Nuclear Morphometry using a Deep Learning-based Algorithm has Prognostic Relevance for Canine Cutaneous Mast Cell Tumors
CoRR(2023)
摘要
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
which was higher than manual morphometry of all pathologists combined (0.868,
95
the proposed thresholds, the hazard ratio for algorithmic morphometry (SD of
nuclear area ≥ 9.0 μ m^2) was 18.3 (95
morphometry (SD of nuclear area ≥ 10.9 μ m^2) 9.0 (95
for karyomegaly estimates 7.6 (95
30.5 (95
estimates was fair (κ = 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.
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关键词
nuclear morphometry,tumors,canine,learning-based
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