Citations

341 Background: Specialist clinicians (oncologists, urologists, radiation oncologists) read through free text reports of prostate MRI and biopsy reports to risk stratify patients during initial consultation of localized PCa. This laborious and time-consuming process is repeated for each day during the clinic. Herein, we propose a hybrid stratification framework utilizing a combination of large language model (LLM) and rule-based programming to automate this process. Methods: This retrospective study included patients with localized PCa (2004-2024) presenting to care at Mayo Clinic with at least one positive prostate biopsy and MRI report available. The state-of-the-art LLM – GPT4 – was utilized using a structured zeroshot prompt to extract relevant phenotypic variables (clinical T stage, Gleason patterns, grade group, extent of specimen/cores involved, prostate size/volume) from unstructured MRI and biopsy reports. Using a rule-based algorithm, the extracted variables and PSA at PCa diagnosis were used to categorize each patient into one of the six NCCN risk categories (very low [VL], low [L], favorable intermediate [FInt], unfavorable intermediate [UFInt], high risk (HR), very high risk [VHR]). Prompts were iteratively developed using ~5% of the total dataset and validated on ~10% of the dataset. Final performance was assessed against a held-out expert annotated test dataset using evaluation metrics (accuracy, F1-score, precision, recall). Additionally, manual annotation was conducted by two independent novice reviewers to compare machine annotated and novice-human annotated risk categorization. Results: A total of 397 patients were included in the evaluation. The median age at diagnosis was 64.8 (IQR: 59.7-68.5); majority of the men were White (n: 366; 92%) and non-Hispanic (n: 371; 94%). The most prevalent risk category was UFint (n: 151; 38%) followed by FInt (n: 80; 20.2%), HR (n: 76; 19.1%), VHR (n: 64; 16.1%), L (21: 5.3%), and VL (n: 5; 1.3%). Novice reviewers achieved an accuracy of 79%, precision of 80%, recall of 82% and a F1-score of 0.81. The hybrid stratification framework using GPT4 achieved an accuracy of 89%, precision of 89%, recall of 88% and a F1-score of 0.88. Among 43 errors by GPT4 risk stratification, majority of the errors were due to miscategorization of clinical T-stage (n: 21; 49%) followed by erroneous number of positive cores (n: 13; 30%). However, additional evaluation showed that GPT4 achieved numerically higher performance (accuracy: 90%; precision: 91%; recall: 90%; F1: 0.90) compared to novice reviewers (accuracy: 77%; precision: 89%; recall: 77%; F1: 0.82) for ascertaining clinical T-stage. Conclusions: Large language models exhibited superior performance than novice clinicians for risk stratification in patients with localized PCa. As the next step, we intend to validate our framework for automated risk classification in a prospective manner.

428 Background: Prostate cancer management presents a significant healthcare burden, with the need to efficiently triage patients for treatment. Our objective is to leverage large language models to predict physician-recommended treatment plans from unstructured clinical notes. By accurately predicting treatment plans, we aim to risk stratify and triage patients effectively, thereby optimizing the allocation of physician resources. Methods: 448 unstructured initial urology consultation patient notes following first positive prostate cancer biopsy were identified. The recommended and final treatments received were manually annotated to establish ground truth labels (Table 1). The dataset was split 80:20 for training and testing, preprocessed to remove plan sections and formatted into question-answer (QA) format. A domain-specific large language model (LLM) inspired by GPT and a specialized tokenizer (PCa- LLM) for prostate cancer terminology were developed. QA models were built using the PCa-LLM and compared with those using GPT-2 as the backbone to predict recommended and final treatments. Results: For the physician-recommended treatment plans, our LLM (PCa-LLM) showed superior performance with higher AUROC scores for curative vs. non-curative treatments (0.78 vs. 0.65), chemo-hormonal vs. other non-curative treatments (0.89 vs. 0.65), and surveillance vs. all other treatments (0.72 vs. 0.70), while both models achieved the same high AUROC of 0.99 for chemo-hormonal vs. all other treatments. For final treatments, PCa-LLM demonstrated better AUROC for curative vs. non-curative treatments (0.77 vs. 0.74) and chemo-hormonal vs. other non-curative treatments (0.71 vs. 0.66), while GPT2 outperformed PCa-LLM for surveillance vs. all other treatments (0.78 vs. 0.70). Both models achieved an AUROC of 0.99 for chemo-hormonal vs. all other treatments. Conclusions: PCa-LLM accurately predicted most treatment categories better than GPT2, with higher AUROC scores, and can be utilized to triage prostate cancer patients using initial consultation notes. Task Physician -Recommended Treatment Plan Final Treatment Received Curative Prostatectomy/Radiation 228 230 Non-Curative Focal Therapy 22 15 Active Surveillance 40 45 Chemo-hormonal 30 30 Model Predictions (AUROC) GPT2 PCa-LLM GPT2 PCa-LLM Curative vs. non-curative 0.65 0.78 0.74 0.77 Chemohormonal vs. other non-curative 0.65 0.89 0.66 0.71 Chemohormonal vs. all other 0.99 0.99 0.99 0.99 Surveillance vs. other non-curative 0.64 0.60 0.67 0.59 Surveillance vs. all other 0.70 0.72 0.78 0.70

