# Confocal video-mosaicking microscopy to guide surgery of superficially spreading skin cancers > **NIH NIH R01** · SLOAN-KETTERING INST CAN RESEARCH · 2022 · $649,426 ## Abstract Superficially spreading types of skin cancers such as lentigo maligna melanomas (LMMs) and non-melanoma skin cancers (NMSCs) occur mostly on older patients, with diffuse sub-clinical sub-surface spread over large areas and with poorly defined margins that are difficult to detect. To treat these cancers, dermatologists rou- tinely perform a large number of mapping biopsies to determine the spread and margins, followed by surgical excision with wide "safety" margins. Not surprisingly, such a "blind" approach results in under-sampling of the margins, over-sampling of normal skin, too many false positives and false negatives, and too much loss of normal skin tissue. What may help address this problem is reflectance confocal microscopy (RCM) imaging to noninvasively delineate margins, directly on patients. RCM imaging detects skin cancers in vivo with sensitivity of 85-95% and specificity 80-70%. In 2016, the Centers for Medicare and Medicaid Services granted reim- bursement codes for RCM imaging of skin. RCM imaging is now being increasingly used to noninvasively guide diagnosis, sparing patients from unnecessary biopsies of benign lesions. While the two-decade effort leading to the granting of these codes was focused on imaging-guided diagnosis, emerging applications are in imaging to guide therapy. We propose to create an approach called RCM video-mosaicking, to noninvasively map skin cancer margins over large areas on patients, with increased sampling, accuracy and sparing of nor- mal tissue. The innovation will be in designing a highly robust (against tissue warping and motion artifacts) and high speed (real-time, seconds) approach for RCM video-mosaicking: we will develop an optical flow ap- proach with a novel hybrid 3-stage deep learning network comprising of 8 parameters that will model global and local rigid and non-rigid tissue motion dynamics, learn and adapt to variable tissue and speckle noise con- ditions in patients, and predict and automatically detect motion blur artifacts. As required by PAR-18-009, our academic-industrial partnership will deliver RCM video-mosaicking to clinicians for real-time implementation at the bedside (translational novelty). Our proposed application is for guiding surgical excision, but the approach will have wider impact, for guiding new and emerging less invasive non-surgical treatments for superficial skin cancers. In a preliminary study, we demonstrated RCM video-mosaicking with real-time speed (125 millisec- onds per frame, 8 frames per second), and registration errors of 1.02 ± 1.3 pixels relative to field-of-view of 1000 x 1000 pixels. Our specific aims are (1) to develop a real-time and robust RCM video-mosaicking ap- proach and incorporate into a handheld confocal microscope for use at the bedside, (2) to test the approach for image quality and clinical acceptability, and (3) to prospectively test on 100 patients, with pre-surgical video- mosaicking of LMM margins and superficial NMSC margins, follo... ## Key facts - **NIH application ID:** 10426308 - **Project number:** 5R01CA240771-04 - **Recipient organization:** SLOAN-KETTERING INST CAN RESEARCH - **Principal Investigator:** Octavia Irma Camps - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2022 - **Award amount:** $649,426 - **Award type:** 5 - **Project period:** 2019-07-01 → 2024-06-30 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10426308 ## Citation > US National Institutes of Health, RePORTER application 10426308, Confocal video-mosaicking microscopy to guide surgery of superficially spreading skin cancers (5R01CA240771-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10426308. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*