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Competing Interests: We declare no competing interests.

Near infrared photoimmunotherapy (NIR-PIT) is a recently approved cancer therapy for recurrent head and neck cancer. It involves the intravenous administration of an antibody-photoabsorber (IRDye700DX: IR700) conjugate (APC) to target cancer cells, followed 24 h later by exposure to near infrared light to activate cell-specific cytotoxicity. NIR-PIT selectively targets cancer cells for destruction and activates a strong anticancer host immunity. The fluorescent signal emitted by IR700 enables the visualization of the APC in vivo using fluorescence imaging. Similarly, the activation of IR700 during therapy can be monitored by loss of fluorescence. NIR-PIT can be used with a variety of antibodies and therefore, a variety of cancer types. However, in most cases, NIR-PIT requires direct light exposure only achieved with interstitial diffuser light fibers that are placed with image-guided interventional needle insertion. In addition, the unique nature of NIR-PIT cell death, means that metabolic molecular imaging techniques such as PET and diffusion MRI can be used to assess therapeutic outcomes. This mini-review focuses on the potential implications of NIR-PIT for interventional radiology and therapeutic monitoring., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

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., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

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 ( 89 Zr) 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 89 Zr-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., Competing Interests: None., (AJCR Copyright © 2024.)

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., Competing Interests: Declaration of interests Some of these experiments were performed while D.S.N. and C.E.M. were employees of, and K. Nakamura was an advisor to, Sana Biotechnology. D.S.N. and C.E.M. are equity holders in Sana Biotechnology., (Published by Elsevier Inc.)

Esophageal cancer remains a highly aggressive malignancy with a poor prognosis, despite ongoing advancements in treatments such as immunotherapy. The tumor microenvironment, particularly cancer-associated fibroblasts (CAF), plays a crucial role in driving the aggressiveness of esophageal cancer. In a previous study utilizing human-derived xenograft models, we successfully developed a novel cancer treatment that targeted CAFs with near-infrared photoimmunotherapy (NIR-PIT), as an adjuvant therapy. In this study, we sought to translate our findings toward clinical practice by employing patient-derived xenograft (PDX) models and utilizing humanized mAbs, specifically sibrotuzumab, which is an antihuman fibroblast activation protein (FAP) Ab and already being investigated in clinical trials as monotherapy. PDX models derived from patients with esophageal cancer were effectively established, preserving the expression of key biomarkers such as EGFR and FAP, as observed in primary tumors. The application of FAP-targeted NIR-PIT using sibrotuzumab, conjugated with the photosensitizer IR700DX, exhibited precise binding and selective elimination of FAP-expressing fibroblasts in vitro. Notably, in our in vivo investigations using both cell line-derived xenograft and PDX models, FAP-targeted NIR-PIT led to significant inhibition of tumor progression compared with control groups, all without inducing adverse events such as weight loss. Immunohistologic assessments revealed a substantial reduction in CAFs exclusively within the tumor microenvironment of both models, further supporting the efficacy of our approach. Thus, our study demonstrates the potential of CAF-targeted NIR-PIT employing sibrotuzumab as a promising therapeutic avenue for the clinical treatment of patients with esophageal cancer., (©2024 American Association for Cancer Research.)

#PSMA is amazing new tech but is using it to expedite the call of disease progression helping #ProstateCancer patients?

Near-infrared photoimmunotherapy (NIR-PIT) is a new type of cancer therapy that employs antibody-IRDye700DX conjugates (AbPCs) and near-infrared (NIR) light at a wavelength of 689 nm, the excitation wavelength of IR700. Administered intravenously, injected AbPCs bind specifically to cells expressing the target antigen, whereupon NIR light exposure causes rapid, selective killing. This process induces an anticancer T cell response, leading to sustained anticancer host immune response. Programmed cell death ligand-1 (PD-L1) is a major inhibitory immune checkpoint molecule expressed in various cancers. In this study, we first assessed the efficacy of PD-L1-targeted NIR-PIT (αPD-L1-PIT) in immune-competent tumor mouse models. αPD-L1-PIT showed a significant therapeutic effect on the tumor models with high PD-L1 expression. Furthermore, αPD-L1-PIT induced an abscopal effect on distant tumors and long-term immunological memory. In contrast, αPD-L1-PIT was not as effective for tumor models with low PD-L1 expression. To improve the efficacy of PD-L1-targeted NIR-PIT, PEGylated interferon-gamma (IFNγ) was administered with αPD-L1-PIT. The combination therapy improved the treatment efficacy by increasing PD-L1 expression leading to more efficient cell killing by αPD-L1-PIT. Furthermore, the PEGylated IFNγ led to a CD8+ T cell-dominant tumor microenvironment (TME) with an enhanced anticancer T cell response after αPD-L1-PIT. As a result, even so-called cold tumors exhibited complete responses after αPD-L1-PIT. Thus, combination therapy of PEGylated IFNγ and PD-L1-targeted NIR-PIT has the potential to be an important future strategy for cancer immunotherapy., (Published 2024. This article is a U.S. Government work and is in the public domain in the USA. Cancer Science published by John Wiley & Sons Australia, Ltd on behalf of Japanese Cancer Association.)

Purpose: Prostate-specific membrane antigen (PSMA)-targeting PET radiotracers reveal physiologic uptake in the urinary system, potentially misrepresenting activity in the prostatic urethra as an intraprostatic lesion. This study examined the correlation between midline 18 F-DCFPyL activity in the prostate and hyperintensity on T2-weighted (T2W) MRI as an indication of retained urine in the prostatic urethra., Patients and Methods: Eighty-five patients who underwent both 18 F-DCFPyL PSMA PET/CT and prostate MRI between July 2017 and September 2023 were retrospectively analyzed for midline radiotracer activity and retained urine on postvoid T2W MRIs. Fisher's exact tests and unpaired t tests were used to compare residual urine presence and prostatic urethra measurements between patients with and without midline radiotracer activity. The influence of anatomical factors including prostate volume and urethral curvature on urinary stagnation was also explored., Results: Midline activity on PSMA PET imaging was seen in 14 patients included in the case group, whereas the remaining 71 with no midline activity constituted the control group. A total of 71.4% (10/14) and 29.6% (21/71) of patients in the case and control groups had urethral hyperintensity on T2W MRI, respectively ( P < 0.01). Patients in the case group had significantly larger mean urethral dimensions, larger prostate volumes, and higher incidence of severe urethral curvature compared with the controls., Conclusions: Stagnated urine within the prostatic urethra is a potential confounding factor on PSMA PET scans. Integrating PET imaging with T2W MRI can mitigate false-positive calls, especially as PSMA PET/CT continues to gain traction in diagnosing localized prostate cancer., Competing Interests: Conflicts of interest and sources of funding: none declared., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

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., Competing Interests: No potential conflict of interest was reported by the author(s)., (This work was authored as part of the Contributor’s official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.)

Background: Cancer-associated fibroblasts (CAFs) in the tumor microenvironment (TME) play a critical role in tumor immunosuppression. However, targeted depletion of CAFs is difficult due to their diverse cells of origin and the resulting lack of specific surface markers. Near-infrared photoimmunotherapy (NIR-PIT) is a novel cancer treatment that leads to rapid cell membrane damage., Methods: In this study, we used anti-mouse fibroblast activation protein (FAP) antibody to target FAP + CAFs (FAP-targeted NIR-PIT) and investigated whether this therapy could suppress tumor progression and improve tumor immunity., Results: FAP-targeted NIR-PIT induced specific cell death in CAFs without damaging adjacent normal cells. Furthermore, FAP-targeted NIR-PIT treated mice showed significant tumor regression in the CAF-rich tumor model accompanied by an increase in CD8 + tumor infiltrating lymphocytes (TILs). Moreover, treated tumors showed increased levels of IFN-γ, TNF-α, and IL-2 in CD8 + TILs compared with non-treated tumors, suggesting enhanced antitumor immunity., Conclusions: Cancers with FAP-positive CAFs in their TME grow rapidly and FAP-targeted NIR-PIT not only suppresses their growth but improves tumor immunosuppression. Thus, FAP-targeted NIR-PIT is a potential therapeutic strategy for selectively targeting the TME of CAF + tumors., (© 2024. The Author(s).)

