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  • Kerschbaumer, Johannes  (2)
  • Scherfler, Christoph  (2)
  • 2020-2024  (2)
  • 1
    Online Resource
    Online Resource
    ADC textural features in patients with single brain metastases improve clinical risk models
    Springer Science and Business Media LLC ; 2022
    In:  Clinical & Experimental Metastasis Vol. 39, No. 3 ( 2022-06), p. 459-466
    Details
    In: Clinical & Experimental Metastasis, Springer Science and Business Media LLC, Vol. 39, No. 3 ( 2022-06), p. 459-466
    Abstract: In this retrospective study we performed a quantitative textural analysis of apparant diffusion coefficient (ADC) images derived from diffusion weighted MRI (DW-MRI) of single brain metastases (BM) patients from different primary tumors and tested whether these imaging parameters may improve established clinical risk models. Methods We identified 87 patients with single BM who had a DW-MRI at initial diagnosis. Applying image segmentation, volumes of contrast-enhanced lesions in T1 sequences, hyperintense T2 lesions (peritumoral border zone (T2PZ)) and tumor-free gray and white matter compartment (GMWMC) were generated and registered to corresponding ADC maps. ADC textural parameters were generated and a linear backward regression model was applied selecting imaging features in association with survival. A cox proportional hazard model with backward regression was fitted for the clinical prognostic models (diagnosis-specific graded prognostic assessment score (DS-GPA) and the recursive partitioning analysis (RPA)) including these imaging features. Results Thirty ADC textural parameters were generated and linear backward regression identified eight independent imaging parameters which in combination predicted survival. Five ADC texture features derived from T2PZ, the volume of the T2PZ, the normalized mean ADC of the GMWMC as well as the mean ADC slope of T2PZ. A cox backward regression including the DS-GPA, RPA and these eight parameters identified two MRI features which improved the two risk scores (HR = 1.14 [1.05;1.24] for normalized mean ADC GMWMC and HR = 0.87 [0.77;0.97] ) for ADC 3D kurtosis of the T2PZ.) Conclusions Textural analysis of ADC maps in patients with single brain metastases improved established clinical risk models. These findings may aid to better understand the pathogenesis of BM and may allow selection of patients for new treatment options.
    Type of Medium: Online Resource
    ISSN: 0262-0898 , 1573-7276
    URL: Article
    DOI: 10.1007/s10585-022-10160-z
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 1496876-9
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  • 2
    Online Resource
    Online Resource
    Somatostatin receptor subtype expression and radiomics from DWI-MRI represent SUV of [68Ga]Ga-DOTATOC PET in patients with meningioma
    Springer Science and Business Media LLC ; 2023
    In:  Journal of Neuro-Oncology
    Details
    In: Journal of Neuro-Oncology, Springer Science and Business Media LLC
    Abstract: This retrospective study aimed to analyse the correlation between somatostatin receptor subtypes (SSTR 1–5) and maximum standardized uptake value (SUV max ) in meningioma patients using Gallium-68 DOTA-D-Phe1-Tyr3-octreotide Positron Emission Tomography ([68Ga]Ga-DOTATOC PET). Secondly, we developed a radiomic model based on apparent diffusion coefficient (ADC) maps derived from diffusion weighted magnetic resonance images (DWI MRI) to reproduce SUV max . Method The study included 51 patients who underwent MRI and [68Ga]Ga-DOTATOC PET before meningioma surgery. SUV max values were quantified from PET images and tumour areas were segmented on post-contrast T1-weighted MRI and mapped to ADC maps. A total of 1940 radiomic features were extracted from the tumour area on each ADC map. A random forest regression model was trained to predict SUV max and the model’s performance was evaluated using repeated nested cross-validation. The expression of SSTR subtypes was quantified in 18 surgical specimens and compared to SUV max values. Results The random forest regression model successfully predicted SUV max values with a significant correlation observed in all 100 repeats (p  〈  0.05). The mean Pearson’s r was 0.42 ± 0.07 SD, and the root mean square error (RMSE) was 28.46 ± 0.16. SSTR subtypes 2A, 2B, and 5 showed significant correlations with SUV max values (p  〈  0.001, R2 = 0.669; p = 0.001, R2 = 0.393; and p = 0.012, R2 = 0.235, respectively). Conclusion SSTR subtypes 2A, 2B, and 5 correlated significantly with SUV max in meningioma patients. The developed radiomic model based on ADC maps effectively reproduces SUV max using [68Ga]Ga-DOTATOC PET.
    Type of Medium: Online Resource
    ISSN: 0167-594X , 1573-7373
    URL: Article
    DOI: 10.1007/s11060-023-04414-3
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2007293-4
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