Citations

Abstract Artificial intelligence (AI) holds significant potential to enhance various aspects of oncology, spanning the cancer care continuum. This review provides an overview of current and emerging AI applications, from risk assessment and early detection to treatment and supportive care. AI‐driven tools are being developed to integrate diverse data sources, including multi‐omics and electronic health records, to improve cancer risk stratification and personalize prevention strategies. In screening and diagnosis, AI algorithms show promise in augmenting the accuracy and efficiency of medical image analysis and histopathology interpretation. AI also offers opportunities to refine treatment planning, optimize radiation therapy, and personalize systemic therapy selection. Furthermore, AI is explored for its potential to improve survivorship care by tailoring interventions and to enhance end‐of‐life care through improved symptom management and prognostic modeling. Beyond care delivery, AI augments clinical workflows, streamlines the dissemination of up‐to‐date evidence, and captures critical patient‐reported outcomes for clinical decision support and outcomes assessment. However, the successful integration of AI into clinical practice requires addressing key challenges, including rigorous validation of algorithms, ensuring data privacy and security, and mitigating potential biases. Effective implementation necessitates interdisciplinary collaboration and comprehensive education for health care professionals. The synergistic interaction between AI and clinical expertise is crucial for realizing the potential of AI to contribute to personalized and effective cancer care. This review highlights the current state of AI in oncology and underscores the importance of responsible development and implementation.

5108 Background: We previously proposed a hybrid framework combining LLMs and rule based algorithm (RBA) for automating risk stratification in locPCa. Herein, we aim to validate the risk stratification agent (RSA) in a prospective cohort, develop & evaluate the treatment recommendation agent (TRA) based on NCCN guidelines, and develop an interactive interface to facilitate clinicians for accurate risk stratification and treatment recommendations at the point of care. Methods: This study included pts with locPCa (2004-2024) presenting at Mayo Clinic with at least 1 positive prostate biopsy and MRI report available. For RSA prospective validation, GPT4 extracted key phenotypic variables (PSA, T stage, prostate volume, number of cores, Gleason patterns, grade group) from unstructured MRI and biopsy reports using a zeroshot prompt. An RBA then classified pts into NCCN risk groups. The agent performance was compared with the treating clinician's documentation and evaluated against gold-standard labels manually annotated by two independent clinicians. For development of TRA, two experiments were performed using GPT4 with and without retrieval-augmented generation (RAG) to generate treatment plans. Generated treatment plans were evaluated using NCCN guideline-informed treatment decision tree based algorithm (DTA). Evaluation metrics, weighted – accuracy (acc) and F1, were computed. A clinician facing interface (lisr.org/risk) was developed to provide accurate risk stratification and treatment plans. Results: A total of 858 pts were included (500 for prospective validation, 358 for treatment recommendations). Prospective validation for RSA demonstrated a higher F1 score of 0.89 compared to the treating clinician (F1: 0.58). Treatment recommendation experiments showed that GPT4 with RAG achieved higher acc (64% full – all correct treatment options and 36% partial – at least 1 correct treatment option) compared to GPT4 alone (35% and 65%, respectively). Sensitivity analysis using DTA-informed GPT4 note generation achieved 94% full acc and 6% partial acc. GPT4 alone generated hallucinated treatment options in 71% of cases, while GPT4 with RAG reduced this to 32%. Conclusions: The multi-agent framework based on LLM and RBA achieves high accuracy in risk stratification and treatment recommendation for locPCa. A multi-agent framework with an interactive interface holds high promise to enable efficient, accurate decision-making, and improve locPCa management at the point of care Performance evaluation. RSA(acc; F1) % TRA(full acc; partial acc) % Clinician RSA* GPT4 RAG+GPT4 DTA+GPT4 Overall 60; 58 89; 89 29; 71 64; 36 94; 6 V. High 94; 33 96; 96 55; 45 69; 31 93; 7 High 46; 52 92; 89 - - - Int. Unf 80; 61 91; 90 5; 95 38; 62 93; 7 Int. Fav 57; 64 84; 87 0; 100 98; 2 100; 0 Low 57; 65 87; 80 100; 0 95; 5 90; 10 V. Low 8; 13 50; 67^ - - - *Statistical significance (p<0.01) except for ^V. Low.

