{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T13:02:25Z","timestamp":1790341345726,"version":"4.1.0"},"reference-count":28,"publisher":"PAGEPress Publications","issue":"3","license":[{"start":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T00:00:00Z","timestamp":1790294400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Arch Ital Urol Androl"],"abstract":"<jats:p>Beginning in the late 1980s, we developed methods for digital image acquisition, virtual microscopy, and quantitative image analysis that anticipated many features of contemporary digital pathology. Our work included machine vision systems for the automated detection and characterization of high-grade prostatic intraepithelial neoplasia, malignancy-associated changes, and prostate cancer, as well as quantitative approaches for identifying cribriform architecture and early decision support systems for diagnostic pathology. These pioneering studies established methodological foundations that have evolved into modern whole-slide imaging, multiplex tissue analysis, and AI-based diagnostic algorithms. Recent advances in deep learning have further expanded these concepts, improving cancer detection, grading, and prognostic assessment. Although AI has considerable potential to enhance the accuracy and efficiency of prostate pathology, its current role is best viewed as an assistive tool integrated with expert pathological interpretation.<\/jats:p>","DOI":"10.4081\/aiua.2026.16186","type":"journal-article","created":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T12:45:42Z","timestamp":1790340342000},"source":"Crossref","is-referenced-by-count":0,"title":["Let us trace our path from glass slide to virtual slide and artificial intelligence"],"prefix":"10.4081","volume":"98","author":[{"given":"Rodolfo","family":"Montironi","sequence":"first","affiliation":[{"name":"Molecular Medicine and Cell Therapy Foundation c\/o Polytechnic University of the Marche Region, Ancona"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessia","family":"Cimadamore","sequence":"additional","affiliation":[{"name":"Department of Medicine (DMED), Institute of Pathological Anatomy, Udine University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alberto","family":"Trinchieri","sequence":"additional","affiliation":[{"name":"CDC Ambrosiana, Cesano Boscone, Milan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2549","published-online":{"date-parts":[[2026,9,25]]},"reference":[{"key":"253215","doi-asserted-by":"crossref","unstructured":"1. Bawahab AA. Artificial Intelligence in Equivocal Prostatic Needle Biopsies: Benign Mimics and Cancer Detection. Arch Ital Urol Androl. 2026; 98:15673.","DOI":"10.4081\/aiua.2026.15673"},{"key":"253216","unstructured":"2. Thompson D, Richards D, Bartels H, et al. Multimegapixel images in histopathology. Anal Quant Cytol Histol 2001; 23:169-77."},{"key":"253217","doi-asserted-by":"crossref","unstructured":"3. Montironi R, Cheng L, Lopez-Beltran A, Scarpelli M. Quantitative Image Analysis on Histologic Virtual Slides for Prostate Pathology Diagnosis, Response to Chemopreventive Agents, and Prognosis. Eur Urol Focus. 2017; 3:467-469.","DOI":"10.1016\/j.euf.2016.06.013"},{"key":"253218","doi-asserted-by":"crossref","unstructured":"4. Montironi R, Thompson D, Scarpelli M, et al. Transcontinental communication and quantitative digital histopathology via the Internet; with special reference to prostate neoplasia. J Clin Pathol. 2002; 55:452-460.","DOI":"10.1136\/jcp.55.6.452"},{"key":"253219","doi-asserted-by":"crossref","unstructured":"5. Saini ML, Hemmerich A, Banach-Ovens A, et al. Comparison of digital displays for efficient diagnosis and accuracy. J Pathol Inform. 2026; 20:100544.","DOI":"10.1016\/j.jpi.2026.100544"},{"key":"253220","unstructured":"6. Thompson D, Bartels PH, Bartels HG, Montironi R. Knowledge guided histometry of the basal cell layer in prostatic intraepithelial neoplasia. Anal Quant Cytol Histol 1996; 18:177-84."},{"key":"253221","doi-asserted-by":"crossref","unstructured":"7. Montironi R, Thompson D, Scarpelli M, et al. Karyometry detects subvisual differences in chromatin organization state between cribriform and flat high-grade prostatic intraepithelial neoplasia. Mod Pathol 2004; 17:928-37.","DOI":"10.1038\/modpathol.3800142"},{"key":"253222","unstructured":"8. da Silva VD, Montironi R, Thompson D, et al. Chromatin texture in high grade prostatic intraepithelial neoplasia and early invasive carcinoma. Anal Quant Cytol Histol 1999;21:113-20."},{"key":"253223","doi-asserted-by":"crossref","unstructured":"9. Montironi R, Bartels PH, DeCensi A, et al. A randomized phase IIb presurgical study of finasteride vs. low-dose flutamide vs. placebo in men with prostate cancer. Efficacy monitored by karyometry. Urol Oncol 2013; 31:557-65.","DOI":"10.1016\/j.urolonc.2011.02.024"},{"key":"253224","doi-asserted-by":"crossref","unstructured":"10. Montironi R, Diamanti L, Thompson D, et al. Analysis of the capillary architecture in the precursors of prostate cancer: recent findings and new concepts. Eur Urol 1996; 30:191-200.","DOI":"10.1159\/000474169"},{"key":"253225","doi-asserted-by":"crossref","unstructured":"11. Lippolis G, Edsjo\u00a8 A, Helczynski L, et al. Automatic registration of multi-modal microscopy images for integrative analysis of prostate tissue sections. BMC Cancer 2013; 13:408.","DOI":"10.1186\/1471-2407-13-408"},{"key":"253226","doi-asserted-by":"crossref","unstructured":"12. Montironi R, Cheng L, Lopez-Beltran A, Scarpelli M. Quantitative Image Analysis on Histologic Virtual Slides for Prostate Pathology Diagnosis, Response to Chemopreventive Agents, and Prognosis. Eur Urol Focus. 