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1. AI-based histopathology image analysis reveals a distinct subset of endometrial cancers

Author

Darbandsari, Amirali, Farahani, Hossein, Asadi, Maryam, Wiens, Matthew, Cochrane, Dawn, Khajegili Mirabadi, Ali, Jamieson, Amy, Farnell, David, Ahmadvand, Pouya, Douglas, Maxwell, Leung, Samuel, Abolmaesumi, Purang, Jones, Steven J. M., Talhouk, Aline, Kommoss, Stefan, Gilks, C. Blake, Huntsman, David G., Singh, Naveena, McAlpine, Jessica N., and Bashashati, Ali

Published
2024
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5. Genomic characterization of DICER1-associated neoplasms uncovers molecular classes

Author

Kommoss, Felix K. F., Chong, Anne-Sophie, Chong, Anne-Laure, Pfaff, Elke, Jones, David T. W., Hiemcke-Jiwa, Laura S., Kester, Lennart A., Flucke, Uta, Gessler, Manfred, Schrimpf, Daniel, Sahm, Felix, Clarke, Blaise A., Stewart, Colin J. R., Wang, Yemin, Gilks, C. Blake, Kommoss, Friedrich, Huntsman, David G., Schüller, Ulrich, Koelsche, Christian, Glenn McCluggage, W., von Deimling, Andreas, and Foulkes, William D.

Published
2023
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8. Variability in endometrial carcinoma pathology practice: opportunities for improvement with molecular classification

Author

Thompson, Emily F., Huvila, Jutta, Jamieson, Amy, Leung, Samuel, Lum, Amy, Offman, Saul, Lytwyn, Alice, Sur, Mona Lisa, Hoang, Lynn, Irving, Julie, van der Westhuizen, Nicholas, Morin, Chantale, Bicamumpaka, Cyrille, Azordegan, Nazilla, Gougeon, François, Ennour-Idrissi, Kaoutar, Senz, Janine, McConechy, Melissa K., Aguirre-Hernandez, Rosalia, Lui, Victoria, Kuo, Carolyn, Bell, Cassidy, Salisbury, Taylor, Lawson, James, He, Ellen, Wang, Shanzhao, Chiu, Derek, Kean, Sarah, Samouëlian, Vanessa, Salvador, Shannon, Gotlieb, Walter, Helpman, Limor, Scott, Stephanie, Wohlmuth, Christoph, Vicus, Danielle, Plante, Marie, Talhouk, Aline, Huntsman, David, Parra-Herran, Carlos, Kinloch, Mary, Grondin, Katherine, Gilks, C. Blake, and McAlpine, Jessica N.

Published
2022
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10. Molecular subtype stratified outcomes according to adjuvant therapy in endometrial cancer

Author

Jamieson, Amy, Huvila, Jutta, Leung, Samuel, Chiu, Derek, Thompson, Emily F., Lum, Amy, Kinloch, Mary, Helpman, Limor, Salvador, Shannon, Vicus, Danielle, Kean, Sarah, Samouelian, Vanessa, Grondin, Katherine, Irving, Julie, Offman, Saul, Parra-Herran, Carlos, Lau, Susie, Scott, Stephanie, Plante, Marie, McConechy, Melissa K., Huntsman, David G., Talhouk, Aline, Kommoss, Stefan, Gilks, C. Blake, and McAlpine, Jessica N.

Published
2023
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12. Assessing the impact of deep‐learning assistance on the histopathological diagnosis of serous tubal intraepithelial carcinoma (STIC) in fallopian tubes.

Author

Bogaerts, Joep MA, Steenbeek, Miranda P, Bokhorst, John‐Melle, van Bommel, Majke HD, Abete, Luca, Addante, Francesca, Brinkhuis, Mariel, Chrzan, Alicja, Cordier, Fleur, Devouassoux‐Shisheboran, Mojgan, Fernández‐Pérez, Juan, Fischer, Anna, Gilks, C Blake, Guerriero, Angela, Jaconi, Marta, Kleijn, Tony G, Kooreman, Loes, Martin, Spencer, Milla, Jakob, and Narducci, Nadine

Subjects
COMPUTATIONAL intelligence, ARTIFICIAL intelligence, DEEP learning, PATHOLOGISTS, DIGITAL technology, FALLOPIAN tubes
Abstract

