{
  "id": 278263,
  "title": "Spatial-And-Context aware (SpACe) \"virtual biopsy\" radiogenomic maps",
  "url": "/competitions/rsna-miccai-brain-tumor-radiogenomic-classification/discussion/278263",
  "author_name": "JasonX",
  "post_date": "2021-10-13T14:42:26.360000",
  "votes": 3,
  "comment_count": 0,
  "views": 0,
  "content": "<p>I've been able to gather good research points from this article. Hope is useful for you too.<br>\nCheers!</p>\n<p>\"With growing emphasis on personalized cancer-therapies,radiogenomics has shown promise in identifying target tumor mutational status on routine imaging (i.e. MRI) scans. These approaches fall into 2 categories: (1) deep-learning/radiomics (context-based), using image features from the entire tumor to identify the gene mutation status, or (2) atlas (spatial)-based to obtain likelihood of gene mutation status based on population statistics. While many genes (i.e. EGFR, MGMT) are spatially variant, a significant challenge in reliable assessment of gene mutation status on imaging has been the lack of available co-localized ground truth for training the models. We present Spatial-And-Context aware (SpACe) \"virtual biopsy\" maps that incorporate context-features from co-localized biopsy site along with spatial-priors from population atlases, within a Least Absolute Shrinkage and Selection Operator (LASSO) regression model, to obtain a per-voxel probability of the presence of a mutation status (M+ vs M-). We then use probabilistic pair-wise Markov model to improve the voxel-wise prediction probability. We evaluate the efficacy of SpACe maps on MRI scans with co-localized ground truth obtained from corresponding biopsy, to predict the mutation status of 2 driver genes in Glioblastoma: (1) EGFR (n=91), and (2) MGMT (n=81). When compared against deep-learning (DL) and radiomic models, SpACe maps obtained training and testing accuracies of 90% (n=71) and 90.48% (n=21) in identifying EGFR amplification status,compared to 80% and 71.4% via radiomics, and 74.28% and 65.5% via DL. For MGMT status, training and testing accuracies using SpACe were 88.3% (n=61) and 71.5% (n=20), compared to 52.4% and 66.7% using radiomics,and 79.3% and 68.4% using DL. Following validation,SpACe maps could provide surgical navigation to improve localization of sampling sites for targeting of specific driver genes in cancer.\"</p>\n<p><a href=\"https://arxiv.org/abs/2006.09878\" target=\"_blank\">Full Article</a></p>",
  "messages": [
    {
      "id": 1543505,
      "postDate": "2021-10-13T14:42:26.360Z",
      "content": "<p>I've been able to gather good research points from this article. Hope is useful for you too.<br>\nCheers!</p>\n<p>\"With growing emphasis on personalized cancer-therapies,radiogenomics has shown promise in identifying target tumor mutational status on routine imaging (i.e. MRI) scans. These approaches fall into 2 categories: (1) deep-learning/radiomics (context-based), using image features from the entire tumor to identify the gene mutation status, or (2) atlas (spatial)-based to obtain likelihood of gene mutation status based on population statistics. While many genes (i.e. EGFR, MGMT) are spatially variant, a significant challenge in reliable assessment of gene mutation status on imaging has been the lack of available co-localized ground truth for training the models. We present Spatial-And-Context aware (SpACe) \"virtual biopsy\" maps that incorporate context-features from co-localized biopsy site along with spatial-priors from population atlases, within a Least Absolute Shrinkage and Selection Operator (LASSO) regression model, to obtain a per-voxel probability of the presence of a mutation status (M+ vs M-). We then use probabilistic pair-wise Markov model to improve the voxel-wise prediction probability. We evaluate the efficacy of SpACe maps on MRI scans with co-localized ground truth obtained from corresponding biopsy, to predict the mutation status of 2 driver genes in Glioblastoma: (1) EGFR (n=91), and (2) MGMT (n=81). When compared against deep-learning (DL) and radiomic models, SpACe maps obtained training and testing accuracies of 90% (n=71) and 90.48% (n=21) in identifying EGFR amplification status,compared to 80% and 71.4% via radiomics, and 74.28% and 65.5% via DL. For MGMT status, training and testing accuracies using SpACe were 88.3% (n=61) and 71.5% (n=20), compared to 52.4% and 66.7% using radiomics,and 79.3% and 68.4% using DL. Following validation,SpACe maps could provide surgical navigation to improve localization of sampling sites for targeting of specific driver genes in cancer.\"</p>\n<p><a href=\"https://arxiv.org/abs/2006.09878\" target=\"_blank\">Full Article</a></p>",
