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Cancers
Special Issues
AI-Enabled Multimodal Data Integration for Clinical Decision-Making in Oncology
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AI-Enabled Multimodal Data Integration for Clinical Decision-Making in Oncology

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

Deadline for manuscript submissions: closed (31 March 2025) | Viewed by 2078

Special Issue Editors

Dr. Jayasree Chakraborty

E-Mail Website
Guest Editor
Memorial Sloan-Kettering Cancer Center, Department of Surgery, New York, NY, USA
Interests: medical image processing; artificial intelligence; colorectal cancer; pancreatic cancer; liver cancer; multimodal data analysis
Dr. Abhishek Midya

E-Mail Website
Guest Editor
Department of Biomedical Engineering, Emory University, Atlanta, GA, USA
Interests: medical image analysis; prostate cancer; brain cancer; artificial intelligence
Dr. Natally Horvat

E-Mail Website
Guest Editor
Mayo Clinic, Department of Radiology, Rochester, MN, USA
Interests: rectal cancer; hepatobiliopancreatic cancer; LI-RADS; precision oncology; radiomics; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Georgios Karagkounis

E-Mail Website
Guest Editor
Memorial Sloan-Kettering Cancer Center, Department of Surgery, New York, NY, USA
Interests: colorectal cancer

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) is revolutionizing precision medicine by enabling more personalized and accurate treatment plans through advanced data analysis. Machine learning algorithms are increasingly used to analyze complex datasets, such as radiographic images, genetic information, and patient records, to identify patterns and predict disease risk. These insights help tailor treatments to individual patients, potentially improving outcomes. While significant progress has been made, challenges remain, specially in integrating AI seamlessly into clinical practice, ensuring generalizability, interpretability, equitability, and data privacy, and addressing ethical concerns.

We are pleased to announce a call for proposals for a Special Issue dedicated to exploring the transformative impact of AI-enabled multimodal data integration in oncology. This Special Issue aims to gather cutting-edge research that demonstrates how AI can enhance clinical decision-making by integrating diverse data sources such as imaging, genomics, electronic health records, and clinical data. This Special Issue aims to inform the community about innovative research on applying artificial intelligence to integrate multimodal data and their associated challenges.

Scope: We invite submissions that address the following topics:

  • Development of AI tools in improving diagnostic accuracy, prediction of outcome, prognostication, and treatment planning.
  • Innovative AI algorithms for integrating multimodal data in oncology.
  • Case studies demonstrating the practical application of AI in clinical settings.
  • Ethical considerations and challenges in AI-driven data integration.
  • Future directions and emerging trends in AI for oncology.

Dr. Jayasree Chakraborty
Dr. Abhishek Midya
Dr. Natally Horvat
Dr. Georgios Karagkounis
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 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

  • artificial intelligence
  • oncology
  • machine learning
  • deep learning
  • multimodal data
  • radiomics
  • radiogenomics
  • digital health
  • digital twin
  • personalized healthcare
  • precision medicine
  • smart healthcare
  • medical imaging
  • medical data analysis
  • data-driven surveillance
  • health informatics
  • bioinformatics
  • big data

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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

22 pages, 2915 KiB  
Article
AI Model for Predicting Anti-PD1 Response in Melanoma Using Multi-Omics Biomarkers
by Axel Gschwind and Stephan Ossowski
Cancers 2025, 17(5), 714; https://doi.org/10.3390/cancers17050714 - 20 Feb 2025
Viewed by 1572
Abstract
Background: Immune checkpoint inhibitors (ICIs) have demonstrated significantly improved clinical efficacy in a minority of patients with advanced melanoma, whereas non-responders potentially suffer from severe side effects and delays in other treatment options. Predicting the response to anti-PD1 treatment in melanoma remains a [...] Read more.
Background: Immune checkpoint inhibitors (ICIs) have demonstrated significantly improved clinical efficacy in a minority of patients with advanced melanoma, whereas non-responders potentially suffer from severe side effects and delays in other treatment options. Predicting the response to anti-PD1 treatment in melanoma remains a challenge because the current FDA-approved gold standard, the nonsynonymous tumor mutation burden (nsTMB), offers limited accuracy. Methods: In this study, we developed a multi-omics-based machine learning model that integrates genomic and transcriptomic biomarkers to predict the response to anti-PD1 treatment in patients with advanced melanoma. We employed least absolute shrinkage and selection operator (LASSO) regression with 49 biomarkers extracted from tumor–normal whole-exome and RNA sequencing as input features. The performance of the multi-omics AI model was thoroughly compared to that of nsTMB alone and to models that use only genomic or transcriptomic biomarkers. Results: We used publicly available DNA and RNA-seq datasets of melanoma patients for the training and validation of our model, forming a meta-cohort of 449 patients for which the outcome was recorded as a RECIST score. The model substantially improved the prediction of anti-PD1 outcomes compared to nsTMB alone, with an ROC AUC of 0.7 in the training set and an ROC AUC of 0.64 in the test set. Using SHAP values, we demonstrated the explainability of the model’s predictions on a per-sample basis. Conclusions: We demonstrated that models using only RNA-seq or multi-omics biomarkers outperformed nsTMB in predicting the response of melanoma patients to ICI. Furthermore, our machine learning approach improves clinical usability by providing explanations of its predictions on a per-patient basis. Our findings underscore the utility of multi-omics data for selecting patients for treatment with anti-PD1 drugs. However, to train clinical-grade AI models for routine applications, prospective studies collecting larger melanoma cohorts with consistent application of exome and RNA sequencing are required. Full article
(This article belongs to the Special Issue AI-Enabled Multimodal Data Integration for Clinical Decision-Making in Oncology)
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