An update of the Gleason grading system

MRI-based virtual pathology of the prostate

Prostate cancer poses significant diagnostic challenges, with conventional methods like prostate-specific antigen (PSA) screening and transrectal ultrasound (TRUS)-guided biopsies often leading to overdiagnosis or miss clinically significant cancers. Multiparametric MRI (mpMRI) has emerged as a more reliable tool. However, it is limited by high inter-observer variability and radiologists missing up to 30% of clinically significant cancers. This article summarizes a few of these recent advancements in quantitative MRI techniques that look at the "Virtual Pathology" of the prostate with an aim to enhance prostate cancer detection and characterization. These techniques include T2 relaxation-based techniques such as luminal water imaging, diffusion based such as vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT) and restriction spectrum imaging or combined relaxation-diffusion techniques such as hybrid multi-dimensional MRI (HM-MRI), time-dependent diffusion imaging, and diffusion-relaxation correlation spectrum imaging. These methods provide detailed insights into underlying prostate microstructure and tissue composition and have shown improved diagnostic accuracy over conventional MRI. These innovative MRI methods hold potential for augmenting mpMRI, reducing variability in diagnosis, and paving the way for MRI as a 'virtual histology' tool in prostate cancer diagnosis. However, they require further validation in larger multi-center clinical settings and rigorous in-depth radiological-pathology correlation are needed for broader implementation.

Histological parameters and stromal desmoplastic status affecting accurate diagnosis of extraprostatic extension of prostate cancer using multi-parametric magnetic resonance imaging

OBJECTIVE

To investigate the clinicopathological factors affecting discrepancies between multi-parametric magnetic resonance imaging (mpMRI) and histopathological evaluation for diagnosis of extraprostatic extension (EPE) of prostate cancer.

METHODS

One hundred-and-three lesions from 96 cases with suspected EPE on preoperative mpMRI, of which 60 and 43 showed bulging and frank capsular breach, respectively, were grouped according to pathological (p)EPE in radical prostatectomy specimens. Additionally, clinicopathological/immunohistochemical findings for periostin reflecting a desmoplastic stromal reaction were compared between these groups.

RESULTS

pEPE was detected in 49 (48%) of the 103 lesions. Of these, 25 (42%) showed bulging and 24 (56%) showed frank capsular breach on MRI. In the total cohort, the absence of pEPE was significantly associated with a lower Gleason Grade Group (GG) (p < 0.0001), anterior location (p = 0.003), absence of intraductal carcinoma of the prostate (IDC-P) (p = 0.026), and high stromal periostin expression (p < 0.0001). These trends were preserved in subgroups defined by MRI findings, except for anterior location/IDC-P in the bulging subgroup.

CONCLUSIONS

GG, anterior location, and periostin expression may cause mpMRI-pathological discrepancies regarding EPE. Periostin expression was a significant pEPE-negative factor in all subgroup analyses. Our results indicate that patients with suspected EPE on MRI, regardless of their pEPE results, should be followed as carefully as those with definite pEPE.

Tailoring pretext tasks to improve self-supervised learning in histopathologic subtype classification of lung adenocarcinomas

Lung adenocarcinoma (LUAD) is a morphologically heterogeneous disease with five predominant histologic subtypes. Fully supervised convolutional neural networks can improve the accuracy and reduce the subjectivity of LUAD histologic subtyping using hematoxylin and eosin (H&E)-stained whole slide images (WSIs). However, developing supervised models with good prediction accuracy usually requires extensive manual data annotation, which is time-consuming and labor-intensive. This work proposes three self-supervised learning (SSL) pretext tasks to reduce labeling effort. These tasks not only leverage the multi-resolution nature of the H&E WSIs but also explicitly consider the relevance to the downstream task of classifying the LUAD histologic subtypes. Two tasks involve predicting the spatial relationship between tiles cropped from lower and higher magnification WSIs. We hypothesize that these tasks induce the model to learn to distinguish different tissue structures presented in the images, thus benefiting the downstream classification. The third task involves predicting the eosin stain from the hematoxylin stain, inducing the model to learn cytoplasmic features relevant to LUAD subtypes. The effectiveness of the three proposed SSL tasks and their ensemble was demonstrated by comparison with other state-of-the-art pretraining and SSL methods using three publicly available datasets. Our work can be extended to any other cancer type where tissue architectural information is important. The model could be used to expedite and complement the process of routine pathology diagnosis tasks. The code is available at https://github.com/rina-ding/ssl_luad_classification.

Development and validation of a nomogram to predict cancer-specific survival in nonsurgically treated elderly patients with prostate cancer

Prostate Cancer (PC) is the most common male nonskin tumour in the world, and most diagnosed patients are over 65 years old. The main treatment for PC includes surgical treatment and nonsurgical treatment. Currently, for nonsurgically treated elderly patients, few studies have evaluated their prognostic factors. Our aim was to construct a nomogram that could predict cancer-specific survival (CSS) in nonsurgically treated elderly PC patients to assess their prognosis-related independent risk factors. Patient information was obtained from the Surveillance, Epidemiology and End Results (SEER) database, and our target population was nonsurgically treated PC patients who were over 65 years old. Independent risk factors were determined using both univariate and multivariate Cox regression models. A nomogram was built using a multivariate Cox regression model. The accuracy and discrimination of the prediction model were tested using the consistency index (C-index), the area under the subject operating characteristic curve (AUC), and the calibration curve. Decision curve analysis (DCA) was used to examine the potential clinical value of this model. A total of 87,831 elderly PC patients with nonsurgical treatment in 2010-2018 were included in the study and were randomly assigned to the training set (N = 61,595) and the validation set (N = 26,236). Univariate and multivariate Cox regression model analyses showed that age, race, marital status, TNM stage, chemotherapy, radiotherapy modality, PSA and GS were independent risk factors for predicting CSS in nonsurgically treated elderly PC patients. The C-index of the training set and the validation set was 0.894 (95% CI 0.888-0.900) and 0.897 (95% CI 0.887-0.907), respectively, indicating the good discrimination ability of the nomogram. The AUC and the calibration curves also show good accuracy and discriminability. We developed a new nomogram to predict CSS in elderly PC patients with nonsurgical treatment. The model is internally validated with good accuracy and reliability, as well as potential clinical value, and can be used for clinical aid in decision-making.

