Diagnostics in Oncology Research
A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Clinical Diagnosis and Prognosis".
Deadline for manuscript submissions: 14 March 2026
Special Issue Editor
Special Issue Information
Dear Colleagues,
Cancer remains one of the leading causes of mortality worldwide, necessitating advancements in early detection, accurate diagnosis, and personalized treatment strategies. The field of oncology diagnostics has witnessed rapid progress due to innovations in molecular biology, imaging technologies, artificial intelligence (AI), and liquid biopsy techniques. This special issue, entitled "Diagnostics in Oncology Research", aims to compile cutting-edge research and reviews on novel diagnostic approaches that enhance cancer detection, prognosis, and therapeutic decision-making.
Objectives and Scope
We are pleased to invite you the articles that will focus on recent advancements in oncology diagnostics, covering technological innovations, biomarker discovery, computational approaches, and clinical applications. The special issue aims to include research articles, reviews, case reports, and perspectives on topics including, but not limited to:
- Molecular Diagnostics:
- Next-generation sequencing (NGS) and genomic profiling
- Circulating tumor DNA (ctDNA) and liquid biopsies
- Epigenetic and transcriptomic biomarkers
- Imaging and Radiomics:
- AI-driven radiology and pathology
- PET/CT, MRI, and novel imaging modalities
- Radiogenomics and tumor heterogeneity analysis
- Biomarkers and Histopathology in early detection
- tissue-based diagnostics, and innovative histopathological techniques that enhance early cancer detection
- novel immunohistochemical markers, liquid biopsy-derived biomarkers, AI-driven digital pathology, and multi-omics approaches that refine diagnostic accuracy
- Point-of-Care and Non-Invasive Diagnostics:
- Microfluidics and lab-on-a-chip technologies
- Exosome-based diagnostics
- Early detection via blood-based biomarkers
- Computational and AI-Driven Approaches:
- Machine learning for cancer diagnosis and classification
- Predictive modeling for treatment response
- Digital pathology and automated image analysis
- Clinical Translation and Challenges:
- Regulatory and ethical considerations
- Integration of diagnostics into precision oncology
Prof. Dr. Riffat Mehboob
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. Diagnostics is an international peer-reviewed open access semimonthly 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 2600 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
- oncology diagnostics
- cancer detection
- precision medicine
- tumor biomarkers
- digital pathology
- artificial intelligence in oncology
- epigenetic biomarkers
- immunohistochemical biomarkers
- predictive modeling
- genomic profiling
- transcriptome signature
- PET/CT and MRI diagnostics
- machine learning for cancer diagnostics
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