Rationale and Objectives: Efficiently detecting and characterizing metastatic bone lesions on staging CT is crucial for prostate cancer (PCa) care. However, it demands significant expert time and additional imaging such as PET/CT. We aimed to develop an ensemble of two automated deep learning AI models for 1) bone lesion detection and segmentation and 2) benign vs. metastatic lesion classification on staging CTs and to compare its performance with radiologists., Materials and Methods: This retrospective study developed two AI models using 297 staging CT scans (81 metastatic) with 4601 benign and 1911 metastatic lesions in PCa patients. Metastases were validated by follow-up scans, bone biopsy, or PET/CT. Segmentation AI (3DAISeg) was developed using the lesion contours delineated by a radiologist. 3DAISeg performance was evaluated with the Dice similarity coefficient, and classification AI (3DAIClass) performance on AI and radiologist contours was assessed with F1-score and accuracy. Training/validation/testing data partitions of 70:15:15 were used. A multi-reader study was performed with two junior and two senior radiologists within a subset of the testing dataset (n = 36)., Results: In 45 unseen staging CT scans (12 metastatic PCa) with 669 benign and 364 metastatic lesions, 3DAISeg detected 73.1% of metastatic (266/364) and 72.4% of benign lesions (484/669). Each scan averaged 12 extra segmentations (range: 1-31). All metastatic scans had at least one detected metastatic lesion, achieving a 100% patient-level detection. The mean Dice score for 3DAISeg was 0.53 (median: 0.59, range: 0-0.87). The F1 for 3DAIClass was 94.8% (radiologist contours) and 92.4% (3DAISeg contours), with a median false positive of 0 (range: 0-3). Using radiologist contours, 3DAIClass had PPV and NPV rates comparable to junior and senior radiologists: PPV (semi-automated approach AI 40.0% vs. Juniors 32.0% vs. Seniors 50.0%) and NPV (AI 96.2% vs. Juniors 95.7% vs. Seniors 91.9%). When using 3DAISeg, 3DAIClass mimicked junior radiologists in PPV (pure-AI 20.0% vs. Juniors 32.0% vs. Seniors 50.0%) but surpassed seniors in NPV (pure-AI 93.8% vs. Juniors 95.7% vs. Seniors 91.9%)., Conclusion: Our lesion detection and classification AI model performs on par with junior and senior radiologists in discerning benign and metastatic lesions on staging CTs obtained for PCa., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Bradford J. Wood: Principal investigator on cooperative research and development agreement (CRADA) between National Institutes of Health (NIH) and Philips and CRADAs with industry partners unrelated to this work; travel support related to CRADAs; royalties from NIH related to Philips licensing agreement; patents planned, issued, or pending. Peter L. Choyke: Receives payment from royalties paid to the U.S. government for patents on MRI US fusion biopsy licensed to Philips Medical. Peter A. Pinto: Institutional CRADA with Philips; royalties from NIH related to Philips licensing agreement; NIH-related patents planned, issued, or pending (U.S. patent nos. 8 447 384 and 10 215 830). Baris Turkbey: CRADAs with NVIDIA and Philips; royalties from NIH; patents planned, issued, or pending in the field of artificial intelligence. Dong Yang, Ziyue Xu, Jesse Tetreault, Daguang Xu: employee of NVIDIA Corporation. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier Inc.)

Background: Image quality evaluation of prostate MRI is important for successful implementation of MRI into localized prostate cancer diagnosis., Purpose: To examine the impact of image quality on prostate cancer detection using an in-house previously developed artificial intelligence (AI) algorithm., Study Type: Retrospective., Subjects: 615 consecutive patients (median age 67 [interquartile range [IQR]: 61-71] years) with elevated serum PSA (median PSA 6.6 [IQR: 4.6-9.8] ng/mL) prior to prostate biopsy., Field Strength/sequence: 3.0T/T2-weighted turbo-spin-echo MRI, high b-value echo-planar diffusion-weighted imaging, and gradient recalled echo dynamic contrast-enhanced., Assessments: Scans were prospectively evaluated during clinical readout using PI-RADSv2.1 by one genitourinary radiologist with 17 years of experience. For each patient, T2-weighted images (T2WIs) were classified as high-quality or low-quality based on evaluation of both general distortions (eg, motion, distortion, noise, and aliasing) and perceptual distortions (eg, obscured delineation of prostatic capsule, prostatic zones, and excess rectal gas) by a previously developed in-house AI algorithm. Patients with PI-RADS category 1 underwent 12-core ultrasound-guided systematic biopsy while those with PI-RADS category 2-5 underwent combined systematic and targeted biopsies. Patient-level cancer detection rates (CDRs) were calculated for clinically significant prostate cancer (csPCa, International Society of Urological Pathology Grade Group ≥2) by each biopsy method and compared between high- and low-quality images in each PI-RADS category., Statistical Tests: Fisher's exact test. Bootstrap 95% confidence intervals (CI). A P value <0.05 was considered statistically significant., Results: 385 (63%) T2WIs were classified as high-quality and 230 (37%) as low-quality by AI. Targeted biopsy with high-quality T2WIs resulted in significantly higher clinically significant CDR than low-quality images for PI-RADS category 4 lesions (52% [95% CI: 43-61] vs. 32% [95% CI: 22-42]). For combined biopsy, there was no significant difference in patient-level CDRs for PI-RADS 4 between high- and low-quality T2WIs (56% [95% CI: 47-64] vs. 44% [95% CI: 34-55]; P = 0.09)., Data Conclusion: Higher quality T2WIs were associated with better targeted biopsy clinically significant cancer detection performance for PI-RADS 4 lesions. Combined biopsy might be needed when T2WI is lower quality., Level of Evidence: 2 TECHNICAL EFFICACY: Stage 1., (© 2023 International Society for Magnetic Resonance in Medicine. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

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.

Competing Interests: The authors declare no conflict of interest.

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. 18 F-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 SUV max and SUV mean 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.

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.

Objective: Automated methods for prostate segmentation on MRI are typically developed under ideal scanning and anatomical conditions. This study evaluates three different prostate segmentation AI algorithms in a challenging population of patients with prior treatments, variable anatomic characteristics, complex clinical history, or atypical MRI acquisition parameters., Materials and Methods: A single institution retrospective database was queried for the following conditions at prostate MRI: prior prostate-specific oncologic treatment, transurethral resection of the prostate (TURP), abdominal perineal resection (APR), hip prosthesis (HP), diversity of prostate volumes (large ≥ 150 cc, small ≤ 25 cc), whole gland tumor burden, magnet strength, noted poor quality, and various scanners (outside/vendors). Final inclusion criteria required availability of axial T2-weighted (T2W) sequence and corresponding prostate organ segmentation from an expert radiologist. Three previously developed algorithms were evaluated: (1) deep learning (DL)-based model, (2) commercially available shape-based model, and (3) federated DL-based model. Dice Similarity Coefficient (DSC) was calculated compared to expert. DSC by model and scan factors were evaluated with Wilcox signed-rank test and linear mixed effects (LMER) model., Results: 683 scans (651 patients) met inclusion criteria (mean prostate volume 60.1 cc [9.05-329 cc]). Overall DSC scores for models 1, 2, and 3 were 0.916 (0.707-0.971), 0.873 (0-0.997), and 0.894 (0.025-0.961), respectively, with DL-based models demonstrating significantly higher performance (p < 0.01). In sub-group analysis by factors, Model 1 outperformed Model 2 (all p < 0.05) and Model 3 (all p < 0.001). Performance of all models was negatively impacted by prostate volume and poor signal quality (p < 0.01). Shape-based factors influenced DL models (p < 0.001) while signal factors influenced all (p < 0.001)., Conclusion: Factors affecting anatomical and signal conditions of the prostate gland can adversely impact both DL and non-deep learning-based segmentation models., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

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., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: B. J. Wood receives support as part of a cooperative research and development agreement (CRADA) between NIH and Philips Healthcare, receives royalties from NIH related to a licensing agreement with Philips Healthcare, and is party to patents or potential patents related to this work. P. L. Choyke receives royalties for MRI/ultrasound fusion biopsy patents licensed to Philips Medical. P. A. Pinto receives royalties from NIH related to a Philips licensing agreement and support as part of a CRADA between NIH and Philips Healthcare. B. Turkbey receives support as part of a CRADA between NIH and NVDIA and between NIH and Philips Healthcare, receives royalties from NIH, and is party to patents or potential patents related to this work. Jesse Tetreault: employee of NVIDIA Corporation. The remaining authors declare that there are no other disclosures relevant to the subject matter of this article. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government., (Published by Elsevier Inc.)

