Portable, handheld, and affordable blood perfusion imager for screening of subsurface cancer in resource-limited settings

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 119; no. 2; pp. 1 - 12
• Main Authors: Bhowmik, Arka, Ghosh, Biswajoy, Pal, Mousumi, Paul, Ranjan Rashmi, Chatterjee, Jyotirmoy, Chakraborty, Suman
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 11.01.2022
• Subjects: Adult; Aged, 80 and over; Algorithms; Biopsy; Blood circulation; Cancer; Data integration; Decision making; Diagnostic Imaging - instrumentation; Diagnostic Imaging - methods; Early Detection of Cancer; Engineering; Humans; Image acquisition; Image analysis; Image contrast; Image processing; Image Processing, Computer-Assisted; Learning algorithms; Lesions; Machine Learning; Male; Mass Screening - instrumentation; Mass Screening - methods; Mathematical analysis; Medical imaging; Microvasculature; Middle Aged; Mouth Neoplasms - diagnostic imaging; Mouth Neoplasms - pathology; Perfusion; Perfusion - instrumentation; Perfusion - methods; Physical Sciences; Physiology; Portable equipment; Selectivity; Squamous Cell Carcinoma of Head and Neck - diagnostic imaging; Surface temperature; Thermal imaging; Tumors; resource-limited setting; blood perfusion sensor; cancer screening
• ISSN: 0027-8424; 1091-6490; 1091-6490
• DOI: 10.1073/pnas.2026201119

Precise information on localized variations in blood circulation holds the key for noninvasive diagnostics and therapeutic assessment of various forms of cancer. While thermal imaging by itself may provide significant insights on the combined implications of the relevant physiological parameters, viz. local blood perfusion and metabolic balance due to active tumors as well as the ambient conditions, knowledge of the tissue surface temperature alone may be somewhat inadequate in distinguishing between some ambiguous manifestations of precancer and cancerous lesions, resulting in compromise of the selectivity in detection. This, along with the lack of availability of a user-friendly and inexpensive portable device for thermal-image acquisition, blood perfusion mapping, and data integration acts as a deterrent against the emergence of an inexpensive, contact-free, and accurate in situ screening and diagnostic approach for cancer detection and management. Circumventing these constraints, here we report a portable noninvasive blood perfusion imager augmented with machine learning–based quantitative analytics for screening precancerous and cancerous traits in oral lesions, by probing the localized alterations in microcirculation. With a proven overall sensitivity >96.66% and specificity of 100% as compared to gold-standard biopsybased tests, the method successfully classified oral cancer and precancer in a resource-limited clinical setting in a double-blinded patient trial and exhibited favorable predictive capabilities considering other complementary modes of medical image analysis as well. The method holds further potential to achieve contrast-free, accurate, and low-cost diagnosis of abnormal microvascular physiology and other clinically vulnerable conditions, when interpreted along with complementary clinically evidenced decision-making perspectives.