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Cancers
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New Statistical and Machine Learning Methods for Cancer Research
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New Statistical and Machine Learning Methods for Cancer Research

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

Deadline for manuscript submissions: 20 May 2027 | Viewed by 718

Special Issue Editor

Dr. Anjishnu Banerjee

E-Mail Website
Guest Editor
Department of Biostatistics, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA
Interests: personalized medicine; statistical learning for cancer research; bayesian methods in cancer; adaptive cancer trials
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Electronic health records are becoming commonplace in cancer care, and this has led to readily available databanks for advanced research. In this context, a multitude of new approaches in the domains of statistical and machine learning are being developed to analyze these cancer-related databases, to gain new insights, and to guide the development of new trials with novel statistical design mechanisms. Computational techniques associated with analyzing cancer research data are an important and impactful research area. In this Special Issue, we invite papers that deal with technologies and methods in this domain, with the following focus areas (but not limited to them):

  • Novel statistical methods that are empirically robust and broadly applicable for new drug development and disease delineation/prognosis/prediction in cancer.
  • Association studies, for example, studies targeting “omics” profiling for new personalized cancer therapies and related novel machine and statistical methods specifically tailored for “omics” research in cancer.
  • New statistical designs for adaptive clinical trials in cancer research.
  • Newer statistical computational approaches across the spectrum of cancer care, from initial drug discovery to population health modeling.

Dr. Anjishnu Banerjee
Guest Editor

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

  • statistical methods
  • machine learning
  • “Omics” research
  • adaptive clinical trials
  • drug development
  • personalized cancer therapies

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Published Papers (1 paper)

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Research

21 pages, 1710 KB  
Article
Multimodal Late-Fusion of Radiomics, Clinical Data, and Demographics Enhances Personalized Survival Prediction in NSCLC
by Zarindokht Helforoush, Mohamed Jaber and Nezamoddin N. Kachouie
Cancers 2026, 18(9), 1407; https://doi.org/10.3390/cancers18091407 - 29 Apr 2026
Viewed by 190
Abstract
Backgrounds/Objectives: Non-small cell lung cancer (NSCLC) exhibits substantial prognostic heterogeneity that is not fully captured by conventional anatomical staging, highlighting the need for individualized risk assessment. Radiomics enables non-invasive characterization of tumor phenotype, yet high dimensionality and inter-feature correlations often limit model stability [...] Read more.
Backgrounds/Objectives: Non-small cell lung cancer (NSCLC) exhibits substantial prognostic heterogeneity that is not fully captured by conventional anatomical staging, highlighting the need for individualized risk assessment. Radiomics enables non-invasive characterization of tumor phenotype, yet high dimensionality and inter-feature correlations often limit model stability and interpretability. Methods: To address these challenges, we developed a multimodal late-fusion framework integrating radiomic, clinical, and demographic information to predict patient-specific absolute risk in the Lung1 cohort (N = 398). Radomic features (N = 107) were extracted from primary tumor volumes and refined using a Group Lasso–penalized Cox model, preserving biological coherence and producing a parsimonious imaging signature. This signature was combined with clinical and demographic variables using five different late-fusion strategies: weighted averaging, Cox regression, logistic stacking, Random Survival Forests (RSF), and XGBoost. Model performance was evaluated using 5-fold cross-validation based on discrimination, calibration, and risk stratification metrics. Results: Using 5-fold cross validation, the radiomics-only model outperformed conventional clinical staging in patients’ risk prediction (C-index 0.5717 vs. 0.5350) and accuracy, demonstrating the prognostic value of imaging biomarkers. All fusion strategies improved risk prediction performance, with the Cox fusion model slightly better than other fusion methods with C-index of 0.58, time-dependent AUC of 0.60, and the distinct risk stratification with log-rank χ2 of 22.85. Conclusions: These findings suggest that multimodal late fusion may provide robust and interpretable risk estimates with potential clinical relevance, supporting personalized risk prediction for informed decision-making in NSCLC. Full article
(This article belongs to the Special Issue New Statistical and Machine Learning Methods for Cancer Research)
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