OCT4‑positive circulating tumor cells may predict a poor prognosis in patients with metastatic castration‑resistant prostate cancer treated with abiraterone plus prednisone therapy

Octamer-binding transcription factor 4 (OCT4) and circulating tumor cells (CTCs) are key factors associated with tumor metastasis and drug resistance in cancer. The present prospective study aimed to investigate the prevalence of OCT4-positive (OCT4+) CTCs and the potential association with the clinical features and survival of patients with metastatic castration-resistant prostate cancer (mCRPC) treated with abiraterone + prednisone. In total, 70 patients with mCRPC treated with abiraterone + prednisone were enrolled in the present study and peripheral blood samples were collected prior to treatment initiation to determine CTC count via a Canpatrol system. RNA in situ hybridization was performed for OCT4+ CTC quantification. Lactate dehydrogenase (LDH) was detected by automatic biochemical analyzer (AU54000, OLYMPUS). Results demonstrated that 34 (48.6%), 21 (30.0%) and 15 (21.4%) patients harbored OCT4+ (CTC+/OCT4+) or OCT4-negative CTCs (CTC+/OCT4-) or were CTC-negative (CTC-), respectively. Notably, CTC+/OCT4+ occurrence was associated with visceral metastasis and high levels of LDH. In addition, radiographic progression-free survival [rPFS; median, 15.0, 95% confidence interval (CI), 9.6-20.4 vs. not reached vs. median, 29.5, 95% CI, 18.6-40.4 months; P=0.001] and overall survival (OS) were significantly decreased (median, 27.3, 95% CI, 20.1-34.5 vs. not reached vs. not reached; P=0.016) in CTC+/OCT4+ compared with CTC+/OCT4- and CTC- patients. Subsequently, the adjustment was performed by multivariate Cox regression models, which revealed that CTC+/OCT4+ (vs. CTC+/OCT4- or CTC-) was independently associated with decreased rPFS [hazard ratio (HR), 3.833; P<0.001] and OS (HR, 3.938; P=0.008). In conclusion, OCT4+ CTCs were highly prevalent in patients with mCRPC and associated with visceral metastasis and increased levels of LDH. Thus, the presence of OCT4+ CTCs may serve as an independent prognostic factor for patients with mCRPC treated with abiraterone + prednisone.

Grossing and reporting of radical prostatectomy specimens: An evidence-based approach

Radical prostatectomy (RP) constitutes the primary treatment option for patients with clinically localized, biopsy-proven prostate cancer that requires local treatment with curative intent. Accurate reporting of radical prostatectomy specimens is required to guide further risk stratification and management of patients. Hence, for the handling and reporting of RP specimens, a standardized protocol should be followed. Many general pathologists may not be well-versed with the guidelines for the handling of radical prostatectomy specimens. This article discusses a detailed approach to grossing techniques, including specimen description, fixation requirements, gross cut-up, and reporting of the grade and stage of RP specimens. This will enable the pathologist to aid in multidisciplinary management.

Real-world outcomes of abiraterone and enzalutamide in first-line treatment of metastatic castration-resistant prostate cancer: which patients benefit most?

OBJECTIVES

Abiraterone and enzalutamide are two oral novel androgen receptor axis-targeted agents approved for the treatment of castration-resistant prostate cancer (mCRPC). Despite the availability of multiple treatments, there is a need to improve the knowledge and management of these drugs in the real-world setting, especially in patient groups under-represented in clinical trials. Our aim was to review the outcome of patients with chemotherapy-naïve mCRPC treated with abiraterone or enzalutamide in routine clinical practice in order to identify factors that are predictive for response.

METHODS

This observational retrospective study was performed in a Spanish tertiary hospital and included men with chemotherapy-naïve mCPRC who started treatment with abiraterone or enzalutamide between September 2012 and November 2018. The study end date was 30 October 2020.

RESULTS

Ninety patients with mCRPC were included, 57 with abiraterone and 33 with enzalutamide. Median overall survival (OS) was 26.87 months (95% CI 19.68 to 34.05), with no difference found between the two treatment groups. Nine variables were related to increased OS in the univariate analysis: Eastern Cooperative Oncology Group (ECOG) performance status (0-1 vs 2), pain (need of opioids for cancer pain), visceral disease, ≥3 bone lesions, exclusively lymph node metastases, baseline prostate specific antigen (PSA) (<50 vs ≥50 ng/dL and <20 vs ≥20 ng/dL), haemoglobin (<12 vs ≥12 g/dL) and alkaline phosphatase (≤116 vs >116 IU/L). A PSA response >50% was observed in 65 patients (76.5%). In the multivariate analysis, ECOG performance status, pain, visceral disease and alkaline phosphatase provided independent prognostic information. Median OS by Kaplan-Meier analysis was significantly longer for patients with a PSA response (32.1 vs 17.9 months; HR 0.46, 95% CI 0.27 to 0.78; p=0.003).

CONCLUSIONS

This study assessed the efficacy of abiraterone and enzalutamide in a real-world setting, including patients under-represented in pivotal studies. Some clinical factors were correlated with improved OS in chemotherapy-naïve men with mCPRC treated with these drugs.

Associations of flow disruptions with patient, staff, and process outcomes: a prospective observational study of robotic-assisted radical prostatectomies

BACKGROUND

Technological advancements in the operating room (OR) have sparked new challenges for surgical workflow, OR professionals, and patient safety. Disruptive events are frequent across all surgical specialties, but little is known about their effects on patient outcomes and the influence of systemic factors. The aim was to explore the associations of intraoperative flow disruptions (FDs) with patient outcomes, staff workload, and surgery duration.

METHODS

Prospective, single-center, and multi-source study comprising direct and standardized OR observations of urologic surgical procedures, clinical patient outcomes, and staff- and patient-reported outcome data (PROMs; 3-month follow-up). All data were recorded between 01/2020 and 10/2021. FDs were assessed using standardized procedure observations. Linear and logistic regression analyses including multiple system factors were used to explore the effects of FDs on surgical outcomes.

RESULTS

61 robotic-assisted radical prostatectomy procedures were captured (with 61 patients and 243 staff reports). High rates of FDs were observed; however, our analyses did not show significant relationships with patient complication rates. Equipment- and patient-related FDs were associated with increased staff workload. No association was found between higher rates of FDs and procedure duration.

CONCLUSIONS

FDs were not related to inferior patient outcomes. Our findings may inform future OR investigations that scrutinize the complex interplay of human, team, process, and technological components that mitigate the effects of FDs during surgery.

