Thorpe, Matthew

Purpose Radioligand therapy (RLT) with [ 177 Lu] Lu-PSMA-617 has shown significant benefits in metastatic castration-resistant prostate cancer (mCRPC), including prolonged overall survival (OS) and progression-free survival (PFS), as well as improved quality of life. For patients previously treated with [ 177 Lu]Lu-PSMA-617 who subsequently experience disease progression, retreatment with [ 177 Lu] Lu-PSMA-617 has emerged as a potential approach, yet data on the outcomes of RLT retreatment remains limited. This study aimed to assess the efficacy and safety of retreatment RLT in mCRPC. Methods Clinical and imaging data of patients who underwent at least two cycles of [ 177 Lu] Lu-PSMA-617, followed by a second course of treatment with [ 177 Lu] Lu-PSMA-617 between March 2018 and December 2024, were retrospectively reviewed. Endpoints included biochemical response, imaging response, toxicity, PSA-PFS, rPFS, and OS. Radiologic and biochemical responses were evaluated using RECIST 1.1 criteria and PSA levels, respectively. Results A total of 589 patients underwent [ 177 Lu] Lu-PSMA-617 RLT, of whom 20 (3.4%, 20/589) received RLT retreatment. The median age was 71.3 years (range: 63.7–95.2), and the median follow-up was 73.3 months (95% CI, 35.7–77.2 months) after the initial treatment and 21.7 months (95% CI, 16.9–26.0 months) after retreatment. The initial treatment involved a median of 4.5 cycles (range: 2–6). Following the first course of RLT, 15 patients (75%, 15/20) had exhibited a >50% decline in PSA levels, including 9 who achieved a >90% decrease. Additionally, 12 patients (60%, 12/20) had demonstrated a complete or partial imaging response, while 3 (15%, 3/20) patients had stable disease, and 5 (25%, 5/20) patients had progressive radiologic disease. Patients received a median of 4 retreatment cycles (range, 1–6), and the median interval between initial RLT and retreatment RLT was 33.1 months (IQR, 16.2–48.9 months; range, 10.7–58.8 months). A PSA decline of ≥50% was observed in 9 out of 20 patients (45%, 9/20). An imaging response was observed in 10 patients (50%, 10/20), with 4 achieving a complete response and 6 achieving a partial response, whereas 10 patients (50%, 10/20) showed progression. The median PSA-PFS was 9.8 months (95% CI, 4.1–17.9 months), and the median rPFS was 8.2 months (95% CI, 3.3–17.9 months). Eight deaths occurred during follow-up. The estimated Kaplan Meier OS rates were 83.6% at 12 months (95% CI, 57.3%–94.4%) and 53.7% at 24 months (95% CI, 28.3%–73.7%). Grade 2 renal toxicity was observed in one patient (5%, 1/20). Three patients (15%, 3/20) developed Grade 2 anemia. Notably, no grade 3 or grade 4 adverse events were observed during either treatment period. Conclusion In our cohort, retreatment with [¹ 77 Lu]Lu-PSMA-617 RLT was associated with biochemical and imaging responses in some patients, with observed survival outcomes that support further investigation. However, given the retrospective single-arm design and small sample size, large prospective studies are needed to more rigorously evaluate the safety, efficacy, and optimal patient selection for retreatment RLT.

