Trends in oncologic hybrid imaging

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Abstract

Hybrid imaging plays a central role in the diagnosis and management of a wide range of malignancies at all stages. In this article, we review the most pertinent historical developments, emerging clinical applications of novel radiotracers and imaging technologies, and potential implications for training and practice. This includes an overview of novel tracers for prostate, breast, and neuroendocrine tumors, assessment of tumor heterogeneity, the concept of image-guided ‘biologically relevant dosing’, and theranostic applications. Recent technological advancements, including time-of-flight PET, PET/MRI, and ‘one-minute whole-body PET’, are also covered. Finally, we discuss how these rapidly evolving applications might affect current training curricula and how imaging-derived big data could be harnessed to the benefit of our patients.

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Most cited references 40

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German Multicenter Study Investigating 177Lu-PSMA-617 Radioligand Therapy in Advanced Prostate Cancer Patients.

Kambiz Rahbar, Hojjat Ahmadzadehfar, Clemens Kratochwil … (2017)

(177)Lu-labeled PSMA-617 is a promising new therapeutic agent for radioligand therapy (RLT) of patients with metastatic castration-resistant prostate cancer (mCRPC). Initiated by the German Society of Nuclear Medicine, a retrospective multicenter data analysis was started in 2015 to evaluate efficacy and safety of (177)Lu-PSMA-617 in a large cohort of patients.

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Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects.

Elaheh Moradi, Antonietta Pepe, Christian Gaser … (2015)

Mild cognitive impairment (MCI) is a transitional stage between age-related cognitive decline and Alzheimer's disease (AD). For the effective treatment of AD, it would be important to identify MCI patients at high risk for conversion to AD. In this study, we present a novel magnetic resonance imaging (MRI)-based method for predicting the MCI-to-AD conversion from one to three years before the clinical diagnosis. First, we developed a novel MRI biomarker of MCI-to-AD conversion using semi-supervised learning and then integrated it with age and cognitive measures about the subjects using a supervised learning algorithm resulting in what we call the aggregate biomarker. The novel characteristics of the methods for learning the biomarkers are as follows: 1) We used a semi-supervised learning method (low density separation) for the construction of MRI biomarker as opposed to more typical supervised methods; 2) We performed a feature selection on MRI data from AD subjects and normal controls without using data from MCI subjects via regularized logistic regression; 3) We removed the aging effects from the MRI data before the classifier training to prevent possible confounding between AD and age related atrophies; and 4) We constructed the aggregate biomarker by first learning a separate MRI biomarker and then combining it with age and cognitive measures about the MCI subjects at the baseline by applying a random forest classifier. We experimentally demonstrated the added value of these novel characteristics in predicting the MCI-to-AD conversion on data obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. With the ADNI data, the MRI biomarker achieved a 10-fold cross-validated area under the receiver operating characteristic curve (AUC) of 0.7661 in discriminating progressive MCI patients (pMCI) from stable MCI patients (sMCI). Our aggregate biomarker based on MRI data together with baseline cognitive measurements and age achieved a 10-fold cross-validated AUC score of 0.9020 in discriminating pMCI from sMCI. The results presented in this study demonstrate the potential of the suggested approach for early AD diagnosis and an important role of MRI in the MCI-to-AD conversion prediction. However, it is evident based on our results that combining MRI data with cognitive test results improved the accuracy of the MCI-to-AD conversion prediction.

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HER2 status and benefit from adjuvant trastuzumab in breast cancer.

Chungyeul Kim, Soonmyung Paik, Norman Wolmark (2008)

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Author and article information

Contributors

Andreas G. Wibmer: wibmera@mskcc.org

Hedvig Hricak: hricakh@mskcc.org

Gary A. Ulaner: ulanerg@mskcc.org

Wolfgang Weber: weberw@mskcc.org

Journal

Journal ID (nlm-ta): Eur J Hybrid Imaging

Journal ID (iso-abbrev): Eur J Hybrid Imaging

Title: European Journal of Hybrid Imaging

Publisher: Springer International Publishing (Cham )

ISSN (Electronic): 2510-3636

Publication date (Electronic): 19 January 2018

Publication date PMC-release: 19 January 2018

Publication date (Print): 2018

Volume: 2

Issue: 1

Electronic Location Identifier: 1

Affiliations

[1 ]ISNI 0000 0001 2171 9952, GRID grid.51462.34, Department of Radiology, , Memorial Sloan Kettering Cancer Center, ; 1275 York Avenue, New York, NY 10065 USA

[2 ]ISNI 0000 0001 2171 9952, GRID grid.51462.34, Molecular Imaging and Therapy Service, , Memorial Sloan Kettering Cancer Center, ; 1275 York Avenue, New York, NY 10065 USA

Article

Publisher ID: 19

DOI: 10.1186/s41824-017-0019-6

PMC ID: 5954767

PubMed ID: 29782605

SO-VID: f9b09f84-03ae-4c37-8979-078242040366

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

History

Date received : 23 October 2017

Date accepted : 7 December 2017

Funding

Funded by: Peter Michael Foundation (US)

Custom metadata

Keywords: oncologic hybrid molecular imaging,time-of-flight positron emission tomography computed tomography,one-minute whole-body pet explorer,18f –fluciclovine,11c–choline,prostate-specific membrane antigen,18f–fluorodehydrotestosterone,89zr-trastuzumab,18f–fluoroestradiol,68ga/ 177lu -dota-tate

Data availability:

Keywords: oncologic hybrid molecular imaging, time-of-flight positron emission tomography computed tomography, one-minute whole-body pet explorer, 18f –fluciclovine, 11c–choline, prostate-specific membrane antigen, 18f–fluorodehydrotestosterone, 89zr-trastuzumab, 18f–fluoroestradiol, 68ga/ 177lu -dota-tate

Trends in oncologic hybrid imaging

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Abstract

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Electron Channelling Contrast Imaging (ECCI)

Most cited references 40

German Multicenter Study Investigating 177Lu-PSMA-617 Radioligand Therapy in Advanced Prostate Cancer Patients.

Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects.

HER2 status and benefit from adjuvant trastuzumab in breast cancer.

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