Evidence map›Paper›PMID 42134421›Full record

ArticleJournal of the American Academy of Dermatology2026

Diagnostic accuracy of two-photon fluorescence microscopy in Mohs surgery of basal cell carcinomas.

Michael G Giacomelli, Tadayuki Yoshitake, Lucas C Cahill, Shang I Brian Jiang, Laura M Sowerby, James G Fujimoto, Beverly E Faulkner-Jones, Daihung Do

Abstract read
In one paragraph

Article in Journal of the American Academy of Dermatology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Michael G GiacomelliDepartment of Biomedical Engineering, University of Rochester, Rochester, New York; Department of Electrical Engineering and Computer Science and Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts. Electronic address: m.giacomelli@rochester.edu.
Tadayuki YoshitakeDepartment of Electrical Engineering and Computer Science and Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts; Verily Life Sciences, Dallas, Texas.
Lucas C CahillDepartment of Electrical Engineering and Computer Science and Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts; Harvard Medical School, Boston, Massachusetts.
Shang I Brian JiangDepartment of Dermatology, University of California San Diego, San Diego, California.
Laura M SowerbyDepartment of Dermatology, Lahey Hospital and Medical Center, Burlington, Massachusetts.
James G FujimotoDepartment of Electrical Engineering and Computer Science and Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts.
Beverly E Faulkner-JonesQuantum Pathology LLC, Waltham, Massachusetts; Department of Pathology, Beth Israel Deaconess Medical Center, Boston, Massachusetts.
Daihung DoDepartment of Dermatology, Beth Israel Deaconess Medical Center, Boston, Massachusetts; Newton Wellesley Dermatology Associates, Wellesley Hills, Massachusetts.

Funding

The Harvard Clinical and Translational Science CenterUL1TR002541 · NCATS · HARVARD MEDICAL SCHOOL · PI NADLER, LEE MARSHALL · 2018 to 2022
$93.0M
Two photon fluorescence microscopy for dermatologic surgery and biopsyR37CA258376 · NCI · UNIVERSITY OF ROCHESTER · PI Michael Gene Giacomelli · 2021 to 2026
$2.0M
Intraoperative real time breast cancer margin assessment with nonlinear microscopR01CA178636 · NCI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI CONNOLLY, JAMES, FUJIMOTO, JAMES G · 2013 to 2018
$1.7M
Fluorescence microscopy for evaluation of Mohs surgical marginsK22CA226035 · NCI · UNIVERSITY OF ROCHESTER · PI GIACOMELLI, MICHAEL GENE · 2019 to 2021
$559k
NCATS NIH HHS UL1 TR002541NCI NIH HHS K22 CA226035NCI NIH HHS R01 CA178636NCI NIH HHS R37 CA258376
6 · The paper itself

Abstract

backgroundMohs surgery has excellent outcomes but is time-consuming due to the use of intraoperative frozen sections. Two-photon fluorescence microscopy (TPFM) is a slide-free technique that generates H&E-like histology images in fresh tissue specimens in real-time which could accelerate treatment.

objectivesTo determine the accuracy of TPFM with H&E coloring for the evaluation of basal cell carcinoma on Mohs specimens.

methodsFrozen section analysis and TPFM images were acquired from 133 specimens following completion of Mohs surgery for basal cell carcinoma. Two Mohs surgeons who had not previously seen TPFM histology evaluated the TPFM images. Their evaluations (TPFM read) were compared to a consensus frozen section analysis (frozen section analysis read) graded by a dermatopathologist and separate Mohs surgeon.

resultsThe average sensitivity and specificity were 93.8% and 82.2%, respectively. If specimens that were discordant due to section plane differences or tissue fragments from frozen sectioning are excluded, average sensitivity and specificity improve to 95.0% and 94.3%, respectively, which is a truer measure of the diagnostic accuracy. LIMITATIONS: This study evaluated agreement between histology after conclusion of surgery.

conclusionMohs surgeons can interpret H&E colored TPFM images with comparable accuracy to frozen sections on Mohs margins taken for basal cell carcinoma with minimal training.

Indexed as

Basal Cell CarcinomaMicroscopy, Fluorescence, MultiphotonMohs SurgerySkin NeoplasmsFemaleFrozen SectionsHumansSensitivity and Specificitybasal cell carcinomaconfocal microscopyfluorescenceMohs surgerytwo-photon

Identifiers

PMID42134421
PMCPMC13440018

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.