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Simple Summary: The proper management of prostate cancer requires accurate assessment of patients diagnosed with prostate cancer, a common and often lethal cancer. Current conventional prostate cancer diagnosis relies on: (1) needle biopsy, which can result in side effects such as hemorrhage or infection, as well as misplacement of needles resulting in evaluation inaccuracy, (2) prostate serum antigen detection that can lead to overdiagnosis, or (3) MRI interpretations using the Prostate Imaging-Reporting and Data System (PI-RADS), which is a subjective method that depends on the experience of the radiologist's interpretation of the images. Quantitative analysis of MRI can alleviate the limitations of conventional approaches. Artificial intelligence applied to MRI requires big training datasets, and its application is restricted to patients scanned under certain restricted conditions. More recently, spectral/statistical techniques, adapted from remote sensing, have been applied and tested in spatially registered multi-parametric MRI. The new spectral/statistical techniques require limited training, are flexible regarding patient scanning conditions, and perform well relative to other techniques. This study extends spectral/statistical techniques to make them more independent of the individual radiologist by autonomously finding the appropriate tumor signature within a prostate and, therefore, providing an objective, non-invasive, accurate means for determining prostate tumor aggressiveness. Background: Accurate, reliable, non-invasive assessment of patients diagnosed with prostate cancer is essential for proper disease management. Quantitative assessment of multi-parametric MRI, such as through artificial intelligence or spectral/statistical approaches, can provide a non-invasive objective determination of the prostate tumor aggressiveness without side effects or potential poor sampling from needle biopsy or overdiagnosis from prostate serum antigen measurements. To simplify and expedite prostate tumor evaluation, this study examined the efficacy of autonomously extracting tumor spectral signatures for spectral/statistical algorithms for spatially registered bi-parametric MRI. Methods: Spatially registered hypercubes were digitally constructed by resizing, translating, and cropping from the image sequences (Apparent Diffusion Coefficient (ADC), High B-value, T2) from 42 consecutive patients in the bi-parametric MRI PI-CAI dataset. Prostate cancer blobs exceeded a threshold applied to the registered set from normalizing the registered set into an image that maximizes High B-value, but minimizes the ADC and T2 images, appearing "green" in the color composite. Clinically significant blobs were selected based on size, average normalized green value, sliding window statistics within a blob, and position within the hypercube. The center of mass and maximized sliding window statistics within the blobs identified voxels associated with tumor signatures. We used correlation coefficients (R) and p-values, to evaluate the linear regression fits of the z-score and SCR (with processed covariance matrix) to tumor aggressiveness, as well as Area Under the Curves (AUC) for Receiver Operator Curves (ROC) from logistic probability fits to clinically significant prostate cancer. Results: The highest R (R > 0.45), AUC (>0.90), and lowest p-values (<0.01) were achieved using z-score and modified registration applied to the covariance matrix and tumor signatures selected from the "greenest" parts from the selected blob. Conclusions: The first autonomous tumor signature applied to spatially registered bi-parametric MRI shows promise for determining prostate tumor aggressiveness. [ABSTRACT FROM AUTHOR]

(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. [ABSTRACT FROM AUTHOR]

Background: Current prostate cancer evaluation can be inaccurate and burdensome. Quantitative evaluation of Magnetic Resonance Imaging (MRI) sequences non-invasively helps prostate tumor assessment. However, including Dynamic Contrast Enhancement (DCE) in the examined MRI sequence set can add complications, inducing possible side effects from the IV placement or injected contrast material and prolonging scanning time. More accurate quantitative MRI without DCE and artificial intelligence approaches are needed. Purpose: Predict the risk of developing Clinically Significant (Insignificant) prostate cancer CsPCa (CiPCa) and correlate with the International Society of Urologic Pathology (ISUP) grade using processed Signal to Clutter Ratio (SCR) derived from spatially registered bi-parametric MRI (SRBP-MRI) and thereby enhance non-invasive management of prostate cancer. Methods: This pilot study retrospectively analyzed 42 consecutive prostate cancer patients from the PI-CAI data collection. BP-MRI (Apparent Diffusion Coefficient, High B-value, T2) were resized, translated, cropped, and stitched to form spatially registered SRBP-MRI. Efficacy of noise reduction was tested by regularizing, eliminating principal components (PC), and minimizing elliptical volume from the covariance matrix to optimize the SCR. MRI guided biopsy (MRBx), Systematic Biopsy (SysBx), combination (MRBx + SysBx), or radical prostatectomy determined the ISUP grade for each patient. ISUP grade ≥ 2 (<2) was judged as CsPCa (CiPCa). Linear and logistic regression were fitted to ISUP grade and CsPCa/CiPCa SCR. Correlation Coefficients (R) and Area Under the Curves (AUC) for Receiver Operator Curves (ROC) evaluated the performance. Results: High correlation coefficients (R) (>0.55) and high AUC (=1.0) for linear and/or logistic fit from processed SCR and z-score for SRBP-MRI greatly exceed fits using prostate serum antigen, prostate volume, and patient age (R ~ 0.17). Patients assessed with combined MRBx + SysBx and from individual MRI scanners achieved higher R (DR = 0.207+/−0.118) than all patients used in the fits. Conclusions: In the first study, to date, spectral approaches for assessing tumor aggressiveness on SRBP-MRI have been applied and tested and achieved high values of R and exceptional AUC to fit the ISUP grade and CsPCA/CiPCA, respectively. [ABSTRACT FROM AUTHOR]

The article reports that in the era of precision medicine, integrated diagnostics is employed for selecting appropriate and optimal therapies tailored for patients' individual characteristics. Topics include considered a highly debated and heterogeneous clinical scenario is to manage the patients who underwent whole-gland therapy for prostate cancer.

This study describes the pattern of failure in patients with biochemical (BCR) recurrence after low-dose-rate (LDR) brachytherapy as a component of definitive treatment for prostate cancer. Patients with BCR after LDR brachytherapy ± external beam radiation therapy (EBRT) were enrolled on prospective IRB approved advanced imaging protocols. Patients underwent 3T multiparametric MRI (mpMRI); a subset underwent prostate specific membrane antigen (PSMA)-based PET/CT. Pathologic confirmation was obtained unless contraindicated. Between January 2011 and April 2021, 51 patients with BCR after brachytherapy (n = 36) or brachytherapy + EBRT (n = 15) underwent mpMRI and were included in this analysis. Of 38 patients with available dosimetry, only two had D90<90%. The prostate and seminal vesicles were a site of failure in 66.7% (n = 34) and 39.2% (n = 20), respectively. PET/CT (n = 32 patients) more often identified lesions pelvic lymph nodes (50%; n = 16) and distant metastases (18.8%; n = 6), than mpMRI. Isolated nodal disease (9.8%; n = 5) and distant metastases (n = 1) without local recurrence were uncommon. Recurrence within the prostate was located in the transition zone in 48.5%, central or midline in 45.5%, and anterior in 36.4% of patients. In this cohort of patients with BCR after LDR brachytherapy ± EBRT, the predominant recurrence pattern was local (prostate ± seminal vesicles) with frequent occurrence in the anterior prostate and transition zone. mpMRI and PSMA PET/CT provided complementary information to localize sites of recurrence, with PSMA PET/CT often confirming mpMRI findings and identifying occult nodal or distant metastases. [ABSTRACT FROM AUTHOR]

