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High-definition Fourier Transform Infrared FT-IR Spectroscopic Imaging of Human Tissue Sections towards Improving Pathology

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Exploring Feature Selection with Deep Learning for Kidney Tissue Microarray Classification Using Infrared Spectral

Zachary Caterer¹, Jordan Langlois², Connor McKeown²

¹Interdisciplinary Quantitative Biology PhD Program, Biofrontier's Institute, University of Colorado Boulder, Boulder, CO 80303, USA.

Bioengineering (Basel, Switzerland)

|April 26, 2025

Summary

This summary is machine-generated.

This study introduces a deep learning framework using infrared imaging to accurately differentiate rare kidney cancers (chromophobe renal cell carcinoma) from benign tumors (Oncocytoma), improving diagnostic speed and accuracy.

Keywords:

deep learning feature selection laser-based infrared spectroscopic imaging quantum cascade lasers renal tumors tissue microarrays

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Area of Science:

Medical diagnostics
Computational pathology
Spectroscopic imaging

Background:

Kidney and renal pelvic cancers cause significant mortality, with renal cell carcinoma (RCC) being the most common type.
Chromophobe RCC diagnosis is challenging due to overlapping features with benign Oncocytoma.
Current biopsy methods have limitations in distinguishing these renal tumors.

Purpose of the Study:

To develop a deep learning framework for automated classification of kidney tumors using infrared (IR) spectroscopic imaging data.
To improve the accuracy and efficiency of distinguishing chromophobe RCC from Oncocytoma.
To create a potential tool for high-throughput, real-time IR imaging diagnostics.

Main Methods:

Utilized a deep learning framework on an IR dataset of kidney tumor tissue microarrays (TMAs).
Employed feature selection algorithms to reduce data dimensionality and optimize spectral analysis.
Implemented a deep learning classification model for automated tumor identification.

Main Results:

Achieved a classification accuracy of 91.3% on validation data.
Demonstrated that using only 13.6% of IR wavelengths reduced training time by 21% while maintaining high accuracy.
Developed a classification pipeline with high predictive power for renal tumors.

Conclusions:

The proposed deep learning framework integrated with feature selection offers a powerful tool for renal tumor classification.
This approach shows potential for integration into real-time IR imaging systems for enhanced diagnostics.
Accurate differentiation of chromophobe RCC and Oncocytoma can significantly impact patient outcomes and treatment strategies.