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Abstract Objective Data extraction from the published literature is the most laborious step in conducting living systematic reviews (LSRs). We aim to build a generalizable, automated data extraction workflow leveraging large language models (LLMs) that mimics the real-world 2-reviewer process. Materials and Methods A dataset of 10 trials (22 publications) from a published LSR was used, focusing on 23 variables related to trial, population, and outcomes data. The dataset was split into prompt development (n = 5) and held-out test sets (n = 17). GPT-4-turbo and Claude-3-Opus were used for data extraction. Responses from the 2 LLMs were considered concordant if they were the same for a given variable. The discordant responses from each LLM were provided to the other LLM for cross-critique. Accuracy, ie, the total number of correct responses divided by the total number of responses, was computed to assess performance. Results In the prompt development set, 110 (96%) responses were concordant, achieving an accuracy of 0.99 against the gold standard. In the test set, 342 (87%) responses were concordant. The accuracy of the concordant responses was 0.94. The accuracy of the discordant responses was 0.41 for GPT-4-turbo and 0.50 for Claude-3-Opus. Of the 49 discordant responses, 25 (51%) became concordant after cross-critique, increasing accuracy to 0.76. Discussion Concordant responses by the LLMs are likely to be accurate. In instances of discordant responses, cross-critique can further increase the accuracy. Conclusion Large language models, when simulated in a collaborative, 2-reviewer workflow, can extract data with reasonable performance, enabling truly “living” systematic reviews.

Introduction Symptom burden in myeloproliferative neoplasms (MPN) is quantified using total symptom scores (TSS) calculated from the patient-reported scores for clinically meaningful symptoms. The impact of demographic, clinical, and laboratory features on these symptoms remains unclear and warrants further investigation. Methods Patients with polycythemia vera (PV), essential thrombocythemia (ET), and myelofibrosis (MF) were identified from the retrospective chart review. TSS, scores of individual symptoms (fatigue, early satiety, abdominal discomfort, inactivity, concentration difficulties, fever, night sweats, itching, bone pain, weight loss), demographic characteristics (race, ethnicity, age, and gender), clinical features (time since diagnosis, depression status, obesity status), laboratory results and season at the time of visit when the first TSS assessment was performed were recorded. Multicollinearity and homoscedasticity were assessed using variance inflation factors and visual inspection of the data, respectively. A multivariable regression analysis was performed using a robust variance estimator. A p-value <0.05 indicated a statistically significant association with the symptom scores. Results The chart review identified 557 (PV: 201; ET: 191; MF: 165) patients. The median age of patients with PV, ET, and MF was 65 (range: 27-93), 65 (20-96), and 69 (27-96) years, respectively. Most patients were white (PV: 92%; ET: 90%; MF: 93%) and non-Hispanic or Latino (PV: 93%; ET: 96%; MF: 92%). Most patients with PV and ET were female (PV: 51%; ET: 73%), while most patients with MF were male (55%). Among the included patients 22% (PV: 21%; ET: 27%; MF: 18%) had the diagnosis of depression at the time of the clinical visit. TSS was highest in MF (mean (SD): 18.1±14.6), followed by PV (15.7±14.5) and ET (15.1±14.0) patients. Fatigue was the most reported symptom by the patients (PV: 81%; ET: 82%; MF: 84%), whereas weight loss was the least reported symptom (PV: 4.5%; ET: 6.3%; MF: 16%). With regards to the TSS multivariable regression analysis showed that age (beta coefficient: 0.2; p=0.01), depression (7.2; 0.008), and low hemoglobin (1.2; 0.003) in PV, depression (11.7; 0.001) in ET, and depression (9.5; 0.001), splenomegaly (6.9; 0.01), and WBC count (0.16; 0.048) in MF patients were significantly associated with TSS. In terms of individual symptoms in PV, depression was associated with fatigue (beta coefficient: 1.31; p-value: 0.01) and concentration difficulties (1.7; <0.001), high WBC count was associated with early satiety (0.09; 0.03), and non-white race was associated with fever (1.4; 0.007). In ET, depression was associated with fatigue (1.6; <0.001), abdominal discomfort (1.4; <0.001), inactivity (2.5; <0.001), concentration difficulties (2.4; <0.001) and night sweats (1.4; <0.001), anemia was associated with fatigue (1.2; 0.03), fewer months since diagnosis was associated with inactivity (0.01; 0.02) and young age was associated with bone pain (0.02; 0.03). In MF, depression was associated with fatigue (1.8; 0.001), concentration difficulties (1.3; 0.01), fever (1.1; 