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Novel Approaches to Machine Learning and Artificial Intelligence in Cancer Research and Care (2nd Edition)

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Informatics and Big Data".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 808

Special Issue Editors

Dr. Caroline Chung

E-Mail Website
Guest Editor
Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
Interests: brain tumors; CNS neoplasms; image-guided therapy; MRI biomarkers; clinical trials
Special Issues, Collections and Topics in MDPI journals
Dr. Chris J. Gibbons
*
E-Mail Website
Guest Editor
1. Division of Internal Medicine, Section of Patient Centered Analytics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
2. MD Anderson Center for INSPiRED Cancer Care (Integrated Systems for Patient-Reported Data), The University of Texas MD Anderson Cancer Center, Houston, TX, USA
Interests: machine learning; statistical analysis; quantitative data analysis; research methodology; clinical trials
* Chief Applied Scientist, Health AI Innovation | Oracle Health

Special Issue Information

Dear Colleagues,

This Special Issue of Cancers will highlight cutting-edge research addressing new strategies to improve the performance and safe clinical translation of machine learning (ML) and artificial intelligence (AI) approaches to oncology research and care. Specifically, we aim to address four key elements of AI/ML development and implementation that will help cancer discovery, driving clinical impact: data, models, evaluation, and systems. Topics of interest regarding data may include new standards for data quality and provenance and opportunities around the inclusion of metadata in model development and implementation. Models may include efficient and interpretable algorithms suitable for deployment in health systems, including deploying and monitoring algorithms enabling accelerated safe deployment across health systems. Regarding model evaluation, we welcome papers presenting new strategies for evaluating ML and AI through clinical trials or ensuring the validity of text-generating tools. From a system perspective, systems for deploying and monitoring algorithms in practice and approaches and standards for flagging model behaviors that may lead to unexpected outcomes are of interest.

This Special Issue is the second edition of a previous issue on the topic of “Novel Approaches to Machine Learning and Artificial Intelligence in Cancer Research and Care” (https://www.mdpi.com/journal/cancers/special_issues/38F0P4P3Q9).

Dr. Caroline Chung
Dr. Chris J. Gibbons
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

  • machine learning
  • deep learning
  • artificial intelligence
  • interpretable AI
  • data quality
  • metadata
  • data quality
  • cancer
  • MLOps
  • data science
  • real-world data

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Published Papers (2 papers)

