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CC BY-NC-ND 4.0 · Appl Clin Inform 2022; 13(01): 091-099
DOI: 10.1055/s-0041-1741481
State of the Art/Best Practice Paper

PCaGuard: A Software Platform to Support Optimal Management of Prostate Cancer

Ioannis Tamposis
1   Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
,
Ioannis Tsougos
2   Department of Medical Physics, Medical School, University of Thessaly, Larisa, Greece
,
Anastasios Karatzas
3   Department of Urology, Medical School, University of Thessaly, Larisa, Greece
,
Katerina Vassiou
4   Radiology and Anatomy Department, Medical School, University of Thessaly, Larisa, Greece
,
Marianna Vlychou
5   Radiology Department, Medical School, University of Thessaly, Larisa, Greece
,
Vasileios Tzortzis
3   Department of Urology, Medical School, University of Thessaly, Larisa, Greece
› Author Affiliations Funding All authors report support from the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH–CREATE–INNOVATE (project code: T1EDK-03264) for nonprofit. All authors report receipt from related university hospital of Larisa for nonprofit.
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Abstract

Background and Objective Prostate cancer (PCa) is a severe public health issue and the most common cancer worldwide in men. Early diagnosis can lead to early treatment and long-term survival. The addition of the multiparametric magnetic resonance imaging in combination with ultrasound (mpMRI-U/S fusion) biopsy to the existing diagnostic tools improved prostate cancer detection. Use of both tools gradually increases in every day urological practice. Furthermore, advances in the area of information technology and artificial intelligence have led to the development of software platforms able to support clinical diagnosis and decision-making using patient data from personalized medicine.

Methods We investigated the current aspects of implementation, architecture, and design of a health care information system able to handle and store a large number of clinical examination data along with medical images, and produce a risk calculator in a seamless and secure manner complying with data security/accuracy and personal data protection directives and standards simultaneously. Furthermore, we took into account interoperability support and connectivity to legacy and other information management systems. The platform was implemented using open source, modern frameworks, and development tools.

Results The application showed that software platforms supporting patient follow-up monitoring can be effective, productive, and of extreme value, while at the same time, aiding toward the betterment medicine clinical workflows. Furthermore, it removes access barriers and restrictions to specialized care, especially for rural areas, providing the exchange of medical images and patient data, among hospitals and physicians.

Conclusion This platform handles data to estimate the risk of prostate cancer detection using current state-of-the-art in eHealth systems and services while fusing emerging multidisciplinary and intersectoral approaches. This work offers the research community an open architecture framework that encourages the broader adoption of more robust and comprehensive systems in standard clinical practice.

Keywords

prevention - diagnosis - clinical workflow - prostate cancer - multiparametric MRI-U/S fusion - health care system framework

Protection of Human and Animal Subjects

The study was performed in compliance with the World Medical Association Declaration of Helsinki Ethical Principles for Medical Research Involving Human Subjects and was reviewed by the University Hospital Ethics Committee, Larisa Greece (reference 4861).




Publication History

Received: 28 July 2021

Accepted: 24 November 2021

Article published online:
19 January 2022

© 2022. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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  • References

