ColonScopeX: Leveraging Explainable Expert Systems with Multimodal Data for Improved Early Diagnosis of Colorectal Cancer

• URL: http://arxiv.org/abs/2504.08824v1
• Date: Wed, 09 Apr 2025 20:45:11 GMT
• Title: ColonScopeX: Leveraging Explainable Expert Systems with Multimodal Data for Improved Early Diagnosis of Colorectal Cancer
• Authors: Natalia Sikora, Robert L. Manschke, Alethea M. Tang, Peter Dunstan, Dean A. Harris, Su Yang,
• Abstract summary: Colorectal cancer (CRC) ranks as the second leading cause of cancer-related deaths and the third most prevalent malignant tumour worldwide.<n>Early detection of CRC remains problematic due to its non-specific and often embarrassing symptoms.<n>We propose ColonScopeX, a machine learning framework utilizing explainable AI (XAI) methodologies to enhance the early detection of CRC and pre-cancerous lesions.
• Score: 3.541280502270993
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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