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Osteosarcomas (OSs) are aggressive bone tumors with many divergent histologic patterns. During pathology review, OSs are subtyped based on the predominant histologic pattern; however, tumors often demonstrate multiple patterns. This high tumor heterogeneity coupled with scarcity of samples compared with other tumor types render histology-based prognosis of OSs challenging. To combat lower case numbers in humans, dogs with spontaneous OSs have been suggested as a model species. Herein, a convolutional neural network was adversarially trained to classify distinct histologic patterns of OS in humans using mostly canine OS data during training. Adversarial training improved domain adaption of a histologic subtype classifier from canines to humans, achieving an average multiclass F1 score of 0.77 (95% CI, 0.74-0.79) and 0.80 (95% CI, 0.78-0.81) when compared with the ground truth in canines and humans, respectively. Finally, this trained model, when used to characterize the histologic landscape of 306 canine OSs, uncovered distinct clusters with markedly different clinical responses to standard-of-care therapy.

Radiomics is the process of extraction of high-throughput quantitative imaging features from medical images. These features represent noninvasive quantitative biomarkers that go beyond the traditional imaging features visible to the human eye. This article first reviews the steps of the radiomics pipeline, including image acquisition, ROI selection and image segmentation, image preprocessing, feature extraction, feature selection, and model development and application. Current evidence for the application of radiomics in abdominopelvic solid-organ cancers is then reviewed. Applications including diagnosis, subtype determination, treatment response assessment, and outcome prediction are explored within the context of hepatobiliary and pancreatic cancer, renal cell carcinoma, prostate cancer, gynecologic cancer, and adrenal masses. This literature review focuses on the strongest available evidence, including systematic reviews, meta-analyses, and large multicenter studies. Limitations of the available literature are highlighted, including marked heterogeneity in radiomics methodology, frequent use of small sample sizes with high risk of overfitting, and lack of prospective design, external validation, and standardized radiomics workflow. Thus, although studies have laid a foundation that supports continued investigation into radiomics models, stronger evidence is needed before clinical adoption.

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

Rationale and objectives Prostate MRI improves detection of clinically significant prostate cancer; however, its diagnostic performance has wide variation. Artificial intelligence (AI) has the potential to assist radiologists in the detection and classification of prostatic lesions. Herein, we aimed to develop and test a cascaded deep learning detection and classification system trained on biparametric prostate MRI using PI-RADS for assisting radiologists during prostate MRI read out. Materials and Methods T2-weighted, diffusion-weighted (ADC maps, high b value DWI) MRI scans obtained at 3 Tesla from two institutions (n = 1043 in-house and n = 347 Prostate-X, respectively) acquired between 2015 to 2019 were used for model training, validation, testing. All scans were retrospectively reevaluated by one radiologist. Suspicious lesions were contoured and assigned a PI-RADS category. A 3D U-Net-based deep neural network was used to train an algorithm for automated detection and segmentation of prostate MRI lesions. Two 3D residual neural network were used for a 4-class classification task to predict PI-RADS categories 2 to 5 and BPH. Training and validation used 89% (n = 1290 scans) of the data using 5 fold cross-validation, the remaining 11% (n = 150 scans) were used for independent testing. Algorithm performance at lesion level was assessed using sensitivities, positive predictive values (PPV), false discovery rates (FDR), classification accuracy, Dice similarity coefficient (DSC). Additional analysis was conducted to compare AI algorithm's lesion detection performance with targeted biopsy results. Results Median age was 66 years (IQR = 60-71), PSA 6.7 ng/ml (IQR = 4.7-9.9) from in-house cohort. In the independent test set, algorithm correctly detected 111 of 198 lesions leading to 56.1% (49.3%-62.6%) sensitivity. PPV was 62.7% (95% CI 54.7%-70.7%) with FDR of 37.3% (95% CI 29.3%-45.3%). Of 79 true positive lesions, 82.3% were tumor positive at targeted biopsy, whereas of 57 false negative lesions, 50.9% were benign at targeted biopsy. Median DSC for lesion segmentation was 0.359. Overall PI-RADS classification accuracy was 30.8% (95% CI 24.6%-37.8%). Conclusion Our cascaded U-Net, residual network architecture can detect, classify cancer suspicious lesions at prostate MRI with good detection, reasonable classification performance metrics.

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 (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.

