Transforming the oncology data paradigm by creating, capturing, and retrieving structured cancer data at the point of care: A Mayo Clinic pilot.

INTRODUCTION: Structured data capture requires defined languages such as minimal Common Oncology Data Elements (mCODE). This pilot assessed the feasibility of capturing 5 mCODE categories (stage, disease status, performance status (PS), intent of therapy and intent to change therapy). METHODS: A tool (SmartPhrase) using existing and custom structured data elements was Built to capture 4 data categories (disease status, PS, intent of therapy and intent to change therapy) typically documented as free-text within notes. Existing functionality for stage was supported by the Build. Participant survey data, presence of data (per encounter), and time in chart were collected prior to go-live and repeat timepoints. The anticipated outcome was capture of >50% sustained over time without undue burden. RESULTS: Pre-intervention (5-weeks before go-live), participants had 1390 encounters (1207 patients). The median percent capture across all participants was 32% for stage; no structured data was available for other categories pre-intervention. During a 6-month pilot with 14 participants across three sites, 4995 encounters (3071 patients) occurred. The median percent capture across all participants and all post-intervention months increased to 64% for stage and 81%-82% for the other data categories post-intervention. No increase in participant time in chart was noted. Participants reported that data were meaningful to capture. CONCLUSIONS: Structured data can be captured (1) in real-time, (2) sustained over time without (3) undue provider burden using note-based tools. Our system is expanding the pilot, with integration of these data into clinical decision support, practice dashboards and potential for clinical trial matching.

Potential application of artificial intelligence in cancer therapy.

PURPOSE OF REVIEW: This review underscores the critical role and challenges associated with the widespread adoption of artificial intelligence in cancer care to enhance disease management, streamline clinical processes, optimize data retrieval of health information, and generate and synthesize evidence. RECENT FINDINGS: Advancements in artificial intelligence models and the development of digital biomarkers and diagnostics are applicable across the cancer continuum from early detection to survivorship care. Additionally, generative artificial intelligence has promised to streamline clinical documentation and patient communications, generate structured data for clinical trial matching, automate cancer registries, and facilitate advanced clinical decision support. Widespread adoption of artificial intelligence has been slow because of concerns about data diversity and data shift, model reliability and algorithm bias, legal oversight, and high information technology and infrastructure costs. SUMMARY: Artificial intelligence models have significant potential to transform cancer care. Efforts are underway to deploy artificial intelligence models in the cancer practice, evaluate their clinical impact, and enhance their fairness and explainability. Standardized guidelines for the ethical integration of artificial intelligence models in cancer care pathways and clinical operations are needed. Clear governance and oversight will be necessary to gain trust in artificial intelligence-assisted cancer care by clinicians, scientists, and patients.

Optimal systemic therapy in men with low-volume prostate cancer.

PURPOSE OF REVIEW: Low-volume prostate cancer is an established prognostic category of metastatic hormone-sensitive prostate cancer. However, the term is often loosely used to reflect the low burden of disease across different prostate cancer states. This review explores the definitions of low-volume prostate cancer, biology, and current evidence for treatment. We also explore future directions, including the impact of advanced imaging modalities, particularly prostate-specific membrane antigen (PSMA) positron emission tomography (PET) scans, on refining patient subgroups and treatment strategies for patients with low-volume prostate cancer. RECENT FINDINGS: Recent investigations have attempted to redefine low-volume disease, incorporating factors beyond metastatic burden. Advanced imaging, especially PSMA PET, offers enhanced accuracy in detecting metastases, potentially challenging the conventional definition of low volume. The prognosis and treatment of low-volume prostate cancer may vary by the timing of metastatic presentation. Biomarker-directed consolidative therapy, metastases-directed therapy, and de-escalation of systemic therapies will be increasingly important, especially in patients with metachronous low-volume disease. SUMMARY: In the absence of validated biomarkers, the management of low-volume prostate cancer as defined by CHAARTED criteria may be guided by the timing of metastatic presentation. For metachronous low-volume disease, we recommend novel hormonal therapy (NHT) doublets with or without consolidative metastasis-directed therapy (MDT), and for synchronous low-volume disease, NHT doublets with or without consolidative MDT and prostate-directed radiation. Docetaxel triplets may be a reasonable alternative in some patients with synchronous presentation. There is no clear role of docetaxel doublets in patients with low-volume disease. In the future, a small subset of low-volume diseases with oligometastases selected by genomics and advanced imaging like PSMA PET may achieve long-term remission with MDT with no systemic therapy.

Applications of Artificial Intelligence in Prostate Cancer Care: A Path to Enhanced Efficiency and Outcomes.

The landscape of prostate cancer care has rapidly evolved. We have transitioned from the use of conventional imaging, radical surgeries, and single-agent androgen deprivation therapy to an era of advanced imaging, precision diagnostics, genomics, and targeted treatment options. Concurrently, the emergence of large language models (LLMs) has dramatically transformed the paradigm for artificial intelligence (AI). This convergence of advancements in prostate cancer management and AI provides a compelling rationale to comprehensively review the current state of AI applications in prostate cancer care. Here, we review the advancements in AI-driven applications across the continuum of the journey of a patient with prostate cancer from early interception to survivorship care. We subsequently discuss the role of AI in prostate cancer drug discovery, clinical trials, and clinical practice guidelines. In the localized disease setting, deep learning models demonstrated impressive performance in detecting and grading prostate cancer using imaging and pathology data. For biochemically recurrent diseases, machine learning approaches are being tested for improved risk stratification and treatment decisions. In advanced prostate cancer, deep learning can potentially improve prognostication and assist in clinical decision making. Furthermore, LLMs are poised to revolutionize information summarization and extraction, clinical trial design and operations, drug development, evidence synthesis, and clinical practice guidelines. Synergistic integration of multimodal data integration and human-AI integration are emerging as a key strategy to unlock the full potential of AI in prostate cancer care.