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 [ 18 F]DCFPyL PET/CT (NCT03181867), and saliva samples were collected between 0-120 minutes post-injection. [ 18 F]DCFPyL salivary excretion was measured over 120 minutes and expressed as %ID/g. Protein-associated binding was estimated by the percentage of [ 18 F]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 [ 18 F]-DCFPyL in salivary glands while 8 patients demonstrated high uptake in the saliva at 45 minutes. The intact [ 18 F]-DCFPyL (98%) was also confirmed in the saliva samples at 120 min with increasing salivary radioactivity between 30-120 min., Conclusion: Systemically injected [ 18 F]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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Fernandes, Roy, Basuli, Warner, Lindenberg, Mena, Adler, Griffiths, Choyke and Lin.)

Uptake segmentation and classification on PSMA PET/CT are important for automating whole-body tumor burden determinations. We developed and evaluated an automated deep learning (DL)-based framework that segments and classifies uptake on PSMA PET/CT. We identified 193 [ 18 F] DCFPyL PET/CT scans of patients with biochemically recurrent prostate cancer from two institutions, including 137 [ 18 F] DCFPyL PET/CT scans for training and internally testing, and 56 scans from another institution for external testing. Two radiologists segmented and labelled foci as suspicious or non-suspicious for malignancy. A DL-based segmentation was developed with two independent CNNs. An anatomical prior guidance was applied to make the DL framework focus on PSMA-avid lesions. Segmentation performance was evaluated by Dice, IoU, precision, and recall. Classification model was constructed with multi-modal decision fusion framework evaluated by accuracy, AUC, F1 score, precision, and recall. Automatic segmentation of suspicious lesions was improved under prior guidance, with mean Dice, IoU, precision, and recall of 0.700, 0.566, 0.809, and 0.660 on the internal test set and 0.680, 0.548, 0.749, and 0.740 on the external test set. Our multi-modal decision fusion framework outperformed single-modal and multi-modal CNNs with accuracy, AUC, F1 score, precision, and recall of 0.764, 0.863, 0.844, 0.841, and 0.847 in distinguishing suspicious and non-suspicious foci on the internal test set and 0.796, 0.851, 0.865, 0.814, and 0.923 on the external test set. DL-based lesion segmentation on PSMA PET is facilitated through our anatomical prior guidance strategy. Our classification framework differentiates suspicious foci from those not suspicious for cancer with good accuracy., (© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

Background: Noninvasive in vivo cell tracking is valuable in understanding the mechanisms that enhance anti-cancer immunity. We have recently developed a new method called phototruncation-assisted cell tracking (PACT), that uses photoconvertible cell tracking technology to detect in vivo cell migration. This method has the advantages of not requiring genetic engineering of cells and employing tissue-penetrant near-infrared light., Methods: We applied PACT to monitor the migration of immune cells between a tumour and its tumour-draining lymph node (TDLN) after near-infrared photoimmunotherapy (NIR-PIT)., Findings: PACT showed a significant increase in the migration of dendritic cells (DCs) and macrophages from the tumour to the TDLN immediately after NIR-PIT. This migration by NIR-PIT was abrogated by inhibiting the sphingosine-1-phosphate pathway or Gαi signaling. These results were corroborated by intranodal immune cell profiles at two days post-treatment; NIR-PIT significantly induced DC maturation and increased and activated the CD8 + T cell population in the TDLN. Furthermore, PACT revealed that NIR-PIT significantly enhanced the migration of CD8 + T cells from the TDLN to the tumour four days post-treatment, which was consistent with the immunohistochemical assessment of tumour-infiltrating lymphocytes and tumour regression., Interpretation: Immune cells dramatically migrated between the tumour and TDLN following NIR-PIT, indicating its potential as an immune-stimulating therapy. Also, PACT is potentially applicable to a wide range of immunological research., Funding: This work was supported by the Intramural Research Program of the National Institutes of Health, National Cancer Institute, Centre for Cancer Research (grant number: ZIA BC011513 and ZIA BC011506)., Competing Interests: Declaration of interests The authors have no competing interests to disclose., (Published by Elsevier B.V.)

Rationale and Objectives: Prostate MRI quality is essential in guiding prostate biopsies. However, assessment of MRI quality is subjective with variation. Quality degradation sources exert varying impacts based on the sequence under consideration, such as T2W versus DWI. As a result, employing sequence-specific techniques for quality assessment could yield more advantageous outcomes. This study aims to develop an AI tool that offers a more consistent evaluation of T2W prostate MRI quality, efficiently identifying suboptimal scans while minimizing user bias., Materials and Methods: This retrospective study included 1046 patients from three cohorts (ProstateX [n = 347], All-comer in-house [n = 602], enriched bad-quality MRI in-house [n = 97]) scanned between January 2011 and May 2022. An expert reader assigned T2W MRIs a quality score. A train-validation-test split of 70:15:15 was applied, ensuring equal distribution of MRI scanners and protocols across all partitions. T2W quality AI classification model was based on 3D DenseNet121 architecture using MONAI framework. In addition to multiclassification, binary classification was utilized (Classes 0/1 vs. 2). A score of 0 was given to scans considered non-diagnostic or unusable, a score of 1 was given to those with acceptable diagnostic quality with some usability but with some quality distortions present, and a score of 2 was given to those considered optimal diagnostic quality and usability. Partial occlusion sensitivity maps were generated for anatomical correlation. Three body radiologists assessed reproducibility within a subgroup of 60 test cases using weighted Cohen Kappa., Results: The best validation multiclass accuracy of 77.1% (121/157) was achieved during training. In the test dataset, multiclassification accuracy was 73.9% (116/157), whereas binary accuracy was 84.7% (133/157). Sub-class sensitivity for binary quality distortion classification for class 0 was 100% (18/18), and sub-class specificity for T2W classification of absence/minimal quality distortions for class 2 was 90.5% (95/105). All three readers showed moderate to substantial agreement with ground truth (R1-R3 κ = 0.588, κ = 0.649, κ = 0.487, respectively), moderate to substantial agreement with each other (R1-R2 κ = 0.599, R1-R3 κ = 0.612, R2-R3 κ = 0.685), fair to moderate agreement with AI (R1-R3 κ = 0.445, κ = 0.410, κ = 0.292, respectively). AI showed substantial agreement with ground truth (κ = 0.704). 3D quality heatmap evaluation revealed that the most critical non-diagnostic quality imaging features from an AI perspective related to obscuration of the rectoprostatic space (94.4%, 17/18)., Conclusion: The 3D AI model can assess T2W prostate MRI quality with moderate accuracy and translate whole sequence-level classification labels into 3D voxel-level quality heatmaps for interpretation. Image quality has a significant downstream impact on ruling out clinically significant cancers. AI may be able to help with reproducible identification of MRI sequences requiring re-acquisition with explainability., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier Inc.)

