Skin cancer margin analysis with morphologic and metabolic micro-tomography (Conference Presentation)

Eugénie Dalimier; Emilie Benoit a la Guillaume; John Durkin; Sarah Hendriks; Thérèse Awada; Diana Mandache; Vannary Meas-Yedid; Clement Apelian; Albert Claude Boccara

doi:10.1117/12.2287761

14 March 2018 Skin cancer margin analysis with morphologic and metabolic micro-tomography (Conference Presentation)

Eugénie Dalimier, Emilie Benoit a la Guillaume, John Durkin, Sarah Hendriks, Thérèse Awada, Diana Mandache, Vannary Meas-Yedid, Clement Apelian, Albert Claude Boccara

Author Affiliations +

Proceedings Volume 10467, Photonics in Dermatology and Plastic Surgery 2018; 104670F (2018) https://doi.org/10.1117/12.2287761
Event: SPIE BiOS, 2018, San Francisco, California, United States

Abstract

Non-melanoma skin cancer is the most common cancer. On cosmetically sensitive areas, Mohs micrographic surgery is the standard of care; intra-operative margin assessment minimizes the surgical defect while helping to reduce the recurrence rate by a factor of 3 compared to surgical excision. The current Mohs technique relies on frozen section tissue slide preparation, which significantly lengthens operative time and requires on-site trained histotechnicians. Full-field micro-tomography is a novel optical imaging technique based on interferometry. It allows the extraction of a full-field optical coherence tomography (FFOCT) image, representative of the morphology of the tissue, and the dynamic cell information, representative of the intra-cellular metabolic activity. Both images are calculated at the micron-level in a few minutes and without tissue preparation. This multi-centric study aimed to evaluate these combined new imaging modalities for the analysis of skin cancer margins during Mohs surgery. Over 200 Mohs specimens were imaged in Drexel University College of Medicine, USA, and GHR Mulhouse Sud Alsace, France. An atlas was established of FFOCT images and corresponding histological slides to reveal FFOCT reading criteria of normal and cancerous structures. Dynamic cell information enhanced visualization of cancerous cells and surrounding immune cells, and yielded metabolic quantification of cancerous area versus benign areas. Finally, deep learning algorithms were developed for preliminary results for the automatic detection of cancerous tissue. Hybrid morphologic and metabolic micro-tomography techniques hold great potential for skin cancer margin assessment. They can potentially reduce recurrence rates and surgery times, optimize clinical workflow, and decrease healthcare costs.

Conference Presentation

Citation Download Citation

Eugénie Dalimier, Emilie Benoit a la Guillaume, John Durkin, Sarah Hendriks, Thérèse Awada, Diana Mandache, Vannary Meas-Yedid, Clement Apelian, and Albert Claude Boccara "Skin cancer margin analysis with morphologic and metabolic micro-tomography (Conference Presentation)", Proc. SPIE 10467, Photonics in Dermatology and Plastic Surgery 2018, 104670F (14 March 2018); https://doi.org/10.1117/12.2287761

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