Simple Summary: Near-infrared photoimmunotherapy (NIR-PIT) is a novel molecularly-targeted therapy that induces rapid cancer cell death by systemically administering an antibody-photoabsorber conjugate (APC) that binds to cancer cells and irradiating NIR light that drives photochemical transformations of the APC. APCs are constructed by using a monoclonal antibody targeting a cancer cell surface antigen and conjugating it to IRDye700DX silica-phthalocyanine dye. NIR-PIT can selectively kill cancer cells while leaving normal tissues unaffected. Moreover, NIR-PIT activates anti-cancer immunity through the induction of immunogenic cell death of cancer cells. Currently, NIR-PIT is being applied clinically in head and neck squamous cell carcinoma. In previous preclinical studies, NIR-PIT showed excellent efficacy against urologic cancers including bladder cancer and prostate cancer. The clinical application of NIR-PIT will expand to urologic cancers in the near future. Near-infrared photoimmunotherapy (NIR-PIT) is a novel molecularly-targeted therapy that selectively kills cancer cells by systemically injecting an antibody-photoabsorber conjugate (APC) that binds to cancer cells, followed by the application of NIR light that drives photochemical transformations of the APC. APCs are synthesized by selecting a monoclonal antibody that binds to a receptor on a cancer cell and conjugating it to IRDye700DX silica-phthalocyanine dye. Approximately 24 h after APC administration, NIR light is delivered to the tumor, resulting in nearly-immediate necrotic cell death of cancer cells while causing no harm to normal tissues. In addition, NIR-PIT induces a strong immunologic effect, activating anti-cancer immunity that can be further boosted when combined with either immune checkpoint inhibitors or immune suppressive cell-targeted (e.g., regulatory T cells) NIR-PIT. Currently, a global phase III study of NIR-PIT in recurrent head and neck squamous cell carcinoma is ongoing. The first APC and NIR laser systems were approved for clinical use in September 2020 in Japan. In the near future, the clinical applications of NIR-PIT will expand to other cancers, including urologic cancers. In this review, we provide an overview of NIR-PIT and its possible applications in urologic cancers. [ABSTRACT FROM AUTHOR]

Prostate cancer (PCa) is the most common cancer type in males in the Western World. MRI has an established role in diagnosis of PCa through guiding biopsies. Due to multistep complex nature of the MRI-guided PCa diagnosis pathway, diagnostic performance has a big variation. Developing artificial intelligence (AI) models using machine learning, particularly deep learning, has an expanding role in radiology. Specifically, for prostate MRI, several AI approaches have been defined in the literature for prostate segmentation, lesion detection and classification with the aim of improving diagnostic performance and interobserver agreement. In this review article, we summarize the use of radiology applications of AI in prostate MRI. [ABSTRACT FROM AUTHOR]

OATP1B3 is expressed de novo in primary prostate cancer tissue and to a greater degree in prostate cancer metastases. Gadoxetate disodium is a substrate of OATP1B3, and its uptake has been shown to correlate with OATP1B3 expression in other cancers. We aimed to evaluate use of gadoxetate disodium to image prostate cancer and to track its utility as a biomarker. A single center open-label non-randomized pilot study recruited men with (1) localized, and (2) metastatic castration resistant prostate cancer (mCRPC). Gadoxetate disodium-enhanced MRI was performed at four timepoints post-injection. The Wilcoxon signed rank test was used to compare MRI contrast enhancement ratio (CER) pre-injection and post-injection. OATP1B3 expression was evaluated via immunohistochemistry (IHC) and a pharmacogenomic analysis of OATP1B3, NCTP and OATP1B1 was conducted. The mCRPC subgroup (n = 9) demonstrated significant enhancement compared to pre-contrast images at 20-, 40- and 60-min timepoints (p < 0.0078). The localized cancer subgroup (n = 11) demonstrated earlier enhancement compared to the mCRPC group, but no retention over time (p > 0.05). OATP1B3 expression on IHC trended higher contrast enhancement between 20–40 min (p ≤ 0.064) and was associated with contrast enhancement at 60 min (p = 0.0422). OATP1B1 haplotype, with N130D and V174A substitutions, impacted enhancement at 40–60 min (p ≤ 0.038). mCRPC lesions demonstrate enhancement after injection of gadoxetate disodium on MRI and retention over 60 min. As inter-individual variability in OATP1B3 expression and function has both predictive and prognostic significance, gadoxetate disodium has potential as a biomarker in prostate cancer. [ABSTRACT FROM AUTHOR]

The 13th sentence of the first paragraph of the Materials and Methods subsection Animals, regarding the antibiotic/antiparasitic treatment regimen, is incorrect. The authors do not believe that the differences in the antibiotic/antiparasitic treatment used prior to the study affected the results and conclusions. The correct text is: Prior to the study and as part of the facility animal management, 22 animals received an oral antibiotic/antiparasitic regimen between 1 and 10 months prior to study which included metronidazole (50 mg + 21mg/kg or 50 mg/kg once or twice daily for 5-10 days) and fenbendazole (50 mg/kg once daily for 3-5 days), with some of these animals (n = 15) also receiving enrofloxacin (10 mg/kg once daily for 7 days). [Extracted from the article]

The Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) was developed with a consensus-based process using a combination of published data, and expert observations and opinions. In the short time since its release, numerous studies have validated the value of PI-RADS v2 but, as expected, have also identified a number of ambiguities and limitations, some of which have been documented in the literature with potential solutions offered. To address these issues, the PI-RADS Steering Committee, again using a consensus-based process, has recommended several modifications to PI-RADS v2, maintaining the framework of assigning scores to individual sequences and using these scores to derive an overall assessment category. This updated version, described in this article, is termed PI-RADS v2.1. It is anticipated that the adoption of these PI-RADS v2.1 modifications will improve inter-reader variability and simplify PI-RADS assessment of prostate magnetic resonance imaging even further. Research on the value and limitations on all components of PI-RADS v2.1 is strongly encouraged. The Prostate Imaging Reporting and Data System (PI-RADS) Committee, using a consensus-based process, has recommended several modifications to PI-RADS version 2 (v2), maintaining the framework of assigning scores to individual sequences and using these scores to derive an overall assessment category. The updated version is termed PI-RADS v2.1. [ABSTRACT FROM AUTHOR]

