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Cancers
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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: 31 December 2024 | Viewed by 4690

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 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

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Published Papers (4 papers)

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Review

29 pages, 3780 KiB  
Review
Artificial Intelligence-Assisted Stimulated Raman Histology: New Frontiers in Vibrational Tissue Imaging
by Manu Krishnan Krishnan Nambudiri, V. G. Sujadevi, Prabaharan Poornachandran, C. Murali Krishna, Takahiro Kanno and Hemanth Noothalapati
Cancers 2024, 16(23), 3917; https://doi.org/10.3390/cancers16233917 - 22 Nov 2024
Viewed by 377
Abstract
Frozen section biopsy, introduced in the early 1900s, still remains the gold standard methodology for rapid histologic evaluations. Although a valuable tool, it is labor-, time-, and cost-intensive. Other challenges include visual and diagnostic variability, which may complicate interpretation and potentially compromise the [...] Read more.
Frozen section biopsy, introduced in the early 1900s, still remains the gold standard methodology for rapid histologic evaluations. Although a valuable tool, it is labor-, time-, and cost-intensive. Other challenges include visual and diagnostic variability, which may complicate interpretation and potentially compromise the quality of clinical decisions. Raman spectroscopy, with its high specificity and non-invasive nature, can be an effective tool for dependable and quick histopathology. The most promising modality in this context is stimulated Raman histology (SRH), a label-free, non-linear optical process which generates conventional H&E-like images in short time frames. SRH overcomes limitations of conventional Raman scattering by leveraging the qualities of stimulated Raman scattering (SRS), wherein the energy gets transferred from a high-power pump beam to a probe beam, resulting in high-energy, high-intensity scattering. SRH’s high resolution and non-requirement of preprocessing steps make it particularly suitable when it comes to intrasurgical histology. Combining SRH with artificial intelligence (AI) can lead to greater precision and less reliance on manual interpretation, potentially easing the burden of the overburdened global histopathology workforce. We review the recent applications and advances in SRH and how it is tapping into AI to evolve as a revolutionary tool for rapid histologic analysis. Full article
(This article belongs to the Special Issue Advanced Research in Oncology in 2024)
22 pages, 2221 KiB  
Review
An Overview for Clinicians on Intraductal Papillary Mucinous Neoplasms (IPMNs) of the Pancreas
by Dimitrios Moris, Ioannis Liapis, Piyush Gupta, Ioannis A. Ziogas, Georgia-Sofia Karachaliou, Nikolaos Dimitrokallis, Brian Nguyen and Pejman Radkani
Cancers 2024, 16(22), 3825; https://doi.org/10.3390/cancers16223825 - 14 Nov 2024
Viewed by 388
Abstract
Currently, there is no reliable method of discerning between low-risk and high-risk intraductal papillary mucinous neoplasms (IPMNs). Operative resection is utilized in an effort to resect those lesions with high-grade dysplasia (HGD) prior to the development of invasive disease. The current guidelines recommend [...] Read more.
Currently, there is no reliable method of discerning between low-risk and high-risk intraductal papillary mucinous neoplasms (IPMNs). Operative resection is utilized in an effort to resect those lesions with high-grade dysplasia (HGD) prior to the development of invasive disease. The current guidelines recommend resection for IPMN that involve the main pancreatic duct. Resecting lesions with HGD before their progression to invasive disease and the avoidance of resection in those patients with low-grade dysplasia is the optimal clinical scenario. Therefore, the importance of developing preoperative models able to discern HGD in IPMN patients cannot be overstated. Low-risk patients should be managed with nonsurgical treatment options (typically MRI surveillance), while high-risk patients would undergo resection, hopefully prior to the formation of invasive disease. Current research is evolving in multiple directions. First, there is an ongoing effort to identify reliable markers for predicting malignant transformation of IPMN, mainly focusing on genomic and transcriptomic data from blood, tissue, and cystic fluid. Also, multimodal models of combining biomarkers with clinical and radiographic data seem promising for providing robust and accurate answers of risk levels for IPMN patients. Full article
(This article belongs to the Special Issue Advanced Research in Oncology in 2024)
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16 pages, 1067 KiB  
Review
The Influence of Microbiota on Breast Cancer: A Review
by Cara-Xenia-Rafaela Neagoe, Mihaela Ionică, Octavian Constantin Neagoe and Adrian Pavel Trifa
Cancers 2024, 16(20), 3468; https://doi.org/10.3390/cancers16203468 - 13 Oct 2024
Viewed by 1217
Abstract
Breast cancer remains one of the leading causes of death among women worldwide, and recent research highlights its growing connection to alterations in the microbiota. This review delves into the intricate relationship between microbiotas and breast cancer, exploring its presence in healthy breast [...] Read more.
Breast cancer remains one of the leading causes of death among women worldwide, and recent research highlights its growing connection to alterations in the microbiota. This review delves into the intricate relationship between microbiotas and breast cancer, exploring its presence in healthy breast tissue, its changes during cancer progression, and its considerable impact on both the tumor microenvironment (TME) and the tumor immune microenvironment (TIME). We extensively analyze how the microbiota influences cancer growth, invasion, metastasis, resistance to drugs, and the evasion of the immune system, with a special focus on its effects on the TIME. Furthermore, we investigate distinct microbial profiles associated with the four primary molecular subtypes of breast cancer, examining how the microbiota in tumor tissues compares with that in adjacent normal tissues. Emerging studies suggest that microbiotas could serve as valuable diagnostic and prognostic biomarkers, as well as targets for therapy. This review emphasizes the urgent need for further research to improve strategies for breast cancer prevention, diagnosis, and treatment. By offering a detailed examination of the microbiota’s critical role in breast cancer, this review aims to foster the development of novel microbiota-based approaches for managing the disease. Full article
(This article belongs to the Special Issue Advanced Research in Oncology in 2024)
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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
Cited by 1 | Viewed by 1438
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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