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Cancers
Special Issues
Advanced Research in Cancer Initiation and Early Detection
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Advanced Research in Cancer Initiation and Early Detection

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 2023) | Viewed by 1390

Special Issue Editors

Dr. Huanhuan Chen

E-Mail Website
Guest Editor
The Pritzker School of Molecular Engineering, The Ben May department of Cancer Research, The University of Chicago, Chicago, IL 60637, USA
Interests: stem cell biology; cancer biology; lung cancer; small cell lung cancer; pluripotent stem cell; lung infection; molecular engineering; cellular engineering; tissue engineering
Dr. Patricia J. Lardone

E-Mail Website
Guest Editor
1. Instituto de Biomedicina de Sevilla, IBiS, Universidad de Sevilla, HUVR, Junta de Andalucía, CSIC, 41013 Seville, Spain
2. Departamento de Bioquímica Médica y Biología Molecular e Inmunología, Facultad de Medicina, Universidad de Sevilla, 41009 Seville, Spain
Interests: cancer immunology; immunotherapy; melatonin; multiple sclerosis immunopathology
Dr. Zhubo Wei

E-Mail Website
Co-Guest Editor
Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA
Interests: cancer genomics; cancer genetics; transposons; cancer mouse models; colorectal cancer; lung cancer
Dr. Abhimanyu Thakur

E-Mail Website
Co-Guest Editor
The Pritzker School of Molecular Engineering, Ben May Department for Cancer Research, The University of Chicago, Chicago, IL 60637, USA
Interests: extracellular vesicles; cancer biology; neuroscience; stem cell; biosensing; theranostic
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Cancer is a deadly disease affecting tens of millions of people around the world, and most cancer-related deaths come from advanced disease stages. Therefore, it is quite understandable that most cancer research is focusing on its late stages and metastasis and related exploration of treatment. However, it is equally important to continue to advance our understanding of the first step of cancer: how does it develop from normal tissues/cells? With decades of diligent efforts from numerous excellent cancer research scientists, we have accumulated a significant amount of knowledge about cancer initiation. In the current era of genomics and other -omics studies, cancer initiation research will achieve much more to finally reach the goal of applying the knowledge to detect cancers effectively and efficiently at early stages. Once that goal is realized, we will be able to not only save many people’s lives but also significantly ease medical burdens to not only patients and their families but also the whole society.

The two components of early detection of cancer are early diagnosis and screening. Therefore, this Special Issue will cover both early detection and screening. Any studies concerning the two directions are welcome here but please make sure that the results to be reported include mechanistic components that promote our understanding of cancer initiation and have the potential to make cancer early detection a reality in the future.

Dr. Huanhuan Chen
Dr. Patricia J. Lardone
Dr. Zhubo Wei
Dr. Abhimanyu Thakur
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

  • cancer initiation
  • early diagnosis
  • screening
  • liquid biopsy
  • cancer genomics
  • single-cell sequencing
  • multi-omics
  • cancer modeling

Published Papers (1 paper)

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Research

14 pages, 2667 KiB  
Article
Machine-Learning-Based Classification Model to Address Diagnostic Challenges in Transbronchial Lung Biopsy
by Hisao Sano, Ethan N. Okoshi, Yuri Tachibana, Tomonori Tanaka, Kris Lami, Wataru Uegami, Yoshio Ohta, Luka Brcic, Andrey Bychkov and Junya Fukuoka
Cancers 2024, 16(4), 731; https://doi.org/10.3390/cancers16040731 - 9 Feb 2024
Viewed by 1086
Abstract
Background: When obtaining specimens from pulmonary nodules in TBLB, distinguishing between benign samples and mis-sampling from a tumor presents a challenge. Our objective is to develop a machine-learning-based classifier for TBLB specimens. Methods: Three pathologists assessed six pathological findings, including interface bronchitis/bronchiolitis (IB/B), [...] Read more.
Background: When obtaining specimens from pulmonary nodules in TBLB, distinguishing between benign samples and mis-sampling from a tumor presents a challenge. Our objective is to develop a machine-learning-based classifier for TBLB specimens. Methods: Three pathologists assessed six pathological findings, including interface bronchitis/bronchiolitis (IB/B), plasma cell infiltration (PLC), eosinophil infiltration (Eo), lymphoid aggregation (Ly), fibroelastosis (FE), and organizing pneumonia (OP), as potential histologic markers to distinguish between benign and malignant conditions. A total of 251 TBLB cases with defined benign and malignant outcomes based on clinical follow-up were collected and a gradient-boosted decision-tree-based machine learning model (XGBoost) was trained and tested on randomly split training and test sets. Results: Five pathological changes showed independent, mild-to-moderate associations (AUC ranging from 0.58 to 0.75) with benign conditions, with IB/B being the strongest predictor. On the other hand, FE emerged to be the sole indicator of malignant conditions with a mild association (AUC = 0.66). Our model was trained on 200 cases and tested on 51 cases, achieving an AUC of 0.78 for the binary classification of benign vs. malignant on the test set. Conclusion: The machine-learning model developed has the potential to distinguish between benign and malignant conditions in TBLB samples excluding the presence or absence of tumor cells, thereby improving diagnostic accuracy and reducing the burden of repeated sampling procedures for patients. Full article
(This article belongs to the Special Issue Advanced Research in Cancer Initiation and Early Detection)
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