5068 Background: 177 Lu-PSMA-617 (LuPSMA) is a groundbreaking therapy for mPC, but biomarkers are urgently needed to improve patient selection. Plasma-derived EVs are emerging blood-based markers of tumor burden. Here, we analyzed levels of EVs of 123 patients (pts) from 4 cohorts: a pre- and during LuPSMA treatment for a contemporary prospective Mayo Clinic cohort, pre-treatment plasma levels of EVs for a historical Tulane cohort, localized prostate cancer (LPC) after radical prostatectomy (RP), and male healthy donors (HD). Methods: Blood samples were collected from 51 pts pre-cycles (pre-C) 1-3 of LuPSMA at Mayo Clinic from 12/2024-12/2025 and 26 pts pre-C1 at Tulane Cancer Center between 12/2018-1/2025. Small and large EVs were incubated with fluorescently conjugated antibodies (PSMA, STEAP1, CEACAM5, and TROP2) and enumerated using nanoscale flow cytometry. EVs from blood samples of 10 HD and 36 LPC post-RP from 6/2020-8/2024 at Mayo Clinic as controls. AUC-ROC and logistic regression analyses were performed to identify marker signatures associated with PSA50 response. Results: Baseline demographics are shown in the table. Median levels of PSMA, STEAP1, CEACAM5, and TROP2 EV/milliliter (mL) were all significantly higher for pts with mPC (pooled Tulane and Mayo cohorts) compared to LPC post-RP (p<0.001) and HD (p<0.001). In the Mayo cohort, 10 of 51 pts (20%) had undetectable PSA pre-C1. There were no significant differences in median pre-C1 plasma EV levels for these 4 different types of EVs comparing pts with undetectable vs detectable PSA. For 41 Mayo pts with detectable PSA pre-C1, 20 achieved PSA50 response at time of data cutoff (49%), while 7 pts (17%) had persistent PSA rise on LuPSMA. For pts who achieved PSA50, there was a significant decline in both CEACAM5 and TROP2 EVs/mL pre-C1 compared to pre-C3 (19 x 10 6 vs 5.8 x 10 6 , p=0.003 and 16 x 10 6 vs 5.5 x 10 6 , p<0.001, respectively), while no significant changes occurred for the 7 pts with no PSA response. A composite score of pre-C1 CEACAM5 and TROP2 EVs (large- and small-sized EVs) demonstrated the strongest association with PSA50 response (OR=2.96, 95% CI: 1.27-6.44, p=0.038), achieving AUC of 0.73. PSMA and STEAP1 EVs did not show any significant changes from pre-C1 to pre-C3. Conclusions: Plasma-derived EVs are a promising biomarker to identify patients who will benefit from LuPSMA. Longer follow-up and enrolling a larger cohort are underway. Clinical trial information: NCT06200103 . Median values for pre-LuPSMA (Mayo and Tulane), post-RP for LPC, and HD. Mayo mPC (n=51) Tulane mPC (n=26) LPC (n=36) HD (n=10) Age (range) 73 (57-87) 71 (54-83) 66 (56-77) 55 (50-57) Prior lines (range) 1 (1-3) 4 (1-11) - - Gleason score 9 8 7 - PSA (range) 1.0 (0-182) 75 (0.8-503) - - PSMA EV/mL 3.7 x 10 6 14 x 10 6 2.0 x 10 6 1.4 x 10 6 STEAP1 EV/mL 30 x 10 6 45 x 10 6 12 x 10 6 12 x 10 6 CEACAM5 EV/mL 17 x 10 6 5.1 x 10 6 3.7 x 10 6 3.9 x 10 6 TROP2 EV/mL 13 x 10 6 5.2 x 10 6 4.5 x 10 6 3.6 x 10 6

Abstract Introduction: Suboptimal response to therapy, early disease relapse, and limited long-term survival remain critical challenges in a substantial number of patients with multiple myeloma (MM). While cross-sectional imaging studies are commonly obtained in clinical practice, only limited information is currently incorporated into risk stratification efforts and clinical decision making. Here, we investigate the use of novel image-based biomarkers to identify patients at high risk for adverse outcomes. Methods: We obtained the pretreatment 18F-FDG PET/CT scans of patients with newly diagnosed MM with available clinical and laboratory data seen at Mayo Clinic between 2006 and 2022. In this analysis, we included 637 patients with detectable metabolic tumor volume on their pretreatment scan. The native DICOM images were pre-processed and subjected to automated image segmentation using nnU-Net (nnU-Net v1.0, inference using a pre-trained hematologic malignancies model). Limitations of this automated segmentation approach include heterogeneity introduced by varying image quality, technical variability in PET reconstruction, and potential inclusion of non-neoplastic FDG-avid lesions in segmented regions. PyRadiomics was used to extract quantitative radiomics features for clinical prediction modeling. We evaluated the predictive power of tumor size-, shape-, and texture-related radiomics features for identifying high-risk disease (advanced ISS stage at diagnosis, the presence of high-risk FISH abnormalities, failure to achieve a stringent complete response [sCR] to first-line therapy by IWMG criteria, and early mortality). All radiomics features were quantile-normalized for downstream modeling. Forward and backward feature selection as well as testing for multicollinearity were used to remove redundant features. Results: Median age at diagnosis of the 637 myeloma patients was 64 years (range 22–90), and 387 patients (61%) were male. After a median follow-up of 4.4 years (95% CI 0.2–11.7), median overall survival (OS) was 7.3 years (95% CI 6.3–8.3). Increased total tumor volume (upper tertile vs lower tertiles), measured