Background: Current prostate cancer evaluation can be inaccurate and burdensome. Quantitative evaluation of MRI sequences non-invasively helps prostate tumor assessment. However, including Dynamic Contrast Enhancement in the examined MRI sequence set can add complications, inducing possible side effects from the IV placement or injected contrast material, and prolongs scanning time. Purpose: Predict the risk of developing Clinically Significant (Insignificant) prostate cancer CsPCa (CiPCa) and 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-invasively 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 (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 (R=0.207+/-0.118) than using all patients in the fits. Conclusions: In the first study to date to apply and test hyperspectral approaches for assessing tumor aggressiveness on SRBP-MRI, high values of R and exceptional AUC to fit the ISUP grade and CsPCA/CiPCA were comparable or better than those from artificial intelligence in other studies.

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

Upfront knowledge of tumor growth rates of clear cell renal cell carcinoma in von Hippel-Lindau syndrome (VHL) patients can allow for a more personalized approach to either surveillance imaging frequency or surgical planning. In this study, we implement a machine learning algorithm utilizing radiomic features of renal tumors identified on baseline magnetic resonance imaging (MRI) in VHL patients to predict the volumetric growth rate category of these tumors.A total of 73 VHL patients with 173 pathologically confirmed Clear Cell Renal Cell Carcinoma (ccRCCs) underwent MRI at least at two different time points between 2015 and 2021. Each tumor was manually segmented in excretory phase contrast T1 weighed MRI and co-registered on pre-contrast, corticomedullary and nephrographic phases. Radiomic features and volumetric data from each tumor were extracted using the PyRadiomics library in Python (4544 total features). Tumor doubling time (DT) was calculated and patients were divided into two groups: DT = 1 year and DT 1 year. Random forest classifier (RFC) was used to predict the DT category. To measure prediction performance, the cohort was randomly divided into 100 training and test sets (80% and 20%). Model performance was evaluated using area under curve of receiver operating characteristic curve (AUC-ROC), as well as accuracy, F1, precision and recall, reported as percentages with 95% confidence intervals (CIs).The average age of patients was 47.2 ± 10.3 years. Mean interval between MRIs for each patient was 1.3 years. Tumors included in this study were categorized into 155 Grade 2; 16 Grade 3; and 2 Grade 4. Mean accuracy of RFC model was 79.0% [67.4-90.6] and mean AUC-ROC of 0.795 [0.608-0.988]. The accuracy for predicting DT classes was not different among the MRI sequences (P-value = 0.56).Here we demonstrate the utility of machine learning in accurately predicting the renal tumor growth rate category of VHL patients based on radiomic features extracted from different T1-weighted pre- and post-contrast MRI sequences.

PURPOSE/OBJECTIVE(S): 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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. Published by Elsevier Inc. on behalf of American Brachytherapy Society.

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.

Flow chart of dose level patient enrollment

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Purpose: Endoglin (CD105) is an endothelial cell membrane receptor highly expressed on proliferating tumor vasculature, including that of hepatocellular carcinoma (HCC), and is associated with poor prognosis. Endoglin is essential for angiogenesis, and its expression is induced by hypoxia and VEGF pathway inhibition. TRC105 is a chimeric IgG1 CD105 mAb that inhibits angiogenesis and causes antibody-dependent cellular cytotoxicity and apoptosis of proliferating endothelium.Experimental Design: Patients with HCC (Child–Pugh A/B7), ECOG 0/1, were enrolled in a phase I study of TRC105 at 3, 6, 10, and 15 mg/kg every 2 weeks given with sorafenib 400 mg twice daily. Correlative biomarkers included DCE-MRI and plasma levels of angiogenic factors, including soluble endoglin. Pharmacokinetics were assessed in serum.Results: Twenty-six patients were enrolled, of whom 25 received treatment, 15 with cirrhosis. Hep B/C: 3/15; M:F 19:6; mean age of 60 (range, 18–76); 1 DLT (grade 3 AST) occurred at 10 mg/kg. The most frequent toxicity was low-grade epistaxis, a known toxicity of TRC105. One patient experienced an infusion reaction and was replaced. One patient with coronary stenosis developed a fatal myocardial infarction, and one patient developed G3 cerebral tumor hemorrhage. MTD was not established and DL4 (15 mg/kg) was expanded. The overall response rate in 24 evaluable patients at all 4 dose levels was 21% [95% confidence interval (CI), 7.1–42.2], and 25% (95% CI, 8.7–49.1) in patients with measureable disease. Four patients had confirmed stable disease, one of whom was treated for 22 months. Median progression-free survival (PFS) for 24 patients evaluable for PFS was 3.8 months (95% CI, 3.2–5.6 months); median overall survival was 15.5 months (95% CI, 8.5–26.3 months).Conclusions: TRC105 combined with sorafenib was well tolerated at the recommended single agent doses of both drugs. Encouraging evidence of activity to date (PR rate 25%) was observed, and the study is now continuing to recruit in the phase II stage as a multicenter study to confirm activity of the combination. Clin Cancer Res; 23(16); 4633–41. ©2017 AACR.

