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, followed by validation against post-surgical pa- thology. We are a highly synergistic team from Memorial Sloan Kettering Cancer Center, Northeastern Uni- versity, and Caliber Imaging and Diagnostics (formerly, Lucid Inc.), with a 13-year record of collaboration.

Public Health Relevance

Reflectance confocal microscopy (RCM) imaging can noninvasively diagnose skin cancers, and spare patients from biopsies of benign skin conditions. We propose to develop an approach to noninvasively delineate skin cancer margins, to help guide less invasive surgery, and help more accurately and completely remove cancer while preserving more of the surrounding normal skin.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
1R01CA240771-01
Application #
9801564
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Henderson, Lori A
Project Start
2019-07-01
Project End
2024-06-30
Budget Start
2019-07-01
Budget End
2020-06-30
Support Year
1
Fiscal Year
2019
Total Cost
Indirect Cost
Name
Sloan-Kettering Institute for Cancer Research
Department
Type
DUNS #
064931884
City
New York
State
NY
Country
United States
Zip Code
10065