Introduction: Prostate cancer (PCa) imaging is a rapidly evolving field. Dramatic improvements in prostate MRI during the last decade will probably change the accuracy of diagnosis. This chapter reviews recent current evidence about MRI diagnostic performance and impact on PCa management.Materials and methods: The International Consultation on Urological Diseases nominated a committee to review the literature on prostate MRI. A search of the PubMed database was conducted to identify articles focussed on MP-MRI detection and staging protocols, reporting and scoring systems, the role of MP-MRI in diagnosing PCa prior to biopsy, in active surveillance, in focal therapy and in detecting local recurrence after treatment.Results: Differences in opinion were reported in the use of the strength of magnets [1.5 Tesla (T) vs. 3T] and coils. More agreement was found regarding the choice of pulse sequences; diffusion-weighted MRI (DW-MRI), dynamic contrast-enhanced MRI (DCE MRI), and/or MR spectroscopy imaging (MRSI) are recommended in addition to conventional T2-weighted anatomical sequences. In 2015, the Prostate Imaging Reporting and Data System (PI-RADS version 2) was described to standardize image acquisition and interpretation. MP-MRI improves detection of clinically significant PCa (csPCa) in the repeat biopsy setting or before the confirmatory biopsy in patients considering active surveillance. It is useful to guide focal treatment and to detect local recurrences after treatment. Its role in biopsy-naive patients or during the course of active surveillance remains debated.Conclusion: MP-MRI is increasingly used to improve detection of csPCa and for the selection of a suitable therapeutic approach. [ABSTRACT FROM AUTHOR]

Dramatic changes in the use of prostate magnetic resonance imaging (MRI) have occurred in the last decade. The recognition that MRI detects and localizes cancers with reasonable accuracy led to the development of directed biopsies. These image-guided biopsies have a higher sensitivity for clinically significant cancers and a lower sensitivity for indolent disease. Prospective trials provide level 1 evidence supporting the use of prostate MRI. For local staging, while the specificity of prostate MRI is high, its sensitivity is lacking for microscopic extraprostatic extension. Computer-aided diagnosis of prostate MRI promises to bring the diagnostic power of MRI to nonexpert readers and thus further integrate MRI into the diagnostic workup. [ABSTRACT FROM AUTHOR]

Imaging is critically important for the diagnosis, staging, and management of men with high-risk prostate cancer. Conventional imaging modalities have been employed for local and metastatic staging with limited performance. Sodium fluoride positron emission tomography is recommended when there is high suspicion of bone metastases despite a negative or indeterminate bone scan. Magnetic resonance imaging has advantages in local staging but its value depends on the extent of disease. Whole-body positron emission tomography/magnetic resonance imaging could provide both local and distant staging. None of the existing positron emission tomography agents are recommended in practice guidelines; however, among them, prostate-specific membrane antigen-based tracers seem to hold the most promise based on sensitivity and specificity. [ABSTRACT FROM AUTHOR]

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. Visual Abstract [ABSTRACT FROM AUTHOR]

Prostate cancer is unique in that unlike other solid organ malignancies, only recently has imaging been employed to routinely detect and localize disease. The introduction of transrectal ultrasound was a significant development, transitioning digitally guided prostate biopsies to ultrasound guidance. The arrival of multiparametric MRI has become the next major step, transforming the way Urologist's diagnose, stage, and treat prostate cancer. Recent recommendations against PSA screening have changed the landscape of urologic oncology with the changing needs being reflected in the initiation of additional robust imaging techniques at different time points in prostate cancer care. The current review aims to provide a clinical perspective in the history, current standard of care, and novel imaging modalities in the evaluation of prostate cancer. [ABSTRACT FROM AUTHOR]

The incidence and importance of bone marrow involvement and/or early bone lesions in multiple myeloma (MM) precursor diseases is largely unknown. This study prospectively compared the sensitivity of several imaging modalities in monoclonal gammopathy of undetermined significance (MGUS), smoldering multiple myeloma (SMM) and MM. Thirty patients (10 each with MGUS, SMM and MM) were evaluated with skeletal survey, [18F]FDG-PET/CT, [18F]NaF-PET/CT and morphologic dynamic contrast enhanced (DCE)-MRI. An additional 16 SMM patients had skeletal surveys and FDG-PET/CT. Among MGUS patients, DCE-MRI found only one focal marrow abnormality; other evaluations were negative. Among 26 SMM patients, five (19%) were re-classified as MM based on lytic bone lesions on CT and six had unifocal or diffuse marrow abnormality. Among MM, marrow abnormalities were observed on FDG-PET/CT in 8/10 patients and on DCE-MRI in nine evaluable patients. Abnormal NaF uptake was observed only in MM patients with lytic lesions on CT, providing no additional clinical information. [ABSTRACT FROM PUBLISHER]

Background: Carbonic anhydrase IX (CA-IX) is a potential imaging biomarker of clear cell renal cell carcinoma (ccRCC). Here, we report the results of a phase II clinical trial of a small molecule radiotracer targeting CA-IX (F-VM4-037) in ccRCC. Methods: Between October 2012 and May 2013, 11 patients with kidney masses underwent F-VM4-037 PET/CT prior to surgery. Dynamic imaging was performed for the first 45 min post injection and whole-body imaging was obtained at 60 min post injection. Tumors were surgically excised or biopsied within 4 weeks of imaging. Results: All patients tolerated the radiotracer well with no adverse events. Ten of the 11 patients had histologically confirmed malignancy. One patient had a Bosniak Type 3 cyst with no tumor found at surgery. Two patients had extrarenal disease and 9 had tumors only in the kidney. Primary ccRCC lesions were difficult to visualize on PET alone due to high uptake of the tracer in the adjacent normal kidney parenchyma, however when viewed in conjunction with CT, the tumors were easily localized. Metastatic lesions were clearly visible on PET. Mean SUV for primary kidney lesions was 2.55 in all patients; in patients with histologically confirmed ccRCC, the mean SUV was 3.16. The time-activity curves (TAC) are consistent with reversible ligand binding with peak activity concentration at 8 min post injection followed by washout. Distribution Volume Ratio (DVR) of the lesions was measured using the Logan graphical analysis method. The mean DVR value across the 9 kidney lesions was 5.2 ± 2.8, (range 0.68-10.34). Conclusion: 18F-VM4-037 is a well-tolerated PET agent that allows same day imaging of CA-IX expression. The agent demonstrated moderate signal uptake in primary tumors and excellent visualization of CA-IX positive metastases. While the evaluation of primary ccRCC lesions is challenging due to high background activity in the normal kidney parenchyma, 18F-VM4-037 may be most useful in the evaluation of metastatic ccRCC lesions. [ABSTRACT FROM AUTHOR]

