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Conventional imaging modalities such as mammography, MRI, and ultrasound face constraints in sensitivity, specificity, cost, and patient comfort. This study presents a handheld Near-Infrared Diffuse Optical Tomography (NIR DOT) probe using multi-wavelength LEDs and a linear CCD sensor to reconstruct breast cross-sectional images from scattering and absorption. Optimized differential optical wavelengths enable functional discrimination between healthy and malignant tissue. Clinical evaluations suggest strong tumor localization and treatment-response monitoring performance (sensitivity 94.7%, specificity 84.2%) supported by machine learning and a modified diffusion equation (MDE) for faster, more accurate reconstruction. 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Incorporating machine learning algorithms and a modified diffusion equation (MDE) improves the accuracy and speed of image reconstruction.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},371252,1790654395,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":41},137441390410,"https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984","Article  \nAdvancing Near-Infrared Probes for Enhanced Breast Cancer Assessment  \nMohammad Pouriayevali 1, Ryley McWilliams 1, Avner Bachar 1,2, Parmveer Atwal 3, Ramani Ramaseshan 1 and Farid Golnaraghi 1, *  \nAcademic Editor: Hai-Feng (Frank) Ji  \nReceived: 15 January 2025  \nRevised: 29 January 2025  \nAccepted: 4 February 2025  \nPublished: 6 February 2025  \nCitation: Pouriayevali, M.;  \nMcWilliams, R.; Bachar, A.; Atwal, P.; Ramaseshan, R.; Golnaraghi, F. Advancing Near-Infrared Probes for Enhanced Breast Cancer Assessment. Sensors 2025, 25, 983. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/s25030983](10.3390/s25030983)  \nCopyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \n1 School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC V3T 0A3, Canada;  \n[mohammad_pouriayevali@sfu.ca](mohammad_pouriayevali@sfu.ca) (M.P.); [rmcwilli@sfu.ca](rmcwilli@sfu.ca) (R.M.); [avner.bachar@ufv.ca](avner.bachar@ufv.ca) (A.B.);  \n[ramani_ramaseshan_2@sfu.ca](ramani_ramaseshan_2@sfu.ca) (R.R.)  \n2 Automation and Robotics Technician, University of Fraser Valley, Chilliwack, BC V2R 0N3, Canada  \n3 Department of Medical Physics, BC Cancer, Abbotsford, BC V2S 0C2, Canada; patwal@bccancer.bc.ca  \n* Correspondence: [farid_golnaraghi@sfu.ca](farid_golnaraghi@sfu.ca)  \nAbstract: Breast cancer remains a leading cause of cancer-related deaths among women, emphasizing the critical need for early detection and monitoring techniques. Conventional imaging modalities such as mammography, MRI, and ultrasound have face sensitivity, specificity, cost, and patient comfort limitations. This study introduces a handheld NearInfrared Diffuse Optical Tomography (NIR DOT) probe for breast cancer imaging. The NIRscan probe utilizes multi-wavelength light-emitting diodes (LEDs) and a linear chargecoupled device (CCD) sensor to acquire real-time optical data, reconstructing cross-sectional images of breast tissue based on scattering and absorption coefficients. With wavelengths optimized for the differential optical properties of tissue components, the probe enables functional imaging, distinguishing between healthy and malignant tissues. Clinical evaluations have demonstrated its potential for precise tumor localization and monitoring therapeutic responses, achieving a sensitivity of 94.7% and specificity of 84.2% . By incorporating machine learning algorithms and a modified diffusion equation (MDE), the system enhances the accuracy and speed of image reconstruction, supporting rapid, non-invasive diagnostics. This development represents a significant step forward in portable, costeffective solutions for breast cancer detection, with potential applications in low-resource settings and diverse clinical environments.  \nKeywords: biosensor; machine learning; breast cancer; chemotherapy; residual cancer burden  \n1. Introduction  \nBreast cancer remains the most prevalent cancer among women and is the second leading cause of cancer-related deaths [1] . In 2024, it was estimated that approximately 42,250 women in the US would die of breast cancer [1] . In recent years, a variety of imaging modalities have been developed to facilitate early detection and reduce breast cancer mortality rates [2,3] . Despite X-ray mammography being the primary screening modality, it has notable limitations, particularly for younger women and those with dense breast tissue, reducing its sensitivity [2,3] . Additionally, exposure to ionizing radiation can pose long-term health risks [4] . Ultrasound (US) has been utilized as an alternative for imaging dense breast tissues, but its effectiveness is hindered by speckle artifacts and low specificity [5] . Magnetic resonance imagi","cbCainZb9IBnoZfP","https://ap.wps.com/l/cbCainZb9IBnoZfP","pdf",1946764,12,"English","# Abstract\n# Keywords\n# 1. Introduction\n## Imaging modalities and limitations\n## Near-infrared optical imaging progress\n# Article metadata","[{\"question\":\"What imaging approach does the study introduce for breast cancer assessment?\",\"answer\":\"The study introduces a handheld Near-Infrared Diffuse Optical Tomography (NIR DOT) probe that reconstructs breast cross-sectional images using optical data.\"},{\"question\":\"How does the NIRscan probe acquire and process data?\",\"answer\":\"It uses multi-wavelength LEDs and a linear CCD sensor to acquire real-time optical measurements, then reconstructs images based on scattering and absorption coefficients.\"},{\"question\":\"What performance and enhancements are reported for the probe’s imaging?\",\"answer\":\"Clinical evaluations indicate sensitivity of 94.7% and specificity of 84.2%. Incorporating machine learning algorithms and a modified diffusion equation (MDE) improves the accuracy and speed of image reconstruction.\"}]","Advancing Near-Infrared Probes for Enhanced Breast Cancer Assessment | PDF",1790191626]