MRI Radiogenomics of Pediatric Medulloblastoma: A Multicenter Study

Michael Zhang, Samuel W. Wong, Jason N. Wright, Matthias W. Wagner, Sebastian Toescu, Michelle Han, Lydia T. Tam, Quan Zhou, Saman S. Ahmadian, Katie Shpanskaya, Seth Lummus, Hollie Lai, Azam Eghbal, Alireza Radmanesh, Jordan Nemelka, Stephen Harward, Michael Malinzak, Suzanne Laughlin, Sébastien Perreault, Kristina R.M. BraunRobert M. Lober, Yoon Jae Cho, Birgit Ertl-Wagner, Chang Y. Ho, Kshitij Mankad, Hannes Vogel, Samuel H. Cheshier, Thomas S. Jacques, Kristian Aquilina, Paul G. Fisher, Michael Taylor, Tina Poussaint, Nicholas A. Vitanza, Gerald A. Grant, Stefan Pfister, Eric Thompson, Alok Jaju, Vijay Ramaswamy, Kristen W. Yeom

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Background: Radiogenomics of pediatric medulloblastoma (MB) offers an opportunity for MB risk stratification, which may aid therapeutic decision making, family counseling, and selection of patient groups suitable for targeted genetic analysis. Purpose: To develop machine learning strategies that identify the four clinically significant MB molecular subgroups. Materials and Methods: In this retrospective study, consecutive pediatric patients with newly diagnosed MB at MRI at 12 international pediatric sites between July 1997 and May 2020 were identified. There were 1800 features extracted from T2- and contrast-enhanced T1-weighted preoperative MRI scans. A two-stage sequential classifier was designed-one that first identifies non-wingless (WNT) and non-sonic hedgehog (SHH) MB and then differentiates therapeutically relevant WNT from SHH. Further, a classifier that distinguishes high-risk group 3 from group 4 MB was developed. An independent, binary subgroup analysis was conducted to uncover radiomics features unique to infantile versus childhood SHH subgroups. The best-performing models from six candidate classifiers were selected, and performance was measured on holdout test sets. CIs were obtained by bootstrapping the test sets for 2000 random samples. Model accuracy score was compared with the no-information rate using the Wald test. Results: The study cohort comprised 263 patients (mean age 6 SD at diagnosis, 87 months 6 60; 166 boys). A two-stage classifier outperformed a single-stage multiclass classifier. The combined, sequential classifier achieved a microaveraged F1 score of 88% and a binary F1 score of 95% specifically for WNT. A group 3 versus group 4 classifier achieved an area under the receiver operating characteristic curve of 98%. Of the Image Biomarker Standardization Initiative features, texture and first-order intensity features were most contributory across the molecular subgroups. Conclusion: An MRI-based machine learning decision path allowed identification of the four clinically relevant molecular pediatric medulloblastoma subgroups.

Original languageEnglish (US)
Pages (from-to)406-416
Number of pages11
JournalRADIOLOGY
Volume304
Issue number2
DOIs
StatePublished - Aug 2022
Externally publishedYes

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

Fingerprint

Dive into the research topics of 'MRI Radiogenomics of Pediatric Medulloblastoma: A Multicenter Study'. Together they form a unique fingerprint.

Cite this