., 467, 99, 10.1016/j.ins.2018.07.063 Houston, 2018, Trends in prostate cancer incidence rates and prevalence of prostate specific antigen screening by socioeconomic status and regions in the United States, 2004 to 2013, J. Urol., 199, 676 Kaggle Kakade, 2009, On the complexity of linear prediction: risk bounds, margin bounds, and regularization, In: Proceedings of the 21st International Conference on Neural Information Processing Systems. Curran Associates Inc., Red Hook, pp. 793–800 Kote-Jarai, 2011, Seven prostate cancer susceptibility loci identified by a multi-stage genome-wide association study, Nat. Genet., 43, 785, 10.1038/ng.882 Kott, 2021, Development of a deep learning algorithm for the histopathologic diagnosis and Gleason grading of prostate cancer biopsies: a pilot study, Eur. Urol. Focus, 7, 347, 10.1016/j.euf.2019.11.003 Kumagai, 2015, Lifestyle modification increases serum testosterone level and decrease central blood pressure in overweight and obese men, Endocr. J., 62, 423, 10.1507/endocrj.EJ14-0555 Kwak, 2017, Lumen-based detection of prostate cancer via convolutional neural networks., Medical Imaging 2017: Digital Pathology. Availlable at: Li, 2018, A comparative study of Gaussian and non-Gaussian diffusion models for differential diagnosis of prostate cancer with in-bore transrectal MR-guided biopsy as a pathological reference, Acta Radiol., 59, 1395, 10.1177/0284185118760961 Li, 2018, Path R-CNN for prostate cancer diagnosis and gleason grading of histological images, IEEE Trans. Med. Imag., 38, 945, 10.1109/TMI.2018.2875868 Liberman, 2016, Toward a differential diagnosis of hidden hearing loss in humans, PLoS One, 11, 1, 10.1371/journal.pone.0162726 Liu, 2019, Prediction of prostate cancer aggressiveness with a combination of radiomics and machine learning-based analysis of dynamic contrast-enhanced MRI, Clin. Radiol., 74, 896, 10.1016/j.crad.2019.07.011 Lokhande, 2020, Carcino-Net: a Deep learning Framework for automated Gleason grading of prostate biopsies Mann, 1990, Differential diagnosis and classification of apathy, Am. J. Psychiatr., 147, 22, 10.1176/ajp.147.1.22 Marley, 2016, Epidemiology of colorectal cancer, Int. J. Mol. Epidemiol. Genet., 7, 105 Moyad, 2002, Is obesity a risk factor for prostate cancer, and does it even matter? A hypothesis and different perspective, Urology, 59, 41, 10.1016/S0090-4295(01)01175-X Mumford, 2009, Computational intelligence: collaboration: fusion and emergence, 10.1007/978-3-642-01799-5 Murtola, 2018, Fasting blood glucose, glycaemic control and prostate cancer risk in the Finnish Randomized Study of Screening for Prostate Cancer, Br. J. Cancer, 118, 1248, 10.1038/s41416-018-0055-4 Nair, 2010, Photonic crystal sensors: an overview, Prog. Quant. Electron., 34, 89, 10.1016/j.pquantelec.2010.01.001 Naji, 2018, Digital rectal examination for prostate cancer screening in primary care: a systematic review and meta-analysis, Ann. Fam. Med., 16, 149, 10.1370/afm.2205 Natarajan, 2011, Clinical application of a 3D ultrasound-guided prostate biopsy system, Urol. Oncol.: seminars and original investigations, 29, 334, 10.1016/j.urolonc.2011.02.014 Ng, 2007, De novo SVM classification of precursor microRNAs from genomic pseudo hairpins using global and intrinsic folding measures, Bioinformatics, 23, 1321, 10.1093/bioinformatics/btm026 Nir, 2019, Comparison of artificial intelligence techniques to evaluate performance of a classifier for automatic grading of prostate cancer from digitized histopathologic images, JAMA Netw. Open, 2, e190442, 10.1001/jamanetworkopen.2019.0442 Parikesit, 2016, The impact of obesity towards prostate diseases, Prostate Int., 4, 1, 10.1016/j.prnil.2015.08.001 Pereira, 2020, Towards the differential diagnosis of prostate cancer by the pre-treatment of human urine using ionic liquids, Sci. Rep., 10, 1, 10.1038/s41598-020-71925-8 Platz, 2009, Men with low serum cholesterol have a lower risk of high-grade prostate cancer in the placebo arm of the prostate cancer prevention trial, Cancer