Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.0
4.5
Journals
Cancers
Special Issues
Feature Review Papers on "Cancer Causes, Screening and Diagnosis"
share announcement

Feature Review Papers on "Cancer Causes, Screening and Diagnosis"

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Causes, Screening and Diagnosis".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 17952

Special Issue Editor

Dr. Taewan Kim

E-Mail Website
Guest Editor
1. Ohio State University Comprehensive Cancer Center, 1070 Biomedical Research Tower, 460W 12th Ave, Columbus, OH 43210, USA
2. Shenzhen University International Cancer Center, Building A1-107, Shenzhen University Xili Campus, 1066 Xili Xueyuan Ave, Nanshan District, Shenzhen, Guangdong, China
Interests: noncoding RNA; microRNA; lncRNA; p53; MYC
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

The future of cancerology, considering environmental pollution and technological advancements in cancer screening and diagnosis, is dependent on our efforts today toward better identifying the causes of cancer, improving screening and, also, the establishment and use of new diagnostic tests.

The special issue should provide essential data for the identification of carcinogens linked to physical, chemical, and biological causes as well as social, mental, and behavioral origins by exploration of the responsible molecules.

The physiological and genetic impacts in relation to the occurrence of cancer will be determined. Modern methods in medical imaging and personalized screening could thus help guide clinical decision-making.

We encourage the submission of manuscripts that will assess the “physical, chemical, biological, and social” environmental impacts in oncology, and also all methodologies for cancer screening and diagnosis. Reviews and Systematic reviews relating to significant progress in this area will be included.

➢   Screening and diagnoses

  • Biopsies, cytology
  • Cancer-associated antigens
  • Circulating DNA and RNA/oligos
  • Viral infection
  • Bacterial infection
  • Endoscopy
  • Sonography
  • Radiology: tomography (TDM, TEP), scintigraphy, MRI, PET scanning, etc.
  • Personalized screening
  • Markers:
    • Protein and carbohydrate biomarkers in biological liquids (blood, pleura, peritonea, urinary)
    • Immunopathological biomarkers
    • Circulating nucleotides
    • DNA, RNA, histone complex
      • miRNAs
      • Circular DNA
    • Gene analysis and mutagenesis
    • Heteroploid cells
    • Circulating cancer cells

Dr. Taewan Kim
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.

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (6 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review, Other

16 pages, 417 KiB  
Article
The Impact of Race–Ethnicity and Diagnosis of Alzheimer’s Disease and Related Dementias on Mammography Use
by Aokun Chen, Yongqiu Li, Jennifer N. Woodard, Jessica Y. Islam, Shuang Yang, Thomas J. George, Elizabeth A. Shenkman, Jiang Bian and Yi Guo
Cancers 2022, 14(19), 4726; https://doi.org/10.3390/cancers14194726 - 28 Sep 2022
Cited by 1 | Viewed by 1839
Abstract
Breast cancer screening (BCS) with mammography is a crucial method for improving cancer survival. In this study, we examined the association of Alzheimer’s disease (AD) and AD-related dementias (ADRD) diagnosis and race–ethnicity with mammography use in BCS-eligible women. In the real-world data from [...] Read more.
Breast cancer screening (BCS) with mammography is a crucial method for improving cancer survival. In this study, we examined the association of Alzheimer’s disease (AD) and AD-related dementias (ADRD) diagnosis and race–ethnicity with mammography use in BCS-eligible women. In the real-world data from the OneFlorida+ Clinical Research Network, we extracted a cohort of 21,715 BCS-eligible women with ADRD and a matching comparison cohort of 65,145 BCS-eligible women without ADRD. In multivariable regression analysis, BCS-eligible women with ADRD were more likely to undergo a mammography than the BCS-eligible women without ADRD (odds ratio [OR] = 1.19, 95% confidence interval [CI] = 1.13–1.26). Stratified by race–ethnicity, BCS-eligible Hispanic women with ADRD were more likely to undergo a mammography (OR = 1.56, 95% CI = 1.39–1.75), whereas BCS-eligible non-Hispanic black (OR = 0.72, 95% CI = 0.62–0.83) and non-Hispanic other (OR = 0.65, 95% CI = 0.45–0.93) women with ADRD were less likely to undergo a mammography. This study was the first to report the impact of ADRD diagnosis and race–ethnicity on mammography use in BCS-eligible women using real-world data. Our results suggest ADRD patients might be undergoing BCS without detailed guidelines to maximize benefits and avoid harms. Full article
(This article belongs to the Special Issue Feature Review Papers on "Cancer Causes, Screening and Diagnosis")
►▼ Show Figures