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Research

13 pages, 643 KB  
Article
Reducing Bias in the Evaluation of Robotic Surgery for Lung Cancer Through Machine Learning
by Alain Bernard, Jonathan Cottenet, Pascale Tubert-Bitter and Catherine Quantin
Cancers 2025, 17(20), 3347; https://doi.org/10.3390/cancers17203347 - 17 Oct 2025
Viewed by 173
Abstract
Background: Robot-assisted surgery (RAS) is a major innovation in the treatment of lung cancer, offering advantages in surgical precision and reducing postoperative complications. However, its impact on 90-day mortality remains controversial due to methodological biases in comparative studies. This study uses machine learning [...] Read more.
Background: Robot-assisted surgery (RAS) is a major innovation in the treatment of lung cancer, offering advantages in surgical precision and reducing postoperative complications. However, its impact on 90-day mortality remains controversial due to methodological biases in comparative studies. This study uses machine learning methods to improve propensity score estimation and reduce selection bias. Methods: We used the French national hospital database (PMSI) to identify patients who underwent lung resection for cancer between 2019 and 2023. Four models were applied for propensity score estimation: logistic regression, Random Forest, Gradient Boosting Machine (GBM), and XGBoost. Group balancing was achieved through propensity score weighting and matching, followed by logistic regression analysis to estimate the effect of RAS on 90-day mortality. Results: Among the 30,988 patients included, 5717 (18.5%) underwent robot-assisted surgery, while 25,271 (81.5%) underwent thoracotomy. RAS patients had a lower prevalence of comorbidities and earlier-stage tumors. XGBoost was the most effective model for propensity score estimation, with an AUC ROC of 0.9984 and a Brier Score of 0.0119. The adjusted analysis showed a significant reduction in 90-day mortality in the RAS group (OR = 0.39, 95% CI: 0.34–0.45) with weighting and (OR = 0.58, 95% CI: 0.48–0.70) with matching. Conclusions: The application of machine learning to adjust for selection bias allowed for better control of confounding factors in the analysis of the effect of RAS on 90-day mortality. Our results suggest a potential benefit of robotic surgery compared to thoracotomy, although further studies are needed to confirm these findings. Full article
(This article belongs to the Special Issue Novel Approaches to Machine Learning and Artificial Intelligence in Cancer Research and Care (2nd Edition))
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15 pages, 1868 KB  
Article
Utility of Same-Modality, Cross-Domain Transfer Learning for Malignant Bone Tumor Detection on Radiographs: A Multi-Faceted Performance Comparison with a Scratch-Trained Model
by Joe Hasei, Ryuichi Nakahara, Yujiro Otsuka, Koichi Takeuchi, Yusuke Nakamura, Kunihiro Ikuta, Shuhei Osaki, Hironari Tamiya, Shinji Miwa, Shusa Ohshika, Shunji Nishimura, Naoaki Kahara, Aki Yoshida, Hiroya Kondo, Tomohiro Fujiwara, Toshiyuki Kunisada and Toshifumi Ozaki
Cancers 2025, 17(19), 3144; https://doi.org/10.3390/cancers17193144 - 27 Sep 2025
Viewed by 330
Abstract
Background/Objectives: Developing high-performance artificial intelligence (AI) models for rare diseases like malignant bone tumors is limited by scarce annotated data. This study evaluates same-modality cross-domain transfer learning by comparing an AI model pretrained on chest radiographs with a model trained from scratch for [...] Read more.
Background/Objectives: Developing high-performance artificial intelligence (AI) models for rare diseases like malignant bone tumors is limited by scarce annotated data. This study evaluates same-modality cross-domain transfer learning by comparing an AI model pretrained on chest radiographs with a model trained from scratch for detecting malignant bone tumors on knee radiographs. Methods: Two YOLOv5-based detectors differed only in initialization (transfer vs. scratch). Both were trained/validated on institutional data and tested on an independent external set of 743 radiographs (268 malignant, 475 normal). The primary outcome was AUC; prespecified operating points were high-sensitivity (≥0.90), high-specificity (≥0.90), and Youden-optimal. Secondary analyses included PR/F1, calibration (Brier, slope), and decision curve analysis (DCA). Results: AUC was similar (YOLO-TL 0.954 [95% CI 0.937–0.970] vs. YOLO-SC 0.961 [0.948–0.973]; DeLong p = 0.53). At the high-sensitivity point (both sensitivity = 0.903), YOLO-TL achieved higher specificity (0.903 vs. 0.867; McNemar p = 0.037) and PPV (0.840 vs. 0.793; bootstrap p = 0.030), reducing ~17 false positives among 475 negatives. At the high-specificity point (~0.902–0.903 for both), YOLO-TL showed higher sensitivity (0.798 vs. 0.764; p = 0.0077). At the Youden-optimal point, sensitivity favored YOLO-TL (0.914 vs. 0.892; p = 0.041) with a non-significant specificity difference. Conclusions: Transfer learning may not improve overall AUC but can enhance practical performance at clinically crucial thresholds. By maintaining high detection rates while reducing false positives, the transfer learning model offers superior clinical utility. Same-modality cross-domain transfer learning is an efficient strategy for developing robust AI systems for rare diseases, supporting tools more readily acceptable in real-world screening workflows. Full article
(This article belongs to the Special Issue Novel Approaches to Machine Learning and Artificial Intelligence in Cancer Research and Care (2nd Edition))
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