      "rawMarkdown": "I've been able to gather good research points from this article. Hope is useful for you too.\nCheers!\n\n\"With growing emphasis on personalized cancer-therapies,radiogenomics has shown promise in identifying target tumor mutational status on routine imaging (i.e. MRI) scans. These approaches fall into 2 categories: (1) deep-learning/radiomics (context-based), using image features from the entire tumor to identify the gene mutation status, or (2) atlas (spatial)-based to obtain likelihood of gene mutation status based on population statistics. While many genes (i.e. EGFR, MGMT) are spatially variant, a significant challenge in reliable assessment of gene mutation status on imaging has been the lack of available co-localized ground truth for training the models. We present Spatial-And-Context aware (SpACe) \"virtual biopsy\" maps that incorporate context-features from co-localized biopsy site along with spatial-priors from population atlases, within a Least Absolute Shrinkage and Selection Operator (LASSO) regression model, to obtain a per-voxel probability of the presence of a mutation status (M+ vs M-). We then use probabilistic pair-wise Markov model to improve the voxel-wise prediction probability. We evaluate the efficacy of SpACe maps on MRI scans with co-localized ground truth obtained from corresponding biopsy, to predict the mutation status of 2 driver genes in Glioblastoma: (1) EGFR (n=91), and (2) MGMT (n=81). When compared against deep-learning (DL) and radiomic models, SpACe maps obtained training and testing accuracies of 90% (n=71) and 90.48% (n=21) in identifying EGFR amplification status,compared to 80% and 71.4% via radiomics, and 74.28% and 65.5% via DL. For MGMT status, training and testing accuracies using SpACe were 88.3% (n=61) and 71.5% (n=20), compared to 52.4% and 66.7% using radiomics,and 79.3% and 68.4% using DL. Following validation,SpACe maps could provide surgical navigation to improve localization of sampling sites for targeting of specific driver genes in cancer.\"\n\n[Full Article](https://arxiv.org/abs/2006.09878)",
      "votes": 3
    }
  ],
  "comments": [],
  "raw_markdown_by_id": {
    "1543505": "I've been able to gather good research points from this article. Hope is useful for you too.\nCheers!\n\n\"With growing emphasis on personalized cancer-therapies,radiogenomics has shown promise in identifying target tumor mutational status on routine imaging (i.e. MRI) scans. These approaches fall into 2 categories: (1) deep-learning/radiomics (context-based), using image features from the entire tumor to identify the gene mutation status, or (2) atlas (spatial)-based to obtain likelihood of gene mutation status based on population statistics. While many genes (i.e. EGFR, MGMT) are spatially variant, a significant challenge in reliable assessment of gene mutation status on imaging has been the lack of available co-localized ground truth for training the models. We present Spatial-And-Context aware (SpACe) \"virtual biopsy\" maps that incorporate context-features from co-localized biopsy site along with spatial-priors from population atlases, within a Least Absolute Shrinkage and Selection Operator (LASSO) regression model, to obtain a per-voxel probability of the presence of a mutation status (M+ vs M-). We then use probabilistic pair-wise Markov model to improve the voxel-wise prediction probability. We evaluate the efficacy of SpACe maps on MRI scans with co-localized ground truth obtained from corresponding biopsy, to predict the mutation status of 2 driver genes in Glioblastoma: (1) EGFR (n=91), and (2) MGMT (n=81). When compared against deep-learning (DL) and radiomic models, SpACe maps obtained training and testing accuracies of 90% (n=71) and 90.48% (n=21) in identifying EGFR amplification status,compared to 80% and 71.4% via radiomics, and 74.28% and 65.5% via DL. For MGMT status, training and testing accuracies using SpACe were 88.3% (n=61) and 71.5% (n=20), compared to 52.4% and 66.7% using radiomics,and 79.3% and 68.4% using DL. Following validation,SpACe maps could provide surgical navigation to improve localization of sampling sites for targeting of specific driver genes in cancer.\"\n\n[Full Article](https://arxiv.org/abs/2006.09878)"
  }
}