Rationale and Objectives: To analyze variables that can predict the positivity of 18 F-DCFPyL- positron emission tomography/computed tomography (PET/CT) and extent of disease in patients with biochemically recurrent (BCR) prostate cancer after primary local therapy with either radical prostatectomy or radiation therapy., Materials and Methods: This is a retrospective analysis of a prospective single institutional review board-approved study. We included 199 patients with biochemical recurrence and negative conventional imaging after primary local therapies (radical prostatectomy n = 127, radiation therapy n = 72). All patients underwent 18 F-DCFPyL-PET/CT. Univariate and multivariate logistic regression analyses were used to determine predictors of a positive scan for both cohort of patients. Regression-based coefficients were used to develop nomograms predicting scan positivity and extra-pelvic disease. Decision curve analysis (DCA) was implemented to quantify nomogram's clinical benefit., Results: Of the 127 (63%) post-radical prostatectomy patients, 91 patients had positive scans - 61 of those with intrapelvic lesions and 30 with extra-pelvic lesions (i.e., retroperitoneal or distant nodes and/or bone/organ lesions). Of the 72 post-radiation therapy patients, 65 patients had positive scans - 39 of them had intrapelvic lesions and 26 extra-pelvic lesions. In the radical prostatectomy cohort, multivariate regression analysis revealed original International Society of Urological Pathology category, prostate-specific antigen (PSA), prostate-specific antigen doubling time (PSAdt), and time from BCR (mo) to scan were predictors for scan positivity and presence of extra-pelvic disease, with an area under the curve of 80% and 78%, respectively. Positive versus negative tumor margin after radical prostatectomy was not related to scan positivity or to the presence of positive extra-pelvic foci. In the radiation therapy cohort, multivariate regression analysis revealed that PSA, PSAdt, and time to BCR (mo) were predictors of extra-pelvic disease, with area under the curve of 82%. Because only seven patients in the radiation therapy cohort had negative scans, a prediction model for scan positivity could not be analyzed and only the presence of extra-pelvic disease was evaluated., Conclusion: PSA and PSAdt are consistently significant predictors of 18 F-DCFPyL PET/CT positivity and extra-pelvic disease in BCR prostate cancer patients. Stratifying the patient population into primary local treatment group enables the use of other variables as predictors, such as time since BCR. This nomogram may guide selection of the most suitable candidates for 18 F-DCFPyL-PET/CT imaging., Competing Interests: Declaration of Competing Interest All authors declare that they have no competing interests., (Published by Elsevier Inc.)

Automatic detection and characterization of cancer are important clinical needs to optimize early treatment. We developed a deep, semisupervised transfer learning approach for fully automated, whole-body tumor segmentation and prognosis on PET/CT. Methods: This retrospective study consisted of 611 18 F-FDG PET/CT scans of patients with lung cancer, melanoma, lymphoma, head and neck cancer, and breast cancer and 408 prostate-specific membrane antigen (PSMA) PET/CT scans of patients with prostate cancer. The approach had a nnU-net backbone and learned the segmentation task on 18 F-FDG and PSMA PET/CT images using limited annotations and radiomics analysis. True-positive rate and Dice similarity coefficient were assessed to evaluate segmentation performance. Prognostic models were developed using imaging measures extracted from predicted segmentations to perform risk stratification of prostate cancer based on follow-up prostate-specific antigen levels, survival estimation of head and neck cancer by the Kaplan-Meier method and Cox regression analysis, and pathologic complete response prediction of breast cancer after neoadjuvant chemotherapy. Overall accuracy and area under the receiver-operating-characteristic (AUC) curve were assessed. Results: Our approach yielded median true-positive rates of 0.75, 0.85, 0.87, and 0.75 and median Dice similarity coefficients of 0.81, 0.76, 0.83, and 0.73 for patients with lung cancer, melanoma, lymphoma, and prostate cancer, respectively, on the tumor segmentation task. The risk model for prostate cancer yielded an overall accuracy of 0.83 and an AUC of 0.86. Patients classified as low- to intermediate- and high-risk had mean follow-up prostate-specific antigen levels of 18.61 and 727.46 ng/mL, respectively ( P < 0.05). The risk score for head and neck cancer was significantly associated with overall survival by univariable and multivariable Cox regression analyses ( P < 0.05). Predictive models for breast cancer predicted pathologic complete response using only pretherapy imaging measures and both pre- and posttherapy measures with accuracies of 0.72 and 0.84 and AUCs of 0.72 and 0.76, respectively. Conclusion: The proposed approach demonstrated accurate tumor segmentation and prognosis in patients across 6 cancer types on 18 F-FDG and PSMA PET/CT scans., (© 2024 by the Society of Nuclear Medicine and Molecular Imaging.)

Enfortumab vedotin (EV), an antibody-drug conjugate (ADC) that targets Nectin-4, has shown promising results in the treatment of bladder cancer. However, multiple resistance mechanisms that are unique to ADCs limit the therapeutic potential of EV in clinical practice. Here, we developed and tested a Nectin-4-targeted near-infrared photoimmunotherapy (NIR-PIT) that utilizes the same target as EV but utilizes a distinct cytotoxic and immunotherapeutic pathway in preclinical models of bladder cancer. NIR-PIT was effective in vitro against luminal subtype human bladder cancer cell lines (RT4, RT112, MGH-U3, SW780, and HT1376-luc), but not against other subtype cell lines (UMUC3 and T24). In vivo, the tumor site was clearly visible by Nectin-4-IR700 fluorescence 24 h after its administration, suggesting the potential as an intraoperative imaging modality. NIR-PIT significantly suppressed tumor growth and prolonged survival in SW780 and RT112 xenograft models. Weekly treatment with NIR-PIT further improved tumor control in RT112 xenograft models. The effectiveness of NIR-PIT was also confirmed in HT1376-luc orthotopic xenograft models. Histological analysis verified that NIR-PIT induced a significant pathologic response. Taken together, Nectin-4-targeted NIR-PIT shows promise as a treatment for luminal subtype bladder cancers., Competing Interests: Declaration of competing interest None of authors does not have conflict of interest to be disclosed., (Published by Elsevier B.V.)

Background and Objective: Focal therapy (FT) is increasingly recognized as a promising approach for managing localized prostate cancer (PCa), notably reducing treatment-related morbidities. However, post-treatment anatomical changes present significant challenges for surveillance using current imaging techniques. This study aimed to evaluate the inter-reader agreement and efficacy of the Prostate Imaging after Focal Ablation (PI-FAB) scoring system in detecting clinically significant prostate cancer (csPCa) on post-FT multiparametric magnetic resonance imaging (mpMRI)., Methods: A retrospective cohort study was conducted involving patients who underwent primary FT for localized csPCa between 2013 and 2023, followed by post-FT mpMRI and a prostate biopsy. Two expert genitourinary radiologists retrospectively evaluated post-FT mpMRI using PI-FAB. The key measures included inter-reader agreement of PI-FAB scores, assessed by quadratic weighted Cohen's kappa ( κ ), and the system's efficacy in predicting in-field recurrence of csPCa, with a PI-FAB score cutoff of 3. Additional diagnostic metrics including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and overall accuracy were also evaluated., Key Findings and Limitations: Scans from 38 patients were analyzed, revealing a moderate level of agreement in PI-FAB scoring ( κ  = 0.56). Both radiologists achieved sensitivity of 93% in detecting csPCa, although specificity, PPVs, NPVs, and accuracy varied., Conclusions and Clinical Implications: The PI-FAB scoring system exhibited high sensitivity with moderate inter-reader agreement in detecting in-field recurrence of csPCa. Despite promising results, its low specificity and PPV necessitate further refinement. These findings underscore the need for larger studies to validate the clinical utility of PI-FAB, potentially aiding in standardizing post-treatment surveillance., Patient Summary: Focal therapy has emerged as a promising approach for managing localized prostate cancer, but limitations in current imaging techniques present significant challenges for post-treatment surveillance. The Prostate Imaging after Focal Ablation (PI-FAB) scoring system showed high sensitivity for detecting in-field recurrence of clinically significant prostate cancer. However, its low specificity and positive predictive value necessitate further refinement. Larger, more comprehensive studies are needed to fully validate its clinical utility.

