Cancer provides a unique medical decision context considering its variegated forms with evolution of disease as well as the individual condition of patients, their ability to receive treatment, and their responses to treatment. Though technologies have improved, challenges of accurate cancer detection, characterization, and monitoring remain. Traditional imaging assessment of cancer most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. Radiomics and artificial intelligence (AI) promises to make great strides in the qualitative and quantitative interpretation of cancer imaging.
Radiomics, the high-throughput mining of quantitative image features from standard-of-care medical imaging, is gaining increasing importance in cancer research. It enables data to be extracted and applied within clinical-decision support systems so to improve accuracy of cancer diagnosis, prognosis, and prediction. AI excels at distinguishing complex patterns in cancer images and thus provides the opportunity to alter image interpretation from a purely qualitative and subjective task to one that can be quantified and effortlessly reproduced. In addition, AI can aggregate multiple data streams into powerful integrated diagnostic systems covering cancer images, genomics, pathology, electronic health records, and social networks and thereby complement clinical decision making. However, radiomics and AI applications in cancer imaging to date have not been vigorously validated for reproducibility and generalizability.
This Research Topic aims to provide a forum to update and discuss new discoveries in the field of applying radiomics and AI in cancer imaging for pushing them to clinical use and to impact future directions in cancer care.
For this Research Topic, we are seeking Original Research, Review, Opinion, or Method articles covering, but not limited to, the following themes:
1. Translational radiomics and AI as the process of converting the basic radiomic methodologies into evidence-based clinically applicable models;
2. Applications of radiomics and AI for differential diagnosis and staging;
3. Connecting radiomics and AI to therapy response and prognosis;
4. Artificial intelligence-assisted therapy decision based on multimodal cancer imaging (such as ultrasound, CT, MRI, PET/CT, and PET/MRI).
Cancer provides a unique medical decision context considering its variegated forms with evolution of disease as well as the individual condition of patients, their ability to receive treatment, and their responses to treatment. Though technologies have improved, challenges of accurate cancer detection, characterization, and monitoring remain. Traditional imaging assessment of cancer most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. Radiomics and artificial intelligence (AI) promises to make great strides in the qualitative and quantitative interpretation of cancer imaging.
Radiomics, the high-throughput mining of quantitative image features from standard-of-care medical imaging, is gaining increasing importance in cancer research. It enables data to be extracted and applied within clinical-decision support systems so to improve accuracy of cancer diagnosis, prognosis, and prediction. AI excels at distinguishing complex patterns in cancer images and thus provides the opportunity to alter image interpretation from a purely qualitative and subjective task to one that can be quantified and effortlessly reproduced. In addition, AI can aggregate multiple data streams into powerful integrated diagnostic systems covering cancer images, genomics, pathology, electronic health records, and social networks and thereby complement clinical decision making. However, radiomics and AI applications in cancer imaging to date have not been vigorously validated for reproducibility and generalizability.
This Research Topic aims to provide a forum to update and discuss new discoveries in the field of applying radiomics and AI in cancer imaging for pushing them to clinical use and to impact future directions in cancer care.
For this Research Topic, we are seeking Original Research, Review, Opinion, or Method articles covering, but not limited to, the following themes:
1. Translational radiomics and AI as the process of converting the basic radiomic methodologies into evidence-based clinically applicable models;
2. Applications of radiomics and AI for differential diagnosis and staging;
3. Connecting radiomics and AI to therapy response and prognosis;
4. Artificial intelligence-assisted therapy decision based on multimodal cancer imaging (such as ultrasound, CT, MRI, PET/CT, and PET/MRI).