Epithelial-to-Mesenchymal Transition-Related Markers in Prostate Cancer: From Bench to Bedside

Prostate cancer (PCa) is the second most frequent type of cancer in men worldwide, with 288,300 new cases and 34,700 deaths estimated in the United States in 2023. Treatment options for early-stage disease include external beam radiation therapy, brachytherapy, radical prostatectomy, active surveillance, or a combination of these. In advanced cases, androgen-deprivation therapy (ADT) is considered the first-line therapy; however, PCa in most patients eventually progresses to castration-resistant prostate cancer (CRPC) despite ADT. Nonetheless, the transition from androgen-dependent to androgen-independent tumors is not yet fully understood. The physiological processes of epithelial-to-non-epithelial ("mesenchymal") transition (EMT) and mesenchymal-to-epithelial transition (MET) are essential for normal embryonic development; however, they have also been linked to higher tumor grade, metastatic progression, and treatment resistance. Due to this association, EMT and MET have been identified as important targets for novel cancer therapies, including CRPC. Here, we discuss the transcriptional factors and signaling pathways involved in EMT, in addition to the diagnostic and prognostic biomarkers that have been identified in these processes. We also tackle the various studies that have been conducted from bench to bedside and the current landscape of EMT-targeted therapies.

Anti-Cancer Stem-Cell-Targeted Therapies in Prostate Cancer

Prostate cancer (PCa) is the second-most commonly diagnosed cancer in men around the world. It is treated using a risk stratification approach in accordance with the National Comprehensive Cancer Network (NCCN) in the United States. The main treatment options for early PCa include external beam radiation therapy (EBRT), brachytherapy, radical prostatectomy, active surveillance, or a combination approach. In those with advanced disease, androgen deprivation therapy (ADT) is considered as a first-line therapy. However, the majority of cases eventually progress while receiving ADT, leading to castration-resistant prostate cancer (CRPC). The near inevitable progression to CRPC has spurred the recent development of many novel medical treatments using targeted therapies. In this review, we outline the current landscape of stem-cell-targeted therapies for PCa, summarize their mechanisms of action, and discuss avenues of future development.

The devil is in the details: a small-lesion sensitive weakly supervised learning framework for prostate cancer detection and grading

Prostate cancer (PCa) is a significant health concern in aging males, and the diagnosis depends primarily on histopathological assessments to determine tumor size and Gleason score. This process is highly time-consuming, subjective, and relies on the extensive experience of the pathologists. Deep learning based artificial intelligence shows an ability to match pathologists on many prostate cancer diagnostic scenarios. However, it is easy to make mistakes on some hard cases with small tumor areas considering the extensively high-resolution of whole slide images (WSIs). The absence of fine-grained and large-scale annotations of such small tumor lesions makes this problem more challenging. Existing methods usually perform uniform cropping of the foreground of WSI and then use convolutional neural networks as the backbone network to predict the classification results. However, cropping can damage the structure of tiny tumors, which affects classification accuracy. To solve this problem, we propose an Intensive-Sampling Multiple Instance Learning Framework (ISMIL), which focuses on tumor regions and improves the recognition of small tumor regions by intensively sampling the crucial regions. Experiments of prostate cancer detection show that our method achieves an area under the receiver operating characteristic curve (AUC) of 0.987 on the PANDA sets, which improves recall by at least 33% with higher specificity over the current primary methods for hard cases. The ISMIL also demonstrates comparable abilities to human experts on the prostate cancer grading task. Moreover, ISMIL has shown good robustness in independent cohorts, which makes it a potential tool to improve the diagnostic efficiency of pathologists.

Motivation and family communication in hereditary prostate cancer genetic testing: Survey of patients from a US tertiary medical center

Identification of a hereditary prostate cancer in an affected individual can guide treatment and may also impact cancer screening and surveillance for patients and their relatives. This study aimed to determine the factors that are associated with the decision-making process of individuals with prostate cancer regarding whether to pursue genetic testing as well as how, why, and with whom genetic test results are shared. We surveyed 113 patients diagnosed with prostate cancer who received cancer genetic counseling through a United States tertiary medical center, inquiring about genetic testing motivations and family communication about results. Among those who pursued genetic testing, (1) learning about my family's possible cancer risk (98%), (2) learning information that may guide cancer treatment (93%), and (3) learning if I am at risk for future cancers (92%) were most frequently identified as slightly or very important factors in their decision. Participants shared their genetic test results in a higher proportion to male first-degree relatives than female first-degree relatives; however, no significant difference was found (p = 0.103). Our study may suggest sex differences related to family communication about genetic testing results. Such findings indicate a critical need for genetic counselors to clearly communicate the impact of genetic test results on both male and female relatives. Further research on motivation and family communication about genetic test results in diverse cohorts is needed.

Identification of the Key Genes Involved in the Tumorigenesis and Prognosis of Prostate Cancer

Background. Prostate cancer (PCa) is a malignant tumor in males, with a majority of the cases advancing to metastatic castration resistance. Metastasis is the leading cause of mortality in PCa. The traditional early detection and prediction approaches cannot differentiate between the different stages of PCa. Therefore, new biomarkers are necessary for early detection and clear differentiation of PCa stages to provide precise therapeutic intervention. Methods. The objective of the study was to find significant differences in genes and combine the three GEO datasets with TCGA-PRAD datasets (DEG). Weighted gene coexpression network analysis (WGCNA) determined the gene set and PCa clinical feature correlation module utilizing the TGGA-PRAD clinical feature data. The correlation module genes were rescreened using the biological information analysis tools, with the three hub genes (TOP2A, NCAPG, and BUB1B) for proper verification. Finally, internal (TCGA) and external (GSE32571, GSE70770) validation datasets were used to validate and predict the value of last hub genes. Results. The hub gene was abnormally upregulated in PCa samples during verification. The expression of each gene was favorably connected with the Gleason score and TN tumor grade in clinical samples but negatively correlated with the overall survival rate. The expression of these genes was linked to CD8 naive cells and macrophages, among other cells. Antitumor immune cells like NK and NKT were favorably and adversely correlated with infiltrating cells, respectively. Simultaneously, the GSCV and GSEA indicated that the hub gene is connected with cell proliferation, death, and androgen receptor, among other signaling pathways. Therefore, these genes could influence the incidence and progression of PCa by participating in or modulating various signaling pathways. Furthermore, using the online tool of CMap, we examined the individual medications for Hughes and determined that tipifarnib could be useful for the clinical therapy of PCa. Conclusion. TOP2A, NCAPG, and BUB1B are important genes intimately linked to the clinical prognosis of PCa and can be employed as reliable biomarkers for early diagnosis and prognosis. Moreover, these genes can provide a theoretical basis for precision differentiation and treatment of PCa.

