Unlocking artificial intelligence, machine learning and deep learning to combat therapeutic resistance in metastatic...

Area of Science:

• Oncology
• Medical Informatics
• Bioinformatics

Background:

• Metastatic castration-resistant prostate cancer (mCRPC) presents significant therapeutic challenges.
• Existing treatments face limitations due to acquired or intrinsic resistance.
• Novel approaches are crucial for improving patient outcomes in mCRPC.

Purpose of the Study:

• To review the application of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in overcoming therapeutic resistance in mCRPC.
• To explore how AI can analyze integrated datasets for mechanistic insights and biomarker discovery.
• To discuss the potential and limitations of AI in developing personalized treatment strategies for mCRPC.

Main Methods:

• Narrative review synthesizing current literature on AI in mCRPC.
• Analysis of AI methodologies utilizing genomics, proteomics, and clinical data.
• Examination of studies focusing on prediction of treatment response and resistance mechanisms.

Main Results:

• AI approaches can integrate multi-omics and clinical data to identify resistance drivers.
• AI shows promise in predicting treatment efficacy and identifying novel biomarkers.
• Personalized treatment strategies can be potentially developed using AI insights.

Conclusions:

• AI holds significant potential for advancing therapeutic strategies against mCRPC resistance.
• Data heterogeneity and regulatory hurdles are key challenges for clinical translation.
• Further research is needed to fully implement AI in mCRPC clinical practice.