Burkett, Brian J.

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.

Abstract INTRODUCTION Centiloid (CL) scaling standardizes amyloid positron emission tomography (PET) quantification across tracers and platforms; however, variability across software implementations may affect diagnostic classification. This study evaluated inter‐software variability and diagnostic performance across five platforms using identical 1 8 F‐florbetapir datasets. METHODS Retrospectively, 192 patients undergoing 1 8 F‐florbetapir PET/computed tomography (CT) and magnetic resonance imaging (MRI) were analyzed. CL values were generated using four US Food and Drug Administration (FDA) ‐cleared platforms and an in‐house Centiloid standard pipeline. Agreement was assessed using intraclass correlation coefficient, with bias and limits of agreement evaluated by linear modeling and Bland–Altman analysis. Diagnostic performance was assessed using receiver operating characteristic (ROC) analysis and classification against visual interpretation. RESULTS Agreement was excellent (intraclass correlation coefficient [ICC] 0.969; 95%CI 0.961–0.975). Some platforms produced systematically higher CL values versus others slightly lower. LoA reached ± 30 CL. Diagnostic accuracy was high (area under the curve [AUC]: 0.949–0.975), with sensitivity 0.923–0.968 and specificity 0.667–0.806. DISCUSSION Despite excellent agreement, systematic differences persist and may affect classification near thresholds, supporting consistent use of a single processing pipeline.

This cross-sectional study evaluates the performance of the US Food and Drug Administration–cleared phosphorylated tau 217 (p-tau217) to β-amyloid 42 ratio cut points used to detect amyloid pathology associated with Alzheimer disease.

Abstract INTRODUCTION Alzheimer's disease neuropathologic change (ADNC) classification identifies not/low and intermediate/high levels of neuropathology. Our goal was to assess how frequently a positive amyloid positron emission tomography (PET) scan indicates not/low ADNC and whether this autopsy finding can occur >10 years after a positive amyloid PET. METHODS Participants with positive amyloid PET scans were categorized by levels of ADNC at autopsy, grouped by time from initial positive amyloid PET to time of death (<5 years, 5 to <10 years, or 10+ years). RESULTS Among those with a positive amyloid PET scan, the majority had intermediate/high ADNC at autopsy (234/259, 90%). In the group with 10+ years between a positive amyloid PET and death ( n = 39) not/low ADNC occurred in 3/39 (8%). DISCUSSION Not/low ADNC is uncommon among those with positive amyloid PET scans. After 10+ years, it is possible but rare for a positive amyloid PET scan to represent an indolent state of neuropathology.

Abstract In epilepsy patients lacking magnetic resonance imaging‐visible lesions such as focal cortical dysplasia (FCD), surgical resections target regions with abnormal electroencephalographic (EEG) activity. However, histopathological identification of subtle cortical architectural abnormalities in these specimens remains challenging. We investigated whether artificial intelligence (AI)‐based morphometric and spatial analysis of NeuN‐stained cortical sections could detect neuronal architectural disorganization in epilepsy resections, including regions without definitive histologic dysplasia. Whole slide images were generated from 83 FCD regions and 19 neurologically normal autopsy controls. Regions of interest were annotated in QuPath as FCD, FCD‐adjacent, FCD‐distant, apparently normal (abnormal EEG without histologic dysplasia), and true normal (autopsy controls). NeuN‐positive neurons were detected using QuPath (90% sensitivity, 12% false positive rate). Spatial and morphometric features—including neuronal clustering, distribution inhomogeneity, and nuclear morphology—were extracted and used to train a multinomial, Least Absolute Shrinkage and Selection Operator‐regularized regression classifier. The classifier achieved 70.6% accuracy in subregion classification and 88.2% accuracy in overall specimen diagnosis. Notably, regions with abnormal EEG but lacking histologic dysplasia exhibited quantifiable architectural disorganization similar to those seen in areas adjacent to FCD and distinct from control tissue. AI‐driven analysis of neuronal morphology and spatial distribution reveals subtle cortical disorganization in epilepsy resections, including in histologically ambiguous regions. Further investigation is warranted to determine if this methodology can enhance the diagnostic yield of neuropathological evaluation and support more precise surgical targeting in epilepsy.

