Statistical considerations in spectral histopathology and performance evaluation

Shachi Mittal; Rohit Bhargava

doi:10.1117/12.2577920

5 March 2021 Statistical considerations in spectral histopathology and performance evaluation

Shachi Mittal, Rohit Bhargava

Proceedings Volume 11656, Advanced Chemical Microscopy for Life Science and Translational Medicine 2021; 1165614 (2021) https://doi.org/10.1117/12.2577920
Event: SPIE BiOS, 2021, Online Only

Abstract

Spectroscopic imaging offers a potential path to all-digital molecular pathology by relating the spectral data to histopathological details of tissue. In the different statistical approaches applied to spectral data, there are gaps in the appropriate sample size estimation for a significant statistical power and accurate model assessment especially for multiclass problems. Underestimation of the sample size can lead to statistically insignificant diagnostic tests while an overestimation can greatly increase experimental costs and time frames.Since the receiver operating characteristic (ROC) curve is designed primarily for a binary test, there are no straightforward approaches to use it for multiple classes in a typical pathology image. In this study, we have described sample size estimation (power analysis) and multiclass diagnostic ROC curve generation for hyperspectral datasets.

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Shachi Mittal and Rohit Bhargava "Statistical considerations in spectral histopathology and performance evaluation", Proc. SPIE 11656, Advanced Chemical Microscopy for Life Science and Translational Medicine 2021, 1165614 (5 March 2021); https://doi.org/10.1117/12.2577920

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