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Cancers
Special Issues
Early Diagnosis of Cancer
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Early Diagnosis of Cancer

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Pathophysiology".

Deadline for manuscript submissions: closed (30 March 2022) | Viewed by 22983

Special Issue Editors

Dr. Hayley C. Whitaker

E-Mail Website
Guest Editor
Division of Surgery & Interventional Science, University College London, London WC1E 6BT, UK
Interests: prostate cancer; biomarkers; diagnosis; patient stratification; molecular pathology; genomics; personalised medicine
Dr. Kelly Coffey

E-Mail Website
Guest Editor
Translational and Clinical Research Institute, University of Newcastle, Newcastle upon Tyne NE2 4HH, UK
Interests: prostate cancer; cell signalling pathways; spatial biology

Special Issue Information

Dear Colleagues,

The early diagnosis of cancer, before it has metastasised, is key to more effective—and often curative—treatment.  For example, more than 90% of women diagnosed with breast cancer at the earliest stage survive their disease for at least 5 years compared to around 15% for women diagnosed with the most advanced stage of disease.

Making improvements in early cancer diagnosis covers a wide range of scientific disciplines, including fundamental research that aims to identify premalignant drivers of tumourigenesis or markers of early disease, translational studies that find ways to apply that research to the disease, and research into implementing advances in early diagnosis into the clinical pathway.

Underpinning all early diagnosis research is a clear definition of the clinically unmet need and an understanding of the current blockages in the diagnostic pathway and these will vary with different cancer types. Hence, while we may want to diagnose all pancreatic cancers, we may only want to diagnose the more aggressive prostate cancers that are more likely to be life limiting.

This Special Issue will highlight state-of-the-art research in improving the early diagnosis of cancers towards advancing the clinical impact of existing scientific knowledge.

Dr. Hayley C. Whitaker
Dr. Kelly Coffey
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 short 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

  • imaging
  • biomarker
  • genetics
  • multidisciplinary
  • translational research
  • fluidics
  • artificial intelligence
  • survival
  • treatment

Published Papers (3 papers)

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Review

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14 pages, 842 KiB  
Review
Deep Neural Network Models for Colon Cancer Screening
by Muthu Subash Kavitha, Prakash Gangadaran, Aurelia Jackson, Balu Alagar Venmathi Maran, Takio Kurita and Byeong-Cheol Ahn
Cancers 2022, 14(15), 3707; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers14153707 - 29 Jul 2022
Cited by 12 | Viewed by 3048
Abstract
Early detection of colorectal cancer can significantly facilitate clinicians’ decision-making and reduce their workload. This can be achieved using automatic systems with endoscopic and histological images. Recently, the success of deep learning has motivated the development of image- and video-based polyp identification and [...] Read more.
Early detection of colorectal cancer can significantly facilitate clinicians’ decision-making and reduce their workload. This can be achieved using automatic systems with endoscopic and histological images. Recently, the success of deep learning has motivated the development of image- and video-based polyp identification and segmentation. Currently, most diagnostic colonoscopy rooms utilize artificial intelligence methods that are considered to perform well in predicting invasive cancer. Convolutional neural network-based architectures, together with image patches and preprocesses are often widely used. Furthermore, learning transfer and end-to-end learning techniques have been adopted for detection and localization tasks, which improve accuracy and reduce user dependence with limited datasets. However, explainable deep networks that provide transparency, interpretability, reliability, and fairness in clinical diagnostics are preferred. In this review, we summarize the latest advances in such models, with or without transparency, for the prediction of colorectal cancer and also address the knowledge gap in the upcoming technology. Full article
(This article belongs to the Special Issue Early Diagnosis of Cancer)
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20 pages, 2006 KiB  
Review
The Role of Artificial Intelligence in Early Cancer Diagnosis
by Benjamin Hunter, Sumeet Hindocha and Richard W. Lee
Cancers 2022, 14(6), 1524; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers14061524 - 16 Mar 2022
Cited by 67 | Viewed by 14960
Abstract
Improving the proportion of patients diagnosed with early-stage cancer is a key priority of the World Health Organisation. In many tumour groups, screening programmes have led to improvements in survival, but patient selection and risk stratification are key challenges. In addition, there are [...] Read more.
Improving the proportion of patients diagnosed with early-stage cancer is a key priority of the World Health Organisation. In many tumour groups, screening programmes have led to improvements in survival, but patient selection and risk stratification are key challenges. In addition, there are concerns about limited diagnostic workforces, particularly in light of the COVID-19 pandemic, placing a strain on pathology and radiology services. In this review, we discuss how artificial intelligence algorithms could assist clinicians in (1) screening asymptomatic patients at risk of cancer, (2) investigating and triaging symptomatic patients, and (3) more effectively diagnosing cancer recurrence. We provide an overview of the main artificial intelligence approaches, including historical models such as logistic regression, as well as deep learning and neural networks, and highlight their early diagnosis applications. Many data types are suitable for computational analysis, including electronic healthcare records, diagnostic images, pathology slides and peripheral blood, and we provide examples of how these data can be utilised to diagnose cancer. We also discuss the potential clinical implications for artificial intelligence algorithms, including an overview of models currently used in clinical practice. Finally, we discuss the potential limitations and pitfalls, including ethical concerns, resource demands, data security and reporting standards. Full article
(This article belongs to the Special Issue Early Diagnosis of Cancer)
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Other

