About this Research Topic
The increasing adoption of medical imaging represents a valuable support for radiologists and pathologists. In particular, one of the most interesting aspects of medical imaging is in the possibility to diagnose, for instance, prostate cancers without invasive intervention but only with a radiological examination. In recent years, medical imaging has become an irreplaceable tool for oncologists. There is a need to manage a large amount of data to extract quantitative knowledge.
In this context, Artificial Intelligence and Radiomics are emerging as a novel approach for optimizing the analysis of massive data from medical images to provide auxiliary guidance in clinical issues. These techniques, in fact, being able to directly process images, has allowed a series of research sub-lines: detection of malignant and benign tumors, segmentation, detection of the degree of the pathology, prediction of the most appropriate therapy, understanding of the evolution of tumors, and effectiveness of prescribed therapies. Moreover, multimodal acquisitions, merging real-time images from different diagnostic modalities such as MRI, CT, PET and ultrasound images, increases diagnostic accuracy and reduces the number of biopsies (approximately 30% of unnecessary biopsies are estimated, which can be avoided, especially in prostatic application)
The aim of this special issue is to collect contributions on innovative methodologies and quantitative imaging techniques to assist the decision-making process performed by oncologists and radiologists for prostate cancers diagnosis and prognosis. In particular, are welcomed prospective and retrospective clinical studies aimed to detect prognosis and diagnosis by exploiting medical imaging but also techniques aimed to mine information from medical big data for precision medicine, by means of multimodal acquisitions combining different types of signals and images.
Contributions related to the design of novel and innovative software for the analysis or imaging technology are also of interest.
- Medical Image Segmentation
- Anatomy Detection in Medical Imaging
- Medical Image Classification and Analysis
- Computational Pathology and Radiology
- Discovery of Predictive and Prognostic Tissue Biomarkers
- Medical Big Data Analytics
- Formal Methods for Disease Detection
- Explainable Medical Image Classification
- Radiomics for Multimodal Quantitative Images
- Biosignals in Oncology
Please note: manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by validation (clinical cohort or biological validation in vitro or in vivo) are out of scope for this section and will not be accepted as part of this Research Topic.
In this context, Artificial Intelligence and Radiomics are emerging as a novel approach for optimizing the analysis of massive data from medical images to provide auxiliary guidance in clinical issues. These techniques, in fact, being able to directly process images, has allowed a series of research sub-lines: detection of malignant and benign tumors, segmentation, detection of the degree of the pathology, prediction of the most appropriate therapy, understanding of the evolution of tumors, and effectiveness of prescribed therapies. Moreover, multimodal acquisitions, merging real-time images from different diagnostic modalities such as MRI, CT, PET and ultrasound images, increases diagnostic accuracy and reduces the number of biopsies (approximately 30% of unnecessary biopsies are estimated, which can be avoided, especially in prostatic application)
The aim of this special issue is to collect contributions on innovative methodologies and quantitative imaging techniques to assist the decision-making process performed by oncologists and radiologists for prostate cancers diagnosis and prognosis. In particular, are welcomed prospective and retrospective clinical studies aimed to detect prognosis and diagnosis by exploiting medical imaging but also techniques aimed to mine information from medical big data for precision medicine, by means of multimodal acquisitions combining different types of signals and images.
Contributions related to the design of novel and innovative software for the analysis or imaging technology are also of interest.
- Medical Image Segmentation
- Anatomy Detection in Medical Imaging
- Medical Image Classification and Analysis
- Computational Pathology and Radiology
- Discovery of Predictive and Prognostic Tissue Biomarkers
- Medical Big Data Analytics
- Formal Methods for Disease Detection
- Explainable Medical Image Classification
- Radiomics for Multimodal Quantitative Images
- Biosignals in Oncology
Please note: manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by validation (clinical cohort or biological validation in vitro or in vivo) are out of scope for this section and will not be accepted as part of this Research Topic.
Keywords: Radiomics, Artificial Intelligence, Machine Learning, Data Analytics, Image Processing, Deep Learning
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.
