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Citing this Article

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Published on 06.01.17 in Vol 3, No 1 (2017): Jan-Jun

This paper is in the following e-collection/theme issue:

Works citing "Rotterdam Prostate Cancer Risk Calculator: Development and Usability Testing of the Mobile Phone App"

Nuno Pereira-Azevedo, Luís Osório, Avelino Fraga, Monique J Roobol

JMIR Cancer 2017 (Jan 06); 3(1):e1 

According to Crossref, the following articles are citing this article (DOI 10.2196/cancer.6750):

(note that this is only a small subset of citations)

  1. Lippi G, Mattiuzzi C, Plebani M. PSA-based, prostate cancer risk on-line calculators: no such thing as a crystal ball?. Diagnosis 2018;5(4):253
    CrossRef
  2. Jamnadass E, Rai BP, Veneziano D, Tokas T, Rivas JG, Cacciamani G, Somani B. Do prostate cancer-related mobile phone apps have a role in contemporary prostate cancer management? A systematic review by EAU young academic urologists (YAU) urotechnology group. World Journal of Urology 2020;38(10):2411
    CrossRef
  3. Tikka T, Paleri V, MacKenzie K. External validation of a cancer risk prediction model for suspected head and neck cancer referrals. Clinical Otolaryngology 2018;43(2):714
    CrossRef
  4. Osses D, Roobol M, Schoots I. Prediction Medicine: Biomarkers, Risk Calculators and Magnetic Resonance Imaging as Risk Stratification Tools in Prostate Cancer Diagnosis. International Journal of Molecular Sciences 2019;20(7):1637
    CrossRef
  5. Tapiero S, Yoon R, Jefferson F, Sung J, Limfueco L, Cottone C, Lu S, Patel RM, Landman J, Clayman RV. Smartphone technology and its applications in urology: a review of the literature. World Journal of Urology 2020;38(10):2393
    CrossRef
  6. McNally CJ, Ruddock MW, Moore T, McKenna DJ.

    Biomarkers That Differentiate Benign Prostatic Hyperplasia from Prostate Cancer: A Literature Review

