MRI textures as outcome predictor for Gamma Knife radiosurgery on vestibular schwannoma

P.P.J.H. Langenhuizen, M.J.W. Legters, S. Zinger, H.B. Verheul, S. Leenstra, P.H.N. de With

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Downloads (Pure)

Abstract

Vestibular schwannomas (VS) are benign brain tumors that can be treated with high-precision focused radiation with the Gamma Knife in order to stop tumor growth. Outcome prediction of Gamma Knife radiosurgery (GKRS) treatment can help in determining whether GKRS will be effective on an individual patient basis. However, at present, prognostic factors of tumor control after GKRS for VS are largely unknown, and only clinical factors, such as size of the tumor at treatment and pre-treatment growth rate of the tumor, have been considered thus far. This research aims at outcome prediction of GKRS by means of quantitative texture feature analysis on conventional MRI scans. We compute first-order statistics and features based on gray-level co-occurrence (GLCM) and run-length matrices (RLM), and employ support vector machines and decision trees for classification. In a clinical dataset, consisting of 20 tumors showing treatment failure and 20 tumors exhibiting treatment success, we have discovered that the second-order statistical metrics distilled from GLCM and RLM are suitable for describing texture, but are slightly outperformed by simple first-order statistics, like mean, standard deviation and median. The obtained prediction accuracy is about 85%, but a final choice of the best feature can only be made after performing more extensive analyses on larger datasets. In any case, this work provides suitable texture measures for successful prediction of GKRS treatment outcome for VS.

Original languageEnglish
Title of host publicationMedical Imaging 2018
Subtitle of host publicationComputer-Aided Diagnosis
Place of PublicationBellingham
PublisherSPIE
Number of pages9
ISBN (Electronic)9781510616394
DOIs
Publication statusPublished - 1 Jan 2018
Event2018 SPIE Medical Imaging: Image Processing - Houston, United States
Duration: 10 Feb 201815 Feb 2018

Publication series

NameProceedings of SPIE
Volume10575

Conference

Conference2018 SPIE Medical Imaging: Image Processing
CountryUnited States
CityHouston
Period10/02/1815/02/18

Fingerprint

Acoustic Neuroma
Radiosurgery
Magnetic resonance imaging
Tumors
tumors
textures
Textures
predictions
Neoplasms
Statistics
statistics
Decision Trees
Gamma Rays
Therapeutics
Growth
Treatment Failure
Brain Neoplasms
matrices
Decision trees
pretreatment

Keywords

  • DT
  • Gamma Knife radiosurgery
  • GLCM
  • machine learning
  • MRI texture features
  • RLM
  • SVM
  • treatment outcome prediction
  • Vestibular schwannoma

Cite this

Langenhuizen, P. P. J. H., Legters, M. J. W., Zinger, S., Verheul, H. B., Leenstra, S., & de With, P. H. N. (2018). MRI textures as outcome predictor for Gamma Knife radiosurgery on vestibular schwannoma. In Medical Imaging 2018: Computer-Aided Diagnosis [105750H] (Proceedings of SPIE; Vol. 10575). Bellingham: SPIE. https://doi.org/10.1117/12.2293464
Langenhuizen, P.P.J.H. ; Legters, M.J.W. ; Zinger, S. ; Verheul, H.B. ; Leenstra, S. ; de With, P.H.N. / MRI textures as outcome predictor for Gamma Knife radiosurgery on vestibular schwannoma. Medical Imaging 2018: Computer-Aided Diagnosis. Bellingham : SPIE, 2018. (Proceedings of SPIE).
@inproceedings{29cc0052c5f8418183f57de2d48798fa,
title = "MRI textures as outcome predictor for Gamma Knife radiosurgery on vestibular schwannoma",
abstract = "Vestibular schwannomas (VS) are benign brain tumors that can be treated with high-precision focused radiation with the Gamma Knife in order to stop tumor growth. Outcome prediction of Gamma Knife radiosurgery (GKRS) treatment can help in determining whether GKRS will be effective on an individual patient basis. However, at present, prognostic factors of tumor control after GKRS for VS are largely unknown, and only clinical factors, such as size of the tumor at treatment and pre-treatment growth rate of the tumor, have been considered thus far. This research aims at outcome prediction of GKRS by means of quantitative texture feature analysis on conventional MRI scans. We compute first-order statistics and features based on gray-level co-occurrence (GLCM) and run-length matrices (RLM), and employ support vector machines and decision trees for classification. In a clinical dataset, consisting of 20 tumors showing treatment failure and 20 tumors exhibiting treatment success, we have discovered that the second-order statistical metrics distilled from GLCM and RLM are suitable for describing texture, but are slightly outperformed by simple first-order statistics, like mean, standard deviation and median. The obtained prediction accuracy is about 85{\%}, but a final choice of the best feature can only be made after performing more extensive analyses on larger datasets. In any case, this work provides suitable texture measures for successful prediction of GKRS treatment outcome for VS.",
keywords = "DT, Gamma Knife radiosurgery, GLCM, machine learning, MRI texture features, RLM, SVM, treatment outcome prediction, Vestibular schwannoma",
author = "P.P.J.H. Langenhuizen and M.J.W. Legters and S. Zinger and H.B. Verheul and S. Leenstra and {de With}, P.H.N.",
year = "2018",
month = "1",
day = "1",
doi = "10.1117/12.2293464",
language = "English",
series = "Proceedings of SPIE",
publisher = "SPIE",
booktitle = "Medical Imaging 2018",
address = "United States",

