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Clinical Studies on Imaging Biomarkers

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A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Clinical Research of Cancer".

Deadline for manuscript submissions: closed (15 November 2021) | Viewed by 14877

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Prof. Dr. Marius E. Mayerhoefer

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Guest Editor

1. Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
2. Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
3. Department of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, Vienna, Austria
4. Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
Interests: PET; PET/CT; PET/MR; MRI; radiomics in the context of lymphoma, myeloma, leukemia, neuroendocrine tumors and melanoma

Dr. Lale Umutlu

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Guest Editor

Department of Diagnostic and Interventional Radiology, University Hospital Essen, University of Duisburg–Essen, Essen, Germany
Interests: PET/MR

Special Issue Information

Dear colleagues,

Imaging biomarkers derived from CT, MRI, PET, SPECT or ultrasound are becoming increasingly relevant in all areas of cancer patient management. One reason is that, contrary to biopsies, imaging can capture information on the entire tumor volume, rather than only on a small and possibly site-specific fraction. Imaging can be qualitative or quantitative, can be used for initial tumor assessment and characterization or treatment response assessment, and may even have value for outcome prognostication. However, many imaging biomarkers never make the transition from the pre-clinical to the clinical setting. Even of those imaging biomarkers that are applied to patients, only very few demonstrate long-term success and are eventually included in clinical management guidelines.

This Special Issue aims to provide a forum for research on cutting edge imaging biomarkers that have not been previously used in a clinical setting, and for research that provides additional supportive or contradictory data on techniques that have already shown promising results but require further validation. The image-based assessment of tumor heterogeneity represents another focus of this Special Issue.

Dr. Marius E. Mayerhoefer
Dr. Lale Umutlu
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.

Published Papers (6 papers)

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Research

13 pages, 1708 KiB

Open AccessArticle

Fully Automated MR Based Virtual Biopsy of Cerebral Gliomas

by Johannes Haubold, René Hosch, Vicky Parmar, Martin Glas, Nika Guberina, Onofrio Antonio Catalano, Daniela Pierscianek, Karsten Wrede, Cornelius Deuschl, Michael Forsting, Felix Nensa, Nils Flaschel and Lale Umutlu

Cancers 2021, 13(24), 6186; https://doi.org/10.3390/cancers13246186 - 8 Dec 2021

Cited by 12 | Viewed by 2557

Abstract

Objective: The aim of this study was to investigate the diagnostic accuracy of a radiomics analysis based on a fully automated segmentation and a simplified and robust MR imaging protocol to provide a comprehensive analysis of the genetic profile and grading of cerebral gliomas for everyday clinical use. Methods: MRI examinations of 217 therapy-naïve patients with cerebral gliomas, each comprising a non-contrast T1-weighted, FLAIR and contrast-enhanced T1-weighted sequence, were included in the study. In addition, clinical and laboratory parameters were incorporated into the analysis. The BraTS 2019 pretrained DeepMedic network was used for automated segmentation. The segmentations generated by DeepMedic were evaluated with 200 manual segmentations with a DICE score of 0.8082 ± 0.1321. Subsequently, the radiomics signatures were utilized to predict the genetic profile of ATRX, IDH1/2, MGMT and 1p19q co-deletion, as well as differentiating low-grade glioma from high-grade glioma. Results: The network provided an AUC (validation/test) for the differentiation between low-grade gliomas vs. high-grade gliomas of 0.981 ± 0.015/0.885 ± 0.02. The best results were achieved for the prediction of the ATRX expression loss with AUCs of 0.979 ± 0.028/0.923 ± 0.045, followed by 0.929 ± 0.042/0.861 ± 0.023 for the prediction of IDH1/2. The prediction of 1p19q and MGMT achieved moderate results, with AUCs of 0.999 ± 0.005/0.711 ± 0.128 for 1p19q and 0.854 ± 0.046/0.742 ± 0.050 for MGMT. Conclusion: This fully automated approach utilizing simplified MR protocols to predict the genetic profile and grading of cerebral gliomas provides an easy and efficient method for non-invasive tumor decoding. Full article

(This article belongs to the Special Issue Clinical Studies on Imaging Biomarkers)

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13 pages, 1758 KiB

Open AccessArticle

A Prospective Study Assessing the Post-Prostatectomy Detection Rate of a Presumed Local Failure at mpMR with Either ⁶⁴CuCl₂ or ⁶⁴CuPSMA PET/CT

by Adriana Faiella, Rosa Sciuto, Diana Giannarelli, Marta Bottero, Alessia Farneti, Luca Bertini, Sandra Rea, Valeria Landoni, Patrizia Vici, Maria Consiglia Ferriero and Giuseppe Sanguineti

