Riaz, Irbaz Bin

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.

e17059 Background: It is unclear if patients with different HRR gene-mutated mCRPC derive consistent benefit from treatment with PARPi and API combination. Approval labels for PARPi+API are inconsistent across regulatory bodies in the US (Food and Drug Administration, FDA) and Europe (European Medical Agency, EMA), adding to confusion among practicing clinicians. Therefore, we aimed to assess the differential efficacy of PARP+API therapy by different genes using up-to-date trial evidence. Methods: We maintain a living meta-analysis on mCRPC treatments that is updated when new data becomes available. Here, we report data from phase III randomized control trials (RCTs) assessing PARPi+API in 1L mCRPC. The outcomes of interest included radiographic progression-free survival (rPFS) and overall survival (OS). Hazard ratios (HR) for rPFS and OS by different genes were summarized using random effects meta-analysis via inverse variance approach. Results: We have screened 27376 references since the inception of this living review. Here, we report data from three trials (15 references) with a total of 1618 patients. Frequently reported HRR gene mutations in the included trials were BRCA2 (35%), ATM (21%), CDK12 (12.2%), CHEK2 (12.2%), BRCA1 (4.6%), and PALB2 (3.4%). In terms of rPFS, statistical benefit was observed in BRCA (0.28; 95% CI: 0.13-0.59) and CDK12 (0.58; 95% CI: 0.35-0.95) subgroups, whereas no statistically significant benefit was seen in PALB2 (0.53; 95% CI: 0.21-1.32), ATM (0.93; 95% CI, 0.57-1.53), and CHEK2 (0.92; 95% CI: 0.53-1.61) subgroups. For OS, benefit was observed in BRCA (0.47; 95%CI: 0.31-0.71) subgroup. While not statistically significant, a signal of meaningful OS benefit was observed in PALB2 (0.33; 95%CI: 0.10-1.16) but no OS benefit was observed in ATM (0.97; 95% CI: 0.57-1.67), CDK12 (0.80; 95% CI: 0.36-1.78), and CHEK2 (0.81; 95% CI: 0.37-1.75) subgroups. Conclusions: Current results show that mCRPC patients with BRCA and CDK12 alterations had delayed disease progression with PARPi+API as 1L therapy. BRCA mutations are also associated with improved OS, while patients with CHEK2 and ATM derived no significant benefit with PARPi+API combination. [Table: see text]

e16619 Background: Should patients (pts) with MIBC receive aIO given the DFS benefit seen in AMBASSADOR and CheckMate 274 trials is an important clinical question. Thus, we aimed to quantify the magnitude of absolute benefit across different subgroups to aid clinicians in shared decision-making with their patients. Methods: Phase III trials evaluating aIO as compared to observation or placebo (obs/plac) in pts with MIBC were considered eligible. Outcomes of interest included DFS and OS. Pre-specified subgroups included disease location, PDL1 status, neoadjuvant chemotherapy (NAC) receipt, and tumor and nodal (TN) staging. Pairwise comparisons were made using a DerSimonian-Lairds (DL) random-effects meta-analysis. Stratified baseline risks (obtained from clinical trials) and relative effect estimates were used to estimate corresponding intervention risks (CIRs). The difference in CIRs and baseline risk was calculated to present absolute risk differences (ARDs) in each subgroup category. Results: Analysis included three trials with 2220 pts (pembrolizumab, nivolumab, and atezolizumab assessed in each). No significant difference was observed in OS with aIO compared to obs/plac (hazard ratio: 0.91; 0.76-1.09). However, aIO significantly improved DFS compared to obs/plac (0.76; 0.64-0.91) in the overall population. In pts with bladder/lower tract disease, aIO improved DFS compared to obs/plac (0.72; 0.56-0.92); however, no significant difference was observed in DFS in pts with upper tract disease. There was a statistically significant effect modification by disease location (p: 0.01). Adjuvant IO significantly improved DFS in PDL1- (0.78; 0.64-0.94) but not in PDL1+ pts (0.75; 0.52-1.08). Likewise, aIO significantly improved DFS in pts who received NAC (0.69; 0.53-0.90) but not in pts who did not receive NAC (0.89; 0.75-1.05). In terms of TN staging, aIO improved DFS in T2-3N0 (0.73; 0.57-0.94), T4N0 (0.76; 0.61-0.95), and in T2-4N1 patients (0.75; 0.59-0.95). No statistically significant modifications were observed among these subgroups. Stratified ARDs for DFS by each subgroup are outlined (Table). Conclusions: When delayed disease progression is a vital consideration, a risk-adapted strategy for the optimal patient selection to receive aIO should be considered. Disease location (lower tract/bladder), node positivity, and receipt of NAC emerged as groups likely to derive greater benefit with aIO. [Table: see text]

