Haghofer Andreas, Parlak Eda, Bartel Alexander, Donovan Taryn A, Assenmacher Charles-Antoine, Bolfa Pompei, Dark Michael J, Fuchs-Baumgartinger Andrea, Klang Andrea, Jäger Kathrin, Klopfleisch Robert, Merz Sophie, Richter Barbara, Schulman F Yvonne, Janout Hannah, Ganz Jonathan, Scharinger Josef, Aubreville Marc, Winkler Stephan M, Kiupel Matti, Bertram Christof A
University of Applied Sciences Upper Austria, Hagenberg, Austria.
Johannes Kepler University Linz, Linz, Austria.
Vet Pathol. 2025 Mar;62(2):161-177. doi: 10.1177/03009858241295399. Epub 2024 Nov 19.
Variation in nuclear size and shape is an important criterion of malignancy for many tumor types; however, categorical estimates by pathologists have poor reproducibility. Measurements of nuclear characteristics can improve reproducibility, but current manual methods are time-consuming. The aim of this study was to explore the limitations of estimates and develop alternative morphometric solutions for canine cutaneous mast cell tumors (ccMCTs). We assessed the following nuclear evaluation methods for accuracy, reproducibility, and prognostic utility: (1) anisokaryosis estimates by 11 pathologists; (2) gold standard manual morphometry of at least 100 nuclei; (3) practicable manual morphometry with stratified sampling of 12 nuclei by 9 pathologists; and (4) automated morphometry using deep learning-based segmentation. The study included 96 ccMCTs with available outcome information. Inter-rater reproducibility of anisokaryosis estimates was low (k = 0.226), whereas it was good (intraclass correlation = 0.654) for practicable morphometry of the standard deviation (SD) of nuclear size. As compared with gold standard manual morphometry (area under the ROC curve [AUC] = 0.839, 95% confidence interval [CI] = 0.701-0.977), the prognostic value (tumor-specific survival) of SDs of nuclear area for practicable manual morphometry and automated morphometry were high with an AUC of 0.868 (95% CI = 0.737-0.991) and 0.943 (95% CI = 0.889-0.996), respectively. This study supports the use of manual morphometry with stratified sampling of 12 nuclei and algorithmic morphometry to overcome the poor reproducibility of estimates. Further studies are needed to validate our findings, determine inter-algorithmic reproducibility and algorithmic robustness, and explore tumor heterogeneity of nuclear features in entire tumor sections.
核大小和形状的变化是许多肿瘤类型恶性程度的重要标准；然而，病理学家的分类估计重复性较差。核特征的测量可以提高重复性，但目前的手工方法耗时。本研究的目的是探讨估计的局限性，并为犬皮肤肥大细胞瘤（ccMCTs）开发替代的形态计量学解决方案。我们评估了以下核评估方法的准确性、重复性和预后效用：（1）11名病理学家的核大小不均一性估计；（2）至少100个核的金标准手工形态计量学；（3）9名病理学家对12个核进行分层抽样的可行手工形态计量学；（4）使用基于深度学习分割的自动形态计量学。该研究包括96例具有可用结局信息的ccMCTs。核大小不均一性估计的评分者间重复性较低（k = 0.226），而核大小标准差的可行形态计量学的重复性良好（组内相关系数 = 0.654）。与金标准手工形态计量学（ROC曲线下面积[AUC] = 0.839，95%置信区间[CI] = 0.701 - 0.977）相比，可行手工形态计量学和自动形态计量学的核面积标准差的预后价值（肿瘤特异性生存）较高，AUC分别为0.868（95% CI = 0.737 - 0.991）和0.943（95% CI = 0.889 - 0.996）。本研究支持使用对12个核进行分层抽样的手工形态计量学和算法形态计量学来克服估计重复性差的问题。需要进一步研究来验证我们的发现，确定算法间的重复性和算法的稳健性，并探索整个肿瘤切片中核特征的肿瘤异质性。