Cancers 2021, 13(21), 5564; https://doi.org/10.3390/cancers13215564 - 6 Nov 2021

Cited by 6 | Viewed by 1785

Abstract

Background: We aimed assess the detection rate (DR) of positron emission tomography/computed tomography with two novel tracers in patients referred for salvage radiotherapy (sRT) with a presumed local recurrence at multiparametric magnetic resonance (mpMR) after radical prostatectomy (RP). Methods: The present prospective study was conducted at a single institution between August 2017 and June 2020. Eligibility criteria were undetectable PSA after RP; subsequent biochemical recurrence (two consecutive PSA rises to 0.2 ng/mL or greater); a presumed local failure at mpMR; no distant metastases at ¹⁸F-fluorocholine PET/CT (CH/PET); no previous history of androgen deprivation therapy. Patients were offered both ⁶⁴CuCl₂ PET/CT (CU/PET) and ⁶⁴Cu-PSMA PET/CT (PSMA/PET) before sRT. After image co-registration, PET findings were compared to mpMR ones in terms of DR and independent predictors of DR investigated at logistic regression. Results: A total of 62 patients with 72 nodules at mpMR were accrued. Compared to mpMR (DR = 100%, 95%CI: 94.9–100%), DRs were 47.2% (95%CI: 36.1–58.6%) and 54.4% (95%CI: 42.7–65.7%) for CU/PET and PSMA/PET, respectively (p < 0.001 for both). Both experimental PET/CT performed particularly poorly at PSA levels consistent with early sRT. Conclusions: The two novel radiotracers are inferior to mpMR in restaging the prostatic fossa for sRT planning purposes, particularly in the context of early salvage radiotherapy. Full article

(This article belongs to the Special Issue Clinical Studies on Imaging Biomarkers)

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13 pages, 1789 KiB

Open AccessArticle

Multiparametric Integrated ¹⁸F-FDG PET/MRI-Based Radiomics for Breast Cancer Phenotyping and Tumor Decoding

by Lale Umutlu, Julian Kirchner, Nils Martin Bruckmann, Janna Morawitz, Gerald Antoch, Marc Ingenwerth, Ann-Kathrin Bittner, Oliver Hoffmann, Johannes Haubold, Johannes Grueneisen, Harald H. Quick, Christoph Rischpler, Ken Herrmann, Peter Gibbs and Katja Pinker-Domenig

Cancers 2021, 13(12), 2928; https://doi.org/10.3390/cancers13122928 - 11 Jun 2021

Cited by 33 | Viewed by 2910

Abstract

Background: This study investigated the performance of simultaneous ¹⁸F-FDG PET/MRI of the breast as a platform for comprehensive radiomics analysis for breast cancer subtype analysis, hormone receptor status, proliferation rate and lymphonodular and distant metastatic spread. Methods: One hundred and twenty-four patients underwent simultaneous ¹⁸F-FDG PET/MRI. Breast tumors were segmented and radiomic features were extracted utilizing CERR software following the IBSI guidelines. LASSO regression was employed to select the most important radiomics features prior to model development. Five-fold cross validation was then utilized alongside support vector machines, resulting in predictive models for various combinations of imaging data series. Results: The highest AUC and accuracy for differentiation between luminal A and B was achieved by all MR sequences (AUC 0.98; accuracy 97.3). The best results in AUC for prediction of hormone receptor status and proliferation rate were found based on all MR and PET data (ER AUC 0.87, PR AUC 0.88, Ki-67 AUC 0.997). PET provided the best determination of grading (AUC 0.71), while all MR and PET analyses yielded the best results for lymphonodular and distant metastatic spread (0.81 and 0.99, respectively). Conclusion: ¹⁸F-FDG PET/MRI enables comprehensive high-quality radiomics analysis for breast cancer phenotyping and tumor decoding, utilizing the perks of simultaneously acquired morphologic, functional and metabolic data. Full article

(This article belongs to the Special Issue Clinical Studies on Imaging Biomarkers)

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12 pages, 1590 KiB

Open AccessArticle

Evaluation of the Predictive Potential of 18F-FDG PET and DWI Data Sets for Relevant Prognostic Parameters of Primary Soft-Tissue Sarcomas

by Michal Chodyla, Aydin Demircioglu, Benedikt M. Schaarschmidt, Stefanie Bertram, Janna Morawitz, Sebastian Bauer, Lars Podleska, Christoph Rischpler, Michael Forsting, Ken Herrmann, Lale Umutlu and Johannes Grueneisen

