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Confocal video-mosaicking microscopy to guide surgery of superficially spreading skin cancers

共聚焦视频马赛克显微镜指导浅表扩散皮肤癌的手术

基本信息

批准号：
10309506
负责人：
Octavia Irma Camps
金额：
$ 8.12万
依托单位：
SLOAN-KETTERING INST CAN RESEARCH
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-07-01 至 2024-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10309506
关键词：
Address Algorithms Area Benign Biopsy Caliber Clinical Code Collaborations Dermatologist Detection Development Diagnosis Diagnostic Diffuse Excision Feedback Grant Hutchinson&apos s Melanotic Freckle Hybrids Image Learning Lesion Machine Learning Malignant - descriptor Malignant Neoplasms Maps Memorial Sloan-Kettering Cancer Center Microscope Microscopy Modeling Morphologic artifacts Mosaicism Motion Noise Normal tissue morphology Operative Surgical Procedures Optics Patients Public Health Research Personnel Resolution Safety Sampling Skin Skin Cancer Skin Carcinoma Skin Tissue Specificity Speed Standardization Surface Surgeon Surgical Pathology Testing Time Tissues United States Centers for Medicare and Medicaid Services Universities Validation Video Microscopy Visual automated algorithm base blind classification algorithm clinical imaging deep learning design image guided imaging approach in vivo innovation learning network machine learning algorithm microscopic imaging noninvasive diagnosis novel older patient parent project preservation prospective test reflectance confocal microscopy segmentation algorithm

项目摘要

1 FROM PARENT PROJECT R01CA240771 2 Superficially spreading types of skin cancers such as lentigo maligna melanomas (LMMs) and non-melanoma 3 skin cancers (NMSCs) occur mostly on older patients, with diffuse sub-clinical sub-surface spread over large 4 areas and with poorly defined margins that are difficult to detect. To treat these cancers, dermatologists rou- 5 tinely perform a large number of mapping biopsies to determine the spread and margins, followed by surgical 6 excision with wide "safety" margins. Not surprisingly, such a "blind" approach results in under-sampling of the 7 margins, over-sampling of normal skin, too many false positives and false negatives, and too much loss of 8 normal skin tissue. What may help address this problem is reflectance confocal microscopy (RCM) imaging to 9 noninvasively delineate margins, directly on patients. RCM imaging detects skin cancers in vivo with sensitivity 10 of 85-95% and specificity 80-70%. In 2016, the Centers for Medicare and Medicaid Services granted reim- 11 bursement codes for RCM imaging of skin. RCM imaging is now being increasingly used to noninvasively 12 guide diagnosis, sparing patients from biopsies of benign lesions. While the two-decade effort to date was fo- 13 cused on imaging-guided diagnosis, emerging applications are in imaging to guide therapy. We propose to 14 create an approach called RCM video-mosaicking, to noninvasively map skin cancer margins over large areas 15 on patients, with increased sampling, accuracy and sparing of normal tissue. The parent project specific aims 16 are (1) to develop a real-time and robust RCM video-mosaicking approach and incorporate into a handheld 17 confocal microscope for use at the bedside, (2) to test the approach for image quality and clinical acceptability, 18 and (3) to prospectively test on 100 patients, with video-mosaicking of LMM margins and superficial NMSC 19 margins, followed by validation against post-surgical pathology. 20 PROPOSED DIVERSITY SUPPLEMENT PROJECT FOR Ms. ANABEL ALFONSO 21 The proposed project for Ms. Anabel Alfonso will build upon the parent project and extend it in a completely 22 novel direction. In the parent project, RCM video-mosaics will be visually read by Mohs surgeons during sur- 23 gery. The innovation in Anabel’s project will be the development of a new machine learning-based algorithm 24 for automated detection and mapping of LMM margins in video-mosaics, toward automating, standardizing and 25 increasing the speed and efficiency of video-mosaicking for Mohs surgeons at the bedside. The specific aims 26 for Anabel’s project are to (1) develop an image classification and segmentation algorithm that automatically 27 analyzes RCM video-mosaics, highlights diagnostically significant areas (malignant versus normal) in LMM 28 margins and provides real-time diagnostic feedback to Mohs surgeons; (2) to test and validate the algorithm on 29 RCM videos and video-mosaics obtained from LMMs on 50 patients. Acquisition of the videos and video- 30 mosaics will occur during the parent project.

1从父项目r01ca240771