Abstract The study of prehabilitation and rehabilitation ([p]rehabilitation) to alleviate the sequelae of bladder cancer and its treatment has generated numerous opportunities to improve the quality of life of bladder cancer survivors. The authors conducted a scoping review of randomized clinical trials (RCTs) to identify knowledge gaps in and research directions for (p)rehabilitative support for those affected by bladder cancer. The authors systematically searched six databases and synthesized key findings from RCTs conducted from January 1, 2004, through March 15, 2022, that enrolled participants with bladder cancer, survivors, or caregivers in outpatient (p)rehabilitative programs (e.g., exercise, nutrition, or psychological support). Outcomes were characterized according to eight prespecified, clinically relevant categories. The search retrieved 10,968 records, 27 of which met the inclusion criteria, and 24 described unique RCTs with 2471 enrolled participants. Of 24 interventions, 17 (71%) yielded statistically significant results for the outcome of interest. Only one RCT included a cost‐effectiveness analysis, and only two characterized the efficacy of interventions for caregivers. Of 11 RCTs involving psychological support, eight yielded statistically significant results, as did nine of 11 RCTs with physical exercise interventions, three of four RCTs with educational interventions, three of four RCTs with nutritional support interventions, one of two RCTs with pharmacologic medications, and zero of one RCT with physical therapy. The most promising interventions for inclusion in multimodal, personalized (p)rehabilitation programs included exercise, stress management training, cognitive training, smoking and alcohol cessation counseling, immunonutrition, stoma education, and penile rehabilitation. Further studies of the cost effectiveness and efficacy for caregivers of such interventions are needed. Plain Language Summary In a scoping review of all randomized clinical trials involving prehabilitative or rehabilitative diet, exercise, and psychological support interventions for patients with bladder cancer, survivors, and their caregivers, 17 of 24 (71%) interventions yielded statistically significant improvements in the outcome of interest. Clinicians should consider implementing such interventions for those affected by bladder cancer.

Acute kidney injury (AKI) is a commonly encountered medical problem that is associated with poor health outcomes in AKI survivors, including increased mortality and re-admission to the hospital. Despite their high-risk status, only a small fraction (< 10%) of patients receive specialized nephrologist follow-up after AKI event. To address the gap in care for AKI patients, this work proposes an artificial intelligence (AI) based fusion technique that combines patient's single-lead electrocardiograph (ECG) and demographics to predict AKI recurrence 3-7 days before onset. The ECG data is analyzed with an on-chip reservoir-computer (RC) prototyped in 28nm CMOS process to create a compressed representation for predicting AKI onset from ECG. After fusion with demographics, the proposed technique is able to predict AKI recurrence 3-7 days before onset with 75.8% accuracy when evaluated on a retrospective patient dataset collected from Mayo Clinic Enterprise.

