Predictability of transient tumor enlargement following gamma knife radiosurgery on vestibular schwannoma

P.P.J.H. Langenhuizen, S.H.P. Sebregts, Sveta Zinger, Sieger Leenstra, P. Hanssens, Peter de With, H.B. Verheul

Onderzoeksoutput: Bijdrage aan congresAbstractAcademic

Uittreksel

IntroductionGamma Knife radiosurgery (GKRS) is a well-established treatment for small- to medium-sized vestibular schwannomas (VS). However, this treatment is controversial for larger VS. One of its drawbacks is that VS can present a radiation-induced transient tumor enlargement (TTE). For larger VS, such a swelling may cause symptoms related to mass effect, necessitating microsurgery. Currently, it is not possible to predict this adverse effect. We evaluated the predictability of TTE by quantitatively analyzing the tumor appearance on MRI. The goal is to determine the optimal treatment strategy, i.e. radiosurgery or microsurgery, on an individual basis. MethodsFrom our database, patients with large VS (>4cc) and minimum follow-up of three years, were identified. The TTE classification was based on evaluation of MRI scans at 6, 12, 24 and 36 months, according to strict volumetric criteria. We evaluated the influence of MRI tumor texture characteristics on TTE. These texture characteristics were quantified by calculating features based on gray-level co-occurrence matrices (GLCM), computed on T1-weighted, T2-weighted, and T1-weighted contrast-enhanced MRIs. Correlation was determined between these characteristics and TTE using machine-learning methods. ResultsBetween 2002 and 2015, 795 VS patients received GKRS as primary treatment at our center. The strict criteria for TTE and non-TTE led to the inclusion of 67 patients, of which 26 exhibited TTE. By employing GLCM-based features, we developed a model to predict TTE. We obtained a prediction sensitivity and specificity of 83% and 79%, respectively, using Support Vector Machines. These results improved for larger tumor volumes, i.e. in 7cc or larger, the results obtained were 85% and 87%, respectively. ConclusionResults from this research clearly show that MRI differences in VS tumor texture can be exploited to predict TTE in large VS. The developed prediction model can lead to an optimal treatment strategy selection on an individual basis.

Congres

Congres14th International Stereotactic Radiosurgery Society Congress
Verkorte titelISRS2019
LandBrazilië
StadRio de Janeiro
Periode9/06/1913/06/19
Internet adres

Vingerafdruk

Acoustic Neuroma
Radiosurgery
Neoplasms
Microsurgery
Therapeutics
Tumor Burden

Trefwoorden

    Citeer dit

    Langenhuizen, P. P. J. H., Sebregts, S. H. P., Zinger, S., Leenstra, S., Hanssens, P., de With, P., & Verheul, H. B. (2019). Predictability of transient tumor enlargement following gamma knife radiosurgery on vestibular schwannoma. Abstract van 14th International Stereotactic Radiosurgery Society Congress, Rio de Janeiro, Brazilië.
    Langenhuizen, P.P.J.H. ; Sebregts, S.H.P. ; Zinger, Sveta ; Leenstra, Sieger ; Hanssens, P. ; de With, Peter ; Verheul, H.B./ Predictability of transient tumor enlargement following gamma knife radiosurgery on vestibular schwannoma. Abstract van 14th International Stereotactic Radiosurgery Society Congress, Rio de Janeiro, Brazilië.
    @conference{1dd7cdbed4ba49e2b696414b63599cbc,
    title = "Predictability of transient tumor enlargement following gamma knife radiosurgery on vestibular schwannoma",
    abstract = "IntroductionGamma Knife radiosurgery (GKRS) is a well-established treatment for small- to medium-sized vestibular schwannomas (VS). However, this treatment is controversial for larger VS. One of its drawbacks is that VS can present a radiation-induced transient tumor enlargement (TTE). For larger VS, such a swelling may cause symptoms related to mass effect, necessitating microsurgery. Currently, it is not possible to predict this adverse effect. We evaluated the predictability of TTE by quantitatively analyzing the tumor appearance on MRI. The goal is to determine the optimal treatment strategy, i.e. radiosurgery or microsurgery, on an individual basis. MethodsFrom our database, patients with large VS (>4cc) and minimum follow-up of three years, were identified. The TTE classification was based on evaluation of MRI scans at 6, 12, 24 and 36 months, according to strict volumetric criteria. We evaluated the influence of MRI tumor texture characteristics on TTE. These texture characteristics were quantified by calculating features based on gray-level co-occurrence matrices (GLCM), computed on T1-weighted, T2-weighted, and T1-weighted contrast-enhanced MRIs. Correlation was determined between these characteristics and TTE using machine-learning methods. ResultsBetween 2002 and 2015, 795 VS patients received GKRS as primary treatment at our center. The strict criteria for TTE and non-TTE led to the inclusion of 67 patients, of which 26 exhibited TTE. By employing GLCM-based features, we developed a model to predict TTE. We obtained a prediction sensitivity and specificity of 83{\%} and 79{\%}, respectively, using Support Vector Machines. These results improved for larger tumor volumes, i.e. in 7cc or larger, the results obtained were 85{\%} and 87{\%}, respectively. ConclusionResults from this research clearly show that MRI differences in VS tumor texture can be exploited to predict TTE in large VS. The developed prediction model can lead to an optimal treatment strategy selection on an individual basis.",
    keywords = "transient tumor enlargement, vestibular schwannoma, gamma knife radiosurgery",
    author = "P.P.J.H. Langenhuizen and S.H.P. Sebregts and Sveta Zinger and Sieger Leenstra and P. Hanssens and {de With}, Peter and H.B. Verheul",
    year = "2019",
    month = "6",
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    note = "14th International Stereotactic Radiosurgery Society Congress, ISRS2019 ; Conference date: 09-06-2019 Through 13-06-2019",
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    Langenhuizen, PPJH, Sebregts, SHP, Zinger, S, Leenstra, S, Hanssens, P, de With, P & Verheul, HB 2019, 'Predictability of transient tumor enlargement following gamma knife radiosurgery on vestibular schwannoma', Rio de Janeiro, Brazilië, 9/06/19 - 13/06/19, .