  • 1 Sehn JK. Prostate cancer pathology: recent updates and controversies. Mo Med 2018; 115 (02) 151-155
    PubMedGoogle Scholar
  • 2 Litwin MS, Tan H-J. The diagnosis and treatment of prostate cancer: a review. JAMA 2017; 317 (24) 2532-2542
    CrossrefPubMedGoogle Scholar
  • 3 Okotie OT, Roehl KA, Han M, Loeb S, Gashti SN, Catalona WJ. Characteristics of prostate cancer detected by digital rectal examination only. Urology 2007; 70 (06) 1117-1120
    CrossrefPubMedGoogle Scholar
  • 4 Catalona WJ. Prostate cancer screening. Med Clin North Am 2018; 102 (02) 199-214
    CrossrefPubMedGoogle Scholar
  • 5 Harvey C, Pilcher J, Richenberg J, Patel U, Frauscher F. Applications of transrectal ultrasound in prostate cancer. Br J Radiol 2012; 85 (special issue 1): S3-S17
    CrossrefPubMedGoogle Scholar
  • 6 Abraham NE, Mendhiratta N, Taneja SS. Patterns of repeat prostate biopsy in contemporary clinical practice. J Urol 2015; 193 (04) 1178-1184
    CrossrefPubMedGoogle Scholar
  • 7 Hegde JV, Mulkern RV, Panych LP. et al. Multiparametric MRI of prostate cancer: an update on state-of-the-art techniques and their performance in detecting and localizing prostate cancer. J Magn Reson Imaging 2013; 37 (05) 1035-1054
    CrossrefPubMedGoogle Scholar
  • 8 Ahmed HU, El-Shater Bosaily A, Brown LC. et al; PROMIS study group. Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study. Lancet 2017; 389 (10071): 815-822
    CrossrefPubMedGoogle Scholar
  • 9 Turkbey B, Brown AM, Sankineni S, Wood BJ, Pinto PA, Choyke PL. Multiparametric prostate magnetic resonance imaging in the evaluation of prostate cancer. CA Cancer J Clin 2016; 66 (04) 326-336
    CrossrefPubMedGoogle Scholar
  • 10 Mowatt G, Scotland G, Boachie C. et al. The diagnostic accuracy and cost-effectiveness of magnetic resonance spectroscopy and enhanced magnetic resonance imaging techniques in aiding the localisation of prostate abnormalities for biopsy: a systematic review and economic evaluation. Health Technol Assess 2013; 17 (20) vii-xix, 1–281
    CrossrefPubMedGoogle Scholar
  • 11 Valerio M, Willis S, van der Meulen J, Emberton M, Ahmed HU. Methodological considerations in assessing the utility of imaging in early prostate cancer. Curr Opin Urol 2015; 25 (06) 536-542
    CrossrefPubMedGoogle Scholar
  • 12 Bossuyt PM, Irwig L, Craig J, Glasziou P. Comparative accuracy: assessing new tests against existing diagnostic pathways. BMJ 2006; 332 (7549): 1089-1092
    CrossrefPubMedGoogle Scholar
  • 13 Fütterer JJ, Briganti A, De Visschere P. et al. Can clinically significant prostate cancer be detected with multiparametric magnetic resonance imaging? A systematic review of the literature. Eur Urol 2015; 68 (06) 1045-1053
    CrossrefPubMedGoogle Scholar
  • 14 Siddiqui MM, Rais-Bahrami S, Turkbey B. et al. Comparison of MR/ultrasound fusion-guided biopsy with ultrasound-guided biopsy for the diagnosis of prostate cancer. JAMA 2015; 313 (04) 390-397
    CrossrefPubMedGoogle Scholar
  • 15 Schoots IG, Petrides N, Giganti F. et al. Magnetic resonance imaging in active surveillance of prostate cancer: a systematic review. Eur Urol 2015; 67 (04) 627-636
    CrossrefPubMedGoogle Scholar
  • 16 Dickinson L, Ahmed HU, Hindley RG. et al. Prostate-specific antigen vs. magnetic resonance imaging parameters for assessing oncological outcomes after high intensity–focused ultrasound focal therapy for localized prostate cancer. Urol Oncol 2017; 35 (01) 30.e9-30.e15
    CrossrefPubMedGoogle Scholar
  • 17 Hegde JV, Demanes DJ, Veruttipong D. et al. Pretreatment 3T multiparametric MRI staging predicts for biochemical failure in high-risk prostate cancer treated with combination high-dose-rate brachytherapy and external beam radiotherapy. Brachytherapy 2017; 16 (06) 1106-1112
    CrossrefPubMedGoogle Scholar
  • 18 Marks L, Young S, Natarajan S. MRI-ultrasound fusion for guidance of targeted prostate biopsy. Curr Opin Urol 2013; 23 (01) 43-50
    CrossrefPubMedGoogle Scholar
  • 19 Tamposis I, Iordanidis E, Tzortzis L. et al. HPVGuard: A software platform to support management and prognosis of cervical cancer. IEEE; 2014: 401-405 ; Athens, Greece
    PubMedGoogle Scholar
  • 20 Klarenbeek SE, Weekenstroo HHA, Sedelaar JPM, Fütterer JJ, Prokop M, Tummers M. The effect of higher level computerized clinical decision support systems on oncology care: a systematic review. Cancers (Basel) 2020; 12 (04) 1032
    CrossrefPubMedGoogle Scholar
  • 21 Mukai TO, Bro F, Olesen F, Vedsted P. To test or not: a registry-based observational study of an online decision support for prostate-specific antigen tests. Int J Med Inform 2013; 82 (10) 973-979
