“…Results shown in Figure 5 , Figure 6 demonstrated that individual lesion features were only weak classifiers, as evidenced by the modest areas under the receiver operating characteristic curve (AUC value), but when artificial intelligence was used to merge the features into lesion signatures, performance substantially improved (last four data points in plot below). Giger et al have been developing and investigating computerized quantitative methods for extracting data from multi‐modality breast images and mining the data to yield image‐based phenotypes relating to breast cancer risk, diagnosis, prognosis, and response to therapy [18] , [19] , [20] . Figure 5 Relationship of MRI-based phenotypes in distinguishing breast cancer subtypes (big data analyses).…”
The National Cancer Institute (NCI) Cancer Imaging Program organized two related workshops on June 26–27, 2013, entitled “Correlating Imaging Phenotypes with Genomics Signatures Research” and “Scalable Computational Resources as Required for Imaging-Genomics Decision Support Systems.” The first workshop focused on clinical and scientific requirements, exploring our knowledge of phenotypic characteristics of cancer biological properties to determine whether the field is sufficiently advanced to correlate with imaging phenotypes that underpin genomics and clinical outcomes, and exploring new scientific methods to extract phenotypic features from medical images and relate them to genomics analyses. The second workshop focused on computational methods that explore informatics and computational requirements to extract phenotypic features from medical images and relate them to genomics analyses and improve the accessibility and speed of dissemination of existing NIH resources. These workshops linked clinical and scientific requirements of currently known phenotypic and genotypic cancer biology characteristics with imaging phenotypes that underpin genomics and clinical outcomes. The group generated a set of recommendations to NCI leadership and the research community that encourage and support development of the emerging radiogenomics research field to address short-and longer-term goals in cancer research.
“…Results shown in Figure 5 , Figure 6 demonstrated that individual lesion features were only weak classifiers, as evidenced by the modest areas under the receiver operating characteristic curve (AUC value), but when artificial intelligence was used to merge the features into lesion signatures, performance substantially improved (last four data points in plot below). Giger et al have been developing and investigating computerized quantitative methods for extracting data from multi‐modality breast images and mining the data to yield image‐based phenotypes relating to breast cancer risk, diagnosis, prognosis, and response to therapy [18] , [19] , [20] . Figure 5 Relationship of MRI-based phenotypes in distinguishing breast cancer subtypes (big data analyses).…”
The National Cancer Institute (NCI) Cancer Imaging Program organized two related workshops on June 26–27, 2013, entitled “Correlating Imaging Phenotypes with Genomics Signatures Research” and “Scalable Computational Resources as Required for Imaging-Genomics Decision Support Systems.” The first workshop focused on clinical and scientific requirements, exploring our knowledge of phenotypic characteristics of cancer biological properties to determine whether the field is sufficiently advanced to correlate with imaging phenotypes that underpin genomics and clinical outcomes, and exploring new scientific methods to extract phenotypic features from medical images and relate them to genomics analyses. The second workshop focused on computational methods that explore informatics and computational requirements to extract phenotypic features from medical images and relate them to genomics analyses and improve the accessibility and speed of dissemination of existing NIH resources. These workshops linked clinical and scientific requirements of currently known phenotypic and genotypic cancer biology characteristics with imaging phenotypes that underpin genomics and clinical outcomes. The group generated a set of recommendations to NCI leadership and the research community that encourage and support development of the emerging radiogenomics research field to address short-and longer-term goals in cancer research.
On 5 August 1968, publication of the Harvard Committee’s report on the subject of “irreversible coma” established a standard for diagnosing death on neurological grounds. On the same day, the 22nd World Medical Assembly met in Sydney, Australia, and announced the Declaration of Sydney, a pronouncement on death, which is less often quoted because it was overshadowed by the impact of the Harvard Report. To put those events into present-day perspective, the authors reviewed all papers published on this subject and the World Medical Association web page and documents, and corresponded with Dr A G Romualdez, the son of Dr A Z Romualdez. There was vast neurological expertise among some of the Harvard Committee members, leading to a comprehensible and practical clinical description of the brain death syndrome and the way to diagnose it. This landmark account had a global medical and social impact on the issue of human death, which simultaneously lessened reception of the Declaration of Sydney. Nonetheless, the Declaration of Sydney faced the main conceptual and philosophical issues on human death in a bold and forthright manner. This statement differentiated the meaning of death at the cellular and tissue levels from the death of the person. This was a pioneering view on the discussion of human death, published as early as in 1968, that should be recognised by current and future generations.
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