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Advances in Artificial Intelligence Methodologies and Applications in Cancer
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Advances in Artificial Intelligence Methodologies and Applications in Cancer

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

Deadline for manuscript submissions: 15 September 2026 | Viewed by 6481

Special Issue Editors

Dr. Jennifer G. Le-Rademacher

E-Mail Website
Guest Editor
Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA
Interests: cancer; clinical trial design; survival analysis; patient-reported outcomes; complex data; symptom management; adverse event
Dr. Nicholas Larson

E-Mail Website
Guest Editor
Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA
Interests: statistical genetics; bioinformatics and computational biology; machine learning; pharmacogenomics; cancer; cardiovascular disease

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is not new, having been introduced in the 1950s. Recent advances in computational power, algorithms, and data storage have significantly enhanced its application in various fields, including medicine and health. AI's ability to process and analyze vast amounts of data is transforming healthcare, particularly cancer research and patient care. AI-powered diagnostic tools can detect the early signs of cancer from medical imaging with remarkable precision, reducing human error and expediting diagnosis to achieve timely intervention. AI tools can identify mutations and biomarkers to enhance diagnosis and predict treatment responses.

However, it is essential to understand AI's limitations and address ethical considerations to ensure that these technologies benefit all patients. Understanding AI's impact and ensuring that it is used responsibly is crucial for advancing cancer research and patient care.

This Special Issue aims to highlight advances in the application of AI, providing insights into the transformative potential of AI in cancer research and patient care. The scope of this Special Issue includes methodological and technical advances, innovative applications, case studies of AI in action, ethical considerations, challenges, and solutions regarding the utilization of AI technology in cancer.

Dr. Jennifer G. Le-Rademacher
Dr. Nicholas Larson
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-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 (AI)
  • AI tools
  • cancer research
  • diagnostic tools
  • medical imaging
  • biomarkers
  • ethical considerations
  • patient care

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Published Papers (4 papers)

