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

• URL: http://arxiv.org/abs/2110.03588v1
• Date: Thu, 7 Oct 2021 16:10:44 GMT
• Title: A transformer-based deep learning approach for classifying brain metastases into primary organ sites using clinical whole brain MRI images
• Authors: Qing Lyu, Sanjeev V. Namjoshi, Emory McTyre, Umit Topaloglu, Richard Barcus, Michael D. Chan, Christina K. Cramer, Waldemar Debinski, Metin N. Gurcan, Glenn J. Lesser, Hui-Kuan Lin, Reginald F. Munden, Boris C. Pasche, Kiran Kumar Solingapuram Sai, Roy E. Strowd, Stephen B. Tatter, Kounosuke Watabe, Wei Zhang, Ge Wang, Christopher T. Whitlow
• Abstract summary: The treatment decisions for brain metastatic disease are driven by knowledge of the primary organ site cancer histology. The use of clinical whole-brain data and the end-to-end pipeline obviate external human intervention. It is convincingly established that whole-brain imaging features would be sufficiently discriminative to allow accurate diagnosis of the primary organ site of malignancy.
• Score: 4.263008461907835
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• AXIAL: Attention-based eXplainability for Interpretable Alzheimer's Localized Diagnosis using 2D CNNs on 3D MRI brain scans [45.630166504856255]
  This study presents an innovative method for Alzheimer's disease diagnosis using 3D MRI designed to enhance the explainability of model decisions. Our approach adopts a soft attention mechanism, enabling 2D CNNs to extract volumetric representations. With voxel-level precision, our method identified which specific areas are being paid attention to, identifying these predominant brain regions.
  arXiv  Detail & Related papers  (2024-07-02T16:44:00Z)
• Cross-modality Guidance-aided Multi-modal Learning with Dual Attention for MRI Brain Tumor Grading [47.50733518140625]
  Brain tumor represents one of the most fatal cancers around the world, and is very common in children and the elderly. We propose a novel cross-modality guidance-aided multi-modal learning with dual attention for addressing the task of MRI brain tumor grading.
  arXiv  Detail & Related papers  (2024-01-17T07:54:49Z)
• A Novel SLCA-UNet Architecture for Automatic MRI Brain Tumor Segmentation [0.0]
  Brain tumor is one of the severe health complications which lead to decrease in life expectancy of the individuals. Timely detection and prediction of brain tumors can be helpful to prevent death rates due to brain tumors. Deep learning-based approaches have emerged as a promising solution to develop automated biomedical image exploration tools.
  arXiv  Detail & Related papers  (2023-07-16T14:06:45Z)
• Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging [59.79875531898648]
  DeepGlioma is an artificial-intelligence-based diagnostic screening system. DeepGlioma can predict the molecular alterations used by the World Health Organization to define the adult-type diffuse glioma taxonomy.
  arXiv  Detail & Related papers  (2023-03-23T18:50:18Z)
• Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI [55.78588835407174]
  We propose a method that reformulates the generation task of diffusion models as a patch-based estimation of healthy brain anatomy. We evaluate our approach on data of tumors and multiple sclerosis lesions and demonstrate a relative improvement of 25.1% compared to existing baselines.
  arXiv  Detail & Related papers  (2023-03-07T09:40:22Z)
• Deep-Learning Tool for Early Identifying Non-Traumatic Intracranial Hemorrhage Etiology based on CT Scan [40.51754649947294]
  The deep learning model was developed with 1868 eligible NCCT scans with non-traumatic ICH collected between January 2011 and April 2018. The model's diagnostic performance was compared with clinicians's performance. The clinicians achieve significant improvements in the sensitivity, specificity, and accuracy of diagnoses of certain hemorrhage etiologies with proposed system augmentation.
  arXiv  Detail & Related papers  (2023-02-02T08:45:17Z)
• Deep learning-based approach to reveal tumor mutational burden status from whole slide images across multiple cancer types [41.61294299606317]
  Tumor mutational burden (TMB) is a potential genomic biomarker of immunotherapy. TMB detected through whole exome sequencing lacks clinical penetration in low-resource settings. In this study, we proposed a multi-scale deep learning framework to address the detection of TMB status from routinely used whole slide images.
  arXiv  Detail & Related papers  (2022-04-07T07:02:32Z)
• A New Deep Hybrid Boosted and Ensemble Learning-based Brain Tumor Analysis using MRI [0.28675177318965034]
  Two-phase deep learning-based framework is proposed to detect and categorize brain tumors in magnetic resonance images (MRIs) In the first phase, a novel deep boosted features and ensemble classifiers (DBF-EC) scheme is proposed to detect tumor MRI images from healthy individuals effectively. In the second phase, a new hybrid features fusion-based brain tumor classification approach is proposed, comprised of dynamic-static feature and ML classifier to categorize different tumor types.
  arXiv  Detail & Related papers  (2022-01-14T10:24:47Z)
• Brain Tumors Classification for MR images based on Attention Guided Deep Learning Model [3.6328238032703806]
  We analyze the existing technology and propose an attention guided deep convolution neural network (CNN) model. Our method can achieve the average accuracy of 99.18% under ten-fold cross-validation for identifying the presence or absence of tumor. It can assist doctors in achieving efficient clinical diagnosis of brain tumors.
  arXiv  Detail & Related papers  (2021-04-06T07:25:52Z)
• Deep Convolutional Neural Networks Model-based Brain Tumor Detection in Brain MRI Images [0.0]
  Our work involves implementing a deep convolutional neural network (DCNN) for diagnosing brain tumors from MR images. Our model can single out the MR images with tumors with an overall accuracy of 96%.
  arXiv  Detail & Related papers  (2020-10-03T07:42:17Z)
• CorrSigNet: Learning CORRelated Prostate Cancer SIGnatures from Radiology and Pathology Images for Improved Computer Aided Diagnosis [1.63324350193061]
  We propose CorrSigNet, an automated two-step model that localizes prostate cancer on MRI. First, the model learns MRI signatures of cancer that are correlated with corresponding histopathology features. Second, the model uses the learned correlated MRI features to train a Convolutional Neural Network to localize prostate cancer.
  arXiv  Detail & Related papers  (2020-07-31T23:44:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.