Background: Differential kidney function assessment is an important part of preoperative evaluation of various urological interventions. It is obtained through dedicated nuclear medical imaging and is not yet implemented through conventional Imaging. Objective: We assess if differential kidney function can be obtained through evaluation of contrast-enhanced computed tomography(CT) using a combination of deep learning and (2D and 3D) radiomic features. Methods: All patients who underwent kidney nuclear scanning at Mayo Clinic sites between 2018–2022 were collected. CT scans of the kidneys were obtained within a 3-month interval before or after the nuclear scans were extracted. Patients who underwent a urological or radiological intervention within this time frame were excluded. A segmentation model was used to segment both kidneys. 2D and 3D radiomics features were extracted and compared between the two kidneys to compute delta radiomics and assess its ability to predict differential kidney function. Performance was reported using receiver operating characteristics, sensitivity, and specificity. Results: Studies from Arizona & Rochester formed our internal dataset ( n = 1,159). Studies from Florida were separately processed as an external test set to validate generalizability. We obtained 323 studies from our internal sites and 39 studies from external sites. The best results were obtained by a random forest model trained on 3D delta radiomics features. This model achieved an area under curve (AUC) of 0.85 and 0.81 on internal and external test sets, while specificity and sensitivity were 0.84,0.68 on the internal set, 0.70, and 0.65 on the external set. Conclusion: This proposed automated pipeline can derive important differential kidney function information from contrast-enhanced CT and reduce the need for dedicated nuclear scans for early-stage differential kidney functional assessment. Clinical Impact: We establish a machine learning methodology for assessing differential kidney function from routine CT without the need for expensive and radioactive nuclear medicine scans.

98 Background: The risk of secondary primary malignancies (SPM) among the survivors of GU cancer is not well-established. In this study, we attempt to describe the patterns of SPMs in patients diagnosed with GU cancers. Methods: The Surveillance, Epidemiology, and End Results (SEER) database (SEER*Stat 8.4.2) was searched to obtain data on the incidence of SPM in adult patients (age >18 years) diagnosed with GU cancers, including prostate, renal, and bladder cancer. Standardized incidence ratios (SIRs) with a 95% confidence interval (CI) were calculated. SIR was defined as the observed SPMS from each category divided by the expected number of SPMs in the age-matched US population for the same period. The data was further stratified by age (<50y, 50-70y, >70y), race (White, Black, Asian/Pacific Islander), ethnicity (Hispanic, non-Hispanic), chemotherapy, and radiotherapy exposure. Results: A total of 1,041,217 patients were included in this analysis. In patients diagnosed with GU cancer, the incidence of overall SPM was 11%. Particularly, patients with GU cancers were observed to have a high incidence of Endocrine system SPM (1.52; 1.45-1.59), and urinary system SPM (1.51; 1.49-1.53) These results were consistent by different sociodemographic groups. In patients diagnosed with prostate cancer, the incidence of thyroid SPM was increased (1.31; 1.23-1.39). Particularly, prostate cancer patients treated with radiation therapy had increased incidence rates of SPM in the peritoneum, omentum and mesentery (1.57; 0.79-2.81) and urinary bladder (1.36;1.32-1.40) was higher as compared to those with no exposure to radiation therapy (1.11;1.08-1.13 and 1.03;1-1.06 respectively). Bladder cancer patients had an overall increased incidence of SPM in the esophagus (1.21; 1.08-1.35), lung, bronchus, trachea, and other respiratory organs (1.97;1.92-2.01), prostate (3.50;3.4-3.6) and kidney (1.86;1.76-1.97). A higher incidence of renal pelvis, ureter and other urinary organ SPM (11.08;10.48-11.07) was also notable in this population. Similarly, kidney cancer patients had a higher incidence of the endocrine system (3.00;2.74-3.27), prostate (1.24;1.2-1.29) and bladder 1.78;1.68-1.79 SPMs. Conclusions: This population-based study shows an increased risk of SPM development in GU cancer survivors compared to the general population. Clinicians should keep a high index of suspicion for SPMs and initiate appropriate workups at early warning signs.