e17155 Background: Localized PCa is classified into six NCCN risk categories, each with specific treatment recommendations. However, treatment concordance across these categories and its variation by race in clinical practice remain unexplored. Therefore, we aim to describe patterns of guideline-concordant treatment receipt for localized PCa across risk groups and race. Methods: Surveillance, Epidemiology, and End Results (SEER) database (2018–2020) was queried to identify patients with localized PCa from 17 registries. Data regarding clinical T stage, Gleason patterns, PSA level, biopsy cores and initial treatment received (radiation, radical prostatectomy, systemic therapy) was obtained. Each patient was then stratified into one of six NCCN-based risk categories using a rule-based algorithm. Using the stratified risk category of each patient, treatment recommendations were obtained from the NCCN guidelines. Concordance was assessed by comparing the received treatment and guideline-recommended treatment. A focused bi-variate logistic regression analysis was performed adjusting for risk groups, and race. Odds ratio (OR) with 95% confidence intervals (CI) were computed to quantify the magnitude of guideline-discordant treatment receipt. Results: This analysis included 69,584 patients with localized PCa (White: 53,875 [77.4%]; Black: 11,265 [16.2%]; Asian/Pacific Islander: 4,133 [5.9%]; American Indian/Alaska Native: 311 0.5%]). In terms of risk groups, patients in higher risk categories had increased odds of discordance compared to the lower risk groups (Table). In terms of race, Black men had significantly higher odds of receiving guideline-discordant therapy (OR:1.67, 95% CI:1.58–1.76), while no statistically significant difference was observed with American Indian/Alaska Native men (OR:1.21, 95% CI: 0.92–1.61) and Asian/Pacific Islander men (OR:1.05, 95% CI:0.97–1.14) compared to White men. Conclusions: Population level data, based on limited sample size in some groups, suggests that treatment concordance with NCCN guidelines varies by risk group and race in localized PCa. Black patients, and those with higher risk groups were more likely to receive guideline-discordant therapy. These findings highlight the need for strategies and prospective studies at individual patient level to evaluate and enhance guideline adherence to promote personalized care for these patients. Variable Discordant Treatment (%) OR (95% CI) White 22168 (41.1) REF Black 5653 (47.5) 1.67 (1.58-1.76) * Asian/Pacific Islander 1822 (44.1) 1.05 (0.97-1.14) American Indian/Alaska Native 191 (61.4) 1.22 (0.92- 1.61) Very Low Risk 770 (9) REF Low Risk 146 (2) 0.16 (0.13-0.19) * Favorable Risk 35 (0.4) 0.03 (0.02-0.04) * Unfavorable Risk 15125 (83.2) 36.30 (33.72-39.25) * High/Very High Risk 13441 (50) 7.32 (6.83-7.86) * * p <0.05.