Low PSA radiographic disease progression on C11-choline PET.

Background: For men with prostate cancer, radiographic progression may occur without a concordant rise in prostate-specific antigen (PSA). Our study aimed to assess the prevalence of radiographic progression using C-11 choline positron emission tomography (PET) imaging in patients achieving ultra-low PSA values and to evaluate clinical outcomes in this patient population. Methods: In a single institution study, we reviewed the prospectively maintained Mayo Clinic C-11 Choline PET metastatic prostate cancer registry to identify patients experiencing radiographic disease progression (rDP) on C-11 choline PET scan while the PSA value was less than 0.5 ng/mL. Disease progression was confirmed by tissue biopsy or response to subsequent therapy. Clinicopathologic variables were abstracted by trained research personnel. Overall survival was estimated using the Kaplan-Meier method. Intergroup differences were assessed using the log-rank test. A univariate and multivariate Cox regression model was performed to investigate variables associated with poor survival after rDP. Results: A total of 1323 patients within the registry experienced rDP between 2011 and 2021, including 220 (16.6%) men with rDP occurring at low PSA level. A median (interquartile range [IQR]) of 54.7 (19.7-106.9) months elapsed between the time of prostate cancer diagnosis and low PSA rDP, during which 173 patients (78%) developed castration-resistant prostate cancer (CRPC). Sites of low PSA rDP included local recurrence (n = 17, 8%), lymph node (n = 90, 41%), bone (n = 94, 43%) and visceral metastases (n = 19, 9%). Biopsy at the time of rDP demonstrated small-cell or neuroendocrine features in 21% of patients with available tissue. Over a median (IQR) follow-up of 49.4 (21.3-95.1) months from the time of low PSA rDP, 46% (n = 102) of patients died. Factors associated with poorer survival outcomes include advanced age at rDP, CRPC status, bone and visceral metastasis (p value <0.05). Visceral metastases were associated with decreased overall survival (p = 0.009 by log-rank) as compared with other sites of rDP. Conclusions: Men with prostate cancer commonly experience metastatic progression at very low or even undetectable PSA levels. Periodic imaging, even at low absolute PSA values, may result in more timely identification of disease progression.

Performance of ChatGPT-4 and Bard chatbots in responding to common patient questions on prostate cancer 177Lu-PSMA-617 therapy.

Background: Many patients use artificial intelligence (AI) chatbots as a rapid source of health information. This raises important questions about the reliability and effectiveness of AI chatbots in delivering accurate and understandable information. Purpose: To evaluate and compare the accuracy, conciseness, and readability of responses from OpenAI ChatGPT-4 and Google Bard to patient inquiries concerning the novel 177Lu-PSMA-617 therapy for prostate cancer. Materials and methods: Two experts listed the 12 most commonly asked questions by patients on 177Lu-PSMA-617 therapy. These twelve questions were prompted to OpenAI ChatGPT-4 and Google Bard. AI-generated responses were distributed using an online survey platform (Qualtrics) and blindly rated by eight experts. The performances of the AI chatbots were evaluated and compared across three domains: accuracy, conciseness, and readability. Additionally, potential safety concerns associated with AI-generated answers were also examined. The Mann-Whitney U and chi-square tests were utilized to compare the performances of AI chatbots. Results: Eight experts participated in the survey, evaluating 12 AI-generated responses across the three domains of accuracy, conciseness, and readability, resulting in 96 assessments (12 responses x 8 experts) for each domain per chatbot. ChatGPT-4 provided more accurate answers than Bard (2.95 ± 0.671 vs 2.73 ± 0.732, p=0.027). Bard's responses had better readability than ChatGPT-4 (2.79 ± 0.408 vs 2.94 ± 0.243, p=0.003). Both ChatGPT-4 and Bard achieved comparable conciseness scores (3.14 ± 0.659 vs 3.11 ± 0.679, p=0.798). Experts categorized the AI-generated responses as incorrect or partially correct at a rate of 16.6% for ChatGPT-4 and 29.1% for Bard. Bard's answers contained significantly more misleading information than those of ChatGPT-4 (p = 0.039). Conclusion: AI chatbots have gained significant attention, and their performance is continuously improving. Nonetheless, these technologies still need further improvements to be considered reliable and credible sources for patients seeking medical information on 177Lu-PSMA-617 therapy.

Prostate Cancer Lung Metastasis: Clinical Insights and Therapeutic Strategies.

Prostate cancer lung metastasis represents a clinical conundrum due to its implications for advanced disease progression and the complexities it introduces in treatment planning. As the disease progresses to distant sites such as the lung, the clinical management becomes increasingly intricate, requiring tailored therapeutic strategies to address the unique characteristics of metastatic lesions. This review seeks to synthesize the current state of knowledge surrounding prostate cancer metastasis to the lung, shedding light on the diverse array of clinical presentations encountered, ranging from subtle radiological findings to overt symptomatic manifestations. By examining the diagnostic modalities utilized in identifying this metastasis, including advanced imaging techniques and histopathological analyses, this review aims to provide insights into the diagnostic landscape and the challenges associated with accurately characterizing lung metastatic lesions in prostate cancer patients. Moreover, this review delves into the nuances of therapeutic interventions employed in managing prostate cancer lung metastasis, encompassing systemic treatments such as hormonal therapies and chemotherapy, as well as metastasis-directed therapies including surgery and radiotherapy.