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Research

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18 pages, 4968 KB  
Article
Integrating Machine Learning and Dynamic Bayesian Networks to Identify the Factors Associated with Subsequent Intrapulmonary Metastasis Classification After Initial Single Primary Lung Cancer
by Wei Liu, Aliss T. C. Chang, Joyce W. Y. Chan, Junko C. S. Chan, Rainbow W. H. Lau, Tony S. K. Mok and Calvin S. H. Ng
Cancers 2026, 18(8), 1185; https://doi.org/10.3390/cancers18081185 - 8 Apr 2026
Viewed by 516
Abstract
Background/Objectives: Intrapulmonary metastasis (IPM) after an initial single primary lung cancer (SPLC) is an adverse follow-up pattern; however, when studying population-based longitudinal records, the determinants remain unclear. We aimed to identify factors associated with subsequent IPM after initial SPLC using artificial intelligence (AI)-driven [...] Read more.
Background/Objectives: Intrapulmonary metastasis (IPM) after an initial single primary lung cancer (SPLC) is an adverse follow-up pattern; however, when studying population-based longitudinal records, the determinants remain unclear. We aimed to identify factors associated with subsequent IPM after initial SPLC using artificial intelligence (AI)-driven analytical approaches. Methods: We used Surveillance, Epidemiology, and End Results (SEER) lung cancer records from 2000 to 2019. Adults with at least two records were restricted to those with SPLC at the first record. Outcome at the second record was registry-classified IPM versus persistent SPLC. A machine learning framework based on random forest models was developed using baseline variables, first record characteristics, and the interval between records. Temporal validation was performed by training on cases from 2000 to 2013 and testing on cases from 2014 to 2019. A dynamic Bayesian network (DBN) supported simulated intervention (SI) analyses to estimate model-implied risk ratios (RRs) with 95% confidence intervals (CIs). Results: Among 3450 patients, 361 had registry-classified IPM at the second record. The random forest model achieved an area under the curve (AUC) of 0.852 in internal validation and 0.929 in temporal validation. Surgery and record timing were the leading predictors. The DBN retained surgery as the only direct parent and achieved an AUC of 0.779. SI analyses showed higher IPM probability for pleural invasion level (PL) 3 versus PL 0, RR 1.378 (95% CI, 1.080–1.657). Lobectomy with mediastinal lymph node dissection versus wedge resection lowered the IPM probability, RR 0.378 (95% CI, 0.219–0.636). Conclusions: AI-based time-sequence modeling integrating machine learning and a DBN allowed for the identification of surgery, pleural invasion, and record timing as key factors associated with subsequent IPM classification after initial SPLC. This framework demonstrates the potential of combining predictive and probabilistic dependency modeling to investigate registry-based disease classification patterns, and may support hypothesis generation for future prospective studies. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence Methodologies and Applications in Cancer)
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19 pages, 2631 KB  
Article
AI-HOPE-TP53: A Conversational Artificial Intelligence Agent for Pathway-Centric Analysis of TP53-Driven Molecular Alterations in Early-Onset Colorectal Cancer
by Ei-Wen Yang, Brigette Waldrup and Enrique Velazquez-Villarreal
Cancers 2025, 17(17), 2865; https://doi.org/10.3390/cancers17172865 - 31 Aug 2025
Cited by 3 | Viewed by 1833
Abstract
Background/Objectives: The incidence of early onset colorectal cancer (EOCRC) is increasing globally, particularly among underrepresented populations such as Hispanic/Latino individuals. TP53 is among the most frequently mutated pathways in CRC; however, its role in EOCRC, especially in relation to disparities and treatment outcomes, [...] Read more.
Background/Objectives: The incidence of early onset colorectal cancer (EOCRC) is increasing globally, particularly among underrepresented populations such as Hispanic/Latino individuals. TP53 is among the most frequently mutated pathways in CRC; however, its role in EOCRC, especially in relation to disparities and treatment outcomes, remains poorly defined. We developed AI-HOPE-TP53, a novel conversational AI agent, to enable a real-time, disparity-aware analysis of TP53 pathway alterations in EOCRC. Methods: AI-HOPE-TP53 integrates a fine-tuned biomedical large language model (LLaMA 3) with harmonized datasets from cBioPortal (TCGA, MSK-IMPACT, AACR Project GENIE). Natural language queries are translated into workflows for mutation profiling, Kaplan–Meier survival analysis, and odds ratio estimation across clinical and demographic subgroups. Results: The platform replicated known genotype–phenotype associations, including elevated TP53 mutation frequency in EOCRC and poorer prognosis in TP53-mutated tumors. Significant findings included a survival benefit for patients with early-onset TP53-mutant CRC treated with FOLFOX (p = 0.0149). Additional exploratory analyses showed a trend toward higher prevalence of TP53 pathway alterations in Hispanic/Latino EOCRC patients (OR = 2.13, p = 0.084) and identified sex-based disparities in treatment, with women being less likely than men to receive FOLFOX (OR = 0.845, p = 0.0138). Conclusions: AI-HOPE-TP53, developed in this study and made publicly available, is the first conversational AI platform tailored for pathway-specific and disparity-aware EOCRC research. By integrating clinical, genomic, and demographic data through natural language interaction, hypothesis generation and equity-focused analyses are enabled, with significant potential to advance precision oncology. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence Methodologies and Applications in Cancer)
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10 pages, 1055 KB  
Article
Artificial Intelligence and Hysteroscopy: A Multicentric Study on Automated Classification of Pleomorphic Lesions
by Miguel Mascarenhas, Carla Peixoto, Ricardo Freire, Joao Cavaco Gomes, Pedro Cardoso, Inês Castro, Miguel Martins, Francisco Mendes, Joana Mota, Maria João Almeida, Fabiana Silva, Luis Gutierres, Bruno Mendes, João Ferreira, Teresa Mascarenhas and Rosa Zulmira
Cancers 2025, 17(15), 2559; https://doi.org/10.3390/cancers17152559 - 3 Aug 2025
Cited by 1 | Viewed by 1586
Abstract
Background/Objectives: The integration of artificial intelligence (AI) in medical imaging is rapidly advancing, yet its application in gynecologic use remains limited. This proof-of-concept study presents the development and validation of a convolutional neural network (CNN) designed to automatically detect and classify endometrial [...] Read more.
Background/Objectives: The integration of artificial intelligence (AI) in medical imaging is rapidly advancing, yet its application in gynecologic use remains limited. This proof-of-concept study presents the development and validation of a convolutional neural network (CNN) designed to automatically detect and classify endometrial polyps. Methods: A multicenter dataset (n = 3) comprising 65 hysteroscopies was used, yielding 33,239 frames and 37,512 annotated objects. Still frames were extracted from full-length videos and annotated for the presence of histologically confirmed polyps. A YOLOv1-based object detection model was used with a 70–20–10 split for training, validation, and testing. Primary performance metrics included recall, precision, and mean average precision at an intersection over union (IoU) ≥ 0.50 (mAP50). Frame-level classification metrics were also computed to evaluate clinical applicability. Results: The model achieved a recall of 0.96 and precision of 0.95 for polyp detection, with a mAP50 of 0.98. At the frame level, mean recall was 0.75, precision 0.98, and F1 score 0.82, confirming high detection and classification performance. Conclusions: This study presents a CNN trained on multicenter, real-world data that detects and classifies polyps simultaneously with high diagnostic and localization performance, supported by explainable AI features that enhance its clinical integration and technological readiness. Although currently limited to binary classification, this study demonstrates the feasibility and potential of AI to reduce diagnostic subjectivity and inter-observer variability in hysteroscopy. Future work will focus on expanding the model’s capabilities to classify a broader range of endometrial pathologies, enhance generalizability, and validate performance in real-time clinical settings. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence Methodologies and Applications in Cancer)
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Review

