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Bioengineering
Special Issues
Metabolic Imaging and Cancers
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Metabolic Imaging and Cancers

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Regenerative Engineering".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 3325

Special Issue Editor

Dr. Rupsa Datta

E-Mail Website
Guest Editor
Morgridge Institute for Research, Madison, WI, USA
Interests: optical imaging; fluorescence lifetime imaging microscopy; non-linear microscopy; cancer metabolism

Special Issue Information

Dear Colleagues,

Metabolic imaging entails targeted measurements of changes in cellular metabolism. As a powerful, non-invasive method to quantify abnormal metabolism present in diseases, metabolic imaging facilitates the understanding of pathogenesis, aids in clinical diagnosis, and detects responses to therapy. Alteration in various metabolic pathways correlates with cancer, thus rendering metabolic imaging especially advantageous in oncology. In vivo metabolic imaging tools such as positron emission tomography (PET) with metabolic probe 18F-FDG and hyperpolarized 13C magnetic resonance spectroscopic imaging (HP 13C-MRSI) are two extensively applied methods for metabolism monitoring in clinical oncology. Fluorescence-based optical redox ratio and fluorescence lifetime imaging microscopy (FLIM) of intrinsic metabolic coenzymes including NAD(P)H, FAD, and FMN and genetically encoded fluorescence-based sensors investigate metabolism in tumors and the tumor microenvironment, both in vitro and in vivo, spatially resolvable to the single-cell level. Mass spectrometry imaging (MSI), a technique that accurately determines molecular species and spatial distribution, has also gained popularity as a metabolic imaging technique. In recent years, metabolic imaging methods have shown immense capability in comprehending pharmacokinetics and deciphering molecular alterations in metabolic pathways.

The journal Bioengineering would like to compile a collection of papers to report on the advancements in metabolic imaging and cancers. The scope of this Special Issue covers, but not limited to:

  • Clinical and biological applications of metabolic imaging in cancer.
  • Imaging based clinical studies.
  • Instrumentation/technology development for cancer metabolism imaging.
  • Machine-learning- and deep-learning-based tools that support metabolic imaging modalities.
  • New image analysis techniques.
  • Machine-learning/deep-learning-based cancer grading

Dr. Rupsa Datta
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. Bioengineering is an international peer-reviewed open access monthly 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 2700 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.

Published Papers (2 papers)

