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  • 1
    Online Resource
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    Radiomics outperforms semantic features for prediction of response to stereotactic radiosurgery in brain metastases
    Elsevier BV ; 2022
    In:  Radiotherapy and Oncology Vol. 166 ( 2022-01), p. 37-43
    Details
    In: Radiotherapy and Oncology, Elsevier BV, Vol. 166 ( 2022-01), p. 37-43
    Type of Medium: Online Resource
    ISSN: 0167-8140
    URL: Article
    DOI: 10.1016/j.radonc.2021.11.010
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2022
    detail.hit.zdb_id: 1500707-8
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  • 2
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    Automated Brain Tumor Detection and Segmentation for Treatment Response Assessment Using Amino Acid PET
    Society of Nuclear Medicine ; 2023
    In:  Journal of Nuclear Medicine Vol. 64, No. 10 ( 2023-10), p. 1594-1602
    Details
    In: Journal of Nuclear Medicine, Society of Nuclear Medicine, Vol. 64, No. 10 ( 2023-10), p. 1594-1602
    Type of Medium: Online Resource
    ISSN: 0161-5505 , 2159-662X
    URL: Article
    DOI: 10.2967/jnumed.123.265725
    RVK:
    XA 55300
    Language: English
    Publisher: Society of Nuclear Medicine
    Publication Date: 2023
    detail.hit.zdb_id: 2040222-3
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  • 3
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    NIMG-04. PREDICTING THE BRAF MUTATIONAL STATUS IN PATIENTS WITH MELANOMA BRAIN METASTASES USING RADIOMICS - A BICENTRIC STUDY
    Oxford University Press (OUP) ; 2021
    In:  Neuro-Oncology Vol. 23, No. Supplement_6 ( 2021-11-12), p. vi127-vi128
    Details
    In: Neuro-Oncology, Oxford University Press (OUP), Vol. 23, No. Supplement_6 ( 2021-11-12), p. vi127-vi128
    Abstract: The BRAF V600E mutation is present in approximately 50% of patients with melanoma and is an important prerequisite for a response to targeted therapies such as BRAF inhibitors. In the majority of patients, the BRAF mutational status is based on the analysis of tissue samples from the extracranial primary tumor only. Since the extracranial and intracranial BRAF mutational status may be discrepant, the additional information on the BRAF mutational status of melanoma brain metastases would be of clinical value, e.g., for the prediction of response to targeted therapies. Here, we evaluated the potential of MRI radiomics for the determination of the intracranial BRAF mutational status in patients with melanoma brain metastases. PATIENTS AND METHODS Fifty-nine patients with melanoma brain metastases from two university hospitals (group 1, 45 patients; group 2, 14 patients) were operated with subsequent genetic analysis of the intracranial BRAF mutational status. All patients underwent structural MRI preoperatively. Areas of contrast enhancement were manually segmented and analyzed. Group 1 was used for model training and validation, group 2 for model testing. After image preprocessing and radiomics feature extraction, a test-retest analysis was performed to identify robust features prior to feature selection. Finally, the best performing radiomics model was applied to the test data. Diagnostic performances were evaluated using receiver operating characteristic (ROC) analyses. RESULTS Twenty-two patients (49%) in group 1, and 6 patients (43%) in group 2 had an intrametastatic BRAF V600E mutation. Using the best performing six parameter radiomics signature, a linear support vector machine classifier yielded an area under the ROC curve (AUC) of 0.92 (sensitivity, 83%; specificity, 88%) in the test data. CONCLUSION The developed radiomics classifier allows a non-invasive prediction of the intracranial BRAF V600E mutational status in patients with melanoma brain metastases and may be of value for treatment decisions.
    Type of Medium: Online Resource
    ISSN: 1522-8517 , 1523-5866
    URL: Article
    DOI: 10.1093/neuonc/noab196.504
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 2094060-9
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  • 4
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    Radiomics for the noninvasive prediction of the BRAF mutation status in patients with melanoma brain metastases
    Oxford University Press (OUP) ; 2022
    In:  Neuro-Oncology Vol. 24, No. 8 ( 2022-08-01), p. 1331-1340
    Details
    In: Neuro-Oncology, Oxford University Press (OUP), Vol. 24, No. 8 ( 2022-08-01), p. 1331-1340
    Abstract: The BRAF V600E mutation is present in approximately 50% of patients with melanoma brain metastases and an important prerequisite for response to targeted therapies, particularly BRAF inhibitors. As heterogeneity in terms of BRAF mutation status may occur in melanoma patients, a wild-type extracranial primary tumor does not necessarily rule out a targetable mutation in brain metastases using BRAF inhibitors. We evaluated the potential of MRI radiomics for a noninvasive prediction of the intracranial BRAF mutation status. Methods Fifty-nine patients with melanoma brain metastases from two university brain tumor centers (group 1, 45 patients; group 2, 14 patients) underwent tumor resection with subsequent genetic analysis of the intracranial BRAF mutation status. Preoperative contrast-enhanced MRI was manually segmented and analyzed. Group 1 was used for model training and validation, group 2 for model testing. After radiomics feature extraction, a test-retest analysis was performed to identify robust features prior to feature selection. Finally, the best performing radiomics model was applied to the test data. Diagnostic performances were evaluated using receiver operating characteristic (ROC) analyses. Results Twenty-two of 45 patients (49%) in group 1, and 8 of 14 patients (57%) in group 2 had an intracranial BRAF V600E mutation. A linear support vector machine classifier using a six-parameter radiomics signature yielded an area under the ROC curve of 0.92 (sensitivity, 83%; specificity, 88%) in the test data. Conclusions The developed radiomics classifier allows a noninvasive prediction of the intracranial BRAF V600E mutation status in patients with melanoma brain metastases with high diagnostic performance.