In recent years, it has become clear that artificial intelligence (AI) models can achieve high accuracy in specific pathology‐related tasks. An example is our deep‐learning model, designed to automatically detect serous tubal intraepithelial carcinoma (STIC), the precursor lesion to high‐grade serous ovarian carcinoma, found in the fallopian tube. However, the standalone performance of a model is insufficient to determine its value in the diagnostic setting. To evaluate the impact of the use of this model on pathologists' performance, we set up a fully crossed multireader, multicase study, in which 26 participants, from 11 countries, reviewed 100 digitalized H&E‐stained slides of fallopian tubes (30 cases/70 controls) with and without AI assistance, with a washout period between the sessions. We evaluated the effect of the deep‐learning model on accuracy, slide review time and (subjectively perceived) diagnostic certainty, using mixed‐models analysis. With AI assistance, we found a significant increase in accuracy (p < 0.01) whereby the average sensitivity increased from 82% to 93%. Further, there was a significant 44 s (32%) reduction in slide review time (p < 0.01). The level of certainty that the participants felt versus their own assessment also significantly increased, by 0.24 on a 10‐point scale (p < 0.01). In conclusion, we found that, in a diverse group of pathologists and pathology residents, AI support resulted in a significant improvement in the accuracy of STIC diagnosis and was coupled with a substantial reduction in slide review time. This model has the potential to provide meaningful support to pathologists in the diagnosis of STIC, ultimately streamlining and optimizing the overall diagnostic process. [ABSTRACT FROM AUTHOR]

Published
2024
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13. The spectrum of oestrogen receptor expression in endometrial carcinomas of no specific molecular profile.

Author

Alafraidi, Mona, Hoang, Lynn, Howitt, Brooke E., Longacre, Teri A., McAlpine, Jessica N., Jamieson, Amy, Singh, Naveena, Gilks, C Blake, and Pors, Jennifer

Subjects
RENAL cell carcinoma, ENDOMETRIAL cancer, YOLK sac, CARCINOMA, HYSTERECTOMY
Abstract

Aims: Decreased oestrogen receptor (ER) expression is a marker of poor prognosis in endometrial carcinomas (EC) of no specific molecular profile (NSMP), but the optimal cut‐off to separate high‐risk 'low ER' versus low‐risk 'high ER' expression has not been defined. Here we characterised the distribution of ER staining in a cohort of ECs. Methods and results: Biopsy specimens from 120 cases of NSMP EC were stained for ER and assigned an Allred score. In 66 additional cases ER staining of matched biopsy and hysterectomy were compared. Twelve of 120 tumours had an Allred score of 0–3, including three endometrioid carcinomas (EEA) (one G1, two G3), four clear cell carcinomas (CCC), two mesonephric‐like adenocarcinoma (MLA) and one each of: gastric‐type adenocarcinoma, carcinosarcoma and endometrial carcinoma NOS. Three had Allred scores of 4–5: two MLA and one high‐grade carcinoma with yolk sac differentiation. Five had Allred scores of 6: four EEA (one G1, one G2, two G3) and one mixed clear cell and endometrioid carcinoma. The remaining 100 tumours with Allred scores ≥ 7 were all EEA (66 G1, 28 G2, five G3 and one grade unknown). Comparing the biopsy versus hysterectomy ER staining (n = 66), the results were within a single Allred score point, except two cases with strong diffuse expression in the biopsy (Allred 8) and moderate expression in the hysterectomy (Allred 5). Conclusions: Most NSMP ECs (> 80%) show high ER expression (Allred score ≥ 7). All non‐endometrioid carcinomas and a few endometrioid carcinomas had lower ER expression (Allred score ≤ 6) or were completely negative. [ABSTRACT FROM AUTHOR]

Published
2024
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15. Molecular characterization of low-grade serous ovarian carcinoma identifies genomic aberrations according to hormone receptor expression

Author

Cheasley, Dane, Fernandez, Marta Llaurado, Köbel, Martin, Kim, Hannah, Dawson, Amy, Hoenisch, Joshua, Bittner, Madison, Chiu, Derek S., Talhouk, Aline, Gilks, C. Blake, Jayawardana, Madawa W., Pishas, Kathleen I., Mes-Masson, Anne-Marie, Provencher, Diane, Nigam, Abhimanyu, Hacker, Neville F., Gorringe, Kylie L., Campbell, Ian G., and Carey, Mark S.

