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2. Gleason score 3+3=6 prostatic adenocarcinoma is not benign and the current debate is unhelpful to clinicians and patients

Author

Samaratunga, Hemamali, Egevad, Lars, Yaxley, John, Perry-Keene, Joanna, Le Fevre, Ian, Kench, James, Matsika, Admire, Bostwick, David, Iczkowski, Kenneth, and Delahunt, Brett

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Cancer, Prostate Cancer, Urologic Diseases, Clinical Research, Male, Humans, Neoplasm Grading, Prostatic Neoplasms, Biopsy, Needle, Carcinoma, Prostatectomy, Adenocarcinoma, Prostate, adenocarcinoma, Gleason score, prognosis, Pathology, Clinical sciences, Oncology and carcinogenesis
Abstract

Prostate adenocarcinoma is a common malignancy associated with a significant morbidity and mortality. In both prostate biopsies and radical prostatectomy specimens Gleason scoring informs both treatment and outcome prediction. The current convention is that in needle biopsies, Gleason patterns 3, 4 and 5 are considered to be malignant. Despite this there is debate as to whether or not Gleason score (GS) 3+3=6 should be diagnosed as cancer due to potential over-treatment and the psychological impact on patients. It is apparent that GS 3+3=6 is indolent disease with a low risk of metastasis. However, it does have the histological features of malignancy and is capable of infiltrating the prostate gland, extraprostatic extension, and metastatic spread. Furthermore GS 3+3=6 carcinoma has immunohistochemical and molecular genetic features similar to those of higher grade prostatic carcinoma. If GS 3+3=6 tumour is considered benign, the question arises should a benign label be given to the Gleason pattern 3 component of tumour that includes Gleason patterns of higher grade? This would seem a logical step as GS 3+3=6 cancers and the pattern 3 component in cancers with multiple patterns are morphologically identical. If pattern 3 is considered to be benign, then Gleason scoring would be limited to 4+4=8, 4+5=9, 5+4=9 and 5+5=10 which is clearly inappropriate. The correct strategy to address potential over-treatment of patients with low-grade cancer is clinician and patient education, not the recalibration of Gleason grading to reclassify malignant tumours as benign.

Published
2023

3. Reinterpretation of prostate cancer pathology by Appl1, Sortilin and Syndecan-1 biomarkers

Author

Logan, Jessica M., Martini, Carmela, Sorvina, Alexandra, Johnson, Ian R. D., Brooks, Robert D., Caruso, Maria C., Huzzell, Chelsea, Moore, Courtney R., Karageorgos, Litsa, Butler, Lisa M., Tewari, Prerna, Prabhakaran, Sarita, Hickey, Shane M., Klebe, Sonja, Samaratunga, Hemamali, Delahunt, Brett, Moretti, Kim, O’Leary, John J., Brooks, Douglas A., and Ung, Ben S.-Y.

Published
2024
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4. Computational pathology in 2030: a Delphi study forecasting the role of AI in pathology within the next decade.

Author

Berbís, M, McClintock, David, Bychkov, Andrey, Van der Laak, Jeroen, Pantanowitz, Liron, Lennerz, Jochen, Cheng, Jerome, Delahunt, Brett, Egevad, Lars, Eloy, Catarina, Farris, Alton, Fraggetta, Filippo, García Del Moral, Raimundo, Hartman, Douglas, Herrmann, Markus, Hollemans, Eva, Iczkowski, Kenneth, Karsan, Aly, Kriegsmann, Mark, Salama, Mohamed, Sinard, John, Tuthill, J, Williams, Bethany, Casado-Sánchez, César, Sánchez-Turrión, Víctor, Luna, Antonio, Aneiros-Fernández, José, and Shen, Jeanne

Subjects
Anatomic pathology, Artificial intelligence, Computational pathology, Digital pathology, Machine learning, Pathologist workflow, Humans, Artificial Intelligence, Delphi Technique, Algorithms, Surveys and Questionnaires, Forecasting
Abstract