    . Cancer Management and Research 2020;Volume 12:5225
    CrossRef
  7. Nakai Y, Miyake M, Anai S, Hori S, Tatsumi Y, Morizawa Y, Onisi S, Tanaka N, Fujimoto K. Spectrophotometric photodynamic diagnosis of prostate cancer cells excreted in voided urine using 5-aminolevulinic acid. Lasers in Medical Science 2018;33(7):1557
    CrossRef
  8. Castaneda P, Ellimoottil C. Current use of telehealth in urology: a review. World Journal of Urology 2020;38(10):2377
    CrossRef
  9. Govorko MH, Fritschi L, White J, Reid A. Identifying Asbestos-Containing Materials in Homes: Design and Development of the ACM Check Mobile Phone App. JMIR Formative Research 2017;1(1):e7
    CrossRef
  10. Remmers S, Roobol MJ. Personalized strategies in population screening for prostate cancer. International Journal of Cancer 2020;147(11):2977
    CrossRef
  11. Salmani H, Ahmadi M, Shahrokhi N. The Impact of Mobile Health on Cancer Screening: A Systematic Review. Cancer Informatics 2020;19:117693512095419
    CrossRef
  12. Lombardo R, Tema G, Cancrini F, Albanesi L, Mavilla L, Tariciotti P, Gentile BC, Aloisi P, Rizzo G, Tardioli S, Giulianelli R. The role of immune PSA complex (iXip) in the prediction of prostate cancer. Biomarkers 2021;26(1):26
    CrossRef
  13. Chen IA, Chu C, Lin J, Tsai J, Yu C, Sridhar AN, Sooriakumaran P, Loureiro RCV, Chand M. Prostate Cancer Risk Calculator Apps in a Taiwanese Population Cohort: Validation Study. Journal of Medical Internet Research 2020;22(12):e16322
    CrossRef
  14. Tully KH, Bahlburg H, Berg S, Hanske J, von Landenberg N, Noldus J, Palisaar R, Roghmann F, Brock M. Changing the Prostate Cancer Detection Paradigm: Clinical Application of European Association of Urology Guideline–recommended Magnetic Resonance Imaging–based Risk Stratification in Men with Suspected Prostate Cancer. European Urology Focus 2021;7(5):1011
    CrossRef
  15. De Nunzio C, Lombardo R, Baldassarri V, Cindolo L, Bertolo R, Minervini A, Sessa F, Muto G, Bove P, Vittori M, Bozzini G, Castellan P, Mugavero F, Falsaperla M, Schips L, Celia A, Bada M, Porreca A, Pastore A, Al Salhi Y, Giampaoli M, Novella G, Rizzetto R, Trabacchin N, Mantica G, Pini G, Remmers S, Antonelli A, Tubaro A. Rotterdam mobile phone app including MRI data for the prediction of prostate cancer: A multicenter external validation. European Journal of Surgical Oncology 2021;47(10):2640
    CrossRef
  16. Chiu PK, Shen X, Wang G, Ho C, Leung C, Ng C, Choi K, Teoh JY. Enhancement of prostate cancer diagnosis by machine learning techniques: an algorithm development and validation study. Prostate Cancer and Prostatic Diseases 2022;25(4):672
    CrossRef
  17. Loeb S, Black PC, Wyatt AW, Nyame YA, Shore N, Tilki D, Castro E, Cooperberg M, Giri V, Ribal MJ, Lughezzani G, Sánchez-Salas R, Moore CM, Rastinehad A, Kerkmeijer L, Ahmed HU, Akamatsu S, de la Taille A, Gleave M, Tanguay S. B2B: Prostate Cancer. Société Internationale d’Urologie Journal 2021;2(Supplement 1):S30
    CrossRef
  18. Bandala-Jacques A, Castellanos Esquivel KD, Pérez-Hurtado F, Hernández-Silva C, Reynoso-Noverón N. Prostate Cancer Risk Calculators for Healthy Populations: Systematic Review. JMIR Cancer 2021;7(3):e30430
    CrossRef
  19. Díaz-Fernández F, Celma A, Salazar A, Moreno O, López C, Cuadras M, Regis L, Planas J, Morote J, Trilla E. Systematic review of methods used to improve the efficacy of magnetic resonance in early detection of clinically significant prostate cancer. Actas Urológicas Españolas (English Edition) 2023;47(3):127
    CrossRef
  20. Berg S, Tully KH, von Landenberg N, Bahlburg H, Roghmann F, Müller G, Hanske J, Noldus J, Brock M. How Many Cores Should Be Sampled during Systematic Prostate Biopsy in Case of Negative Multiparametric Magnetic Resonance Imaging? Analysis of 274 Men with Clinical Suspicion of Prostate Cancer. Urologia Internationalis 2022;106(9):914
    CrossRef
  21. Gebeyehu S, Twinomurinzi H. A Collaborative Consumption Digital Platform for Government Organizations using Design Science. Digital Government: Research and Practice 2022;3(1):1
