Cancers of the Kidney and the Renal Pelvis are the 8th most commonly diagnosed type of cancer annually according to SEER databases, with over 75,000 cases anticipated to be diagnosed this year and causing an estimated 13,000 deaths worldwide. In relative terms, the survival outcomes for patients of renal cancers is fairly high, with a 5-year survival rate of over 75%, and most renal cancers are diagnosed in the early stages, in part due to more readily noticeable symptoms like hematuria.
Imagining, along with tissue biopsies, are the two main tools used by clinicians in diagnosis of renal cancers. As well as diagnosis, imaging of the kidneys in potential or confirmed renal cancer cases is used to assess extent of disease, to understand tumor biology, to assess the efficacy of the prescribed treatment, and to confirm remission has been achieved and maintained in future. Imaging techniques can also be utilized when predicting implications of organ function reduction, as the sites and size of tumors within the kidneys are key predictors of this, and can also be used to plan surgical interventions and provide a prognosis for success and postsurgical outcomes. Metastasis is another concern with renal cancers, with metastatic spread to the lungs, lymph nodes, liver and adrenals all being common due to the location of the kidneys, and keen monitoring using imaging directives can help predict this, and enable preventative interventions sooner rather than later.
We welcome Original Research, leading-edge Reviews and Clinical Trials related but not limited to the aspects below:
- Existing cancer imaging techniques showing effectiveness in diagnoses in renal cancers
- Single/multiple imaging modalities-guided renal cancer treatment
- Novel techniques proving efficacious in renal cancers
- Integrative prognostic models used in renal cancers
- Predictive factors for prognosis identifiable via imaging methodologies
- Novel Deep learning, Machine learning and Artificial Intelligence based models in the management of Renal cancers
Please note: manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by validation (independent 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.
Cancers of the Kidney and the Renal Pelvis are the 8th most commonly diagnosed type of cancer annually according to SEER databases, with over 75,000 cases anticipated to be diagnosed this year and causing an estimated 13,000 deaths worldwide. In relative terms, the survival outcomes for patients of renal cancers is fairly high, with a 5-year survival rate of over 75%, and most renal cancers are diagnosed in the early stages, in part due to more readily noticeable symptoms like hematuria.
Imagining, along with tissue biopsies, are the two main tools used by clinicians in diagnosis of renal cancers. As well as diagnosis, imaging of the kidneys in potential or confirmed renal cancer cases is used to assess extent of disease, to understand tumor biology, to assess the efficacy of the prescribed treatment, and to confirm remission has been achieved and maintained in future. Imaging techniques can also be utilized when predicting implications of organ function reduction, as the sites and size of tumors within the kidneys are key predictors of this, and can also be used to plan surgical interventions and provide a prognosis for success and postsurgical outcomes. Metastasis is another concern with renal cancers, with metastatic spread to the lungs, lymph nodes, liver and adrenals all being common due to the location of the kidneys, and keen monitoring using imaging directives can help predict this, and enable preventative interventions sooner rather than later.
We welcome Original Research, leading-edge Reviews and Clinical Trials related but not limited to the aspects below:
- Existing cancer imaging techniques showing effectiveness in diagnoses in renal cancers
- Single/multiple imaging modalities-guided renal cancer treatment
- Novel techniques proving efficacious in renal cancers
- Integrative prognostic models used in renal cancers
- Predictive factors for prognosis identifiable via imaging methodologies
- Novel Deep learning, Machine learning and Artificial Intelligence based models in the management of Renal cancers
Please note: manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by validation (independent 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.