Objective To describe the detection rate of anteriorly located prostate cancer ( PCa) with the addition of magnetic resonance imaging ( MRI)/ultrasonography ( US) fusion-guided biopsy ( FGB) to the standard transrectal ultrasonography ( TRUS)-guided biopsy. Patients and Methods All patients, regardless of their biopsy history, who were referred for clinical suspicion of PCa (i.e elevated prostate-specific antigen ( PSA) level and abnormal digital rectal examination) underwent 3T multiparametric- MRI ( mpMRI) screening; and those with suspicious lesions in the anterior region of the prostate were identified. Patients then received a FGB of all suspicious lesions in addition to a systematic 12-core extended sextant TRUS-guided biopsy. We conducted a lesion-based analysis comparing cancer detection rates of anterior targets using FGB vs systematic cores taken from the same anatomic sextant within the prostate. Lengths of cancer in the most involved core were also compared between the two biopsy techniques used. Patients with only anterior targets were analysed separately. Results Of 499 patients undergoing FGB, 162 had a total of 241 anterior lesions. The mean age, PSA level and prostate volume in this group were 62 years, 12.7 ng/dL, and 57 mL, respectively. In total, PCa was diagnosed in 121 anterior lesions (50.2%) identified on mpMRI. Sixty-two (25.7%) of these anterior lesions were documented as positive for cancer on systematic 12-core TRUS-guided biopsy cores, while 97 (40.2%) were positive on the targeted FGB cores ( P = 0.001). In lesions that were positive on both FGB and TRUS biopsy, the most involved core was 112% longer on FGB (3.7 vs 1.6 mm, P ≤ 0.01). Forty-two patients had only anterior lesions on mpMRI; of these, 24 (57.1%) were found to have cancer on the FGB + TRUS biopsy platform. Six patients were positive on FGB only and 13 were positive on both biopsy techniques; however, 7/13 patients were upgraded to a higher Gleason score after FGB. All five patients positive on TRUS biopsy only were candidates for active surveillance. Conclusion The results showed that FGB detects significantly more anteriorly located PCa than does TRUS-guided biopsy alone and it may serve as an effective tool for the subset of patients with such tumours. [ABSTRACT FROM AUTHOR]

Imaging has played an important role in the administration of personalized medicine. From diagnosing diseases to guiding therapies, imaging has become an all-encompassing modality. With respect to prostate cancer, personalized management of the disease has been transformed by imaging. Specifically, multiparametric magnetic resonance imaging has emerged as a vital player in the detection, characterization, and localization of the disease thus making the incorporation of imaging in personalized prostate cancer management integral. In this review, the current role of imaging in personalized medicine for the management of prostate cancer is discussed. [ABSTRACT FROM AUTHOR]

Over 80% of sexual HIV-1 transmissions originate from a single viral variant, but the underlying basis for this transmission bottleneck remains to be elucidated. Nonhuman primate models of mucosal virus transmission allow opportunities to gain insight into the basis of this mucosal bottleneck. We used simulated inocula consisting of either non-infectious vital dye or contrast dye with non-invasive magnetic resonance imaging (MRI) to visualize mucosal exposure and passive lymphatic drainage patterns following vaginal and rectal exposures in Indian origin rhesus macaques. Results revealed a limited overall distance of dye coverage from the anal verge following 1 ml (n  = 8) intrarectally administered, which greatly increased with a 3 ml (n = 8) volume. Intravaginal dye exposure using 2 ml revealed complete coverage of the mucosa of the vagina and ectocervix, however dye was not detectable in the endocervix, uterus, fallopian tubes or ovaries in nuliparous sexually mature rhesus macaques (n = 9). In addition, following submucosal and intranodal injections of vital dye or MRI contrast dye in the rectum (n = 9), or distal and proximal vagina (n = 4), the lymphatic drainage pathways were identified as first the internal then common iliac chain followed by para-aortic lymph nodes. Drainage from the distal descending colon (n = 8) was via the para-colonic lymph nodes followed by the inferior mesenteric and para-aortic lymph nodes. Analysis after vaginal challenge with infectious SIVmac239 followed by euthanasia at day 3 revealed a pattern of viral dissemination consistent with the imaging results. These results provide insights into potential patterns of viral dissemination that can help guide efforts to better elucidate the earliest events of virus transmission and potential intervention strategies. [ABSTRACT FROM AUTHOR]

Study Type - Diagnosis (case series) Level of Evidence 4 What's known on the subject? and What does the study add? MRI has been shown to improve prostate cancer detection rates. Pinto et al. ( J Urol 2011; 86: 1281-5) reported their outcomes on 101 patients with low, moderate or high suspicion lesions on multiparametric MRI that were subsequently targeted via an MRI/ultrasound fusion biopsy platform. The prostate cancer detection rates were 27%, 66% and 89% respectively. Sciarra et al. ( Clin Cancer Res 2010; 16: 1875-83) performed a prospective trial in 180 patients with prior negative biopsy and persistent PSA elevation. Patients were randomized to either MRI targeted biopsy followed by random 12-core TRUS biopsy vs random TRUS guided biopsy alone. Prostate cancer detection in the MRI targeted group was 45.5% vs 24.4% in the random group. Although MRI has been shown to improve prostate cancer detection rates, there has not previously been any work looking at the ability of MRI to detect prostate cancer localized to the very distal apex of the prostate. This is an important topic in that it might lead clinicians to counsel their patients in treatment decisions if it is felt that a treatment might not treat this section of the prostate well, e.g. high intensity focused ultrasound therapy that might spare the distal apex. OBJECTIVE To describe an undescribed 'very distal' apical prostate cancer on multiparametric MRI (mpMRI) since apical prostate cancer can be difficult to detect in transrectal ultrasound guided biopsy and might therefore be missed in treatment decisions such as high intensity focused ultrasound or surgical therapy., PATIENTS AND METHODS From January 2011 to December 2012 a total of 210 consecutive patients underwent 3 T mpMRI with endorectal coil followed by our previously described MRI/ultrasound image fused and directed TRUS biopsies., Patients also underwent 12-core TRUS sextant biopsies., The inclusion criteria required at least one distal apical prostate lesion visualized on mpMRI and targeted for biopsy., RESULTS A total of 38 men (median age 62 years, median PSA 7.68 ng/dL) were identified as having distal apical prostate cancer on mpMRI., Thirteen patients (34%) had a prior diagnosis of cancer and were on active surveillance protocols while 25 (66%) did not. Of those patients, 21 (55%) had undergone a median of two prior negative biopsies., Twenty-two patients (58%) were positive on biopsy for prostate cancer. On breakdown of patients who were positive, 17 (77%) were positive on TRUS random biopsies and 21 (95%) were positive on MRI targeted biopsies with the majority of patients having multifocal disease., At the distal apical lesions of interest, 80% were positive on MRI targeted biopsy. In addition 33% of these patients were upgraded based on MRI targeted biopsy at the distal lesion., CONCLUSIONS Very distal apical prostate cancer can be accurately detected and sampled with mpMRI and subsequent MRI/ultrasound fusion biopsy., This may aid clinicians and patients in decision making for therapeutic modalities. [ABSTRACT FROM AUTHOR]

