ColonScopeX: Leveraging Explainable Expert Systems with Multimodal Data for Improved Early Diagnosis of Colorectal Cancer
Journal:
arXiv
Published Date:
Apr 9, 2025
Abstract
Colorectal cancer (CRC) ranks as the second leading cause of cancer-related
deaths and the third most prevalent malignant tumour worldwide. Early detection
of CRC remains problematic due to its non-specific and often embarrassing
symptoms, which patients frequently overlook or hesitate to report to
clinicians. Crucially, the stage at which CRC is diagnosed significantly
impacts survivability, with a survival rate of 80-95\% for Stage I and a stark
decline to 10\% for Stage IV. Unfortunately, in the UK, only 14.4\% of cases
are diagnosed at the earliest stage (Stage I).
In this study, we propose ColonScopeX, a machine learning framework utilizing
explainable AI (XAI) methodologies to enhance the early detection of CRC and
pre-cancerous lesions. Our approach employs a multimodal model that integrates
signals from blood sample measurements, processed using the Savitzky-Golay
algorithm for fingerprint smoothing, alongside comprehensive patient metadata,
including medication history, comorbidities, age, weight, and BMI. By
leveraging XAI techniques, we aim to render the model's decision-making process
transparent and interpretable, thereby fostering greater trust and
understanding in its predictions. The proposed framework could be utilised as a
triage tool or a screening tool of the general population.
This research highlights the potential of combining diverse patient data
sources and explainable machine learning to tackle critical challenges in
medical diagnostics.