Artificial intelligence system shows performance at the level of uropathologists for the detection and grading of prostate cancer in core needle biopsy: an independent external validation study

Accurate diagnosis and grading of needle biopsies are crucial for prostate cancer management. A uropathologist-level artificial intelligence (AI) system could help make unbiased decisions and improve pathologists' efficiency. We previously reported an artificial neural network-based, automated, diagnostic software for prostate biopsy, DeepDx^® Prostate (DeepDx). Using an independent external dataset, we aimed to validate the performance of DeepDx at the levels of prostate cancer diagnosis and grading and evaluate its potential value to the general pathologist. A dataset composed of 593 whole-slide images of prostate biopsies (130 normal and 463 adenocarcinomas) was assembled, including their original pathology reports. The Gleason scores (GSs) and grade groups (GGs) determined by three uropathology experts were considered as the reference standard. A general pathologist conducted user validation by scoring the dataset with and without AI assistance. DeepDx was accurate for prostate cancer detection at a similar level to the original pathology report, whereas it was more concordant than the latter with the reference GGs and GSs (kappa/quadratic-weighted kappa = 0.713/0.922 vs. 0.619/0.873 for GGs and 0.654/0.904 vs. 0.576/0.858 for GSs). Notably, it outperformed the original report, especially in the detection of Gleason patterns 4/5, and achieved excellent agreement in quantifying the Gleason pattern 4. When the general pathologist used AI assistance, the concordance of GG between the user and the reference standard increased (kappa/quadratic-weighted kappa, 0.621/0.876 to 0.741/0.925), while the average slide examination time was substantially decreased (55.7 to 36.8 s/case). Overall, DeepDx was capable of making expert-level diagnosis in prostate core biopsies. In addition, its remarkable performance in detecting high-grade Gleason patterns and enhancing the general pathologist's diagnostic performance supports its potential value in routine practice.

AI based prostate analysis system trained without human supervision to predict patient outcome from tissue samples

In order to plan the best treatment for prostate cancer patients, the aggressiveness of the tumor is graded based on visual assessment of tissue biopsies according to the Gleason scale. Recently, a number of AI models have been developed that can be trained to do this grading as well as human pathologists. But the accuracy of the AI grading will be limited by the accuracy of the subjective “ground truth” Gleason grades used for the training. We have trained an AI to predict patient outcome directly based on image analysis of a large biobank of tissue samples with known outcome without input of any human knowledge about cancer grading. The model has shown similar and in some cases better ability to predict patient outcome on an independent test-set than expert pathologists doing the conventional grading.

The direct cost incurred by patients and caregivers in diagnosing and managing prostate cancer in Ghana

Background

Over the years, the prevalence of prostate cancer (PCa) has been on the increase. Poor prognosis has been a reflection of increased advance-staged diagnosis and inadequate financial assistance. The prioritization of resources cannot be effective enough to factor in the unexpected economic burden resulting from ill health unless health economic approaches are utilized to estimate the cost of diseases including PCa. With the absence of data on the cost of PCa in Ghana, and the evidence of the benefits of PCa cost-of-illness studies on cancer financing, it has become imperative to investigate the direct health cost of PCa on patients and careers. Hence, we investigate the cost of PCa diagnosis and management, the availability and prices of PCa medications, and the affordability of PCa care in Ghana.

Methods

The prevalence approach to cost-of-illness studies was adopted in this study through a random selection of two (2) hospitals, four (4) private laboratories, and ten (10) private community pharmacies in the Ashanti Region of Ghana. The diagnostic and management cost of PCa was investigated through the application of validated data collection instruments to representatives of the selected hospitals and laboratories. The availability and prices of PCa medications were studied with the administration of a validated tool to representatives of the selected pharmacies. The data were analyzed with Microsoft Excel Spreadsheet and the affordability of care was assessed considering the 2021 Ghana National Daily Minimum Wage (GNDMW).

Results

The cost of diagnosing non-metastatic and metastatic PCa were respectively estimated at GHC 1686.00 ($ 290.58) and GHC 6876.00 ($ 1185.09). Radical prostatectomy, as a management option, was estimated at GHC 2150.00 ($ 370.56) higher than Extended Beam Radiotherapy (GHC 2150.00: $ 370.56). The mean PCa drug availability for the sampled pharmacies around the public hospital, all the sampled pharmacies, and around the private hospital were respectively 61.54, 51.54, and 41.54%. None of the sampled drugs at the stated strengths had a 100% availability. A 6-month androgen deprivation therapy employing goserelin was GHC 3000.00 ($ 517.05). The median drug price ratio (MDPR) was 0.72 - 15.38, with generic bicalutamide 150 mg tablets as the cheapest and generic flutamide 250 mg tablets as the most expensive.

Conclusion

The diagnostic and management cost of PCa currently overwhelms the average Ghanaian because the minimum daily wage in 2021 is GHC 12.53 ($ 0.46). A higher economic burden was associated with metastatic PCa and hence, the need for strategies to improve early detection. Also, the inclusion of PCa management in the National Health Insurance Scheme would lessen the financial burden of the disease on patients and careers, and improve management outcomes.

Concordance between Gleason score of prostate biopsies and radical prostatectomy specimens and its predictive factors

Objective:

The Gleason score is an essential factor for making decisions about prostate cancer management and its prognosis. Thus, we conducted this research to discover the histologic-grading accuracy of needle biopsy specimens, and to identify preoperative clinical and pathological factors that predict upgrading and downgrading from biopsy to radical prostatectomy specimen.

Patients and methods:

This study was performed on 570 patients who were referred to the medical centers affiliated with Shiraz University of Medical Sciences and underwent radical prostatectomy from 2013 to 2017. Concordance was evaluated between the Gleason score of needle biopsy and radical prostatectomy specimens. Predictors of upgrades and downgrades were assessed in univariate and multivariate logistic regression analyses.

Results:

Scores were the same in 50% of cases, downgraded in 26%, and upgraded in 24%. The variables predicting a Gleason score upgrade were higher Prostate specific antigen level, larger tumors, and older age. Lower tumor volume, lower Prostate specific antigen, and low maximum percentage of cancer in cores were predictors of downgrading from Gleason score>6 to ⩽6. Also, Body mass index>30, smaller tumor size, and negative lymph nodes were predictors of downgrading from Gleason score>7 to 7.

Conclusion:

The correlation between biopsy and Radical prostatectomy Gleason scores was only 50%. After dividing them into the new grading groups, this coordination increased by only 5.6%. Physicians need to consider possible limitations of the Gleason score of biopsy and factors that can be predictive of upgrading to high-risk prostate cancer before making treatment decisions.

Predicting biochemical recurrence of prostate cancer with artificial intelligence

Background

The first sign of metastatic prostate cancer after radical prostatectomy is rising PSA levels in the blood, termed biochemical recurrence. The prediction of recurrence relies mainly on the morphological assessment of prostate cancer using the Gleason grading system. However, in this system, within-grade morphological patterns and subtle histopathological features are currently omitted, leaving a significant amount of prognostic potential unexplored.