by the total tumor surface area, was associated with advanced ISS stage (III vs I+II; OR 1.73, 95% CI 1.24–2.42, p=0.001, n=637). Increased tumor density (dichotomized at the median), measured by the median gray level intensity (OR 1.93, 95% CI 1.94–3.12, p=0.007, n=303), and decreased tumor heterogeneity (lower tertile vs upper tertiles), measured by the variability of gray-level intensities (OR 1.88, 95% CI 1.49–3.12, p=0.012, n=303), were associated with the presence of high-risk cytogenetics. Both increased total tumor volume (4.9% increase per tertile, 95% CI 2.2–7.6, p&lt;0.001, n=600) and tumor density (8.9% increase per tertile, 95% CI 4.5–13.3, p&lt;0.001, n=600) were independently associated with more extensive bone marrow involvement at diagnosis. Increased tumor heterogeneity (upper tertiles vs lower tertile), as measured by rapid changes of gray level intensities, was associated with failure to achieve a stringent complete response to first-line therapy (OR 0.50, 95% CI 0.30–0.82, p=0.006, n=351). Increased tumor volume was associated with shorter OS (HR 1.35 per tertile, 95% CI 1.11–1.63, p=0.002, n=301), independent of age, sex, ISS stage, and the presence of high-risk cytogenetics. A simple additive score consisting of tertiles of total tumor volume, ISS stage, and plasma cell labeling index ≥2% identified patients at risk of mortality within 3 years of diagnosis (OR 2.00, 95% CI 1.40–2.85, p&lt;0.001, n=126), with moderate discrimination (AUC=0.71). Sensitivity analysis demonstrated total tumor volume and texture as most predictive of overall survival in a random forest survival model (C-index 0.712, 95% CI 0.649–0.775) and tumor texture as most predictive of response to therapy in a random forest classification model (C-index 0.693, 95% CI 0.607–0.779). Conclusions: Automated image segmentation and radiomics feature extraction from 18F-FDG PET/CT scans produces biomarkers that capture disease stage, burden, and risk in newly diagnosed MM. Total tumor volume and heterogeneity are strongly associated with important disease characteristics and can be used to identify patients at high risk for therapeutic resistance, early mortality, and inferior long-term outcomes.

114 Background: Oncologists have traditionally relied upon serial assessments of PSA and periodic imaging (i.e., every 2 to 3 cycles) to monitor treatment response. Early identification of disease progression allows for faster transitions to alternative therapies that might be more effective. In this study, we evaluated the utility of post-treatment SPECT/CT for earlier detection of treatment failure in patients receiving Lutetium-177–PSMA-617 (LuPSMA). Methods: We queried an IRB-approved registry including all patients starting LuPSMA at Mayo Clinic, MN, in the interval of March 2022 to March 2023. Those receiving fewer than 2 cycles of treatment and missing SPECT/CT data were excluded. We reviewed clinical notes to document the date and reason(s) for treatment discontinuation. The assembled cohort consists of patients stopping LuPSMA for progressive disease ([PD], categorized as biochemical, radiographic, or both). Results of the prior SPECT/CT imaging reports were reviewed. At our institution SPECT/CT images are typically acquired on the day after each infusion. Metrics collected from the radiology reports included: new foci of PSMA localization since the prior PSMA PET or SPECT/CT, suspicion for new non-PSMA avid disease, and a visual determination of PSMA-avid tumor volume. Results: A total of 256 patients received an initial cycle of LuPSMA. SPECT/CT imaging data for at least 2 consecutive treatment cycles was available for 68 patients who developed PD after a median (IQR) of 3 (2-4) cycles. There were 55 patients (81%) with both biochemical and radiographic evidence of PD, 7 (10%) with biochemical only progression, and 6 (9%) with radiographic only progression. The most used imaging modality for response assessment was PSMA PET/CT (n=46, 67%), followed by conventional imaging (n=9, 13%), and choline PET/CT (n=5, 7%). The median time between most recent post-therapy SPECT/CT and determination of PD was 39 days. The prior SPECT/CT had detected new foci of PSMA avid disease in 12 (18%) cases and new non-PSMA avid disease in 5 (7%) cases. Two patients had both new PSMA-avid and non-PSMA avid lesions. The most common sites of new PSMA-avid disease on SPECT/CT were bone (n=10) and liver (n=2). A total of 20 (29%) patients had visually increased tumor volume on SPECT/CT, 9 (45%) of which had no new lesions. In total, 24 of 68 patients (35%) had early detection of treatment futility detected by SPECT/CT. At a median (IQR) follow-up time of 8 (6.4-12) mo, the median OS [95% CI] was similar for patients with PD detected on SPECT/CT (7.8 [6.6 – 9] mo) compared to those without clear evidence of PD on SPECT/CT(8.4 [6.4-10.3] mo, p=0.885). Conclusions: Post-treatment SPECT/CT imaging may complement traditional forms of response assessment and provide an earlier warning of treatment failure through identification of new PSMA-avid lesions, non-PSMA avid disease, and increased tumor volume.

