Citations

269 Background: Metastatic visceral involvement in prostate cancer patients conveys a poor prognosis. Survival patterns of site-specific visceral metastasis are poorly understood. Here, we sought to investigate survival patterns in prostate cancer patients according to their first detected site of visceral metastasis. Methods: Retrospectively, we identified 200 patients with visceral metastatic prostate cancer. Patients were divided into three groups according to first site detected with visceral metastases; first-site lung metastases, first-site brain metastases, and first-site liver metastases. Visceral metastases were detected on either conventional imaging (CT/MRI), metabolic imaging (C-11 choline), or PSMA PET-CT scan. Follow up duration of our study was 80 months. Results: Clinicopathological variables are shown. The K-M curve of overall survival of the entire cohort suggests better survival patterns in patients with first-site lung metastases compared to patients with first-site brain or liver metastases (p<0.0001). In subset analysis of patients with CRPC, the K-M curve of overall survival, which demonstrates better survival outcomes in patients with first-site lung metastases in comparison with patients with first-site brain or liver metastases (p<0.0001). Conclusions: Our data suggests that prostate cancer patients with visceral metastatic disease have different survival patterns according to the first site detected with visceral metastasis. In our cohort, patients with first-site lung metastasis demonstrated better survival outcomes than patients with first-site brain or liver metastasis. Further studies are warranted. [Table: see text]

The NCCN Guidelines for Prostate Cancer Early Detection provide recommendations for individuals with a prostate who opt to participate in an early detection program after receiving the appropriate counseling on the pros and cons. These NCCN Guidelines Insights provide a summary of recent updates to the NCCN Guidelines with regard to the testing protocol, use of multiparametric MRI, and management of negative biopsy results to optimize the detection of clinically significant prostate cancer and minimize the detection of indolent disease.

Evaluate the quantitative, subjective (Deauville score [DS]) and reader agreement differences between standard ordered subset expectation maximization (OSEM) and Bayesian penalized likelihood (BPL) positron emission tomography (PET) reconstruction methods. A retrospective review of 104 F-18 fluorodeoxyglucose PET/computed tomography (CT) exams among 52 patients with diffuse large B-cell lymphoma. An unblinded radiologist moderator reviewed both BPL and OSEM PET/CT exams. Four blinded radiologists then reviewed the annotated cases to provide a visual DS for each annotated lesion. Significant (P < .001) differences in BPL and OSEM PET methods were identified with greater standard uptake value (SUV) maximum and SUV mean for BPL. The DS was altered in 25% of cases when BPL and OSEM were reviewed by the same radiologist. Interobserver DS agreement was higher for OSEM (>1 cm lesion = 0.89 and ≤1 cm lesion = 0.84) compared to BPL (>1 cm lesion = 0.85 and ≤1 cm lesion = 0.81). Among the 4 readers, average intraobserver visual DS agreement between OSEM and BPL was 0.67 for lesions >1cm and 0.4 for lesions ≤1 cm. F-18 Fluorodeoxyglucose PET/CT of diffuse large B-cell lymphoma reconstructed with BPL has higher SUV values, altered DSs and reader agreement when compared to OSEM. This report finds volumetric PET measurements such as metabolic tumor volume to be similar between BPL and OSEM PET reconstructions. Efforts such as adoption of European Association Research Ltd accreditation should be made to harmonize PET data with an aim at balancing the need for harmonization and sensitivity for lesion detection.

