Gastroenteropancreatic neuroendocrine neoplasms comprise a heterogeneous group of tumors that differ in their pathogenesis,hormonal syndromes produced,biological behavior and consequently,in their requirement for and/...Gastroenteropancreatic neuroendocrine neoplasms comprise a heterogeneous group of tumors that differ in their pathogenesis,hormonal syndromes produced,biological behavior and consequently,in their requirement for and/or response to specific chemotherapeutic agents and molecular targeted therapies.Various imaging techniques are available for functional and morphological evaluation of these neoplasms and the selection of investigations performed in each patient should be customized to the clinical question.Also,with the increased availability of cross sectional imaging,these neoplasms are increasingly being detected incidentally in routine radiology practice.This article is a review of the various imaging modalities currently used in the evaluation of neuroendocrine neoplasms,along with a discussion of the role of advanced imaging techniques and a glimpse into the newer imaging horizons,mostly in the research stage.展开更多
The use of machine learning and deep learning has enabled many applications,previously thought of as being impossible.Among all medical fields,cancer care is arguably the most significantly impacted,with precision med...The use of machine learning and deep learning has enabled many applications,previously thought of as being impossible.Among all medical fields,cancer care is arguably the most significantly impacted,with precision medicine now truly being a possibility.The effect of these technologies,loosely known as artificial intelligence,is particularly striking in fields involving images(such as radiology and pathology)and fields involving large amounts of data(such as genomics).Practicing oncologists are often confronted with new technologies claiming to predict response to therapy or predict the genomic make-up of patients.Understanding these new claims and technologies requires a deep understanding of the field.In this review,we provide an overview of the basis of deep learning.We describe various common tasks and their data requirements so that oncologists could be equipped to start such projects,as well as evaluate algorithms presented to them.展开更多
文摘Gastroenteropancreatic neuroendocrine neoplasms comprise a heterogeneous group of tumors that differ in their pathogenesis,hormonal syndromes produced,biological behavior and consequently,in their requirement for and/or response to specific chemotherapeutic agents and molecular targeted therapies.Various imaging techniques are available for functional and morphological evaluation of these neoplasms and the selection of investigations performed in each patient should be customized to the clinical question.Also,with the increased availability of cross sectional imaging,these neoplasms are increasingly being detected incidentally in routine radiology practice.This article is a review of the various imaging modalities currently used in the evaluation of neuroendocrine neoplasms,along with a discussion of the role of advanced imaging techniques and a glimpse into the newer imaging horizons,mostly in the research stage.
文摘The use of machine learning and deep learning has enabled many applications,previously thought of as being impossible.Among all medical fields,cancer care is arguably the most significantly impacted,with precision medicine now truly being a possibility.The effect of these technologies,loosely known as artificial intelligence,is particularly striking in fields involving images(such as radiology and pathology)and fields involving large amounts of data(such as genomics).Practicing oncologists are often confronted with new technologies claiming to predict response to therapy or predict the genomic make-up of patients.Understanding these new claims and technologies requires a deep understanding of the field.In this review,we provide an overview of the basis of deep learning.We describe various common tasks and their data requirements so that oncologists could be equipped to start such projects,as well as evaluate algorithms presented to them.