639 Background: Immunotherapy combined with chemotherapy (IO/C) as the first-line management of locally advanced or metastatic urothelial carcinoma (LA/mUC) has been investigated in multiple trials with conflicting outcomes. Therefore, we conducted a meta-analysis of available trials to compare objective response rates (ORR) of this combination regimen, both in overall and in cisplatin-ineligible populations. Methods: EMABSE and MEDLINE were searched from 2000 through July 20th, 2023, to identify phase II and III clinical trials assessing IO, C or both in LA/mUC. Main outcome of interest was objective response rates (ORR) as defined in the included trials. An inverse variance random-effects meta-analysis was performed to estimate pooled ORR using the restricted maximum likelihood estimation method. Subgroup differences were assessed among IO/C, IO alone, and C alone. The threshold for statistical significance was established at 0.1. Results: Of 5975 citations identified, a total of 26 trials with a total of 4,628 participants were included in this systematic review. In the overall group, the ORR in IO/C trials (5 trials, 1,236 patients) was 44.70% (95% CI: 21.88%- 70.0%). In IO trials (7 trials, 2,189 patients), ORR was 23.68% (19.55%- 28.36%) and in C trials (26 trials, 4,628 patients) was 43.46% (37.43%-49.68%]). In the cisplatin-ineligible group, the ORR in IO/C trials (2 trials, 240 patients) was 60.06% (33.38%-81.86%), in IO trials (6 trials, 1,103 patients) was 26.15% (23.01%; 29.56%), and in C trials (8 trials, 767 patients) was 42.45% (35.67%-49.52%). In terms of subgroup differences, the difference in ORR was statistically significant between IO/C and IO trials (p= 0.0844) and between IO and chemotherapy trials (p< 0.0001) in the overall population. Similarly, we found a statistically significant difference in ORR between IO/C and IO (p= 0.0108) and between IO and chemotherapy trials (p< 0.0001) in the cisplatin-ineligible population. No statistically significant differences were observed between IO/C and C alone. Conclusions: First-line IO/C may achieve a better objective response compared to IO alone in patients diagnosed with locally advanced/metastatic UC. Although, there appears to be a trend of greater benefit with IO/C compared to C alone, the difference was not statistically significant. The results will be updated as soon data from new trials (CheckMate 901 and EV 302) becomes available which could offer new insights. [Table: see text]

412 Background: Previously we have reported comparative effectiveness data regarding survival of first line (1L) treatment options in mRCC. However, it is not known how the life-prolonging frontline combinations compare to patient-reported outcomes. Methods: Electronic databases including MEDLINE and EMBASE were searched from each database’s inception through July 1st 2023. Phase III randomized controlled trials (RCTs) assessing 1L immune-checkpoint inhibitor (ICI) combination therapies for mRCC and reporting HrQoL were included. Small sample size adjusted standardized mean differences (SMD; Hedges’ g) with 95% confidence intervals (CI) were computed for global, physical, functional, and emotional QoL. Positive SMD indicated an improvement. A network meta-analysis was conducted to assess the comparative HrQoL across different treatment options. P-scores were computed to assess relative treatment rankings. Results: Of 5770 citations identified, five trials (nine references) were included in this analysis. Patient-reported outcomes have not been reported in the JAVELIN Renal 101 or COSMIC-313 trials yet. Global QoL was significantly improved with cabozantinib-nivolumab (CaboNivo) as compared to Nivo-ipilimumab (SMD 2.74, 95% CI 2.54; 2.93), lenvatinib-pembrolizumab (LenPem; 2.79, 2.53; 3.06), sunitinib (2.87, 2.72; 3.03), Pem-axitinib (PemAxi; 3.09, 2.89; 3.30) and atezolizumab-bevacizumab (AteBev; 3.24, 3.04; 3.45). Global QoL improvement was also observed with NivoIpi compared to sunitinib (0.14, 0.02; 0.25), PemAxi (0.36, 0.18; 0.54) and AteBev (0.51, 0.33; 0.68). Physical QoL improvement were consistent with CaboNivo. Likewise, LenPem significantly improved QoL compared to sunitinib (0.26, 0.04; 0.47), PemAxi (0.38, 0.13; 0.64) and NivoIpi (0.51, 0.27; 0.76). Significant functional QoL benefits were only observed with CaboNivo (0.32, 0.17; 0.48) and NivoIpi (0.19, 0.08; 0.31) compared to sunitinib. No significant mixed treatment comparisons were observed for emotional QoL, however, currently available data suggests that LenPem (rank 1) may potentially improve QoL (Table). Conclusions: CaboNivo and NivoIpi as 1L therapy may offer an improved overall HrQoL compared to other contemporary treatment options in mRCC. [Table: see text]