# MALDI imaging mass spectrometry differentiates basal cell carcinoma from trichoblastoma and trichoepithelioma: A proof of principle study

## TL;DR

This study shows that MALDI imaging mass spectrometry can distinguish between basal cell carcinoma and benign skin tumors, offering a new diagnostic tool.

## Contribution

The novel use of MALDI IMS for differentiating BCC from TB/TE in dermatopathology is demonstrated.

## Key findings

- MALDI IMS achieved high sensitivity (98.9%) and specificity (88.4%) in differentiating BCC from TB/TE using tumor nests.
- A combined model of tumor nests and stroma improved diagnostic accuracy with 90.26% sensitivity and 97.1% specificity.
- The study confirms distinct proteomic profiles between BCC and TB/TE, suggesting potential for clinical application.

## Abstract

Basal cell carcinoma (BCC) comprises a large portion of dermatopathology specimens; however, benign mimics such as trichoblastoma/trichoepithelioma (TB/TE) place accurate diagnosis at risk and consequently lead to inappropriate clinical management and overuse of healthcare resources. This study aims to address the challenges of traditional histopathological evaluation by utilizing matrix-assisted laser desorption ionization imaging mass spectrometry (MALDI IMS).

Formalin-fixed paraffin-embedded BCC and TB/TE tissue blocks were taken from archival tissue. A cohort of 69 BCC and TB/TE specimens were identified, each having three concordant diagnoses given by Dermatopathologists after a blinded analysis. H&E stained sections of each specimen were imaged for pathological analysis and uploaded to a digital annotation software with the following classifications: BCC, TB, TE, BCC stroma, TB stroma, and TE stroma. Mass spectra were collected from unstained serial sections guided by the areas annotated by the Dermatopathologists on the H&E stained serial sections. Before informatics, the data from the cohort were divided randomly into a training set (n = 55) and a validation set (n = 14). Prediction models were developed using a support vector machine (SVM) classification model from the training set data.

The platform predicted BCC and TB/TE in model 2 (tumor nests alone) with a sensitivity of 98.9% (95% CI 98.3–99.4%) and specificity of 88.4% (95% CI 78.4–94.5%) at the spectral level in the validation set. Model 1 (stroma alone) had a sensitivity of 46.1% (95% CI 43.0–49.1%) and specificity of 99.2% (95% CI 97.1–99.9%). A combined model 3 (tumor nests and stroma) had a sensitivity of 90.26% (95% CI 89.1%-91.3%) and a specificity of 97.1% (95% CI 94.6% to 98.7%). The limitations of this study included a small sample set, which included easily identifiable cases obtained from a single tissue source.

Our study proves that BCC and TB/TE exhibit different proteomic profiles that one can use to enable accurate differential diagnosis. While our findings are not yet validated for clinical use, this merits further research to support IMS as an ancillary diagnostic tool for adequately and efficiently identifying the most common cutaneous malignancy in the United States. We recommend that future studies obtain a more extensive set of histologically challenging cases from multiple institutions and adequate clinical follow-up to confirm diagnostic accuracy.