e16579 Background: Recent results from EV 302 and CheckMate 901 have transformed the treatment landscape of untreated locally advanced/metastatic urothelial cancer (Ia/mUC). Here, we present the most up-to-date evidence of comparative efficacy and safety of first line (1L) treatment options in Ia/mUC with emphasis on Enfortumab vedotin (EV)-pembrolizumab (P) and gemcitabine (Gem)-cisplatin (Cis)-nivolumab (N). Methods: This living systematic review used the living interactive evidence (LIvE) synthesis framework. Full-text and abstract publications of phase II/III randomized clinical trials comparing 1L immunotherapy combined with chemotherapy (IO+C) or immunotherapy alone (IO) regimens with platinum-based chemotherapy (Plat) in Ia/mUC were included. Outcomes of interest included overall survival (OS), progression-free survival (PFS) and treatment-related grade 3 or greater adverse events (≥G3 TRAE). Mixed treatment comparisons were made using network meta-analysis. P-scores were computed to assess relative treatment rankings. Results: A total of 5 trials (8 references) with 4,749 patients and 9 unique treatments were included. EV-P and Gem-Cis-N were ranked first and second in terms of OS and PFS improvement. EV-P improved OS compared to Gem-Cis-N (HR: 0.60; 0.45-0.81), Gem-Plat-atezolizumab(A) (0.55; 0.42-0.72), durvalumab(D)- tremelimumab(T) (0.55; 0.42-0.72), Gem-Plat-P (0.55; 0.42-0.72), P (0.51; 0.39-0.68), D (0.47; 0.36-0.63), A (0.48; 0.36-0.63) and Gem-Plat (0.47; 0.38-0.58). EV-P also improved PFS compared to Gem-Cis-N (0.63; 0.48-0.82), Gem-Plat-A (0.55; 0.43-0.69), D-T (0.42; 0.33-0.53), Gem-Plat-P (0.58; 0.45-0.74), P (0.34; 0.26-0.44), D (0.35; 0.27-0.44) and Gem-Plat (0.45; 0.38- 0.54). For ≥G3 TRAE, A was ranked first with the lowest adverse events compared to others. EV-P (rank 5) had significantly fewer events compared to Gem-Plat, Gem-Plat-A, Gem-Cis-N and Gem-Plat-P. Gem-Cis-N was ranked last with significantly greater events compared to A, P, D, D-T, Gem-Plat, EV-P, and Gem-Plat-A (Table with ranks [p-scores]). Conclusions: Current evidence suggests that patients with untreated la/mUC experience improved survival and slower disease progression with EV-P when compared to other contemporary options. Gem-Cis-N may be a reasonable alternative in patients who are cisplatin-eligible. Cost and access to these treatments are crucial and should be considered to facilitate shared decision making. [Table: see text]

11079 Background: Phase 3 randomized clinical trials (RCTs) inform the standard of care in cancer treatment yet have a mixed track record of success. In this study, we sought to 1) determine the overall success rate of phase 3 RCTs; 2) assess whether authorship patterns differ between successful (positive) and unsuccessful (negative) trials; and 3) explore if individual authors have distinct track records of success. Methods: The HemOnc knowledgebase (KB) was queried for initial publications of phase 3 RCTs of systemic anticancer therapy; those without primary outcome described were excluded. RCTs were labeled positive if the primary endpoint was met per HemOnc standard criteria (P value ≤ 0.05 or hazard ratio upper bound ≤ 1 for any experimental arm[s]) and negative if they did not meet these criteria or if the experimental arm was statistically inferior. Trial metadata and author information were extracted from the HemOnc KB. Author positive publication rate (PPR) was defined as % of publications reporting a positive result divided by total number of eligible publications. Prolific authors were defined as those with 6+ eligible publications. Author count for manuscripts reporting positive vs negative results was evaluated with the Mann-Whitney U test; other statistics were descriptive. Results: 3328 studies met criteria as of February 5, 2024; these were associated with 3305 initial publications between 1960-2023, involving 25,412 unique authors. 1474 (44.3%) of the studies were positive. Publications reporting positive trial results had, on average, 3 more authors, median (interquartile range) of 18 (12-23) vs 15 (11-20), p<0.0001. The mean (±SD) PPR for n=1967 prolific authors was 47.7% (±22.3%). The PPR was not related to individual author output; mean PPR for non-prolific authors was 48.0% (SD not reported). Differences in PPR were apparent by the disease under study and the years of publication (Table). Among the 46 prolific authors who had a PPR 2+ SD above the mean, 26 (55%) were from China followed by Italy with 3 (6%). Conclusions: Despite known positive publication bias, the success rate of published phase 3 RCTs remains <50%. Publications reporting positive results had significantly more authors; this finding requires further study including adjustment for trial sample size. Significant variation in PPR amongst fields could be due to watershed therapeutic advances, differential selection of primary endpoints, and/or selective reporting of results. Finally, a signal for geographic association of outlier PPRs suggests the need for further investigation. [Table: see text]