Cancer-associated fibroblasts (CAFs) constitute a prominent cellular component of the tumor stroma, with various pro-tumorigenic roles. Numerous attempts to target fibroblast activation protein (FAP), a highly expressed marker in immunosuppressive CAFs, have failed to demonstrate anti-tumor efficacy in human clinical trials. Near-infrared photoimmunotherapy (NIR-PIT) is a highly selective tumor therapy that utilizes an antibody-photo-absorbing conjugate activated by near-infrared light. In this study, we examined the therapeutic efficacy of CAF depletion by NIR-PIT in two mouse tumor models. Using CAF-rich syngeneic lung and spontaneous mammary tumors, NIR-PIT against FAP or podoplanin was performed. Anti-FAP NIR-PIT effectively depleted FAP + CAFs, as well as FAP + myeloid cells, and suppressed tumor growth, whereas anti-podoplanin NIR-PIT was ineffective. Interferon-gamma production by CD8 T and natural killer cells was induced within hours after anti-FAP NIR-PIT. Additionally, lung metastases were reduced in the treated spontaneous mammary cancer model. Depletion of FAP + stromal as well as FAP + myeloid cells effectively suppressed tumor growth in bone marrow chimeras, suggesting that the depletion of both cell types in one treatment is an effective therapeutic approach. These findings highlight a promising therapy for selectively eliminating immunosuppressive FAP + cells within the tumor microenvironment.

BACKGROUND. Precise risk stratification through MRI/ultrasound (US) fusion-guided targeted biopsy (TBx) can guide optimal prostate cancer (PCa) management. OBJECTIVE. The purpose of this study was to compare PI-RADS version 2.0 (v2.0) and PI-RADS version 2.1 (v2.1) in terms of the rates of International Society of Urological Pathology (ISUP) grade group (GG) upgrade and downgrade from TBx to radical prostatectomy (RP). METHODS. This study entailed a retrospective post hoc analysis of patients who underwent 3-T prostate MRI at a single institution from May 2015 to March 2023 as part of three prospective clinical trials. Trial participants who underwent MRI followed by MRI/US fusion-guided TBx and RP within a 1-year interval were identified. A single genitourinary radiologist performed clinical interpretations of the MRI examinations using PI-RADS v2.0 from May 2015 to March 2019 and PI-RADS v2.1 from April 2019 to March 2023. Upgrade and downgrade rates from TBx to RP were compared using chi-square tests. Clinically significant cancer was defined as ISUP GG2 or greater. RESULTS. The final analysis included 308 patients (median age, 65 years; median PSA density, 0.16 ng/mL 2 ). The v2.0 group ( n = 177) and v2.1 group ( n = 131) showed no significant difference in terms of upgrade rate (29% vs 22%, respectively; p = .15), downgrade rate (19% vs 21%, p = .76), clinically significant upgrade rate (14% vs 10%, p = .27), or clinically significant downgrade rate (1% vs 1%, p > .99). The upgrade rate and downgrade rate were also not significantly different between the v2.0 and v2.1 groups when stratifying by index lesion PI-RADS category or index lesion zone, as well as when assessed only in patients without a prior PCa diagnosis (all p > .01). Among patients with GG2 or GG3 at RP ( n = 121 for v2.0; n = 103 for v2.1), the concordance rate between TBx and RP was not significantly different between the v2.0 and v2.1 groups (53% vs 57%, p = .51). CONCLUSION. Upgrade and downgrade rates from TBx to RP were not significantly different between patients whose MRI examinations were clinically interpreted using v2.0 or v2.1. CLINICAL IMPACT. Implementation of the most recent PI-RADS update did not improve the incongruence in PCa grade assessment between TBx and surgery.

Purpose: To evaluate CycleGAN's ability to enhance T2-weighted image (T2WI) quality., Method: A CycleGAN algorithm was used to enhance T2WI quality. 96 patients (192 scans) were identified from patients who underwent multiple axial T2WI due to poor quality on the first attempt (RAD1) and improved quality on re-acquisition (RAD2). CycleGAN algorithm gave DL classifier scores (0-1) for quality quantification and produced enhanced versions of QI1 and QI2 from RAD1 and RAD2, respectively. A subset (n = 20 patients) was selected for a blinded, multi-reader study, where four radiologists rated T2WI on a scale of 1-4 for quality. The multi-reader study presented readers with 60 image pairs (RAD1 vs RAD2, RAD1 vs QI1, and RAD2 vs QI2), allowing for selecting sequence preferences and quantifying the quality changes., Results: The DL classifier correctly discerned 71.9 % of quality classes, with 90.6 % (96/106) as poor quality and 48.8 % (42/86) as diagnostic in original sequences (RAD1, RAD2). CycleGAN images (QI1, QI2) demonstrated quantitative improvements, with consistently higher DL classifier scores than original scans (p < 0.001). In the multi-reader analysis, CycleGAN demonstrated no qualitative improvements, with diminished overall quality and motion in QI2 in most patients compared to RAD2, with noise levels remaining similar (8/20). No readers preferred QI2 to RAD2 for diagnosis., Conclusion: Despite quantitative enhancements with CycleGAN, there was no qualitative boost in T2WI diagnostic quality, noise, or motion. Expert radiologists didn't favor CycleGAN images over standard scans, highlighting the divide between quantitative and qualitative metrics., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier B.V.)

Competing Interests: Declaration of interests The authors declare no competing interests. All of the authors are employees of the US government. This work is the personal opinion of the authors.

BACKGROUND. Currently most clinical models for predicting biochemical recurrence (BCR) of prostate cancer (PCa) after radical prostatectomy (RP) incorporate staging information from RP specimens, creating a gap in preoperative risk assessment. OBJECTIVE. The purpose of our study was to compare the utility of presurgical staging information from MRI and postsurgical staging information from RP pathology in predicting BCR in patients with PCa. METHODS. This retrospective study included 604 patients (median age, 60 years) with PCa who underwent prostate MRI before RP from June 2007 to December 2018. A single genitourinary radiologist assessed MRI examinations for extraprostatic extension (EPE) and seminal vesicle invasion (SVI) during clinical interpretations. The utility of EPE and SVI on MRI and RP pathology for BCR prediction was assessed through Kaplan-Meier and Cox proportional hazards analyses. Established clinical BCR prediction models, including the University of California San Francisco Cancer of the Prostate Risk Assessment (UCSF-CAPRA) model and the Cancer of the Prostate Risk Assessment Postsurgical (CAPRA-S) model, were evaluated in a subset of 374 patients with available Gleason grade groups from biopsy and RP pathology; two CAPRA-MRI models (CAPRA-S model with modifications to replace RP pathologic staging features with MRI staging features) were also assessed. RESULTS. Univariable predictors of BCR included EPE on MRI (HR = 3.6), SVI on MRI (HR = 4.4), EPE on RP pathology (HR = 5.0), and SVI on RP pathology (HR = 4.6) (all p < .001). Three-year BCR-free survival (RFS) rates for patients without versus with EPE were 84% versus 59% for MRI and 89% versus 58% for RP pathology, and 3-year RFS rates for patients without versus with SVI were 82% versus 50% for MRI and 83% versus 54% for RP histology (all p < .001). For patients with T3 disease on RP pathology, 3-year RFS rates were 67% and 41% for patients without and with T3 disease on MRI. AUCs of CAPRA models, including CAPRA-MRI models, ranged from 0.743 to 0.778. AUCs were not significantly different between CAPRA-S and CAPRA-MRI models ( p > .05). RFS rates were significantly different between low- and intermediate-risk groups for only CAPRA-MRI models (80% vs 51% and 74% vs 44%; both p < .001). CONCLUSION. Presurgical MRI-based staging features perform comparably to postsurgical pathologic staging features for predicting BCR. CLINICAL IMPACT. MRI staging can preoperatively identify patients at high BCR risk, helping to inform early clinical decision-making. TRIAL REGISTRATION. ClinicalTrials.gov NCT00026884 and NCT02594202.