Study Type - Diagnosis (case series) Level of Evidence 4 What's known on the subject? and What does the study add? Benign prostatic hyperplasia is the most common symptomatic disorder of the prostate and its severity varies greatly in the population. Various methods have been used to estimate prostate volumes in the past including the digital rectal examination and ultrasound measurements. High-resolution T2 weighted MRI can provide accurate measurements of zonal volumes and total volumes, which can be used to better understand the etiology of lower urinary tract symptoms of men. OBJECTIVE To use ability of magnetic resonance imaging (MRI) to investigate age-related changes in zonal prostate volumes., PATIENTS AND METHODS This Institutional Review Board approved, Health Insurance Portability and Accountability Act-compliant study consisted of 503 patients who underwent 3 T prostate MRI before any treatment for prostate cancer., Whole prostate (WP) and central gland (CG) volumes were manually contoured on T2-weighted MRI using a semi-automated segmentation tool. WP, CG, peripheral zone (PZ) volumes were measured for each patient., WP, CG, PZ volumes were correlated with age, serum prostate-specific antigen (PSA) level, International Prostate Symptom Score (IPSS), Sexual Health Inventory for Men (SHIM) scores., RESULTS Linear regression analysis showed positive correlations between WP, CG volumes and patient age ( P < 0.001); there was no correlation between age and PZ volume ( P= 0.173)., There was a positive correlation between WP, CG volumes and serum PSA level ( P < 0.001), as well as between PZ volume and serum PSA level ( P= 0.002)., At logistic regression analysis, IPSS positively correlated with WP, CG volumes ( P < 0.001)., SHIM positively correlated with WP ( P= 0.015) and CG ( P= 0.023) volumes., As expected, the IPSS of patients with prostate volumes (WP, CG) in first decile for age were significantly lower than those in tenth decile., CONCLUSIONS Prostate MRI is able to document age-related changes in prostate zonal volumes., Changes in WP and CG volumes correlated inversely with changes in lower urinary tract symptoms., These findings suggest a role for MRI in measuring accurate prostate zonal volumes; have interesting implications for study of age-related changes in the prostate. [ABSTRACT FROM AUTHOR]

Purpose: There is a growing need to localize prostate cancers on magnetic resonance imaging (MRI) to facilitate the use of image guided biopsy, focal therapy, and active surveillance follow up. Our goal was to develop a decision support system (DSS) for detecting and localizing peripheral zone prostate cancers by using machine learning approach to calculate a cancer probability map from multiparametric MR images (MP-MRI). Methods: This IRB approved Health Insurance Portability and Accountability Act compliant retrospective study consisted of 31 patients (mean age and serum prostate specific antigen of 60.4 and 6.62 ng/ml, respectively) who had MP-MRI at 3 T followed by radical prostatectomy. Seven patients were excluded due to technical issues with their MP-MRI (e.g., motion artifact, failure to perform all sequences). Cancer and normal regions were identified in the peripheral zone by correlating them to whole mount histology slides of the excised prostatectomy specimens. To facilitate the correlation, tissue blocks matching the MR slices were obtained using a MR-based patient-specific mold. Segmented regions on the MP-MRI were correlated to histopathology and used as training sets for the learning system that generated the cancer probability maps. Leave-one-patient-out cross-validation on the cancer and normal regions was performed to determine the learning system's efficacy, an evolutionary strategies approach (also known as a genetic algorithm) was used to find the optimal values for a set of parameters, and finally a cancer probability map was generated. Results: For the 24 patients that were used in the study, 225 cancer and 264 noncancerous regions were identified from the region maps. The efficacy of DSS was first determined without optimizing support vector machines (SVM) parameters, where a region having a cancer probability greater than or equal to 50% was considered as a correct classification. The nonoptimized system had an f-measure of 85% and the Kappa coefficient of 71% (Rater's agreement, where raters are DSS and ground truth histology). The efficacy of the DSS after optimizing SVM parameters using a genetic algorithm had an f-measure of 89% and a Kappa coefficient of 80%. Thus, after optimization of the DSS there was a 4% increase in the f-measure and a 9% increase in the Kappa coefficient. Conclusions: This DSS provides a cancer probability map for peripheral zone prostate tumors based on endorectal MP-MRI. These cancer probability maps can potentially aid radiologists in accuratelylocalizing peripheral zone prostate cancers for planning targeted biopsies, focal therapy, and follow up for active surveillance. [ABSTRACT FROM AUTHOR]

Objective: To demonstrate the use of a patient-specific magnetic resonance imaging (MRI)-based prostate mold to generate histologic sections that directly correlate to axial MRI slices in a patient with anteriorly located prostate cancer. Anteriorly located prostate cancer has traditionally been difficult to detect on digital rectal examination and transrectal ultrasound-guided biopsy. Multiparametric MRI has potential as a valuable tool for the diagnosis and focal treatment of prostate cancer. A significant difficulty to date has been accurate correlation between the magnetic resonance images and histopathologic specimens. Methods: A patient-specific mold from a preoperative T2-weighted MRI scan was created to hold and shape the prostate specimen. Slots for slicing were positioned at 6-mm increments coplanar to the axial MRI slices. After surgical excision, the specimen was inked to maintain the orientation and fixed in formalin. The seminal vesicles were excised, and the prostate was oriented in the mold such that the color-coding matched the anatomic labels on the mold. The specimen was sliced with a single blade and the resultant 6-mm tissue blocks were used for histologic analysis. Results: Preoperative multiparametric MRI revealed a lesion in the right anterior transition zone that was positive on T2-weighed MRI, apparent diffusion coefficient maps of diffusion-weighted MRI, magnetic resonance spectroscopy, and dynamic contrast-enhanced MRI. The histologic sections obtained using the mold demonstrated a similar Gleason score 6 (3 + 3) lesion in the right anterior transition zone, correlating with the MRI findings. Conclusion: The use of patient-specific prostate molds to register the MRI findings with the histopathologic specimen in prostate cancer could offer several benefits compared with current specimen processing techniques. This technique might further validate MRI as an accurate tool for prostate cancer localization and staging. [ABSTRACT FROM AUTHOR]

Abstract: Autosomal recessive polycystic kidney disease (ARPKD), characterized by progressive cystic degeneration of the kidneys and congenital hepatic fibrosis (CHF), is the most common childhood onset ciliopathy, with an estimated frequency of 1 in 20,000 births. It is caused by mutations in PKHD1. The carrier frequency for ARPKD in the general population is estimated at 1 in 70. Given the recessive inheritance pattern, individuals who are heterozygous for PKHD1 mutations are not expected to have clinical findings. We performed ultrasound (USG) evaluations on 110 parents from 64 independent ARPKD families and identified increased medullary echogenicity in 6 (5.5%) and multiple small liver cysts in 10 parents (9%). All ARPKD parents with these abnormal imaging findings were asymptomatic; kidney and liver function tests were unremarkable. Complete sequencing of PKHD1 in the 16 ARPKD parents with abnormal imaging confirmed the mutation transmitted to the proband, but did not reveal any other pathogenic variants. Our data suggest that carrier status for ARPKD is a predisposition to polycystic liver disease and renal involvement associated with increased medullary echogenicity on USG. Whether some of these individuals become symptomatic as they age remains to be determined. [Copyright &y& Elsevier]

Small bowel wall thickening detected on computed tomography is a frequent finding in patients referring to emergency room with acute abdominal pain. In this pictorial review, we aim to discuss patterns of small bowel wall thickening and to explain hints for differential diagnosis with imaging findings. [ABSTRACT FROM AUTHOR]