    CrossRef
  22. Murtha JA, Liu N, Birstler J, Hanlon BM, Venkatesh M, Hanrahan LP, Borza T, Kushner DM, Funk LM. Obesity and “obesity-related” cancers: are there body mass index cut-points?. International Journal of Obesity 2022;46(10):1770
    CrossRef
  23. Stocks J, Choi Y, Ibrahim S, Huchko M. Iterative Development of a Mobile Phone App to Support Community Health Volunteers During Cervical Cancer Screening in Western Kenya: Qualitative Study. JMIR Formative Research 2022;6(2):e27501
    CrossRef
  24. Pospelova IV, Bragin DS, Cherepanova IV, Serebryakova VN, Sokolov AA, Kaveshnikov VS. Development of Mobile Application for Assessment of Basic Echocardiographic Parameters in Apparently Healthy Population. Telemedicine and e-Health 2021;
    CrossRef
  25. Díaz-Fernández F, Celma A, Salazar A, Moreno O, López C, Cuadras M, Regis L, Planas J, Morote J, Trilla E. Revisión sistemática de los métodos para incrementar la eficacia de la resonancia magnética en el diagnóstico precoz de cáncer de próstata clínicamente significativo. Actas Urológicas Españolas 2023;47(3):127
    CrossRef
  26. Berg S, Tully KH, Hoffmann V, Bahlburg H, Roghmann F, Müller G, Noldus J, Reike M. Assessment of complications after transperineal and transrectal prostate biopsy using a risk-stratified pathway identifying patients at risk for post-biopsy infections. Scandinavian Journal of Urology 2023;57(1-6):41
    CrossRef
  27. van Breugel SJ, Low I, Christie ML, Pokorny MR, Nagarajan R, Holtkamp HU, Srinivasa K, Amirapu S, Nieuwoudt MK, Simpson MC, Zargar‐Shoshtari K, Aguergaray C. Raman spectroscopy system for real‐time diagnosis of clinically significant prostate cancer tissue. Journal of Biophotonics 2023;16(5)
    CrossRef
  28. Greenberg JW, Koller CR, Casado C, Triche BL, Krane LS. A narrative review of biparametric MRI (bpMRI) implementation on screening, detection, and the overall accuracy for prostate cancer. Therapeutic Advances in Urology 2022;14:175628722210963
    CrossRef
  29. Handke AE, Albers P, Schimmöller L, Bonekamp D, Asbach P, Schlemmer H, Hadaschik BA, Radtke JP. Systematische oder gezielte Fusionsbiopsie der Prostata. Die Urologie 2023;62(5):464
    CrossRef
  30. Dite GS, Spaeth E, Murphy NM, Allman R. Development and validation of a simple prostate cancer risk prediction model based on age, family history, and polygenic risk. The Prostate 2023;83(10):962
    CrossRef
  31. Bahlburg H, Tully K, Hoffmann V, Hanske J, von Landenberg N, Roghmann F, Palisaar R, Noldus J, Berg S, Brock M. Avoiding Prostate Biopsies in Patients at Low Risk for Prostate Cancer: A Prospective Evaluation of a PSA-Density-Based Safety Net. Urologia Internationalis 2023;107(5):454
    CrossRef
  32. Bouarroudj K, Kitouni I, Lechekhab A, Leghelimi Z, Kara I. Quality evaluation of commercially available healthcare applications for prostate cancer management. Multimedia Tools and Applications 2023;82(20):31793
    CrossRef
  33. Singh A, Randive S, Breggia A, Ahmad B, Christman R, Amal S. Enhancing Prostate Cancer Diagnosis with a Novel Artificial Intelligence-Based Web Application: Synergizing Deep Learning Models, Multimodal Data, and Insights from Usability Study with Pathologists. Cancers 2023;15(23):5659
    CrossRef
  34. Lieslehto J, Tiihonen J, Lähteenvuo M, Leucht S, Correll CU, Mittendorfer-Rutz E, Tanskanen A, Taipale H. Development and Validation of a Machine Learning–Based Model of Mortality Risk in First-Episode Psychosis. JAMA Network Open 2024;7(3):e240640
    CrossRef

According to Crossref, the following books are citing this article (DOI 10.2196/cancer.6750):

  1. . Evaluation of Novel Approaches to Software Engineering. 2020. Chapter 9:188
    CrossRef
  2. Teles A, Barros F, Rodrigues I, Barbosa A, Silva F, Coutinho L, Teixeira S. IoT and ICT for Healthcare Applications. 2020. Chapter 4:33
    CrossRef
  3. McGrowder DA, Miller FG, Brown JG, Wilson-Clarke C, Anderson-Jackson L. Diagnostic Applications of Health Intelligence and Surveillance Systems. 2021. chapter 7:144
    CrossRef