A transformer-based deep learning approach for classifying brain metastases into primary organ sites using clinical whole brain MRI images

10/07/2021
by   Qing Lyu, et al.
11

The treatment decisions for brain metastatic disease are driven by knowledge of the primary organ site cancer histology, often requiring invasive biopsy. This study aims to develop a novel deep learning approach for accurate and rapid non-invasive identification of brain metastatic tumor histology with conventional whole-brain MRI. The use of clinical whole-brain data and the end-to-end pipeline obviate external human intervention. This IRB-approved single-site retrospective study was comprised of patients (n=1,293) referred for MRI treatment-planning and gamma knife radiosurgery from July 2000 to May 2019. Contrast-enhanced T1-weighted contrast enhanced and T2-weighted-Fluid-Attenuated Inversion Recovery brain MRI exams (n=1,428) were minimally preprocessed (voxel resolution unification and signal-intensity rescaling/normalization), requiring only seconds per an MRI scan, and input into the proposed deep learning workflow for tumor segmentation, modality transfer, and primary site classification associated with brain metastatic disease in one of four classes (lung, melanoma, renal, and other). Ten-fold cross-validation generated the overall AUC of 0.941, lung class AUC of 0.899, melanoma class AUC of 0.882, renal class AUC of 0.870, and other class AUC of 0.885. It is convincingly established that whole-brain imaging features would be sufficiently discriminative to allow accurate diagnosis of the primary organ site of malignancy. Our end-to-end deep learning-based radiomic method has a great translational potential for classifying metastatic tumor types using whole-brain MRI images, without additional human intervention. Further refinement may offer invaluable tools to expedite primary organ site cancer identification for treatment of brain metastatic disease and improvement of patient outcomes and survival.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 25

page 26

page 27

06/24/2020

Deep Learning-based Computational Pathology Predicts Origins for Cancers of Unknown Primary

Cancers of unknown primary (CUP), represent 1-3 enigmatic disease where ...
03/09/2021

Deep and Statistical Learning in Biomedical Imaging: State of the Art in 3D MRI Brain Tumor Segmentation

Clinical diagnostic and treatment decisions rely upon the integration of...
04/04/2021

Contrast-enhanced MRI Synthesis Using 3D High-Resolution ConvNets

Gadolinium-based contrast agents (GBCAs) have been widely used to better...
01/19/2021

Meningioma segmentation in T1-weighted MRI leveraging global context and attention mechanisms

Meningiomas are the most common type of primary brain tumor, accounting ...
01/16/2022

Is it Possible to Predict MGMT Promoter Methylation from Brain Tumor MRI Scans using Deep Learning Models?

Glioblastoma is a common brain malignancy that tends to occur in older a...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.