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Cancers
Special Issues
Advanced Research in Oncology in 2024
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Advanced Research in Oncology in 2024

A special issue of Cancers (ISSN 2072-6694).

Deadline for manuscript submissions: 25 October 2024 | Viewed by 438

Special Issue Editor

Dr. Dimitrios Moris

E-Mail Website
Guest Editor
Department of Surgery, Duke University Medical Center, 2301 Erwin Rd, Durham, NC, USA
Interests: transplantation; surgical oncology; Hepato-Pancreato-Biliary (HPB) surgery
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce a Special Issue entitled “Advanced Research in Oncology in 2024”, which will be the New Year Special Issue Series of Cancers.

For this Special Issue, we are seeking comprehensive review papers from all oncology-related fields from our Editorial Board Members, societies, authors, and reviewers. The papers in this Special Issue will be published via our open access platform after a thorough peer review.

We look forward to receiving your contributions.

Dr. Dimitrios Moris
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 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
  • tumour
  • oncology

Published Papers (1 paper)

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Review

27 pages, 965 KiB  
Review
Clinical Prediction Models for Prognosis of Colorectal Liver Metastases: A Comprehensive Review of Regression-Based and Machine Learning Models
by Stamatios Kokkinakis, Ioannis A. Ziogas, Jose D. Llaque Salazar, Dimitrios P. Moris and Georgios Tsoulfas
Cancers 2024, 16(9), 1645; https://doi.org/10.3390/cancers16091645 - 25 Apr 2024
Viewed by 221
Abstract
Colorectal liver metastasis (CRLM) is a disease entity that warrants special attention due to its high frequency and potential curability. Identification of “high-risk” patients is increasingly popular for risk stratification and personalization of the management pathway. Traditional regression-based methods have been used to [...] Read more.
Colorectal liver metastasis (CRLM) is a disease entity that warrants special attention due to its high frequency and potential curability. Identification of “high-risk” patients is increasingly popular for risk stratification and personalization of the management pathway. Traditional regression-based methods have been used to derive prediction models for these patients, and lately, focus has shifted to artificial intelligence-based models, with employment of variable supervised and unsupervised techniques. Multiple endpoints, like overall survival (OS), disease-free survival (DFS) and development or recurrence of postoperative complications have all been used as outcomes in these studies. This review provides an extensive overview of available clinical prediction models focusing on the prognosis of CRLM and highlights the different predictor types incorporated in each model. An overview of the modelling strategies and the outcomes chosen is provided. Specific patient and treatment characteristics included in the models are discussed in detail. Model development and validation methods are presented and critically appraised, and model performance is assessed within a proposed framework. Full article
(This article belongs to the Special Issue Advanced Research in Oncology in 2024)
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