    Type of Medium: Online Resource
    ISSN: 1522-8517 , 1523-5866
    URL: Article
    DOI: 10.1093/neuonc/noab294
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 2094060-9
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  • 5
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    Multicentric 68Ga-PSMA PET radiomics for treatment response assessment of 177Lu-PSMA-617 radioligand therapy in patients with metastatic castration-resistant prostate cancer
    Frontiers Media SA ; 2023
    In:  Frontiers in Nuclear Medicine Vol. 3 ( 2023-9-14)
    Details
    In: Frontiers in Nuclear Medicine, Frontiers Media SA, Vol. 3 ( 2023-9-14)
    Abstract: The treatment with 177 Lutetium PSMA ( 177 Lu-PSMA) in patients with metastatic castration-resistant prostate cancer (mCRPC) has recently been approved by FDA and EMA. Since treatment success is highly variable between patients, the prediction of treatment response and identification of short- and long-term survivors after treatment could help to tailor mCRPC diagnosis and treatment accordingly. The aim of this study is to investigate the value of radiomics parameters extracted from pretreatment 68 Ga-PSMA PET images for prediction of treatment response. Methods Forty-five mCRPC patients treated with 177 Lu-PSMA-617 from two university hospital centers were retrospectively reviewed for this study. Radiomics features were extracted from the volumetric segmentations of metastases in the bone. A random forest model was trained and validated to predict treatment response based on age and conventionally used PET parameters, radiomics features, and combinations thereof. Further, overall survival was predicted by using the identified radiomics signature and compared to a Cox regression model based on age and PET parameters. Results The machine learning model based on a combined radiomics signature of three features and patient age achieved an AUC of 0.82 in 5-fold cross validation and outperformed models based on age and PET parameters or radiomics features (AUC, 0.75 and 0.76, respectively). A Cox regression model based on this radiomics signature showed the best performance to predict the overall survival (C-index, 0.67). Conclusion Our results demonstrate that a machine learning model to predict response to 177 Lu-PSMA treatment based on a combination of radiomics and patient age outperforms a model based on age and PET parameters. Moreover, the identified radiomics signature based on pretreatment 68 Ga-PSMA PET images might be able to identify patients with an improved outcome and serve as a supportive tool in clinical decision making.
    Type of Medium: Online Resource
    ISSN: 2673-8880
    URL: Article
    DOI: 10.3389/fnume.2023.1234853
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2023
    detail.hit.zdb_id: 3110523-3
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  • 6
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    FET PET Radiomics for Differentiating Pseudoprogression from Early Tumor Progression in Glioma Patients Post-Chemoradiation
    MDPI AG ; 2020
    In:  Cancers Vol. 12, No. 12 ( 2020-12-18), p. 3835-
    Details
    In: Cancers, MDPI AG, Vol. 12, No. 12 ( 2020-12-18), p. 3835-
    Abstract: Currently, a reliable diagnostic test for differentiating pseudoprogression from early tumor progression is lacking. We explored the potential of O-(2-[18F]fluoroethyl)-L-tyrosine (FET) positron emission tomography (PET) radiomics for this clinically important task. Thirty-four patients (isocitrate dehydrogenase (IDH)-wildtype glioblastoma, 94%) with progressive magnetic resonance imaging (MRI) changes according to the Response Assessment in Neuro-Oncology (RANO) criteria within the first 12 weeks after completing temozolomide chemoradiation underwent a dynamic FET PET scan. Static and dynamic FET PET parameters were calculated. For radiomics analysis, the number of datasets was increased to 102 using data augmentation. After randomly assigning patients to a training and test dat aset, 944 features were calculated on unfiltered and filtered images. The number of features for model generation was limited to four to avoid data overfitting. Eighteen patients were diagnosed with early tumor progression, and 16 patients had pseudoprogression. The FET PET radiomics model correctly diagnosed pseudoprogression in all test cohort patients (sensitivity, 100%; negative predictive value, 100%). In contrast, the diagnostic performance of the best FET PET parameter (TBRmax) was lower (sensitivity, 81%; negative predictive value, 80%). The results suggest that FET PET radiomics helps diagnose patients with pseudoprogression with a high diagnostic performance. Given the clinical significance, further studies are warranted.
