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Artificial Intelligence and Machine Learning in Lung Cancer Screening: Current...
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Artificial Intelligence and Machine Learning in Lung Cancer Screening: Current Applications and Future Directions

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Causes, Screening and Diagnosis".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 206

Special Issue Editors

Dr. Jianjun Zhang

E-Mail Website
Guest Editor
1. Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
2. Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
Interests: cancer genomics; cancer immunology; tumor heterogeneity
Special Issues, Collections and Topics in MDPI journals
Dr. Jia Wu

E-Mail Website
Guest Editor
Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
Interests: radiogenomics; machine learning; tumor heterogeneity
Dr. Morteza Salehjahromi

E-Mail Website
Guest Editor
The University of Texas MD Anderson Cancer Center, Houston, TX, USA
Interests: imaging physics; computational machine learning; deep learning; cancer; prognostics

Special Issue Information

Dear Colleagues,

We are excited to announce this call for submissions for a Special Issue of Cancers titled “Artificial Intelligence and Machine Learning in Lung Cancer Screening: Current Applications and Future Directions”. Lung cancer remains a major global health challenge, and the integration of AI and ML into screening offers great promise for earlier detection and better patient outcomes. This Special Issue will highlight recent advances and applications in this area, and we welcome original research and reviews covering topics such as the following:

  1. AI algorithms for early detection and individualized risk assessment;
  2. Multimodal integration (low-dose CT, clinical biomarkers) to improve diagnostic accuracy;
  3. Risk-based screening intervals and personalized follow-up protocols;
  4. Robustness, bias, explainability, and ethical considerations in development and deployment;
  5. Comparative studies evaluating the efficacy of AI-assisted screening programs.

All submissions will undergo rigorous peer review to ensure quality and relevance. We welcome contributions from researchers, clinicians, data scientists, and healthcare professionals engaged in lung cancer screening and AI.

By bringing together the latest work in this area, the Special Issue will advance the field and promote more effective screening strategies. If you have any questions or require further support, please feel free to contact us.

Thank you, and we look forward to receiving your valuable contributions.

Dr. Jianjun Zhang
Dr. Jia Wu
Dr. Morteza Salehjahromi
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
  • machine learning
  • lung cancer screening
  • low-dose CT
  • early detection

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Published Papers (1 paper)

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Research

16 pages, 4636 KB  
Article
Radiomics for Dynamic Lung Cancer Risk Prediction in USPSTF-Ineligible Patients
by Morteza Salehjahromi, Hui Li, Eman Showkatian, Maliazurina B. Saad, Mohamed Qayati, Sherif M. Ismail, Sheeba J. Sujit, Amgad Muneer, Muhammad Aminu, Lingzhi Hong, Xiaoyu Han, Simon Heeke, Tina Cascone, Xiuning Le, Natalie Vokes, Don L. Gibbons, Iakovos Toumazis, Edwin J. Ostrin, Mara B. Antonoff, Ara A. Vaporciyan, David Jaffray, Fernando U. Kay, Brett W. Carter, Carol C. Wu, Myrna C. B. Godoy, J. Jack Lee, David E. Gerber, John V. Heymach, Jianjun Zhang and Jia Wuadd Show full author list remove Hide full author list
Cancers 2025, 17(21), 3406; https://doi.org/10.3390/cancers17213406 - 23 Oct 2025
Viewed by 55
Abstract
Background: Non-smokers and individuals with minimal smoking history represent a significant proportion of lung cancer cases but are often overlooked in current risk assessment models. Pulmonary nodules are commonly detected incidentally—appearing in approximately 24–31% of all chest CT scans regardless of smoking [...] Read more.
Background: Non-smokers and individuals with minimal smoking history represent a significant proportion of lung cancer cases but are often overlooked in current risk assessment models. Pulmonary nodules are commonly detected incidentally—appearing in approximately 24–31% of all chest CT scans regardless of smoking status. However, most established risk models, such as the Brock model, were developed using cohorts heavily enriched with individuals who have substantial smoking histories. This limits their generalizability to non-smoking and light-smoking populations, highlighting the need for more inclusive and tailored risk prediction strategies. Purpose: We aimed to develop a longitudinal radiomics-based approach for lung cancer risk prediction, integrating time-varying radiomic modeling to enhance early detection in USPSTF-ineligible patients. Methods: Unlike conventional models that rely on a single scan, we conducted a longitudinal analysis of 122 patients who were later diagnosed with lung cancer, with a total of 622 CT scans analyzed. Of these patients, 69% were former smokers, while 30% had never smoked. Quantitative radiomic features were extracted from serial chest CT scans to capture temporal changes in nodule evolution. A time-varying survival model was implemented to dynamically assess lung cancer risk. Additionally, we evaluated the integration of handcrafted radiomic features and the deep learning-based Sybil model to determine the added value of combining local nodule characteristics with global lung assessments. Results: Our radiomic analysis identified specific CT patterns associated with malignant transformation, including increased nodule size, voxel intensity, textural entropy, as indicators of tumor heterogeneity and progression. Integrating radiomics, delta-radiomics, and longitudinal imaging features resulted in the optimal predictive performance during cross-validation (concordance index [C-index]: 0.69), surpassing that of models using demographics alone (C-index: 0.50) and Sybil alone (C-index: 0.54). Compared to the Brock model (67% accuracy, 100% sensitivity, 33% specificity), our composite risk model achieved 78% accuracy, 89% sensitivity, and 67% specificity, demonstrating improved early cancer risk stratification. Kaplan–Meier curves and individualized cancer development probability functions further validated the model’s ability to track dynamic risk progression for individual patients. Visual analysis of longitudinal CT scans confirmed alignment between predicted risk and evolving nodule characteristics. Conclusions: Our study demonstrates that integrating radiomics, sybil, and clinical factors enhances future lung cancer risk prediction in USPSTF-ineligible patients, outperforming existing models and supporting personalized screening and early intervention strategies. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Lung Cancer Screening: Current Applications and Future Directions)
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