ArticleCancers2022
Multi-Class Cancer Subtyping in Salivary Gland Carcinomas with MALDI Imaging and Deep Learning.
Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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.
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.
Who cites it
11 citing papers in PubMed, 21 citations in OpenAlex.
- The SALV-Dataset Registry: An Expertly Curated Digital Clinicopathological Dataset for Salivary Gland Tumor Research and AI-Assisted Diagnostic Tools.Head and neck pathology · 2026Article
- Comprehensive Immunophenotypic Profiling and Prognostic Value in Salivary Gland Mucoepidermoid Carcinoma.International dental journal · 2026Article
- Interpreting MALDI imaging data for rare types of ampullary cancer using machine learning.NPJ systems biology and applications · 2026Article
- A comprehensive review of explainable artificial intelligence in healthcare methods, evaluation, and clinical integration.iScience · 2026Review
- Spatial proteomics for the analysis of host-pathogen interactions in mice lungs infected withmicroLife · 2026Article
- Evaluation of Deep Learning Convolutional Neural Networks for Classification of Carcinoma Ex Pleomorphic Adenoma and Pleomorphic Adenoma in Whole-Slide Images.Head and neck pathology · 2025Article
- Integrating MALDI-MSI-Based Spatial Proteomics and Machine Learning to Predict Chemoradiotherapy Outcomes in Head and Neck Cancer.International journal of molecular sciences · 2025Article
- Article
- Current landscape and future trends in salivary gland oncology research-a bibliometric evaluation.Gland surgery · 2024Article
- Current Developments in Diagnosis of Salivary Gland Tumors: From Structure to Artificial Intelligence.Life (Basel, Switzerland) · 2024Review
- Advancements in Oncoproteomics Technologies: Treading toward Translation into Clinical Practice.Proteomes · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 3 institutions in 1 country.
Funding
Abstract
Salivary gland carcinomas (SGC) are a heterogeneous group of tumors. The prognosis varies strongly according to its type, and even the distinction between benign and malign tumor is challenging. Adenoid cystic carcinoma (AdCy) is one subgroup of SGCs that is prone to late metastasis. This makes accurate tumor subtyping an important task. Matrix-assisted laser desorption/ionization (MALDI) imaging is a label-free technique capable of providing spatially resolved information about the abundance of biomolecules according to their mass-to-charge ratio. We analyzed tissue micro arrays (TMAs) of 25 patients (including six different SGC subtypes and a healthy control group of six patients) with high mass resolution MALDI imaging using a 12-Tesla magnetic resonance mass spectrometer. The high mass resolution allowed us to accurately detect single masses, with strong contributions to each class prediction. To address the added complexity created by the high mass resolution and multiple classes, we propose a deep-learning model. We showed that our deep-learning model provides a per-class classification accuracy of greater than 80% with little preprocessing. Based on this classification, we employed methods of explainable artificial intelligence (AI) to gain further insights into the spectrometric features of AdCys.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.