Nascent Prostate Cancer Heterogeneity Drives Evolution and Resistance to Intense Hormonal Therapy.

BACKGROUND: Patients diagnosed with high risk localized prostate cancer have variable outcomes following surgery. Trials of intense neoadjuvant androgen deprivation therapy (NADT) have shown lower rates of recurrence among patients with minimal residual disease after treatment. The molecular features that distinguish exceptional responders from poor responders are not known. OBJECTIVE: To identify genomic and histologic features associated with treatment resistance at baseline. DESIGN, SETTING, AND PARTICIPANTS: Targeted biopsies were obtained from 37 men with intermediate- to high-risk prostate cancer before receiving 6 mo of ADT plus enzalutamide. Biopsy tissues were used for whole-exome sequencing and immunohistochemistry (IHC). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: We assessed the relationship of molecular features with final pathologic response using a cutpoint of 0.05 cm3 for residual cancer burden to compare exceptional responders to incomplete and nonresponders. We assessed intratumoral heterogeneity at the tissue and genomic level, and compared the volume of residual disease to the Shannon diversity index for each tumor. We generated multivariate models of resistance based on three molecular features and one histologic feature, with and without multiparametric magnetic resonance imaging estimates of baseline tumor volume. RESULTS AND LIMITATIONS: Loss of chromosome 10q (containing PTEN) and alterations to TP53 were predictive of poor response, as were the expression of nuclear ERG on IHC and the presence of intraductal carcinoma of the prostate. Patients with incompletely and nonresponding tumors harbored greater tumor diversity as estimated via phylogenetic tree reconstruction from DNA sequencing and analysis of IHC staining. Our four-factor binary model (area under the receiver operating characteristic curve [AUC] 0.89) to predict poor response correlated with greater diversity in our cohort and a validation cohort of 57 Gleason score 8-10 prostate cancers from The Cancer Genome Atlas. When baseline tumor volume was added to the model, it distinguished poor response to NADT with an AUC of 0.98. Prospective use of this model requires further retrospective validation with biopsies from additional trials. CONCLUSIONS: A subset of prostate cancers exhibit greater histologic and genomic diversity at the time of diagnosis, and these localized tumors have greater fitness to resist therapy. PATIENT SUMMARY: Some prostate cancer tumors do not respond well to a hormonal treatment called androgen deprivation therapy (ADT). We used tumor volume and four other parameters to develop a model to identify tumors that will not respond well to ADT. Treatments other than ADT should be considered for these patients.

Quick guide on radiology image pre-processing for deep learning applications in prostate cancer research.

Purpose: Deep learning has achieved major breakthroughs during the past decade in almost every field. There are plenty of publicly available algorithms, each designed to address a different task of computer vision in general. However, most of these algorithms cannot be directly applied to images in the medical domain. Herein, we are focused on the required preprocessing steps that should be applied to medical images prior to deep neural networks. Approach: To be able to employ the publicly available algorithms for clinical purposes, we must make a meaningful pixel/voxel representation from medical images which facilitates the learning process. Based on the ultimate goal expected from an algorithm (classification, detection, or segmentation), one may infer the required pre-processing steps that can ideally improve the performance of that algorithm. Required pre-processing steps for computed tomography (CT) and magnetic resonance (MR) images in their correct order are discussed in detail. We further supported our discussion by relevant experiments to investigate the efficiency of the listed preprocessing steps. Results: Our experiments confirmed how using appropriate image pre-processing in the right order can improve the performance of deep neural networks in terms of better classification and segmentation. Conclusions: This work investigates the appropriate pre-processing steps for CT and MR images of prostate cancer patients, supported by several experiments that can be useful for educating those new to the field (https://github.com/NIH-MIP/Radiology_Image_Preprocessing_for_Deep_Learning).

PI-RADSv2.1: Current status.

