Artificial Intelligence Driven Innovations in Cancer Research, Diagnosis and Treatment - Review
Adaptive Treatment of Metastatic Prostate Cancer Using Generative Artificial Intelligence
Despite the expanding therapeutic options available to cancer patients, therapeutic resistance, disease recurrence, and metastasis persist as hallmark challenges in the treatment of cancer. The rise to prominence of generative artificial intelligence (GenAI) in many realms of human activities is compelling the consideration of its capabilities as a potential lever to advance the development of effective cancer treatments. This article presents a hypothetical case study on the application of generative pre-trained transformers (GPTs) to the treatment of metastatic prostate cancer (mPC). The case explores the design of GPT-supported adaptive intermittent therapy for mPC. Testosterone and prostate-specific antigen (PSA) are assumed to be repeatedly monitored while treatment may involve a combination of androgen deprivation therapy (ADT), androgen receptor-signalling inhibitors (ARSI), chemotherapy, and radiotherapy. The analysis covers various questions relevant to the configuration, training, and inferencing of GPTs for the case of mPC treatment with a particular attention to risk mitigation regarding the hallucination problem and its implications to clinical integration of GenAI technologies. The case study provides elements of an actionable pathway to the realization of GenAI-assisted adaptive treatment of metastatic prostate cancer. As such, the study is expected to help facilitate the design of clinical trials of GenAI-supported cancer treatments.
Unlocking Precision Oncology with GenAI
Generative AI is poised to revolutionize cancer treatment by enabling adaptive, personalized therapies. Our analysis highlights key areas where GenAI will deliver measurable impact.
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Increased Treatment Efficacy
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Reduced Treatment Resistance
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Potential Treatment Cycles Analyzed Annually
Deep Analysis & Enterprise Applications
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GenAI in Oncology
Generative Artificial Intelligence (GenAI), particularly Large Language Models (LLMs) like GPTs, is rapidly gaining traction in oncology. This paper explores its potential for adaptive treatment strategies in metastatic prostate cancer (mPC). While traditional AI has assisted in diagnosis, GenAI offers predictive capabilities for treatment response, enhancing personalized care and overcoming resistance challenges. However, critical considerations include data privacy, explainability, and mitigating 'hallucinations' for clinical safety.
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Possible Disease States with Biomarker Discretization
Enterprise Process Flow
Desired Treatment Response
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Adaptive Therapeutic Strategy
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OncoGPT Inference
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Ground Truth Injection
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Patient Treatment
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Data & Training
Effective GenAI for oncology relies on vast, high-quality, multimodal datasets. The paper outlines the need for extensive patient treatment cycle data, potentially millions of records, curated from major cancer centers or synthetic generation. Discretization of biomarkers (PSA, testosterone) and therapeutic controls (drug dosages) is crucial for model training. Addressing data privacy, ensuring generalizability, and mitigating bias are paramount for clinical adoption.
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Discretized Therapeutic Control Options
OncoGPT Training Scenario
Imagine a major cancer center treating 15,000 prostate cancer patients annually. Over a decade, this would yield 150,000 treatment cycles, providing a substantial dataset for training or retraining GenAI models like OncoGPT. Further data sharing through consortia (e.g., PCCTC) or synthetic data generation can augment this, ensuring the model learns from diverse patient trajectories and treatment responses, including comprehensive biomarker data.
Clinical Integration & Validation
Integrating GenAI into clinical practice requires rigorous validation. The paper proposes accuracy metrics (e.g., Euclidean distance for MSE) and reliability assessments for treatment response predictions. A three-pronged FDA framework (valid clinical association, analytical validation, clinical validation) is suggested. Clinical trials (Phase 1b adaptive ADT trial as baseline) will be essential to establish efficacy against clinical endpoints like time to progression and overall survival, while actively managing hallucination risks through fine-tuning.
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Intersection Observer Threshold
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