Background: Meniscal and chondral damage is common in the patient undergoing revision anterior cruciate ligament (ACL) reconstruction. Purpose: To determine if meniscal and/or articular cartilage pathology at the time of revision ACL surgery significantly influences a patient’s outcome at 6-year follow-up. Study Design: Cohort study; Level of evidence, 3. Methods: Patients undergoing revision ACL reconstruction were prospectively enrolled between 2006 and 2011. Data collection included baseline demographics, surgical technique, pathology, treatment, and scores from 4 validated patient-reported outcome instruments: International Knee Documentation Committee (IKDC), Knee injury and Osteoarthritis Outcome Score (KOOS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and Marx Activity Rating Scale. Patients were followed up at 6 years and asked to complete the identical set of outcome instruments. Regression analysis assessed the meniscal and articular cartilage pathology risk factors for clinical outcomes 6 years after revision ACL reconstruction. Results: An overall 1234 patients were enrolled (716 males, 58%; median age, 26 years). Surgeons reported the pathology at the time of revision surgery in the medial meniscus (45%), lateral meniscus (36%), medial femoral condyle (43%), lateral femoral condyle (29%), medial tibial plateau (11%), lateral tibial plateau (17%), patella (30%), and trochlea (21%). Six-year follow-up was obtained on 79% of the sample (980/1234). Meniscal pathology and articular cartilage pathology (medial femoral condyle, lateral femoral condyle, lateral tibial plateau, trochlea, and patella) were significant drivers of poorer patient-reported outcomes at 6 years (IKDC, KOOS, WOMAC, and Marx). The most consistent factors driving outcomes were having a medial meniscal excision (either before or at the time of revision surgery) and patellofemoral articular cartilage pathology. Six-year Marx activity levels were negatively affected by having either a repair/excision of the medial meniscus (odds ratio range, 1.45-1.72; P≤ .04) or grade 3-4 patellar chondrosis (odds ratio, 1.72; P = .04). Meniscal pathology occurring before the index revision surgery negatively affected scores on all KOOS subscales except for sports/recreation ( P < .05). Articular cartilage pathology significantly impaired all KOOS subscale scores ( P < .05). Lower baseline outcome scores, higher body mass index, being a smoker, and incurring subsequent surgery all significantly increased the odds of reporting poorer clinical outcomes at 6 years. Conclusion: Meniscal and chondral pathology at the time of revision ACL reconstruction has continued significant detrimental effects on patient-reported outcomes at 6 years after revision surgery.

641 Background: Tumors of neuroendocrine origin are a rare, heterogenous group of neoplasms. Neuroendocrine neoplasms (NENs) are categorized by site of origin, differentiation status, and by grade (Ki-67 expression and/or mitotic rate), with prognostic variability accordingly. These tumors frequently metastasize to bone, with reported incidence between 6-12% by older SSTR imaging. Our study evaluates patients with well-differentiated tumors of neuroendocrine origin to determine the incidence of osseous metastases when evaluated with higher-sensitivity Ga68 DOTATATE PET scans. The study characterizes the clinical features. Methods: This study was performed at a single, 3-site, US tertiary-care institution. IRB approval was obtained. An automated data extraction tool was used to mine the electronic medical record by searching all positron emission tomography (PET) studies for keywords. Identified scans had to include a combination of the following keywords: “Dotatate” AND “met*” or “lesion” AND “bone” or “osse*” or “skel*”. The individual medical records from the generated report were reviewed to include only patients with 1) well-differentiated NETs of GI and pancreatic origin, lung carcinoid, paraganglioma/pheochromocytoma, or other/unknown primary site, and 2) patients with confirmed osseous metastatic disease. Patient data was entered into a database and evaluated in aggregate. Results: 1,948 PET scans of 1,473 patients were extracted from the EMR, from which 424 patients were identified for inclusion; scans were performed between 5/2018 and 5/2021. Calculated incidence of bone metastasis by Ga68 DOTATATE PET was 28.8%. Median age of included population was 61 years (range 14-92), 49.5% being male. Site of origin was 47.2% bowel NET, 18.9% pancreatic NET, 10.8% lung carcinoid, 10.6% paraganglioma/pheochromocytoma, 2.1% other site, and 10.4% unknown primary. Majority of patients were asymptomatic (64.0%), had sclerotic appearance (76.7%), Krenning 4 (71.4%), and >3 sites (68.3%) of osseous disease. 94.6% of the population had disease of the axial skeleton; 65.6% appendicular. Only 57 patients (13.4%) with osseous disease suffered a fracture, despite metastases at high-risk sites. Fracture occurred at disproportionately low rates in NETs originating in bowel (22.8% of fractures), with proportionately higher rates among pancreatic NETS and paragangliomas/pheochromocytomas (31.6% and 22.8%, respectively). Fractures occurred at proportionately higher rates in higher-grade disease compared to low-grade. Conclusions: Osseous metastatic disease in well-differentiated NENs is evident at much higher rates when imaging with Ga68 DOTATATE PET compared with previously reported data. Nevertheless, fracture occurred at a low rate, suggesting that these patients are at a relatively low risk for skeletal-related events.

