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Cancers
Special Issues
Integrating Bioinformatics, AI, Imaging, and Clinical Informatics in Cancer Research
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Integrating Bioinformatics, AI, Imaging, and Clinical Informatics 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: 5 June 2025 | Viewed by 1075

Special Issue Editor

Dr. Jason Li

E-Mail Website
Guest Editor
Peter Maccallum Cancer Centre, Melbourne, Australia
Interests: bioinformatics; cancer; artificial intelligence; genomics; imaging; large language models; facility management

Special Issue Information

Dear Colleagues,

Bioinformatics and artificial intelligence (AI) are reshaping cancer research, offering unprecedented opportunities to enhance our understanding and treatment of this complex disease.

We invite contributions that employ multi-omics, single-cell RNA sequencing (scRNAseq), spatial transcriptomic profiling, functional genomics screening, and advanced imaging techniques, including cell imaging and histology imaging, in the context of cancer research. These methodologies have transformative potential, enabling researchers to uncover new cellular subtypes, elucidate complex molecular pathways, and map tumor microenvironments in unprecedented detail.

This Special Issue also encourages work that spans the disciplines of bioinformatics, medical imaging, clinical informatics, and generative artificial intelligence, including large language models. Enhanced integrative approaches across these fields will foster advancements in cancer prognosis, diagnosis, therapy, and fundamental research. This Special Issue contributes towards the convergence of all aspects of cancer informatics by embracing traditionally cross-disciplinary topics in a unified collection.

Dr. Jason Li
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

  • bioinformatics
  • multiomics
  • genomics
  • transcriptomics artificial intelligence
  • deep learning
  • clinical informatics
  • medical imaging
  • functional genomics
  • multi-discipline

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

Published Papers (1 paper)

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Research

27 pages, 17565 KiB  
Article
Ulcerative Colitis, LAIR1 and TOX2 Expression, and Colorectal Cancer Deep Learning Image Classification Using Convolutional Neural Networks
by Joaquim Carreras, Giovanna Roncador and Rifat Hamoudi
Cancers 2024, 16(24), 4230; https://doi.org/10.3390/cancers16244230 - 19 Dec 2024
Viewed by 842
Abstract
Background: Ulcerative colitis is a chronic inflammatory bowel disease of the colon mucosa associated with a higher risk of colorectal cancer. Objective: This study classified hematoxylin and eosin (H&E) histological images of ulcerative colitis, normal colon, and colorectal cancer using artificial intelligence (deep [...] Read more.
Background: Ulcerative colitis is a chronic inflammatory bowel disease of the colon mucosa associated with a higher risk of colorectal cancer. Objective: This study classified hematoxylin and eosin (H&E) histological images of ulcerative colitis, normal colon, and colorectal cancer using artificial intelligence (deep learning). Methods: A convolutional neural network (CNN) was designed and trained to classify the three types of diagnosis, including 35 cases of ulcerative colitis (n = 9281 patches), 21 colon control (n = 12,246), and 18 colorectal cancer (n = 63,725). The data were partitioned into training (70%) and validation sets (10%) for training the network, and a test set (20%) to test the performance on the new data. The CNNs included transfer learning from ResNet-18, and a comparison with other CNN models was performed. Explainable artificial intelligence for computer vision was used with the Grad-CAM technique, and additional LAIR1 and TOX2 immunohistochemistry was performed in ulcerative colitis to analyze the immune microenvironment. Results: Conventional clinicopathological analysis showed that steroid-requiring ulcerative colitis was characterized by higher endoscopic Baron and histologic Geboes scores and LAIR1 expression in the lamina propria, but lower TOX2 expression in isolated lymphoid follicles (all p values < 0.05) compared to mesalazine-responsive ulcerative colitis. The CNN classification accuracy was 99.1% for ulcerative colitis, 99.8% for colorectal cancer, and 99.1% for colon control. The Grad-CAM heatmap confirmed which regions of the images were the most important. The CNNs also differentiated between steroid-requiring and mesalazine-responsive ulcerative colitis based on H&E, LAIR1, and TOX2 staining. Additional classification of 10 new cases of colorectal cancer (adenocarcinoma) were correctly classified. Conclusions: CNNs are especially suited for image classification in conditions such as ulcerative colitis and colorectal cancer; LAIR1 and TOX2 are relevant immuno-oncology markers in ulcerative colitis. Full article
(This article belongs to the Special Issue Integrating Bioinformatics, AI, Imaging, and Clinical Informatics in Cancer Research)
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