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Cancers
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Application of Biostatistics in Cancer Research
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Application of Biostatistics in 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: 31 March 2025 | Viewed by 2192

Special Issue Editors

Prof. Dr. Alan Hutson

E-Mail Website
Guest Editor
Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14263, USA
Interests: clinical trial design; nonparametric statistics; randomization and permutation tests; computational methods in statistics
Special Issues, Collections and Topics in MDPI journals
Dr. Han Yu

E-Mail Website
Guest Editor
Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14263, USA
Interests: computational biology; graphical models; network analysis; predictive modeling; statistical inference; clinical trial design

Special Issue Information

Dear Colleagues,

Advances in statistical methods and cancer research are intrinsically linked, driving forward innovations in both fields. In an era where experimental therapies are increasingly expensive, cutting-edge and efficient clinical trial designs are crucial. These designs can significantly reduce costs and expedite the journey of successful treatments to the market.

Emerging technologies in radiomics, genomics, proteomics, metabolomics, and spatial transcriptomics demand sophisticated statistical and bioinformatics approaches. These include graphical models, machine learning, and artificial intelligence (AI). Moreover, new statistical methods in genome-wide association studies (GWASs) are instrumental in identifying individuals at increased risk of cancer, thereby enhancing prevention strategies and improving early detection through screening.

Additionally, the application of statistical approaches to natural language processing (NLP)—a technology that translates human language into machine-readable data—is a pioneering area in cancer research. This Special Issue will showcase breakthrough statistical methods poised to make a significant impact on advancing cancer research.

We look forward to receiving your contributions.

Sincerely,

Prof. Dr. Alan Hutson
Dr. Han Yu
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

  • clinical trial design
  • nonparametric statistics
  • randomization and permutation tests
  • computational methods in statistics
  • Bayesian methods
  • computational biology
  • graphical models
  • network analysis
  • machine learning
  • artificial intelligence
  • predictive modeling

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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14 pages, 799 KiB  
Article
Deep Learning for Melanoma Detection: A Deep Learning Approach to Differentiating Malignant Melanoma from Benign Melanocytic Nevi
by Magdalini Kreouzi, Nikolaos Theodorakis, Georgios Feretzakis, Evgenia Paxinou, Aikaterini Sakagianni, Dimitris Kalles, Athanasios Anastasiou, Vassilios S. Verykios and Maria Nikolaou
Cancers 2025, 17(1), 28; https://doi.org/10.3390/cancers17010028 - 25 Dec 2024
Viewed by 673
Abstract
Background/Objectives: Melanoma, an aggressive form of skin cancer, accounts for a significant proportion of skin-cancer-related deaths worldwide. Early and accurate differentiation between melanoma and benign melanocytic nevi is critical for improving survival rates but remains challenging because of diagnostic variability. Convolutional neural [...] Read more.
Background/Objectives: Melanoma, an aggressive form of skin cancer, accounts for a significant proportion of skin-cancer-related deaths worldwide. Early and accurate differentiation between melanoma and benign melanocytic nevi is critical for improving survival rates but remains challenging because of diagnostic variability. Convolutional neural networks (CNNs) have shown promise in automating melanoma detection with accuracy comparable to expert dermatologists. This study evaluates and compares the performance of four CNN architectures—DenseNet121, ResNet50V2, NASNetMobile, and MobileNetV2—for the binary classification of dermoscopic images. Methods: A dataset of 8825 dermoscopic images from DermNet was standardized and divided into training (80%), validation (10%), and testing (10%) subsets. Image augmentation techniques were applied to enhance model generalizability. The CNN architectures were pre-trained on ImageNet and customized for binary classification. Models were trained using the Adam optimizer and evaluated based on accuracy, area under the receiver operating characteristic curve (AUC-ROC), inference time, and model size. The statistical significance of the differences was assessed using McNemar’s test. Results: DenseNet121 achieved the highest accuracy (92.30%) and an AUC of 0.951, while ResNet50V2 recorded the highest AUC (0.957). MobileNetV2 combined efficiency with competitive performance, achieving a 92.19% accuracy, the smallest model size (9.89 MB), and the fastest inference time (23.46 ms). NASNetMobile, despite its compact size, had a slower inference time (108.67 ms), and slightly lower accuracy (90.94%). Performance differences among the models were statistically significant (p < 0.0001). Conclusions: DenseNet121 demonstrated a superior diagnostic performance, while MobileNetV2 provided the most efficient solution for deployment in resource-constrained settings. The CNNs show substantial potential for improving melanoma detection in clinical and mobile applications. Full article
(This article belongs to the Special Issue Application of Biostatistics in Cancer Research)
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11 pages, 505 KiB  
Article
Robustness Assessment of Oncology Dose-Finding Trials Using the Modified Fragility Index
by Amy X. Shi, Heng Zhou, Lei Nie, Lifeng Lin, Hongjian Li and Haitao Chu
Cancers 2024, 16(20), 3504; https://doi.org/10.3390/cancers16203504 - 17 Oct 2024
Viewed by 837
Abstract
Objectives: The sample sizes of phase I trials are typically small; some designs may lead to inaccurate estimation of the maximum tolerated dose (MTD). The objective of this study was to propose a metric assessing whether the MTD decision is sensitive to enrolling [...] Read more.
Objectives: The sample sizes of phase I trials are typically small; some designs may lead to inaccurate estimation of the maximum tolerated dose (MTD). The objective of this study was to propose a metric assessing whether the MTD decision is sensitive to enrolling a few additional subjects in a phase I dose-finding trial. Methods: Numerous model-based and model-assisted designs have been proposed to improve the efficiency and accuracy of finding the MTD. The Fragility Index (FI) is a widely used metric quantifying the statistical robustness of randomized controlled trials by estimating the number of events needed to change a statistically significant result to non-significant (or vice versa). We propose a modified Fragility Index (mFI), defined as the minimum number of additional participants required to potentially change the estimated MTD, to supplement existing designs identifying fragile phase I trial results. Findings: Three oncology trials were used to illustrate how to evaluate the fragility of phase I trials using mFI. The results showed that two of the trials were not sensitive to additional subjects’ participation while the third trial was quite fragile to one or two additional subjects. Conclusions: The mFI can be a useful metric assessing the fragility of phase I trials and facilitating robust identification of MTD. Full article
(This article belongs to the Special Issue Application of Biostatistics in Cancer Research)
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