e13551 Background: The landscape of cancer care is evolving rapidly. In 2023, there were 83 new FDA oncology approvals, and 145 interventional treatment (tx) studies opened across Mayo Clinic. We are exploring novel precision outreach approaches utilizing an LLM-integrated chatbot application to “ping” patients (pts) with specific clinical or genomic characteristics about newly available drugs or clinical trials. As novel outreach methods are considered, we surveyed a patient (pt) support group to understand communication preferences and awareness of adjuvant CDK4/6 inhibitors in tx of breast cancer (BC). Methods: On October 7, 2024, we administered a cross-sectional survey to 41 members of the Breast Cancer Support Network (BCSN) in Rochester, MN as a part of an IRB exempt quality improvement project. Leaders of the BCSN shared information to all participants, noting that the survey’s intent was to represent the pt voice and participation was voluntary. Anonymous survey data were collected with descriptive results reported. Results: 34/41 participants in the BCSN shared demographic information. The majority (83%) were > 50 years of age. 47% reported stage I BC, 12% stage II, 6% stage III, and 35% were unsure of their stage. Most participants (26/34, 76%) reported that their BC was hormone receptor positive (HR+), 2 HER-2 positive, and 6 unsure of their BC receptor status. 24/26 (92%) of pts with HR+ BC were taking or planning to start adjuvant endocrine therapy. 10/34 (29%) were aware that adjuvant CDK 4/6 inhibitors were approved. Of these 10 participants, 2 learned of it from online health resources, 6 from their clinician, 1 from social media, and 1 from a support group. The majority (66%) were unsure if they qualified for these drugs. Regarding receipt of information on new drug approvals/tx options, the highest ranked communication preference was to receive this information in person at an upcoming visit (ranked 1st by 50% of participants), followed by EHR-based message (28%), email (10%), phone call (6%), letter (4%), or an interactive chatbot (0%). Most respondents noted that communication from their healthcare provider was very important (30/37, 81%), with others stating that this was somewhat important (5/37, 14%) or neutral (2/37, 5%). Most noted that receiving information about tx options via video or visual aid before a visit would be very helpful (33/41, 80%). When asked about follow up after learning of a new drug approval, 17/32 (53%) of pts preferred an appointment, 9/32 (28%) a phone call, 3/32 (9%) a decision aid tool, and 3/32 (9%) a questionnaire to assess their understanding. Conclusions: Providing personalized, timely communication about new FDA approved tx options is important for BC survivors and critical for improving care delivery. While technological solutions could broaden awareness, this requires balancing pt preferences for direct communication with clinicians.

5076 Background: C and Lu are both life-prolonging therapies for pts with mCRPC after progression on androgen receptor pathway inhibitor (ARPI) and docetaxel (D). Herein, our objective was to assess the comparative effectiveness of C vs. Lu in pts with mCRPC with prior progression on D and ARPI in real-world pts in the USA. Methods: A de-identified nationwide Flatiron Health electronic health record (EHR)-derived database was used to extract pt-level data. Eligibility: pts with mCRPC who progressed on D and ARPI and received for the first-time single-agent C or Lu. Pts who received alternative Lu or C in a later line of therapy (LOT) were excluded. Endpoints: real-world time to next therapy (rwTTNT) and real-world overall survival (rwOS). These were summarized via Kaplan-Meier survival estimates with a 95% confidence interval (CI) and compared in the context of propensity score (PS) matching weighted analysis using the Cox proportional hazard model. PS model included the following covariates: age, race-ethnicity, socioeconomic status, treatment year, Gleason score, ECOG performance status, log2PSA, alkaline phosphatase, hemoglobin, creatinine, LOT, insurance, and practice type. All the covariates achieved balance after PS matching weighting. Results: Among 24,105 pts with metastatic prostate cancer in the dataset, 1,445 met the eligibility criteria and were included (1,227 treated with C, 218 treated with Lu). In C cohort: median age was 73 (IQR 67 – 78), 66.9% had Gleason score ≥ 8, and median LOT was 4 (IQR 3-4). In Lu cohort: median age was 75 (IQR 67.25 – 80), 68.1% had Gleason score ≥ 8, and median LOT was 4 (IQR 3-5). In PS matching weighting analysis, there was evidence that pts receiving Lu had a significantly improved rwTTNT (median 8.3 months [mo], 95% CI 6.3 – 9.8) compared to those receiving C (median 4.7 mo, 95% CI 4.2 – 5.4) (HR 0.49, 95% CI 0.37 – 0.63, p < 0.001), which persisted after adjusting for covariates (HR 0.43, 95% CI 0.32 – 0.57, p < 0.001). Additionally, there was evidence that rwOS was longer in pts receiving Lu (median 10.3, 95% CI 8.9 – 12.8) compared to those receiving C (median 8.8 mo, 95% CI 6.6 – 10.8) (HR 0.69, 95% CI 0.51 – 0.92, p = 0.01). This improvement in rwOS with Lu compared to C persisted after adjusting for covariates (HR 0.61, 95% CI 0.44 – 0.84, p < 0.001). Conclusions: This is the largest real-world data to date assessing the comparative effectiveness of Lu vs. C, and it showed significantly longer rwTTNT and rwOS with Lu compared to C in pts with mCRPC pretreated with ARPI and D. Limitations: retrospective nature, selection bias, missingness, lack of randomization, etc. and residual confounding in real-world datasets. Upon external validation, these findings could guide treatment selection in the clinic and design of clinical trials.

