AI-Driven Oncology: Advancing Cancer Detection, Diagnosis, and Personalized Treatment

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 603

Special Issue Editors


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Guest Editor
Department of Bioengineering, Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA
Interests: medical image analysis; artificial intelligence in medicine; deep learning; computer-aided diagnostics; precsion medicine; diagnostics and prognostic markers; bigdata in medicine
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Guest Editor Assistant
1. Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh 33516, Egypt
2. Faculty of Computer Science & Engineering, New Mansoura University, Gamasa 35712, Egypt
Interests: explainable artificial intelligence (XAI); deep learning; continuous learning systems; risk management; cardiovascular health prediction; computer-aided diagnostics; genetic algorithms; nanotechnology applications; visual impairment assistance systems; congestion prediction algorithms

Special Issue Information

Dear Colleagues,

Recent developments in machine learning (ML) and artificial intelligence (AI) have revolutionized oncology by providing creative ways to improve cancer diagnosis, detection, and individualized care. While early and precise diagnosis is still essential for bettering patient outcomes, conventional methods frequently depend on clinical criteria and a small number of biomarkers, which may not adequately account for the complexity of cancer progression. Large-scale datasets, such as genetic profiles, clinical records, and medical imaging, can be analyzed by AI-driven models to find trends and more accurately forecast treatment outcomes.

The application of AI and ML in oncology has the potential to enhance diagnostic precision, identify novel biomarkers, predict clinical outcomes, and recommend the most effective treatment regimens. This Special Issue will gather state-of-the-art research on the application of AI and ML in cancer detection, diagnosis, prognosis, and treatment planning. Additionally, the capacity of AI to handle multi-omics data, including transcriptomics, proteomics, and genomes, creates new opportunities for the identification of predictive and prognostic biomarkers. For these AI models to be adopted in clinical practice and to build patient and healthcare provider trust, it is vital that they are explainable and interpretable.

 We invite submissions that address novel algorithms, models, and frameworks for the following topics:

  1. Cancer Detection and Diagnosis;
  2. Predicting Cancer Prognosis and Clinical Outcomes;
  3. Treatment Response Prediction;
  4. Integration of Multi-Omics Data;
  5. Explainability and Interpretability in AI Models.

We welcome original research articles and reviews that explore innovative AI/ML methodologies, real-world applications, and challenges in the field of oncology. Submissions should provide significant contributions to the understanding and development of AI techniques in cancer care. We encourage authors working in diverse disciplines, including but not limited to oncology, data science, bioinformatics, and computer science, to submit their work to this Special Issue. All submissions will be subject to rigorous peer review, and we particularly welcome studies that combine theoretical and practical perspectives, demonstrate clinical relevance, and present real-world applications of AI in cancer care.

Dr. Mohamed Shehata
Guest Editor

Dr. Fatma M. Talaat
Guest Editor Assistant

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 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. Cancers 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 2900 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

  • cancer detection and diagnosis
  • AI in oncology
  • explainable AI (XAI)
  • precision oncology
  • medical imaging
  • prognostic biomarkers
  • clinical decision support systems

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Published Papers (2 papers)