Figure 1

Review

Jump to: Research, Other

15 pages, 3039 KiB  
Review
Possible Bias in Supervised Deep Learning Algorithms for CT Lung Nodule Detection and Classification
by Nikos Sourlos, Jingxuan Wang, Yeshaswini Nagaraj, Peter van Ooijen and Rozemarijn Vliegenthart
Cancers 2022, 14(16), 3867; https://doi.org/10.3390/cancers14163867 - 10 Aug 2022
Cited by 15 | Viewed by 3336
Abstract
Artificial Intelligence (AI) algorithms for automatic lung nodule detection and classification can assist radiologists in their daily routine of chest CT evaluation. Even though many AI algorithms for these tasks have already been developed, their implementation in the clinical workflow is still largely [...] Read more.
Artificial Intelligence (AI) algorithms for automatic lung nodule detection and classification can assist radiologists in their daily routine of chest CT evaluation. Even though many AI algorithms for these tasks have already been developed, their implementation in the clinical workflow is still largely lacking. Apart from the significant number of false-positive findings, one of the reasons for that is the bias that these algorithms may contain. In this review, different types of biases that may exist in chest CT AI nodule detection and classification algorithms are listed and discussed. Examples from the literature in which each type of bias occurs are presented, along with ways to mitigate these biases. Different types of biases can occur in chest CT AI algorithms for lung nodule detection and classification. Mitigation of them can be very difficult, if not impossible to achieve completely. Full article
(This article belongs to the Special Issue Feature Review Papers on "Cancer Causes, Screening and Diagnosis")
►▼ Show Figures

Figure 1

16 pages, 1198 KiB  
Review
Management of Superficial Esophageal Squamous Cell Carcinoma and Early Gastric Cancer following Non-Curative Endoscopic Resection
by Waku Hatta, Tomoyuki Koike, Kaname Uno, Naoki Asano and Atsushi Masamune
Cancers 2022, 14(15), 3757; https://doi.org/10.3390/cancers14153757 - 2 Aug 2022
Cited by 7 | Viewed by 2918
Abstract
According to the European and Japanese guidelines, additional treatment is recommended for cases of superficial esophageal squamous cell carcinoma (ESCC) and early gastric cancer (EGC) that do not meet the curability criteria for endoscopic resection (ER), i.e., non-curative ER, owing to the risk [...] Read more.
According to the European and Japanese guidelines, additional treatment is recommended for cases of superficial esophageal squamous cell carcinoma (ESCC) and early gastric cancer (EGC) that do not meet the curability criteria for endoscopic resection (ER), i.e., non-curative ER, owing to the risk of lymph node metastasis (LNM). However, the rates of LNM in such cases were relatively low (e.g., 8% for EGC). Several recent advances have been made in this field. First, pathological risk stratification for metastatic recurrence following non-curative ER without additional treatment was developed for both superficial ESCC and EGC. Second, the pattern of metastatic recurrence and prognosis after recurrence following non-curative ER without additional treatment was found to be considerably different between superficial ESCC and EGC. Third, a combination of ER and selective chemoradiotherapy was developed as a minimally invasive treatment method for clinical T1b-SM ESCC. These findings may help clinicians decide the treatment strategy for patients following non-curative ER; however, for optimal therapeutic decision-making in such patients, it is also important to predict the prognosis other than SESCC or EGC and impaired quality of life. Thus, a novel algorithm that considers these factors, as well as metastatic recurrence, should be developed. Full article
(This article belongs to the Special Issue Feature Review Papers on "Cancer Causes, Screening and Diagnosis")
►▼ Show Figures

Figure 1

20 pages, 1014 KiB  
Review
Setting the Research Agenda for Clinical Artificial Intelligence in Pancreatic Adenocarcinoma Imaging
by Megan Schuurmans, Natália Alves, Pierpaolo Vendittelli, Henkjan Huisman and John Hermans
Cancers 2022, 14(14), 3498; https://doi.org/10.3390/cancers14143498 - 19 Jul 2022
Cited by 4 | Viewed by 2725
Abstract
Pancreatic ductal adenocarcinoma (PDAC), estimated to become the second leading cause of cancer deaths in western societies by 2030, was flagged as a neglected cancer by the European Commission and the United States Congress. Due to lack of investment in research and development, [...] Read more.
Pancreatic ductal adenocarcinoma (PDAC), estimated to become the second leading cause of cancer deaths in western societies by 2030, was flagged as a neglected cancer by the European Commission and the United States Congress. Due to lack of investment in research and development, combined with a complex and aggressive tumour biology, PDAC overall survival has not significantly improved the past decades. Cross-sectional imaging and histopathology play a crucial role throughout the patient pathway. However, current clinical guidelines for diagnostic workup, patient stratification, treatment response assessment, and follow-up are non-uniform and lack evidence-based consensus. Artificial Intelligence (AI) can leverage multimodal data to improve patient outcomes, but PDAC AI research is too scattered and lacking in quality to be incorporated into clinical workflows. This review describes the patient pathway and derives touchpoints for image-based AI research in collaboration with a multi-disciplinary, multi-institutional expert panel. The literature exploring AI to address these touchpoints is thoroughly retrieved and analysed to identify the existing trends and knowledge gaps. The results show absence of multi-institutional, well-curated datasets, an essential building block for robust AI applications. Furthermore, most research is unimodal, does not use state-of-the-art AI techniques, and lacks reliable ground truth. Based on this, the future research agenda for clinically relevant, image-driven AI in PDAC is proposed. Full article
(This article belongs to the Special Issue Feature Review Papers on "Cancer Causes, Screening and Diagnosis")
►▼ Show Figures

