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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: 30 June 2025 | Viewed by 4881

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 policies can be found here.

Published Papers (6 papers)

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Research

21 pages, 2798 KiB  
Article
High-Speed Videoendoscopy and Stiffness Mapping for AI-Assisted Glottic Lesion Differentiation
by Magdalena M. Pietrzak, Justyna Kałuża-Olszewska, Ewa Niebudek-Bogusz, Artur Klepaczko and Wioletta Pietruszewska
Cancers 2025, 17(8), 1376; https://doi.org/10.3390/cancers17081376 - 21 Apr 2025
Viewed by 165
Abstract
Objectives: This study evaluates the potential of high-speed videoendoscopy (HSV) in differentiating between benign and malignant glottic lesions, offering a non-invasive diagnostic tool for clinicians. Moreover, a new parameter derived from high-speed videoendoscopy (HSV) had been proposed and implemented in the analysis [...] Read more.
Objectives: This study evaluates the potential of high-speed videoendoscopy (HSV) in differentiating between benign and malignant glottic lesions, offering a non-invasive diagnostic tool for clinicians. Moreover, a new parameter derived from high-speed videoendoscopy (HSV) had been proposed and implemented in the analysis for an objective assessment of the vocal fold stiffness. Methods: High-speed videoendoscopy (HSV) was conducted on 102 participants, including 21 normophonic individuals, 39 patients with benign vocal fold lesions, and 42 with glottic cancer. Laryngotopographic parameter describing the stiffness of vocal fold (SAI) and kymographic parameters describing amplitude, symmetry, and glottal dynamics were quantified. Statistical differences between groups were assessed using receiver operating characteristic (ROC) analysis and lesion classification was performed using a machine learning model. Results: Univariate receiver operating characteristic (ROC) analysis revealed that SAI (AUC = 0.91, 95% CI: 0.839–0.962) and weighted amplitude asymmetry (AUC = 0.92, 95% CI: 0.85–0.974) were highly effective in distinguishing between normophonic and organic lesions (p < 0.01). Further multivariate analysis using machine learning models demonstrated improved accuracy, with the SVM classifier achieving an AUC of 0.93 for detecting organic lesions and 0.83 for distinguishing benign from malignant lesions. Conclusions: The study demonstrates the potential value of parameter describing the pliability of infiltrated vocal fold (SAI) as a non-invasive tool to support histopathological evaluation in laryngeal lesions, with machine learning models enhancing diagnostic performance. Full article
(This article belongs to the Special Issue Application of Biostatistics in Cancer Research)
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21 pages, 1986 KiB  
Article
Dynamic Prediction of Rectal Cancer Relapse and Mortality Using a Landmarking-Based Machine Learning Model: A Multicenter Retrospective Study from the Italian Society of Surgical Oncology—Colorectal Cancer Network Collaborative Group
by Rossella Reddavid, Ugo Elmore, Jacopo Moro, Paola De Nardi, Alberto Biondi, Roberto Persiani, Leonardo Solaini, Donato P. Pafundi, Desiree Cianflocca, Diego Sasia, Marco Milone, Giulia Turri, Michela Mineccia, Francesca Pecchini, Gaetano Gallo, Daniela Rega, Simona Gili, Fabio Maiello, Andrea Barberis, Federico Costanzo, Monica Ortenzi, Andrea Divizia, Caterina Foppa, Gabriele Anania, Antonino Spinelli, Giuseppe S. Sica, Mario Guerrieri, Roberto Polastri, Francesco Bianco, Paolo Delrio, Giuseppe Sammarco, Micaela Piccoli, Alessandro Ferrero, Corrado Pedrazzani, Michele Manigrasso, Felice Borghi, Claudio Coco, Davide Cavaliere, Domenico D’Ugo, Riccardo Rosati and Danila Azzolinaadd Show full author list remove Hide full author list
Cancers 2025, 17(8), 1294; https://doi.org/10.3390/cancers17081294 - 11 Apr 2025
Viewed by 568
Abstract
Background: Almost 30% of patients with rectal cancer (RC) who submit to comprehensive treatment experience relapse. Surveillance plays a leading role in early detection. The landmark approach provides a more flexible and dynamic framework for survival prediction. Objective: This large retrospective [...] Read more.
Background: Almost 30% of patients with rectal cancer (RC) who submit to comprehensive treatment experience relapse. Surveillance plays a leading role in early detection. The landmark approach provides a more flexible and dynamic framework for survival prediction. Objective: This large retrospective study aims to develop a machine learning algorithm to profile the patient prognosis, especially the risk and the onset of RC relapse after curative resection. Methods: A cohort of 2450 RC patients were analyzed using landmark analysis. Model A applied a classical cause-specific Cox approach with a landmarking approach, while Model B implemented a landmarking-based RSF (random survival forest) competing risk algorithm. The two models were compared in terms of predictive and interpretative ability. A bootstrapped validation strategy was employed to validate the model’s performance and prevent overfitting. The best-performing hyperparameters were selected systematically, ensuring the model’s robustness within the landmark approach. The study assessed these factors’ importance and interactions using RSF and compared the predictive accuracy to that of the classical Cox model. Results: Model B outperformed Model A (mean C-index 0.95 vs. 0.78), capturing complex interactions and providing dynamic, individualized relapse predictions. Clinical factors influencing survival outcomes were identified across time with the landmark approach allowing for more accurate and timely predictions. Conclusions: The landmark approach offers an improvement over traditional methods in survival analysis. By accommodating time-dependent variables and the evolving nature of patient data, this approach provides a precise tool for profiling RC survival, thereby supporting more informed and dynamic clinical decision-making. Full article