Published
2022
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20. Refined cut-off for TP53 immunohistochemistry improves prediction of TP53 mutation status in ovarian mucinous tumors: implications for outcome analyses

Author

Kang, Eun Young, Cheasley, Dane, LePage, Cecile, Wakefield, Matthew J., da Cunha Torres, Michelle, Rowley, Simone, Salazar, Carolina, Xing, Zhongyue, Allan, Prue, Bowtell, David D.L., Mes-Masson, Anne-Marie, Provencher, Diane M., Rahimi, Kurosh, Kelemen, Linda E., Fasching, Peter A., Doherty, Jennifer A., Goodman, Marc T., Goode, Ellen L., Deen, Suha, Pharoah, Paul D.P., Brenton, James D., Sieh, Weiva, Mateoiu, Constantina, Sundfeldt, Karin, Cook, Linda S., Le, Nhu D., Anglesio, Michael S., Gilks, C. Blake, Huntsman, David G., Kennedy, Catherine J., Traficante, Nadia, Bowtell, D., Chenevix-Trench, G., Green, A., Webb, P., DeFazio, A., Gertig, D., Traficante, N., Fereday, S., Moore, S., Hung, J., Harrap, K., Sadkowsky, T., Pandeya, N., Malt, M., Mellon, A., Robertson, R., Bergh, T. Vanden, Jones, M., Mackenzie, P., Maidens, J., Nattress, K., Chiew, Y.E., Stenlake, A., Sullivan, H., Alexander, B., Ashover, P., Brown, S., Corrish, T., Green, L., Jackman, L., Ferguson, K., Martin, K., Martyn, A., Ranieri, B., White, J., Jayde, V., Mamers, P., Bowes, L., Galletta, L., Giles, D., Hendley, J., Alsop, K., Schmidt, T., Shirley, H., Ball, C., Young, C., Viduka, S., Tran, Hoa, Bilic, Sanela, Glavinas, Lydia, Brooks, Julia, Stuart-Harris, R., Kirsten, F., Rutovitz, J., Clingan, P., Glasgow, A., Proietto, A., Braye, S., Otton, G., Shannon, J., Bonaventura, T., Stewart, J., Begbie, S., Friedlander, M., Bell, D., Baron-Hay, S., Ferrier, A., Gard, G., Nevell, D., Pavlakis, N., Valmadre, S., Young, B., Camaris, C., Crouch, R., Edwards, L., Hacker, N., Marsden, D., Robertson, G., Beale, P., Beith, J., Carter, J., Dalrymple, C., Houghton, R., Russell, P., Links, M., Grygiel, J., Hill, J., Brand, A., Byth, K., Jaworski, R., Harnett, P., Sharma, R., Wain, G., Ward, B., Papadimos, D., Crandon, A., Cummings, M., Horwood, K., Obermair, A., Perrin, L., Wyld, D., Nicklin, J., Davy, M., Oehler, M.K., Hall, C., Dodd, T., Healy, T., Pittman, K., Henderson, D., Miller, J., Pierdes, J., Blomfield, P., Challis, D., McIntosh, R., Parker, A., Brown, B., Rome, R., Allen, D., Grant, P., Hyde, S., Laurie, R., Robbie, M., Healy, D., Jobling, T., Manolitsas, T., McNealage, J., Rogers, P., Susil, B., Sumithran, E., Simpson, I., Phillips, K., Rischin, D., Fox, S., Johnson, D., Lade, S., Loughrey, M., O'Callaghan, N., Murray, W., Waring, P., Billson, V., Pyman, J., Neesham, D., Quinn, M., Underhill, C., Bell, R., Ng, L.F., Blum, R., Ganju, V., Hammond, I., Leung, Y., McCartney, A., Buck, M., Haviv, I., Purdie, D., Whiteman, D., Zeps, N., DeFazio, Anna, Kaufmann, Scott, Churchman, Michael, Gourley, Charlie, Stephens, Andrew N., Meagher, Nicola S., Ramus, Susan J., Antill, Yoland C., Campbell, Ian, Scott, Clare L., Köbel, Martin, Gorringe, Kylie L., Ryland, Georgina L., Allan, Prue E., Alsop, Kathryn, Ananda, Sumitra, Au-Yeung, George, Böhm, Maret, Brand, Alison, Chenevix-Trench, Georgia, Christie, Michael, Chiew, Yoke-Eng, Dudley, Rhiannon, Fairweather, Nicole, Fereday, Sian, Fox, Stephen B., Hacker, Neville F., Hadley, Alison M., Hendley, Joy, Ho, Gwo-Yaw, Hunter, Sally M., Jobling, Tom W., Kalli, Kimberly R., Kaufmann, Scott H., Le Page, Cecile, McNally, Orla M., McAlpine, Jessica N., Mileshkin, Linda, Pyman, Jan, Samimi, Goli, Sharma, Ragwha, and Campbell, Ian G.

