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Bioengineering
Special Issues
Label-Free Cancer Detection
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Label-Free Cancer Detection

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biomedical Engineering and Biomaterials".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 976

Special Issue Editor

Dr. Raphaël Canadas

E-Mail Website
Guest Editor
Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
Interests: cancer diagnosis; microfluidics; biomaterials; in vitro modelling

Special Issue Information

Dear Colleagues,

We are pleased to announce a call for submissions to a Special Issue on label-free cancer detection, to be published in Bioengineering (MDPI). The aim of this Special Issue is to explore innovative approaches, methodologies, and technologies that leverage label-free detection strategies to advance cancer diagnostics.

Label-free techniques have emerged as transformative tools in biomedical research and clinical applications, offering cancer detection capabilities without the need for external labeling agents. These approaches minimize sample preparation time and reduce potential perturbations, paving the way for real-time, cost-effective practice. Label-free detection also fosters minimally and non-invasive identification of cancer biomarkers, enabling home and point-of-care sampling of liquid biopsies.

This Special Issue seeks to highlight cutting-edge advancements in the field, including but not limited to optical, spectroscopic, nanotechnological, and biosensing methods, as well as their integration into diagnostic platforms (e.g., microfluidics and arrays). We welcome contributions that showcase the development, optimization, and clinical translation of label-free systems for cancer detection, with the goal of enhancing early diagnosis and improving patient outcomes.

We encourage researchers, engineers, and clinicians to submit original research articles, reviews, and case studies to this Special Issue, contributing to the growing body of knowledge in this rapidly evolving domain.

Dr. Raphaël Canadas
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • label-free
  • microfluidics
  • biomarkers
  • cancer
  • non-invasive
  • biosensors
  • nanotechnology

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

23 pages, 39861 KiB  
Article
Integrated Machine Learning Algorithms-Enhanced Predication for Cervical Cancer from Mass Spectrometry-Based Proteomics Data
by Da Zhang, Lihong Zhao, Bo Guo, Aihong Guo, Jiangbo Ding, Dongdong Tong, Bingju Wang and Zhangjian Zhou
Bioengineering 2025, 12(3), 269; https://doi.org/10.3390/bioengineering12030269 - 9 Mar 2025
Viewed by 772
Abstract
Early diagnosis is critical for improving outcomes in cancer patients; however, the application of diagnostic markers derived from serum proteomic screening remains challenging. Artificial intelligence (AI), encompassing deep learning and machine learning (ML), has gained increasing prominence across various scientific disciplines. In this [...] Read more.
Early diagnosis is critical for improving outcomes in cancer patients; however, the application of diagnostic markers derived from serum proteomic screening remains challenging. Artificial intelligence (AI), encompassing deep learning and machine learning (ML), has gained increasing prominence across various scientific disciplines. In this study, we utilized cervical cancer (CC) as a model to develop an AI-driven pipeline for the identification and validation of serum biomarkers for early cancer diagnosis, leveraging mass spectrometry-based proteomics data. By processing and normalizing serum polypeptide differential peaks from 240 patients, we employed eight distinct ML algorithms to classify and analyze these differential polypeptide peaks, subsequently constructing receiver operating characteristic (ROC) curves and confusion matrices. Key performance metrics, including accuracy, precision, recall, and F1 score, were systematically evaluated. Furthermore, by integrating feature importance values, Shapley values, and local interpretable model-agnostic explanation (LIME) values, we demonstrated that the diagnostic area under the curve (AUC) achieved by our multi-dimensional learning models approached 1, significantly outperforming the diagnostic AUC of single markers derived from the PRIDE database. These findings underscore the potential of proteomics-driven integrated machine learning as a robust strategy to enhance early cancer diagnosis, offering a promising avenue for clinical translation. Full article
(This article belongs to the Special Issue Label-Free Cancer Detection)
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