BACKGROUND: Artificial intelligence (AI) is rapidly fuelling a fundamental transformation in the practice of pathology. However, clinical integration remains challenging, with no AI algorithms to date in routine adoption within typical anatomic pathology (AP) laboratories. This survey gathered current expert perspectives and expectations regarding the role of AI in AP from those with first-hand computational pathology and AI experience. METHODS: Perspectives were solicited using the Delphi method from 24 subject matter experts between December 2020 and February 2021 regarding the anticipated role of AI in pathology by the year 2030. The study consisted of three consecutive rounds: 1) an open-ended, free response questionnaire generating a list of survey items; 2) a Likert-scale survey scored by experts and analysed for consensus; and 3) a repeat survey of items not reaching consensus to obtain further expert consensus. FINDINGS: Consensus opinions were reached on 141 of 180 survey items (78.3%). Experts agreed that AI would be routinely and impactfully used within AP laboratory and pathologist clinical workflows by 2030. High consensus was reached on 100 items across nine categories encompassing the impact of AI on (1) pathology key performance indicators (KPIs) and (2) the pathology workforce and specific tasks performed by (3) pathologists and (4) AP lab technicians, as well as (5) specific AI applications and their likelihood of routine use by 2030, (6) AIs role in integrated diagnostics, (7) pathology tasks likely to be fully automated using AI, and (8) regulatory/legal and (9) ethical aspects of AI integration in pathology. INTERPRETATION: This systematic consensus study details the expected short-to-mid-term impact of AI on pathology practice. These findings provide timely and relevant information regarding future care delivery in pathology and raise key practical, ethical, and legal challenges that must be addressed prior to AIs successful clinical implementation. FUNDING: No specific funding was provided for this study.

Published
2023

9. Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge

Author

Bulten, Wouter, Kartasalo, Kimmo, Chen, Po-Hsuan Cameron, Ström, Peter, Pinckaers, Hans, Nagpal, Kunal, Cai, Yuannan, Steiner, David F, van Boven, Hester, Vink, Robert, Hulsbergen-van de Kaa, Christina, van der Laak, Jeroen, Amin, Mahul B, Evans, Andrew J, van der Kwast, Theodorus, Allan, Robert, Humphrey, Peter A, Grönberg, Henrik, Samaratunga, Hemamali, Delahunt, Brett, Tsuzuki, Toyonori, Häkkinen, Tomi, Egevad, Lars, Demkin, Maggie, Dane, Sohier, Tan, Fraser, Valkonen, Masi, Corrado, Greg S, Peng, Lily, Mermel, Craig H, Ruusuvuori, Pekka, Litjens, Geert, and Eklund, Martin

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Cancer, Urologic Diseases, Prostate Cancer, Algorithms, Biopsy, Cohort Studies, Humans, Male, Neoplasm Grading, Prostatic Neoplasms, Reproducibility of Results, PANDA challenge consortium, Medical and Health Sciences, Immunology, Biomedical and clinical sciences, Health sciences
Abstract

Artificial intelligence (AI) has shown promise for diagnosing prostate cancer in biopsies. However, results have been limited to individual studies, lacking validation in multinational settings. Competitions have been shown to be accelerators for medical imaging innovations, but their impact is hindered by lack of reproducibility and independent validation. With this in mind, we organized the PANDA challenge-the largest histopathology competition to date, joined by 1,290 developers-to catalyze development of reproducible AI algorithms for Gleason grading using 10,616 digitized prostate biopsies. We validated that a diverse set of submitted algorithms reached pathologist-level performance on independent cross-continental cohorts, fully blinded to the algorithm developers. On United States and European external validation sets, the algorithms achieved agreements of 0.862 (quadratically weighted κ, 95% confidence interval (CI), 0.840-0.884) and 0.868 (95% CI, 0.835-0.900) with expert uropathologists. Successful generalization across different patient populations, laboratories and reference standards, achieved by a variety of algorithmic approaches, warrants evaluating AI-based Gleason grading in prospective clinical trials.

Published
2022

12. International Society of Urological Pathology (ISUP) Consensus Conference on Current Issues in Bladder Cancer. Working Group 2: Grading of Mixed Grade, Invasive Urothelial Carcinoma Including Histologic Subtypes and Divergent Differentiations, and Non-Urothelial Carcinomas