2017; 3:467-469.","DOI":"10.1016\/j.euf.2016.06.013"},{"key":"253227","doi-asserted-by":"crossref","unstructured":"13. Montironi R, Hamilton PW, Scarpelli M, Thompson D, Bartels PH. Subtle morphological and molecular changes in normal-looking epithelium in prostates with prostatic intraepithelial neoplasia or cancer. Eur Urol 1999; 35:468-73.","DOI":"10.1159\/000019881"},{"key":"253228","doi-asserted-by":"crossref","unstructured":"14. Montironi R, Filho AL, Santinelli A, et al. Nuclear changes in the normal-looking columnar epithelium adjacent to and distant from prostatic intraepithelial neoplasia and prostate cancer. Morphometric analysis in whole-mount sections. Virchows Arch 2000; 437:625-34.","DOI":"10.1007\/s004280000290"},{"key":"253229","doi-asserted-by":"crossref","unstructured":"15. Lee G, Veltri RW, Ali Set al. Nuclear shape and architecture in benign fields predict biochemical recurrence in prostate cancer patients following radical prostatectomy. Eur Urol Focus. 2017; 3:457-466.","DOI":"10.1016\/j.euf.2016.05.009"},{"key":"253230","doi-asserted-by":"crossref","unstructured":"16. Montironi R, Cimadamore A, Massari F, et al. Whole Slide Imaging of Large Format Histology in Prostate Pathology: Potential for Information Fusion. Arch Pathol Lab Med. 2017; 141:1460-1461.","DOI":"10.5858\/arpa.2017-0198-LE"},{"key":"253231","unstructured":"17. Bartels PH, Bartels HG, Montironi R, et al. Machine vision in the detection of prostate lesions in histologic sections. Anal Quant Cytol Histol. 1998; 20:358-64."},{"key":"253232","doi-asserted-by":"crossref","unstructured":"18. Pantanowitz L, Quiroga-Garza GM, Bien L, et al. An artificial intelligence algorithm for prostate cancer diagnosis in whole slide images of core needle biopsies: a blinded clinical validation and deployment study. Lancet Digit Health. 2020; 2:e407-e416.","DOI":"10.1016\/S2589-7500(20)30159-X"},{"key":"253233","doi-asserted-by":"crossref","unstructured":"19. Montironi R, Cimadamore A, Scarpelli M, et al. Let us not forget about our past contributions to the field of prostatic neoplasms: To some extent what we value now was already there. Pathol Res Pract. 2021; 219:153377.","DOI":"10.1016\/j.prp.2021.153377"},{"key":"253234","doi-asserted-by":"crossref","unstructured":"20. Bartels PH, Thompson D, Bartels HG, et al. Machine visionbased histometry of premalignant and malignant prostatic lesions. Pathol Res Pract. 1995; 191:935-44.","DOI":"10.1016\/S0344-0338(11)80979-9"},{"key":"253235","unstructured":"21. Thompson D, Bartels PH, Bartels HG, Montironi R. Image segmentation of cribriform gland tissue. Anal Quant Cytol Histol. 1995;17:314-22."},{"key":"253236","doi-asserted-by":"crossref","unstructured":"22. Montironi R, Cimadamore A, Gasparrini S, et al. Prostate cancer with cribriform morphology: diagnosis, aggressiveness, molecular pathology and possible relationships with intraductal carcinoma. Expert Rev Anticancer Ther. 2018; 18:685-693.","DOI":"10.1080\/14737140.2018.1469406"},{"key":"253237","doi-asserted-by":"crossref","unstructured":"23. Ambrosini P, Hollemans E, Kweldam CF, et al. Automated detection of cribriform growth patterns in prostate histology images. Sci Rep. 2020; 10:14904.","DOI":"10.1038\/s41598-020-71942-7"},{"key":"253238","doi-asserted-by":"crossref","unstructured":"24. Bartels PH, Thompson D, Montironi R, et al. Diagnostic decision support for prostate lesions. Pathol Res Pract. 1995; 191:945-57.","DOI":"10.1016\/S0344-0338(11)80980-5"},{"key":"253239","doi-asserted-by":"crossref","unstructured":"25. Montironi R, Cheng L, Lopez-Beltran A, et al. Decision support systems for morphology-based diagnosis and prognosis of prostate neoplasms: a methodological approach. Cancer. 2009; 115(13Suppl):3068-77.","DOI":"10.1002\/cncr.24345"},{"key":"253240","doi-asserted-by":"crossref","unstructured":"26. Goldenberg SL, Nir G, Salcudean SE. A new era: artificial intelligence and machine learning in prostate cancer. Nat Rev Urol. 2019;16:391-403.","DOI":"10.1038\/s41585-019-0193-3"},{"key":"253241","doi-asserted-by":"crossref","unstructured":"27. Thenault R, Kaulanjan K, Darde T, et al. The application of artificial intelligence in prostate cancer management\u2014what improvements can be expected? A systematic review. Appl Sci. 2020;10:6428.","DOI":"10.3390\/app10186428"},{"key":"253242","doi-asserted-by":"crossref","unstructured":"28. Cimadamore A, Lopez-Beltran A, Scarpelli M, et al. Artificial intelligence and prostate cancer: Advances and challenges. 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