}

Langenhuizen, PPJH, Legters, MJW, Zinger, S, Verheul, HB, Leenstra, S & de With, PHN 2018, MRI textures as outcome predictor for Gamma Knife radiosurgery on vestibular schwannoma. in Medical Imaging 2018: Computer-Aided Diagnosis., 105750H, Proceedings of SPIE, vol. 10575, SPIE, Bellingham, 2018 SPIE Medical Imaging: Image Processing, Houston, United States, 10/02/18. https://doi.org/10.1117/12.2293464

MRI textures as outcome predictor for Gamma Knife radiosurgery on vestibular schwannoma. / Langenhuizen, P.P.J.H.; Legters, M.J.W.; Zinger, S.; Verheul, H.B.; Leenstra, S.; de With, P.H.N.

Medical Imaging 2018: Computer-Aided Diagnosis. Bellingham : SPIE, 2018. 105750H (Proceedings of SPIE; Vol. 10575).

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

TY - GEN

T1 - MRI textures as outcome predictor for Gamma Knife radiosurgery on vestibular schwannoma

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

AU - Legters, M.J.W.

AU - Zinger, S.

AU - Verheul, H.B.

AU - Leenstra, S.

AU - de With, P.H.N.

PY - 2018/1/1

Y1 - 2018/1/1

N2 - Vestibular schwannomas (VS) are benign brain tumors that can be treated with high-precision focused radiation with the Gamma Knife in order to stop tumor growth. Outcome prediction of Gamma Knife radiosurgery (GKRS) treatment can help in determining whether GKRS will be effective on an individual patient basis. However, at present, prognostic factors of tumor control after GKRS for VS are largely unknown, and only clinical factors, such as size of the tumor at treatment and pre-treatment growth rate of the tumor, have been considered thus far. This research aims at outcome prediction of GKRS by means of quantitative texture feature analysis on conventional MRI scans. We compute first-order statistics and features based on gray-level co-occurrence (GLCM) and run-length matrices (RLM), and employ support vector machines and decision trees for classification. In a clinical dataset, consisting of 20 tumors showing treatment failure and 20 tumors exhibiting treatment success, we have discovered that the second-order statistical metrics distilled from GLCM and RLM are suitable for describing texture, but are slightly outperformed by simple first-order statistics, like mean, standard deviation and median. The obtained prediction accuracy is about 85%, but a final choice of the best feature can only be made after performing more extensive analyses on larger datasets. In any case, this work provides suitable texture measures for successful prediction of GKRS treatment outcome for VS.

AB - Vestibular schwannomas (VS) are benign brain tumors that can be treated with high-precision focused radiation with the Gamma Knife in order to stop tumor growth. Outcome prediction of Gamma Knife radiosurgery (GKRS) treatment can help in determining whether GKRS will be effective on an individual patient basis. However, at present, prognostic factors of tumor control after GKRS for VS are largely unknown, and only clinical factors, such as size of the tumor at treatment and pre-treatment growth rate of the tumor, have been considered thus far. This research aims at outcome prediction of GKRS by means of quantitative texture feature analysis on conventional MRI scans. We compute first-order statistics and features based on gray-level co-occurrence (GLCM) and run-length matrices (RLM), and employ support vector machines and decision trees for classification. In a clinical dataset, consisting of 20 tumors showing treatment failure and 20 tumors exhibiting treatment success, we have discovered that the second-order statistical metrics distilled from GLCM and RLM are suitable for describing texture, but are slightly outperformed by simple first-order statistics, like mean, standard deviation and median. The obtained prediction accuracy is about 85%, but a final choice of the best feature can only be made after performing more extensive analyses on larger datasets. In any case, this work provides suitable texture measures for successful prediction of GKRS treatment outcome for VS.

KW - DT

KW - Gamma Knife radiosurgery

KW - GLCM

KW - machine learning

KW - MRI texture features

KW - RLM

KW - SVM

KW - treatment outcome prediction

KW - Vestibular schwannoma

UR - http://www.scopus.com/inward/record.url?scp=85046268855&partnerID=8YFLogxK

U2 - 10.1117/12.2293464

DO - 10.1117/12.2293464

M3 - Conference contribution

AN - SCOPUS:85046268855

T3 - Proceedings of SPIE

BT - Medical Imaging 2018

PB - SPIE

CY - Bellingham

ER -

Langenhuizen PPJH, Legters MJW, Zinger S, Verheul HB, Leenstra S, de With PHN. MRI textures as outcome predictor for Gamma Knife radiosurgery on vestibular schwannoma. In Medical Imaging 2018: Computer-Aided Diagnosis. Bellingham: SPIE. 2018. 105750H. (Proceedings of SPIE). https://doi.org/10.1117/12.2293464