Methods

To discover additional prognostic information using artificial intelligence, we trained a deep learning system to predict biochemical recurrence from tissue in H&E-stained microarray cores directly. We developed a morphological biomarker using convolutional neural networks leveraging a nested case-control study of 685 patients and validated on an independent cohort of 204 patients. We use concept-based explainability methods to interpret the learned tissue patterns.

Results

The biomarker provides a strong correlation with biochemical recurrence in two sets (n = 182 and n = 204) from separate institutions. Concept-based explanations provided tissue patterns interpretable by pathologists.

Conclusions

These results show that the model finds predictive power in the tissue beyond the morphological ISUP grading.

To determine the prognosis of patients with prostate cancer, several clinical factors are taken into account. One of these is the cancer grade, assigned by a pathologist based on the cancer’s appearance under a microscope. The grade ranges from 1 to 5, where 5 is the most aggressive tumour type. This study explored whether deep learning—a technique in which computer software learns patterns from multiple examples—can learn to predict the risk of patients’ cancers recurring from microscopic images of the tumours. We show, on two clinical datasets from different institutions, that such a system can help to better predict prognosis, beyond the information provided by grade alone. In the future, this type of method could help clinicians to predict the prognosis of individual prostate cancer patients.

Pinckaers et al. develop a deep learning system to predict biochemical recurrence in prostate cancer patients treated with radical prostatectomy. The authors’ morphological biomarker provides predictive power beyond traditional Gleason grading, based on analysis of two clinical datasets from different institutions.

A multitask deep representation for Gleason score classification to support grade annotations

The Gleason grade system is the main standard to quantify the aggressiveness and progression of prostate cancer. Currently, exists a high disagreement among experts in the diagnosis and stratification of this disease. Deep learning models have emerged as an alternative to classify and support experts automatically. However, these models are limited to learn a rigid stratification rule that can be biased during training to a specific observer. Therefore, this work introduces an embedding representation that integrates an auxiliary task learning to deal with the high inter and intra appearance of the Gleason system. The proposed strategy implements as a main task a triplet loss scheme that builds a feature embedding space with respect to batches of positive and negative histological training patches. As an auxiliary task is added a cross-entropy that helps with inter-class variability of samples while adding robust representations to the main task. The proposed approach shows promising results achieving an average accuracy of 66% and 64%, for two experts without statistical difference. Additionally, reach and average accuracy of 73% in patches where both pathologists are agree, showing the robustness patterns learning from the approach.

MRI-detectability and histological factors of prostate cancer including intraductal carcinoma and cribriform pattern

BACKGROUND

Histopathological characteristics affecting the detectability of clinically significant prostate cancer (csPCa) on magnetic resonance imaging (MRI) remain unclear. This study aimed to compare the histopathology between MRI-detectable and MRI-undetectable cancers, emphasizing intraductal carcinoma of the prostate (IDC-P) and predominant Gleason pattern 4 subtype.

METHODS

This single-center retrospective study enrolled 153 consecutive patients with 191 lesions who underwent preoperative multiparametric MRI and subsequent radical prostatectomy. MRI/histopathological findings and area fractions of histological components (cancer cells, stroma, and luminal spaces) of MRI-detectable and MRI-undetectable cancers were compared. Data were analyzed using Fisher's exact, independent t, or Mann-Whitney U tests.

RESULTS

Overall, 148 (77%) and 43 (23%) cancers were MRI-detectable and MRI-undetectable, respectively. MRI-detectable cancers were significantly larger than MRI-undetectable cancers (p = 0.03). The percentage of lesions in Grade Group 3 or higher was significantly higher among MRI-detectable cancers than among MRI-undetectable cancers (p = 0.02). MRI detectability of csPCa was associated with increases in relative area fractions of cancer cells (p < 0.001) and decreases in those of stroma (p < 0.001) and luminal spaces (p < 0.001) in prostate cancer (PCa) than the percentage of Gleason pattern 4 (p = 0.09). The percentage of lesions containing IDC-P was similar for MRI-detectable and MRI-undetectable cancers (40% vs. 33%; p = 0.48). The distribution of cribriform gland subtypes was not significantly different between MRI-detectable and MRI-undetectable Gleason pattern 4 subtype cancers (p > 0.99). Contrarily, the ratio of fused gland subtype was significantly higher in MRI-detectable than in MRI-undetectable cancers (p = 0.03). Furthermore, the ratio of poorly-formed gland subtype was significantly higher in MRI-undetectable than in MRI-detectable cancers (p = 0.01).

CONCLUSIONS

MRI detectability of csPCa is strongly associated with the relative area fractions of cancer cells, stroma, and luminal spaces in PCa rather than conventional histopathological parameters. Neither the presence nor the percentage of IDC-P affected MRI detectability.

A data-driven ultrasound approach discriminates pathological high grade prostate cancer

Accurate prostate cancer screening is imperative for reducing the risk of cancer death. Ultrasound imaging, although easy, tends to have low resolution and high inter-observer variability. Here, we show that our integrated machine learning approach enabled the detection of pathological high-grade cancer by the ultrasound procedure. Our study included 772 consecutive patients and 2899 prostate ultrasound images obtained at the Nippon Medical School Hospital. We applied machine learning analyses using ultrasound imaging data and clinical data to detect high-grade prostate cancer. The area under the curve (AUC) using clinical data was 0.691. On the other hand, the AUC when using clinical data and ultrasound imaging data was 0.835 (p = 0.007). Our data-driven ultrasound approach offers an efficient tool to triage patients with high-grade prostate cancers and expands the possibility of ultrasound imaging for the prostate cancer detection pathway.

Increased Expression and Altered Cellular Localization of Fibroblast Growth Factor Receptor-Like 1 (FGFRL1) Are Associated with Prostate Cancer Progression

Simple Summary

Prostate cancer (PCa) is one of the most frequently diagnosed malignancies in men. PCa is primarily regulated by androgens, but other mechanisms, such as fibroblast growth factor receptor (FGFR) signaling, are also involved. In some patients, PCa relapses after surgical removal of prostate, and androgen deprivation therapy (ADT) is used as the first-line treatment. Unfortunately, the patients often lose response to ADT and progress by other mechanisms to castration-resistant, currently non-curable PCa. In our study, we aimed to identify better diagnostic markers and therapeutic targets against PCa. We analyzed patient PCa tissue samples from radical prostatectomies and biopsies, and used physiologically relevant 3D organoids and mouse xenografts to study FGFR signaling in PCa. We found that FGFRL1, a protein belonging to the FGFR family, plays a role in PCa. Our results suggest that FGFRL1 has significant effects on PCa progression and has potential as a prognostic biomarker.

