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The Use of Artificial Intelligence in Predicting Response to Cancer Therapy

A special issue of Cancers (ISSN 2072-6694).

Deadline for manuscript submissions: 30 June 2026 | Viewed by 2516

Special Issue Editor


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Guest Editor
Hematology and Oncology, St Luke's University Health Network, Bethlehem, PA, USA
Interests: artificial intelligence in oncology; clinical trial optimization and patient matching; precision medicine and biomarker-driven therapies; multimodal data integration (genomics, imaging, real-world data); predictive modeling of treatment response; agentic and neurosymbolic AI systems; oncology informatics and digital health; health equity and AI-enabled access to care; real-world evidence and outcomes research

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is rapidly transforming oncology by enabling deeper, multimodal understanding of how patients respond to cancer therapies. Advances in machine learning, knowledge graph models, multimodal embeddings, and agentic clinical decision-support systems are accelerating our ability to integrate genomics, pathology, imaging, clinical trajectories, and real-world data at an unprecedented scale. As precision oncology expands, the ability to predict therapeutic benefit, resistance patterns, toxicity risk, and dynamic treatment response has become central to improving outcomes and reducing disparities.

This Special Issue aims to highlight cutting-edge AI approaches that enhance response prediction across targeted therapies, immunotherapy, cell and gene therapies, radiotherapy, and combination strategies. We welcome original research, methodological innovations, clinical validation studies, and comprehensive reviews that advance trustworthy, explainable, and clinically actionable AI for oncology. Submissions addressing equity, real-world implementation, federated learning, regulatory considerations, and human–AI collaboration are particularly encouraged.

We look forward to your contributions to this rapidly evolving field.

Dr. Arturo Loaiza-Bonilla
Guest Editor

Manuscript Submission Information

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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
  • response prediction
  • precision oncology
  • machine learning
  • multimodal data integration
  • immunotherapy biomarkers
  • treatment resistance
  • clinical decision support
  • real-world data
  • explainable AI

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Published Papers (2 papers)

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Research

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14 pages, 390 KB  
Article
Revisiting AI Interpretability in Precision Oncology: Why Predictive Accuracy Does Not Ensure Stable Feature Importance
by Souichi Oka and Yoshiyasu Takefuji
Cancers 2026, 18(4), 593; https://doi.org/10.3390/cancers18040593 - 11 Feb 2026
Cited by 2 | Viewed by 907
Abstract
Background: Artificial intelligence (AI) is becoming important in oncology, supporting risk prediction, treatment planning, and biomarker discovery. However, current evaluation practices often assume that high predictive accuracy implies reliable interpretation—a misconception that may undermine reproducibility and clinical decision-making. This study aims to reassess [...] Read more.
Background: Artificial intelligence (AI) is becoming important in oncology, supporting risk prediction, treatment planning, and biomarker discovery. However, current evaluation practices often assume that high predictive accuracy implies reliable interpretation—a misconception that may undermine reproducibility and clinical decision-making. This study aims to reassess interpretability by introducing feature ranking order consistency as a stability-focused metric to evaluate how model explanations respond to minimal input perturbations. Methods: Using The Cancer Genome Atlas (TCGA) breast cancer multi-omics dataset, we compared supervised models—Linear Regression, Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest, and Extreme Gradient Boosting (XGBoost)—with unsupervised and statistical methods, including Principal Component Analysis (PCA), Highly Variable Gene Selection, and Spearman’s rank correlation. Each method produced a Top 20 feature ranking, and stability was assessed by testing whether rankings remained consistent after removing the top-ranked feature. Predictive performance was evaluated using a Random Forest classifier with stratified 10-fold cross-validation. Results: Supervised models exhibited unstable feature importance rankings even under minimal perturbations (<0.1% feature removal), suggesting that high predictive accuracy may obscure fragile or misleading explanations. In contrast, Highly Variable Gene Selection and Spearman’s correlation consistently produced stable, biologically coherent feature sets and maintained competitive predictive performance. Conclusions: Interpretive instability is a major limitation of many machine learning models in oncology. Incorporating stability-based criteria—such as feature ranking consistency—into evaluation frameworks is essential for ensuring reproducible, trustworthy, and clinically actionable AI. As AI adoption accelerates, prioritizing interpretability alongside accuracy is critical for responsible deployment in precision oncology. Full article
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Review

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23 pages, 909 KB  
Review
Defining a Multi-Omic, AI-Enabled Stool Screening Paradigm for Colorectal Cancer: A Consensus Framework for Clinical Translation
by Arturo Loaiza-Bonilla, Yan Leyfman, Viviana Cortiana, Rhys Crawford and Shivani Modi
Cancers 2026, 18(6), 909; https://doi.org/10.3390/cancers18060909 - 11 Mar 2026
Cited by 2 | Viewed by 1138
Abstract
Colorectal cancer (CRC) develops through both conventional adenoma–carcinoma and serrated neoplasia pathways, yet noninvasive screening still under-detects the advanced precursor lesions that enable true cancer prevention. Stool-based screening reduces CRC mortality, but its preventive impact remains constrained by limited detection of advanced precancerous [...] Read more.
Colorectal cancer (CRC) develops through both conventional adenoma–carcinoma and serrated neoplasia pathways, yet noninvasive screening still under-detects the advanced precursor lesions that enable true cancer prevention. Stool-based screening reduces CRC mortality, but its preventive impact remains constrained by limited detection of advanced precancerous lesions (APLs), including advanced adenomas and sessile serrated lesions. Next-generation multitarget stool DNA assays (mt-sDNA; e.g., Cologuard Plus) have established high sensitivity for CRC and specificity approaching 94%, leaving improved APL detection as the principal opportunity for innovation. This review presents a consensus framework for a multi-omic stool screening paradigm that integrates host epigenetic markers (DNA methylation) with gut microbiome features using artificial intelligence (AI). Multi-omics capture complementary layers of early tumor biology: epithelial shedding and field effects reflected in host methylation signals together with luminal ecological and inflammatory changes represented by microbial features. Evidence from cross-cohort microbiome studies indicates that microbial signatures provide an additive—rather than standalone—axis of information for CRC and its precursor lesions. Because microbiome-based models are highly susceptible to batch effects arising from collection devices, extraction chemistry, sequencing platforms, and bioinformatic pipelines, practical mitigation strategies are outlined, including harmonized pre-analytics, batch-aware study design, leakage-resistant validation, and computational harmonization. A translational roadmap linking analytical validity, locked-model development, and prospective colonoscopy-verified clinical validation is proposed, aligned with TRIPOD + AI, STARD, PROBAST-AI, SPIRIT-AI, CONSORT-AI, and DECIDE-AI reporting standards. Scenario modeling using BLUE-C prevalence estimates suggests that improving APL sensitivity from approximately 43% to 55–65% at ~94% specificity could translate to detecting roughly 13–23 additional advanced precancerous lesions per 1000 individuals screened, highlighting the potential prevention impact of a multi-omic approach. This framework aims to guide developers and clinical investigators toward next-generation stool tests capable of materially improving precursor-lesion detection while maintaining clinically acceptable specificity. Full article
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