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Advances in the Detection and Diagnosis of Cancer and Their Clinical Applications

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A special issue of Applied Sciences (ISSN 2076-3417).

Deadline for manuscript submissions: 20 August 2026 | Viewed by 1511

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Special Issue Editors

Dr. Ana Ramos

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Guest Editor

H&TRC—Health & Technology Research Center, ESTeSL—Escola Superior de Tecnologia da Saúde, Instituto Politécnico de Lisboa, 1990-096 Lisboa, Portugal
Interests: oncobiology; drug development; mTOR signalling; deep intronic mutations

Prof. Dr. Fernando Mendes

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Guest Editor

1. Coimbra Health School (ESTeSC), Polytechnique University of Coimbra, Rua 5 de Outubro, São Martinho do Bispo, 3045-043 Coimbra, Portugal
2. H&TRC—Health & Technology Research Center, Coimbra Health School, Polytechnic University of Coimbra, Rua 5 de Outubro, 3045-043 Coimbra, Portugal
3. Coimbra Institute for Clinical and Biomedical Research (iCBR) Area of Environment Genetics and Oncobiology (CIMAGO), Institute of Biophysics, Faculty of Medicine, Universidade de Coimbra, Pólo III—Pólo das Ciências da Saúde, Azinhaga de Santa Comba, 3000-548 Coimbra, Portugal
4. Center for Innovative Biomedicine and Biotechnology, University of Coimbra, 3000-548 Coimbra, Portugal
5. European Association for Professions in Biomedical Sciences, 1000 Brussels, Belgium
Interests: lung cancer; inflammation; radiation effects; immune oncology; biomarkers
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Special Issue Information

Dear Colleagues,

This Special Issue aims to explore the clinical applications of emerging methods and innovative approaches for the detection and diagnosis of cancer. Topics of interest include, but are not limited to, liquid biopsy, multiparametric biomarkers, artificial intelligence (AI) in digital pathology, nanotechnology and quantum dots, and CRISPR-based molecular detection. These advances are reshaping the landscape of cancer diagnostics, providing more precise, less invasive, and increasingly personalized tools for clinical practice.

Dr. Ana Ramos
Prof. Dr. Fernando Mendes
Guest Editors

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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. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

cancer detection
cancer diagnosis
clinical applications
liquid biopsy
multiparametric biomarkers
artificial intelligence
digital pathology
nanotechnology
quantum dots
CRISPR
molecular diagnostics
precision oncology
non-invasive techniques
personalized medicine
emerging technologies in oncology

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Research

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13 pages, 644 KB

Open AccessArticle

Expression Profile of CEACAM-5, CA125 and HE4 Proteins in Tumor and Corresponding Margin Samples in a Group of Patients with Gastroenteropancreatic Neuroendocrine Tumors (GEP-NET)

by Agata Świętek, Joanna Katarzyna Strzelczyk, Dorota Hudy, Zenon P. Czuba, Karolina Snopek-Miśta, Mariusz Kryj, Katarzyna Kuśnierz, Sławomir Mrowiec, Marcin Zeman, Małgorzata Roś-Mazurczyk and Janusz Strzelczyk

Appl. Sci. 2026, 16(2), 692; https://doi.org/10.3390/app16020692 - 9 Jan 2026

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Abstract

Biomarkers such as CEACAM-5, CA125 and HE4 have been implicated in tumor progression, invasion, and microenvironment modulation in several cancers, but their protein expression in GEP-NET remains poorly characterized. This study aimed to evaluate CEACAM-5, CA125 and HE4 levels in tumors and matched surgical margin samples from 59 GEP-NET patients and assess correlations with clinical and demographic variables. Total protein concentration was measured spectrophotometrically, and selected cytokines by multiplex immunoassay. No significant differences in CEACAM-5, CA125 and HE4 protein concentrations were found between tumor and margin samples. However, in tumor tissue, CA125 protein levels showed a statistically significant association with T and M status. A significantly higher level of all proteins was observed in ileum or colon tumors compared to pancreas. Analysis of HE4 revealed differences in protein levels between male and female tumor samples. CEACAM-5, CA125 and HE4 proteins showed distinct expression patterns in GEP-NET according to tumor stage, metastasis, primary tumor location, and sex, highlighting their potential as tissue biomarkers of tumor aggressiveness and microenvironmental activity. These findings provide a basis for future studies on their prognostic and therapeutic relevance. Full article

