A nuclear circularity-based classifier for diagnostic distinction of desmoplastic from spindle cell melanoma in digitized histological images.
BACKGROUND
Distinction of spindle cell melanoma (SM) and desmoplastic melanoma (DM) is clinically important due to differences in metastatic rate and prognosis; however, histological distinction is not always straightforward. During a routine review of cases, we noted differences in nuclear circularity between SM and DM.


OBJECTIVE
The primary aim in our study was to determine whether these differences in nuclear circularity, when assessed using a basic ImageJ-based threshold extraction, can serve as a diagnostic classifier to distinguish DM from SM.


METHODS
Our retrospective analysis of an established patient cohort (SM n = 9, DM n = 9) was employed to determine discriminatory power.


METHODS
Regions of interest (total n = 108; 6 images per case) were selected from scanned H and E-stained histological sections, and nuclear circularity was extracted and quantified by computational image analysis using open source tools (plugins for ImageJ).


METHODS
Using analysis of variance, t-tests, and Fisher's exact tests, we compared extracted quantitative shape measures; statistical significance was defined as P < 0.05.


RESULTS
Classifying circularity values into four shape categories (spindled, elongated, oval, round) demonstrated significant differences in the spindled and round categories. Paradoxically, DM contained more spindled nuclei than SM (P = 0.011) and SM contained more round nuclei than DM (P = 0.026). Performance assessment using a combined shape-classification of the round and spindled fractions showed 88.9% accuracy and a Youden index of 0.77.


CONCLUSIONS
Spindle cell melanoma and DM differ significantly in their nuclear morphology with respect to fractions of round and spindled nuclei. Our study demonstrates that quantifying nuclear circularity can be used as an adjunct diagnostic tool for distinction of DM and SM.
Institute of Pathology, University Ulm, Ulm, Germany.
Manuel
M
Schöchlin
Institute of Pathology, University Ulm, Ulm, Germany.
Stephanie E
SE
Weissinger
Institut für Lasertechnologien in der Medizin und Meβtechnik, University Ulm, Ulm, Germany.
Arnd R
AR
Brandes
Institute of Pathology, University Ulm, Ulm, Germany ; Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland.
Markus
M
Herrmann
Institute of Pathology, University Ulm, Ulm, Germany.
Peter
P
Möller
Institute of Pathology, University Ulm, Ulm, Germany.
Jochen K
JK
Lennerz
Digital pathology; morphometry; numerical histology