Abstract

Fibroblast growth factor receptors (FGFRs) 1–4 are involved in prostate cancer (PCa) regulation, but the role of FGFR-like 1 (FGFRL1) in PCa is unclear. FGFRL1 expression was studied by qRT-PCR and immunohistochemistry of patient tissue microarrays (TMAs) and correlated with clinical patient data. The effects of FGFRL1 knockdown (KD) in PC3M were studied in in vitro culture models and in mouse xenograft tumors. Our results showed that FGFRL1 was significantly upregulated in PCa. The level of membranous FGFRL1 was negatively associated with high Gleason scores (GSs) and Ki67, while increased cytoplasmic and nuclear FGFRL1 showed a positive correlation. Cox regression analysis indicated that nuclear FGFRL1 was an independent prognostic marker for biochemical recurrence after radical prostatectomy. Functional studies indicated that FGFRL1-KD in PC3M cells increases FGFR signaling, whereas FGFRL1 overexpression attenuates it, supporting decoy receptor actions of membrane-localized FGFRL1. In accordance with clinical data, FGFRL1-KD markedly suppressed PC3M xenograft growth. Transcriptomics of FGFRL1-KD cells and xenografts revealed major changes in genes regulating differentiation, ECM turnover, and tumor–stromal interactions associated with decreased growth in FGFRL1-KD xenografts. Our results suggest that FGFRL1 upregulation and altered cellular compartmentalization contribute to PCa progression. The nuclear FGFRL1 could serve as a prognostic marker for PCa patients.

PVT1 is a prognostic marker associated with immune invasion of bladder urothelial carcinoma

Plasmacytoma variant translocation 1 (PVT1) is involved in multiple signaling pathways and plays an important regulatory role in a variety of malignant tumors. However, its role in the prognosis and immune invasion of bladder urothelial carcinoma (BLCA) remains unclear. This study investigated the expression of PVT1 in tumor tissue and its relationship with immune invasion, and determined its prognostic role in patients with BLCA. Patients were identified from the cancer genome atlas (TCGA). The enrichment pathway and function of PVT1 were explained by gene ontology (GO) term analysis, gene set enrichment analysis (GSEA) and single-sample gene set enrichment analysis (ssGSEA), and the degree of immune cell infiltration was quantified. Kaplan-Meier analysis and Cox regression were used to analyze the correlation between PVT1 and survival rate. PVT1-high BLCA patients had a lower 10-year disease-specific survival (DSS P < 0.05) and overall survival (OS P < 0.05). Multivariate Cox regression analysis showed that PVT1 (high vs. low) (P = 0.004) was an independent prognostic factor. A nomogram was used to predict the effect of PVT1 on the prognosis. PVT1 plays an important role in the progression and prognosis of BLCA and can be used as a medium biomarker to predict survival after cystectomy.

Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge

Artificial intelligence (AI) has shown promise for diagnosing prostate cancer in biopsies. However, results have been limited to individual studies, lacking validation in multinational settings. Competitions have been shown to be accelerators for medical imaging innovations, but their impact is hindered by lack of reproducibility and independent validation. With this in mind, we organized the PANDA challenge-the largest histopathology competition to date, joined by 1,290 developers-to catalyze development of reproducible AI algorithms for Gleason grading using 10,616 digitized prostate biopsies. We validated that a diverse set of submitted algorithms reached pathologist-level performance on independent cross-continental cohorts, fully blinded to the algorithm developers. On United States and European external validation sets, the algorithms achieved agreements of 0.862 (quadratically weighted κ, 95% confidence interval (CI), 0.840-0.884) and 0.868 (95% CI, 0.835-0.900) with expert uropathologists. Successful generalization across different patient populations, laboratories and reference standards, achieved by a variety of algorithmic approaches, warrants evaluating AI-based Gleason grading in prospective clinical trials.

A review of artificial intelligence in prostate cancer detection on imaging

A multitude of studies have explored the role of artificial intelligence (AI) in providing diagnostic support to radiologists, pathologists, and urologists in prostate cancer detection, risk-stratification, and management. This review provides a comprehensive overview of relevant literature regarding the use of AI models in (1) detecting prostate cancer on radiology images (magnetic resonance and ultrasound imaging), (2) detecting prostate cancer on histopathology images of prostate biopsy tissue, and (3) assisting in supporting tasks for prostate cancer detection (prostate gland segmentation, MRI-histopathology registration, MRI-ultrasound registration). We discuss both the potential of these AI models to assist in the clinical workflow of prostate cancer diagnosis, as well as the current limitations including variability in training data sets, algorithms, and evaluation criteria. We also discuss ongoing challenges and what is needed to bridge the gap between academic research on AI for prostate cancer and commercial solutions that improve routine clinical care.

Dynamic contrast-enhanced magnetic resonance imaging for risk stratification in patients with prostate cancer

BACKGROUND

To investigate the usefulness of perfusion parameters derived from dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) of patients diagnosed as prostate cancer (PCa) in differentiating clinically significant cancer [CSC, Gleason score (GS) ≥7] from non-CSC (GS 6).

METHODS

A total of 94 patients diagnosed between August 2018 and September 2020 as PCa by radical prostatectomy were included in this retrospective study (mean age: 68.7 years, range, 47-83 years). All of the patients had undergone DCE-MRI on a single 3T-MR scanner. Whole-tumor volume was measured by reviewing a pathologic topographic map as a reference standard. The quantitative DCE perfusion parameters, including volume transfer constant (K^trans), rate constant (k_ep), extracellular extravascular space (EES) volume fraction (v_e), plasma volume fraction (v_p) and area of region of interest (ROI) were calculated under an extended Tofts model. A receiver operating characteristic (ROC) curve analysis by pair-wise comparison was performed to compare the diagnostic performances of the perfusion parameters.

RESULTS

The study population comprised GS 6 (n=17), GS 7 (n=57), GS 8 (n=9) and GS 9 (n=11) cases. Among the perfusion parameters, v_e differed significantly between CSC (0.238±0.095) and non-CSC (0.300±0.126) (P=0.0308). Area under the curve (AUC) was 0.643 (95% CI, 0.538-0.739), and a maximum accuracy of 64%, a sensitivity of 66%, and a specificity of 53% were estimated. Area of ROI also differed significantly between CSC (201.89±163.87 mm²) and non-CSC (84.99±85.82 mm²) (P=0.0054). AUC was 0.807 (95% CI, 0.713-0.881), and maximum accuracy, sensitivity, and specificity were 81%, 82%, and 76%, respectively.

