About this Research Topic
In the new era of precision medicine in oncology, medical imaging is pivotal for a broad spectrum of indications ranging from early detection of malignant lesions to response assessment in advanced metastatic disease.
This Research Topic aims to share disruptive technologies in the field of imaging-guided precision medicine in oncology. The goal is to discuss new concepts and discoveries in the field of quantitative imaging biomarkers derived from a quantitative analysis of data contained in medical images. These imaging biomarkers can decipher the imaging phenotype of tumors, patients’ anthropomorphic characteristics, and tumor microenvironment.
We would like to communicate imaging biomarkers that can be used as clinical decision tools that benefit the lives of cancer patients. Original Research and Review articles should be at the crossroads between radiology, nuclear medicine, computer science, biochemistry, pathology, and oncology. The following technologies should focus on the clinical topics discussed below:
1. New imaging technologies for precision medicine:
a. Software: Radiomics, machine-learning, deep-learning, artificial intelligence.
b. Hardware: new devices, new acquisition protocols.
c. Multimodal hybrid imaging.
d. Metabolic and molecular imaging
e. Augmented reality
2. Guiding the initial treatment decision:
a. Precision diagnosis: grade, stage, genomics.
b. Baseline prognostic and predictive imaging biomarkers.
3. Assessing tumor sensitivity to treatments:
a. New patterns of response: abscopal effect, delayed efficacy, dissociated response.
b. New patterns of progression: progression, pseudoprogression, hyperprogression.
c. Breakthrough in systemic therapies: Immuno-oncology, targeted therapies.
d. Breakthrough in interventional radiology.
e. Breakthrough in radiation therapy.
4. Diagnosis and management of COVID-19 in cancer patients
This Research Topic aims to share disruptive technologies in the field of imaging-guided precision medicine in oncology. The goal is to discuss new concepts and discoveries in the field of quantitative imaging biomarkers derived from a quantitative analysis of data contained in medical images. These imaging biomarkers can decipher the imaging phenotype of tumors, patients’ anthropomorphic characteristics, and tumor microenvironment.
We would like to communicate imaging biomarkers that can be used as clinical decision tools that benefit the lives of cancer patients. Original Research and Review articles should be at the crossroads between radiology, nuclear medicine, computer science, biochemistry, pathology, and oncology. The following technologies should focus on the clinical topics discussed below:
1. New imaging technologies for precision medicine:
a. Software: Radiomics, machine-learning, deep-learning, artificial intelligence.
b. Hardware: new devices, new acquisition protocols.
c. Multimodal hybrid imaging.
d. Metabolic and molecular imaging
e. Augmented reality
2. Guiding the initial treatment decision:
a. Precision diagnosis: grade, stage, genomics.
b. Baseline prognostic and predictive imaging biomarkers.
3. Assessing tumor sensitivity to treatments:
a. New patterns of response: abscopal effect, delayed efficacy, dissociated response.
b. New patterns of progression: progression, pseudoprogression, hyperprogression.
c. Breakthrough in systemic therapies: Immuno-oncology, targeted therapies.
d. Breakthrough in interventional radiology.
e. Breakthrough in radiation therapy.
4. Diagnosis and management of COVID-19 in cancer patients
Keywords: Artificial Intelligence, Deep learning, Immunotherapy, Machine learning, Medical Imaging, Oncology, Radiomics
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