e16157 Background: The optimal treatment for resectable gastric/GEJ AC is unclear due to limited direct comparisons among chemotherapy and chemoradiation regimens. Addressing this gap is critical to improve patient outcomes. We performed this network meta-analysis (NMA) to determine the relative efficacy of multimodality regimens for resectable gastric/GEJ AC. Methods: MEDLINE, EMBASE, Scopus, and CENTRAL were searched from inception till September 20, 2024, to identify phase III trials evaluating perioperative/neoadjuvant systemic therapy ± radiation in resectable gastric/GEJ AC. Primary outcomes were disease-free survival (DFS) and overall survival (OS). A frequentist NMA was conducted to compare hazard ratios (HR) and 95% CI for each outcome. Relative treatment rankings were assessed using P-scores. Results: Fifteen trials (8,072 patients) were analyzed. pFLOT (perioperative fluorouracil, leucovorin, oxaliplatin, and docetaxel) ranked highest for DFS in the overall population (P score 91%) (Table), significantly outperforming surgery alone (HR 0.48, 95% CI 0.38-0.60) and nCROSS (neoadjuvant paclitaxel, carboplatin and radiotherapy) (0.67, 0.56-0.81). For OS, nPLF+nCRT(EP) (neoadjuvant cisplatin, leucovorin and fluorouracil for induction, followed by etoposide, cisplatin, and radiotherapy) (P score 90%) and pFLOT (P score 87%) were the top regimens, with pFLOT significantly outperforming surgery alone (0.57, 0.45-0.72), and nCROSS (0.73, 0.60-0.89). Adding neoadjuvant chemoradiation to pFLOT provided no additional OS (1.14, 0.76-1.72) or DFS (1.22, 0.83-1.82) benefit. Similarly, adding pembrolizumab to perioperative chemotherapy (CF/CX) (cisplatin and fluorouracil/capecitabine) was not superior to pFLOT in terms of DFS (1.10, 0.71-1.69) or OS (1.09, 0.69-1.72). Conclusions: pFLOT demonstrated superior efficacy in resectable gastric/GEJ AC, surpassing surgery alone, nCROSS, and other perioperative regimens. The additive role of immunotherapy to pFLOT requires further investigation to refine patient selection and optimize outcomes. Regimen Overall Population GEJ cohort OS DFS OS DFS nPLF+nCRT(EP) 1 (90) 2 (84) 1 (85) NA pFLOT 2 (87) 1 (91) 2 (79) 2 (88) pPembrolizumab+CF/CX 3 (75) 3 (81) 5 (41) - nECX 4 (61) 5 (55) - - pCF 5 (59) 8 (51) 3 (72) - pECF/ECX 6 (47) 6 (55) 7 (40) 3 (57) nECX+aCRT(CX) 7 (45) 7 (52) 9 (31) - nCF 8 (42) 10 (28) 6 (40) - nCROSS 9 (41) 9 (35) 8 (34) 4 (43) pECF/ECX/FLOT+nCRT(F/X) 10 (38) 4 (57) 4 (72) 1 (90) nCRT(CF) 11 (9) 11 (6) - 5 (14) Surgery 12 (7) 12 (5) 10 (5) 6 (9) Data reported as rank (P-score). Abbreviations: n: neoadjuvant; p: perioperative; ECX: epirubicin + cisplatin + capecitabine; CF: cisplatin + fluorouracil; ECF: epirubicin + cisplatin + fluorouracil; aCRT(CX): adjuvant radiotherapy + cisplatin + capecitabine; CRT(F/X): fluorouracil/capecitabine + radiotherapy; CRT(CF): cisplatin + fluorouracil + radiotherapy.