(This article belongs to the Special Issue Advances in the Detection and Diagnosis of Cancer and Their Clinical Applications)

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13 pages, 1045 KB

Open AccessArticle

Development of a Nomogram for Predicting Lymphovascular Invasion at Initial Transurethral Resection of Bladder Tumors

by Takatoshi Somoto, Takanobu Utsumi, Rino Ikeda, Naoki Ishitsuka, Takahide Noro, Yuta Suzuki, Shota Iijima, Yuka Sugizaki, Ryo Oka, Takumi Endo, Naoto Kamiya, Nobuyuki Hiruta and Hiroyoshi Suzuki

Appl. Sci. 2025, 15(24), 12979; https://doi.org/10.3390/app152412979 - 9 Dec 2025

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Abstract

Lymphovascular invasion (LVI) is a potent yet underutilized prognostic marker in bladder cancer, particularly in non–muscle-invasive disease (NMIBC). We aimed to develop and internally validate a predictive nomogram to estimate the probability of LVI at initial transurethral resection of bladder tumors (TURBT), utilizing preoperative clinical parameters. In this retrospective cohort study, 413 patients with histologically confirmed urothelial carcinoma who underwent initial TURBT were included. LVI was identified histologically in 9.2% of cases. Univariate and multivariate logistic regression, in conjunction with the least absolute shrinkage and selection operator modeling, revealed eight significant predictors: papillary architecture, Box–Cox–transformed tumor size, urinary cytology classification, age ≥ 75 years, pedunculated morphology, gender, hydronephrosis, and tumor multiplicity. The resulting nomogram demonstrated excellent discriminative performance, with an AUC of 0.888 in the training cohort and 0.827 in the validation cohort, and exhibited good calibration based on weighted plots. This model facilitates individualized prediction of LVI using routinely available clinical data. Early detection of LVI may inform risk-adapted management strategies, including repeat resection, or intensified surveillance in patients with bladder cancer. The model complements existing predictive frameworks and can contribute to more personalized and effective bladder cancer care. Full article

(This article belongs to the Special Issue Advances in the Detection and Diagnosis of Cancer and Their Clinical Applications)

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23 pages, 2800 KB

Open AccessSystematic Review

Artificial Intelligence for Artifact Reduction in Cone Beam Computed Tomographic Images: A Systematic Review

by Parisa Soltani, Gianrico Spagnuolo, Francesca Angelone, Asal Rezaeiyazdi, Mehdi Mohammadzadeh, Giuseppe Maisto, Amirhossein Moaddabi, Mariangela Cernera, Niccolò Giuseppe Armogida, Francesco Amato and Alfonso Maria Ponsiglione

Appl. Sci. 2026, 16(1), 396; https://doi.org/10.3390/app16010396 - 30 Dec 2025

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Abstract

Cone beam computed tomography (CBCT) allows for rapid and accessible acquisition of three-dimensional images with a lower radiation dose compared to conventional computed tomography (CT) scans. However, the quality of CBCT images is limited by a variety of artifacts. This systematic review attempts to explore different artificial intelligence-based solutions for enhancing the quality of CBCT scans and reducing different types of artifacts in these three-dimensional images. PubMed, Web of Science, Scopus, Embase, Cochrane, and Google Scholar were searched up to March 2025. Risk of bias of included studies was assessed using the QUADAS-II tool. Extracted data included bibliographic information, aim, imaging modality, anatomical site of interest, artificial intelligence modeling approach and details, data and dataset details, qualitative and quantitative performance metrics, and main findings. A total of 27 papers from 2018 to 2025 were included. These studies focused on five areas: metal artifact reduction, scatter correction, image reconstruction improvement, motion artifact reduction, and noise reduction. Artificial intelligence models mainly used U-Net variants, though hybrid and transformer-based models were also explored. The thoracic region was the most analyzed, and the structural similarity index measure and peak signal-to-noise-ratio were common performance metrics. Data availability was limited, with only 26% of studies providing public access and 15% sharing model source codes. Artificial intelligence-driven approaches have demonstrated promising results for CBCT artifact reduction. This review highlights a wide variability in performance assessments and that most studies have not received diagnostic validation, limiting conclusions on the true clinical impact of these artificial intelligence-based improvements. Full article

(This article belongs to the Special Issue Advances in the Detection and Diagnosis of Cancer and Their Clinical Applications)

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