This presentation addressed how combined analysis of mass spectrometry imaging data and digitized H&E histological images produced more accurate classification skin samples versus approaches that used either the histological images or MSI data on their own.

‍

‍

Dermatopathologists annotated digital H&E images of melanocytic lesions as either melanoma, a type of skin cancer, or nevus. These annotations were used to guide MALDI imaging experiments. Data analysis strategies, including using a pre-trained deep neural network, were applied to classify benign nevi samples from melanoma using individually either the H&E images and the MSI data, and then from the fused H&E and MSI data. The results showed that the multimodal pipeline improved both classification performance and stability. This study provides an example where specialized bioinformatics produced better results than a more typical approach.

VIEW SLIDES

‍

If you want to learn more about this work, feel free to contact us.

‍

Publication details

Wanqiu Zhang^1,2, Nathan Heath Patterson^3,4, Nico Verbeeck², Jessica Moore³, Alice Ly², Richard M. Caprioli^3,4, Bart De Moor¹, Jeremy L. Norris^3,4, Marc Claesen². Incorporating morphology via deep learning improves classification performance of MALDI imaging for skin lesions , 2022

‍

AFFILIATIONS:

1. KU LEUVEN, ESAT-STADIUS, LEUVEN, BELGIUM
2. ASPECT ANALYTICS NV, GENK, BELGIUM
3. FRONTIER DIAGNOSTICS, LLC, NASHVILLE, TENNESSEE
4. MASS SPECTROMETRY RESEARCH CENTER, DEPARTMENT OF BIOCHEMISTRY, VANDERBILT UNIVERSITY, NASHVILLE, TENNESSEE