Cancers 2021, 13(11), 2753; https://doi.org/10.3390/cancers13112753 - 1 Jun 2021

Cited by 7 | Viewed by 2296

Abstract

Background: To evaluate the potential of simultaneously acquired 18F-FDG PET- and MR-derived quantitative imaging data sets of primary soft-tissue sarcomas for the prediction of neoadjuvant treatment response, the metastatic status and tumor grade. Methods: A total of 52 patients with a high-risk soft-tissue sarcoma underwent a 18F-FDG PET/MR examination within one week before the start of neoadjuvant treatment. For each patient, the maximum tumor size, metabolic activity (SUVs), and diffusion-restriction (ADC values) of the tumor manifestations were determined. A Mann–Whitney-U test was used, and ROC analysis was performed to evaluate the potential to predict histopathological treatment response, the metastatic status or tumor grade. The results from the histopathological analysis served as reference standard. Results: Soft-tissue sarcomas with a histopathological treatment response revealed a significantly higher metabolic activity than tumors in the non-responder group. In addition, grade 3 tumors showed a significant higher 18F-FDG uptake than grade 2 tumors. Furthermore, no significant correlation between the different outcome variables and tumor size or calculated ADC-values could be identified. Conclusion: Measurements of the metabolic activity of primary and untreated soft-tissue sarcomas could non-invasively deliver relevant information that may be used for treatment planning and risk-stratification of high-risk sarcoma patients in a pretherapeutic setting. Full article

(This article belongs to the Special Issue Clinical Studies on Imaging Biomarkers)

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10 pages, 1126 KiB

Open AccessArticle

Iodine Map Radiomics in Breast Cancer: Prediction of Metastatic Status

by Lukas Lenga, Simon Bernatz, Simon S. Martin, Christian Booz, Christine Solbach, Rotraud Mulert-Ernst, Thomas J. Vogl and Doris Leithner

Cancers 2021, 13(10), 2431; https://doi.org/10.3390/cancers13102431 - 18 May 2021

Cited by 19 | Viewed by 2314

Abstract

Dual-energy CT (DECT) iodine maps enable quantification of iodine concentrations as a marker for tissue vascularization. We investigated whether iodine map radiomic features derived from staging DECT enable prediction of breast cancer metastatic status, and whether textural differences exist between primary breast cancers and metastases. Seventy-seven treatment-naïve patients with biopsy-proven breast cancers were included retrospectively (41 non-metastatic, 36 metastatic). Radiomic features including first-, second-, and higher-order metrics as well as shape descriptors were extracted from volumes of interest on iodine maps. Following principal component analysis, a multilayer perceptron artificial neural network (MLP-NN) was used for classification (70% of cases for training, 30% validation). Histopathology served as reference standard. MLP-NN predicted metastatic status with AUCs of up to 0.94, and accuracies of up to 92.6 in the training and 82.6 in the validation datasets. The separation of primary tumor and metastatic tissue yielded AUCs of up to 0.87, with accuracies of up to 82.8 in the training, and 85.7 in the validation dataset. DECT iodine map-based radiomic signatures may therefore predict metastatic status in breast cancer patients. In addition, microstructural differences between primary and metastatic breast cancer tissue may be reflected by differences in DECT radiomic features. Full article

(This article belongs to the Special Issue Clinical Studies on Imaging Biomarkers)

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10 pages, 1315 KiB

Open AccessArticle

Evaluation of the Temporal Muscle Thickness as an Independent Prognostic Biomarker in Patients with Primary Central Nervous System Lymphoma

by Julia Furtner, Karl-Heinz Nenning, Thomas Roetzer, Johanna Gesperger, Lukas Seebrecht, Michael Weber, Astrid Grams, Stefan L. Leber, Franz Marhold, Camillo Sherif, Johannes Trenkler, Barbara Kiesel, Georg Widhalm, Ulrika Asenbaum, Ramona Woitek, Anna S. Berghoff, Daniela Prayer, Georg Langs, Matthias Preusser and Adelheid Wöhrer

Cancers 2021, 13(3), 566; https://doi.org/10.3390/cancers13030566 - 2 Feb 2021

Cited by 21 | Viewed by 2117

Abstract

In this study, we assessed the prognostic relevance of temporal muscle thickness (TMT), likely reflecting patient’s frailty, in patients with primary central nervous system lymphoma (PCNSL). In 128 newly diagnosed PCNSL patients TMT was analyzed on cranial magnetic resonance images. Predefined sex-specific TMT cutoff values were used to categorize the patient cohort. Survival analyses, using a log-rank test as well as Cox models adjusted for further prognostic parameters, were performed. The risk of death was significantly increased for PCNSL patients with reduced muscle thickness (hazard ratio of 3.189, 95% CI: 2–097–4.848, p < 0.001). Importantly, the results confirmed that TMT could be used as an independent prognostic marker upon multivariate Cox modeling (hazard ratio of 2.504, 95% CI: 1.608–3.911, p < 0.001) adjusting for sex, age at time of diagnosis, deep brain involvement of the PCNSL lesions, Eastern Cooperative Oncology Group (ECOG) performance status, and methotrexate-based chemotherapy. A TMT value below the sex-related cutoff value at the time of diagnosis is an independent adverse marker in patients with PCNSL. Thus, our results suggest the systematic inclusion of TMT in further translational and clinical studies designed to help validate its role as a prognostic biomarker. Full article

(This article belongs to the Special Issue Clinical Studies on Imaging Biomarkers)

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