(This article belongs to the Special Issue Application of Biostatistics in Cancer Research)
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21 pages, 4615 KiB  
Article
Improving the Estimation of Prediction Increment Measures in Logistic and Survival Analysis
by Danielle M. Enserro and Austin Miller
Cancers 2025, 17(8), 1259; https://doi.org/10.3390/cancers17081259 - 8 Apr 2025
Viewed by 247
Abstract
Background/Objectives: Proper confidence interval estimation of the area under the receiver operating characteristic curve (AUC), the net reclassification index (NRI), and the integrated discrimination improvement (IDI) is an area of ongoing research. The most common confidence interval estimation methods employ asymptotic theory. However, [...] Read more.
Background/Objectives: Proper confidence interval estimation of the area under the receiver operating characteristic curve (AUC), the net reclassification index (NRI), and the integrated discrimination improvement (IDI) is an area of ongoing research. The most common confidence interval estimation methods employ asymptotic theory. However, developments demonstrate that degeneration of the normal distribution assumption under the null hypothesis exists for measures such as the change in AUC (ΔAUC) and IDI, and confidence intervals estimated under the normal distribution assumption may be invalid. We aim to study the performance of confidence intervals derived assuming asymptotic theory and those derived with non-parametric bootstrapping methods. Methods: We examine the performance of ΔAUC, NRI, and IDI in both the logistic and survival regression context. We explore empirical distributions and compare coverage probabilities of asymptotic confidence intervals with those produced from bootstrapping methods through simulation. Results: The primary finding in both the logistic framework and the survival analysis framework is that the percentile CIs performed well regarding coverage, without compromise to their width; this finding was robust in most scenarios. Conclusions: Our results suggest that the asymptotic intervals are only appropriate when a strong effect size of the added parameter exists, and that the percentile bootstrap interval exhibits at least a reasonable coverage while maintaining the shortest width in nearly all simulated scenarios, making this interval the most reliable choice. The intent is that these recommendations improve the accuracy in the estimation and the overall assessment of discrimination improvement. Full article
(This article belongs to the Special Issue Application of Biostatistics in Cancer Research)
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18 pages, 30809 KiB  
Article
Identifying Rural Hotspots for Head and Neck Cancer Using the Bayesian Mapping Approach
by Poornima Ramamurthy, John Adeoye, Siu-Wai Choi, Peter Thomson and Dileep Sharma
Cancers 2025, 17(5), 819; https://doi.org/10.3390/cancers17050819 - 26 Feb 2025
Viewed by 543
Abstract
Background: The Bayesian mapping approach has not been used to identify head and neck cancer hotspots in Australia previously. This study aims to identify rural communities at risk of head and neck cancer (HNC) for targeted prevention programs. Methods: This study [...] Read more.
Background: The Bayesian mapping approach has not been used to identify head and neck cancer hotspots in Australia previously. This study aims to identify rural communities at risk of head and neck cancer (HNC) for targeted prevention programs. Methods: This study included data from 23,853 cases recorded in the Queensland Cancer Register between 1982 and 2018. Outcomes for mapping included incidence, overall mortality, 3-year mortality, and 5-year mortality. Local government areas (LGAs) with a general population aged 15 years and above (according to 2016 census data from the Australian Bureau of Statistics) were utilized for mapping. Results: Of the 59 LGAs with higher-than-average risk, 22 predominantly rural and remote LGAs showed statistically significant higher risks of head and neck cancer occurrence. Estimated median standardized mortality ratios (SMRs) ranged from 0.57 to 3.44 and were higher than the state average in 38 LGAs. Four LGAs had the highest mortality—the Shires of Quilpie, Yarrabah, Murweh, and Hinchinbrook. Conclusions: Whilst reasons for some LGAs exhibiting higher HNC are unknown, Bayesian mapping highlights these rural and remote regions as worthy of further investigation. In conclusion, the Bayesian disease mapping approach is effective in identifying high-risk communities for HNC. Findings from this study will aid in designing targeted screening and interventions for the prevention and management of head and neck cancer in regional and remote communities through support services such as a cancer navigator. Full article
(This article belongs to the Special Issue Application of Biostatistics in Cancer Research)
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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 1288
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 1002
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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