Study Type - Diagnostic (exploratory cohort) Level of Evidence 2b Currently, systematic prostate biopsies are obtained with minimal information about their actual location. This study demonstrates that a electromagnetically tracked ultrasound probe can be used to guide biopsies into specific areas of the prostate. By registering the ultrasound to an MRI scan of the prostate, obtained prior to biopsy, it is possible to accurately map the location of biopsies. Thus, if a patient requires a repeat biopsy, or there is a question about whether a specific area of the prostate was sampled, this system can be used to more accurately guide biopsies in the future. To develop a system that documents the location of transrectal ultrasonography (TRUS)-guided prostate biopsies by fusing them to MRI scans obtained prior to biopsy, as the actual location of prostate biopsies is rarely known. Fifty patients (median age 61) with a median prostate-specific antigen (PSA) of 5.8 ng/ml underwent 3T endorectal coil MRI prior to biopsy. 3D TRUS images were obtained just prior to standard TRUS-guided 12-core sextant biopsies wherein an electromagnetic positioning device was attached to the needle guide and TRUS probe in order to track the position of each needle pass. The 3D-TRUS image documenting the location of each biopsy was fused electronically to the T2-weighted MRI. Each biopsy needle track was marked on the TRUS images and these were then transposed onto the MRI. Each biopsy site was classified pathologically as positive or negative for cancer and the Gleason score was determined. The location of all ( n= 605) needle biopsy tracks was successfully documented on the T2-weighted (T2W) MRI. Among 50 patients, 20 had 56 positive cores. At the sites of biopsy, T2W signal was considered 'positive' for cancer (i.e. low in signal intensity) in 34 of 56 sites. It is feasible to document the location of TRUS-guided prostate biopsies on pre-procedure MRI by fusing the pre-procedure TRUS to an endorectal coil MRI using electromagnetic needle tracking. This procedure may be useful in documenting the location of prior biopsies, improving quality control and thereby avoiding under-sampling of the prostate as well as directing subsequent biopsies to regions of the prostate not previously sampled. [ABSTRACT FROM AUTHOR]

Abstract: Prostate cancer is the most common non-cutaneous malignancy among American men. Imaging has recently become more important in detection of prostate cancer since screening techniques such as digital rectal examination (DRE), prostate specific and transrectal ultrasound guided biopsy have considerable limitations in diagnosis and localization of prostate cancer. In this manuscript, we reviewed conventional, functional and targeted imaging modalities used in diagnosis and local staging of prostate cancer with exquisite images. [Copyright &y& Elsevier]

ARPKD/CHF is an inherited disease characterized by non-obstructive fusiform dilatation of the renal collecting ducts leading to enlarged spongiform kidneys and ductal plate malformation of the liver resulting in congenital hepatic fibrosis. ARPKD/CHF has a broad spectrum of clinical presentations involving the kidney and liver. Imaging plays an important role in the diagnosis and follow-up of ARPKD/CHF. Combined use of conventional and high-resolution US with MR cholangiography in ARPKD/CHF patients allows detailed definition of the extent of kidney and hepatobiliary manifestations without requiring ionizing radiation and contrast agents. [ABSTRACT FROM AUTHOR]

Abstract: Purpose: The objective of this study is to describe the computed tomography findings of peritoneal carcinomatosis of urinary bladder cancer. Material and Method: Patients with urinary bladder cancer were reviewed from patient database of our hospital, and 384 patients with urinary bladder cancer were identified. Computed tomography scans of 105 patients with urinary bladder cancer were retrospectively reviewed. Images were reviewed for presence of peritoneal implant, ascites, solid organ metastases, and retroperitoneal lymphadenopathy. Surgical history and follow-up courses of the patients were reviewed. Results: Computed tomography findings of peritoneal metastases were found in eight (7.6%) patients. Peritoneal involvement was diffuse in three patients and was in the form of implantation in five patients. In two patients, liver metastases were detected; additionally, one patient had accompanying pleural implants and one patient had adrenal gland metastases. Peritoneal involvement was detected in the preoperative stage in five patients. Conclusion: Presence of peritoneal carcinomatosis in cancer patients is an indicator of poor prognosis. Detection of peritoneal involvement in urinary bladder cancer patients can alter treatment strategy and may prevent unnecessary aggressive surgical interventions. [Copyright &y& Elsevier]

To assess the impact of scanning direction on the image quality of coronary artery bypass grafts (CABGs), native coronary arteries (NCAs) were examined by electrocardiographically (ECG) gated 16-row multidetector computed tomography (16-MDCT). Eighty-two patients with 209 grafts were studied by 16-MDCT. Forty-one patients with 111 grafts were scanned craniocaudally. Forty-one patients with 98 grafts were scanned caudocranially. CABG, native coronary arteries were examined in four (proximal, middle, distal, distal anastomoses), three (proximal, middle, distal) segments, respectively. Subjective image quality on a four-point scale was calculated for segments. Scores of groups were compared. Results Image quality scores of proximal, distal segments of the right coronary artery (RCA) were better in caudocranially scanned group (P<0.05). When we subgrouped patients according to initial heart rates (IHR) (group 1, <65 beats/min; group 2, > or =65 beats/min), there was no statistical significance between image quality scores of coronary arteries, CABG when IHR was <65 beats/min in groups regardless of scanning direction. Scores of anastomotic segment of CABG to RCA, middle segments of circumflex coronary artery, proximal and distal segments of RCA in caudocranially scanned group were better when the IHR is > or =65 beats/min compared with the craniocaudally scanned group. When the IHR of the patient is > or =65 beats/min, performing ECG-gated 16-MDCT angiography in the caudocranial direction provides better image quality for evaluation of coronary arteries and CABGs. [ABSTRACT FROM AUTHOR]

We describe a 10-year-old girl with congenital absence of the portal vein (CAPV) and multiple hyperplastic nodules in the liver. MRI appearances of the liver lesions and the portocaval anastomosis between the inferior mesenteric vein and internal iliac veins are presented. In addition, the relevance of CAPV and nodular lesions of the liver is reviewed. [ABSTRACT FROM AUTHOR]

The Society of Abdominal Radiology (SAR) Prostate Cancer Disease-Focused Panel (DFP) was originally chaired by Sadhna Verma and Andrew Rosenkrantz, who led it to become the most popular DFP within a couple years. Although other DFP's have grown in popularity, the prostate cancer DFP remains not only one of the largest, it is also very active in terms of academic, educational, and profession accomplishments, with prominent showings from multiple panelists and ambitious inter-societal impact. Verma and Rosenkrantz, panelists have collaborated with organizations involved in prostate cancer management. [Extracted from the article]

Prostate segmentation in transrectal ultrasound (TRUS) image is an essential prerequisite for many prostate-related clinical procedures, which, however, is also a long-standing problem due to the challenges caused by the low image quality and shadow artifacts. In this paper, we propose a Shadow-consistent Semi-supervised Learning (SCO-SSL) method with two novel mechanisms, namely shadow augmentation (Shadow-AUG) and shadow dropout (Shadow-DROP), to tackle this challenging problem. Specifically, Shadow-AUG enriches training samples by adding simulated shadow artifacts to the images to make the network robust to the shadow patterns. Shadow-DROP enforces the segmentation network to infer the prostate boundary using the neighboring shadow-free pixels. Extensive experiments are conducted on two large clinical datasets (a public dataset containing 1,761 TRUS volumes and an in-house dataset containing 662 TRUS volumes). In the fully-supervised setting, a vanilla U-Net equipped with our Shadow-AUG&Shadow-DROP outperforms the state-of-the-arts with statistical significance. In the semi-supervised setting, even with only 20% labeled training data, our SCO-SSL method still achieves highly competitive performance, suggesting great clinical value in relieving the labor of data annotation. Source code is released at https://github.com/DIAL-RPI/SCO-SSL. [ABSTRACT FROM AUTHOR]