0.02), and night sweats (2.2; 0.03), low hemoglobin was associated with fatigue (0.46; <0,001) and early satiety (0.31; 0.003), high WBC count was associated with fatigue (0.03; 0.04) and early satiety (0.04; 0.02), low platelet count was associated with abdominal discomfort (0.002; 0.03), splenomegaly was associated with inactivity (2.27; 0.003) and female gender was associated with itching (0.57; 0.01). Conclusions This study demonstrates that depression in PV and ET and depression, low hemoglobin, and a high WBC count in MF contribute to multiple symptoms seen in these patients. Identifying demographic groups at risk of a higher symptom burden and addressing the comorbidities that drive symptoms, in addition to disease-directed therapy, is crucial to improving the quality of life in these patients.

Introduction Prognostication in myelofibrosis (MF) currently relies on manual assessment of clinical, pathological, and laboratory variables, which is inefficient and error-prone. The potential of large language models (LLMs) and rule-based programming to automate data extraction and prognostication in these patients remains unexplored and merits investigation. Methods Patients with MF were identified from a retrospective chart review. Data regarding clinical (age, constitutional symptoms, transfusion requirements), laboratory (hemoglobin, circulating white blood cell, platelet, and blast counts), and pathological (grade of bone marrow fibrosis, chromosomal analyses, cytogenetics) features was extracted from the latest free-text clinical notes and pathology reports using the generative pretrained transformer-4 (GPT-4) model in a zero-shot setting with the prompting parameters set to generate deterministic responses. The patients were randomly sampled into development (15%) and test (85%) sets, with the documents of patients in the development set used for iterative prompt engineering. The finalized prompts were used for data extraction in the test set. Rule-based programming was used for post-processing of the extracted data and ascertaining prognosis via Dynamic International Prognostic Scoring System (DIPSS), DIPSS-plus (DIPSS+), and Mutation-enhanced International Prognostic Scoring System-70 (MIPSS-70). Automatically extracted data and prognostic categories were compared with the manually generated human annotations. Mean accuracy, precision, and recall with 95% confidence intervals (CI) were computed. Detailed error and sensitivity analyses were conducted to assess performance. Results The study included 100 patients (development: 15; test: 85) with 100 clinical notes (development: 15; test: 85) and 90 pathology reports (development: 13; test: 77). The mean age of these patients was 70 (SD: 10.6); 55 (55%) patients were men, and 93 (93%) patients were White. In the development set, the mean accuracy, precision, and recall for data extraction from clinical notes were 94% (95% CI: 90-98%), 92% (84-99%), and 99% (97-100%). For data extraction from pathology reports, the mean accuracy, precision, and recall were 96% (93-100%), 98% (92-100%), and 83% (63-100%). For prognostication, the mean accuracy, precision, and recall were 87% (79-94%), 80% (73-87%), and 100% using DIPSS (accuracy: 80%; precision: 73%; recall: 100%), DIPSS+ (accuracy: 93%; precision: 83%; recall: 100%) and MIPSS-70 (accuracy: 87%, precision: 83%; recall: 100%). In the test set, the mean accuracy, precision, and recall for data extraction from clinical notes were 96% (95% CI: 93-98%), 91% (87-96%), and 98% (97-100%). For data extraction from pathology reports, the mean accuracy, precision, and recall were 95% (92-98%), 93% (87-99%), and 90% (80-100%). For prognostication the mean accuracy, precision, and recall were 95% (94-96%), 89% (88-90%) and 99% (96-100%) using DIPSS (accuracy: 94%; precision: 89%; recall: 100%), DIPSS+ (accuracy: 95%; precision: 88%; recall: 95%) and MIPSS-70 (accuracy: 94%, precision: 90%; recall: 100%). Error analyses identified incorrect extractions by GPT-4 were related to the circulating blasts and constitutional symptoms variables from the clinical notes. Sensitivity analyses excluding the associated variables improved overall accuracy from 94% to 97% and 96% to 97% in the development and test sets, respectively. Similarly, incorrect extractions from the pathology reports were related to the CALR gene mutation variables. Sensitivity analyses excluding the associated variables improved the overall accuracy from 96% to 98% and 95% to 97% in the development and test sets, respectively. Conclusion LLMs and rule-based programming can accurately extract data from the electronic health records and prognosticate MF patients via DIPSS, DIPSS+, and MIPSS-70. The automated approach could significantly streamline clinical and research workflows, providing an efficient and less resource-intensive alternative to manual prognostication.