Background: Cribriform (CBFM) pattern on prostate biopsy has been implicated as a predictor for high-risk features, potentially leading to adverse outcomes after definitive treatment. This study aims to investigate whether the CBFM pattern containing prostate cancers (PCa) were associated with false negative magnetic resonance imaging (MRI) and determine the association between MRI and histopathological disease burden., Methods: Patients who underwent multiparametric magnetic resonance imaging (mpMRI), combined 12-core transrectal ultrasound (TRUS) guided systematic (SB) and MRI/US fusion-guided biopsy were retrospectively queried for the presence of CBFM pattern at biopsy. Biopsy cores and lesions were categorized as follows: C0 = benign, C1 = PCa with no CBFM pattern, C2 = PCa with CBFM pattern. Correlation between cancer core length (CCL) and measured MRI lesion dimension were assessed using a modified Pearson correlation test for clustered data. Differences between the biopsy core groups were assessed with the Wilcoxon-signed rank test with clustering., Results: Between 2015 and 2022, a total of 131 consecutive patients with CBFM pattern on prostate biopsy and pre-biopsy mpMRI were included. Clinical feature analysis included 1572 systematic biopsy cores (1149 C0, 272 C1, 151 C2) and 736 MRI-targeted biopsy cores (253 C0, 272 C1, 211 C2). Of the 131 patients with confirmed CBFM pathology, targeted biopsy (TBx) alone identified CBFM in 76.3% (100/131) of patients and detected PCa in 97.7% (128/131) patients. SBx biopsy alone detected CBFM in 61.1% (80/131) of patients and PCa in 90.8% (119/131) patients. TBx and SBx had equivalent detection in patients with smaller prostates (p = 0.045). For both PCa lesion groups there was a positive and significant correlation between maximum MRI lesion dimension and CCL (C1 lesions: p < 0.01, C2 lesions: p < 0.001). There was a significant difference in CCL between C1 and C2 lesions for T2 scores of 3 and 5 (p ≤ 0.01, p ≤ 0.01, respectively) and PI-RADS 5 lesions (p ≤ 0.01), with C2 lesions having larger CCL, despite no significant difference in MRI lesion dimension., Conclusions: The extent of disease for CBFM-containing tumors is difficult to capture on mpMRI. When comparing MRI lesions of similar dimensions and PIRADS scores, CBFM-containing tumors appear to have larger cancer yield on biopsy. Proper staging and planning of therapeutic interventions is reliant on accurate mpMRI estimation. Special considerations should be taken for patients with CBFM pattern on prostate biopsy., (Published 2023. This article is a U.S. Government work and is in the public domain in the USA.)

Epidermal growth factor receptor (EGFR) has emerged as an important therapeutic target in many cancers, and overexpression of EGFR is frequently observed in hepatocellular carcinomas (HCCs). Near-infrared photoimmunotherapy (NIR-PIT) is a new anticancer treatment that selectively damages the cell membrane of cancer cells after NIR light-induced photochemical reaction of IR700, which is bound to a targeting antibody on the cell membrane. NIR-PIT using cetuximab-IR700 has already been approved in Japan, is under review by the US Food and Drug Administration (FDA) for advanced head and neck cancers, and its safety has been established. However, EGFR has not been investigated as a target in NIR-PIT in HCCs. Here, we investigate the application of NIR-PIT using cetuximab-IR700 to HCCs using xenograft mouse models of EGFR-expressing HCC cell lines, Hep3B, HuH-7, and SNU-449. In vitro NIR-PIT using EGFR-targeted cetuximab-IR700 killed cells in a NIR light dose-dependent manner. In vivo NIR-PIT resulted in a delayed growth compared with untreated controls. In addition, in vivo NIR-PIT in both models showed histological signs of cancer cell damage, such as cytoplasmic vacuolation and nuclear dysmorphism. A significant decrease in Ki-67 positivity was also observed after NIR-PIT, indicating decreased cancer cell proliferation. This study suggests that NIR-PIT using cetuximab-IR700 has potential for the treatment of EGFR-expressing HCCs., (Published 2023. This article is a U.S. Government work and is in the public domain in the USA. Cancer Science published by John Wiley & Sons Australia, Ltd on behalf of Japanese Cancer Association.)

Since the development of fibroblast activation protein-targeted radiopharmaceuticals, 68 Ga-fibroblast activation protein inhibitor (FAPI) PET/CT has been found to be suitable for detecting primary and metastatic lesions in many types of tumors. However, there is currently a lack of reliable data regarding the clinical impact of this family of probes. To address this gap, the present study aimed to analyze the clinical impact of 68 Ga-FAPI PET/CT by examining a large cohort of patients with various tumors. Methods: In total, 226 patients (137 male and 89 female) were included in this retrospective analysis. Pancreatic cancer and head and neck cancers were the most common tumor types in this cohort. TNM stage and oncologic management were initially determined with gold standard imaging, and these results were compared with 68 Ga-FAPI PET/CT. Changes were classified as major and minor. Results: For 42% of all patients, TNM stage was changed by 68 Ga-FAPI PET/CT results. Most of these changes resulted in upstaging. A change in clinical management occurred in 117 of 226 patients. Although a major change in management occurred in only 12% of patients, there was a significant improvement in the ability to accurately plan radiation therapy. In general, the highest clinical impact of 68 Ga-FAPI PET/CT imaging was found in patients with lung cancer, pancreatic cancer, and head and neck tumors. Conclusion: 68 Ga-FAPI PET/CT is a promising imaging probe that has a significant impact on TNM stage and clinical management. 68 Ga-FAPI PET/CT promises to be a crucial new technology that will improve on conventional radiologic imaging methods such as contrast-enhanced CT and contrast-enhanced MRI typically acquired for cancer staging., (© 2023 by the Society of Nuclear Medicine and Molecular Imaging.)

Objective: To evaluate a biparametric MRI (bpMRI)-based artificial intelligence (AI) model for the detection of local prostate cancer (PCa) recurrence in patients with radiotherapy history., Materials and Methods: This study included post-radiotherapy patients undergoing multiparametric MRI and subsequent MRI/US fusion-guided and/or systematic biopsy. Histopathology results were used as ground truth. The recurrent cancer detection sensitivity of a bpMRI-based AI model, which was developed on a large dataset to primarily identify lesions in treatment-naïve patients, was compared to a prospective radiologist assessment using the Wald test. Subanalysis was conducted on patients stratified by the treatment modality (external beam radiation treatment [EBRT] and brachytherapy) and the prostate volume quartiles., Results: Of the 62 patients included (median age = 70 years; median PSA = 3.51 ng/ml; median prostate volume = 27.55 ml), 56 recurrent PCa foci were identified within 46 patients. The AI model detected 40 lesions in 35 patients. The AI model performance was lower than the prospective radiology interpretation (Rad) on a patient-(AI: 76.1% vs. Rad: 91.3%, p = 0.02) and lesion-level (AI: 71.4% vs. Rad: 87.5%, p = 0.01). The mean number of false positives per patient was 0.35 (range: 0-2). The AI model performance was higher in EBRT group both on patient-level (EBRT: 81.5% [22/27] vs. brachytherapy: 68.4% [13/19]) and lesion-level (EBRT: 79.4% [27/34] vs. brachytherapy: 59.1% [13/22]). In patients with gland volumes >34 ml (n = 25), detection sensitivities were 100% (11/11) and 94.1% (16/17) on patient- and lesion-level, respectively., Conclusion: The reported bpMRI-based AI model detected the majority of locally recurrent prostate cancer after radiotherapy. Further testing including external validation of this model is warranted prior to clinical implementation., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Bradford J. Wood: Principal investigator on cooperative research and development agreement (CRADA) between National Institutes of Health (NIH) and Philips and CRADAs with industry partners unrelated to this work; travel support related to CRADAs; royalties from NIH related to Philips licensing agreement; patents planned, issued, or pending. Peter L. Choyke: Receives payment from royalties paid to the U.S. government for patents on MRI US fusion biopsy licensed to Philips Medical. Peter A. Pinto: Institutional CRADA with Philips; royalties from NIH related to Philips licensing agreement; NIH-related patents planned, issued, or pending (U.S. patent nos. 8 447 384 and 10 215 830). Baris Turkbey: CRADAs with NVIDIA and Philips; royalties from NIH; patents planned, issued, or pending in the field of artificial intelligence., (Published by Elsevier B.V.)