Artificial intelligence developments are essential to the successful deployment of community-wide, MRI-driven prostate cancer diagnosis. AI systems should ensure that the main benefits of biopsy avoidance are delivered while maintaining consistent high specificities, at a range of disease prevalences. Since all current artificial intelligence / computer-aided detection systems for prostate cancer detection are experimental, multiple developmental efforts are still needed to bring the vision to fruition. Initial work needs to focus on developing systems as diagnostic supporting aids so their results can be integrated into the radiologists' workflow including gland and target outlining tasks for fusion biopsies. Developing AI systems as clinical decision-making tools will require greater efforts. The latter encompass larger multicentric, multivendor datasets where the different needs of patients stratified by diagnostic settings, disease prevalence, patient preference, and clinical setting are considered. AI-based, robust, standard operating procedures will increase the confidence of patients and payers, thus enabling the wider adoption of the MRI-directed approach for prostate cancer diagnosis. Key Points: • AI systems need to ensure that the benefits of biopsy avoidance are delivered with consistent high specificities, at a range of disease prevalence. • Initial work has focused on developing systems as diagnostic supporting aids for outlining tasks, so they can be integrated into the radiologists' workflow to support MRI-directed biopsies. • Decision support tools require a larger body of work including multicentric, multivendor studies where the clinical needs, disease prevalence, patient preferences, and clinical setting are additionally defined. [ABSTRACT FROM AUTHOR]

Introduction: Accurate diagnosis and treatment of kidney tumors greatly benefit from automated solutions for detection and classification on MRI. In this study, we explore the application of a deep learning algorithm, YOLOv7, for detecting kidney tumors on contrast-enhanced MRI. Material and methods: We assessed the performance of YOLOv7 tumor detection on excretory phase MRIs in a large institutional cohort of patients with RCC. Tumors were segmented on MRI using ITK-SNAP and converted to bounding boxes. The cohort was randomly divided into ten benchmarks for training and testing the YOLOv7 algorithm. The model was evaluated using both 2-dimensional and a novel in-house developed 2.5-dimensional approach. Performance measures included F1, Positive Predictive Value (PPV), Sensitivity, F1 curve, PPV-Sensitivity curve, Intersection over Union (IoU), and mean average PPV (mAP). Results: A total of 326 patients with 1034 tumors with 7 different pathologies were analyzed across ten benchmarks. The average 2D evaluation results were as follows: Positive Predictive Value (PPV) of 0.69 ± 0.05, sensitivity of 0.39 ± 0.02, and F1 score of 0.43 ± 0.03. For the 2.5D evaluation, the average results included a PPV of 0.72 ± 0.06, sensitivity of 0.61 ± 0.06, and F1 score of 0.66 ± 0.04. The best model performance demonstrated a 2.5D PPV of 0.75, sensitivity of 0.69, and F1 score of 0.72. Conclusion: Using computer vision for tumor identification is a cutting-edge and rapidly expanding subject. In this work, we showed that YOLOv7 can be utilized in the detection of kidney cancers. [ABSTRACT FROM AUTHOR]

Introduction: Classification of clear cell renal cell carcinoma (ccRCC) growth rates in patients with Von Hippel–Lindau (VHL) syndrome has several ramifications for tumor monitoring and surgical planning. Using two separate machine-learning algorithms, we sought to produce models to predict ccRCC growth rate classes based on qualitative MRI-derived characteristics. Material and Methods: We used a prospectively maintained database of patients with VHL who underwent surgical resection for ccRCC between January 2015 and June 2022. We employed a threshold growth rate of 0.5 cm per year to categorize ccRCC tumors into two distinct groups—'slow-growing' and 'fast-growing'. Utilizing a questionnaire of qualitative imaging features, two radiologists assessed each lesion on different MRI sequences. Two machine-learning models, a stacked ensemble technique and a decision tree algorithm, were used to predict the tumor growth rate classes. Positive predictive value (PPV), sensitivity, and F1-score were used to evaluate the performance of the models. Results: This study comprises 55 patients with VHL with 128 ccRCC tumors. Patients' median age was 48 years, and 28 patients were males. Each patient had an average of two tumors, with a median size of 2.1 cm and a median growth rate of 0.35 cm/year. The overall performance of the stacked and DT model had 0.77 ± 0.05 and 0.71 ± 0.06 accuracies, respectively. The best stacked model achieved a PPV of 0.92, a sensitivity of 0.91, and an F1-score of 0.90. Conclusion: This study provides valuable insight into the potential of machine-learning analysis for the determination of renal tumor growth rate in patients with VHL. This finding could be utilized as an assistive tool for the individualized screening and follow-up of this population. [ABSTRACT FROM AUTHOR]

Key Points: • Identify, assure, and measure major sources of variability affecting the MRI-directed biopsy pathway for prostate cancer diagnosis. • Develop strategies to control and minimize variations that impair pathway effectiveness including the performance of main players and team working. • Assure end-to-end quality of the diagnostic chain with robust multidisciplinary team working. [ABSTRACT FROM AUTHOR]

An editorial is presented on multiple researchers developing computer-aided detection (CAD) algorithms enabling the detection of clinically significant prostate cancer (csPCa) on MRI. Topics include developments being welcomed by radiologists, urologists, and healthcare administrators facing the increased demand for MRI-influenced prostate biopsies; and number of acceptable false detections depending on the urologist or patient showing biopsy versus cancer averse.

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. 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. 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. Fully TP biopsy is feasible and may detect prostate cancer with detection rates comparable to conventional TP biopsy. [ABSTRACT FROM AUTHOR]