4591 Background: EV-302 and CheckMate 901 demonstrated significant survival benefit with enfortumab vedotin-pembrolizumab (EVPembro) and gemcitabine-cisplatin-nivolumab (GemCisNivo) as 1L therapy, respectively, in patients (pts) with la/mUC. However, whether these treatments are cost-effective or not remains unclear. Methods: A three-state Markov model (progression-free, progression and death) was developed. State utilities were derived from published literature. State transition probabilities were informed from point-probabilities and hazard ratios for OS and PFS obtained from the latest follow-up of eligible trials. Average sales price (2024 USD) for individual treatments (excluding ancillary charges) were obtained from the center of medicare and medicaid services (US payer’s perspective). Treatment strategies were modeled in accordance with the dose/schedule reported in the eligible trials. Pts who received GemCis or Gem-carboplatin (GemCarbo) and did not progress were modeled to receive Avelumab maintenance. Half-cycle corrected costs and utilities were accrued over a 10-year lifetime horizon and were discounted at 3%. Monte Carlo simulation was used to estimate quality adjusted life years (QALYs) and incremental cost-effectiveness ratios (ICERs). A willingness-to-pay threshold (WTP) of 100,000$ per QALY was used. Results: In cisplatin-eligible pts, GemCisNivo demonstrated a 0.8 QALY gain over GemCis at an ICER of 21,127 ($/QALY). GemCisNivo was also associated with an incremental net monetary benefit (INMB) of 63,099$ relative to GemCis. EVPembro demonstrated a 2.4 QALY gain over GemCis at an ICER of 915,283 and a 1.6 QALY gain over GemCisNivo at an ICER of 1,406,308. In cisplatin-ineligible pts, EVPembro demonstrated a 2.4 QALY gain over Gem-carboplatin (GemCarbo) at an ICER of 934,761. Price threshold analyses demonstrated that EVPembro is likely to be cost-effective at a cost of 12,344$ for cisplatin-eligible and 13,374$ per cycle for cisplatin-ineligible pts (original cost: 35,665.60$ per cycle). Sensitivity analysis using a total of 12 cycles for EVPembro showed cost-effectiveness at an ICER of 69,594 relative to GemCisNivo in cisplatin-eligible pts, and at an ICER of 53,099 relative to GemCarbo in cisplatin-ineligible pts. Conclusions: In terms of US payer’s perspective, GemCisNivo is most likely to be cost-effective in cisplatin-eligible pts at the WTP of 100,000 $/QALY. Limiting the number of cycles for EVPembro may render it as a cost-effective 1L treatment in la/mUC pts regardless of cisplatin-eligibility status.

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.

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.

