Location-based Radiology Report-Guided Semi-supervised Learning for Prostate Cancer Detection.

Prostate cancer is one of the most prevalent malignancies in the world. While deep learning has potential to further improve computer-aided prostate cancer detection on MRI, its efficacy hinges on the exhaustive curation of manually annotated images. We propose a novel methodology of semisupervised learning (SSL) guided by automatically extracted clinical information, specifically the lesion locations in radiology reports, allowing for use of unannotated images to reduce the annotation burden. By leveraging lesion locations, we refined pseudo labels, which were then used to train our location-based SSL model. We show that our SSL method can improve prostate lesion detection by utilizing unannotated images, with more substantial impacts being observed when larger proportions of unannotated images are used.

AI-ADC: Channel and Spatial Attention-Based Contrastive Learning to Generate ADC Maps from T2W MRI for Prostate Cancer Detection.

BACKGROUND/OBJECTIVES: Apparent Diffusion Coefficient (ADC) maps in prostate MRI can reveal tumor characteristics, but their accuracy can be compromised by artifacts related with patient motion or rectal gas associated distortions. To address these challenges, we propose a novel approach that utilizes a Generative Adversarial Network to synthesize ADC maps from T2-weighted magnetic resonance images (T2W MRI). METHODS: By leveraging contrastive learning, our model accurately maps axial T2W MRI to ADC maps within the cropped region of the prostate organ boundary, capturing subtle variations and intricate structural details by learning similar and dissimilar pairs from two imaging modalities. We trained our model on a comprehensive dataset of unpaired T2-weighted images and ADC maps from 506 patients. In evaluating our model, named AI-ADC, we compared it against three state-of-the-art methods: CycleGAN, CUT, and StyTr2. RESULTS: Our model demonstrated a higher mean Structural Similarity Index (SSIM) of 0.863 on a test dataset of 3240 2D MRI slices from 195 patients, compared to values of 0.855, 0.797, and 0.824 for CycleGAN, CUT, and StyTr2, respectively. Similarly, our model achieved a significantly lower Fréchet Inception Distance (FID) value of 31.992, compared to values of 43.458, 179.983, and 58.784 for the other three models, indicating its superior performance in generating ADC maps. Furthermore, we evaluated our model on 147 patients from the publicly available ProstateX dataset, where it demonstrated a higher SSIM of 0.647 and a lower FID of 113.876 compared to the other three models. CONCLUSIONS: These results highlight the efficacy of our proposed model in generating ADC maps from T2W MRI, showcasing its potential for enhancing clinical diagnostics and radiological workflows.

External Validation of a Previously Developed Deep Learning-based Prostate Lesion Detection Algorithm on Paired External and In-House Biparametric MRI Scans.

Purpose To evaluate the performance of an artificial intelligence (AI) model in detecting overall and clinically significant prostate cancer (csPCa)-positive lesions on paired external and in-house biparametric MRI (bpMRI) scans and assess performance differences between each dataset. Materials and Methods This single-center retrospective study included patients who underwent prostate MRI at an external institution and were rescanned at the authors' institution between May 2015 and May 2022. A genitourinary radiologist performed prospective readouts on in-house MRI scans following the Prostate Imaging Reporting and Data System (PI-RADS) version 2.0 or 2.1 and retrospective image quality assessments for all scans. A subgroup of patients underwent an MRI/US fusion-guided biopsy. A bpMRI-based lesion detection AI model previously developed using a completely separate dataset was tested on both MRI datasets. Detection rates were compared between external and in-house datasets with use of the paired comparison permutation tests. Factors associated with AI detection performance were assessed using multivariable generalized mixed-effects models, incorporating features selected through forward stepwise regression based on the Akaike information criterion. Results The study included 201 male patients (median age, 66 years [IQR, 62-70 years]; prostate-specific antigen density, 0.14 ng/mL2 [IQR, 0.10-0.22 ng/mL2]) with a median interval between external and in-house MRI scans of 182 days (IQR, 97-383 days). For intraprostatic lesions, AI detected 39.7% (149 of 375) on external and 56.0% (210 of 375) on in-house MRI scans (P < .001). For csPCa-positive lesions, AI detected 61% (54 of 89) on external and 79% (70 of 89) on in-house MRI scans (P < .001). On external MRI scans, better overall lesion detection was associated with a higher PI-RADS score (odds ratio [OR] = 1.57; P = .005), larger lesion diameter (OR = 3.96; P < .001), better diffusion-weighted MRI quality (OR = 1.53; P = .02), and fewer lesions at MRI (OR = 0.78; P = .045). Better csPCa detection was associated with a shorter MRI interval between external and in-house scans (OR = 0.58; P = .03) and larger lesion size (OR = 10.19; P < .001). Conclusion The AI model exhibited modest performance in identifying both overall and csPCa-positive lesions on external bpMRI scans. Keywords: MR Imaging, Urinary, Prostate Supplemental material is available for this article. © RSNA, 2024.

Comparison of Transatlantic Recommendations for Prostate Gland Evaluation with MRI after Focal Therapy (TARGET) and Prostate Imaging after Focal Ablation (PI-FAB) for Detecting Recurrent Prostate Cancer at Prostate MRI.

RATIONALE AND OBJECTIVES: The increasing use of focal therapy (FT) in localized prostate cancer (PCa) management requires a standardized MRI interpretation system to detect recurrent clinically significant PCa (csPCa). This pilot study evaluates the novel Transatlantic Recommendations for Prostate Gland Evaluation with MRI after Focal Therapy (TARGET) and compares its performance to that of the Prostate Imaging after Focal Ablation (PI-FAB) system. MATERIALS AND METHODS: This retrospective study included 38 patients who underwent primary FT for localized PCa, with follow-up multiparametric MRI (mpMRI) and biopsy. Two radiologists assessed the mpMRIs using both PI-FAB and TARGET independently. Diagnostic performance metrics and area under the receiver operating characteristic curve (AUC) were calculated. Inter-reader and intrareader agreement were assessed using Cohen's κ and Kendall's τ. RESULTS: 14 patients had recurrent csPCa. PI-FAB showed high sensitivity (92.9% for both readers) and NPV (reader 1: 93.8%, reader 2: 92.9%) but moderate specificity (reader 1: 62.5%, reader 2: 54.2%). TARGET demonstrated lower sensitivity for one reader (reader 1: 78.6%, reader 2: 92.9%) but higher specificity (reader 1: 79.2%, reader 2: 62.5%) for both readers. Both systems displayed moderate inter-reader agreement (κ = 0.56 for PI-FAB, 0.57 for TARGET). CONCLUSION: PI-FAB and TARGET exhibit similar performances in post-FT MRI. While PI-FAB had consistently high sensitivity, TARGET offered higher specificity for one reader. Moderate agreement levels demonstrate the viability of these systems in clinical settings and a promise for improvement.

Large Scale Comparative Analysis of Canine and Human Osteosarcomas Uncovers Conserved Clinically Relevant Tumor Microenvironment Subtypes.

PURPOSE: Osteosarcoma is an aggressive bone cancer lacking robust biomarkers for personalized treatment. Despite its scarcity in humans, it is relatively common in adult pet dogs. This study aimed to analyze clinically annotated bulk tumor transcriptomic datasets of canine and human osteosarcoma patients to identify potentially conserved patterns of disease progression. EXPERIMENTAL DESIGN: Bulk transcriptomic data from 245 pet dogs with treatment-naïve appendicular osteosarcoma were analyzed using deconvolution to characterize the tumor microenvironment (TME). The TME of both primary and metastatic tumors derived from the same dog was compared, and its impact on canine survival was assessed. A machine learning model was developed to classify the TME based on its inferred composition using canine tumor data. This model was applied to 8 independent human osteosarcoma datasets to assess its generalizability and prognostic value. RESULTS: This study found three distinct TME subtypes of canine osteosarcoma based on cell type composition of bulk tumor samples: Immune Enriched (IE), Immune Enriched Dense Extra-Cellular Matrix-like (IE-ECM), and Immune Desert (ID). These three TME-based subtypes of canine osteosarcomas were conserved in humans and could predict progression-free survival outcomes of human patients, independent of conventional prognostic factors such as percent tumor necrosis post standard of care chemotherapy treatment and disease stage at diagnosis. CONCLUSIONS: These findings demonstrate the potential of leveraging data from naturally occurring cancers in canines to model the complexity of the human osteosarcoma TME, offering a promising avenue for the discovery of novel biomarkers and developing more effective precision oncology treatments.

A Phase 1 Trial of Image Guided Risk Volume-Adapted Postprostatectomy Radiation Therapy.

PURPOSE: This was a phase 1 trial with the primary objective of identifying the most compressed dose schedule (DS) tolerable using risk volume-adapted, hypofractionated, postoperative radiation therapy (PORT) for biochemically recurrent prostate cancer. Secondary endpoints included biochemical progression-free survival and quality of life (QOL). METHODS AND MATERIALS: Patients were treated with 1 of 3 isoeffective DSs (DS1: 20 fractions, DS2: 15 fractions, and DS3: 10 fractions) that escalated the dose to the imaging-defined local recurrence (73 Gy3 equivalent dose in 2Gy fractions) and de-escalated the dose to the remainder of the prostate bed (48 Gy3 equivalent dose in 2Gy fractions). Escalation followed a standard 3 + 3 design with a 6-patient expansion at the maximally tolerated hypofractionated DS. Dose-limiting toxicity was defined as Common Terminology Criteria for Adverse Events v.4.0 grade (G) 3 toxicity lasting >4 days within 21 days of PORT completion or G4 gastrointestinal (GI) or genitourinary toxicities thereafter. QOL was assessed longitudinally through 24 months with the Expanded Prostate Cancer Index Composite short form. RESULTS: Between January 2018 and December 2023, 15 patients were treated (3 with DS1, 3 with DS2, and 9 with DS3). The median follow-up was 48 months. No dose-limiting toxicities were observed on any DS, and thus, expansion occurred at DS3. The cumulative incidence of G3 GI and genitourinary toxicity was 7% and 9% at 24 months, respectively, with no G4 events observed. Transient, acute G2+ GI toxicity was the most common. QOL worsened transiently during study follow-up in urinary incontinence, GI, and sexual subdomains but was similar to baseline by 24 months. The biochemical progression-free survival was 91% at both 24 and 60 months. CONCLUSIONS: The maximally tolerated hypofractionated DS for hypofractionated, risk volume-adapted PORT was determined to be DS3 (36.4 Gy to the prostate bed and 47.1 Gy to the imaging-defined recurrence in 10 daily fractions). No >G3 events were observed. Transient declines in QOL did not persist through 24 months.

Editorial for Special Topics: Imaging-Based Diagnosis for Prostate Cancer-State of the Art.

This Special Topics Issue, "Imaging-based Diagnosis of Prostate Cancer-State of the Art", of Diagnostics compiles 10 select articles [...].

Glypican-3 deficiency in liver cancer upregulates MAPK/ERK pathway but decreases cell proliferation.

Glypican-3 (GPC3) is overexpressed in hepatocellular carcinomas and hepatoblastomas and represents an important therapeutic target but the biologic importance of GPC3 in liver cancer is unclear. To date, there are limited data characterizing the biological implications of GPC3 knockout (KO) in liver cancers that intrinsically express this target. Here, we report on the development and characterization of GPC3-KO liver cancer cell lines and compare to them to parental lines. GPC3-KO variants were established in HepG2 and Hep3B liver cancer cell lines using a lentivirus-mediated CRISPR/Cas9 system. We assessed the effects of GPC3 deficiency on oncogenic properties in vitro and in murine xenograft models. Downstream cellular signaling pathway changes induced by GPC3 deficiency were examined by RNAseq and western blot. To confirm the usefulness of the models for GPC3-targeted drug development, we evaluated the target engagement of a GPC3-selective antibody, GC33, conjugated to the positron-emitting zirconium-89 (89Zr) in subcutaneous murine xenografts of wild type (WT) and KO liver cancer cell lines. Deletion of GPC3 significantly reduced liver cancer cell proliferation, migration, and invasion compared to the parental cell lines. Additionally, the tumor growth of GPC3-KO liver cancer xenografts was significantly slower compared with control xenografts. RNA sequencing analysis also showed GPC3-KO resulted in a reduction in the expression of genes associated with cell cycle regulation, invasion, and migration. Specifically, we observed the downregulation of components in the AKT/NFκB/WNT signaling pathways and of molecules related to cell cycle regulation with GPC3-KO. In contrast, pMAPK/ERK1/2 was upregulated, suggesting an adaptive compensatory response. KO lines demonstrated increased sensitivity to ERK (GDC09994), while AKT (MK2206) inhibition was more effective in WT lines. Using antibody-based positron emission tomography (immunoPET) imaging, we confirmed that 89Zr-GC33 accumulated exclusively in GPC3-expression xenografts but not in GPC3-KO xenografts with high tumor uptake and tumor-to-liver signal ratio. We show that GPC3-KO liver cancer cell lines exhibit decreased tumorigenicity and altered signaling pathways, including upregulated pMAPK/ERK1/2, compared to parental lines. Furthermore, we successfully distinguished between GPC3+ and GPC3- tumors using the GPC3-targeted immunoPET imaging agent, demonstrating the potential utility of these cell lines in facilitating GPC3-selective drug development.

A selective CutMix approach improves generalizability of deep learning-based grading and risk assessment of prostate cancer.

The Gleason score is an important predictor of prognosis in prostate cancer. However, its subjective nature can result in over- or under-grading. Our objective was to train an artificial intelligence (AI)-based algorithm to grade prostate cancer in specimens from patients who underwent radical prostatectomy (RP) and to assess the correlation of AI-estimated proportions of different Gleason patterns with biochemical recurrence-free survival (RFS), metastasis-free survival (MFS), and overall survival (OS). Training and validation of algorithms for cancer detection and grading were completed with three large datasets containing a total of 580 whole-mount prostate slides from 191 RP patients at two centers and 6218 annotated needle biopsy slides from the publicly available Prostate Cancer Grading Assessment dataset. A cancer detection model was trained using MobileNetV3 on 0.5 mm × 0.5 mm cancer areas (tiles) captured at 10× magnification. For cancer grading, a Gleason pattern detector was trained on tiles using a ResNet50 convolutional neural network and a selective CutMix training strategy involving a mixture of real and artificial examples. This strategy resulted in improved model generalizability in the test set compared with three different control experiments when evaluated on both needle biopsy slides and whole-mount prostate slides from different centers. In an additional test cohort of RP patients who were clinically followed over 30 years, quantitative Gleason pattern AI estimates achieved concordance indexes of 0.69, 0.72, and 0.64 for predicting RFS, MFS, and OS times, outperforming the control experiments and International Society of Urological Pathology system (ISUP) grading by pathologists. Finally, unsupervised clustering of test RP patient specimens into low-, medium-, and high-risk groups based on AI-estimated proportions of each Gleason pattern resulted in significantly improved RFS and MFS stratification compared with ISUP grading. In summary, deep learning-based quantitative Gleason scoring using a selective CutMix training strategy may improve prognostication after prostate cancer surgery.

Deep learning-based image quality assessment: impact on detection accuracy of prostate cancer extraprostatic extension on MRI.

OBJECTIVE: To assess impact of image quality on prostate cancer extraprostatic extension (EPE) detection on MRI using a deep learning-based AI algorithm. MATERIALS AND METHODS: This retrospective, single institution study included patients who were imaged with mpMRI and subsequently underwent radical prostatectomy from June 2007 to August 2022. One genitourinary radiologist prospectively evaluated each patient using the NCI EPE grading system. Each T2WI was classified as low- or high-quality by a previously developed AI algorithm. Fisher's exact tests were performed to compare EPE detection metrics between low- and high-quality images. Univariable and multivariable analyses were conducted to assess the predictive value of image quality for pathological EPE. RESULTS: A total of 773 consecutive patients (median age 61 [IQR 56-67] years) were evaluated. At radical prostatectomy, 23% (180/773) of patients had EPE at pathology, and 41% (131/318) of positive EPE calls on mpMRI were confirmed to have EPE. The AI algorithm classified 36% (280/773) of T2WIs as low-quality and 64% (493/773) as high-quality. For EPE grade ≥ 1, high-quality T2WI significantly improved specificity for EPE detection (72% [95% CI 67-76%] vs. 63% [95% CI 56-69%], P = 0.03), but did not significantly affect sensitivity (72% [95% CI 62-80%] vs. 75% [95% CI 63-85%]), positive predictive value (44% [95% CI 39-49%] vs. 38% [95% CI 32-43%]), or negative predictive value (89% [95% CI 86-92%] vs. 89% [95% CI 85-93%]). Sensitivity, specificity, PPV, and NPV for EPE grades ≥ 2 and ≥ 3 did not show significant differences attributable to imaging quality. For NCI EPE grade 1, high-quality images (OR 3.05, 95% CI 1.54-5.86; P < 0.001) demonstrated a stronger association with pathologic EPE than low-quality images (OR 1.76, 95% CI 0.63-4.24; P = 0.24). CONCLUSION: Our study successfully employed a deep learning-based AI algorithm to classify image quality of prostate MRI and demonstrated that better quality T2WI was associated with more accurate prediction of EPE at final pathology.

Near-infrared duocarmycin photorelease from a Treg-targeted antibody-drug conjugate improves efficacy of PD-1 blockade in syngeneic murine tumor models.

Regulatory T cells (Tregs) play a crucial role in mediating immunosuppression in the tumor microenvironment. Furthermore, Tregs contribute to the lack of efficacy and hyperprogressive disease upon Programmed cell death protein 1 (PD-1) blockade immunotherapy. Thus, Tregs are considered a promising therapeutic target, especially when combined with PD-1 blockade. However, systemic depletion of Tregs causes severe autoimmune adverse events, which poses a serious challenge to Treg-directed therapy. Here, we developed a novel treatment to locally and predominantly damage Tregs by near-infrared duocarmycin photorelease (NIR-DPR). In this technology, we prepared anti-CD25 F(ab')2 conjugates, which site-specifically uncage duocarmycin in CD25-expressing cells upon exposure to NIR light. In vitro, CD25-targeted NIR-DPR significantly increased apoptosis of CD25-expressing HT2-A5E cells. When tumors were irradiated with NIR light in vivo, intratumoral CD25+ Treg populations decreased and Ki-67 and Interleukin-10 expression was suppressed, indicating impaired functioning of intratumoral CD25+ Tregs. CD25-targeted NIR-DPR suppressed tumor growth and improved survival in syngeneic murine tumor models. Of note, CD25-targeted NIR-DPR synergistically enhanced the efficacy of PD-1 blockade, especially in tumors with higher CD8+/Treg PD-1 ratios. Furthermore, the combination therapy induced significant anti-cancer immunity including maturation of dendritic cells, extensive intratumoral infiltration of cytotoxic CD8+ T cells, and increased differentiation into CD8+ memory T cells. Altogether, CD25-targeted NIR-DPR locally and predominantly targets Tregs in the tumor microenvironment and synergistically improves the efficacy of PD-1 blockade, suggesting that this combination therapy can be a rational anti-cancer combination immunotherapy.

Long-term engraftment and maturation of autologous iPSC-derived cardiomyocytes in two rhesus macaques.

Cellular therapies with cardiomyocytes produced from induced pluripotent stem cells (iPSC-CMs) offer a potential route to cardiac regeneration as a treatment for chronic ischemic heart disease. Here, we report successful long-term engraftment and in vivo maturation of autologous iPSC-CMs in two rhesus macaques with small, subclinical chronic myocardial infarctions, all without immunosuppression. Longitudinal positron emission tomography imaging using the sodium/iodide symporter (NIS) reporter gene revealed stable grafts for over 6 and 12 months, with no teratoma formation. Histological analyses suggested capability of the transplanted iPSC-CMs to mature and integrate with endogenous myocardium, with no sign of immune cell infiltration or rejection. By contrast, allogeneic iPSC-CMs were rejected within 8 weeks of transplantation. This study provides the longest-term safety and maturation data to date in any large animal model, addresses concerns regarding neoantigen immunoreactivity of autologous iPSC therapies, and suggests that autologous iPSC-CMs would similarly engraft and mature in human hearts.

In vivo tracking of ex vivo generated 89 Zr-oxine labeled plasma cells by PET in a non-human primate model.

B cells are an attractive platform for engineering to produce protein-based biologics absent in genetic disorders, and potentially for the treatment of metabolic diseases and cancer. As part of pre-clinical development of B cell medicines, we demonstrate a method to collect, ex vivo expand, differentiate, radioactively label, and track adoptively transferred non-human primate (NHP) B cells. These cells underwent 10- to 15-fold expansion, initiated IgG class switching, and differentiated into antibody secreting cells. Zirconium-89-oxine labeled cells were infused into autologous donors without any preconditioning and tracked by PET/CT imaging. Within 24 hours of infusion, 20% of the initial dose homed to the bone marrow and spleen and distributed stably and equally between the two. Interestingly, approximately half of the dose homed to the liver. Image analysis of the bone marrow demonstrated inhomogeneous distribution of the cells. The subjects experienced no clinically significant side effects or laboratory abnormalities. A second infusion of B cells into one of the subjects resulted in an almost identical distribution of cells, suggesting a non-limiting engraftment niche and feasibility of repeated infusions. This work supports the NHP as a valuable model to assess the potential of B cell medicines as potential treatment for human diseases.

Evaluation of a Cascaded Deep Learning-based Algorithm for Prostate Lesion Detection at Biparametric MRI.

Background Multiparametric MRI (mpMRI) improves prostate cancer (PCa) detection compared with systematic biopsy, but its interpretation is prone to interreader variation, which results in performance inconsistency. Artificial intelligence (AI) models can assist in mpMRI interpretation, but large training data sets and extensive model testing are required. Purpose To evaluate a biparametric MRI AI algorithm for intraprostatic lesion detection and segmentation and to compare its performance with radiologist readings and biopsy results. Materials and Methods This secondary analysis of a prospective registry included consecutive patients with suspected or known PCa who underwent mpMRI, US-guided systematic biopsy, or combined systematic and MRI/US fusion-guided biopsy between April 2019 and September 2022. All lesions were prospectively evaluated using Prostate Imaging Reporting and Data System version 2.1. The lesion- and participant-level performance of a previously developed cascaded deep learning algorithm was compared with histopathologic outcomes and radiologist readings using sensitivity, positive predictive value (PPV), and Dice similarity coefficient (DSC). Results A total of 658 male participants (median age, 67 years [IQR, 61-71 years]) with 1029 MRI-visible lesions were included. At histopathologic analysis, 45% (294 of 658) of participants had lesions of International Society of Urological Pathology (ISUP) grade group (GG) 2 or higher. The algorithm identified 96% (282 of 294; 95% CI: 94%, 98%) of all participants with clinically significant PCa, whereas the radiologist identified 98% (287 of 294; 95% CI: 96%, 99%; P = .23). The algorithm identified 84% (103 of 122), 96% (152 of 159), 96% (47 of 49), 95% (38 of 40), and 98% (45 of 46) of participants with ISUP GG 1, 2, 3, 4, and 5 lesions, respectively. In the lesion-level analysis using radiologist ground truth, the detection sensitivity was 55% (569 of 1029; 95% CI: 52%, 58%), and the PPV was 57% (535 of 934; 95% CI: 54%, 61%). The mean number of false-positive lesions per participant was 0.61 (range, 0-3). The lesion segmentation DSC was 0.29. Conclusion The AI algorithm detected cancer-suspicious lesions on biparametric MRI scans with a performance comparable to that of an experienced radiologist. Moreover, the algorithm reliably predicted clinically significant lesions at histopathologic examination. ClinicalTrials.gov Identifier: NCT03354416 © RSNA, 2024 Supplemental material is available for this article.

Deep learning-based whole-body PSMA PET/CT attenuation correction utilizing Pix-2-Pix GAN.

PURPOSE: Sequential PET/CT studies oncology patients can undergo during their treatment follow-up course is limited by radiation dosage. We propose an artificial intelligence (AI) tool to produce attenuation-corrected PET (AC-PET) images from non-attenuation-corrected PET (NAC-PET) images to reduce need for low-dose CT scans. METHODS: A deep learning algorithm based on 2D Pix-2-Pix generative adversarial network (GAN) architecture was developed from paired AC-PET and NAC-PET images. 18F-DCFPyL PSMA PET-CT studies from 302 prostate cancer patients, split into training, validation, and testing cohorts (n = 183, 60, 59, respectively). Models were trained with two normalization strategies: Standard Uptake Value (SUV)-based and SUV-Nyul-based. Scan-level performance was evaluated by normalized mean square error (NMSE), mean absolute error (MAE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR). Lesion-level analysis was performed in regions-of-interest prospectively from nuclear medicine physicians. SUV metrics were evaluated using intraclass correlation coefficient (ICC), repeatability coefficient (RC), and linear mixed-effects modeling. RESULTS: Median NMSE, MAE, SSIM, and PSNR were 13.26%, 3.59%, 0.891, and 26.82, respectively, in the independent test cohort. ICC for SUVmax and SUVmean were 0.88 and 0.89, which indicated a high correlation between original and AI-generated quantitative imaging markers. Lesion location, density (Hounsfield units), and lesion uptake were all shown to impact relative error in generated SUV metrics (all p < 0.05). CONCLUSION: The Pix-2-Pix GAN model for generating AC-PET demonstrates SUV metrics that highly correlate with original images. AI-generated PET images show clinical potential for reducing the need for CT scans for attenuation correction while preserving quantitative markers and image quality.

Salivary excretion of systemically injected [18F]DCFPyL in prostate cancer patients undergoing PSMA scans.

Introduction: Prostate-specific membrane antigen (PSMA) is present in high amounts in salivary glands, but it is unclear whether labeled binders of PSMA are excreted in the saliva. Methods: Ten patients with prostate cancer underwent whole-body [18F]DCFPyL PET/CT (NCT03181867), and saliva samples were collected between 0-120 minutes post-injection. [18F]DCFPyL salivary excretion was measured over 120 minutes and expressed as %ID/g. Protein-associated binding was estimated by the percentage of [18F]DCFPyL versus parent radiotracer. Results: All PET scans of 10 patients (69 ± 8 years) with histologically confirmed prostate cancer (PSA= 2.4 ± 2.4, and Gleason Grade = 6-9) showed high uptake of [18F]-DCFPyL in salivary glands while 8 patients demonstrated high uptake in the saliva at 45 minutes. The intact [18F]-DCFPyL (98%) was also confirmed in the saliva samples at 120 min with increasing salivary radioactivity between 30-120 min. Conclusion: Systemically injected [18F]DCFPyL shows salivary gland uptake, an increasing amount of which is secreted in saliva over time and is not maximized by 120 minutes post-injection. Although probably insignificant for diagnostic studies, patients undergoing PSMA-targeted therapies should be aware of radioactivity in saliva.

Automated Detection and Grading of Extraprostatic Extension of Prostate Cancer at MRI via Cascaded Deep Learning and Random Forest Classification.

RATIONALE AND OBJECTIVES: Extraprostatic extension (EPE) is well established as a significant predictor of prostate cancer aggression and recurrence. Accurate EPE assessment prior to radical prostatectomy can impact surgical approach. We aimed to utilize a deep learning-based AI workflow for automated EPE grading from prostate T2W MRI, ADC map, and High B DWI. MATERIAL AND METHODS: An expert genitourinary radiologist conducted prospective clinical assessments of MRI scans for 634 patients and assigned risk for EPE using a grading technique. The training set and held-out independent test set consisted of 507 patients and 127 patients, respectively. Existing deep-learning AI models for prostate organ and lesion segmentation were leveraged to extract area and distance features for random forest classification models. Model performance was evaluated using balanced accuracy, ROC AUCs for each EPE grade, as well as sensitivity, specificity, and accuracy compared to EPE on histopathology. RESULTS: A balanced accuracy score of .390 ± 0.078 was achieved using a lesion detection probability threshold of 0.45 and distance features. Using the test set, ROC AUCs for AI-assigned EPE grades 0-3 were 0.70, 0.65, 0.68, and 0.55 respectively. When using EPE≥ 1 as the threshold for positive EPE, the model achieved a sensitivity of 0.67, specificity of 0.73, and accuracy of 0.72 compared to radiologist sensitivity of 0.81, specificity of 0.62, and accuracy of 0.66 using histopathology as the ground truth. CONCLUSION: Our AI workflow for assigning imaging-based EPE grades achieves an accuracy for predicting histologic EPE approaching that of physicians. This automated workflow has the potential to enhance physician decision-making for assessing the risk of EPE in patients undergoing treatment for prostate cancer due to its consistency and automation.