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Photoacoustic-Integrated Multimodal Approach for Colorectal Cancer Diagnosis
Shimul Biswas1, Diya Pratish Chohan1,2, Mrunmayee Wankhede1
1Department of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India.
ACS Biomaterials Science & Engineering
|July 1, 2025
Summary
Photoacoustic (PA) spectroscopy offers a promising, minimally invasive method for early colorectal cancer detection. Combining PA imaging with machine learning achieves high accuracy in identifying tumors and guiding personalized treatment.
Area of Science:
- Biomedical optics
- Cancer diagnostics
- Medical imaging
Background:
- Colorectal cancer (CRC) poses a significant global health burden, necessitating advanced diagnostic solutions.
- Early and accurate detection of CRC is crucial for improving patient outcomes.
- Current diagnostic methods face limitations in sensitivity and invasiveness.
Purpose of the Study:
- To evaluate the potential of photoacoustic (PA) spectroscopy as an advanced tool for colorectal cancer diagnostics.
- To explore the integration of PA techniques with other imaging modalities and machine learning (ML) for enhanced tumor detection and characterization.
- To assess the diagnostic accuracy and clinical utility of PA-based approaches in colorectal cancer management.
Main Methods:
- Utilizing photoacoustic (PA) spectroscopy, a hybrid optical-acoustic technique, to detect biochemical changes in the tumor microenvironment.
- Integrating PA imaging with ultrasound (US), photoacoustic microscopy (PAM), and nanoparticle-enhanced imaging for comprehensive tissue analysis.
- Combining PA technology with endoscopy and machine learning (ML) algorithms for real-time, minimally invasive tumor detection and data analysis.
Main Results:
- PA spectroscopy enables the detection of biochemical alterations indicative of early-stage malignancies.
- Integrated PA imaging provides detailed mapping of tissue structure, vascularity, and molecular markers.
- PA combined with ML demonstrated high diagnostic accuracy (AUC up to 0.96, accuracy >89%) for colorectal tumor detection.
- This approach facilitates tumor classification, therapy monitoring, and identification of features like hypoxia and tumor-associated bacteria.
Conclusions:
- Photoacoustic spectroscopy, particularly when enhanced with ML and other imaging modalities, represents a powerful, minimally invasive tool for precise colorectal cancer detection.
- The technology shows significant potential for real-time, noninvasive diagnosis, tumor classification, and personalized treatment monitoring.
- Continued advancements in PA technology, including nanoparticle design and ML analytics, are poised to revolutionize colorectal cancer management.