Author

Paner, Gladell P., Kamat, Ashish, Netto, George J., Samaratunga, Hemamali, Varma, Murali, Bubendorf, Lukas, van der Kwast, Theodorus H., Cheng, Liang, Adeniran, Adebowale, Al-Ahimadie, Hikmat, Algaba, Fernando, Bhattarai, Yves Allory Selina, Black, Peter, Berman, David, Bertz, Simone, Bubendorf, Lukas, Carlsen, Birgitte, Carvalho, Rita, Cheng, Liang, Chijioke, Obinna, Cho, Yong Mee, Comperat, Eva, di Cunha, Isabela, Dagher, Julien, de Cunha, Isabella, Delahunt, Brett, Downes, Michelle, Flaig, Thomas, Fleischmann, Achim, Florescu, Cosmin, Fontugne, Jacqueline, Genitsch, Vera, Gløersen, Guro Horni, von Gunten, Michael, Grobholz, Rainer, Hansel, Donna, Hartmann, Arndt, Hernandez, Loren Herrera, Hogland, Mattias, Huang, Jiaoti, Jensen, Brad, Iczkowski, Kenneth, Jimenez, Rafael, Jovanovic, Bojana, Kamat, Ashish, Koellermann, Jens, Levin, Trevor, Liedberg, Fredrik, Lopez-Beltran, Antonio, Loya, Anand C, Lyngra, Marianne, McHale, Teresa, Mokeddem, Khadidja, Netto, George, Nourieh, Maya, O’Rourke, Declan, Ostahi, Irina-Alexandra, Paner, Gladell, Picken, Maria M, Pineda, Flavia Guzman, Raspollini, Camelia Radulescu Maria, Reis, Henning, Ruoyu, Shi, Samaratunga, Hemamali, Semba, Remi, Shah, Nigam, Shen, Steven, Smith, Steven, Smith, Tovia, Srigley, John, Suzigan, Sueli, Swarbrick, Nicole, Szalay, Ildiko, Talleraas, Inger, Tille, Jean-Christophe, Toma, Marieta, Trias, Isabel, Tsuzuki, Toyonori, van der Kwast, Theodorus, van Rhijn, Bas, Varma, Murali, Verbeke, Sofie, Verma, Sangeeta, Vlajnic, Tatjana, Vlatkovic, Ljiljana, Warren, Anne, Warrick, Joshua, Williamson, Sean, Wobker, Sara, Woods, Clifton, Yang, Chen, and Yoon, Ghilsuk

Published
2024
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13. International Society of Urological Pathology Consensus Conference on Current Issues in Bladder Cancer. Working Group 4: Molecular Subtypes of Bladder Cancer—Principles of Classification and Emerging Clinical Utility

Author

Warrick, Joshua I., Al-Ahmadie, Hikmat, Berman, David M., Black, Peter C., Flaig, Thomas W., Höglund, Mattias, Bubendorf, Lukas, van der Kwast, Theodorus H., Cheng, Liang, Adeniran, Adebowale, Al-Ahimadie, Hikmat, Algaba, Fernando, Allory, Yves, Bhattarai, Selina, Black, Peter, Berman, David, Bertz, Simone, Bubendorf, Lukas, Carlsen, Birgitte, Carvalho, Rita, Cheng, Liang, Chijioke, Obinna, Cho, Yong Mee, Comperat, Eva, di Cunha, Isabela, Dagher, Julien, de Cunha, Isabella, Delahunt, Brett, Downes, Michelle, Flaig, Thomas, Fleischmann, Achim, Florescu, Cosmin, Fontugne, Jacqueline, Genitsch, Vera, Gløersen, Guro Horni, von Gunten, Michael, Grobholz, Rainer, HanselArndt Hartmann, Donna, Hernandez, Loren Herrera, Hogland, Mattias, Huang, Jiaoti, Jensen, Brad, Iczkowski, Kenneth, Jimenez, Rafael, Jovanovic, Bojana, Kamat, Ashish, Koellermann, Jens, Levin, Trevor, Liedberg, Fredrik, Lopez-Beltran, Antonio, Loya, Anand C, Lyngra, Marianne, McHale, Teresa, Mokeddem, Khadidja, Netto, George, Nourieh, Maya, O’Rourke, Declan, Ostahi, Irina-Alexandra, Paner, Gladell, Picken, Maria M, Pineda, Flavia Guzman, Raspollini, Camelia Radulescu Maria, Reis, Henning, Ruoyu, Shi, Samaratunga, Hemamali, Semba, Remi, Shah, Nigam, Shen, Steven, Smith, Steven, Smith, Tovia, Srigley, John, Suzigan, Sueli, Swarbrick, Nicole, Szalay, Ildiko, Talleraas, Inger, Tille, Jean-Christophe, Toma, Marieta, Trias, Isabel, Tsuzuki, Toyonori, van der Kwast, Theodorus, van Rhijn, Bas, Varma, Murali, Verbeke, Sofie, Verma, Sangeeta, Vlajnic, Tatjana, Vlatkovic, Ljiljana, Warren, Anne, Warrick, Joshua, Williamson, Sean, Wobker, Sara, Woods, Clifton, Yang, Chen, and Yoon, Ghilsuk