Objective: To report an initial experience with a novel, "fully" transperineal (TP) prostate fusion biopsy using an unconstrained ultrasound transducer placed on the perineal skin to guide biopsy needles inserted via a TP approach., Methods: Conventional TP prostate biopsies for detection of prostate cancer have been performed with transrectal ultrasound, requiring specialized hardware, imposing limitations on needle trajectory, and contributing to patient discomfort. Seventy-six patients with known or suspected prostate cancer underwent 78 TP biopsy sessions in an academic center between June 2018 and April 2022 and were included in this study. These patients underwent TP prostate fusion biopsy using a grid or freehand device with transrectal ultrasound as well as TP prostate fusion biopsy using TP ultrasound in the same session. Per-session and per-lesion cancer detection rates were compared for conventional and fully TP biopsies using Fisher exact and McNemar's tests., Results: After a refinement period in 30 patients, 92 MRI-visible prostate lesions were sampled in 46 subsequent patients, along with repeat biopsies in 2 of the 30 patients from the refinement period. Grade group ≥2 cancer was diagnosed in 24/92 lesions (26%) on conventional TP biopsy (17 lesions with grid, 7 with freehand device), and in 25/92 lesions (27%) on fully TP biopsy (P = 1.00), with a 73/92 (79%) rate of agreement for grade group ≥2 cancer between the two methods., Conclusion: Fully TP biopsy is feasible and may detect prostate cancer with detection rates comparable to conventional TP biopsy., Competing Interests: Declaration of Competing Interest Peter Pinto Institutional CRADA with Philips; royalties from NIH related to Philips licensing agreement; NIH-related patents planned, issued, or pending (U.S. patent nos. 8 447 384 and 10 215830). Peter L. Choyke Receives payment from royalties paid to the U.S. government for patents on MRI US fusion biopsy licensed to Philips Medical. Baris Turkbey CRADAs with NVIDIA and Philips; royalties from NIH; patents planned, issued, or pending in the field of artificial intelligence. Nicole Varble employee of Philips. Bradford J. Wood Principal investigator on cooperative research and development agreement (CRADA) between National Institutes of Health (NIH) and Philips and CRADAs with industry partners unrelated to this work; travel support related to CRADAs; royalties from NIH related to Philips licensing agreement; patents planned, issued, or pending. The other authors have no conflict of interest to declare., (Published by Elsevier Inc.)

Head and neck squamous cell carcinoma (HNSCC) contribute to a significant global cancer burden. Developments in current therapeutic approaches have improved patient outcomes but have limited efficacy in patients with unresectable and/or recurrent HNSCC. RM-1929 near-infrared photoimmunotherapy (NIR-PIT) is an emerging treatment that is currently being investigated in a Phase III clinical trial and has been conditionally approved for the treatment of unresectable and/or recurrent HNSCC in Japan. Here, we collect a series of case reports and clinical trial data to assess the efficacy of RM-1929 NIR-PIT. Disease control rates ranged from 66.7 to 100% across these studies, and overall response rates ranged from 43.3 to 100%, suggesting positive clinical outcomes. Low-grade postoperative localized pain and edema were the most frequently reported side effects, and preliminary reports on quality of life and pain levels suggest that RM-1929 NIR-PIT does not significantly decrease quality of life and is manageable with existing pain management strategies, including opioids. These preliminary data in real-world use of RM-1929 NIR-PIT show that it is a well-tolerated therapy that has clinically meaningful outcomes for patients with unresectable and/or recurrent HNSCC.

(1) Background: Non-invasive prostate cancer assessments using multi-parametric MRI are essential to the reliable detection of lesions and proper management of patients. While current guidelines call for the administration of Gadolinium-containing intravenous contrast injections, eliminating such injections would simplify scanning and reduce patient risk and costs. However, augmented image analysis is necessary to extract important diagnostic information from MRIs. Purpose: This study aims to extend previous work on the signal to clutter ratio and test whether prostate tumor eccentricity and volume are indicators of tumor aggressiveness using bi-parametric (BP)-MRI. (2) Methods: This study retrospectively processed 42 consecutive prostate cancer patients from the PI-CAI data collection. BP-MRIs (apparent diffusion coefficient, high b-value, and T2 images) were resized, translated, cropped, and stitched to form spatially registered BP-MRIs. The International Society of Urological Pathology (ISUP) grade was used to judge cases of prostate cancer as either clinically significant prostate cancer (CsPCa) (ISUP ≥ 2) or clinically insignificant prostate cancer (CiPCa) (ISUP < 2). The Adaptive Cosine Estimator (ACE) algorithm was applied to the BP-MRIs, followed by thresholding, and then eccentricity and volume computations, from the labeled and blobbed detection maps. Then, univariate and multivariate linear regression fittings of eccentricity and volume were applied to the ISUP grade. The fits were quantitatively evaluated by computing correlation coefficients (R) and p -values. Area under the curve (AUC) and receiver operator characteristic (ROC) curve scores were used to assess the logistic fitting to CsPCa/CiPCa. (3) Results: Modest correlation coefficients (R) (>0.35) and AUC scores (0.70) for the linear and/or logistic fits from the processed prostate tumor eccentricity and volume computations for the spatially registered BP-MRIs exceeded fits using the parameters of prostate serum antigen, prostate volume, and patient age (R~0.17). (4) Conclusions: This is the first study that applied spectral approaches to BP-MRIs to generate tumor eccentricity and volume metrics to assess tumor aggressiveness. This study found significant values of R and AUC (albeit below those from multi-parametric MRI) to fit and relate the metrics to the ISUP grade and CsPCA/CiPCA, respectively.

IL15 is a potent inducer of differentiation and proliferation of CD8+ T and natural killer (NK) cells, making it a promising candidate for cancer immunotherapy. However, limited efficacy of systemic monotherapy utilizing intravenous IL15 suggests the needs for alternative routes of administration or combination treatment with other therapies. Near-infrared photoimmunotherapy (NIR-PIT) is a highly selective anticancer treatment that elicits a massive release of tumor antigens and immunogenic signals. Here, we investigated whether intratumoral IL15 can enhance the effectiveness of cancer cell-targeted NIR-PIT using syngeneic murine tumor models. Intratumoral injection of IL15 was more effective than intraperitoneal IL15 in vivo in suppressing tumor growth and inducing intratumoral immune responses. When the efficacy of CD44-targeted NIR-PIT was compared in vivo between IL15-secreting MC38 (hIL15-MC38) and parental MC38 tumors, the hIL15-MC38/NIR-PIT group showed the best tumor growth inhibition and survival. In addition, the hIL15-MC38/NIR-PIT group showed significant dendritic cell maturation and significant increases in the number and Granzyme B expression of tumor-infiltrating CD8+ T, NK, and natural killer T cells compared with the treated parental line. Furthermore, intratumoral IL15 injection combined with CD44-targeted NIR-PIT showed significant tumor control in MC38 and Pan02-luc tumor models. In bilateral tumor models, CD44-targeted NIR-PIT in hIL15-MC38 tumors significantly suppressed the growth of untreated MC38 tumors, suggesting abscopal effects. Mice that achieved complete response after the combination therapy completely rejected later tumor rechallenge. In conclusion, local IL15 administration synergistically improves the efficacy of cancer cell-targeted NIR-PIT probably by inducing stronger anticancer immunity, indicating its potential as an anticancer treatment strategy., (©2023 American Association for Cancer Research.)