CONCLUSIONS

Size of the tumor and interstitial space volume fraction are significant parameters in differentiating aggressiveness in PCa.

Multi-resolution super learner for voxel-wise classification of prostate cancer using multi-parametric MRI

Multi-parametric MRI (mpMRI) is a critical tool in prostate cancer (PCa) diagnosis and management. To further advance the use of mpMRI in patient care, computer aided diagnostic methods are under continuous development for supporting/supplanting standard radiological interpretation. While voxel-wise PCa classification models are the gold standard, few if any approaches have incorporated the inherent structure of the mpMRI data, such as spatial heterogeneity and between-voxel correlation, into PCa classification. We propose a machine learning-based method to fill in this gap. Our method uses an ensemble learning approach to capture regional heterogeneity in the data, where classifiers are developed at multiple resolutions and combined using the super learner algorithm, and further account for between-voxel correlation through a Gaussian kernel smoother. It allows any type of classifier to be the base learner and can be extended to further classify PCa sub-categories. We introduce the algorithms for binary PCa classification, as well as for classifying the ordinal clinical significance of PCa for which a weighted likelihood approach is implemented to improve the detection of less prevalent cancer categories. The proposed method has shown important advantages over conventional modeling and machine learning approaches in simulations and application to our motivating patient data.

Vasohibin-1 expression as a biomarker of aggressive nature in ductal adenocarcinoma of the prostate: a retrospective cohort study at two centres in Japan

OBJECTIVES

Vasohibin-1 (VASH1) is an endogenous angiogenesis regulator expressed in activated vascular endothelial cells. We previously reported that high VASH1 expression is a predictor of progression in acinar adenocarcinoma of the prostate. In this study, we evaluated the characteristics of ductal adenocarcinoma of the prostate by comparing the level of VASH1 expression between ductal and acinar adenocarcinoma specimens.

DESIGN AND SETTING

A retrospective cohort study at two centres in Japan.

PARTICIPANTS

Among the 1495 patients who underwent radical prostatectomy or transurethral resection for the past 15 years, a total of 14 patients diagnosed with ductal adenocarcinoma and 20 patients diagnosed with acinar adenocarcinoma with a Gleason score of 4+4 were included.

INTERVENTIONS

We immunohistochemically examined the CD34 expression as the microvessel density (MVD) and activated endothelial cells as the VASH1 density (vessels per mm²).

PRIMARY AND SECONDARY OUTCOME MEASURES

The primary outcome was the association of MVD and VASH1 density between ductal and acinar adenocarcinoma, and the secondary outcome was their oncological outcomes.

RESULTS

Nine patients (64.3%) with ductal adenocarcinoma were diagnosed at an advanced clinical stage, and five patients (35.7%) died from cancer during a median follow-up of 56.0 months. The VASH1 densities (mean±SD) in ductal and acinar adenocarcinoma were 45.1±18.5 vs 16.1±21.0 (p<0.001), respectively, while the MVD (mean±SD) in ductal and acinar adenocarcinoma were 65.3±21.9 vs 80.8±60.7 (p=0.666), respectively. The 5-year cancer-specific survival rates for high and low VASH1 expression were 70.0% and 100.0% (p=0.006), respectively. High VASH1 expression and a diagnosis of ductal adenocarcinoma were significant predictors of cancer-specific survival.

CONCLUSIONS

Ductal adenocarcinoma was more aggressive and had higher VASH1 expression than acinar adenocarcinoma, although MVD was equivalent. These results indicate that VASH1 expression may serve as a novel biomarker for the aggressive nature of ductal adenocarcinoma.

Development and Validation of an Artificial Intelligence-Powered Platform for Prostate Cancer Grading and Quantification

IMPORTANCE

The Gleason grading system has been the most reliable tool for the prognosis of prostate cancer since its development. However, its clinical application remains limited by interobserver variability in grading and quantification, which has negative consequences for risk assessment and clinical management of prostate cancer.

OBJECTIVE

To examine the impact of an artificial intelligence (AI)-assisted approach to prostate cancer grading and quantification.

DESIGN, SETTING, AND PARTICIPANTS

This diagnostic study was conducted at the University of Wisconsin-Madison from August 2, 2017, to December 30, 2019. The study chronologically selected 589 men with biopsy-confirmed prostate cancer who received care in the University of Wisconsin Health System between January 1, 2005, and February 28, 2017. A total of 1000 biopsy slides (1 or 2 slides per patient) were selected and scanned to create digital whole-slide images, which were used to develop and validate a deep convolutional neural network-based AI-powered platform. The whole-slide images were divided into a training set (n = 838) and validation set (n = 162). Three experienced academic urological pathologists (W.H., K.A.I., and R.H., hereinafter referred to as pathologists 1, 2, and 3, respectively) were involved in the validation. Data were collected between December 29, 2018, and December 20, 2019, and analyzed from January 4, 2020, to March 1, 2021.

MAIN OUTCOMES AND MEASURES

Accuracy of prostate cancer detection by the AI-powered platform and comparison of prostate cancer grading and quantification performed by the 3 pathologists using manual vs AI-assisted methods.

RESULTS

Among 589 men with biopsy slides, the mean (SD) age was 63.8 (8.2) years, the mean (SD) prebiopsy prostate-specific antigen level was 10.2 (16.2) ng/mL, and the mean (SD) total cancer volume was 15.4% (20.1%). The AI system was able to distinguish prostate cancer from benign prostatic epithelium and stroma with high accuracy at the patch-pixel level, with an area under the receiver operating characteristic curve of 0.92 (95% CI, 0.88-0.95). The AI system achieved almost perfect agreement with the training pathologist (pathologist 1) in detecting prostate cancer at the patch-pixel level (weighted κ = 0.97; asymptotic 95% CI, 0.96-0.98) and in grading prostate cancer at the slide level (weighted κ = 0.98; asymptotic 95% CI, 0.96-1.00). Use of the AI-assisted method was associated with significant improvements in the concordance of prostate cancer grading and quantification between the 3 pathologists (eg, pathologists 1 and 2: 90.1% agreement using AI-assisted method vs 84.0% agreement using manual method; P < .001) and significantly higher weighted κ values for all pathologists (eg, pathologists 2 and 3: weighted κ = 0.92 [asymptotic 95% CI, 0.90-0.94] for AI-assisted method vs 0.76 [asymptotic 95% CI, 0.71-0.80] for manual method; P < .001) compared with the manual method.

CONCLUSIONS AND RELEVANCE

In this diagnostic study, an AI-powered platform was able to detect, grade, and quantify prostate cancer with high accuracy and efficiency and was associated with significant reductions in interobserver variability. These results suggest that an AI-powered platform could potentially transform histopathological evaluation and improve risk stratification and clinical management of prostate cancer.