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32 pages, 5857 KiB  
Systematic Review
Biorecognition Engineering Technologies for Cancer Diagnosis: A Systematic Literature Review of Non-Conventional and Plausible Sensor Development Methods
by Kalaumari Mayoral-Peña, Omar Israel González Peña, Alexia María Orrantia Clark, Rosario del Carmen Flores-Vallejo, Goldie Oza, Ashutosh Sharma and Marcos De Donato
Cancers 2022, 14(8), 1867; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers14081867 - 07 Apr 2022
Cited by 9 | Viewed by 3858
Abstract
Cancer is the second cause of mortality worldwide. Early diagnosis of this multifactorial disease is challenging, especially in populations with limited access to healthcare services. A vast repertoire of cancer biomarkers has been studied to facilitate early diagnosis; particularly, the use of antibodies [...] Read more.
Cancer is the second cause of mortality worldwide. Early diagnosis of this multifactorial disease is challenging, especially in populations with limited access to healthcare services. A vast repertoire of cancer biomarkers has been studied to facilitate early diagnosis; particularly, the use of antibodies against these biomarkers has been of interest to detect them through biorecognition. However, there are certain limitations to this approach. Emerging biorecognition engineering technologies are alternative methods to generate molecules and molecule-based scaffolds with similar properties to those presented by antibodies. Molecularly imprinted polymers, recombinant antibodies, and antibody mimetic molecules are three novel technologies commonly used in scientific studies. This review aimed to present the fundamentals of these technologies and address questions about how they are implemented for cancer detection in recent scientific studies. A systematic analysis of the scientific peer-reviewed literature regarding the use of these technologies on cancer detection was carried out starting from the year 2000 up to 2021 to answer these questions. In total, 131 scientific articles indexed in the Web of Science from the last three years were included in this analysis. The results showed that antibody mimetic molecules technology was the biorecognition technology with the highest number of reports. The most studied cancer types were: multiple, breast, leukemia, colorectal, and lung. Electrochemical and optical detection methods were the most frequently used. Finally, the most analyzed biomarkers and cancer entities in the studies were carcinoembryonic antigen, MCF-7 cells, and exosomes. These technologies are emerging tools with adequate performance for developing biosensors useful in cancer detection, which can be used to improve cancer diagnosis in developing countries. Full article
(This article belongs to the Special Issue Early Diagnosis of Cancer)
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