Published
2021
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23. Learning generalizable AI models for multi-center histopathology image classification.

Author

Asadi-Aghbolaghi, Maryam, Darbandsari, Amirali, Zhang, Allen, Contreras-Sanz, Alberto, Boschman, Jeffrey, Ahmadvand, Pouya, Köbel, Martin, Farnell, David, Huntsman, David G., Churg, Andrew, Black, Peter C., Wang, Gang, Gilks, C. Blake, Farahani, Hossein, and Bashashati, Ali

Subjects
IMAGE recognition (Computer vision), MACHINE learning, ARTIFICIAL intelligence, DEEP learning, CONVOLUTIONAL neural networks, OBJECT recognition (Computer vision)
Abstract

Investigation of histopathology slides by pathologists is an indispensable component of the routine diagnosis of cancer. Artificial intelligence (AI) has the potential to enhance diagnostic accuracy, improve efficiency, and patient outcomes in clinical pathology. However, variations in tissue preparation, staining protocols, and histopathology slide digitization could result in over-fitting of deep learning models when trained on the data from only one center, thereby underscoring the necessity to generalize deep learning networks for multi-center use. Several techniques, including the use of grayscale images, color normalization techniques, and Adversarial Domain Adaptation (ADA) have been suggested to generalize deep learning algorithms, but there are limitations to their effectiveness and discriminability. Convolutional Neural Networks (CNNs) exhibit higher sensitivity to variations in the amplitude spectrum, whereas humans predominantly rely on phase-related components for object recognition. As such, we propose Adversarial fourIer-based Domain Adaptation (AIDA) which applies the advantages of a Fourier transform in adversarial domain adaptation. We conducted a comprehensive examination of subtype classification tasks in four cancers, incorporating cases from multiple medical centers. Specifically, the datasets included multi-center data for 1113 ovarian cancer cases, 247 pleural cancer cases, 422 bladder cancer cases, and 482 breast cancer cases. Our proposed approach significantly improved performance, achieving superior classification results in the target domain, surpassing the baseline, color augmentation and normalization techniques, and ADA. Furthermore, extensive pathologist reviews suggested that our proposed approach, AIDA, successfully identifies known histotype-specific features. This superior performance highlights AIDA's potential in addressing generalization challenges in deep learning models for multi-center histopathology datasets. [ABSTRACT FROM AUTHOR]

Published
2024
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27. Low-grade serous carcinoma (LGSC): A Canadian multicenter review of practice patterns and patient outcomes

Author

Scott, Stephanie A., Llaurado Fernandez, Marta, Kim, Hannah, Elit, Laurie, Nourmoussavi, Melica, Glaze, Sarah, Roberts, Lesley, Offman, Saul L., Rahimi, Kurosh, Lytwyn, Alice, Sur, Monalisa, Gilks, C. Blake, Matheson, Kara, Köbel, Martin, Dawson, Amy, Tinker, Anna V., Kwon, Janice S., Hoskins, Paul, Santos, Jennifer L., Cheung, Andrea, Provencher, Diane, and Carey, Mark S.