Background: von Hippel–Lindau syndrome (VHL) is an autosomal dominant hereditary syndrome with an increased predisposition of developing numerous cysts and tumors, almost exclusively clear cell renal cell carcinoma (ccRCC). Considering the lifelong surveillance in such patients to monitor the disease, patients with VHL are preferentially imaged using MRI to eliminate radiation exposure. Purpose: Segmentation of kidney and tumor structures on MRI in VHL patients is useful in lesion characterization (e.g., cyst vs. tumor), volumetric lesion analysis, and tumor growth prediction. However, automated tasks such as ccRCC segmentation on MRI is sparsely studied. We develop segmentation methodology for ccRCC on T1 weighted precontrast, corticomedullary, nephrogenic, and excretory contrast phase MRI. Methods: We applied a new neural network approache using a novel differentiable decision forest, called hinge forest (HF), to segment kidney parenchyma, cyst, and ccRCC tumors in 117 images from 115 patients. This data set represented an unprecedented 504 ccRCCs with 1171 cystic lesions obtained at five different MRI scanners. The HF architecture was compared with U‐Net on 10 randomized splits with 75% used for training and 25% used for testing. Both methods were trained with Adam using default parameters (α=0.001,β1=0.9,β2=0.999$\alpha = 0.001,\ \beta _1 = 0.9,\ \beta _2 = 0.999$) over 1000 epochs. We further demonstrated some interpretability of our HF method by exploiting decision tree structure. Results: The HF achieved an average kidney, cyst, and tumor Dice similarity coefficient (DSC) of 0.75 ± 0.03, 0.44 ± 0.05, 0.53 ± 0.04, respectively, while U‐Net achieved an average kidney, cyst, and tumor DSC of 0.78 ± 0.02, 0.41 ± 0.04, 0.46 ± 0.05, respectively. The HF significantly outperformed U‐Net on tumors while U‐Net significantly outperformed HF when segmenting kidney parenchymas (α<0.01$\alpha < 0.01$). Conclusions: For the task of ccRCC segmentation, the HF can offer better segmentation performance compared to the traditional U‐Net architecture. The leaf maps can glean hints about deep learning features that might prove to be useful in other automated tasks such as tumor characterization. [ABSTRACT FROM AUTHOR]

Radiomics and radiogenomics are attractive research topics in prostate cancer. Radiomics mainly focuses on extraction of quantitative information from medical imaging, whereas radiogenomics aims to correlate these imaging features to genomic data. The purpose of this review is to provide a brief overview summarizing recent progress in the application of radiomics-based approaches in prostate cancer and to discuss the potential role of radiogenomics in prostate cancer. [ABSTRACT FROM AUTHOR]

Highlights • We introduce a paradigm shift CAD system, called collaborative CAD (C-CAD), that unifies both CAD and eye-tracking to improve radiographical image analysis. • We develop an eye-tracking interface that provides a real radiology reading room experience and perform an attention based clustering and sparsification of dense eye-tracking data. • We propose a new attention based data sparsification method applied to gaze patterns of radiologists which allows local and global analysis of visual search patterns based on visual attention concepts. • By utilizing gaze patterns, we build a new CAD system based on a 3D deep learning algorithm in a newly designed multi-task learning platform where both segmentation and diagnosis tasks are jointly modeled. • We show the efficacy of the system in lung cancer screening experiments with low dose CT, and then we extend the proposed eye-tracking based CAD system into a multi-modality image analysis framework where users can utilize multiple screens as in prostate screening with multi-parametric MRI. Abstract Computer aided diagnosis (CAD) tools help radiologists to reduce diagnostic errors such as missing tumors and misdiagnosis. Vision researchers have been analyzing behaviors of radiologists during screening to understand how and why they miss tumors or misdiagnose. In this regard, eye-trackers have been instrumental in understanding visual search processes of radiologists. However, most relevant studies in this aspect are not compatible with realistic radiology reading rooms. In this study, we aim to develop a paradigm shifting CAD system, called collaborative CAD (C-CAD), that unifies CAD and eye-tracking systems in realistic radiology room settings. We first developed an eye-tracking interface providing radiologists with a real radiology reading room experience. Second, we propose a novel algorithm that unifies eye-tracking data and a CAD system. Specifically, we present a new graph based clustering and sparsification algorithm to transform eye-tracking data (gaze) into a graph model to interpret gaze patterns quantitatively and qualitatively. The proposed C-CAD collaborates with radiologists via eye-tracking technology and helps them to improve their diagnostic decisions. The C-CAD uses radiologists' search efficiency by processing their gaze patterns. Furthermore, the C-CAD incorporates a deep learning algorithm in a newly designed multi-task learning platform to segment and diagnose suspicious areas simultaneously. The proposed C-CAD system has been tested in a lung cancer screening experiment with multiple radiologists, reading low dose chest CTs. Promising results support the efficiency, accuracy and applicability of the proposed C-CAD system in a real radiology room setting. We have also shown that our framework is generalizable to more complex applications such as prostate cancer screening with multi-parametric magnetic resonance imaging (mp-MRI). [ABSTRACT FROM AUTHOR]

The article discusses the use of social media to engage and educate patients about prostate imaging. Topics covered include a key challenge in managing prostate cancer (PCa), the effectiveness of multi-parametric magnetic resonance imaging in detecting PCa, and social media alternatives that enable patients to rapidly search and access information regarding their diagnosis and treatment.

Despite the success of deep learning in medical image segmentation, most deep learning-based segmentation methods utilize pixel-wise losses as their training objectives, which focus on the local information around each pixel but lack global context. In this paper, we propose a set of shape description losses to supervise the training of the segmentation networks by extracting and quantifying geometric features of the targets, including volume, surface area, center of mass, and bounding box. We believe that these loss functions can introduce global geometrical and statistical constraints into the training procedure. The proposed shape losses are differentiable and can be computed efficiently. We demonstrate that using the proposed shape losses alone for weakly supervised learning can obtain promising performance. The results of our study illustrate the effectiveness of the shape descriptions with physical meanings on segmenting different organs from various modalities, including liver from computed tomography, left atrium from magnetic resonance imaging, and prostate from ultrasound volume. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the cancer detection rates of reduced-core biopsy schemes in patients with unilateral mpMRI-visible intraprostatic lesions and to analyze the contribution of systematic biopsy cores in clinically significant prostate cancer (csPCa) detection. Methods: 212 patients with mpMRI-visible unilateral intraprostatic lesions undergoing MRI/TRUS fusion-guided targeted biopsy (TBx) and systematic biopsy (SBx) were included. Cancer detection rates of TBx + SBx, as determined by highest Gleason Grade Group (GG), were compared to 3 reduced-core biopsy schemes: TBx alone, TBx + ipsilateral systematic biopsy (IBx; MRI-positive hemigland), and TBx + contralateral systematic biopsy (CBx; MRI-negative hemigland). Patient-level and biopsy core-level data were analyzed using descriptive statistics with confidence intervals. Univariable and multivariable logistic regression analysis was conducted to identify predictors of csPCa (≥ GG2) detected in MRI-negative hemiglands at p < 0.05. Results: Overall, 43.4% (92/212) of patients had csPCa and 66.0% (140/212) of patients had any PCa detected by TBx + SBx. Of patients with csPCa, 81.5% had exclusively ipsilateral involvement (MRI-positive), 7.6% had only contralateral involvement (MRI-negative), and 10.9% had bilateral involvement. The csPCa detection rates of reduced-core biopsy schemes were 35.4% (75/212), 40.1% (85/212), and 39.6% (84/212) for TBx alone, TBx + IBx, and TBx + CBx, respectively, with detection sensitivities of 81.5%, 92.4%, and 91.3% compared to TBx + SBx. Conclusion: Reduced-core prostate biopsy strategies confined to the ipsilateral hemigland underestimate csPCa burden by at least 8% in patients with unilateral mpMRI-visible intraprostatic lesions. The combined TBx + SBx strategy maximizes csPCa detection. [ABSTRACT FROM AUTHOR]