Illuminating Pathology Comprehensive Hyper Spectral and RGB Imaging for Cholangiocarcinoma Analysis

Abstract

Cholangiocarcinoma (CCA) has historically been challenging to detect due to its subtle appearance and tendency to develop deep in the bile ducts. Traditional methods, such as biopsy and standard imaging (CT, MRI), have limited accuracy and often require invasive procedures to confirm the presence and extent of the disease. The objective is to develop a non-invasive, highly accurate method for diagnosing cholangiocarcinoma using hyper spectral and RGB imaging technologies, thereby reducing reliance on invasive procedures and improving diagnostic precision. Before machine learning, pathologists relied on histopathology, radiographic imaging (e.g., CT, MRI), and patient biopsies to detect cholangiocarcinoma. These methods, while effective, are invasive, time-consuming, and less accurate for early-stage detection. The current diagnostic methods for cholangiocarcinoma lack precision and often necessitate invasive biopsies, which are risky for patients. Non-invasive imaging techniques like CT and MRI provide insufficient detail to reliably distinguish between benign and malignant tissues, limiting early diagnosis and effective treatment planning. A machine learning-based model, when trained on hyper spectral and RGB imaging data, can detect subtle, unique patterns associated with cholangiocarcinoma. Such a system automates the analysis, eliminating subjectivity, and achieves a high accuracy rate, enabling more reliable diagnosis and reducing the need for invasive biopsies.