Published
2020
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29. Therapeutic options for mucinous ovarian carcinoma

Author

Gorringe, Kylie L., Cheasley, Dane, Wakefield, Matthew J., Ryland, Georgina L., Allan, Prue E., Alsop, Kathryn, Amarasinghe, Kaushalya C., Ananda, Sumitra, Bowtell, David D.L., Christie, Michael, Chiew, Yoke-Eng, Churchman, Michael, DeFazio, Anna, Fereday, Sian, Gilks, C. Blake, Gourley, Charlie, Hadley, Alison M., Hendley, Joy, Hunter, Sally M., Kaufmann, Scott H., Kennedy, Catherine J., Köbel, Martin, Le Page, Cecile, Li, Jason, Lupat, Richard, McNally, Orla M., McAlpine, Jessica N., Pyman, Jan, Rowley, Simone M., Salazar, Carolina, Saunders, Hugo, Semple, Timothy, Stephens, Andrew N., Thio, Niko, Torres, Michelle C., Traficante, Nadia, Zethoven, Magnus, Antill, Yoland C., Campbell, Ian G., and Scott, Clare L.

Published
2020
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33. DNA methylation-based profiling of uterine neoplasms: a novel tool to improve gynecologic cancer diagnostics

Author

Kommoss, Felix K. F., Stichel, Damian, Schrimpf, Daniel, Kriegsmann, Mark, Tessier-Cloutier, Basile, Talhouk, Aline, McAlpine, Jessica N., Chang, Kenneth T. E., Sturm, Dominik, Pfister, Stefan M., Romero-Pérez, Laura, Kirchner, Thomas, Grünewald, Thomas G. P., Buslei, Rolf, Sinn, Hans-Peter, Mechtersheimer, Gunhild, Schirmacher, Peter, Schmidt, Dietmar, Lehr, Hans-Anton, Sahm, Felix, Huntsman, David G., Gilks, C. Blake, Kommoss, Friedrich, von Deimling, Andreas, and Koelsche, Christian

Published
2020
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34. Pathological chemotherapy response score is prognostic in tubo-ovarian high-grade serous carcinoma: A systematic review and meta-analysis of individual patient data

Author

Aggarwal, Simi, Bronger, Holger, Brown, Elizabeth B., Buck, Martin, Bukhari, Syed A., Coghlan, Edwina, Cope, Nichola, de Almeida, Michelle Samora, De Kroon, Cornelius D., Dean, Andrew, Devlin, Michael-John, Ditzel, Helena M., Drecoll, Enken, Ebata, Takahiro, Fagotti, Anna, Faruqi, Asma, Feeney, Laura, Gupta, Kavita, Harley, Ian, Inzani, Frediano, Jeyarajah, Arjun R., Koay, M.H. Eleanor, Kroep, Judith R., Lee, Jung-Yun, Leung, Yee, Lockley, Michelle, Loft, Alice R., MaGee, Daniel, Manchanda, Ranjit, McKenna, Sarah, Midha, Divya, Millan, David, Millar, Joanne, Miller, Rowan, Mohan, Ganendra R., Mughal, Sohail, Mukhopadhyay, Asima, Nicolau, Sergio Mancini, Nevin, James, Oakley, Abigail S., Quigley, Mary, Rai, Bhavana, Rajwanshi, Arvind, Salfinger, Stuart G., Scambia, Giovanni, Scatchard, Kate, Schmalfeldt, Barbara, Simcock, Bryony, Singh, Priya, Strickland, Kyle C., Suri, Vainta, Syed, Sheeba, Sykes, Peter, Tamura, Kenji, Tan, Adeline, Tan, Jason, Thompson, Emily, Tinker, Anna V., Trevisan, Georgia, Uyeda, Maria Gabriela Baumgarten Kuster, Vaughan, Michelle M., Weichert, Wilko, Williams, Anthony, Williams, Sarah, Yoshida, Hiroshi, Zorzato, Pier Carlo, Cohen, Paul A., Powell, Aime, Böhm, Steffen, Gilks, C. Blake, Stewart, Colin J.R., Meniawy, Tarek M., Bulsara, Max, Avril, Stefanie, Brockbank, Eleanor C., Bosse, Tjalling, de Azevedo Focchi, Gustavo Rubino, Ganesan, Raji, Glasspool, Rosalind M., Howitt, Brooke E., Kim, Hyun-Soo, Le, Nhu D., Mandalia, Trupti, McCluggage, W. Glenn, McNeish, Iain, Srinivasan, Radhika, Tan, Yun Yi, van der Griend, Rachael, Yunokawa, Mayu, Zannoni, Gian F., and Singh, Naveena