    CrossrefPubMedGoogle Scholar
  • 22 Yu SH, Kim MS, Chung HS. et al. Early experience with Watson for Oncology: a clinical decision-support system for prostate cancer treatment recommendations. World J Urol 2021; 39 (02) 407-413
    CrossrefPubMedGoogle Scholar
  • 23 Lin H-C, Wu H-C, Chang C-H, Li T-C, Liang W-M, Wang J-YW. Development of a real-time clinical decision support system upon the Web MVC-based architecture for prostate cancer treatment. BMC Med Inform Decis Mak 2011; 11 (01) 16
    CrossrefPubMedGoogle Scholar
  • 24 Tamposis I, Pouliakis A, Fezoulidis I, Karakitsos P. Mobile platforms supporting health professionals: need, technical requirements, and applications. In: Medical Imaging: Concepts, Methodologies, Tools, and Applications. Hershey, PA: IGI Global; 2017: 1020-1043
    CrossrefGoogle Scholar
  • 25 Pouliakis A, Spathis A, Kottaridi C. et al. Cloud computing for BioLabs. Cloud Computing Applications for Quality Health Care Delivery. Hershey, PA: IGI Global; 2014: 228-249
    Google Scholar
  • 26 Iroju O, Soriyan A, Gambo I, Olaleke J. Interoperability in healthcare: benefits, challenges and resolutions. International Journal of Innovation and Applied Studies. 2013; 3 (01) 262-270
    PubMedGoogle Scholar
  • 27 Blazona B, Koncar M. HL7 and DICOM based integration of radiology departments with healthcare enterprise information systems. Int J Med Inform 2007; 76 (Suppl. 03) S425-S432
    CrossrefPubMedGoogle Scholar
  • 28 Cardoso L, Marins F, Quintas C. et al. Interoperability in healthcare. Health Care Delivery and Clinical Science: Concepts, Methodologies, Tools, and Applications. Hershey, PA: IGI Global; 2018: 689-714
    CrossrefGoogle Scholar
  • 29 Goossen W, Langford LH. Exchanging care records using HL7 V3 care provision messages. J Am Med Inform Assoc 2014; 21 (e2) e363-e368
    CrossrefPubMedGoogle Scholar
  • 30 Bidgood Jr WD, Horii SC, Prior FW, Van Syckle DE. Understanding and using DICOM, the data interchange standard for biomedical imaging. J Am Med Inform Assoc 1997; 4 (03) 199-212
    CrossrefPubMedGoogle Scholar
  • 31 Flanders AE, Carrino JA. Understanding DICOM and IHE. Semin Roentgenol 2003; 38 (03) 270-281
    CrossrefPubMedGoogle Scholar
  • 32 Walters B. VMware virtual platform. Linux journal 1999; 1999 (63es): 6
    PubMedGoogle Scholar
  • 33 Warnock MJ, Toland C, Evans D, Wallace B, Nagy P. Benefits of using the DCM4CHE DICOM archive. J Digit Imaging 2007; 20 (1, suppl 1): 125-129
    CrossrefPubMedGoogle Scholar
  • 34 Rosset A, Spadola L, Ratib O. OsiriX: an open-source software for navigating in multidimensional DICOM images. J Digit Imaging 2004; 17 (03) 205-216
    CrossrefPubMedGoogle Scholar
  • 35 Bender D, Sartipi K. HL7 FHIR: An Agile and RESTful approach to healthcare information exchange. IEEE; 2013: 326-331
    PubMedGoogle Scholar
  • 36 Mildenberger P, Eichelberg M, Martin E. Introduction to the DICOM standard. Eur Radiol 2002; 12 (04) 920-927
    CrossrefPubMedGoogle Scholar
  • 37 Tamposis I, Iordanidis E, Tzortzis L, Papachatzis S. OraHealthX ImaginX A next generation cost effective medical imaging workflow management platform. IEEE; 2014: 406-410
    PubMedGoogle Scholar
  • 38 Becker M, Böckmann B, Jöckel K-H. et al. Mapping patient data to colorectal cancer clinical algorithms for personalized guideline-based treatment. Appl Clin Inform 2020; 11 (02) 200-209
    Article in Thieme ConnectPubMedGoogle Scholar
  • 39 Melzer G, Maiwald T, Prokosch H-U, Ganslandt T. Leveraging real-world data for the selection of relevant eligibility criteria for the implementation of electronic recruitment support in clinical trials. Appl Clin Inform 2021; 12 (01) 17-26
    Article in Thieme ConnectPubMedGoogle Scholar
  • 40 Unberath P, Prokosch HU, Gründner J, Erpenbeck M, Maier C, Christoph J. EHR-independent predictive decision support architecture based on OMOP. Appl Clin Inform 2020; 11 (03) 399-404
    Article in Thieme ConnectPubMedGoogle Scholar
  • 41 Kostrinsky-Thomas AL, Hisama FM, Payne TH. Searching the PDF Haystack: automated knowledge discovery in scanned EHR documents. Appl Clin Inform 2021; 12 (02) 245-250
    Article in Thieme ConnectPubMedGoogle Scholar
  • 42 Wang H, Yatawara M, Huang S-C. et al. The integrated proactive surveillance system for prostate cancer. Open Med Inform J 2012; 6: 1-8
    CrossrefPubMedGoogle Scholar
  • 43 Sherman S, Shats O, Ketcham MA. et al. PCCR: pancreatic cancer collaborative registry. Cancer Inform 2011; 10: 83-91
    PubMedGoogle Scholar
  • 44 Sherman S, Shats O, Fleissner E. et al. Multicenter breast cancer collaborative registry. Cancer Inform 2011; 10: 217-226
    PubMedGoogle Scholar