Epidemiol. Prevent. Biomark., 18, 2807, 10.1158/1055-9965.EPI-09-0472 National Cancer Institute Rahman, 2016, Prostate cancer, Anwer Khan Mod. Med. Coll. J., 7, 36, 10.3329/akmmcj.v7i2.31644 Rice, 2005, Mining protein function from text using term-based support vector machines, BMC Bioinf., 6, 1, 10.1186/1471-2105-6-1 Roweis, 2000, Nonlinear dimensionality reduction by locally linear embedding, Science, 290, 2323, 10.1126/science.290.5500.2323 Sand, 2015, Classification, diagnosis, and differential diagnosis of multiple sclerosis, Curr. Opin. Neurol., 28, 193, 10.1097/WCO.0000000000000206 Schoots, 2015, Magnetic resonance imaging-targeted biopsy may enhance the diagnostic accuracy of significant prostate cancer detection compared to standard transrectal ultrasound-guided biopsy: a systematic review and meta-analysis, Eur. Urol., 68, 438, 10.1016/j.eururo.2014.11.037 Siddiqui, 2015, Comparison of MR/ultrasound fusion-guided biopsy with ultrasound-guided biopsy for the diagnosis of prostate cancer, JAMA, 313, 390, 10.1001/jama.2014.17942 Silva-Rodríguez, 2020, Going deeper through the Gleason scoring scale: an automatic end-to-end system for histology prostate grading and cribriform pattern detection, Comput. Methods Progr. Biomed., 195, 1 Smith, 2015, Denosumab for the prevention of skeletal complications in metastatic castration-resistant prostate cancer: comparison of skeletal-related events and symptomatic skeletal events, Ann. Oncol., 26, 368, 10.1093/annonc/mdu519 Steinestel, 2019, Detecting predictive androgen receptor modifications in circulating prostate cancer cells, Oncotarget, 10, 4213, 10.18632/oncotarget.3925 Sung, 2021, Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries, CA Cancer J. Clin., 71, 209, 10.3322/caac.21660 Taitt, 2018, Global trends and prostate cancer: a review of incidence, detection, and mortality as influenced by race, ethnicity, and geographic location, Am. J. Men’s Health, 12, 1807, 10.1177/1557988318798279 Tenenbaum, 2000, A global geometric framework for nonlinear dimensionality reduction, Science, 290, 2319, 10.1126/science.290.5500.2319 Tian, 2015, Prevalent latent adenocarcinoma of the prostate in forensic autopsies, J. Clin. Pathol. Forensic Med., 6, 11, 10.5897/JCPFM2015.0063 Tian, 2018, Deep convolutional neural network for prostate MR segmentation, Int. J. Comput. Assist. Radiol. Surg., 13, 1687, 10.1007/s11548-018-1841-4 To, 2018, Deep dense multi-path neural network for prostate segmentation in magnetic resonance imaging, Int. J. Comput. Assist. Radiol. Surg., 13, 1687, 10.1007/s11548-018-1841-4 Tse, 2017, Bisphenol A and other environmental risk factors for prostate cancer in Hong Kong, Environ. Int., 107, 1, 10.1016/j.envint.2017.06.012 Turner, 2007, BRCA1 dysfunction in sporadic basal-like breast cancer, Oncogene, 26, 2126, 10.1038/sj.onc.1210014 Uzoka, 2016, A framework for early differential diagnosis of tropical confusable diseases using the fuzzy cognitive map engine, World Academy of Science, Engineering and Technology, Int. J. Comput., 10, 346 Vaidyanathan, 2017, Environmental factors and risk of aggressive prostate cancer among a population of New Zealand men–a genotypic approach, Mol. Biosyst., 13, 681, 10.1039/C6MB00873A Wolk, 2017, Potential health hazards of eating red meat, J. Intern. Med., 281, 106, 10.1111/joim.12543 Yaghi, 2015, Oral antibiotics in trans-rectal prostate biopsy and its efficacy to reduce infectious complications: systematic review, Urol. Ann., 7, 417, 10.4103/0974-7796.164860 Yaxley, 2016, Robot-assisted laparoscopic prostatectomy versus open radical retropubic prostatectomy: early outcomes from a randomised controlled phase 3 study, Lancet, 388, 1057, 10.1016/S0140-6736(16)30592-X Zhang, 2016, Prostatic adenocarcinoma presenting with metastases to the testis and epididymis: a case report, Oncol. Lett., 11, 792, 10.3892/ol.2015.3920 Zhou, 2016, Prostate cancer incidence in 43 populations worldwide: an analysis of time trends overall and by age group, Int. J. Cancer, 138, 1388, 10.1002/ijc.29894