    Type of Medium: Online Resource
    ISSN: 2072-6694
    URL: Article
    DOI: 10.3390/cancers12123835
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2527080-1
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  • 7
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    Evaluation of FET PET Radiomics Feature Repeatability in Glioma Patients
    MDPI AG ; 2021
    In:  Cancers Vol. 13, No. 4 ( 2021-02-05), p. 647-
    Details
    In: Cancers, MDPI AG, Vol. 13, No. 4 ( 2021-02-05), p. 647-
    Abstract: Amino acid PET using the tracer O-(2-[18F]fluoroethyl)-L-tyrosine (FET) has attracted considerable interest in neurooncology. Furthermore, initial studies suggested the additional diagnostic value of FET PET radiomics in brain tumor patient management. However, the conclusiveness of radiomics models strongly depends on feature generalizability. We here evaluated the repeatability of feature-based FET PET radiomics. A test–retest analysis based on equivalent but statistically independent subsamples of FET PET images was performed in 50 newly diagnosed and histomolecularly characterized glioma patients. A total of 1,302 radiomics features were calculated from semi-automatically segmented tumor volumes-of-interest (VOIs). Furthermore, to investigate the influence of the spatial resolution of PET on repeatability, spherical VOIs of different sizes were positioned in the tumor and healthy brain tissue. Feature repeatability was assessed by calculating the intraclass correlation coefficient (ICC). To further investigate the influence of the isocitrate dehydrogenase (IDH) genotype on feature repeatability, a hierarchical cluster analysis was performed. For tumor VOIs, 73% of first-order features and 71% of features extracted from the gray level co-occurrence matrix showed high repeatability (ICC 95% confidence interval, 0.91–1.00). In the largest spherical tumor VOIs, 67% of features showed high repeatability, significantly decreasing towards smaller VOIs. The IDH genotype did not affect feature repeatability. Based on 297 repeatable features, two clusters were identified separating patients with IDH-wildtype glioma from those with an IDH mutation. Our results suggest that robust features can be obtained from routinely acquired FET PET scans, which are valuable for further standardization of radiomics analyses in neurooncology.
    Type of Medium: Online Resource
    ISSN: 2072-6694
    URL: Article
    DOI: 10.3390/cancers13040647
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2527080-1
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  • 8
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    NIMG-83. MULTIMODAL PET/MRI RADIOMICS AND CLINICAL PARAMETERS FOR OVERALL SURVIVAL PREDICTION IN PATIENTS WITH IDH WILDTYPE GLIOBLASTOMA
    Oxford University Press (OUP) ; 2022
    In:  Neuro-Oncology Vol. 24, No. Supplement_7 ( 2022-11-14), p. vii184-vii184
    Details
    In: Neuro-Oncology, Oxford University Press (OUP), Vol. 24, No. Supplement_7 ( 2022-11-14), p. vii184-vii184
    Abstract: Currently, most radiomics studies on survival prediction in brain tumor patients are based on MRI only. The goal of our study was to evaluate multimodal radiomics derived from amino acid PET/MRI and clinical parameters for survival prediction in patients with newly diagnosed IDH wildtype glioblastoma. METHODS Sixty-three patients with newly diagnosed IDH wildtype glioblastoma were evaluated retrospectively. At initial diagnosis, all patients underwent structural MRI and O-(2-[18F]fluoroethyl)-L-tyrosine (FET) PET. Tumor volumes were automatically segmented using a deep learning-based tool followed by visual inspection. Predefined and deep radiomics features were extracted from both imaging modalities. Feature repeatability analyses and feature selection were performed to avoid overfitting. Cox regression models for overall survival were built from clinical parameters such as age or the extent of resection, radiomics features, and combinations thereof, and finally validated using 5-fold cross-validation. Further evaluation of the model in an external test dataset is ongoing. RESULTS The median overall survival was 12 months (range, 0-64 months). Higher age and larger FET PET tumor volumes were significantly correlated with shorter overall survival (age, r=-0.39, p & lt; 0.001; volume, r=-0.31, p & lt; 0.05). Models solely based on predefined FET PET or MRI radiomics features showed a similar mean concordance index (C-index) as the model based on clinical parameters (C-indices, 0.68±0.04; 0.64±0.03; and 0.69±0.08, respectively). Multimodal radiomics based on predefined and deep features yielded improved C-indices of 0.75±0.06 and 0.72±0.09, respectively. A model based on multimodal radiomics and clinical parameters achieved the best prognostic performance (C-index, 0.80±0.04). CONCLUSION Our results suggest an added clinical value of multimodal FET PET/MRI radiomics with clinical parameters for the non-invasive survival prediction in patients with IDH wildtype glioblastoma.