112 Background: We now have more than a decade of experience using Androgen Receptor Pathway Inhibitors (ARPIs) across various disease states of prostate cancer, and the safety of their combination with Lutetium-177–PSMA-617 has been demonstrated in VISION and other large randomized clinical trials. However, real-world data on treatment patterns and outcomes involving combination therapies with 177 Lu-PSMA-617 remain limited. Methods: For this analysis, we utilized the Mayo Clinic Rochester radiopharmaceutical database, a prospectively maintained retrospective database containing all patients who received 177 Lu-PSMA-617 at our institution. We focused on patients who started treatment from March 2022 to March 2023. Patients receiving an ARPI (abiraterone acetate, enzalutamide, apalutamide, or darolutamide) during the course of 177 Lu-PSMA-617 treatment were identified, inclusive of patients who continued using an ARPI prescribed as a prior line of treatment and those who started (i.e., switched) to a new ARPI. Baseline clinicopathologic and imaging characteristics were abstracted and compared using the Mann-Whitney U and Chi-square tests. Best PSA response during treatment was reported as a percent decline from baseline. Survival was calculated from the date of the first cycle of 177 Lu-PSMA-617. PSA50 response and overall survival (OS) outcomes for the two groups (concurrent use of ARPI vs. 177 Lu-PSMA-617 alone) were compared using the Chi-square test and Kaplan-Meier method, respectively. A multivariate Cox regression analysis was performed, including established prognostic factors. Results: An ARPI was prescribed to 106 of the 256 patients (41.4%) starting 177 Lu-PSMA-617 in the interval of March 2022 to March 2023. With regards to baseline characteristics, those receiving an ARPI concurrently with 177 Lu-PSMA-617 had a lower PSA (3.4 vs 29.7 ng/mL, p < 0.001) and a lower frequency of bone (77.4% vs. 87.4%, p = 0.035) and visceral metastases (18.9 vs 34%, p = 0.008) at start of treatment. Median follow-up for the overall cohort (IQR) was 19.1 months (8.7 – 23.6). Patients receiving an ARPI plus 177 Lu-PSMA-617, as compared to 177 Lu-PSMA-617 alone, were more likely to complete all 6 planned doses of treatment (63.2% vs. 48.7%, p < 0.001), though the PSA50 response rate was similar (49.1% vs 47.3%, p = 0.786). Median OS [95% CI] for the overall cohort was 21.3 [16.7 – 25.9] months and significantly longer for those receiving ARPI with 177 Lu-PSMA-617 (NR [NE – NE]) as compared to 177 Lu-PSMA-617 alone (15.3 mo [11.6 – 19.1], p < 0.001). However, on multivariate analysis including known prognostic variables, use of ARPI was not independently associated with improved survival (HR = 1.03 [CI: 0.68 to 1.55], p = 0.891). Conclusions: Patients receiving an ARPI with 177 Lu-PSMA-617 were more likely to complete all 6 cycles of treatment, but no clear differences in survival were observed on multivariate analysis.