    Predictability of transient tumor enlargement following gamma knife radiosurgery on vestibular schwannoma. / Langenhuizen, P.P.J.H.; Sebregts, S.H.P.; Zinger, Sveta; Leenstra, Sieger; Hanssens, P.; de With, Peter; Verheul, H.B.

    2019. Abstract van 14th International Stereotactic Radiosurgery Society Congress, Rio de Janeiro, Brazilië.

    Onderzoeksoutput: Bijdrage aan congresAbstractAcademic

    TY - CONF

    T1 - Predictability of transient tumor enlargement following gamma knife radiosurgery on vestibular schwannoma

    AU - Langenhuizen,P.P.J.H.

    AU - Sebregts,S.H.P.

    AU - Zinger,Sveta

    AU - Leenstra,Sieger

    AU - Hanssens,P.

    AU - de With,Peter

    AU - Verheul,H.B.

    PY - 2019/6

    Y1 - 2019/6

    N2 - IntroductionGamma Knife radiosurgery (GKRS) is a well-established treatment for small- to medium-sized vestibular schwannomas (VS). However, this treatment is controversial for larger VS. One of its drawbacks is that VS can present a radiation-induced transient tumor enlargement (TTE). For larger VS, such a swelling may cause symptoms related to mass effect, necessitating microsurgery. Currently, it is not possible to predict this adverse effect. We evaluated the predictability of TTE by quantitatively analyzing the tumor appearance on MRI. The goal is to determine the optimal treatment strategy, i.e. radiosurgery or microsurgery, on an individual basis. MethodsFrom our database, patients with large VS (>4cc) and minimum follow-up of three years, were identified. The TTE classification was based on evaluation of MRI scans at 6, 12, 24 and 36 months, according to strict volumetric criteria. We evaluated the influence of MRI tumor texture characteristics on TTE. These texture characteristics were quantified by calculating features based on gray-level co-occurrence matrices (GLCM), computed on T1-weighted, T2-weighted, and T1-weighted contrast-enhanced MRIs. Correlation was determined between these characteristics and TTE using machine-learning methods. ResultsBetween 2002 and 2015, 795 VS patients received GKRS as primary treatment at our center. The strict criteria for TTE and non-TTE led to the inclusion of 67 patients, of which 26 exhibited TTE. By employing GLCM-based features, we developed a model to predict TTE. We obtained a prediction sensitivity and specificity of 83% and 79%, respectively, using Support Vector Machines. These results improved for larger tumor volumes, i.e. in 7cc or larger, the results obtained were 85% and 87%, respectively. ConclusionResults from this research clearly show that MRI differences in VS tumor texture can be exploited to predict TTE in large VS. The developed prediction model can lead to an optimal treatment strategy selection on an individual basis.

    AB - IntroductionGamma Knife radiosurgery (GKRS) is a well-established treatment for small- to medium-sized vestibular schwannomas (VS). However, this treatment is controversial for larger VS. One of its drawbacks is that VS can present a radiation-induced transient tumor enlargement (TTE). For larger VS, such a swelling may cause symptoms related to mass effect, necessitating microsurgery. Currently, it is not possible to predict this adverse effect. We evaluated the predictability of TTE by quantitatively analyzing the tumor appearance on MRI. The goal is to determine the optimal treatment strategy, i.e. radiosurgery or microsurgery, on an individual basis. MethodsFrom our database, patients with large VS (>4cc) and minimum follow-up of three years, were identified. The TTE classification was based on evaluation of MRI scans at 6, 12, 24 and 36 months, according to strict volumetric criteria. We evaluated the influence of MRI tumor texture characteristics on TTE. These texture characteristics were quantified by calculating features based on gray-level co-occurrence matrices (GLCM), computed on T1-weighted, T2-weighted, and T1-weighted contrast-enhanced MRIs. Correlation was determined between these characteristics and TTE using machine-learning methods. ResultsBetween 2002 and 2015, 795 VS patients received GKRS as primary treatment at our center. The strict criteria for TTE and non-TTE led to the inclusion of 67 patients, of which 26 exhibited TTE. By employing GLCM-based features, we developed a model to predict TTE. We obtained a prediction sensitivity and specificity of 83% and 79%, respectively, using Support Vector Machines. These results improved for larger tumor volumes, i.e. in 7cc or larger, the results obtained were 85% and 87%, respectively. ConclusionResults from this research clearly show that MRI differences in VS tumor texture can be exploited to predict TTE in large VS. The developed prediction model can lead to an optimal treatment strategy selection on an individual basis.

    KW - transient tumor enlargement

    KW - vestibular schwannoma

    KW - gamma knife radiosurgery

    M3 - Abstract

    ER -

    Langenhuizen PPJH, Sebregts SHP, Zinger S, Leenstra S, Hanssens P, de With P et al. Predictability of transient tumor enlargement following gamma knife radiosurgery on vestibular schwannoma. 2019. Abstract van 14th International Stereotactic Radiosurgery Society Congress, Rio de Janeiro, Brazilië.