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 (morphometry) can
improve reproducibility, but manual methods are time consuming. In this study,
we evaluated fully automated morphometry using a deep learning-based algorithm
in 96 canine cutaneous mast cell tumors with information on patient survival.
Algorithmic morphometry was compared with karyomegaly estimates by 11
pathologists, manual nuclear morphometry of 12 cells by 9 pathologists, and the
mitotic count as a benchmark. The prognostic value of automated morphometry was
high with an area under the ROC curve regarding the tumor-specific survival of
0.943 (95% CI: 0.889 - 0.996) for the standard deviation (SD) of nuclear area,
which was higher than manual morphometry of all pathologists combined (0.868,
95% CI: 0.737 - 0.991) and the mitotic count (0.885, 95% CI: 0.765 - 1.00). At
the proposed thresholds, the hazard ratio for algorithmic morphometry (SD of
nuclear area $\geq 9.0 \mu m^2$) was 18.3 (95% CI: 5.0 - 67.1), for manual
morphometry (SD of nuclear area $\geq 10.9 \mu m^2$) 9.0 (95% CI: 6.0 - 13.4),
for karyomegaly estimates 7.6 (95% CI: 5.7 - 10.1), and for the mitotic count
30.5 (95% CI: 7.8 - 118.0). Inter-rater reproducibility for karyomegaly
estimates was fair ($\kappa$ = 0.226) with highly variable
sensitivity/specificity values for the individual pathologists. Reproducibility
for manual morphometry (SD of nuclear area) was good (ICC = 0.654). This study
supports the use of algorithmic morphometry as a prognostic test to overcome
the limitations of estimates and manual measurements.

通过深度学习算法的自动形态测量，该研究支持使用自动形态测量作为一种预后测试，以克服估计和手动测量的限制。

基于深度学习算法的核形态测量对犬皮肤鳞状细胞癌具有预后意义

Nuclear Morphometry using a Deep Learning-based Algorithm has Prognostic  Relevance for Canine Cutaneous Mast Cell Tumors

The morphometry of a kidney tumor revealed by contrast-enhanced Computed
Tomography (CT) imaging is an important factor in clinical decision making
surrounding the lesion's diagnosis and treatment. Quantitative study of the
relationship between kidney tumor morphology and clinical outcomes is difficult
due to data scarcity and the laborious nature of manually quantifying imaging
predictors. Automatic semantic segmentation of kidneys and kidney tumors is a
promising tool towards automatically quantifying a wide array of morphometric
features, but no sizeable annotated dataset is currently available to train
models for this task. We present the KiTS19 challenge dataset: A collection of
multi-phase CT imaging, segmentation masks, and comprehensive clinical outcomes
for 300 patients who underwent nephrectomy for kidney tumors at our center
between 2010 and 2018. 210 (70%) of these patients were selected at random as
the training set for the 2019 MICCAI KiTS Kidney Tumor Segmentation Challenge
and have been released publicly. With the presence of clinical context and
surgical outcomes, this data can serve not only for benchmarking semantic
segmentation models, but also for developing and studying biomarkers which make
use of the imaging and semantic segmentation masks.

本研究提出了一个基于自动语义分割的肾脏和肾脏肿瘤形态学特征检测方法，并建立了覆盖肾性肿瘤磨损模式的数据集，通过这个数据集开展肾性肿瘤分割的任务以提高磨损模式分析效率。