Oral cancer is a major health issue in India, largely due to the widespread use of tobacco products. Screening high-risk populations for oral cancer is crucial for early detection and improved survival rates. The current gold standard for diagnosis and identifying potential malignancy is histopathological analysis after surgical excision. To minimize uncertainties in the diagnostic gap between clinical examination and histopathological analysis, the development of non-invasive methods capable of classifying oral cancer tissue as malignant or non-malignant in real-time, with a low misclassification rate, is highly desirable. Spectroscopy, known for detecting changes in tissue characteristics indicative of malignant transformation, offers a promising solution. In light of this, BARC has indigenously developed an AI-based, portable, handheld, non-invasive spectroscopy system with a custom-designed pistol-grip spectroscopy probe, known as SCOR, to facilitate large-scale patient screening for early cancer detection.
Oral cancer is one of the most common cancers globally, with over 77,000 new cases and 52,000 deaths reported in India in 2020. Despite therapeutic advances, the five-year survival rate in India remains around 60%. Although the oral cavity is easily accessible, most cases are diagnosed at advanced stages (III and IV). Early detection (Stages I and II) could raise the survival rate to 70-90%. The current gold standard for diagnosis is histopathological analysis following surgical excision. To address the diagnostic gap between clinical examination and histopathology, there is a need for non-invasive methods that can accurately classify oral cancer tissues as malignant or non-malignant in real-time, with minimal misclassification. Spectroscopy techniques, widely used for non-invasive screening, provide molecular information about the tissue composition and are promising for oral cancer diagnosis. Indigenously developed spectroscopy system includes a suitable laser source to excite the target sample for frequency shift and an advanced optical module to collect the low-intensity spectrum with minimal artifacts. Additionally, a data acquisition module and software have been developed to process the captured raw data for spectral identification. Multivariate analysis and AI-based deep learning algorithms have been devised for noise reduction and the classification of various cancerous tissues.
Infrastructure Requirements
| Dimensions, SCOR system | 40 x 70 x 30 mm
(W x D x H) |
Environment
| Temperature | 10-30°C |
| Humidity | 80% non-condensing |
POWER
| Mains Voltage | 240V, 60 Hz, Single phase |
| Power Consumption | 200 W |
MANPOWER
TEST EQUIPMENT