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

Supplementary Figures S1-S2; Supplementary Tables S1-S3.

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Purpose: Sorafenib, a multikinase inhibitor targeting Raf and VEGFR, has shown activity in unselected patients with non–small-cell lung cancer (NSCLC). At present there are no validated biomarkers indicative of sorafenib activity.Experimental Design: Patients received sorafenib 400 mg BID daily to determine activity and tolerability and to measure its biological effects. KRAS mutation status (N = 34), angiogenesis markers (VEGF, bFGF, FLT-1, PLGF-1) and imaging with DCE-MRI (dynamic contrast enhanced MRI) to determine early changes in tumor vascular characteristics were evaluated. Three parameters Ktrans, Kep, and Ve were measured by DCE-MRI at baseline and day 14 of cycle 1. Cytokine analysis was done on days 0, 14, 28, and 54.Results: Thirty-seven patients with previously treated stage IV NSCLC were enrolled in this single-center phase II trial. In 34 evaluable patients, 2 had partial responses and 20 had stable disease for 3 to 17 months, a disease control rate of 65%. The median progression-free survival (PFS) was 3.4 months, and median overall survival (OS) was 11.6 months. Toxicity was consistent with the known side effects of sorafenib. KRAS (32%) and EGFR mutations (22%) showed no correlation with response, PFS, or OS. Kep, was significant in predicting an improvement in OS (P = 0.035) and PFS (P = 0.029). Cytokine analysis demonstrated an improved OS for bFGF day 0 (6 pg/mL; P = 0.042), whereas a PFS benefit was seen with bFGF at day 28 (6; P = 0.028).Conclusions: KRAS and EGFR mutational status showed no correlation with response, PFS, or OS. Radiologic and cytokine changes may act as biomarkers indicative of early angiogenesis inhibition. Clin Cancer Res; 17(5); 1190–9. ©2011 AACR.

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Purpose: Hypoxia-inducible factor 1 (HIF-1) α is frequently overexpressed in human tumors and is associated with angiogenesis and metastasis. Topotecan, a topoisomerase I inhibitor, has been shown to inhibit HIF-1α expression in preclinical models. We designed a pilot trial to measure HIF-1α inhibition in tumor biopsies from patients with advanced solid tumors overexpressing HIF-1α, after treatment with oral topotecan.Experimental Design: Topotecan was administered orally at 1.6 mg/m2 once daily for 5 days/week for 2 weeks, in 28-day cycles. Objectives were to determine inhibition of expression of HIF-1α and HIF-1 target genes in tumor; to assess tumor blood flow by dynamic contrast–enhanced magnetic resonance imaging (DCE-MRI); and to measure pharmacokinetics. Tumor biopsies were collected at baseline and during the second cycle of treatment.Results: Sixteen patients were enrolled. The dose of topotecan was reduced to 1.2 mg/m2/day due to myelosuppression. Seven patients had paired tumor biopsies. In 4 patients, HIF-1α nuclear staining became undetectable after treatment (7.5%–50% staining at baseline). Decreased levels of VEGF and GLUT-1 mRNA were measured in 4 patients; the changes were concordant with reduction in HIF-1α in 3 patients. Decreased tumor blood flow and permeability were observed by DCE-MRI in 7 of 10 patients after 1 cycle. One patient had a partial response accompanied by inhibition of HIF-1α in tumor and reduction in tumor blood flow on DCE-MRI.Conclusions: This multihistology, target assessment trial of a small molecule inhibitor of HIF-1α showed that topotecan could decrease HIF-1α expression in advanced solid tumors. Clin Cancer Res; 17(15); 5123–31. ©2011 AACR.

Contains supplementary methods and 3 supplementary tables.

Supplemental Data legends

The risks of development of colorectal and endometrial cancers in individuals with Lynch syndrome (LS) are well known and have been widely studied. In recent years, the potential association of other malignancies, including prostate cancer, with LS has been considered. Decision-making regarding screening for prostate cancer in the generalized population can be complicated; accounting for the possibility of a higher risk of cancer conferred by a potential genetic predisposition confounds the creation of salient guidelines even further. Although tissue-agnostic treatment approvals have been granted to several immune checkpoint inhibitors (ICIs) for their use in the treatment of subsets of patients whose tumors exhibit high levels of microsatellite instability or high tumor mutational burden, a paucity of data exists regarding the use of ICIs in the first line treatment of patients with locally advanced prostate cancer harboring these features. A significant reduction in tumor volume in response to the combination of immune checkpoint inhibition and androgen deprivation therapy is described in this report of a male with Muir-Torre syndrome who was found to have locally advanced adenocarcinoma of the prostate. While anecdotal, the anti-tumor activity of this combination of therapy is notable and calls attention to the importance of considering further investigation of the use of immune checkpoint blockade as a primary therapeutic option in patients with localized prostate cancer.

Background Prostate cancer local recurrence location and extent must be determined in an accurate and timely manner. Because of the lack of a standardized MRI approach after whole-gland treatment, a panel of international experts recently proposed the Prostate Imaging for Recurrence Reporting (PI-RR) assessment score. Purpose To determine the diagnostic accuracy of PI-RR for detecting local recurrence in patients with biochemical recurrence (BCR) after radiation therapy (RT) or radical prostatectomy (RP) and to evaluate the interreader variability of PI-RR scoring. Materials and Methods This retrospective observational study included patients who underwent multiparametric MRI between September 2016 and May 2021 for BCR after RT or RP. MRI scans were analyzed, and a PI-RR score was assigned independently by four radiologists. The reference standard was defined using histopathologic findings, follow-up imaging, or clinical response to treatment. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated to assess PI-RR performance for each reader. The intraclass correlation coefficient was used to determine interreader agreement. Results A total of 100 men were included: 48 patients after RT (median age, 76 years [IQR, 70-82 years]) and 52 patients after RP (median age, 70 years [IQR, 66-74 years]). After RT, with PI-RR of 3 or greater as a cutoff (assigned when recurrence is uncertain), diagnostic performance ranges were 71%-81% sensitivity, 74%-93% specificity, 71%-89% PPV, 79%-86% NPV, and 77%-88% accuracy across the four readers. After RP, with PI-RR of 3 or greater as a cutoff, performance ranges were 59%-83% sensitivity, 87%-100% specificity, 88%-100% PPV, 66%-80% NPV, and 75%-85% accuracy. The intraclass correlation coefficient was 0.87 across the four readers for both the RT and RP groups. Conclusion MRI scoring with the Prostate Imaging for Recurrence Reporting assessment provides structured, reproducible, and accurate evaluation of local recurrence after definitive therapy for prostate cancer. © RSNA, 2022

PET imaging with targeted novel tracers has been commonly used in the clinical management of prostate cancer. The use of artificial intelligence (AI) in PET imaging is a relatively new approach and in this review article, we will review the current trends and categorize the currently available research into the quantification of tumor burden within the organ, evaluation of metastatic disease, and translational/supplemental research which aims to improve other AI research efforts.

BackgroundCurrent prostate cancer evaluation can be inaccurate and burdensome. To help non-invasive prostate tumor assessment, recent algorithms applied to spatially registered multi-parametric (SRMP) MRI extracted novel clinically relevant metrics, namely the tumor’s eccentricity (shape), signal-to-clutter ratio (SCR), and volume.PurposeConduct a pilot study to predict the risk of developing clinically significant prostate cancer using nomograms and employing Decision Curves Analysis (DCA) from the SRMP MRI-based features to help clinicians non-invasively manage prostate cancer.MethodsThis study retrospectively analyzed 25 prostate cancer patients. MP-MRI (T1, T2, diffusion, dynamic contrast-enhanced) were resized, translated, and stitched to form SRMP MRI. Target detection algorithm [adaptive cosine estimator (ACE)] applied to SRMP MRI determines tumor’s eccentricity, noise reduced SCR (by regularizing or eliminating principal components (PC) from the covariance matrix), and volume. Pathology assessed wholemount prostatectomy for Gleason score (GS). Tumors with GS >=4+3 (ResultsCombining eccentricity (0.45 ACE threshold), SCR (3, 4 PCs), SCR (regularized, modified regularization) with tumor volume (0.65 ACE threshold) improved AUC (>0.70) for ROC curves and p-values (ConclusionsPerformance of nomograms and DCA based on metrics derived from SRMP-MRI in this pilot study were comparable to those using prostate serum antigen, age, and PI-RADS.