Published
2024
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14. Detection of Perineural Invasion in Prostate Needle Biopsies with Deep Neural Networks

Author

Ström, Peter, Kartasalo, Kimmo, Ruusuvuori, Pekka, Grönberg, Henrik, Samaratunga, Hemamali, Delahunt, Brett, Tsuzuki, Toyonori, Egevad, Lars, and Eklund, Martin

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Background: The detection of perineural invasion (PNI) by carcinoma in prostate biopsies has been shown to be associated with poor prognosis. The assessment and quantification of PNI is; however, labor intensive. In the study we aimed to develop an algorithm based on deep neural networks to aid pathologists in this task. Methods: We collected, digitized and pixel-wise annotated the PNI findings in each of the approximately 80,000 biopsy cores from the 7,406 men who underwent biopsy in the prospective and diagnostic STHLM3 trial between 2012 and 2014. In total, 485 biopsy cores showed PNI. We also digitized more than 10% (n=8,318) of the PNI negative biopsy cores. Digitized biopsies from a random selection of 80% of the men were used to build deep neural networks, and the remaining 20% were used to evaluate the performance of the algorithm. Results: For the detection of PNI in prostate biopsy cores the network had an estimated area under the receiver operating characteristics curve of 0.98 (95% CI 0.97-0.99) based on 106 PNI positive cores and 1,652 PNI negative cores in the independent test set. For the pre-specified operating point this translates to sensitivity of 0.87 and specificity of 0.97. The corresponding positive and negative predictive values were 0.67 and 0.99, respectively. For localizing the regions of PNI within a slide we estimated an average intersection over union of 0.50 (CI: 0.46-0.55). Conclusion: We have developed an algorithm based on deep neural networks for detecting PNI in prostate biopsies with apparently acceptable diagnostic properties. These algorithms have the potential to aid pathologists in the day-to-day work by drastically reducing the number of biopsy cores that need to be assessed for PNI and by highlighting regions of diagnostic interest., Comment: 20 pages, 5 figures

Published
2020

16. Appl1, Sortilin and Syndecan-1 immunohistochemistry on intraductal carcinoma of the prostate provides evidence of retrograde spread

Author

Sorvina, Alexandra, Martini, Carmela, Prabhakaran, Sarita, Logan, Jessica M., S-Y. Ung, Benjamin, Moore, Courtney, Johnson, Ian R.D., Lazniewska, Joanna, Tewari, Prerna, Malone, Victoria, Brooks, Robert D., Hickey, Shane M., Caruso, Maria C., Klebe, Sonja, Karageorgos, Litsa, O'Leary, John J., Delahunt, Brett, Samaratunga, Hemamali, and Brooks, Doug A.

Published
2023
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21. Identification of areas of grading difficulties in prostate cancer and comparison with artificial intelligence assisted grading.

Author

Egevad, Lars, Swanberg, Daniela, Delahunt, Brett, Ström, Peter, Kartasalo, Kimmo, Olsson, Henrik, Berney, Dan, Bostwick, David, Evans, Andrew, Humphrey, Peter, Iczkowski, Kenneth, Kench, James, Kristiansen, Glen, Leite, Katia, McKenney, Jesse, Oxley, Jon, Pan, Chin-Chen, Samaratunga, Hemamali, Srigley, John, Takahashi, Hiroyuki, Tsuzuki, Toyonori, van der Kwast, Theo, Varma, Murali, Zhou, Ming, Clements, Mark, and Eklund, Martin

Subjects
Artificial intelligence, Grading, Pathology, Prostate cancer, Reproducibility, Standardization, Artificial Intelligence, Databases, Factual, Humans, Image Interpretation, Computer-Assisted, Male, Neoplasm Grading, Observer Variation, Prostatic Neoplasms
Abstract

