Radiation injury vs. recurrent brain metastasis: combining textural feature radiomics analysis and standard parameters may increase 18F-FET PET accuracy without dynamic scans

Lohmann, Philipp; Stoffels, Gabriele; Ceccon, Garry; Rapp, Marion; Sabel, Michael; Filß, Christian; Kamp, Marcel A.; Stegmayr, Carina; Neumaier, Bernd; Shah, N. Jon; Langen, Karl‐Josef; Galldiks, Norbert

doi:10.1007/s00330-016-4638-2

Cited by 87 publications

(56 citation statements)

References 59 publications

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“…Over the past years, several studies have demonstrated that amino acid PET alone is a potent imaging method for the identification of treatment-related changes such as pseudoprogression (Galldiks et al, 2015; Kebir et al, 2016a; Kebir et al, 2016b; Galldiks, 2017) or radiation injury (Ceccon et al, 2017; Galldiks et al, 2012; Lohmann et al, 2017) in patients with glioma and brain metastasis. For the differentiation of radiation injury from brain metastasis recurrence, the diagnostic accuracy of static (i.e., tumor/brain ratios) and dynamic FET PET parameters (i.e., time-to-peak values and the slope of time-activity curves) has been evaluated in a pilot study by Galldiks et al (2012).…”

Section: Discussionmentioning

confidence: 99%

“…However, in all these studies, dynamic FET PET parameters that require a time-consuming (i.e., 40–50 min acquisition time) and hence more expensive PET acquisition, were evaluated. To facilitate data acquisition and analysis, Lohmann and colleagues combined for the first-time textural features derived from static FET PET for the discrimination of radiation injury from recurrent brain metastasis and achieved a diagnostic accuracy of 85% without the acquisition of dynamic FET PET scans (Lohmann et al, 2017). …”

Section: Discussionmentioning

confidence: 99%

“…Textural feature analysis of inconclusive lesions on PET (Lohmann et al, 2017) and MR images (Larroza et al, 2015; Nardone et al, 2016; Pallavi et al, 2014; Tiwari et al, 2016) is another promising approach. It is based on the assumption that the microstructure of a process depends on the underlying pathology and is reflected in subtle differences in the radiological image that cannot be detected by means of human perception but can be made accessible by high-dimensional quantitative image analysis often referred to as “radiomics”.…”

Section: Introductionmentioning

confidence: 99%

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Combined FET PET/MRI radiomics differentiates radiation injury from recurrent brain metastasis

Lohmann

Köcher

Ceccon

et al. 2018

NeuroImage: Clinical

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BackgroundThe aim of this study was to investigate the potential of combined textural feature analysis of contrast-enhanced MRI (CE-MRI) and static O-(2-[18F]fluoroethyl)-L-tyrosine (FET) PET for the differentiation between local recurrent brain metastasis and radiation injury since CE-MRI often remains inconclusive.MethodsFifty-two patients with new or progressive contrast-enhancing brain lesions on MRI after radiotherapy (predominantly stereotactic radiosurgery) of brain metastases were additionally investigated using FET PET. Based on histology (n = 19) or clinicoradiological follow-up (n = 33), local recurrent brain metastases were diagnosed in 21 patients (40%) and radiation injury in 31 patients (60%). Forty-two textural features were calculated on both unfiltered and filtered CE-MRI and summed FET PET images (20–40 min p.i.), using the software LIFEx. After feature selection, logistic regression models using a maximum of five features to avoid overfitting were calculated for each imaging modality separately and for the combined FET PET/MRI features. The resulting models were validated using cross-validation. Diagnostic accuracies were calculated for each imaging modality separately as well as for the combined model.ResultsFor the differentiation between radiation injury and recurrence of brain metastasis, textural features extracted from CE-MRI had a diagnostic accuracy of 81% (sensitivity, 67%; specificity, 90%). FET PET textural features revealed a slightly higher diagnostic accuracy of 83% (sensitivity, 88%; specificity, 75%). However, the highest diagnostic accuracy was obtained when combining CE-MRI and FET PET features (accuracy, 89%; sensitivity, 85%; specificity, 96%).ConclusionsOur findings suggest that combined FET PET/CE-MRI radiomics using textural feature analysis offers a great potential to contribute significantly to the management of patients with brain metastases.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Combined FET PET/MRI radiomics differentiates radiation injury from recurrent brain metastasis

Lohmann

Köcher

Ceccon

et al. 2018

NeuroImage: Clinical

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124

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“…Radiomics quantifies a variety of MR features such as texture, shape, and heterogeneity, generating the ability to extract a wealth of data from scans, some of which may be invisible to the unaided eye (Zhang et al, 2016; Lohmann et al, 2017). The hope is that computer algorithms can then be trained to recognize different lesions, so that classification can be done automatically, presumably resulting in less user-dependent bias.…”

Section: Caveats To Interpreting Posttreatment Imaging Changesmentioning

confidence: 99%

Brain metastases: neuroimaging

Pope

2018

Handbook of Clinical Neurology

154

130

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Magnetic resonance imaging (MRI) is the cornerstone for evaluating patients with brain masses such as primary and metastatic tumors. Important challenges in effectively detecting and diagnosing brain metastases and in accurately characterizing their subsequent response to treatment remain. These difficulties include discriminating metastases from potential mimics such as primary brain tumors and infection, detecting small metastases, and differentiating treatment response from tumor recurrence and progression. Optimal patient management could be benefited by improved and well-validated prognostic and predictive imaging markers, as well as early response markers to identify successful treatment prior to changes in tumor size. To address these fundamental needs, newer MRI techniques including diffusion and perfusion imaging, MR spectroscopy, and positron emission tomography (PET) tracers beyond traditionally used 18-fluorodeoxyglucose are the subject of extensive ongoing investigations, with several promising avenues of added value already identified. These newer techniques provide a wealth of physiologic and metabolic information that may supplement standard MR evaluation, by providing the ability to monitor and characterize cellularity, angiogenesis, perfusion, pH, hypoxia, metabolite concentrations, and other critical features of malignancy. This chapter reviews standard and advanced imaging of brain metastases provided by computed tomography, MRI, and amino acid PET, focusing on potential biomarkers that can serve as problem-solving tools in the clinical management of patients with brain metastases.

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“…One concept of radiomics is the use of textural feature analysis as a tool that objectively and quantitatively describes one of the intrinsic properties of cancer, particularly heterogeneity. For FET PET, it could be demonstrated for the first time that radiomic textural feature analysis provides noninvasively quantitative information about brain metastasis heterogeneity and, moreover, may be helpful for the distinction between radiation injury and disease progression [23]. Thus, radiomics provides novel imaging biomarkers that could be potentially helpful in the field of neuro-oncology.…”

Section: Newer Indications and Recent Developmentsmentioning

confidence: 99%