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Research

15 pages, 4829 KiB  
Article
Multiple Instance Learning for the Detection of Lymph Node and Omental Metastases in Carcinoma of the Ovaries, Fallopian Tubes and Peritoneum
by Katie E. Allen, Jack Breen, Geoff Hall, Georgia Mappa, Kieran Zucker, Nishant Ravikumar and Nicolas M. Orsi
Cancers 2025, 17(11), 1789; https://doi.org/10.3390/cancers17111789 - 27 May 2025
Abstract
Background/Objectives: Surgical pathology of tubo-ovarian and peritoneal cancer carries a well-recognised diagnostic workload, partly due to the large amount of non-primary tumour-related tissue requiring assessment for the presence of metastatic disease. The lymph nodes and omentum are almost universally included in such [...] Read more.
Background/Objectives: Surgical pathology of tubo-ovarian and peritoneal cancer carries a well-recognised diagnostic workload, partly due to the large amount of non-primary tumour-related tissue requiring assessment for the presence of metastatic disease. The lymph nodes and omentum are almost universally included in such resection cases and contribute considerably to this burden, principally due to volume rather than task complexity. To date, artificial intelligence (AI)-based studies have reported good success rates in identifying nodal spread in other malignancies, but the development of such time-saving assistive digital solutions has been neglected in ovarian cancer. This study aimed to detect the presence or absence of metastatic ovarian carcinoma in the lymph nodes and omentum. Methods: We used attention-based multiple-instance learning (ABMIL) with a vision-transformer foundation model to classify whole-slide images (WSIs) as either containing ovarian carcinoma metastases or not. Training and validation were conducted with a total of 855 WSIs of surgical resection specimens collected from 404 patients at Leeds Teaching Hospitals NHS Trust. Results: Ensembled classification from hold-out testing reached an AUROC of 0.998 (0.985–1.0) and a balanced accuracy of 100% (100.0–100.0%) in the lymph node set, and an AUROC of 0.963 (0.911–0.999) and a balanced accuracy of 98.0% (94.8–100.0%) in the omentum set. Conclusions: This model shows great potential in the identification of ovarian carcinoma nodal and omental metastases, and could provide clinical utility through its ability to pre-screen WSIs prior to histopathologist review. In turn, this could offer significant time-saving benefits and streamline clinical diagnostic workflows, helping to address the chronic staffing shortages in histopathology. Full article
20 pages, 2736 KiB  
Article
Clinical Validation and Post-Implementation Performance Monitoring of a Neural Network-Assisted Approach for Detecting Chronic Lymphocytic Leukemia Minimal Residual Disease by Flow Cytometry
by Jansen N. Seheult, Gregory E. Otteson, Matthew J. Weybright, Michael M. Timm, Wenchao Han, Dragan Jevremovic, Pedro Horna, Horatiu Olteanu and Min Shi
Cancers 2025, 17(10), 1688; https://doi.org/10.3390/cancers17101688 - 17 May 2025
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Abstract
Background: Flow cytometric detection of minimal residual disease (MRD) in chronic lymphocytic leukemia (CLL) is complex, time-consuming, and subject to inter-operator variability. Deep neural networks (DNNs) offer potential for standardization and efficiency improvement, but require rigorous validation and monitoring for safe clinical [...] Read more.
Background: Flow cytometric detection of minimal residual disease (MRD) in chronic lymphocytic leukemia (CLL) is complex, time-consuming, and subject to inter-operator variability. Deep neural networks (DNNs) offer potential for standardization and efficiency improvement, but require rigorous validation and monitoring for safe clinical implementation. Methods: We evaluated a DNN-assisted human-in-the-loop approach for CLL MRD detection. Initial validation included method comparison against manual analysis (n = 240), precision studies, and analytical sensitivity verification. Post-implementation monitoring comprised four components: daily electronic quality control, input data drift detection, error analysis, and attribute acceptance sampling. Laboratory efficiency was assessed through a timing study of 161 cases analyzed by five technologists. Results: Method comparison demonstrated 97.5% concordance with manual analysis for qualitative classification (sensitivity 100%, specificity 95%) and excellent correlation for quantitative assessment (r = 0.99, Deming slope = 0.99). Precision studies confirmed high repeatability and within-laboratory precision across multiple operators. Analytical sensitivity was verified at 0.002% MRD. Post-implementation monitoring identified 2.97% of cases (26/874) with input data drift, primarily high-burden CLL and non-CLL neoplasms. Error analysis showed the DNN alone achieved 97% sensitivity compared to human-in-the-loop-reviewed results, with 13 missed cases (1.5%) showing atypical immunophenotypes. Attribute acceptance sampling confirmed 98.8% of reported negative cases were true negatives. The DNN-assisted workflow reduced average analysis time by 60.3% compared to manual analysis (4.2 ± 2.3 vs. 10.5 ± 5.8 min). Conclusions: The implementation of a DNN-assisted approach for CLL MRD detection in a clinical laboratory provides diagnostic performance equivalent to expert manual analysis while substantially reducing analysis time. Comprehensive performance monitoring ensures ongoing safety and effectiveness in routine clinical practice. This approach provides a model for responsible AI integration in clinical laboratories, balancing automation benefits with expert oversight. Full article
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