In metastatic cancer, the role of heterogeneity at the tumor-immune microenvironment, its molecular underpinnings and clinical relevance remain largely unexplored. To understand tumor-immune dynamics at baseline and upon chemotherapy treatment, we performed unbiased pathway and cell type-specific immunogenomics analysis of treatment-naive (38 5 samples from 8 patients) and paired chemotherapy treated (80 paired samples from 40 patients) high-grade serous ovarian cancer (HGSOC) samples. Whole transcriptome analysis and imagebased quantification of T cells from treatment-naive tumors revealed ubiquitous variability in immune signaling and distinct immune microenvironments co-existing within the same individuals and within tumor deposits at diagnosis. To systematically explore cell type 10 composition of the tumor microenvironment using bulk mRNA, we derived consensus immune and stromal cell gene signatures by intersecting state-of-the-art deconvolution methods, providing improved accuracy and sensitivity when compared to HGSOC immunostaining and leukocyte methylation data sets. Cell-type deconvolution and pathway analyses revealed that Myc and Wnt signaling associate with immune cell exclusion in untreated HGSOC. To evaluate 15 the effect of chemotherapy on the intrinsic tumor-immune heterogeneity, we compared sitematched and site-unmatched tumors before and after neoadjuvant chemotherapy.Transcriptomic and T-cell receptor sequencing analyses showed that site-matched samples had increased cytotoxic immune activation and oligoclonal expansion of T cells after chemotherapy, which was not seen in site-unmatched samples where heterogeneity could not be accounted 20for. These results demonstrate that the tumor-immune interface in advanced HGSOC is intrinsically heterogeneous, and thus requires site-specific analysis to reliably unmask the impact of therapy on the tumor-immune microenvironment. 1050 Conceptualization [Ideas; formulation or evolution of overarching research goals and aims] AS, AJS, MLM, ES Data curation [Management activities to annotate (produce metadata), scrub data and maintain research data (including software code, where necessary for interpreting the data itself) for 1055 initial use and later re-use] AJS, KL, PC Formal analysis [Application of statistical, mathematical, computational, or other formal techniques to analyse or synthesize study data] 1060 AJS Funding acquisition [Acquisition of the financial support for the project leading to this publication] AV, ES, AS, MLM 1065 Investigation [Conducting a research and investigation process, specifically performing the experiments, or data/evidence collection] PC, KL, TW, YM, IN, BW, DC, ES 1070 50 Methodology [Development or design of methodology; creation of models] ES Project administration [Management and coordination responsibility for the research activity planning and execution] 1075 AS, MLM, ES Resources [Provision of study materials, reagents, materials, patients, laboratory samples, animals, instrumentation, computing resources, or other a...

Learning Objectives: On successful completion of this activity, participants should be able to describe (1) the various distinct clinical scenarios for prostate cancer; (2) the various imaging modalities available for use in prostate cancer; (3) the role of these imaging modalities in primary or recurrent prostate cancer and their advantages and disadvantages; and (4) the future potential of theranostic approaches and the evolving indications for contemporary hybrid imaging modalities such as PET/MRI and whole-body MRI. Financial Disclosure: Dr. Burger has received research grants and speaker honorarium from GE Healthcare. The authors of this article have indicated no other relevant relationships that could be perceived as a real or apparent conflict of interest. CME Credit: SNMMI is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to sponsor continuing education for physicians. SNMMI designates each JNM continuing education article for a maximum of 2.0 AMA PRA Category 1 Credits. Physicians should claim only credit commensurate with the extent of their participation in the activity. For CE credit, SAM, and other credit types, participants can access this activity through the SNMMI website (http://www.snmmilearningcenter.org) through October 2022. Prostate cancer is a very heterogeneous disease, and contemporary management is focused on identification and treatment of the prognostically adverse high-risk tumors while minimizing overtreatment of indolent, low-risk tumors. In recent years, imaging has gained increasing importance in the detection, staging, posttreatment assessment, and detection of recurrence of prostate cancer. Several imaging modalities including conventional and functional methods are used in different clinical scenarios with their very own advantages and limitations. This continuing medical education article provides an overview of available imaging modalities currently in use for prostate cancer followed by a more specific section on the value of these different imaging modalities in distinct clinical scenarios, ranging from initial diagnosis to advanced, metastatic castration-resistant prostate cancer. In addition to established imaging indications, we will highlight some potential future applications of contemporary imaging modalities in prostate cancer.

Ongoing technical innovation in combination with a broad research activity has resulted in increased adoption and widespread utilization of magnetic resonance imaging (MRI) of the prostate. The Prostate Imaging Reporting and Data System (PI-RADS), first introduced in 2012 and subsequently updated in 2015 and 2019, standardized image acquisition and reporting and facilitated the communication of imaging findings to referring physician teams and is now considered an obligatory key element in prostate MRI. This has had a tremendous impact on the diagnostic workup of patients with suspected prostate cancer. Indications for MRI have been incorporated in multiple prostate cancer guidelines (e.g., NICE, AUA, EAU, German S3-Guideline), and in turn imaging-based targeted prostate biopsy has markedly increased. Referring physicians not only heavily rely on accurate interpretation of MRI of the prostate but actively seek high-quality MRI scans for their daily practice because prostate MRI has direct impact on their cancer detection rate. Furthermore, a paradigm shift is taking place in the prostate cancer community regarding the care of low-risk prostate cancer patients, where active surveillance (AS) is increasingly favored over definitive therapy. Prostate MRI plays an important role in AS not only during the initial assessment to determine eligibility but also over the course of follow-up of the disease.

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The aim of this work is to develop a data-driven quantitative dynamic contrastenhanced (DCE) MRI technique using Golden-angle RAdial Sparse Parallel (GRASP) MRI with high spatial resolution and high flexible temporal resolution and pharmacokinetic (PK) analysis with an arterial input function (AIF) estimated directly from the data obtained from each patient. DCE-MRI was performed on 13 patients with gynecological malignancy using a 3-T MRI scanner with a single continuous golden-angle stack-of-stars acquisition and image reconstruction with two temporal resolutions, by exploiting a unique feature in GRASP that reconstructs acquired data with userdefined temporal resolution. Joint estimation of the AIF (both AIF shape and delay) and PK parameters was performed with an iterative algorithm that alternates between AIF and PK estimation. Computer simulations were performed to determine the accuracy (expressed as percentage error [PE]) and precision of the estimated parameters. PK parameters (volume transfer constant [K trans ], fractional volume of the extravascular extracellular space [v e ], and blood plasma volume fraction [v p ]) and normalized root-mean-square error [nRMSE] (%) of the fitting errors for the tumor contrast kinetic data were measured both with population-averaged and data-driven AIFs. On patient data, the Wilcoxon signed-rank test was performed to compare nRMSE. Simulations demonstrated that GRASP image reconstruction with a temporal resolution of 1 s/frame for AIF estimation and 5 s/frame for PK analysis resulted in an absolute PE of less than 5% in the estimation of K trans and v e , and less than 11% in the estimation of v p . The nRMSE (mean ± SD) for the dual temporal resolution image reconstruction and data-driven AIF was 0.16 ± 0.04 compared with 0.27 ± 0.10 (p < 0.001) with 1 s/frame using population-averaged AIF, and 0.23 ± 0.07 with 5 s/frame using population-averaged AIF (p < 0.001). We conclude that DCE-MRI data acquired and reconstructed with the GRASP technique at dual temporal resolution can successfully be applied to jointly estimate the AIF and PK