Multiparametric magnetic resonance imaging (mpMRI) has played an increasing role in the detection and local staging of prostate cancer over the last 15 years. Prostate mpMRI, due to various factors, is prone to high inter-reader variability necessitating standardized reporting guidelines that provide accurate and actionable information to the ordering clinician. The Prostate Imaging-Reporting and Data System version 2.1 (PI-RADSv2.1) was released in March 2019 as an update to PI-RADSv2.0 with the hope of further standardizing the reporting process of prostate mpMRI, improving the detection of clinically significant cancer, reducing the biopsy rate of indolent tumors, and decreasing inter-reader variability. Early data show an improved performance of PI-RADSv2.1 over PI-RADSv2.0. Updates included in PI-RADSv2.1 and its current experience in clinic will be reviewed in this review.

Sequential Prostate Magnetic Resonance Imaging in Newly Diagnosed High-risk Prostate Cancer Treated with Neoadjuvant Enzalutamide is Predictive of Therapeutic Response.

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 <0.05 cc or pathologic complete response. Low initial mpMRI relative tumor burden was most predictive of MRD on final pathology. 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.

Role of mpMRI in Benign Prostatic Hyperplasia Assessment and Treatment.

PURPOSE OF REVIEW: To summarize the role of prostate MRI in the assessment and treatment of benign prostatic hyperplasia (BPH) and associated lower urinary tract symptoms (LUTS). RECENT FINDINGS: BPH/LUTS is a very common source of morbidity in aging men. mpMRI has become an increasingly popular modality for prostate imaging due to its ability to provide exceptional anatomic detail. This has allowed for MRI classification of BPH and detailed assessment of response to various BPH treatment modalities, including simple prostatectomy and transurethral resection of the prostate as well as newer methods like UroLift and prostatic artery embolization. MRI will continue to be a useful tool in the diagnosis and management of BPH/LUTS and provides useful information about the efficacy of a variety of available treatment options. Further studies are needed to fully characterize the effects of newer BPH interventions on mpMRI.

Prospective Evaluation of PI-RADS Version 2.1 for Prostate Cancer Detection.

OBJECTIVE. The purpose of this study was to prospectively evaluate Prostate Imaging Reporting and Data and System version 2.1 (PI-RADSv2.1), which was released in March 2019 to update version 2.0, for prostate cancer detection with transrectal ultrasound-MRI fusion biopsy and 12-core systematic biopsy. SUBJECTS AND METHODS. This prospective study included 110 consecutively registered patients who underwent multiparametric MRI evaluated with PI-RADSv2.1 criteria followed by fusion biopsy and systematic biopsy between April and September 2019. Lesion-based cancer detection rates (CDRs) were calculated for prostate cancer (Gleason grade group, > 0) and clinically significant prostate cancer (Gleason grade group, > 1). RESULTS. A total of 171 lesions (median size, 1.1 cm) in 110 patients were detected and evaluated with PI-RADSv2.1. In 16 patients no lesion was detected, and only systematic biopsy was performed. Lesions were categorized as follows: PI-RADS category 1, 1 lesion; PI-RADS category 2, 34 lesions; PI-RADS category 3, 54 lesions; PI-RADS category 4, 52 lesions; and PI-RADS category 5, 30 lesions. Histopathologic analysis revealed prostate cancer in 74 of 171 (43.3%) lesions and clinically significant prostate cancer in 57 of 171 (33.3%) lesions. The CDRs of prostate cancer for PI-RADS 2, 3, 4, and 5 lesions were 20.0%, 24.1%, 51.9%, and 90.0%. The CDRs of clinically significant prostate cancer for PI-RADS 1, 2, 3, 4, and 5 lesions were 0%, 5.7%, 14.8%, 44.2%, and 80.0%. In 16 patients with normal multiparametric MRI findings (PI-RADS 1), the CDRs were 50.0% for PCa and 18.8% for clinically significant prostate cancer. CONCLUSION. This investigation yielded CDRs assessed with prospectively assigned PI-RADSv2.1 scores. CDRs increased with higher PI-RADSv2.1 scores. These results can be compared with previously published outcomes derived with PI-RADS version 2.0.

Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets.

Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to aid in rapid evaluation of CT scans for differentiation of COVID-19 findings from other clinical entities. Here we show that a series of deep learning algorithms, trained in a diverse multinational cohort of 1280 patients to localize parietal pleura/lung parenchyma followed by classification of COVID-19 pneumonia, can achieve up to 90.8% accuracy, with 84% sensitivity and 93% specificity, as evaluated in an independent test set (not included in training and validation) of 1337 patients. Normal controls included chest CTs from oncology, emergency, and pneumonia-related indications. The false positive rate in 140 patients with laboratory confirmed other (non COVID-19) pneumonias was 10%. AI-based algorithms can readily identify CT scans with COVID-19 associated pneumonia, as well as distinguish non-COVID related pneumonias with high specificity in diverse patient populations.

Update on Hereditary Renal Cancer and Imaging Implications.

Up to 8% of renal cancers are thought to have a hereditary component. Several hereditary renal cancer syndromes have been identified over the last few decades. It is important for the radiologist to be aware of findings associated with hereditary renal cancer syndromes to detect tumors early, enroll patients in appropriate surveillance programs, and improve outcomes for the patient and affected family members. This review discusses from a radiologist's perspective well-known hereditary renal cancer syndromes and emerging genetic mutations associated with renal cancer that are less well characterized, focusing on imaging features and known associations.

Advances in Prostate Magnetic Resonance Imaging.

Prostate magnetic resonance (MR) imaging is a widely used imaging technique to detect intraprostatic lesions and guide prostate biopsies, with continuous technical advances for better accuracy in prostate cancer diagnosis. Current evaluation of prostate multiparametric MR imaging mainly depends on qualitative evaluation, which is prone to inter-reader variation. Recent advances in prostate MR imaging, such as quantitative T2 mapping and abbreviated MR imaging protocols (eg, biparametric MR imaging), are designed to simplify prostate MR imaging acquisition and interpretation.

Use of multiparametric magnetic resonance imaging (mpMRI) in localized prostate cancer.

Introduction: Prostate magnetic resonance imaging (MRI) is commonly used for localized disease mainly to detect intraprostatic lesions and to guide biopsies. Despite its documented success in clinical practice, limitations still exist for prostate MRI. In this review, we discuss common clinical uses of prostate MRI, its limitations, and potential solutions for those limitations.Areas covered: Current uses of prostate MRI and challenges discussed. Literature search in PubMed was completed using the keywords "prostate MRI, prostate cancer."Expert opinion: Prostate MRI is a useful method for localization, biopsy, and treatment guidance of prostate cancer. Certain limitations of prostate MRI such as false negatives due to spatial resolution and relatively low repeatability between different radiologists can potentially be solved by investing more on education training and artificial intelligence technology.

What You Need to Know Before Reading Multiparametric MRI for Prostate Cancer.

OBJECTIVE. Multiparametric MRI (mpMRI) has become the main imaging modality for the detection, localization, and local staging of prostate cancer over the past decade. For radiologists to achieve consistent and reproducible reporting of prostate mpMRI, a comprehensive evaluation of the gland including detailed knowledge of anatomy, pathology, and clinical data is required. This article familiarizes radiologists with common pitfalls and conditions that affect mpMRI performance during readouts. CONCLUSION. Consistent, accurate, and reproducible reporting of prostate mpMRI is vital. Additionally, radiologists should be aware of common diagnostic pitfalls that can hinder mpMRI performance.

Positron emission tomography (PET) radiotracers for prostate cancer imaging.

Imaging plays an increasing role in prostate cancer diagnosis and staging. Accurate staging of prostate cancer is required for optimal treatment planning. In detecting extraprostatic cancer and sites of early recurrence, traditional imaging methods (computed tomography, magnetic resonance imaging, radionuclide bone scan) have suboptimal performance. This leaves a gap between known disease recurrence as indicated by rising prostate-specific antigen and the ability to localize the recurrence on imaging. Novel positron emission tomography (PET) agents including radiolabeled choline, fluciclovine (18F-FACBC), and agents targeting prostate-specific membrane antigen are being developed and tested to increase diagnostic performance of non-invasive prostate cancer localization. When combined with CT or MRI, these tracers offer a combination of functional information and anatomic localization that is superior to conventional imaging methods. These PET radiotracers have varying mechanisms and excretion patterns affecting their pharmacokinetics and diagnostic performance, which will be reviewed in this article.