The International Society of Urological Pathology (ISUP) hosts a reference image database supervised by experts with the purpose of establishing an international standard in prostate cancer grading. Here, we aimed to identify areas of grading difficulties and compare the results with those obtained from an artificial intelligence system trained in grading. In a series of 87 needle biopsies of cancers selected to include problematic cases, experts failed to reach a 2/3 consensus in 41.4% (36/87). Among consensus and non-consensus cases, the weighted kappa was 0.77 (range 0.68-0.84) and 0.50 (range 0.40-0.57), respectively. Among the non-consensus cases, four main causes of disagreement were identified: the distinction between Gleason score 3 + 3 with tangential cutting artifacts vs. Gleason score 3 + 4 with poorly formed or fused glands (13 cases), Gleason score 3 + 4 vs. 4 + 3 (7 cases), Gleason score 4 + 3 vs. 4 + 4 (8 cases) and the identification of a small component of Gleason pattern 5 (6 cases). The AI system obtained a weighted kappa value of 0.53 among the non-consensus cases, placing it as the observer with the sixth best reproducibility out of a total of 24. AI may serve as a decision support and decrease inter-observer variability by its ability to make consistent decisions. The grading of these cancer patterns that best predicts outcome and guides treatment warrants further clinical and genetic studies. Results of such investigations should be used to improve calibration of AI systems.

Published
2020

22. Shock

Author

Delahunt, Brett

Published
1996

26. Pathologist-Level Grading of Prostate Biopsies with Artificial Intelligence

Author

Ström, Peter, Kartasalo, Kimmo, Olsson, Henrik, Solorzano, Leslie, Delahunt, Brett, Berney, Daniel M., Bostwick, David G., Evans, Andrew J., Grignon, David J., Humphrey, Peter A., Iczkowski, Kenneth A., Kench, James G., Kristiansen, Glen, van der Kwast, Theodorus H., Leite, Katia R. M., McKenney, Jesse K., Oxley, Jon, Pan, Chin-Chen, Samaratunga, Hemamali, Srigley, John R., Takahashi, Hiroyuki, Tsuzuki, Toyonori, Varma, Murali, Zhou, Ming, Lindberg, Johan, Bergström, Cecilia, Ruusuvuori, Pekka, Wählby, Carolina, Grönberg, Henrik, Rantalainen, Mattias, Egevad, Lars, and Eklund, Martin

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Background: An increasing volume of prostate biopsies and a world-wide shortage of uro-pathologists puts a strain on pathology departments. Additionally, the high intra- and inter-observer variability in grading can result in over- and undertreatment of prostate cancer. Artificial intelligence (AI) methods may alleviate these problems by assisting pathologists to reduce workload and harmonize grading. Methods: We digitized 6,682 needle biopsies from 976 participants in the population based STHLM3 diagnostic study to train deep neural networks for assessing prostate biopsies. The networks were evaluated by predicting the presence, extent, and Gleason grade of malignant tissue for an independent test set comprising 1,631 biopsies from 245 men. We additionally evaluated grading performance on 87 biopsies individually graded by 23 experienced urological pathologists from the International Society of Urological Pathology. We assessed discriminatory performance by receiver operating characteristics (ROC) and tumor extent predictions by correlating predicted millimeter cancer length against measurements by the reporting pathologist. We quantified the concordance between grades assigned by the AI and the expert urological pathologists using Cohen's kappa. Results: The performance of the AI to detect and grade cancer in prostate needle biopsy samples was comparable to that of international experts in prostate pathology. The AI achieved an area under the ROC curve of 0.997 for distinguishing between benign and malignant biopsy cores, and 0.999 for distinguishing between men with or without prostate cancer. The correlation between millimeter cancer predicted by the AI and assigned by the reporting pathologist was 0.96. For assigning Gleason grades, the AI achieved an average pairwise kappa of 0.62. This was within the range of the corresponding values for the expert pathologists (0.60 to 0.73)., Comment: 45 pages, 11 figures