Figure 1

13 pages, 682 KiB  
Review
Implementation of HPV Tests in Latin America: What We Learned; What Should We Have Learned, and What Can We Do Better?
by Luani Rezende Godoy, Júlio César Possati-Resende, Yasmin Medeiros Guimarães, Priscila Grecca Pedrão, Ricardo dos Reis and Adhemar Longatto-Filho
Cancers 2022, 14(11), 2612; https://doi.org/10.3390/cancers14112612 - 25 May 2022
Cited by 6 | Viewed by 3307
Abstract
Cervical cancer is caused by HPV. Although it is the fourth most common type of cancer diagnosed and the fourth cause of cancer death, cervical cancer is nearly completely preventable because of the vaccination and screening available. The present review aims to map [...] Read more.
Cervical cancer is caused by HPV. Although it is the fourth most common type of cancer diagnosed and the fourth cause of cancer death, cervical cancer is nearly completely preventable because of the vaccination and screening available. The present review aims to map the initiatives conducted to implement or evaluate the implementation of HPV testing in Latin American countries. We performed the review by searching on PubMed in the English language and on grey literature, as most of the information about the guidelines used was found in governmental websites in the Spanish language. We only found information in eight countries concerning HPV testing as primary screening. Only Mexico has established HPV-based screening in all territories. There are three countries with regional implementation. Two countries with pilot studies indicated results that supported implementation. Finally, there are another two countries with a national recommendation. We have learned that HPV implementation is feasible and a very promising tool for reducing cervical cancer morbidity and mortality. The costs associated with saving lives and reducing suffering due to morbidity of a preventable disease must be pragmatically evaluated by the Latin America governments, and improving outcomes must be a mandatory priority for those that are responsible for addressing an organized system of cervical cancer screening. Full article
(This article belongs to the Special Issue Feature Review Papers on "Cancer Causes, Screening and Diagnosis")
►▼ Show Figures

Figure 1

Other

Jump to: Research, Review

19 pages, 777 KiB  
Systematic Review
Current Applications of Artificial Intelligence to Classify Cervical Lymph Nodes in Patients with Head and Neck Squamous Cell Carcinoma—A Systematic Review
by Matthias Santer, Marcel Kloppenburg, Timo Maria Gottfried, Annette Runge, Joachim Schmutzhard, Samuel Moritz Vorbach, Julian Mangesius, David Riedl, Stephanie Mangesius, Gerlig Widmann, Herbert Riechelmann, Daniel Dejaco and Wolfgang Freysinger
Cancers 2022, 14(21), 5397; https://doi.org/10.3390/cancers14215397 - 2 Nov 2022
Cited by 23 | Viewed by 3238
Abstract
Locally-advanced head and neck squamous cell carcinoma (HNSCC) is mainly defined by the presence of pathologic cervical lymph nodes (LNs) with or without extracapsular spread (ECS). Current radiologic criteria to classify LNs as non-pathologic, pathologic, or pathologic with ECS are primarily shape-based. However, [...] Read more.
Locally-advanced head and neck squamous cell carcinoma (HNSCC) is mainly defined by the presence of pathologic cervical lymph nodes (LNs) with or without extracapsular spread (ECS). Current radiologic criteria to classify LNs as non-pathologic, pathologic, or pathologic with ECS are primarily shape-based. However, significantly more quantitative information is contained within imaging modalities. This quantitative information could be exploited for classification of LNs in patients with locally-advanced HNSCC by means of artificial intelligence (AI). Currently, various reviews exploring the role of AI in HNSCC are available. However, reviews specifically addressing the current role of AI to classify LN in HNSCC-patients are sparse. The present work systematically reviews original articles that specifically explore the role of AI to classify LNs in locally-advanced HNSCC applying Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines and the Study Quality Assessment Tool of National Institute of Health (NIH). Between 2001 and 2022, out of 69 studies a total of 13 retrospective, mainly monocentric, studies were identified. The majority of the studies included patients with oropharyngeal and oral cavity (9 and 7 of 13 studies, respectively) HNSCC. Histopathologic findings were defined as reference in 9 of 13 studies. Machine learning was applied in 13 studies, 9 of them applying deep learning. The mean number of included patients was 75 (SD ± 72; range 10–258) and of LNs was 340 (SD ± 268; range 21–791). The mean diagnostic accuracy for the training sets was 86% (SD ± 14%; range: 43–99%) and for testing sets 86% (SD ± 5%; range 76–92%). Consequently, all of the identified studies concluded AI to be a potentially promising diagnostic support tool for LN-classification in HNSCC. However, adequately powered, prospective, and randomized control trials are urgently required to further assess AI’s role in LN-classification in locally-advanced HNSCC. Full article
(This article belongs to the Special Issue Feature Review Papers on "Cancer Causes, Screening and Diagnosis")
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop