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The combined use of prostate cancer radiotherapy and MRI planning is increasingly being used in the treatment of clinically significant prostate cancers. The radiotherapy dosage quantity is limited by toxicity in organs with de-novo genitourinary toxicity occurrence remaining unperturbed. Estimation of the urethral radiation dose via anatomical contouring may improve our understanding of genitourinary toxicity and its related symptoms. Yet, urethral delineation remains an expert-dependent and time-consuming procedure. In this study, we aim to develop a fully automated segmentation tool for the prostatic urethra.This study incorporated 939 patients' T2-weighted MRI scans (train/validation/test/excluded: 657/141/140/1 patients), including in-house and public PROSTATE-x datasets, and their corresponding ground truth urethral contours from an expert genitourinary radiologist. The AI model was developed using MONAI framework and was based on a 3D-UNet. AI model performance was determined by Dice score (volume-based) and the Centerline Distance (CLD) between the prediction and ground truth centers (slice-based). All predictions were compared to ground truth in a systematic failure analysis to elucidate the model's strengths and weaknesses. The Wilcoxon-rank sum test was used for pair-wise comparison of group differences.The overall organ-adjusted Dice score for this model was 0.61 and overall CLD was 2.56 mm. When comparing prostates with symmetrical (n = 117) and asymmetrical (n = 23) benign prostate hyperplasia (BPH), the AI model performed better on symmetrical prostates compared to asymmetrical in both Dice score (0.64 vs. 0.51 respectively, p0.05) and mean CLD (2.3 mm vs. 3.8 mm respectively, p0.05). When calculating location-specific performance, the performance was highest at the apex and lowest at the base location of the prostate for Dice and CLD. Dice location dependence: symmetrical (Apex, Mid, Base: 0.69 vs. 0.67 vs. 0.54 respectively, p0.05) and asymmetrical (Apex, Mid, Base: 0.68 vs. 0.52 vs. 0.39 respectively, p0.05). CLD location dependence: symmetrical (Apex, Mid, Base: 1.43 mm vs. 2.15 mm vs. 3.28 mm, p0.05) and asymmetrical (Apex, Mid, Base: 1.83 mm vs. 3.1 mm vs. 6.24 mm, p0.05).We developed a fully automated prostatic urethra segmentation AI tool yielding its best performance in prostate glands with symmetric BPH features. This system can potentially be used to assist treatment planning in patients who can undergo whole gland radiation therapy or ablative focal therapy.

STRUCTURED ABSTRACTBackgroundPatients with localized prostate cancer have historically been assigned to clinical risk groups based on local disease extent, serum prostate specific antigen (PSA), and tumor grade. Clinical risk grouping is used to determine the intensity of treatment with external beam radiotherapy (EBRT) and androgen deprivation therapy (ADT), yet a substantial proportion of patients with intermediate and high risk localized prostate cancer will develop biochemical recurrence (BCR) and require salvage therapy. Prospective identification of patients destined to experience BCR would allow treatment intensification or selection of alternative therapeutic strategies.MethodsTwenty-nine individuals with intermediate or high risk prostate cancer were prospectively recruited to a clinical trial designed to profile the molecular and imaging features of prostate cancer in patients undergoing EBRT and ADT. Whole transcriptome cDNA microarray and whole exome sequencing were performed on pretreatment targeted biopsy of prostate tumors (n=60). All patients underwent pretreatment and 6-month post EBRT multiparametric MRI (mpMRI), and were followed with serial PSA to assess presence or absence of BCR. Genes differentially expressed in the tumor of patients with and without BCR were investigated using pathways analysis tools and were similarly explored in alternative datasets. Differential gene expression and predicted pathway activation were evaluated in relation to tumor response on mpMRI and tumor genomic profile. A novel TGF-β gene signature was developed in the discovery dataset and applied to a validation dataset.FindingsBaseline MRI lesion volume andPTEN/TP53status in prostate tumor biopsies correlated with the activation state of TGF-β signaling measured using pathway analysis. All three measures correlated with the risk of BCR after definitive RT. A prostate cancer-specific TGF-β signature discriminated between patients that experienced BCR vs. those that did not. The signature retained prognostic utility in an independent cohort.InterpretationTGF-β activity is a dominant feature of intermediate-to-unfavorable risk prostate tumors prone to biochemical failure after EBRT with ADT. TGF-β activity may serve as a prognostic biomarker independent of existing risk factors and clinical decision-making criteria.FundingThis research was supported by the Prostate Cancer Foundation, the Department of Defense Congressionally Directed Medical Research Program, National Cancer Institute, and the Intramural Research Program of the NIH, National Cancer Institute, Center for Cancer Research.

Purpose:For high-risk prostate cancer, standard treatment options include radical prostatectomy (RP) or radiotherapy plus androgen deprivation therapy (ADT). Despite definitive therapy, many patients will have disease recurrence. Imaging has the potential to better define characteristics of response and resistance. In this study, we evaluated prostate multiparametric MRI (mpMRI) before and after neoadjuvant enzalutamide plus ADT.Patients and Methods:Men with localized intermediate- or high-risk prostate cancer underwent a baseline mpMRI and mpMRI-targeted biopsy followed by a second mpMRI after 6 months of enzalutamide and ADT prior to RP. Specimens were sectioned in the same plane as mpMRI using patient-specific 3D-printed molds to permit mpMRI-targeted biopsies to be compared with the same lesion from the RP. Specimens were analyzed for imaging and histologic correlates of response.Results:Of 39 patients enrolled, 36 completed imaging and RP. Most patients (92%) had high-risk disease. Fifty-eight lesions were detected on baseline mpMRI, of which 40 (69%) remained measurable at 6-month follow-up imaging. Fifty-five of 59 lesions (93%) demonstrated >50% volume reduction on posttreatment mpMRI. Three of 59 lesions (5%) demonstrated growth in size at follow-up imaging, with two lesions increasing more than 3-fold in volume. On whole-mount pathology, 15 patients demonstrated minimal residual disease (MRD) of Conclusions:Low relative lesion volume at baseline mpMRI was predictive of pathologic response. A subset of patients had limited response. Selection of patients based on these metrics may improve outcomes in high-risk disease.

Contains supplementary methods and 3 supplementary tables.

PURPOSE: Neoadjuvant intense androgen deprivation therapy (iADT) can exert a wide range of histologic responses, which in turn are reflected in the final prostatectomy specimen. Accurate identification and measurement of residual tumor volumes are critical for tracking and stratifying patient outcomes. MATERIALS AND METHODS: The goal of this current study was to evaluate the ability of antibodies against prostate-specific membrane antigen (PSMA) to specifically detect residual tumor in a cohort of 35 patients treated with iADT plus enzalutamide for six months prior to radical prostatectomy. RESULTS: Residual carcinoma was detected in 31 patients, and PSMA reacted positively with tumor in all cases. PSMA staining was 96% sensitive for tumor, with approximately 82% of benign regions showing no reactivity. By contrast, PSMA positively reacted with 72% of benign regions in a control cohort of 37 untreated cases, resulting in 28% specificity for tumor. PSMA further identified highly dedifferentiated prostate carcinomas including tumors with evidence of neuroendocrine differentiation. CONCLUSIONS: We propose that anti-PSMA immunostaining be a standardized marker for identifying residual cancer in the setting of neoadjuvant intense androgen deprivation therapy.

To determine the rigor, generalizability, and reproducibility of published classification and detection artificial intelligence (AI) models for prostate cancer (PCa) on MRI using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) guidelines, a 42-item checklist that is considered a measure of best practice for presenting and reviewing medical imaging AI research.This review searched English literature for studies proposing PCa AI detection and classification models on MRI. Each study was evaluated with the CLAIM checklist. The additional outcomes for which data were sought included measures of AI model performance (eg, area under the curve [AUC], sensitivity, specificity, free-response operating characteristic curves), training and validation and testing group sample size, AI approach, detection versus classification AI, public data set utilization, MRI sequences used, and definition of gold standard for ground truth. The percentage of CLAIM checklist fulfillment was used to stratify studies into quartiles. Wilcoxon's rank-sum test was used for pair-wise comparisons.In all, 75 studies were identified, and 53 studies qualified for analysis. The original CLAIM items that most studies did not fulfill includes item 12 (77% no): de-identification methods; item 13 (68% no): handling missing data; item 15 (47% no): rationale for choosing ground truth reference standard; item 18 (55% no): measurements of inter- and intrareader variability; item 31 (60% no): inclusion of validated interpretability maps; item 37 (92% no): inclusion of failure analysis to elucidate AI model weaknesses. An AUC score versus percentage CLAIM fulfillment quartile revealed a significant difference of the mean AUC scores between quartile 1 versus quartile 2 (0.78 versus 0.86, P = .034) and quartile 1 versus quartile 4 (0.78 versus 0.89, P = .003) scores. Based on additional information and outcome metrics gathered in this study, additional measures of best practice are defined. These new items include disclosure of public dataset usage, ground truth definition in comparison to other referenced works in the defined task, and sample size power calculation.A large proportion of AI studies do not fulfill key items in CLAIM guidelines within their methods and results sections. The percentage of CLAIM checklist fulfillment is weakly associated with improved AI model performance. Additions or supplementations to CLAIM are recommended to improve publishing standards and aid reviewers in determining study rigor.

To present fully automated DL-based prostate cancer detection system for prostate MRI.MRI scans from two institutions, were used for algorithm training, validation, testing. MRI-visible lesions were contoured by an experienced radiologist. All lesions were biopsied using MRI-TRUS-guidance. Lesions masks, histopathological results were used as ground truth labels to train UNet, AH-Net architectures for prostate cancer lesion detection, segmentation. Algorithm was trained to detect any prostate cancer ≥ ISUP1. Detection sensitivity, positive predictive values, mean number of false positive lesions per patient were used as performance metrics.525 patients were included for training, validation, testing of the algorithm. Dataset was split into training (n = 368, 70%), validation (n = 79, 15%), test (n = 78, 15%) cohorts. Dice coefficients in training, validation sets were 0.403, 0.307, respectively, for AHNet model compared to 0.372, 0.287, respectively, for UNet model. In validation set, detection sensitivity was 70.9%, PPV was 35.5%, mean number of false positive lesions/patient was 1.41 (range 0-6) for UNet model compared to 74.4% detection sensitivity, 47.8% PPV, mean number of false positive lesions/patient was 0.87 (range 0-5) for AHNet model. In test set, detection sensitivity for UNet was 72.8% compared to 63.0% for AHNet, mean number of false positive lesions/patient was 1.90 (range 0-7), 1.40 (range 0-6) in UNet, AHNet models, respectively.We developed a DL-based AI approach which predicts prostate cancer lesions at biparametric MRI with reasonable performance metrics. While false positive lesion calls remain as a challenge of AI-assisted detection algorithms, this system can be utilized as an adjunct tool by radiologists.

OBJECTIVE. The purpose of this study was to assess the utility of PET with (2S)-2-[[(1S)-1-carboxy-5-[(6-((18)F)fluoranylpyridine-3-carbonyl)amino]pentyl]carbamoylamino]pentanedioic acid ((18)F-DCFPyL), a prostate-specific membrane antigen (PSMA)-targeted radiotracer, in the detection of high-risk localized prostate cancer as compared with multiparametric MRI (mpMRI). SUBJECTS AND METHODS. This HIPAA-compliant prospective study included 26 consecutive patients with localized high-risk prostate cancer (median age, 69.5 years [range, 53–81 years]; median prostate-specific antigen [PSA] level, 18.88 ng/mL [range, 1.03–20.00 ng/mL]) imaged with (18)F-DCFPyL PET/CT and mpMRI. Images from PET/CT and mpMRI were evaluated separately, and suspicious areas underwent targeted biopsy. Lesion-based sensitivity and tumor detection rate were compared for PSMA PET and mpMRI. Standardized uptake value (SUV) and PSMA PET parameters were correlated with histopathology score, and uptake in tumor was compared with that in nonmalignant tissue. On a patient level, SUV and PSMA tumor volume were correlated with PSA density. RESULTS. Forty-four tumors (one in Gleason grade [GG] group 1, 12 in GG group 2, seven in GG group 3, nine in GG group 4, and 15 in GG group 5) were identified at histopathology. Sensitivity and tumor detection rate of (18)F-DCFPyL PET/CT and mpMRI were similar (PET/CT, 90.9% and 80%; mpMRI, 86.4% and 88.4%; p = 0.58/0.17). Total lesion PSMA and PSMA tumor volume showed a relationship with GG (τ = 0.27 and p = 0.08, τ = 0.30 and p = 0.06, respectively). Maximum SUV in tumor was significantly higher than that in nonmalignant tissue (p < 0.05). Tumor burden density moderately correlated with PSA density (r = 0.47, p = 0.01). Five true-positive tumors identified on (18)F-DCFPyL PET/CT were not identified on mpMRI. CONCLUSION. In patients with high-risk prostate cancer, (18)F-DCFPyL PET/CT is highly sensitive in detecting intraprostatic tumors and can detect tumors missed on mpMRI. Measured uptake is significantly higher in tumor tissue, and PSMA-derived tumor burden is associated with severity of disease.

Background Prostate cancer recurrence is found in up to 40% of men with prior definitive (total prostatectomy or whole-prostate radiation) treatment. Prostate-specific membrane antigen PET agents such as 2-(3-{1-carboxy-5-[(6-[18F]fluoro-pyridine 3-carbonyl)-amino]-pentyl}-ureido)-pentanedioic acid (

Purpose Prostate-Specific Membrane Antigen (PSMA) PET/CT has been introduced as a sensitive method for characterizing metastatic prostate cancer. The purpose of this study is to compare the spatial concordance of 18F-NaF PET/CT and 18F-PSMA-targeted PET/CT within prostate cancer bone metastases. Methods Prostate cancer patients with known bone metastases underwent PSMA-targeted PET/CT (18F-DCFBC or 18F-DCFPyL) and 18F-NaF PET/CT. In pelvic and spinal lesions detected by both radiotracers, regions-of-interest (ROIs) derived by various thresholds of uptake intensity were compared for spatial colocalization. Overlap volume was correlated with uptake characteristics and disease status. Results The study included 149 lesions in 19 patients. Qualitatively, lesions exhibited a heterogeneous range of spatial concordance between PSMA and NaF uptake from completely matched to completely discordant. Quantitatively, overlap volume decreased as a function of tracer intensity. and disease status, where lesions from patients with castration-sensitive disease showed higher spatial concordance while lesions from patients with castration-resistant disease demonstrated more frequent spatial discordance. Conclusion As metastatic prostate cancer progresses from castration-sensitive to castration-resistant, greater discordance is observed between NaF PET and PSMA PET uptake. This may indicate a possible phenotypic shift to tumor growth that is more independent of bone remodeling via osteoblastic formation.

BACKGROUND: Bone flare has been observed on (99m)Tc-MDP bone scans of patients with metastatic castration-resistant prostate cancer (mCRPC). This exploratory study investigates bone flare in mCRPC patients receiving androgen receptor (AR) inhibitors using (18)F-NaF PET/CT. METHODS: Twenty-nine mCRPC patients undergoing AR-inhibiting therapy (abiraterone, orteronel, enzalutamide) received NaF PET/CT scans at baseline, week 6, and week 12 of treatment. SUV metrics were extracted globally for each patient (SUV) and for each individual lesion (iSUV). Bone flare was defined as increasing SUV metrics or lesion number at week 6 followed by subsequent week 12 decrease. Differences in metrics across timepoints were compared using Wilcoxon tests. Cox proportional hazard regression was conducted between global metrics and progression-free survival (PFS). RESULTS: Total SUV was most sensitive for flare detection and was identified in 14/23 (61%) patients receiving CYP17A1-inhibitors (abiraterone, orteronel), and not identified in any of six patients receiving enzalutamide. The appearance of new lesions did not account for initial increases in SUV metrics. iSUV metrics followed patient-level trends: bone flare positive patients showed a median of 72% (range: 0–100%) of lesions with total iSUV flare. Increasing mean SUV at week 6 correlated with extended PFS (HR = 0.58, p = 0.02). CONCLUSION: NaF PET bone flare was present on 61% of mCRPC patients in the first 6 weeks of treatment with CYP17A1-inhibitors. Characterization provided in this study suggests favorable PFS in patients showing bone flare. This characterization of NaF flare is important for guiding treatment assessment schedules to better distinguish between patients showing bone flare and those truly progressing, and should be performed for all emerging mCRPC treatments and imaging agents.

The purpose of this study was to compare the diagnostic performance of (18)F-DCFBC PET/CT, a first-generation (18)F-labeled prostate-specific membrane antigen (PSMA)–targeted agent, and (18)F-NaF PET/CT, a sensitive marker of osteoblastic activity, in a prospective cohort of patients with metastatic prostate cancer. Methods: Twenty-eight prostate cancer patients with metastatic disease on conventional imaging prospectively received up to 4 PET/CT scans. All patients completed baseline (18)F-DCFBC PET/CT and (18)F-NaF PET/CT scans, and 23 patients completed follow-up imaging, with a median follow-up interval of 5.7 mo (range, 4.2–12.6 mo). Lesion detection was compared across the 2 PET/CT agents at each time point. Detection and SUV characteristics of each PET/CT agent were compared with serum prostate-specific antigen (PSA) levels and treatment status at the time of baseline imaging using nonparametric statistical testing (Spearman correlation, Wilcoxon rank). Results: Twenty-six patients had metastatic disease detected on (18)F-NaF or (18)F-DCFBC at baseline, and 2 patients were negative on both scans. Three patients demonstrated soft tissue–only disease. Of 241 lesions detected at baseline, 56 were soft-tissue lesions identified by (18)F-DCFBC only and 185 bone lesions detected on (18)F-NaF or (18)F-DCFBC. (18)F-NaF detected significantly more bone lesions than (18)F-DCFBC (P < 0.001). Correlation of PSA with patient-level SUV metrics was strong in (18)F-DCFBC (ρ > 0.5, P < 0.01) and poor in (18)F-NaF (ρ < 0.3, P > 0.1). When PSA levels were combined with treatment status, patients with below-median levels of PSA (

Genotyping-in-Thousands by sequencing (GT-seq) is a method that uses next-generation sequencing of multiplexed PCR products to generate genotypes from relatively small panels (50-500) of targeted single-nucleotide polymorphisms (SNPs) for thousands of individuals in a single Illumina HiSeq lane. This method uses only unlabelled oligos and PCR master mix in two thermal cycling steps for amplification of targeted SNP loci. During this process, sequencing adapters and dual barcode sequence tags are incorporated into the amplicons enabling thousands of individuals to be pooled into a single sequencing library. Post sequencing, reads from individual samples are split into individual files using their unique combination of barcode sequences. Genotyping is performed with a simple perl script which counts amplicon-specific sequences for each allele, and allele ratios are used to determine the genotypes. We demonstrate this technique by genotyping 2068 individual steelhead trout (Oncorhynchus mykiss) samples with a set of 192 SNP markers in a single library sequenced in a single Illumina HiSeq lane. Genotype data were 99.9% concordant to previously collected TaqMan(™) genotypes at the same 192 loci, but call rates were slightly lower with GT-seq (96.4%) relative to Taqman (99.0%). Of the 192 SNPs, 187 were genotyped in ≥90% of the individual samples and only 3 SNPs were genotyped in

The breeding population of Wood Storks (Mycteria americana) in the southeastern United States has declined since the 1930s, resulting in the listing of all United States populations of the species as endangered in 1984. We assessed genetic structure within and among nine colonies of Wood Storks from Georgia and Florida. Levels of band sharing based on multilocus oligonucleotide DNA fingerprinting of individuals from seven colonies ranged from 58% among nests within the same tree to approximately 55% within the same colony. Levels of band sharing were similar to those reported for other populations of birds that have experienced drastic reductions in population size. A more thorough analysis of four polymorphic microsatellite loci for 136 individuals from nine colonies indicated low levels of allelic diversity and low genetic divergence among colonies. Genetic differentiation (F ST = 0.015) was similar to levels detected from allozymes for 15 colonies of Wood Storks in Florida (F ST = 0.019). These data, together with demographic studies of these populations, indicated high levels of gene flow among colonies (N m = 16.4). In agreement with previous studies, we recommend that all colonies of Wood Storks in the southeastern United States be managed on a regional basis as a single interbreeding population. Similar genetic surveys of the disjunct breeding populations of Wood Storks in South America and Central America would be beneficial in understanding the total genetic differentiation in the species.

The purpose of this tutorial is to provide an introduction to reinforcement learning (RL) at a level easily understood by students and researchers in a wide range of disciplines. The intent is not to present a rigorous mathematical discussion that requires a great deal of effort on the part of the reader, but rather to present a conceptual framework that might serve as an introduction to a more rigorous study of RL. The fundamental principles and techniques used to solve RL problems are presented. The most popular RL algorithms are presented. Section (1) presents an overview of RL and provides a simple example to develop intuition of the underlying dynamic programming mechanism. In Section (2) the parts of a reinforcement learning problem are discussed. These include the environment, reinforcement function, and value function. Section (3) gives a description of the most widely used reinforcement learning algorithms. These include TD(lambda) and both the residual and direct forms of value iteration, Q-learning, and advantage learning. In Section (4) some of the ancillary issues of RL are briefly discussed, such as choosing an exploration strategy and a discount factor. The conclusion is given in Section (5). Finally, Section (6) is a glossary of commonly used terms followed by references and bibliography.