Publications
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Optical Spectroscopy as a Method for Skin Cancer Risk Assessment
Eladio Rodriguez-Diaz, Danielle Manolakos, Holly Christman, Michael A Bonning, John K Geisse, Ousama M A'Amar, David J Leffell, Irving J Bigio (2019) -
Elastic Scattering Spectroscopy in Assessing Skin Lesions: An "In Vivo" Study
Tahwinder Upile 1, Waseem Jerjes, Hani Radhi, Jaspal Mahil, Anuja Rao, Colin Hopper (2011) -
A new tool to inform intra-operative decision making in skin cancer treatment: The non-invasive assessment of basal cell carcinoma of the skin using elastic scattering spectroscopy
T Upile, Waseem Jerjes, O Johal, Simione Lew-Gor, J Mahil, Holger H Sudhoff (2012) -
Elastic scattering spectroscopy in the diagnosis of pigmented lesions: comparison with clinical and histopathological diagnosis
J. J. Scarisbrick, C. D. O. Pickard, Andrew C. Lee, Gavin M. Briggs, Kristie Johnson, Stephen G. Bown, Marco Novelli, M. R. S. Keshtgar, Irving J. Bigio, R. Yu (2003) -
Comparison between ultraviolet-visible and near-infrared elastic scattering spectroscopy of chemically induced melanomas in an animal model
Ousama M. A'amar, Ronald D. Ley, Irving J. Bigio (2004) -
Spectroscopic Sensing of Cancer and Cancer Therapy: Current Status of Translational Research
Irving J Bigio 1, Stephen G Bown (2004) -
Real-time pathology to guide breast surgery: seeing alone is not believing
Irving J Bigio (2012) -
The Color of Cancer: Margin Guidance for Oral Cancer Resection Using Elastic Scattering Spectroscopy
Gregory A Grillone, Zimmern Wang, Gintas P Krisciunas, Angela C Tsai, Vishnu R Kannabiran, Robert W Pistey, Qing Zhao, Eladio Rodriguez-Diaz, Ousama M A'Amar, Irving J Bigio (2017) -
Preoperative Discrimination of Benign From Malignant Disease in Thyroid Nodules With Indeterminate Cytology Using Elastic Light-Scattering Spectroscopy
Jennifer E Rosen, Hyunsuk Suh, Nicholas J Giordano, Ousama M Aamar, Eladio Rodriguez-Diaz, Irving I Bigio, Stephanie L Lee (2014) -
Endoscopic Histological Assessment of Colonic Polyps by Using Elastic Scattering Spectroscopy
Eladio Rodriguez-Diaz, Qin Huang, Sandra R Cerda, Michael J O'Brien, Irving J Bigio, Satish K Singh (2014) -
Skin Cancer: Precancers
Paul Bruner, Benjamin Bashline (2019) -
Safety and Effectiveness of Elastic Scattering Spectroscopy and Machine Learning in the Evaluation of Skin Lesions for Cancer
Benvenuto-Andrade, C., Manolakos, D., Cognetta, A.B. (2020)
Indications for Use
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The DermaSensor™ device is indicated for use as an objective tool to assist qualified healthcare professionals in evaluating skin lesions suggestive of melanoma, basal cell carcinoma, and/or squamous cell carcinoma. The DermaSensor device is intended to assist the user in deciding whether skin lesions require further clinical care and is not intended to be used for direct diagnosis of skin cancer. DermaSensor is only for use by qualified healthcare professionals appropriately trained in the assessment of skin lesions for cancer.
CLAIMS #1 - “faster [and easier] route to reliable information”
SUBSTANTIATION: “Faster” when compared to other technologies that require extensive training, skin preparation and/or image interpretation. "ESS requires no skin preparation and is easy to administer, requiring minimal practitioner training. [It] generates a simple dichotomous output in a non-invasive way." (Rodriguez-Diaz et al (Photochemistry and Photobiology, 2019) https://onlinelibrary.wiley.com/doi/abs/10.1111/php.13140).
SUBSTANTIATION: “reliable information” – Published data on DermaSensor’s ESS and Machine Learning technology shows sensitivity >90% across all skin cancers combined (Rodriguez-Diaz et al (Photochemistry and Photobiology, 2019) https://onlinelibrary.wiley.com/doi/abs/10.1111/php.13140).
CLAIMS #2 - “Smarter clinical choices”
SUBSTANTIATION: DermaSensor’s ESS and Machine Learning technology sensitivity in published data surpasses the sensitivity of naked eye examination for skin cancers, while decreasing the morbidity associated with skin cancers . In a published 2018 meta-analysis physician naked eye sensitivity was greater than 90% in only six of 28 in‐person‐based evaluations and physician experience may influence accuracy. (“Diagnosing skin cancer.” Posted December 6, 2018. https://onlinelibrary.wiley.com/doi/abs/10.1111/php.13140).
CLAIMS #3 - “DermaSensor may help you to avoid benign excisions with poor reimbursement rates and unnecessary morbidity”
SUBSTANTIATION: If the DermaSensor device is used with lesions suggestive of skin cancer that the healthcare professional intended to excise and the true negative device results lead to the healthcare professional deciding to not excise certain benign lesions, that could decrease benign excisions. Published data on DermaSensor’s ESS and Machine Learning technology shows specificity >30% across all benign lesion pathologies (Rodriguez-Diaz et al (Photochemistry and Photobiology, 2019) https://onlinelibrary.wiley.com/doi/abs/10.1111/php.13140).
CLAIM #4 - “DermaSensor is the new, quicker route to better outcomes”
CLAIM #5 - “Enabling early detection using non-invasive spectroscopy”
CLAIM #6 - “DermaSensor may help you detect more of your patients' skin cancer”
SUBSTANTIATION: “Quicker” when compared to other technologies that require extensive training, skin preparation and/or image interpretation. "
SUBSTANTIATION: “...route to better outcomes”, "Enabling early detection" and “DermaSensor may help you detect more of your patients’ skin cancer”– Published data on DermaSensor’s ESS and Machine Learning technology shows sensitivity >90% across all skin cancers combined (Rodriguez-Diaz et al (
