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Biomedicines
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Multidisciplinary Advances in Cancer Care—Innovations in Early Detection, Precision Medicine,...
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Multidisciplinary Advances in Cancer Care—Innovations in Early Detection, Precision Medicine, and AI Driven Strategies

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Cancer Biology and Oncology".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 1206

Editors

Dr. Eva Y W Cheung

E-Mail Website
Guest Editor
Department of Diagnostic Radiology, University of Hong Kong, Hong Kong, China
Interests: advancing early detection; precision medicine; AI-driven strategies across multiple cancer types, including lung cancer, cervical cancer, colorectal cancer, and brain tumors; multi-cancer early detection (MCED) platforms
Dr. Ricky Wu

E-Mail Website
Guest Editor
Department of Biological and Biomedical Sciences, Glasgow Caledonian University, Glasgow, UK
Interests: photodynamic therapy (PDT)
Prof. Dr. Ellie S.M. Chu

E-Mail Website
Guest Editor
School of Health and Medical Sciences, Tung Wah College Hong Kong, Hong Kong, China
Interests: mechanistic studies of photodynamic therapy for in vitro and in vivo cancer models; method development of miRNA profiling using NGS; AI development for cancer characterization and classification on histopathological slides

Special Issue Information

Dear Colleagues,

Cancer remains a leading global health challenge, with lung cancer, cervical cancer, colorectal cancer, and brain tumors among the most prevalent and deadly. Recent breakthroughs in early detection, precision therapeutics, and AI enabled diagnostics are transforming care across these tumor types. This Special Issue aims to showcase cutting edge research and expert perspectives that accelerate translation from bench to bedside and improve patient outcomes worldwide.

We invite submissions that address innovative approaches to screening, molecularly guided therapies, immuno-oncology, tumor microenvironment biology, and AI/radiomics integration, as well as studies focused on risk stratification and equity in cancer care.

Topics of Interest

  • Early Detection & Screening
    • Liquid biopsy and multi‑cancer early detection (MCED) platforms
    • Imaging innovations for lung, cervical, colorectal, and brain cancers
    • AI‑powered risk prediction and screening optimization
  • Precision Medicine & Targeted Therapies
    • Molecular profiling and targeted treatments across cancer types
    • Combination strategies and resistance mechanisms
    • Antibody‑drug conjugates and novel therapeutic targets
    • Advancement in Radiotherapy, Chemotherapy and others
  • Immunotherapy & Tumor Microenvironment
    • Biomarkers predicting response across solid tumors
    • Stromal and immune cell interactions as therapeutic targets
    • Novel checkpoint inhibitors and combination regimens
  • Radiomics & AI Integration
    • Prognostic and predictive models using imaging and omics

Dr. Eva Y W Cheung
Dr. Ricky Wu
Prof. Dr. Ellie S.M. Chu
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 short 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. Biomedicines is an international peer-reviewed open access monthly 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

  • cancer early detection
  • multi-cancer screening
  • liquid biopsy
  • cancer biomarkers
  • tumor imaging and prognostics radiomics and artificial intelligence
  • precision oncology
  • targeted therapies
  • immunotherapy and tumor microenvironment
  • cancer prevention

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

13 pages, 1641 KB  
Article
Ki-67 Proliferation Index in Pulmonary Neuroendocrine Neoplasms: Interobserver Agreement Among Pathologists and Comparison of Two Artificial Intelligence-Based Image Analysis Systems
by Gizem Teoman, Zeynep Turkmen Usta, Zeynep Sagnak Yilmaz and Safak Ersoz
Biomedicines 2026, 14(3), 627; https://doi.org/10.3390/biomedicines14030627 - 11 Mar 2026
Cited by 1 | Viewed by 954
Abstract
Background/Objectives: Although Ki-67 is not formally incorporated into the grading system of pulmonary neuroendocrine neoplasms (PNENs), it is widely used as an adjunct marker to reflect proliferative activity and support diagnostic stratification. Manual Ki-67 assessment is subject to interobserver variability and methodological limitations. [...] Read more.
Background/Objectives: Although Ki-67 is not formally incorporated into the grading system of pulmonary neuroendocrine neoplasms (PNENs), it is widely used as an adjunct marker to reflect proliferative activity and support diagnostic stratification. Manual Ki-67 assessment is subject to interobserver variability and methodological limitations. This study aimed to evaluate the reliability and performance of two artificial intelligence (AI)-based image analysis systems in Ki-67 index assessment and to compare their results with expert pathologist evaluation in pulmonary neuroendocrine tumors. Methods: A total of 63 pulmonary neuroendocrine neoplasm cases, including typical carcinoid (n = 29), atypical carcinoid (n = 13), and large cell neuroendocrine carcinoma (n = 21), were retrospectively analyzed. Ki-67 proliferation indices were independently assessed by four pathologists within predefined hotspot regions, counting approximately 2000 tumor cells per case. The same regions were analyzed using two AI-based image analysis systems (Roche uPath Ki-67 and Virasoft Virasight Ki-67). Interobserver agreement among pathologists was evaluated using the intraclass correlation coefficient (ICC), and concordance between manual and AI-based assessments was assessed using Spearman’s correlation and linear regression analyses. To account for potential scanner/platform effects, slides were digitized using two different whole-slide scanners (VENTANA DP® 600 and Leica Aperio AT2), and color normalization and quality control procedures were applied prior to AI-based analysis. For clinical interpretability, Ki-67 indices were stratified into categorical groups based on tumor subtype-specific thresholds (0–<10%: low, 10–25%: intermediate, >25%: high), and agreement between manual and AI-based categorical scoring was evaluated using Cohen’s kappa coefficient. Results: Among the 63 pulmonary neuroendocrine neoplasm cases, Ki-67 proliferation indices varied across tumor subtypes, with typical carcinoids showing low, atypical carcinoids intermediate, and large cell neuroendocrine carcinomas high proliferative activity. Interobserver agreement among four pathologists was excellent (ICC = 0.998, 95% CI: 0.996–0.998). Strong correlations were observed between manual Ki-67 assessments and AI-derived indices, with Spearman correlation coefficients of 0.961 (95% CI: 0.918–0.982) for Roche AI and 0.904 (95% CI: 0.821–0.949) for Virasoft AI, and 0.926 (95% CI: 0.842–0.968) between the two AI systems. Bland–Altman analyses demonstrated minimal mean differences and most cases within the 95% limits of agreement, indicating high concordance without systematic bias. Categorical agreement analysis, using subtype-specific Ki-67 thresholds (0–<10%: low; 10–25%: intermediate; >25%: high), showed excellent concordance between manual and AI-based scoring (Cohen’s kappa 0.877 for Roche AI and 0.827 for Virasoft AI; p < 0.001), confirming the clinical interpretability and reproducibility of AI-based Ki-67 assessment. Conclusions: AI-based Ki-67 index assessment shows strong concordance with expert pathologist evaluation and reflects biologically relevant differences among pulmonary neuroendocrine neoplasm subtypes. These results suggest that AI-assisted Ki-67 analysis may serve as a reproducible and objective adjunct to routine diagnostic practice in pulmonary neuroendocrine tumors. Full article
(This article belongs to the Special Issue Multidisciplinary Advances in Cancer Care—Innovations in Early Detection, Precision Medicine, and AI Driven Strategies)
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