Introduction: In myeloma, accurate risk stratification remains a challenge due to substantial biological heterogeneity and highly variable survival outcomes. Cross-sectional imaging is commonly obtained at the time of diagnosis, but imaging findings are not currently incorporated into risk stratification efforts in a standardized manner, despite their prognostic significance. Here, we investigate the use of novel image-based prognostic biomarkers obtained by automated image segmentation and radiomics feature extraction using machine learning methods. Methods: We obtained the pre-treatment 18F-FDG PET/CT scans of 443 patients with newly diagnosed myeloma and complete demographic and cytogenetic data (seen at Mayo Clinic between 2005 and 2019). The native DICOM images were pre-processed and subjected to automated image segmentation using nnU-Net (v1.0, inference using a pre-trained hematologic malignancies model). Limitations of this automated segmentation approach include heterogeneity introduced by varying image quality, technical differences related to PET reconstruction, and potential inclusion of non-neoplastic FDG-avid lesions in segmented regions. The quality of the automated image segmentation was evaluated by comparing the radiomics features obtained from ground-truth segmentation (manual lesion segmentation) and model segmentation in a separate cohort of patients with hematologic malignancies (n=72). We selected 42 radiomics features that moderately (r≥0.30, p&lt;0.005, n=23) or strongly (r≥0.50, p&lt;0.001, n=19) correlated between ground-truth (manual) and model (automated) segmentation for further evaluation in myeloma patients. PyRadiomics was used to extract discrete radiomics features for clinical prediction modeling. We evaluated the prognostic significance of tumor size-, shape-, and texture-related radiomics features and compared their predictive power to established prognostic factors (International Staging System [ISS], Revised ISS [R-ISS] stage and high-risk FISH cytogenetics). The outcome of interest was overall survival and the predictive power of competing proportional hazards regression models was assessed using Harrell's C-statistic. For modeling purposes, patients without measurable tumor volume were categorized as having undetectable radiomics features (0 for all features). All radiomics features were median dichotomized for downstream modeling. Forward feature selection and testing for multicollinearity were used to remove redundant features. Select radiomics features were divided into quintiles to generate an additive radiomics risk score and associated risk strata. Results: Median age at diagnosis of the 443 myeloma patients was 63 years (32-91) and 270 patients (62%) were men. After a median follow-up of 2.4 years (95% CI 0.1-7.4), median overall survival was 6.5 years (95% CI 5.5-NR). Most patients had detectable metabolic tumor volume (n=309, 70%) on their pre-treatment 18F-FDG PET/CT scan. Several of the 42 radiomics features were intercorrelated and two features were selected for further investigation based on their independent prognostic significance (forward feature selection). Higher maximum axial tumor diameter (HR 1.90, 95% CI 1.31-2.76, p=0.001) and a lower concentration of low gray-level values in the tumor (HR 1.73, 95% CI 1.20-2.49, p=0.004) were independently associated with overall survival in newly diagnosed myeloma. These associations were independent of age at diagnosis, sex, and R-ISS stage. We devised a simple additive risk score representing the sum of quintiles of the two radiomics features and divided the patients into three risk categories akin to the R-ISS. The predictive power of this radiomics risk score (Harrell's C=0.60) was comparable to the ISS (C=0.59) and the R-ISS (C=0.57). Conclusions: Automated image segmentation and radiomics feature extraction from 18F-FDG PET/CT scans is feasible. This approach yields image-based biomarkers that correlate with results obtained by manual imaging segmentation. Several radiomics features such as tumor size and heterogeneity have independent prognostic significance in newly diagnosed myeloma. These novel biomarkers rival the predictive power of the currently established risk stratification methods and may be used to improve risk stratification in myeloma.

Abstract Bone metastases occur frequently in common malignancies such as breast and prostate cancer. They are responsible for considerable morbidity and skeletal-related events. Fortunately, there are now several systemic, focal, and targeted therapies that can improve quality and length of life, including radionuclide therapies. It is therefore important that bone metastases can be detected as early as possible and that treatment can be accurately and sensitively monitored. Several bone-specific and tumor-specific single-photon emission computed tomography and positron emission tomography molecular imaging agents are available, for detection and monitoring response to systemic therapeutics, as well as theranostic agents to confirm target expression and predict response to radionuclide therapies.