    Type of Medium: Online Resource
    ISSN: 1522-8517 , 1523-5866
    URL: Article
    DOI: 10.1093/neuonc/noac209.701
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 2094060-9
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  • 9
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    Use of advanced neuroimaging and artificial intelligence in meningiomas
    Wiley ; 2022
    In:  Brain Pathology Vol. 32, No. 2 ( 2022-03)
    Details
    In: Brain Pathology, Wiley, Vol. 32, No. 2 ( 2022-03)
    Abstract: Anatomical cross‐sectional imaging methods such as contrast‐enhanced MRI and CT are the standard for the delineation, treatment planning, and follow‐up of patients with meningioma. Besides, advanced neuroimaging is increasingly used to non‐invasively provide detailed insights into the molecular and metabolic features of meningiomas. These techniques are usually based on MRI, e.g., perfusion‐weighted imaging, diffusion‐weighted imaging, MR spectroscopy, and positron emission tomography. Furthermore, artificial intelligence methods such as radiomics offer the potential to extract quantitative imaging features from routinely acquired anatomical MRI and CT scans and advanced imaging techniques. This allows the linking of imaging phenotypes to meningioma characteristics, e.g., the molecular‐genetic profile. Here, we review several diagnostic applications and future directions of these advanced neuroimaging techniques, including radiomics in preclinical models and patients with meningioma.
    Type of Medium: Online Resource
    ISSN: 1015-6305 , 1750-3639
    URL: Issue
    URL: Article
    DOI: 10.1111/bpa.v32.2
    DOI: 10.1111/bpa.13015
    Language: English
    Publisher: Wiley
    Publication Date: 2022
    detail.hit.zdb_id: 2029927-8
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  • 10
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    Static FET PET radiomics for the differentiation of treatment-related changes from glioma progression
    Springer Science and Business Media LLC ; 2022
    In:  Journal of Neuro-Oncology Vol. 159, No. 3 ( 2022-09), p. 519-529
    Details
    In: Journal of Neuro-Oncology, Springer Science and Business Media LLC, Vol. 159, No. 3 ( 2022-09), p. 519-529
    Abstract: To investigate the potential of radiomics applied to static clinical PET data using the tracer O-(2-[ 18 F]fluoroethyl)- l -tyrosine (FET) to differentiate treatment-related changes (TRC) from tumor progression (TP) in patients with gliomas. Patients and Methods One hundred fifty-one (151) patients with histologically confirmed gliomas and post-therapeutic progressive MRI findings according to the response assessment in neuro-oncology criteria underwent a dynamic amino acid PET scan using the tracer O-(2-[ 18 F]fluoroethyl)- l -tyrosine (FET). Thereof, 124 patients were investigated on a stand-alone PET scanner (data used for model development and validation), and 27 patients on a hybrid PET/MRI scanner (data used for model testing). Mean and maximum tumor to brain ratios (TBR mean , TBR max ) were calculated using the PET data from 20 to 40 min after tracer injection. Logistic regression models were evaluated for the FET PET parameters TBR mean , TBR max , and for radiomics features of the tumor areas as well as combinations thereof to differentiate between TP and TRC. The best performing models in the validation dataset were finally applied to the test dataset. The diagnostic performance was assessed by receiver operating characteristic analysis. Results Thirty-seven patients (25%) were diagnosed with TRC, and 114 (75%) with TP. The logistic regression model comprising the conventional FET PET parameters TBR mean and TBR max resulted in an AUC of 0.78 in both the validation (sensitivity, 64%; specificity, 80%) and the test dataset (sensitivity, 64%; specificity, 80%). The model combining the conventional FET PET parameters and two radiomics features yielded the best diagnostic performance in the validation dataset (AUC, 0.92; sensitivity, 91%; specificity, 80%) and demonstrated its generalizability in the independent test dataset (AUC, 0.85; sensitivity, 81%; specificity, 70%). Conclusion The developed radiomics classifier allows the differentiation between TRC and TP in pretreated gliomas based on routinely acquired static FET PET scans with a high diagnostic accuracy.
    Type of Medium: Online Resource
    ISSN: 0167-594X , 1573-7373
    URL: Article
    DOI: 10.1007/s11060-022-04089-2
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2007293-4
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