Abstract Allogeneic stem cell transplantation (alloSCT) is a highly effective, curative therapy for leukemia yet does not provide a survival benefit above autologous SCT in patients with multiple myeloma (MM). To explore this, we developed preclinical models of SCT using C57Bl/6 recipient mice and either C57Bl/6 (ASCT) or C3H.SW (alloSCT) donor grafts. Importantly, these models recapitulated the clinical setting whereby alloSCT provided superior outcomes, compared to ASCT, in recipients bearing MLL-AF9-driven acute myeloid leukemia (AML) but not in those bearing Vk*MYC-MM. Interestingly, we found that MM-specific, T cell-mediated immunity was generated after ASCT, which failed due to MM-induced T cell exhaustion. MM relapse after ASCT could be prevented by TIGIT or PD-1 targeted immune checkpoint inhibition or via depletion of suppressive CSF1-R+ myeloid cells. Conversely, after alloSCT, T cell exhaustion was driven principally by alloantigen and CD8 T cells expressed high levels of PD-1, TIGIT and TIM-3. Furthermore, Vk*MYC myeloma exploited this alloantigen-driven T cell exhaustion via expression of high levels of PD-L1 and CD155 (the cognate ligands for PD-1 and TIGIT), which were expressed minimally on MLL-AF9 AML. To exploit alloSCT in MM, we used post-transplant cyclophosphamide (PT-Cy) to delete high affinity alloreactive T cells that generate the exhausted donor T cell pool. Subsequent administration of CD137 agonists enhanced T cell activation and cytolytic activity within bone marrow, without exacerbating GVHD. Thus, PT-Cy provides a platform for optimizing immunotherapy after alloSCT.

Abstract Multiple myeloma (MM) is a largely incurable hematological malignancy characterized by clonal expansion of plasma cells in bone marrow (BM). Autologous stem cell transplantation (ASCT) remains a standard treatment for MM, allows intensive cytoreduction and induces inflammation in the context of lymphodepletion. We hypothesized that SCT may overcome myeloma-induced immune defects to restore immune-equilibrium. To address this, we developed a model of ASCT for MM using Vk*MYC myeloma. Surprisingly, we demonstrate the induction of T cell-dependent MM control after SCT as recipients of syngeneic BM grafts containing T cells had prolonged survival and reduced tumor burden compared to BM alone. This anti-myeloma activity was independent of γδ T, natural killer (NK) and NK T cells and was dependent on CD4+and CD8+ T cells. MM-primed T cells, harvested from SCT recipients with controlled MM, protected against relapse in secondary recipients and were Vk*MYC clone-specific. Recipients of MM-primed T cells had increased central memory CD8+ T cells and transfer of MM-primed CD8+ T cells was sufficient to prevent relapse. Furthermore, TCR sequencing of CD8+ T cells from mice with controlled MM revealed a higher clonotype overlap relative to MM-free SCT recipients, consistent with MM-specific T cells. Donor IFNγ secretion and signaling was critical to protective immunity and CD8+ T cells from mice with relapsed MM had impaired IFNγ production compared to those with controlled disease. Importantly, IFNγ production was profoundly augmented by CD137 agonist treatment such that majority of mice controlled disease. These data provide new insights into the mechanisms of action of SCT in myeloma and suggest the use of CD137 agonists to prevent relapse.

