Skip to main content
Clinical Trials/CTRI/2025/06/089329
CTRI/2025/06/089329RecruitingNot Applicable

Detecting normal benign and malignant colonic tissues by photoacoustic spectroscopy

DBT-BUILDER, Govt. of India1 site in 1 country44 target enrollmentStarted: July 7, 2025Last updated:

Trial Snapshot

Phase
Not Applicable
Status
Recruiting
Sponsor
Enrollment
44
Locations
1
Primary Endpoint
The newly developed photoacoustic endoscopic probe can classify different

Study Overview

Brief Summary

The development of a photoacoustic probe for detecting colonic mucosal conditions is a significant advancement in the diagnosis and treatment of colorectal cancer. This innovative technology aims to offer a quick and real-time method for identifying cancerous tissues. It addresses the limitations of conventional colonoscopy, which only allows for visual inspection without the ability to distinguish between different mucosal conditions like adenoma and carcinoma. The photoacoustic probe uses the unique optical absorption properties of tissue components such as collagen, NADH, elastin, and FAD, which can serve as biomarkers for cancer diagnosis. By detecting the specific spectral patterns of these biomarkers, the probe can accurately identify and differentiate between normal, adenomatous, and carcinomatous tissues. To improve the diagnostic capabilities of this technology, we will develop a machine-learning model. This model will be trained using spectral data obtained from the photoacoustic probe, enabling it to recognize complex patterns associated with different colonic mucosal conditions. Integrating machine learning will not only enhance the accuracy and reliability of the diagnosis but also aid in the development of a highly sensitive and specific diagnostic tool. This approach has the potential to revolutionize colorectal cancer screening by providing a non-invasive, realtime diagnostic method that can lead to early detection and timely treatment, thus improving patient outcomes and reducing the burden of colorectal cancer. The proposed study has the potential to significantly improve cancer diagnosis and treatment. It involves developing a photoacoustic probe specifically for detecting conditions in the colon’s mucosal lining. This technology can be seamlessly integrated into existing endoscopy and colonoscopy procedures, allowing for real-time, accurate diagnosis of colorectal cancers during routine examinations. This can greatly improve the speed and efficiency of cancer detection compared to traditional methods that rely on biopsy and histopathology, which can be timeconsuming and may delay treatment. The photoacoustic probe provides immediate results, enabling healthcare providers to make informed decisions on the spot. This real-time diagnostic capability can lead to earlier detection of malignant tissues, more precise characterization of mucosal abnormalities, and timely intervention, ultimately enhancing patient outcomes. The clinical translation of this technology represents a significant advancement in colorectal cancer screening and diagnosis, potentially setting a new standard in gastroenterological practice.

Study Design

Study Type
Interventional
Allocation
Na
Masking
None

Eligibility Criteria

Ages
19.00 Year(s) to 80.00 Year(s) (—)
Sex
All

Inclusion Criteria

  • Adults above the age of 18 years Patients undergoing colonoscopy for any clinical indication Patients undergoing surgical removal of part of the colon for any clinical indication.

Exclusion Criteria

  • Patients are not able to give consent.
  • Emergency colonoscopy procedures.
  • Hemodynamically unstable patients.
  • Patients with serious comorbid illnesses preventing full colonoscopy from being performed.

Outcomes

Primary Outcomes

The newly developed photoacoustic endoscopic probe can classify different

Time Frame: Three years

tissue pathological conditions in colorectal mucosa by utilizing the distinct optical absorption properties

Time Frame: Three years

of biomarkers such as collagen, NADH, elastin, and FAD. These biomarkers allow the probe to

Time Frame: Three years

distinguish between normal, adenomatous, and carcinomatous tissues. Integrating machine learning

Time Frame: Three years

into the system further enhances diagnostic accuracy by recognizing complex patterns in the spectral

Time Frame: Three years

data, offering a non-invasive, real-time tool for early detection and improved treatment of colorectal

Time Frame: Three years

cancer.

Time Frame: Three years

Secondary Outcomes

  • This study has the potential for translation into clinical practice.(5 years)

Investigators

Sponsor
DBT-BUILDER, Govt. of India
Sponsor Class
Government funding agency
Responsible Party
Principal Investigator
Principal Investigator

Krishna Kishore Mahato

Manipal School of Life Sciences, Manipal Academy of Higher Education

Study Sites (1)

Loading locations...

Similar Trials