TY - JOUR A1 - Bertram, Christof A1 - Aubreville, Marc A1 - Donovan, Taryn A1 - Bartel, Alexander A1 - Wilm, Frauke A1 - Marzahl, Christian A1 - Assenmacher, Charles-Antoine A1 - Becker, Kathrin A1 - Bennett, Mark A1 - Corner, Sarah M. A1 - Cossic, Brieuc A1 - Denk, Daniela A1 - Dettwiler, Martina A1 - Garcia Gonzalez, Beatriz A1 - Gurtner, Corinne A1 - Haverkamp, Ann-Kathrin A1 - Heier, Annabelle A1 - Lehmbecker, Annika A1 - Merz, Sophie A1 - Noland, Erica L. A1 - Plog, Stephanie A1 - Schmidt, Anja A1 - Sebastian, Franziska A1 - Sledge, Dodd G. A1 - Smedley, Rebecca C. A1 - Tecilla, Marco A1 - Thaiwong, Tuddow A1 - Fuchs-Baumgartinger, Andrea A1 - Meuten, Donald J. A1 - Breininger, Katharina A1 - Kiupel, Matti A1 - Maier, Andreas A1 - Klopfleisch, Robert T1 - Computer-assisted mitotic count using a deep learning–based algorithm improves interobserver reproducibility and accuracy JF - Veterinary Pathology N2 - The mitotic count (MC) is an important histological parameter for prognostication of malignant neoplasms. However, it has inter- and intraobserver discrepancies due to difficulties in selecting the region of interest (MC-ROI) and in identifying or classifying mitotic figures (MFs). Recent progress in the field of artificial intelligence has allowed the development of high-performance algorithms that may improve standardization of the MC. As algorithmic predictions are not flawless, computer-assisted review by pathologists may ensure reliability. In the present study, we compared partial (MC-ROI preselection) and full (additional visualization of MF candidates and display of algorithmic confidence values) computer-assisted MC analysis to the routine (unaided) MC analysis by 23 pathologists for whole-slide images of 50 canine cutaneous mast cell tumors (ccMCTs). Algorithmic predictions aimed to assist pathologists in detecting mitotic hotspot locations, reducing omission of MFs, and improving classification against imposters. The interobserver consistency for the MC significantly increased with computer assistance (interobserver correlation coefficient, ICC = 0.92) compared to the unaided approach (ICC = 0.70). Classification into prognostic stratifications had a higher accuracy with computer assistance. The algorithmically preselected hotspot MC-ROIs had a consistently higher MCs than the manually selected MC-ROIs. Compared to a ground truth (developed with immunohistochemistry for phosphohistone H3), pathologist performance in detecting individual MF was augmented when using computer assistance (F1-score of 0.68 increased to 0.79) with a reduction in false negatives by 38%. The results of this study demonstrate that computer assistance may lead to more reproducible and accurate MCs in ccMCTs. UR - https://doi.org/10.1177/03009858211067478 KW - canine cutaneous mast cell tumors KW - artificial intelligence KW - digital pathology KW - deep learning KW - mitotic figures KW - mitotic count KW - automated image analysis KW - computer assistance Y1 - 2021 UR - https://doi.org/10.1177/03009858211067478 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-13141 SN - 1544-2217 VL - 59 IS - 2 SP - 211 EP - 226 PB - Sage CY - London ER - TY - JOUR A1 - Donovan, Taryn A1 - Moore, Frances M. A1 - Bertram, Christof A1 - Luong, Richard A1 - Bolfa, Pompei A1 - Klopfleisch, Robert A1 - Tvedten, Harold A1 - Salas, Elisa N. A1 - Whitley, Derick A1 - Aubreville, Marc A1 - Meuten, Donald J. T1 - Mitotic Figures - Normal, Atypical, and Imposters: A Guide to Identification JF - Veterinary pathology UR - https://doi.org/10.1177/0300985820980049 KW - mitotic figure KW - mitotic count KW - MC KW - prophase KW - prometaphase KW - metaphase KW - anaphase KW - telophase KW - computational pathology KW - CPATH KW - artificial intelligence KW - AI KW - whole slide image KW - WSI KW - pathology KW - oncology Y1 - 2021 UR - https://doi.org/10.1177/0300985820980049 SN - 1544-2217 VL - 58 IS - 2 SP - 243 EP - 257 PB - Sage CY - London ER - TY - JOUR A1 - Meuten, Donald J. A1 - Moore, Frances M. A1 - Donovan, Taryn A1 - Bertram, Christof A1 - Klopfleisch, Robert A1 - Foster, Robert A. A1 - Smedley, Rebecca C. A1 - Dark, Michael A1 - Milovancev, Milan A1 - Stromberg, Paul A1 - Williams, Bruce H. A1 - Aubreville, Marc A1 - Avallone, Giancarlo A1 - Bolfa, Pompei A1 - Cullen, John A1 - Dennis, Michelle M. A1 - Goldschmidt, Michael A1 - Luong, Richard A1 - Miller, Andrew D. A1 - Miller, Margaret A. A1 - Munday, John S. A1 - Roccabianca, Paola A1 - Salas, Elisa N. A1 - Schulman, F. Yvonne A1 - Laufer-Amorim, Renee A1 - Asakawa, Midori G. A1 - Craig, Linden A1 - Dervisis, Nick A1 - Esplin, D. Glen A1 - George, Jeanne W. A1 - Hauck, Marlene A1 - Kagawa, Yumiko A1 - Kiupel, Matti A1 - Linder, Keith A1 - Meichner, Kristina A1 - Marconato, Laura A1 - Oblak, Michelle L. A1 - Santos, Renato L. A1 - Simpson, R. Mark A1 - Tvedten, Harold A1 - Whitley, Derick T1 - International Guidelines for Veterinary Tumor Pathology: A Call to Action JF - Veterinary Pathology UR - https://doi.org/10.1177/03009858211013712 KW - standardization KW - oncology KW - guidelines KW - protocols KW - validation Y1 - 2021 UR - https://doi.org/10.1177/03009858211013712 SN - 1544-2217 VL - 58 IS - 5 SP - 766 EP - 794 PB - Sage CY - London ER -