220 Background: There is a critical need to identify biomarkers of response and resistance to 177Lu-PSMA-617. Post-hoc analyses of VISION have explored the prognostic and predictive value of select clinical parameters. However, we are still in the early stages of deciphering which molecular changes are associated with favorable or adverse treatment outcomes. Methods: We conducted a retrospective analysis of patients with metastatic castration-resistant prostate cancer (mCRPC), who received 177Lu-PSMA-617 at Mayo Clinic in Rochester, Minnesota, in the interval of March 2022 to March 2023. We included patients who provided research authorization, had a baseline PSA of at least 2 ng/mL, received at least 2 cycles of treatment, and had circulating tumor DNA (ctDNA) genomic profiling performed by Guardant (83 gene panel) within 60 days of treatment initiation. Clinical, laboratory, genomic, and radiomic parameters were abstracted by trained research personnel. Two independent analyses of “response” and “resistance” were performed. Response to treatment was defined as 50% or greater decrease in PSA from baseline (PSA50 response: dichotomized yes versus no), and resistance was defined as non-decreasing or rising PSA during treatment (PSA non-response: dichotomized yes versus no). Associations between gene alterations and response and resistance were evaluated via Fisher’s exact tests. Results: Between March 2022 and March 2023, 273 patients received cycle 1 of 177Lu-PSMA-617. A total of 108 patients met the inclusion criteria. The median number of cycles at data cutoff was 6 (IQR: 4,6) with 57 (53%) patients receiving all 6 planned cycles. The median PSA at baseline was 16.65 ng/ml (IQR: 5.20, 66.12). A total of 65 patients (60%) experienced a PSA50 response. No molecular alterations were associated with a higher rate of PSA50 response; however, alterations in ARID1A, AR, and CHEK2 were associated with lack of PSA50 response (Fisher’s exact p < 0.05). Of the 108 evaluable patients, 20 (19%) met criteria for PSA non-response. Alterations in AR, ARID1A, MYC, TP53, CHEK2, ATM, EGFR, and NOTCH1 were significantly associated with PSA non-response (Fisher’s exact p < 0.05); in each case, the presence of the alteration was positively associated with resistance (i.e., more instance of PSA non-response). No biomarkers were found to be inversely associated with PSA non-response. Conclusions: ctDNA assessed by commercially available, targeted sequencing panels prior to treatment may be useful in identifying the patients least likely to benefit from 177Lu-PSMA-617. Further work is ongoing to integrate ctDNA findings with clinical parameters in the hopes of understanding the factors mediating response and resistance to therapy.

175 Background: It is well-known that patients with liver metastases from metastatic castration resistant prostate cancer (mCRPC) have poorer or transient responses to many forms of systemic therapy. Data on outcomes following treatment with Lutetium-177–PSMA-617 is scarce. The VISION trial reports hazard ratios for overall survival in the subgroup of patients with liver metastases without disclosing the absolute duration of survival. Using real-world clinical data, we examine this important subgroup of patients, describing their PSA response rates and survival. Methods: A single institution database was assembled to include all patients receiving 177-Lu-PSMA-617 at Mayo Clinic Rochester, MN, and starting treatment in the interval of March 2022 to March 2023. Baseline clinicopathologic and imaging characteristics were abstracted. Patients were categorized based on the presence or absence of measurable liver metastases on their baseline PSMA PET scan. Best PSA response while on treatment was annotated and reported as percent decline from baseline. Survival was calculated from date of first cycle of 177-Lu-PSMA-617. PSA50 response and overall survival (OS) outcomes for the two groups (liver metastases versus no liver metastases) were compared using Chi square test and Kaplan-Meier method, respectively. Those with available pre-treatment circulating tumor DNA (ctDNA) genomic profiling, performed by Guardant (83 gene panel), were assessed for the presence of pathogenic and likely pathogenic alterations. Findings are summarized using descriptive statistics. Results: The overall cohort consisted of 212 patients, including 29 (14%) with liver metastases on pre-treatment PSMA PET/CT. The median number of cycles received was 4 (range, 1-6) for patients with liver metastases and 6 (range, 1-6) for those without hepatic involvement. The observed PSA50 response trended lower for those with liver metastases (34.5% [10/29] versus 51% [94/183], p= 0.091). At median (IQR) follow-up of 10 (8-12) months, there was a numerical but not statistically significant difference in median OS (11.28 months versus NR, p=0.066). In the exploratory analysis of baseline ctDNA, BRCA2 copy number loss was relatively enriched among patients with liver metastases (4 of 19, 21%) versus patients without liver disease (5.7%, 9/158). Conclusions: Our data suggest that the presence of liver metastases predict poorer outcomes in patients receiving Lutetium-177–PSMA-617 treatment, which is similar to data reported with other forms of systemic therapy for mCRPC. To maximize the anti-tumor activity of radioligand therapy in the liver, combination approaches should be explored.

Abstract 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.