Digital and AI-Powered Pathologic Diagnostics and Precision Therapeutics in Cancers

Dear Colleagues,

We are pleased to announce a Special Issue of Cancers dedicated to “Digital and AI-Powered Pathologic Diagnostics and Precision Therapeutics in Cancers.”

Pathology is undergoing a major transformation through the convergence of whole-slide imaging (WSI), digital image analysis, artificial intelligence (AI), computational pathology and molecular technologies. Over the past two decades, digital pathology has evolved from research technology into an increasingly important component of clinical practice, supporting primary diagnosis, consultation, quality assurance, education, research and clinical trials. The widespread adoption of WSI has transformed histopathology into a data-rich discipline in which tissue morphology can be digitally captured, quantified and computationally analyzed. Advances in machine learning and deep learning have created new opportunities for tumor detection and classification, grading and staging, biomarker assessment, prognostic prediction and identification of clinically meaningful morphologic patterns. AI-powered analysis of immunohistochemical biomarkers, including ER, PR, HER2, Ki-67 and PD-L1, may improve objectivity, reproducibility and efficiency of pathologic assessment.

Importantly, the potential of digital and AI-powered pathology extends beyond diagnosis to precision oncology and therapeutic decision-making. Integration of digital histomorphology with genomic, transcriptomic, radiologic and clinical data may enable more accurate patient stratification, prediction of treatment response and resistance, identification of therapeutic targets and development of novel prognostic and predictive biomarkers. Emerging foundation models, multimodal AI and generative AI technologies may further expand the role of pathology in precision medicine.

At the same time, clinical translation presents important challenges, including algorithm validation and generalizability, data quality and interoperability, workflow integration, regulatory oversight, algorithmic bias, explainability, data privacy and cybersecurity. Demonstrating clinical utility, cost-effectiveness and impact on patient outcomes will be essential for successful implementation. Human–AI collaboration, in which computational tools augment the expertise and judgment of pathologists, will remain central to responsible adoption.

This Special Issue will highlight recent advances and emerging applications in digital pathology, computational pathology, AI-powered diagnostics and precision therapeutics in cancer. Topics of interest include, but are not limited to:

1. AI applications across pathology subspecialties and cancer types;
2. AI-assisted biomarker evaluation and quantitative pathology in cancer;
3. Computational analysis of the tumor microenvironment and spatial pathology in cancer;
4. AI-enabled molecular and genomic pathology and multimodal data integration in cancer;
5. AI- powered prediction of prognosis, treatment response and therapeutic resistance in cancer;
6. Foundation models, generative AI and emerging AI technologies in oncologic pathology;
7. Regulatory, ethical, legal and implementation considerations; and
8. Digital pathology and AI in translational research and clinical trials.

We particularly welcome multidisciplinary studies addressing clinical validation, real-world implementation, reproducibility and measurable clinical impact. Original research articles, reviews, perspectives, methodological studies and clinical studies are welcome.

This Special Issue of Cancers aims to provide a timely overview of this rapidly evolving field and to explore how digital pathology and AI can transform pathology from a primarily descriptive discipline into a quantitative, predictive and integrative science, ultimately advancing precision diagnosis and personalized cancer treatment.

We look forward to your contributions and to advancing the development of digital, AI-enabled and precision pathology for the next generation of cancer care.

Prof. Dr. Zaibo Li
Prof. Dr. Anil V. Parwani
Guest Editors

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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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.