6582 Background: Although high symptom burden indicates poor survival and informs treatment decisions, little is known about the impact of demographic, clinical, and laboratory features on total symptom score (TSS) in patients with myeloproliferative neoplasms (MPN). Methods: Patients with MPN (polycythemia vera (PV), essential thrombocythemia (ET), and myelofibrosis (MF)) were identified from the retrospective chart review. TSS, individual symptom scores (fatigue, early satiety, abdominal discomfort, inactivity, concentration problems, fever, night sweats, itching, bone pain, weight loss), demographic characteristics (race, ethnicity, age, gender), clinical features (time since diagnosis, depression status, obesity status, spleen size), laboratory results and season at the time of visit were recorded from the clinical encounter when index assessment of TSS was performed for each patient. Normality was assessed using visual inspection of data distribution, whereas multicollinearity was assessed using various inflation factors. A univariable regression followed by a multivariable regression analysis was conducted using a backward selection approach. A p-value <0.05 indicated a statistically significant association of a given feature with TSS. Results: The chart review identified 252 patients (PV: 78; ET: 81; MF: 93). Mean age was 59 (SD: 17.7), 67 (SD: 13.0), and 68 (SD: 10.9) years for ET, PV, and MF respectively. Most patients were white (PV, MF: 92%; ET: 83%) and females (ET: 75%; PV: 60%; MF: 53%). The TSS of patients was highest with PV (mean: 18.5; SD: 16.9) followed by MF (mean: 18.1; SD: 15.4) and ET (mean: 14.3; SD: 15.9). Fatigue was the most reported symptom whereas the least reported symptoms were fever and weight loss. Univariable regression analyses showed depression (B: 17.7; p=0.02), female gender (B: 10.6; p=0.01), platelet count (B: 0.03; p=0.03), and hemoglobin (Hb) (B: -2.6; p=0.01) in PV patients, depression (B: 19.8, p=2x10-5) in ET patients and depression (B: 11.0, p=0.03), white blood cell (WBC) count (B: 0.2; p=0.01), neutrophil count (B: 0.3, p=0.01), and non-neutrophil WBC count (B: 0.6; p=0.02) in MF patients to have significant association with TSS. Multivariable regression analyses ( Table) showed Hb (B: -2.5; p=0.01) and platelet count (B: 0.02; p=0.03) in PV patients, depression (B: 19.7; p=2x10-5) in ET patients and depression (B: 12.3, p=0.01) and WBC count (B: 0.3; p=0.002) in MF patients to have a significant association with TSS. Conclusions: Depression in ET and MF and low Hb in PV were identified as significant drivers of symptom burden. Identifying and managing patients with these comorbidities could improve their quality of life with a potential survival benefit. [Table: see text]

12079 Background: Many troublesome symptoms experienced by men with prostate cancer are not specific to a particular disease state or treatment modality. As such, these issues' scope is neither fully appreciated nor characterized in the reporting of clinical trial data. A modern accounting of poorly controlled symptoms is needed to better inform survivorship and supportive care efforts. Methods: Data are drawn from the Enhanced, EHR-facilitated Cancer Symptom Control (E2C2) study, a cluster-randomized stepped wedge trial examining the effectiveness of routine symptom assessment and management on patient-centered outcomes and healthcare utilization (NCT03892967). This is a population-level study supporting patients with cancer, who are cared for in medical oncology clinics across the Midwest United States. The trial utilizes an automated symptom monitoring system with EHR-enabled algorithms for symptom triage and intervention. The current analysis focuses on pre-intervention surveys. The cohort includes E2C2 participants with prostate cancer (as determined by the Mayo Clinic Cancer Registry) receiving curative or palliative-intent therapies, along with prostate cancer survivors who previously completed their treatment. The prevalence of SPPADE symptoms (sleep disturbance, pain, impaired physical function, anxiety, depression, fatigue [low energy]) was assessed using 0 to 10 numerical rating scales (NRS), with scores of 4-6 indicating moderate symptoms and 7-10 indicating severe symptoms. Results: A total of 1,388 men with prostate cancer completed a pre-intervention survey between March 2019 and January 2023. The median age at completion of first survey was 74 years. The cohort includes patients living in diverse settings, including urban (n=718, 52%), micropolitan (n=250, 18%), small town (n=218, 16%), and rural (n=200, 14%) environments. The proportion of patients experiencing moderate-to-severe symptoms (NRS score ≥4) was 47% for fatigue, 40% for impaired physical function, 32% for sleep disturbance, 27% for pain, 22% for anxiety, and 20% for depression/emotional distress. The three domains with the highest prevalence of severe symptoms (NRS score ≥7) were fatigue (17%), impaired physical function (12%), and sleep disturbance (10%). Conclusions: Proactive symptom monitoring identifies a high overall burden of moderate-to-severe symptoms, particularly fatigue and impaired physical function, among prostate cancer patients and survivors. Collaborative care approaches and symptom control trials are needed to mitigate and manage these burdensome symptoms. Clinical trial information: NCT03892967 . [Table: see text]

e19534 Background: Immunomodulatory drugs, proteasome inhibitors plus dexamethasone are the standard triplet induction regimen for TENDMM. We analyzed the efficacy of adding either anti-CD38 monoclonal antibodies (mAbs) or anti-SLAMF7 antibody to the triplet therapy. Methods: Ovid MEDLINE, EMBASE and published abstracts were systematically searched from each database’s inception through 01-31-2024 to identify II/III trials assessing quadruplet therapy versus triplet therapy in TENDMM patients. Hazard ratios (HR) for progression-free survival (PFS) and overall survival (OS), odds ratios (OR) for overall response rates (ORR), measurable residual disease (MRD) negativity rate (with sensitivity of 10 -5) and grade 3 or higher treatment-related adverse events (G≥3 TRAE) were summarized using random effects meta-analysis via inverse variance approach. Additional subgroup analyses based on the cytogenetic risk were performed. P-value of interaction (Pint ) < 0.1 was pre-specified as statistically significant for the difference between the two groups. Results: 3806 studies were screened and seven trials were included with a total of 3546 patients. Four trials (2303 patients) assessed the addition of anti-CD-38 mAbs and three (1243 patients) assessed anti-SLAMF7 mAb, elotuzumab to the triplet regimen. Quadruplet therapy significantly improved PFS as compared to triplet (HR: 0.63; 95% CI: 0.43-0.92), and the PFS benefit was also observed in standard risk group (HR 0.5; 95% CI 0.2-0.9); however, results were insignificant for high-risk group (HR 0.8; 95% CI 0.62-1.93). No significant OS benefit was observed (HR: 0.79; 95% CI 0.49–1.26); however, data for three trials (CASSIOPEIA, DSMM XVII, PERSEUS) is immature, with longer-term follow-up ongoing. Quadruplet therapy showed significant results for very good partial response or better (OR: 1.7; 95% CI 1.2-2.36), stringent complete response (OR: 1.74; 95% CI 1.27-2.07) and complete response or better (OR: 1.91; 95% CI 1.19-3.06). MRD rate shows benefit with quadruplet therapy (OR: 2.59; 95% CI 2.22-3.01). Improved MRD rate was also observed for high-risk (OR 2.13; 95% CI 1.4-3.2) and standard-risk group (OR 2.58; 95% CI 1.68-3.94). Secondary analysis for efficacy of anti-CD38 mAbs showed benefit for PFS (HR: 0.44; 95% CI: 0.35-0.56), ORR (OR: 1.77;95% CI: 1.02-3.06), and MRD (OR: 2.98; 95% CI 1.8-4.8). G ≥3 TRAE did not significantly differ between the groups (OR: 1.05; 95% CI 0.97-1.12). However, quadruplet therapy showed significant risk of infection (OR: 1.2; 95% 1.05-1.35) and thrombocytopenia (OR: 1.41; 95% CI 1.24-1.59). Conclusions: Quadruplet therapy improved PFS, ORR, and MRD rates compared to triplet regimen and has demonstrated promising benefits to be considered the new standard of care for TENDMM patients.