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Cancers
Special Issues
Imaging and Liquid Biopsy Biomarkers for Cancer Diagnosis and Treatment
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Imaging and Liquid Biopsy Biomarkers for Cancer Diagnosis and Treatment

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

Deadline for manuscript submissions: 31 August 2024 | Viewed by 1302

Special Issue Editors

Dr. Wen Li

E-Mail Website
Guest Editor
Department of Radiology and Biomedical Imaging, University of California, 550 16th Street, San Francisco, CA 94158, USA
Interests: biomedical imaging; quantitative imaging biomarkers; MRI for breast cancer treatment response
Dr. Mark Jesus M. Magbanua

E-Mail Website
Guest Editor
Department of Laboratory Medicine, University of California, 2340 Sutter Street, San Francisco, CA 94115, USA
Interests: liquid biopsy; breast cancer; genomics

Special Issue Information

Dear Colleagues,

Biomarkers are essential for cancer diagnosis and treatment. Imaging and liquid biopsy are both noninvasive methods that can be used to extract quantitative biomarkers repeatedly. A liquid biopsy involves testing blood and other bodily fluids for diagnostic and therapeutic purposes. In the past, imaging has mostly been used to measure tumor size. Recently, radiomics and machine learning (ML)/ deep learning (DL) methods have been developed to study tumor heterogeneity. Imaging focuses on the tumor extent and heterogeneity measurement, while liquid biopsy detects tumor-derived molecules in circulation. In this Special Issue, we are interested in manuscripts that report work combining both imaging and liquid biopsy to diagnosis or treat various kinds of cancer. Imaging modalities include, but are not limited to, magnetic resonance imaging (MRI), X-Ray imaging, computed tomography (CT), ultrasound, and nuclear medicine scans. The liquid biopsy includes tests that can evaulate circulating tumor cells (CTC), disseminated tumor cells (DTC), and circulating tumor DNA or RNA (ctDNA, ctRNA). We welcome original research articles or comprehensive review articles focusing on the combination of quantitative imaging and liquid biopsy biomarkers for cancer detection, staging, prognosis, or monitoring treatment response.

Dr. Wen Li
Dr. Mark Jesus M. Magbanua
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 imaging
  • liquid biopsy
  • cancer diagnosis
  • treatment response
  • prognosis
  • metastasis
  • circulating tumor cells
  • disseminated tumor cells
  • circulating tumor DNA
  • circulating tumor RNA

Published Papers (2 papers)

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Review

11 pages, 537 KiB  
Review
Prostate-Specific Membrane Antigen Positron Emission Tomography/Computed Tomography-Derived Radiomic Models in Prostate Cancer Prognostication
by Linda My Huynh, Shea Swanson, Sophia Cima, Eliana Haddadin and Michael Baine
Cancers 2024, 16(10), 1897; https://doi.org/10.3390/cancers16101897 - 16 May 2024
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Abstract
The clinical integration of prostate membrane specific antigen (PSMA) positron emission tomography and computed tomography (PET/CT) scans represents potential for advanced data analysis techniques in prostate cancer (PC) prognostication. Among these tools is the use of radiomics, a computer-based method of extracting and [...] Read more.
The clinical integration of prostate membrane specific antigen (PSMA) positron emission tomography and computed tomography (PET/CT) scans represents potential for advanced data analysis techniques in prostate cancer (PC) prognostication. Among these tools is the use of radiomics, a computer-based method of extracting and quantitatively analyzing subvisual features in medical imaging. Within this context, the present review seeks to summarize the current literature on the use of PSMA PET/CT-derived radiomics in PC risk stratification. A stepwise literature search of publications from 2017 to 2023 was performed. Of 23 articles on PSMA PET/CT-derived prostate radiomics, PC diagnosis, prediction of biopsy Gleason score (GS), prediction of adverse pathology, and treatment outcomes were the primary endpoints of 4 (17.4%), 5 (21.7%), 7 (30.4%), and 7 (30.4%) studies, respectively. In predicting PC diagnosis, PSMA PET/CT-derived models performed well, with receiver operator characteristic curve area under the curve (ROC-AUC) values of 0.85–0.925. Similarly, in the prediction of biopsy and surgical pathology results, ROC-AUC values had ranges of 0.719–0.84 and 0.84–0.95, respectively. Finally, prediction of recurrence, progression, or survival following treatment was explored in nine studies, with ROC-AUC ranging 0.698–0.90. Of the 23 studies included in this review, 2 (8.7%) included external validation. While explorations of PSMA PET/CT-derived radiomic models are immature in follow-up and experience, these results represent great potential for future investigation and exploration. Prior to consideration for clinical use, however, rigorous validation in feature reproducibility and biologic validation of radiomic signatures must be prioritized. Full article
(This article belongs to the Special Issue Imaging and Liquid Biopsy Biomarkers for Cancer Diagnosis and Treatment)
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13 pages, 544 KiB  
Review
Integrating Imaging and Circulating Tumor DNA Features for Predicting Patient Outcomes
by Mark Jesus M. Magbanua, Wen Li and Laura J. van ’t Veer
Cancers 2024, 16(10), 1879; https://doi.org/10.3390/cancers16101879 - 15 May 2024
Viewed by 400
Abstract
Biomarkers for evaluating tumor response to therapy and estimating the risk of disease relapse represent tremendous areas of clinical need. To evaluate treatment efficacy, tumor response is routinely assessed using different imaging modalities like positron emission tomography/computed tomography or magnetic resonance imaging. More [...] Read more.
Biomarkers for evaluating tumor response to therapy and estimating the risk of disease relapse represent tremendous areas of clinical need. To evaluate treatment efficacy, tumor response is routinely assessed using different imaging modalities like positron emission tomography/computed tomography or magnetic resonance imaging. More recently, the development of circulating tumor DNA detection assays has provided a minimally invasive approach to evaluate tumor response and prognosis through a blood test (liquid biopsy). Integrating imaging- and circulating tumor DNA-based biomarkers may lead to improvements in the prediction of patient outcomes. For this mini-review, we searched the scientific literature to find original articles that combined quantitative imaging and circulating tumor DNA biomarkers to build prediction models. Seven studies reported building prognostic models to predict distant recurrence-free, progression-free, or overall survival. Three discussed building models to predict treatment response using tumor volume, pathologic complete response, or objective response as endpoints. The limited number of articles and the modest cohort sizes reported in these studies attest to the infancy of this field of study. Nonetheless, these studies demonstrate the feasibility of developing multivariable response-predictive and prognostic models using regression and machine learning approaches. Larger studies are warranted to facilitate the building of highly accurate response-predictive and prognostic models that are generalizable to other datasets and clinical settings. Full article
(This article belongs to the Special Issue Imaging and Liquid Biopsy Biomarkers for Cancer Diagnosis and Treatment)
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