Published
2019

29. Aberrant protein expression of Appl1, Sortilin and Syndecan-1 during the biological progression of prostate cancer

Author

Martini, Carmela, Logan, Jessica M., Sorvina, Alexandra, Gordon, Colin, Beck, Andrew R., S-Y. Ung, Ben, Caruso, Maria C., Moore, Courtney, Hocking, Ashleigh, Johnson, Ian R.D., Li, Ka Lok, Karageorgos, Litsa, Hopkins, Ashley M., Esterman, Adrian J., Huzzell, Chelsea, Brooks, Robert D., Lazniewska, Joanna, Hickey, Shane M., Bader, Christie, Parkinson-Lawrence, Emma, Weigert, Roberto, Sorich, Michael J., Tewari, Prerna, Martin, Cara, O'Toole, Sharon, Bates, Mark, Ward, Mark, Mohammed, Bashir, Keegan, Helen, Watson, William, Prendergast, Sophie, Heffernan, Sheena, NiMhaolcatha, Sarah, O'Connor, Roisin, Malone, Victoria, Carter, Marguerite, Ryan, Katie, Brady, Nathan, Clarke, Andres, Sokol, Filip, Prabhakaran, Sarita, Stahl, Jürgen, Klebe, Sonja, Samaratunga, Hemamali, Delahunt, Brett, Selemidis, Stavros, Moretti, Kim L., Butler, Lisa M., O'Leary, John J., and Brooks, Douglas A.

Published
2023
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31. Primary tumour PSMA intensity is an independent prognostic biomarker for biochemical recurrence-free survival following radical prostatectomy

Author

Roberts, Matthew J., Morton, Andrew, Papa, Nathan, Franklin, Anthony, Raveenthiran, Sheliyan, Yaxley, William J., Coughlin, Geoffrey, Gianduzzo, Troy, Kua, Boon, McEwan, Louise, Wong, David, Delahunt, Brett, Egevad, Lars, Samaratunga, Hemamali, Brown, Nicholas, Parkinson, Robert, Emmett, Louise, and Yaxley, John W.

Published
2022
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32. International Society of Urological Pathology Consensus Conference on Current Issues in Bladder Cancer. Working Group 4: Molecular Subtypes of Bladder Cancer—Principles of Classification and Emerging Clinical Utility

Author

Warrick, Joshua I., Al-Ahmadie, Hikmat, Berman, David M., Black, Peter C., Flaig, Thomas W., Höglund, Mattias, Bubendorf, Lukas, van der Kwast, Theodorus H., Cheng, Liang, Adeniran, Adebowale, Al-Ahimadie, Hikmat, Algaba, Fernando, Allory, Yves, Bhattarai, Selina, Black, Peter, Berman, David, Bertz, Simone, Bubendorf, Lukas, Carlsen, Birgitte, Carvalho, Rita, Cheng, Liang, Chijioke, Obinna, Cho, Yong Mee, Comperat, Eva, di Cunha, Isabela, Dagher, Julien, de Cunha, Isabella, Delahunt, Brett, Downes, Michelle, Flaig, Thomas, Fleischmann, Achim, Florescu, Cosmin, Fontugne, Jacqueline, Genitsch, Vera, Gløersen, Guro Horni, von Gunten, Michael, Grobholz, Rainer, HanselArndt Hartmann, Donna, Hernandez, Loren Herrera, Hogland, Mattias, Huang, Jiaoti, Jensen, Brad, Iczkowski, Kenneth, Jimenez, Rafael, Jovanovic, Bojana, Kamat, Ashish, Koellermann, Jens, Levin, Trevor, Liedberg, Fredrik, Lopez-Beltran, Antonio, Loya, Anand C, Lyngra, Marianne, McHale, Teresa, Mokeddem, Khadidja, Netto, George, Nourieh, Maya, O’Rourke, Declan, Ostahi, Irina-Alexandra, Paner, Gladell, Picken, Maria M, Pineda, Flavia Guzman, Raspollini, Camelia Radulescu Maria, Reis, Henning, Ruoyu, Shi, Samaratunga, Hemamali, Semba, Remi, Shah, Nigam, Shen, Steven, Smith, Steven, Smith, Tovia, Srigley, John, Suzigan, Sueli, Swarbrick, Nicole, Szalay, Ildiko, Talleraas, Inger, Tille, Jean-Christophe, Toma, Marieta, Trias, Isabel, Tsuzuki, Toyonori, van der Kwast, Theodorus, van Rhijn, Bas, Varma, Murali, Verbeke, Sofie, Verma, Sangeeta, Vlajnic, Tatjana, Vlatkovic, Ljiljana, Warren, Anne, Warrick, Joshua, Williamson, Sean, Wobker, Sara, Woods, Clifton, Yang, Chen, and Yoon, Ghilsuk

Published
2023
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38. Utility of Pathology Imagebase for standardisation of prostate cancer grading.