PURPOSE: Multiple studies demonstrate MRI-targeted biopsy detects more clinically significant cancer than systematic biopsy, however some clinically significant cancers are detected by systematic biopsy only. While these events are rare, we sought to perform a retrospective analysis of these cases to ascertain the reasons that MRI-targeted biopsy missed clinically significant cancer, which was subsequently detected on systematic prostate biopsy. METHODS: Patients were enrolled in a prospective study comparing cancer detection rates by transrectal MRI-targeted fusion biopsy and systematic 12-core biopsy. Patients with an elevated PSA, abnormal digital rectal exam, or imaging findings concerning for prostate cancer underwent prostate MRI and subsequent MRI-targeted and systematic biopsy in the same setting. The subset of patients with grade group (GG) ≥3 cancer found on systematic biopsy and GG≤2 cancer (or no cancer) on MRI-targeted biopsy were classified as MRI-targeted biopsy misses. A retrospective analysis of the MRI and MRI-targeted biopsy real-time screen captures determined the cause of MRI-targeted biopsy miss. Multivariable logistic regression analysis compared baseline characteristics of patients with MRI-targeted biopsy misses to GG-matched patients whose clinically significant cancer was detected by MRI-targeted biopsy. RESULTS: Over the study period of 2007 to 2019, 2103 patients met study inclusion criteria and underwent combined MRI-targeted and systematic prostate biopsies. 41 (1.9%) men were classified as MRI-targeted biopsy misses. Most MRI-targeted biopsy misses were due to errors in lesion targeting (n=21, 51.2%), followed by MRI-invisible lesions (n=17, 40.5%), and MRI lesions missed by the radiologist (n=3, 7.1%). On logistic regression analysis, lower PI-RADS score was associated with having clinically significant cancer missed on MRI-targeted biopsy. CONCLUSION: While uncommon, most MRI-targeted biopsy misses are due to errors in lesions targeting, which highlights the importance of accurate co-registration and targeting when using software-based fusion platforms. Additionally, some patients will harbor MRI-invisible lesions which are un-targetable by MRI-targeted platforms. The presence of a low PI-RADS score despite a high PSA is suggestive of harboring an MRI-invisible lesion.

Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe to train a FL model, called EXAM (electronic medical record (EMR) chest X-ray AI model), that predicts the future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays. EXAM achieved an average area under the curve (AUC) >0.92 for predicting outcomes at 24 and 72 h from the time of initial presentation to the emergency room, and it provided 16% improvement in average AUC measured across all participating sites and an average increase in generalizability of 38% when compared with models trained at a single site using that site’s data. For prediction of mechanical ventilation treatment or death at 24 h at the largest independent test site, EXAM achieved a sensitivity of 0.950 and specificity of 0.882. In this study, FL facilitated rapid data science collaboration without data exchange and generated a model that generalized across heterogeneous, unharmonized datasets for prediction of clinical outcomes in patients with COVID-19, setting the stage for the broader use of FL in healthcare. Federated learning, a method for training artificial intelligence algorithms that protects data privacy, was used to predict future oxygen requirements of symptomatic patients with COVID-19 using data from 20 different institutes across the globe.

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.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.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.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.

Hereditary leiomyomatosis and renal cell carcinoma (HLRCC) syndrome is associated with an aggressive form of renal cell carcinoma with high risk of metastasis, even in small primary tumors with unequivocal imaging findings. In this study, we compare the performance of ultra-high b-value diffusion-weighted imaging (DWI) sequence (b = 2000 s/mmTwenty-eight patients (n = 18 HLRCC patients with 22 lesions, n = 10 controls) were independently evaluated by three abdominal radiologists with different levels of experience using four combinations of MRI sequences in two separate sessions (session 1: DWI with b-800, session 2: DWI with b-2000). T1 precontrast, T2-weighted (T2WI), and apparent diffusion coefficient (ADC) sequences were similar in both sessions. Each identified lesion was subjectively assessed using a six-point cancer likelihood score based on individual sequences and overall impression.The ability to distinguish benign versus malignant renal lesions improved with the use of b-2000 for more experienced radiologists (Reader 1 AUC: Session 1-0.649 and Session 2-0.938, p = 0.017; Reader 2 AUC: Session 1-0.781 and Session 2-0.921, p = 0.157); whereas no improvement was observed for the less experienced reader (AUC: Session 1-0.541 and Session 2-0.607, p = 0.699).The inclusion of ultra-high b-value DWI sequence improved the ability of classification of renal lesions in patients with HLRCC for experienced radiologists. Consideration should be given toward incorporation of DWI with b-2000 s/mm