The use of imaging techniques such as magnetic resonance imaging (MRI) and computed tomography (CT) have become standard in identifying, diagnosing and staging various cancers. With recent refinements to imaging technologies, such as Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) and enhanced CT scans, practitioners are provided with more detailed images which can differentiate textural details of tumors. With this extra detail in images obtained in a non-invasive manner it opens up a number of opportunities to clinicians at each stage of the cancer care cascade. This impact can be seen in initial diagnosis and characterization, to potential uses as a prognostic biomarker, as well as a method of tracking the success of treatment post-intervention.
Currently, the benefits provided by the ability to analyze textures of tumors include the quantification of tumor heterogeneity, a key feature of malignancy, which can be a key determinant of disease progression predictions. Taken alone or in combination with other acquired patient data, such as genetic profiles and tissue biopsies, this data can influence initial treatment decisions, predictions to the outcomes of these interventions, and aid urgent decision making in instances of failing treatment.
With this Research Topic we aim to compile research that shows the role of texture analysis in cancer imaging in the toolkit of radiologists and clinicians in the cancer care team, with the motive of showing how these technologies can positively impact patient outcomes. We also invite manuscript submissions which identify where technological or procedural updates can optimize outcomes further.
Important Note: Manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) will not be accepted in any of the sections of Frontiers in Oncology.
The use of imaging techniques such as magnetic resonance imaging (MRI) and computed tomography (CT) have become standard in identifying, diagnosing and staging various cancers. With recent refinements to imaging technologies, such as Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) and enhanced CT scans, practitioners are provided with more detailed images which can differentiate textural details of tumors. With this extra detail in images obtained in a non-invasive manner it opens up a number of opportunities to clinicians at each stage of the cancer care cascade. This impact can be seen in initial diagnosis and characterization, to potential uses as a prognostic biomarker, as well as a method of tracking the success of treatment post-intervention.
Currently, the benefits provided by the ability to analyze textures of tumors include the quantification of tumor heterogeneity, a key feature of malignancy, which can be a key determinant of disease progression predictions. Taken alone or in combination with other acquired patient data, such as genetic profiles and tissue biopsies, this data can influence initial treatment decisions, predictions to the outcomes of these interventions, and aid urgent decision making in instances of failing treatment.
With this Research Topic we aim to compile research that shows the role of texture analysis in cancer imaging in the toolkit of radiologists and clinicians in the cancer care team, with the motive of showing how these technologies can positively impact patient outcomes. We also invite manuscript submissions which identify where technological or procedural updates can optimize outcomes further.
Important Note: Manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) will not be accepted in any of the sections of Frontiers in Oncology.