Published
2019
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38. TERT promoter mutation in adult granulosa cell tumor of the ovary

Author

Pilsworth, Jessica A., Cochrane, Dawn R., Xia, Zhouchunyang, Aubert, Geraldine, Färkkilä, Anniina E.M., Horlings, Hugo M., Yanagida, Satoshi, Yang, Winnie, Lim, Jamie L.P., Wang, Yi Kan, Bashashati, Ali, Keul, Jacqueline, Wong, Adele, Norris, Kevin, Brucker, Sara Y., Taran, Florin-Andrei, Krämer, Bernhard, Staebler, Annette, van Meurs, Hannah, Oliva, Esther, Shah, Sohrab P., Kommoss, Stefan, Kommoss, Friedrich, Gilks, C. Blake, Baird, Duncan M., and Huntsman, David G.

Published
2018
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42. Mesonephric‐like adenocarcinoma harbours characteristic copy number variations and a distinct DNA methylation signature closely related to mesonephric adenocarcinoma of the cervix.

Author

Kommoss, Felix KF, Lee, Cheng‐Han, Tessier‐Cloutier, Basile, Gilks, C Blake, Stewart, Colin JR, von Deimling, Andreas, and Köbel, Martin

Subjects
DNA copy number variations, DNA methylation, EPIGENOMICS, RENAL cell carcinoma, ADENOCARCINOMA, GENITALIA
Abstract

Mesonephric‐like adenocarcinoma (MLA) of the female genital tract is an uncommon histotype that can arise in both the endometrium and the ovary. The exact cell of origin and histogenesis currently remain unknown. Here, we investigated whole genome DNA methylation patterns and copy number variations (CNVs) in a series of MLAs in the context of a large cohort of various gynaecological carcinoma types. CNV analysis of 19 MLAs uncovered gains of chromosomes 1q (18/19, 95%), 10 (15/19, 79%), 12 (14/19, 74%), and 2 (10/19, 53%), as well as loss of chromosome 1p (7/19, 37%). Gains of chromosomes 1q, 10, and 12 were also identified in the majority of mesonephric adenocarcinomas of the uterine cervix (MAs) as well as subsets of endometrioid carcinomas (ECs) and low‐grade serous carcinomas of the ovary (LGSCs) but only in a minority of serous carcinomas of the uterine corpus (USCs), clear cell carcinomas (CCCs), and tubo‐ovarian high‐grade serous carcinomas (HGSCs). While losses of chromosome 1p together with gains of chromosome 1q were also identified in both MA and LGSC, gains of chromosome 2 were almost exclusively identified in MLA and MA. Unsupervised hierarchical clustering and t‐SNE analysis of DNA methylation data (Illumina EPIC array) identified a co‐clustering for MLAs and MAs, which was distinct from clusters of ECs, USCs, CCCs, LGSCs, and HGSCs. Group‐wise comparisons confirmed a close epigenetic relationship between MLA and MA. These findings, in conjunction with the established histological and immunophenotypical overlap, suggest bona fide mesonephric differentiation, and support a more precise terminology of mesonephric‐type adenocarcinoma instead of MLA in these tumours. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland. [ABSTRACT FROM AUTHOR]

Published
2024
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43. Practical Guidance for Measuring and Reporting Surgical Margins in Vulvar Cancer

Author

Kortekaas, Kim E., Van de Vijver, Koen K., van Poelgeest, Mariëtte I.E., Gilks, C. Blake, Smit, Vincent T.H.B.M., Arif, Saimah, Arora, Deep, Faruqi, Asma, Ganesan, Raji, Griffin, Nicholas R., Hale, Richard, Hock, Yelin E., Horn, Lars-Christian, McCluggage, W. Glenn, Mukonoweshuro, Pinias, Park, Kay J., Rous, Brian, Tanchel, Bruce, Van Rompuy, Anne-Sophie, van Schalkwyk, Gerry, Vella, Jo, Vergine, Marco, Singh, Naveena, and Bosse, Tjalling

Published
2020
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