Abstract

BACKGROUND AND PURPOSE

CAMs depicting the areas of the input slice that the model preferentially emphasizes when predicting tumor subtype on individual scan slices. The upper row of each subpanel shows the T2 slice with tumor areas manually denoted (upper left) and CAM overlay of the most confident prediction of the model (upper right). The lower row of each panel shows less confident predictions. Examples of correct predictions of PA (A) and MB (B) and incorrect predictions of PA (C) and MB (D) are shown.

Posterior fossa tumors are the most common pediatric brain tumors. MR imaging is key to tumor detection, diagnosis, and therapy guidance. We sought to develop an MR imaging–based deep learning model for posterior fossa tumor detection and tumor pathology classification.

MATERIALS AND METHODS

The study cohort comprised 617 children (median age, 92 months; 56% males) from 5 pediatric institutions with posterior fossa tumors: diffuse midline glioma of the pons (n = 122), medulloblastoma (n = 272), pilocytic astrocytoma (n = 135), and ependymoma (n = 88). There were 199 controls. Tumor histology served as ground truth except for diffuse midline glioma of the pons, which was primarily diagnosed by MR imaging. A modified ResNeXt-50-32x4d architecture served as the backbone for a multitask classifier model, using T2-weighted MRIs as input to detect the presence of tumor and predict tumor class. Deep learning model performance was compared against that of 4 radiologists.

RESULTS

Model tumor detection accuracy exceeded an AUROC of 0.99 and was similar to that of 4 radiologists. Model tumor classification accuracy was 92% with an F1 score of 0.80. The model was most accurate at predicting diffuse midline glioma of the pons, followed by pilocytic astrocytoma and medulloblastoma. Ependymoma prediction was the least accurate. Tumor type classification accuracy and F1 score were higher than those of 2 of the 4 radiologists.

CONCLUSIONS

We present a multi-institutional deep learning model for pediatric posterior fossa tumor detection and classification with the potential to augment and improve the accuracy of radiologic diagnosis.

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jross

Jeffrey Ross

• Mayo Clinic, Phoenix

Dr. Jeffrey S. Ross is a Professor of Radiology at the Mayo Clinic College of Medicine, and practices neuroradiology at the Mayo Clinic in Phoenix, Arizona. His publications include over 100 peer-reviewed articles, nearly 60 non-refereed articles, 33 book chapters, and 10 books. He was an AJNR Senior Editor from 2006-2015, is a member of the editorial board for 3 other journals, and a manuscript reviewer for 10 journals. He became Editor-in-Chief of the AJNR in July 2015. He received the Gold Medal Award from the ASSR in 2013.



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