Abstract
Determining the optimal treatment strategy for individuals diagnosed with prostate cancer (PCa) can be challenging given tumor heterogeneity and a diverse, prolonged natural history. Although PCa is the most common noncutaneous cancer in American men, there is no universally agreed-upon strategy for its diagnosis and management. Physicians must be cautious of overtreatment of men with low-risk disease, but avoid undertreatment in those with high-risk disease. The majority of PCa deaths is due to metastatic disease. Eventually, all patients with metastatic disease develop castration-resistant prostate cancer (mCRPC), which has a very high mortality rate. Several new drugs have been developed to treat mCRPC, but this has also further complicated clinical decision-making. Research efforts are now focused on the timing, sequencing, and combinations of these novel agents. In this chapter, we review the various PCa treatments and discuss technologies that can help guide treatment decisions tailored to the individual needs of PCa patients. Risk stratification methods that incorporate genomic information, improved imaging and biopsy technology, and next-generation sequencing tools are facilitating precision-guided, personalized care.
| Original language | English |
|---|---|
| Title of host publication | Unraveling the Complexities of Metastasis |
| Subtitle of host publication | Transition from a Segmented View to a Conceptual Continuum |
| Publisher | Elsevier |
| Pages | 23-47 |
| Number of pages | 25 |
| ISBN (Electronic) | 9780128217894 |
| ISBN (Print) | 9780128217900 |
| DOIs | |
| State | Published - Jan 1 2022 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2022 Elsevier Inc. All rights reserved.
ASJC Scopus Subject Areas
- General Agricultural and Biological Sciences
- General Biochemistry,Genetics and Molecular Biology
Keywords
- Cell-free DNA
- Circulating tumor cells
- Gene signature panels
- Localized prostate cancer
- Metastatic castration-resistant prostate cancer
- Multiparametric magnetic resonance imaging