AbstractBackgroundMonitoring changes in cognitive impairment is an important aspect of care for aging patients. Traditionally, this is done with pen‐and‐paper assessments such as the Mini‐Mental State Exam (MMSE). EEG is a low‐cost and widely available neurophysiology tool that provides a data‐rich measure of brain health, but signal complexity has limited its impact. Advances in machine learning bring new possibilities to decode those complex signals and leverage EEG in patient care. Here, we engineer novel EEG features for assessing brain health with the [Banded Fractal Variability] (BFV) technique. We use [BFV] and other EEG metrics to assess patients’ cognitive function via a continuous score (a [Cognitive Impairment Index], or [CII]). We validate the diagnostic value of the EEG features by comparing our model against reduced models lacking the EEG features.MethodThe patient sample (N=97, mean (sd) age = 69.1 (11.0), 52.6% female) was collected at the Pacific Brain Health Center. Diagnostic categories included subjective cognitive impairment (n=44), mild cognitive impairment (n=26), and dementia (n=27). Mixed disease (AD, DLB, FTD, vascular, diabetes) was common in the impaired sample, and 39.6% were diagnosed with AD. We developed a continuous [CII] model trained on patients’ MMSE scores (range = 11 to 30, mean = 26.6) using a gradient boosting model with [Banded Fractal Variability] and other EEG features computed from clinical wake EEGs. Model development and testing took place within a rigorous nested cross‐validation (NCV) procedure to prevent overfitting. Performance was compared to reduced (no‐EEG features) models using the non‐parametric Wilcoxon sign‐rank test.ResultThe approach showed a Mean Absolute Error of 1.98 (sd = 0.28) across 5 test folds in the NCV outer loops, significantly outperforming both the baseline intercept‐only model (p = 0.031) and the demographics (age and years of education) model (p = 0.031).ConclusionThese preliminary results suggest that our [CII] derived from machine‐learning and quantitative EEG using [BFV] is a viable approach to assess and track patients’ cognitive impairment regardless of disease status across all impairment levels, raising the possibility that a brief EEG scan could shed light on patient health normally accessible only through extensive cognitive and neuropsychological testing.

AbstractBackgroundAlzheimer’s disease (AD) lacks a fast, easy, reliable, and inexpensive method of diagnosis. Currently, diagnosis is based on time‐consuming behavioral tests and the exclusion of other potential causes of impairment. Several biomarkers show good or promising diagnostic performance (e.g. CSF, tau PET, MRI, blood), but are either expensive, invasive, or still in development and while some can detect preclinical disease stages, all appear slow to change relative to the rate of cognitive decline. Here we develop a prototype diagnostic classifier based on novel metrics of brain activity in resting state electroencephalography (EEG) that correlates well with mental status.MethodArchival resting‐state EEG recordings of older adults (N=248) came from a memory clinic and university‐based clinic with a range of clinical diagnoses including subjective cognitive impairment (SCI), mild cognitive impairment (MCI), and dementia, representing AD, vascular dementia, and Lewy body dementia, TBI, and depression. We developed XGBoost classifiers to detect AD using EEG, age, and sex under increasingly challenging conditions. We computed metrics of periodic and aperiodic brain activity using the FOOOF algorithm. Furthermore, we developed a novel technique called [Banded Fractal Variability], which yields a set of features based on fluctuations in the fractal dimension within canonical frequency bands. We trained classifiers using cross‐validation to avoid overfitting during hyperparameter selection.ResultAlong with ROCAUC, we report an optimal sensitivity, specificity, and accuracy for the point on the ROC curve that maximizes Youden’s j. The classification tasks were healthy vs. probable AD (ROCAUC = 98%, sensitivity = 90%, specificity = 99%, accuracy = 96%), SCI vs. mild AD (ROCAUC = 89%, sensitivity = 76%, specificity = 87%, accuracy = 84%), and AD vs. other pathologies (no AD diagnosis) in MCI and dementia patients (ROCAUC = 82%, sensitivity = 72%, specificity = 87%, accuracy = 80%). All ROCAUC values were stronger than would be expected by chance (ps < 0.001).ConclusionThese preliminary results suggest that AD could be diagnosed in the clinic on the basis of machine‐learning classifiers and resting‐state EEG. Furthermore, they demonstrate that [Banded Fractal Variability] carries clinically‐relevant information about AD.