Phosphatase and tensin homolog (PTEN) loss is associated with adverse outcomes in prostate cancer and can be measured via immunohistochemistry. The purpose of the study was to establish the clinical application of an in-house developed artificial intelligence (AI) image analysis workflow for automated detection of PTEN loss on digital images for identifying patients at risk of early recurrence and metastasis. Postsurgical tissue microarray sections from the Canary Foundation (n = 1264) stained with anti-PTEN antibody were evaluated independently by pathologist conventional visual scoring (cPTEN) and an automated AI-based image analysis pipeline (AI-PTEN). The relationship of PTEN evaluation methods with cancer recurrence and metastasis was analyzed using multivariable Cox proportional hazard and decision curve models. Both cPTEN scoring by the pathologist and quantification of PTEN loss by AI (high-risk AI-qPTEN) were significantly associated with shorter metastasis-free survival (MFS) in univariable analysis (cPTEN hazard ratio [HR], 1.54; CI, 1.07-2.21; P = .019; AI-qPTEN HR, 2.55; CI, 1.83-3.56; P < .001). In multivariable analyses, AI-qPTEN showed a statistically significant association with shorter MFS (HR, 2.17; CI, 1.49-3.17; P < .001) and recurrence-free survival (HR, 1.36; CI, 1.06-1.75; P = .016) when adjusting for relevant postsurgical clinical nomogram (Cancer of the Prostate Risk Assessment [CAPRA] postsurgical score [CAPRA-S]), whereas cPTEN does not show a statistically significant association (HR, 1.33; CI, 0.89-2; P = .2 and HR, 1.26; CI, 0.99-1.62; P = .063, respectively) when adjusting for CAPRA-S risk stratification. More importantly, AI-qPTEN was associated with shorter MFS in patients with favorable pathological stage and negative surgical margins (HR, 2.72; CI, 1.46-5.06; P = .002). Workflow also demonstrated enhanced clinical utility in decision curve analysis, more accurately identifying men who might benefit from adjuvant therapy postsurgery. This study demonstrates the clinical value of an affordable and fully automated AI-powered PTEN assessment for evaluating the risk of developing metastasis or disease recurrence after radical prostatectomy. Adding the AI-qPTEN assessment workflow to clinical variables may affect postoperative surveillance or management options, particularly in low-risk patients., (Copyright © 2023 United States & Canadian Academy of Pathology. All rights reserved.)

Tumor vasculature plays a critical role in the progression and metastasis of tumors, antitumor immunity, drug delivery, and resistance to therapies. The morphological and functional changes of tumor vasculature in response to therapy take place in a spatiotemporal-dependent manner, which can be predictive of treatment outcomes. Dynamic monitoring of intratumor vasculature contributes to an improved understanding of the mechanisms of action of specific therapies or reasons for treatment failure, leading to therapy optimization. There is a rich history of methods used to image the vasculature. This review describes recent advances in imaging technologies to visualize the tumor vasculature, with a focus on enhanced intravital imaging techniques and tumor window models. We summarize new insights on spatial-temporal vascular responses to various therapies, including changes in vascular perfusion and permeability and immune-vascular crosstalk, obtained from intravital imaging. Finally, we briefly discuss the clinical applications of intravital imaging techniques., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)

Background: Osteosarcoma (OS) is an aggressive pediatric cancer with unmet therapeutic needs. Glutaminase 1 (GLS1) inhibition, alone and in combination with metformin, disrupts the bioenergetic demands of tumor progression and metastasis, showing promise for clinical translation. Materials and Methods: Three positron emission tomography (PET) clinical imaging agents, [ 18 F]fluoro-2-deoxy-2-D-glucose ([ 18 F]FDG), 3'-[ 18 F]fluoro-3'-deoxythymidine ([ 18 F]FLT), and (2S, 4R)-4-[ 18 F]fluoroglutamine ([ 18 F]GLN), were evaluated in the MG63.3 human OS xenograft mouse model, as companion imaging biomarkers after treatment for 7 d with a selective GLS1 inhibitor (CB-839, telaglenastat) and metformin, alone and in combination. Imaging and biodistribution data were collected from tumors and reference tissues before and after treatment. Results: Drug treatment altered tumor uptake of all three PET agents. Relative [ 18 F]FDG uptake decreased significantly after telaglenastat treatment, but not within control and metformin-only groups. [ 18 F]FLT tumor uptake appears to be negatively affected by tumor size. Evidence of a flare effect was seen with [ 18 F]FLT imaging after treatment. Telaglenastat had a broad influence on [ 18 F]GLN uptake in tumor and normal tissues. Conclusions: Image-based tumor volume quantification is recommended for this paratibial tumor model. The performance of [ 18 F]FLT and [ 18 F]GLN was affected by tumor size. [ 18 F]FDG may be useful in detecting telaglenastat's impact on glycolysis. Exploration of kinetic tracer uptake protocols is needed to define clinically relevant patterns of [ 18 F]GLN uptake in patients receiving telaglenastat.

Prostate cancer remains a major cause of mortality and morbidity, affecting millions of men, with a large percentage expected to develop the disease as they reach advanced ages. Treatment and management advances have been dramatic over the past 50 years or so, and one aspect of these improvements is reflected in the multiple advances in diagnostic imaging techniques. Much attention has been focused on molecular imaging techniques that offer high sensitivity and specificity and can now more accurately assess disease status and detect recurrence earlier. During development of molecular imaging probes, single-photon emission computed tomography (SPECT) and positron emission tomography (PET) must be evaluated in preclinical models of the disease. If such agents are to be translated to the clinic, where patients undergoing these imaging modalities are injected with a molecular imaging probe, these agents must first be approved by the FDA and other regulatory agencies prior to their adoption in clinical practice. Scientists have worked assiduously to develop preclinical models of prostate cancer that are relevant to the human disease to enable testing of these probes and related targeted drugs. Challenges in developing reproducible and robust models of human disease in animals are beset with practical issues such as the lack of natural occurrence of prostate cancer in mature male animals, the difficulty of initiating disease in immune-competent animals and the sheer size differences between humans and conveniently smaller animals such as rodents. Thus, compromises in what is ideal and what can be achieved have had to be made. The workhorse of preclinical animal models has been, and remains, the investigation of human xenograft tumor models in athymic immunocompromised mice. Later models have used other immunocompromised models as they have been found and developed, including the use of directly derived patient tumor tissues, completely immunocompromised mice, orthotopic methods for inducing prostate cancer within the mouse prostate itself and metastatic models of advanced disease. These models have been developed in close parallel with advances in imaging agent chemistries, radionuclide developments, computer electronics advances, radiometric dosimetry, biotechnologies, organoid technologies, advances in in vitro diagnostics, and overall deeper understandings of disease initiation, development, immunology, and genetics. The combination of molecular models of prostatic disease with radiometric-based studies in small animals will always remain spatially limited due to the inherent resolution sensitivity limits of PET and SPECT decay processes, fundamentally set at around a 0.5 cm resolution limit. Nevertheless, it is central to researcher's efforts and to successful clinical translation that the best animal models are adopted, accepted, and scientifically verified as part of this truly interdisciplinary approach to addressing this important disease., (Published by Elsevier Inc.)

Genitourinary (GU) system is among the most commonly involved malignancy sites in the human body. Imaging plays a crucial role not only in diagnosis of cancer but also in disease management and its prognosis. However, interpretation of conventional imaging methods such as CT or MR imaging (MRI) usually demonstrates variability across different readers and institutions. Artificial intelligence (AI) has emerged as a promising technology that could improve the patient care by providing helpful input to human readers through lesion detection algorithms and lesion classification systems. Moreover, the robustness of these models may be valuable in automating time-consuming tasks such as organ and lesion segmentations. Herein, we review the current state of imaging and existing challenges in GU malignancies, particularly for cancers of prostate, kidney and bladder; and briefly summarize the recent AI-based solutions to these challenges., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.