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Research

19 pages, 4648 KiB  
Article
Development of a Molecular-Subtype-Associated Immune Prognostic Signature That Can Be Recognized by MRI Radiomics Features in Bladder Cancer
by Shenghua Liu, Haotian Chen, Zongtai Zheng, Yanyan He and Xudong Yao
Bioengineering 2023, 10(3), 318; https://doi.org/10.3390/bioengineering10030318 - 2 Mar 2023
Cited by 4 | Viewed by 1529
Abstract
Background: Bladder cancer (BLCA) is highly heterogeneous with distinct molecular subtypes. This research aimed to investigate the heterogeneity of different molecular subtypes from a tumor microenvironment perspective and develop a molecular-subtype-associated immune prognostic signature that can be recognized by MRI radiomics features. Methods: [...] Read more.
Background: Bladder cancer (BLCA) is highly heterogeneous with distinct molecular subtypes. This research aimed to investigate the heterogeneity of different molecular subtypes from a tumor microenvironment perspective and develop a molecular-subtype-associated immune prognostic signature that can be recognized by MRI radiomics features. Methods: Individuals with BLCA in The Cancer Genome Atlas (TCGA) and IMvigor210 were classified into luminal and basal subtypes according to the UNC classification. The proportions of tumor-infiltrating immune cells (TIICs) were examined using The Cell Type Identification by Estimating Relative Subsets of RNA Transcripts algorithm. Immune-linked genes that were expressed differentially between luminal and basal subtypes and associated with prognosis were selected to develop the immune prognostic signature (IPS) and utilized for the classification of the selected individuals into low- and high-risk groups. Functional enrichment analysis (GSEA) was performed on the IPS. The data from RNA-sequencing and MRI images of 111 BLCA samples in our center were utilized to construct a least absolute shrinkage and selection operator (LASSO) model for the prediction of patients’ IPSs. Results: Half of the TIICs showed differential distributions between the luminal and basal subtypes. IPS was highly associated with molecular subtypes, critical immune checkpoint gene expression, prognoses, and immunotherapy response. The prognostic value of the IPS was further verified through several validation data sets (GSE32894, GSE31684, GSE13507, and GSE48277) and meta-analysis. GSEA revealed that some oncogenic pathways were co-enriched in the group at high risk. A novel performance of a LASSO model developed as per ten radiomics features was achieved in terms of IPS prediction in both the validation (area under the curve (AUC): 0.810) and the training (AUC: 0.839) sets. Conclusions: Dysregulation of TIICs contributed to the heterogeneity between the luminal and basal subtypes. The IPS can facilitate molecular subtyping, prognostic evaluation, and personalized immunotherapy. A LASSO model developed as per the MRI radiomics features can predict the IPSs of affected individuals. Full article
(This article belongs to the Special Issue Metabolic Imaging and Cancers)
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15 pages, 1151 KiB  
Article
Adverse Pathology after Radical Prostatectomy of Patients Eligible for Active Surveillance—A Summary 7 Years after Introducing mpMRI-Guided Biopsy in a Real-World Setting
by Benedikt Ebner, Maria Apfelbeck, Nikolaos Pyrgidis, Tobias Nellessen, Stephan Ledderose, Paulo Leonardo Pfitzinger, Yannic Volz, Elena Berg, Benazir Enzinger, Severin Rodler, Michael Atzler, Troya Ivanova, Dirk-André Clevert, Christian Georg Stief and Michael Chaloupka
Bioengineering 2023, 10(2), 247; https://doi.org/10.3390/bioengineering10020247 - 13 Feb 2023
Viewed by 1321
Abstract
Objective: Over the last decade, active surveillance (AS) of low-risk prostate cancer has been increasing. The mpMRI fusion-guided biopsy of the prostate (FBx) is considered to be the gold standard in preoperative risk stratification. However, the role of FBx remains unclear in terms [...] Read more.
Objective: Over the last decade, active surveillance (AS) of low-risk prostate cancer has been increasing. The mpMRI fusion-guided biopsy of the prostate (FBx) is considered to be the gold standard in preoperative risk stratification. However, the role of FBx remains unclear in terms of risk stratification of low-risk prostate cancer outside high-volume centers. The aim of this study was to evaluate adverse pathology after radical prostatectomy (RP) in a real-world setting, focusing on patients diagnosed with Gleason score (GS) 6 prostate cancer (PCa) and eligible for AS by FBx. Subjects and Methods: Between March 2015 and March 2022, 1297 patients underwent FBx at the Department of Urology, Ludwig-Maximilians-University of Munich, Germany. MpMRI for FBx was performed by 111 different radiology centers. FBx was performed by 14 urologists from our department with different levels of experience. In total, 997/1297 (77%) patients were diagnosed with prostate cancer; 492/997 (49%) of these patients decided to undergo RP in our clinic and were retrospectively included. Univariate and multivariable logistic regression analyses were performed to evaluate clinical and histopathological parameters associated with adverse pathology comparing FBx and RP specimens. To compare FBx and systematic randomized biopsies performed in our clinic before introducing FBx (SBx, n = 2309), we performed a propensity score matching on a 1:1 ratio, adjusting for age, number of positive biopsy cores, and initial PSA (iPSA). Results: A total of 492 patients undergoing FBx or SBx was matched. In total, 55% of patients diagnosed with GS 6 by FBx were upgraded to clinically significant PCa (defined as GS ≥ 7a) after RP, compared to 52% of patients diagnosed by SBx (p = 0.76). A time delay between FBx and RP was identified as the only correlate associated with upgrading. A total of 5.9% of all FBx patients and 6.1% of all SBx patients would have been eligible for AS (p > 0.99) but decided to undergo RP. The positive predictive value of AS eligibility (diagnosis of low-risk PCa after biopsy and after RP) was 17% for FBx and 6.7% for SBx (p = 0.39). Conclusions: In this study, we show, in a real-world setting, that introducing FBx did not lead to significant change in ratio of adverse pathology for low-risk PCa patients after RP compared to SBx. Full article
(This article belongs to the Special Issue Metabolic Imaging and Cancers)
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