Author

Egevad, Lars, Delahunt, Brett, Berney, Daniel, Bostwick, David, Cheville, John, Comperat, Eva, Evans, Andrew, Fine, Samson, Grignon, David, Humphrey, Peter, Hörnblad, Jonas, Iczkowski, Kenneth, Kench, James, Kristiansen, Glen, Leite, Katia, Magi-Galluzzi, Cristina, McKenney, Jesse, Oxley, Jon, Pan, Chin-Chen, Samaratunga, Hemamali, Srigley, John, Takahashi, Hiroyuki, True, Lawrence, Tsuzuki, Toyonori, van der Kwast, Theo, Varma, Murali, Zhou, Ming, and Clements, Mark

Subjects
International Society of Urological Pathology, database, grading, prostate, standardisation, Databases, Factual, Humans, Male, Neoplasm Grading, Pathology, Clinical, Prostatic Neoplasms
Abstract

AIMS: Despite efforts to standardise grading of prostate cancer, even among experts there is still a considerable variation in grading practices. In this study we describe the use of Pathology Imagebase, a novel reference image library, for setting an international standard in prostate cancer grading. METHODS AND RESULTS: The International Society of Urological Pathology (ISUP) recently launched a reference image database supervised by experts. A panel of 24 international experts in prostate pathology reviewed independently microphotographs of 90 cases of prostate needle biopsies with cancer. A linear weighted kappa of 0.67 (95% confidence interval = 0.62-0.72) and consensus was reached in 50 cases. The interobserver weighted kappa varied from 0.48 to 0.89. The highest level of agreement was seen for Gleason score (GS) 3 + 3 = 6 (ISUP grade 1), while higher grades and particularly GS 4 + 3 = 7 (ISUP grade 3) showed considerable disagreement. Once a two-thirds majority was reached, images were moved automatically into a public database available for all ISUP members at www.isupweb.org. Non-members are able to access a limited number of cases. CONCLUSIONS: It is anticipated that the database will assist pathologists to calibrate their grading and, hence, decrease interobserver variability. It will also help to identify instances where definitions of grades need to be clarified.

Published
2018

43. Artificial intelligence assistance significantly improves Gleason grading of prostate biopsies by pathologists

Author

Bulten, Wouter, Balkenhol, Maschenka, Belinga, Jean-Joël Awoumou, Brilhante, Américo, Çakır, Aslı, Egevad, Lars, Eklund, Martin, Farré, Xavier, Geronatsiou, Katerina, Molinié, Vincent, Pereira, Guilherme, Roy, Paromita, Saile, Günter, Salles, Paulo, Schaafsma, Ewout, Tschui, Joëlle, Vos, Anne-Marie, ISUP Pathology Imagebase Expert Panel, van Boven, Hester, Vink, Robert, van der Laak, Jeroen, Hulsbergen-van der Kaa, Christina, Litjens, Geert, Delahunt, Brett, Samaratunga, Hemamali, Grignon, David J., Evans, Andrew J., M.Berney, Daniel, Pan, Chin-Chen, Kristiansen, Glen, Kench, James G., Oxley, Jon, Leite, Katia R.M., McKenney, Jesse K., Humphrey, Peter A., Fine, Samson W., Tsuzuki, Toyonori, Varma, Murali, Zhou, Ming, Comperat, Eva, Bostwick, David G., Iczkowski, Kenneth A., Magi-Galluzzi, Cristina, Srigley, John R., Takahashi, Hiroyuki, and van der Kwast, Theo

Published
2021
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48. Histological findings of totally embedded robot assisted laparoscopic radical prostatectomy (RALP) specimens in 1197 men with a negative (low risk) preoperative multiparametric magnetic resonance imaging (mpMRI) prostate lobe and clinical implications

Author

Yaxley, William John, Nouhaud, François-Xavier, Raveenthiran, Sheliyan, Franklin, Anthony, Donato, Peter, Coughlin, Geoff, Kua, Boon, Gianduzzo, Troy, Wong, David, Parkinson, Robert, Brown, Nicholas, Samaratunga, Hemamali, Delahunt, Brett, Egevad, Lars, Roberts, Matthew, and Yaxley, John William

Published
2021
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