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16 pages, 430 KB  
Review
Artificial Intelligence and Rectal Cancer: Beyond Images
by Tommaso Novellino, Carlotta Masciocchi, Andrada Mihaela Tudor, Calogero Casà, Giuditta Chiloiro, Angela Romano, Andrea Damiani, Giovanni Arcuri, Maria Antonietta Gambacorta and Vincenzo Valentini
Cancers 2025, 17(13), 2235; https://doi.org/10.3390/cancers17132235 - 3 Jul 2025
Cited by 1 | Viewed by 1766
Abstract
Introduction: The variability of cancers and medical big data can be addressed using artificial intelligence techniques. Artificial intelligence models can accept different input types, including images as well as other formats such as numerical data, predefined categories, and free text. Non-image sources are [...] Read more.
Introduction: The variability of cancers and medical big data can be addressed using artificial intelligence techniques. Artificial intelligence models can accept different input types, including images as well as other formats such as numerical data, predefined categories, and free text. Non-image sources are as important as images in clinical practice and the literature; nevertheless, the secondary literature tends to focus exclusively on image-based inputs. This article reviews such models, using non-image components as a use case in the context of rectal cancer. Methods: A literature search was conducted using PubMed and Scopus, without temporal limits and in English; for the secondary literature, appropriate filters were employed. Results and Discussion: We classified artificial intelligence models into three categories: image (image-based input), non-image (non-image input), and combined (hybrid input) models. Non-image models performed significantly well, supporting our hypothesis that disproportionate attention has been given to image-based models. Combined models frequently outperform their unimodal counterparts, in agreement with the literature. However, multicenter and externally validated studies assessing both non-image and combined models remain under-represented. Conclusions: To the best of our knowledge, no previous reviews have focused on non-image inputs, either alone or in combination with images. Non-image components require substantial attention in both research and clinical practice. The importance of multimodality—extending beyond images—is particularly relevant in the context of rectal cancer and potentially other pathologies. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence Methodologies and Applications in Cancer)
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