Abstract Background Clinically available plasma p ‐tau217 assays may be utilized in the determination of eligibility for amyloid‐lowering therapies. Our institution recently introduced a plasma p ‐tau217 assay in clinical practice using a two‐cutpoint approach that optimizes the accuracy while minimizing false positive risk and categorizing ≤20% of results as intermediate. In this study, we prospectively evaluate the performance of a plasma p ‐tau217 assay for detection of amyloid pathology using these predefined cutpoints. Method Patients recruited were being considered for lecanemab therapy at a tertiary care subspecialty clinic. Plasma p ‐tau217 was measured using the clinically available Fujirebio Lumipulse p ‐tau217 assay at Mayo Clinic, and results (pg/mL) were classified as either negative (≤0.185), intermediate (0.186–0.324) or positive (≥0.325). Amyloid pathology was determined by amyloid‐PET and/or CSF p ‐tau181/Aβ42. Result Ninety‐three patients were recruited between November 2023 and December 2024. Of these, 49 underwent amyloid‐PET, 4 had CSF p ‐tau181/Aβ42 measurements, and 40 had both PET and CSF results (with 3/40 discordant cases), for use in the assessment of amyloid pathology. The three discordant cases were considered amyloid positive for purposes of plasma p ‐tau217 comparison. Overall amyloid positivity was 90%. Plasma p ‐tau217 results were positive in 83% (77/93), negative in 6% (6/93), and intermediate in 11% (10/93) of patients. An elevated p ‐tau217 predicted the presence of amyloid positivity with a diagnostic accuracy of 96% (95% CI 90% − 99%). In the intermediate range, 80% of patients were positive for amyloid pathology. In patients with a definitive p ‐tau217 result (positive or negative), the positive and negative predictive values were 97% and 83%. There were three patients with discordant results: one PET+/p‐tau217‐, one PET‐/p‐tau217+, and one CSF‐/p‐tau217+. Clinical presentation of these discordant cases was examined, and relevant findings will be discussed. Conclusion The Lumipulse plasma p ‐tau217 assay exhibited acceptable diagnostic accuracy for the determination of amyloid pathology when compared to either amyloid‐PET or CSF p ‐tau181/Aβ42. This prospective study confirms the utility a two‐cutpoint approach for interpreting plasma p ‐tau217 for the assessment of amyloid pathology eligibility for amyloid‐lowering therapies in a heterogenous clinical cohort.

Abstract Background Neuropathologic evaluation is essential for diagnosing and treating dementia, but definitive diagnoses is only available after post‐mortem brain autopsy. Positron emission tomography (PET) offers a potential in vivo proxy for predicting neuropathology. This study proposes a semi‐supervised learning (SSL) model using a single FDG‐PET image to predict underlying neuropathology. Method The SSL model leverages a self‐organized graph structure based on a patient similarity network (PSN) created from 1,495 FDG‐PET images across the aging and neurodegenerative spectrum. Each patient is represented by a node, with edges indicating image similarity derived from principal component analysis features and cosine similarity weights. The PSN structure connects 20 nearest neighbors. Neuropathologic labels were available for 204 autopsied patients. The Poisson Learning Method was employed to propagate labels from these annotated nodes to unlabeled nodes, guided by the PSN structure. FDG‐PET images were collected from clinical and research participants at the Mayo Clinic and processed using an MRI‐free pipeline. Neuropathologic labels were assigned based on widely accepted diagnostic criteria. Leave‐one‐out cross‐validation (LOOCV) was used to assess model performance. Results The confusion matrix demonstrated high accuracy in predicting Alzheimer's disease, frontotemporal lobar degeneration with tau inclusions, and Lewy body disease pathology. A Sankey diagram and label distribution analysis revealed strong correlations between clinical and predicted neuropathologic labels. Conclusion This study highlights the potential of a graph‐based SSL pipeline to predict neuropathological changes using FDG‐PET imaging data, despite limited pathology labels. The findings underscore the value of SSL in bridging the gap between abundant neuroimaging data and scarce neuropathologic labels, contributing to improved dementia diagnosis and understanding.