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This study presents a progressive approach using multi-wavelength interference (MWI) phase imaging with diffuse reflection hyperspectral imaging, measuring interference between 446.6 nm and 632 nm. Fast Fourier transform–based processing and refractive-index variance analysis train a classifier for ex-vivo tissue, achieving 94% specificity and 90.9% sensitivity, and enabling 3D phase-resolved discrimination of unstained cancerous versus healthy tissue at depth.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/multi-wavelength-interference-phase-imaging-for-automatic-breast-cancer-detection-and-delineation-using-diffuse-reflection-imaging/329960/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/multi-wavelength-interference-phase-imaging-for-automatic-breast-cancer-detection-and-delineation-using-diffuse-reflection-imaging/329960.png","ImageObject",300,407,{"name":92,"@type":93},"Jiven","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-27","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What imaging technique does the study propose for breast cancer detection?","Question",{"text":113,"@type":114},"The study proposes multi-wavelength interference (MWI) phase imaging based on diffuse reflection hyperspectral imaging, using interference measurements at 446.6 nm and 632 nm.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How is image processing performed to distinguish tumor from normal tissue?",{"text":118,"@type":114},"Fast Fourier (FF) transform analysis is used to process the interference pattern and quantify changes in refractive index, with increased refractive index variance indicating tumor growth.",{"name":120,"@type":111,"acceptedAnswer":121},"What classification performance does the method achieve?",{"text":122,"@type":114},"After signal normalization with the FF transform algorithm, the method reports 94% specificity and 90.9% sensitivity for categorization of the 632 nm acquired image on preliminary breast specimen data.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},329960,1790192634,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":145,"read_time":146},1099513958607,"https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nMulti‑wavelength interference phase imaging for automatic breast cancer detection and delineation using diffuse reflection imaging  \nAlaaeldin Mahmoud* & Yasser H. El‑Sharkawy  \nMillions of women globally are impacted by the major health problem of breast cancer (BC) . Early detection of BC is critical for successful treatment and improved survival rates. In this study, we provide a progressive approach for BC detection using multi‑wavelength interference (MWI) phase imaging based on diffuse reflection hyperspectral (HS) imaging. The proposed findings are based on the measurement of the interference pattern between the blue (446.6 nm) and red (632 nm) wavelengths. We consider implementing a comprehensive image processing and categorization method based on the use of Fast Fourier (FF) transform analysis pertaining to a change in the refractive index between tumor and normal tissue. We observed that cancer growth affects tissue organization dramatically, as seen by persistently increased refractive index variance in tumors compared normal areas. Both malignant and normal tissue had different depth data collected from it that was analyzed. To enhance the categorization of ex‑vivo BC tissue, we developed and validateda training classifier algorithm specifically designed for categorizing HS cube data. Following the application of signal normalization with the FF transform algorithm, our methodology achieved a high level of performance with a specificity (Spec) of 94% and a sensitivity (Sen) of 90.9% for the 632 nm acquired image categorization, based on preliminary findings from breast specimens under investigation. Notably, we successfully leveraged unstained tissue samples to create 3D phase‑ resolved images that effectively highlight the distinctions in diffuse reflectance features between cancerous and healthy tissue. Preliminary data revealed that our imaging method might be able to assist specialists in safely excising malignant areas and assessing the tumor bed following resection automatically at different depths. This preliminary investigation might result in an effective \"in‑vivo\"disease description utilizing optical technology using a typical RGB camera with wavelength‑specific operation with our quantitative phase MWI imaging methodology.  \nBC is the most prevalent type of cancer in the world and the main reason why women die from cancer1. This disease was contributing for almost 570 thousand deaths in the last five years2. Furthermore, with 2.3 million newly diagnosed cases on average have been with malignant tumors annually3,4. Invasive BC tumor cell that can expand to various parts of the body such as the brain, liver, bone, and lungs, indicating its severity5. Consequently, one of the critical clinical challenges in BC management is the need for precise intraoperative margin assessment during BC surgery. This challenge arises from the complex nature of breast tissue, where ensuring cancer-free margins is essential to minimize the risk of recurrence and improve patient outcomes. Due to the lack of adequate operational tumor edge assessment techniques, complete tumor evacuation is tough6. As a result, a tumor is discovered at the resected edge of the excised specimen, and upwards to 37% of female patients undergoing breast-conserving surgeries are affected by this problem7. This considerable amount of tumor is still present in the patient, increasing the threat of a tumor recurrence and decreasing the likelihood of long-term disease-specific survival8. For efficient treatment and better results, early detection and correct diagnosis, it is crucial to develop new quantitative approaches that combine imaging and processing that can provide a more accurate diagnosis of BC. Many techniques have been developed to achieve this based on the light interaction with different specimens. Although phase contrast microscopy9 alleviated the issue of del","cbCaicq4XdVp2jlZ","https://ap.wps.com/l/cbCaicq4XdVp2jlZ","pdf",4347517,17,"English","# Introduction\n## Clinical need for intraoperative margin assessment\n## Quantitative phase imaging and hyperspectral approaches\n# Methods\n## Multi-wavelength interference phase imaging and hyperspectral pipeline\n## FF-transform analysis and refractive index variance\n## Training classifier for hyperspectral cube categorization\n# Results and validation\n## Performance metrics for image categorization\n## 3D phase-resolved visualization with unstained samples\n# Discussion and potential applications\n## Assisting excision and automated tumor bed assessment","[{\"question\":\"What imaging technique does the study propose for breast cancer detection?\",\"answer\":\"The study proposes multi-wavelength interference (MWI) phase imaging based on diffuse reflection hyperspectral imaging, using interference measurements at 446.6 nm and 632 nm.\"},{\"question\":\"How is image processing performed to distinguish tumor from normal tissue?\",\"answer\":\"Fast Fourier (FF) transform analysis is used to process the interference pattern and quantify changes in refractive index, with increased refractive index variance indicating tumor growth.\"},{\"question\":\"What classification performance does the method achieve?\",\"answer\":\"After signal normalization with the FF transform algorithm, the method reports 94% specificity and 90.9% sensitivity for categorization of the 632 nm acquired image on preliminary breast specimen data.\"}]","Multi-wavelength interference phase imaging for automatic breast cancer detection and delineation using diffuse reflection imaging | PDF",1789986323,43]