Previous Article in Journal
Design of a Low-Latency Video Encoder for Reconfigurable Hardware on an FPGA
Previous Article in Special Issue
A Unified Deep Learning Framework for Robust Multi-Class Tumor Classification in Skin and Brain MRI
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Technologies
Volume 13
Issue 10
10.3390/technologies13100434
technologies-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Application of Foundation Models for Colorectal Cancer Tissue Classification in Mass Spectrometry Imaging

by
Alon Gabriel
1,
Amoon Jamzad
1,*,
Mohammad Farahmand
1,
Martin Kaufmann
2,3,
Natasha Iaboni
4,
David Hurlbut
4,
Kevin Yi Mi Ren
4,
Christopher J. B. Nicol
4,5,
John F. Rudan
2,
Sonal Varma
4,
Gabor Fichtinger
1 and
Parvin Mousavi
1
1
School of Computing, Queen’s University, Kingston, ON K7L 2N8, Canada
2
Department of Surgery, Kingston Health Sciences Centre, Kingston, ON K7L 2V7, Canada
3
Gastrointestinal Diseases Research Unit, Kingston Health Sciences Centre, Kingston, ON K7L 2V7, Canada
4
Department of Pathology and Molecular Medicine, Queen’s University and Kingston Health Sciences Centre, Kingston, ON K7L 3N6, Canada
5
Queen’s Cancer Research Institute, Division of Cancer Biology and Genetics, Kingston, ON K7L 3N6, Canada
*
Author to whom correspondence should be addressed.
Technologies 2025, 13(10), 434; https://doi.org/10.3390/technologies13100434 (registering DOI)
Submission received: 7 August 2025 / Revised: 6 September 2025 / Accepted: 21 September 2025 / Published: 27 September 2025
(This article belongs to the Special Issue Application of Artificial Intelligence in Medical Image Analysis)
Browse Figure
Review Reports Versions Notes

Abstract

Colorectal cancer (CRC) remains a leading global health challenge, with early and accurate diagnosis crucial for effective treatment. Histopathological evaluation, the current diagnostic gold standard, faces limitations including subjectivity, delayed results, and reliance on well-prepared tissue slides. Mass spectrometry imaging (MSI) offers a complementary approach by providing molecular-level information, but its high dimensionality and the scarcity of labeled data present unique challenges for traditional supervised learning. In this study, we present the first implementation of foundation models for MSI-based cancer classification using desorption electrospray ionization (DESI) data. We evaluate multiple architectures adapted from other domains, including a spectral classification model known as FACT, which leverages audio–language pretraining. Compared to conventional machine learning approaches, these foundation models achieved superior performance, with FACT achieving the highest cross-validated balanced accuracy (93.27%±3.25%) and AUROC (98.4%±0.7%). Ablation studies demonstrate that these models retain strong performance even under reduced data conditions, highlighting their potential for generalizable and scalable MSI-based cancer diagnostics. Future work will explore the integration of spatial and multi-modal data to enhance clinical utility.
Keywords: colorectal cancer; foundation models; mass spectrometry imaging colorectal cancer; foundation models; mass spectrometry imaging
Graphical Abstract

Share and Cite

MDPI and ACS Style

Gabriel, A.; Jamzad, A.; Farahmand, M.; Kaufmann, M.; Iaboni, N.; Hurlbut, D.; Ren, K.Y.M.; Nicol, C.J.B.; Rudan, J.F.; Varma, S.; et al. Application of Foundation Models for Colorectal Cancer Tissue Classification in Mass Spectrometry Imaging. Technologies 2025, 13, 434. https://doi.org/10.3390/technologies13100434

AMA Style

Gabriel A, Jamzad A, Farahmand M, Kaufmann M, Iaboni N, Hurlbut D, Ren KYM, Nicol CJB, Rudan JF, Varma S, et al. Application of Foundation Models for Colorectal Cancer Tissue Classification in Mass Spectrometry Imaging. Technologies. 2025; 13(10):434. https://doi.org/10.3390/technologies13100434

Chicago/Turabian Style

Gabriel, Alon, Amoon Jamzad, Mohammad Farahmand, Martin Kaufmann, Natasha Iaboni, David Hurlbut, Kevin Yi Mi Ren, Christopher J. B. Nicol, John F. Rudan, Sonal Varma, and et al. 2025. "Application of Foundation Models for Colorectal Cancer Tissue Classification in Mass Spectrometry Imaging" Technologies 13, no. 10: 434. https://doi.org/10.3390/technologies13100434

APA Style

Gabriel, A., Jamzad, A., Farahmand, M., Kaufmann, M., Iaboni, N., Hurlbut, D., Ren, K. Y. M., Nicol, C. J. B., Rudan, J. F., Varma, S., Fichtinger, G., & Mousavi, P. (2025). Application of Foundation Models for Colorectal Cancer Tissue Classification in Mass Spectrometry Imaging. Technologies, 13(10), 434. https://doi.org/10.3390/technologies13100434

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Article metric data becomes available approximately 24 hours after publication online.
Back to TopTop