8053 Background: In the US, only 27.4% of lung cancer cases are diagnosed early, with 5-year survival rates of 63% for localized and 27% for late-stage cancers. Despite recommendations since 2012 for screening high-risk individuals with low-dose CT, uptake has been limited, and most lung cancer diagnoses occur after symptoms appear. Similarly, chest x-rays, while commonly used as an initial test to investigate patients with symptomatic suspected lung cancer, have demonstrated limited sensitivities between 50-70% and specificities over 80%. Symptoms often overlap with common conditions, making detection challenging, and studies have identified median delays of 187 days from symptom onset to diagnosis. This prolonged interval presents an opportunity for improvement. This study examines the use of the AI cancer prediction platform, C the Signs, to passively screen for lung cancer by leveraging electronic medical records (EMRs) for early lung cancer detection. Methods: Utilizing the Mayo data platform, we conducted a retrospective analysis of EMR data from 894,409 patients, including 7,395 individuals diagnosed with lung cancer. We assessed the sensitivity and specificity of the AI cancer prediction platform, in identifying patients at risk of lung cancer. Additionally, we compared the timing of lung cancer risk identification by the AI cancer prediction platform with the timing of diagnoses made by physicians to determine whether the platform enabled earlier detection. Results: The AI cancer prediction platform detected 6,749 cases of lung cancer among the 7,395 individuals diagnosed, resulting in an early detection sensitivity of 91.5%. The platform identified 423,249 false positives among the 887,014 patients who did not have lung cancer, leading to a specificity of 52.3%. Additionally, it identified the risk of a lung cancer diagnosis in 26.6% of patients up to five years earlier than the diagnoses made by physicians. Conclusions: This study highlights the potential of leveraging EMR data and AI platforms like C the Signs to enhance early lung cancer detection. Chest X-rays, with their reduced sensitivity for early-stage lesions and reliance on symptom-driven use, remain limited as a screening tool. In contrast, the AI platform achieved a sensitivity of 91.5% and identified 26.6% of cases up to five years earlier than traditional diagnoses. These findings underscore the promise of AI-based platforms as supplementary tools for improving early detection and facilitating timely intervention to enhance patient outcomes.

Abstract Background Optimal treatment for resectable gastric and gastroesophageal junction (GEJ) adenocarcinoma remains unclear due to limited head-to-head comparisons among chemotherapy and chemoradiation regimens. This network meta-analysis aimed to determine the relative efficacy of available multimodality treatment regimens in these patients. Methods MEDLINE, EMBASE, Scopus, Web of Science, and CENTRAL were searched till September 20, 2024. Phase 3 randomized trials evaluating perioperative/neoadjuvant systemic therapy ± radiation in resectable gastric/GEJ adenocarcinoma were included. Primary outcomes were disease-free survival (DFS) and overall survival (OS). Frequentist network meta-analysis was conducted to compare hazard ratios (HRs) and 95% confidence intervals (CIs). Results Fifteen trials (8072 patients) were analyzed. pFLOT (perioperative fluorouracil, leucovorin, oxaliplatin, and docetaxel) ranked highest for DFS in the overall population (P-score 91%), achieving significant improvements compared to surgery alone (HR 0.48, 95% CI, 0.38-0.60) and nCROSS (neoadjuvant paclitaxel, carboplatin, and radiotherapy) (0.67, 0.56-0.81). For OS, nPLF + nCRT(EP) (neoadjuvant cisplatin, leucovorin, and fluorouracil for induction, followed by etoposide, cisplatin, and radiotherapy) (P-score 90%) and pFLOT (P-score 87%) were the top regimens. pFLOT significantly outperformed surgery alone (0.57, 0.45-0.72) and nCROSS (0.73, 0.60-0.89). Based on the limited data available, adding neoadjuvant chemoradiation to pFLOT provided no additional OS (1.14, 0.76-1.72) or DFS (1.22, 0.83-1.82) benefit. Similarly, adding pembrolizumab to perioperative chemotherapy (cisplatin and fluorouracil/capecitabine) was not superior to pFLOT (DFS: 1.10, 0.71-1.69; OS: 1.09, 0.69-1.72). Conclusions pFLOT demonstrated superior efficacy in resectable gastric/GEJ adenocarcinoma, outperforming surgery alone, nCROSS, and alternative perioperative regimens. The additive role of immunotherapy requires further investigation to optimize patient selection and outcomes.