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Cancers
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Advances in Bone Metastatic Cancer Research
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Advances in Bone Metastatic Cancer Research

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

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 1569

Special Issue Editors

Dr. Maria Teresa Valenti

E-Mail Website
Guest Editor
Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, 37128 Verona, Italy
Interests: bone degenerative diseases; cancer; transcription factors
Special Issues, Collections and Topics in MDPI journals
Dr. Luca G. Dalle Carbonare

E-Mail Website
Guest Editor
Department of Medicine, University of Verona, Verona, Italy
Interests: bone diseases; cardiovascular diseases; osteo-oncology; bone histomorphometry; mesenchymal stem cells in bone and degenerative diseases; nutraceuticals; preclinical, clinical and post-marketing studies

Special Issue Information

Dear Colleagues,

Bone metastases affect quality of life and survival of cancer patients. Cancer cells metastasize into bone via a multistep process, and complex interactions between transformed cells and hematopoietic, endothelial, and bone cells occur. Despite the improvement of diagnostic tools, the appearance of bone metastases discloses an advanced disease stage for which there are no appropriate therapies. Breast and prostate cancer are solid tumors that often metastasize in the bone. In addition, other types of cancer, such as lung carcinoma, also have a high tropism for the bone environment. The ability to form bone lesions in other types of cancer, such as melanomas, has also been underestimated for many decades. Patients with bone metastases suffer from many complications, including recurring fractures or hypercalcemia. Cancer cells interact with bone cells either to modulate their dormancy or their drug resistance. Cells of mesenchymal origin, such as osteoblasts and fibroblasts, represent “endosteal niche cells”, which are considered an important component of the metastatic niche. An early diagnosis of bone metastasis represents a crucial test in order to prevent disease progression. It has been reported that protein or gene expression profiles analyzed in primary tumors as well as circulating DNA or RNA markers associated with tumor cells may predict the tropism and the ability of cancer cells to metastasize in bone.

This Special Issue will focus on the mechanisms and pathways involved in the formation of bone metastases and on the possible identification of therapeutic approaches to counteract the onset of bone metastases.

We are pleased to invite authors to submit original articles or reviews focused on the latest discoveries in this field.

Dr. Maria Teresa Valenti
Dr. Luca G. Dalle Carbonare
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

  • bone
  • cancer
  • osteoblasts
  • transcription factors
  • metastatic niche
  • endothelial cells
  • osteoclasts

Published Papers (1 paper)

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Research

13 pages, 2662 KiB  
Article
Prediction of Primary Tumor Sites in Spinal Metastases Using a ResNet-50 Convolutional Neural Network Based on MRI
by Ke Liu, Siyuan Qin, Jinlai Ning, Peijin Xin, Qizheng Wang, Yongye Chen, Weili Zhao, Enlong Zhang and Ning Lang
Cancers 2023, 15(11), 2974; https://doi.org/10.3390/cancers15112974 - 30 May 2023
Cited by 4 | Viewed by 1244
Abstract
We aim to investigate the feasibility and evaluate the performance of a ResNet-50 convolutional neural network (CNN) based on magnetic resonance imaging (MRI) in predicting primary tumor sites in spinal metastases. Conventional sequences (T1-weighted, T2-weighted, and fat-suppressed T2-weighted sequences) MRIs of spinal metastases [...] Read more.
We aim to investigate the feasibility and evaluate the performance of a ResNet-50 convolutional neural network (CNN) based on magnetic resonance imaging (MRI) in predicting primary tumor sites in spinal metastases. Conventional sequences (T1-weighted, T2-weighted, and fat-suppressed T2-weighted sequences) MRIs of spinal metastases patients confirmed by pathology from August 2006 to August 2019 were retrospectively analyzed. Patients were partitioned into non-overlapping sets of 90% for training and 10% for testing. A deep learning model using ResNet-50 CNN was trained to classify primary tumor sites. Top-1 accuracy, precision, sensitivity, area under the curve for the receiver-operating characteristic (AUC-ROC), and F1 score were considered as the evaluation metrics. A total of 295 spinal metastases patients (mean age ± standard deviation, 59.9 years ± 10.9; 154 men) were evaluated. Included metastases originated from lung cancer (n = 142), kidney cancer (n = 50), mammary cancer (n = 41), thyroid cancer (n = 34), and prostate cancer (n = 28). For 5-class classification, AUC-ROC and top-1 accuracy were 0.77 and 52.97%, respectively. Additionally, AUC-ROC for different sequence subsets ranged between 0.70 (for T2-weighted) and 0.74 (for fat-suppressed T2-weighted). Our developed ResNet-50 CNN model for predicting primary tumor sites in spinal metastases at MRI has the potential to help prioritize the examinations and treatments in case of unknown primary for radiologists and oncologists. Full article
(This article belongs to the Special Issue Advances in Bone Metastatic Cancer Research)
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