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

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

Deadline for manuscript submissions: 20 August 2026 | Viewed by 532

Special Issue Editors

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

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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
Special Issues, Collections and Topics in MDPI journals

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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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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Published Papers (1 paper)

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Research

13 pages, 1045 KB  
Article
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
Viewed by 79
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 [...] Read more.
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
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