Back to Search
Start Over
Optimized Identification of High-Grade Prostate Cancer by Combining Different PSA Molecular Forms and PSA Density in a Deep Learning Model
- Source :
- Diagnostics, Vol 11, Iss 335, p 335 (2021), Diagnostics, Volume 11, Issue 2
- Publication Year :
- 2021
- Publisher :
- MDPI AG, 2021.
-
Abstract
- After skin cancer, prostate cancer (PC) is the most common cancer among men. The gold standard for PC diagnosis is based on the PSA (prostate-specific antigen) test. Based on this preliminary screening, the physician decides whether to proceed with further tests, typically prostate biopsy, to confirm cancer and evaluate its aggressiveness. Nevertheless, the specificity of the PSA test is suboptimal and, as a result, about 75% of men who undergo a prostate biopsy do not have cancer even if they have elevated PSA levels. Overdiagnosis leads to unnecessary overtreatment of prostate cancer with undesirable side effects, such as incontinence, erectile dysfunction, infections, and pain. Here, we used artificial neuronal networks to develop models that can diagnose PC efficiently. The model receives as an input a panel of 4 clinical variables (total PSA, free PSA, p2PSA, and PSA density) plus age. The output of the model is an estimate of the Gleason score of the patient. After training on a dataset of 190 samples and optimization of the variables, the model achieved values of sensitivity as high as 86% and 89% specificity. The efficiency of the method can be improved even further by training the model on larger datasets.
- Subjects :
- Oncology
medicine.medical_specialty
Prostate biopsy
Clinical Biochemistry
Psa density
030232 urology & nephrology
urologic and male genital diseases
Article
03 medical and health sciences
Prostate cancer
0302 clinical medicine
PSA molecular form
Internal medicine
medicine
Overdiagnosis
lcsh:R5-920
medicine.diagnostic_test
business.industry
Cancer
Gold standard (test)
prostate cancer
medicine.disease
PSA density
Erectile dysfunction
tumor markers
PSA molecular forms
030220 oncology & carcinogenesis
Skin cancer
lcsh:Medicine (General)
business
artificial neural network
Subjects
Details
- ISSN :
- 20754418
- Volume :
- 11
- Database :
- OpenAIRE
- Journal :
- Diagnostics
- Accession number :
- edsair.doi.dedup.....f0b82b5af475d445f6aec4b41fd81fd6
- Full Text :
- https://doi.org/10.3390/diagnostics11020335