Uncovering the invisible-prevalence, characteristics, and radiomics feature-based detection of visually undetectable intraprostatic tumor lesions in 68GaPSMA-11 PET images of patients with primary prostate cancer.
Constantinos ZamboglouAlisa S BettermannChristian GratzkeMichael MixJuri RufSelina KieferCordula A JilgMatthias BenndorfSimon SpohnThomas F FassbenderPeter BronsertMengxia ChenHongqian GuoFeng WangXuefeng QiuAnca-Ligia GrosuPublished in: European journal of nuclear medicine and molecular imaging (2020)
Visual PSMA-PET image interpretation may miss small but clinically significant PCa in a relevant number of patients and RFs can be implemented to uncover them. This could be used for guiding personalized treatments.
Keyphrases
- prostate cancer
- pet ct
- deep learning
- end stage renal disease
- pet imaging
- computed tomography
- ejection fraction
- newly diagnosed
- positron emission tomography
- chronic kidney disease
- peritoneal dialysis
- machine learning
- risk factors
- radical prostatectomy
- prognostic factors
- squamous cell carcinoma
- magnetic resonance imaging
- loop mediated isothermal amplification
- convolutional neural network
- contrast enhanced
- lymph node metastasis
- optical coherence tomography
- sensitive detection