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Cancers
Special Issues
Feature Papers in Section “Cancer Informatics and Big Data”
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Feature Papers in Section “Cancer Informatics and Big Data”

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 December 2025 | Viewed by 1626

Special Issue Editor

Dr. Jason Roszik

E-Mail Website
Guest Editor
Department of Melanoma Medical Oncology, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
Interests: computational cancer genomics; next generation sequencing; targeted therapy; immunotherapy; target discovery; drug repurposing; rare cancers
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the last decade, cancer informatics and big data analytics methods have become a key component in cancer research, as well as in the diagnosis and treatment of cancer. Artificial intelligence-based methods, and other methods utilizing large datasets, are still rapidly developing. In the “Cancer Informatics and Big Data” section of Cancers, we have been focusing on publishing research related to novel tools and methods to facilitate research, as well as understanding how to optimally use and exploit large databases and other resources for developing novel cancer therapies and improving cancer care. The goal of this Special Issue of Cancers, entitled “Feature Papers in Section “Cancer Informatics and Big Data””, is to share the latest innovations in this field to help facilitate the dissemination of knowledge. We invite and encourage the submission of original research papers, as well as review articles, related to the most important challenges and opportunities in this area.

Dr. Jason Roszik
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 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
  • deep learning
  • next-generation sequencing
  • precision medicine and clinical decision-making
  • single-cell data analytics

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

Published Papers (2 papers)

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Research

17 pages, 2239 KiB  
Article
Radiomics in Dermatological Optical Coherence Tomography (OCT): Feature Repeatability, Reproducibility, and Integration into Diagnostic Models in a Prospective Study
by Yousif Widaatalla, Tom Wolswijk, Muhammad Danial Khan, Iva Halilaj, Klara Mosterd, Henry C. Woodruff and Philippe Lambin
Cancers 2025, 17(5), 768; https://doi.org/10.3390/cancers17050768 - 24 Feb 2025
Viewed by 438
Abstract
Background/Objectives: Radiomics has seen substantial growth in medical imaging; however, its potential in optical coherence tomography (OCT) has not been widely explored. We systematically evaluate the repeatability and reproducibility of handcrafted radiomics features (HRFs) from OCT scans of benign nevi and examine the [...] Read more.
Background/Objectives: Radiomics has seen substantial growth in medical imaging; however, its potential in optical coherence tomography (OCT) has not been widely explored. We systematically evaluate the repeatability and reproducibility of handcrafted radiomics features (HRFs) from OCT scans of benign nevi and examine the impact of bin width (BW) selection on HRF stability. The effect of using stable features on a radiomics classification model was also assessed. Methods: In this prospective study, 20 volunteers underwent test–retest OCT imaging of 40 benign nevi, resulting in 80 scans. The repeatability and reproducibility of HRFs extracted from manually delineated regions of interest (ROIs) were assessed using concordance correlation coefficients (CCCs) across BWs ranging from 5 to 50. A unique set of stable HRFs was identified at each BW after removing highly correlated features to eliminate redundancy. These robust features were incorporated into a multiclass radiomics classifier trained to distinguish benign nevi, basal cell carcinoma (BCC), and Bowen’s disease. Results: Six stable HRFs were identified across all BWs, with a BW of 25 emerging as the optimal choice, balancing repeatability and the ability to capture meaningful textural details. Additionally, intermediate BWs (20–25) yielded 53 reproducible features. A classifier trained with six stable features achieved a 90% accuracy and AUCs of 0.96 and 0.94 for BCC and Bowen’s disease, respectively, compared to a 76% accuracy and AUCs of 0.86 and 0.80 for a conventional feature selection approach. Conclusions: This study highlights the critical role of BW selection in enhancing HRF stability and provides a methodological framework for optimizing preprocessing in OCT radiomics. By demonstrating the integration of stable HRFs into diagnostic models, we establish OCT radiomics as a promising tool to aid non-invasive diagnosis in dermatology. Full article
(This article belongs to the Special Issue Feature Papers in Section “Cancer Informatics and Big Data”)
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12 pages, 569 KiB  
Article
Genomic Characterization of Chordoma: Insights from the AACR Project GENIE Database
by Beau Hsia, Gabriel Bitar, Saif A. Alshaka, Jeeho D. Kim, Bastien A. Valencia-Sanchez, Farhoud Faraji, Michael G. Brandel, Mariko Sato, John Ross Crawford, Michael L. Levy, Vijay A. Patel and Sean P. Polster
Cancers 2025, 17(3), 536; https://doi.org/10.3390/cancers17030536 - 5 Feb 2025
Viewed by 912
Abstract
Background: Chordoma is a rare primary tumor originating from embryonic notochord remnants, with limited systemic therapeutic options due to a poor understanding of its genomic landscape. This study aims to characterize the genetic alterations in chordoma using a large national patient-level genomic repository, [...] Read more.
Background: Chordoma is a rare primary tumor originating from embryonic notochord remnants, with limited systemic therapeutic options due to a poor understanding of its genomic landscape. This study aims to characterize the genetic alterations in chordoma using a large national patient-level genomic repository, the AACR Project GENIE, to identify potential therapeutic targets and improve disease modeling. Methods: A retrospective analysis of chordoma samples was conducted using the AACR Project GENIE database. Targeted sequencing data were analyzed for recurrent somatic mutations, tumor mutational burden, and chromosomal copy number variations, with significance set at p < 0.05. Results: Frequent mutations were observed in genes associated with SWI/SNF complex affecting chromatin remodeling (SETD2, PBRM1, ARID1A). Mutations were also common among the TERT promoter regions, and cell cycle regulation (CDKN2A). Significant co-occurrences were identified among PBRM1, BRCA2, and KMT2D mutations. CDKN2A/B deletions were enriched in metastatic tumors, and pediatric cases demonstrated distinct mutation profiles compared to adults. Conclusions: This study provides a genomic profile of chordoma, identifying key mutations and potential therapeutic targets. These findings highlight the roles of chromatin remodeling and cell cycle pathways in chordoma biology, offering insights for future precision medicine approaches and therapeutic interventions. Full article
(This article belongs to the Special Issue Feature Papers in Section “Cancer Informatics and Big Data”)
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