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Original Investigation
September 07, 2016

Use of Digitally Stained Multimodal Confocal Mosaic Images to Screen for Nonmelanoma Skin Cancer

Author Affiliations
  • 1The Ronald O. Perelman Department of Dermatology, New York University School of Medicine, New York
  • 2Department of Dermatology, Columbia University, New York, New York
  • 3Department of Pathology, Dermatopathology Section, New York University School of Medicine, New York
  • 4Laboratory of Investigative Dermatology, The Rockefeller University, New York, New York
JAMA Dermatol. Published online September 7, 2016. doi:10.1001/jamadermatol.2016.2997
Abstract

Importance  Confocal microscopy has the potential to provide rapid bedside pathologic analysis, but clinical adoption has been limited in part by the need for physician retraining to interpret grayscale images. Digitally stained confocal mosaics (DSCMs) mimic the colors of routine histologic specimens and may increase adaptability of this technology.

Objective  To evaluate the accuracy and precision of 3 physicians using DSCMs before and after training to detect basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) in Mohs micrographic surgery fresh-tissue specimens.

Design  This retrospective study used 133 DSCMs from 64 Mohs tissue excisions, which included clear margins, residual BCC, or residual SCC. Discarded tissue from Mohs surgical excisions from the dermatologic surgery units at Memorial Sloan Kettering Cancer Center and Oregon Health & Science University were collected for confocal imaging from 2006 to 2011. Final data analysis and interpretation took place between 2014 and 2016. Two Mohs surgeons and a Mohs fellow, who were blinded to the correlating gold standard frozen section diagnoses, independently reviewed the DSCMs for residual nonmelanoma skin cancer (NMSC) before and after a brief training session (about 5 minutes). The 2 assessments were separated by a 6-month washout period.

Main Outcomes and Measures  Diagnostic accuracy was characterized by sensitivity and specificity of detecting NMSC using DSCMs vs standard frozen histopathologic specimens. The diagnostic precision was calculated based on interobserver agreement and κ scores. Paired 2-sample t tests were used for comparative means analyses before and after training.

Results  The average respective sensitivities and specificities of detecting NMSC were 90% (95% CI, 89%-91%) and 79% (95% CI, 52%-100%) before training and 99% (95% CI, 99%-99%) (P = .001) and 93% (95% CI, 90%-96%) (P = .18) after training; for BCC, they were 83% (95% CI, 59%-100%) and 92% (95% CI, 81%-100%) before training and 98% (95% CI, 98%-98%) (P = .18) and 97% (95% CI, 95%-100%) (P = .15) after training; for SCC, they were 73% (95% CI, 65%-81%) and 89% (95% CI, 72%-100%) before training and 100% (P = .004) and 98% (95% CI, 95%-100%) (P = .21) after training. The pretraining interobserver agreement was 72% (κ = 0.58), and the posttraining interobserver agreement was 98% (κ = 0.97) (P = .04).

Conclusions and Relevance  Diagnostic use of DSCMs shows promising correlation to frozen histologic analysis, but image quality was affected by variations in image contrast and mosaic-stitching artifact. With training, physicians were able to read DSCMs with significantly improved accuracy and precision to detect NMSC.

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