[Localized prostate cancer]

Prostate cancer is the most prevalent noncutaneous cancer in men. The Gleason grading is considered to be the strongest prognostic parameter regarding progression-free survival and overall survival. The original grading system has been modified during the last decade resulting in a more precise prognostic tool. The pretreatment Gleason score guides clinical management and is a key component in S3 guidelines for prostate cancer. In addition to Gleason score several other histologic findings in prostate needle biopsy influence patient management. In this second part of our CME series about prostate cancer, we will discuss the diagnosis of prostate cancer and current guidelines for reporting prostate cancer. In addition, we will highlight prostate lesions of urothelial origin and neuroendocrine prostate cancer as well as prognostic biomarkers.

JPX and LINC00641 ncRNAs expression in prostate tissue: a case-control study

Background and purpose

Prostate cancer (PC) is the second most prevalent cancer in men. Prostate-specific antigen (PSA) is the main biomarker for screening PC. An increase in PSA could lead to false-positive results. Thus, more appropriate markers should be investigated. In the present study, JPX and LINC00641 expression levels were measured in tumoral prostate tissue compared with the non-tumor tissue.

Experimental approach

43 pairs of prostate tumoral and non-tumor tissue were prepared. The expression levels of JPX and LINC00641 were investigated by RT-qPCR.

Findings/Results

Significant upregulation of LINC00641 (2.47 ± 0.5 vs 1.41 ± 0.2) and downregulation of JPX (1.42 ± 0.6 vs 2.83 ± 1.0) were observed in PC tissues compared with the normal tissues (their adjacent non-tumoral tissues).

Conclusion and implications

Dysregulation of JPX and LINC00641 in PC patients could be used in the future as a prognostic biomarker in PC.

PTEN and DNA Ploidy Status by Machine Learning in Prostate Cancer

Simple Summary

Molecular tissue-based prognostic biomarkers are anticipated to complement the current risk stratification systems in prostate cancer, but their manual assessment is subjective and time-consuming. Objective assessment of such biomarkers by machine learning-based methods could advance their adoption in a clinical workflow. PTEN and DNA ploidy status are well-studied biomarkers, which can provide clinically relevant information in prostate cancer at a low cost. Using a cohort of 253 patients who received radical prostatectomy, we developed a novel, fully-automated PTEN scoring in immunohistochemically-stained tissue slides, which could be used to assess PTEN status in a reliable and reproducible manner. In an independent validation cohort of 259 patients, automatically assessed PTEN status was significantly associated with time to biochemical recurrence after radical prostatectomy, and the combination of PTEN and DNA ploidy status further improved risk stratification. These results demonstrate the utility of machine learning in biomarker assessment.

Abstract

Machine learning (ML) is expected to improve biomarker assessment. Using convolution neural networks, we developed a fully-automated method for assessing PTEN protein status in immunohistochemically-stained slides using a radical prostatectomy (RP) cohort (n = 253). It was validated according to a predefined protocol in an independent RP cohort (n = 259), alone and by measuring its prognostic value in combination with DNA ploidy status determined by ML-based image cytometry. In the primary analysis, automatically assessed dichotomized PTEN status was associated with time to biochemical recurrence (TTBCR) (hazard ratio (HR) = 3.32, 95% CI 2.05 to 5.38). Patients with both non-diploid tumors and PTEN-low had an HR of 4.63 (95% CI 2.50 to 8.57), while patients with one of these characteristics had an HR of 1.94 (95% CI 1.15 to 3.30), compared to patients with diploid tumors and PTEN-high, in univariable analysis of TTBCR in the validation cohort. Automatic PTEN scoring was strongly predictive of the PTEN status assessed by human experts (area under the curve 0.987 (95% CI 0.968 to 0.994)). This suggests that PTEN status can be accurately assessed using ML, and that the combined marker of automatically assessed PTEN and DNA ploidy status may provide an objective supplement to the existing risk stratification factors in prostate cancer.

Immunohistochemical investigation of cytokine expression levels as biomarkers in transrectal ultrasound-guided needle biopsy specimens of prostate adenocarcinoma

Cytokines influence the biological behaviour of prostate cancer (PC) and may influence patient outcome and serve as useful prognostic biomarkers. The aim of the present study was to evaluate cytokine expression levels in prostatic needle biopsy specimens and the association with clinicopathological characteristics of patients with PC. A total of 18 patients with PC who underwent transrectal ultrasound (TRUS) guided prostate biopsy were included in the clinical study. These patients were naïve to radiotherapy (RT) or androgen deprivation therapy prior to TRUS biopsy and clinical follow up data was collected. Cytokine expression levels were analysed by using immunohistochemistry and Spearman's correlation test was used to determine the correlation between cytokine expression and clinicopathological characteristics. Expression levels of pro-inflammatory TNF-α and IL-6 decreased as Gleason score (GS) increased; however, a statistically significant difference was not detected. A statically significant correlation was observed between needle biopsy specimen and pre-RT plasma sample expression levels of pro-inflammatory TNF-α and IL-6 (P=0.01 and P=0.05, respectively) and anti-inflammatory TGF-β1 (P=0.05). However, further studies are needed to confirm these results using a larger sample size to confirm the prognostic value of pro-inflammatory TNF-α and IL-6 and anti-inflammatory TGF-β1 in patients with PC.

Clinical characteristics and oncological outcomes in negative multiparametric MRI patients undergoing robot-assisted radical prostatectomy

BACKGROUND

Efforts are ongoing to try and find ways to reduce the number of unnecessary prostate biopsies without missing clinically significant prostate cancers (csPCa). The utility of multiparametric magnetic resonance imaging (mpMRI) in detecting prostate cancer (PCa) shows promise to be used as triage test for systematic prostate biopsy. Our aim is to Study clinical parameters and oncological outcomes in men with negative mpMRI (nMRI; PI-RADS v2 scores of ≤ 2) who underwent robot-assisted radical prostatectomy (RARP) to evaluate nMRI's practicality as a biopsy triage test.

METHODS

Retrospective analysis of 331 men with nMRI who underwent RARP between 2014 and 2020 compared with men with positive mpMRI (pMRI; PI-RADS v2 scores ≥ 3, N = 1770). csPCa was defined as Gleason score ≥ 3 + 4 and biochemical recurrence (BCR) was defined as PSA > 0.2 ng/ml on two occasions. Biopsies were graded with the International Society of Urologic Pathology [ISUP] grade. Descriptive statistics for nMRI and pMRI were performed. Mann-Whitney U test was used for continuous variables and χ ² for categorical variables. Univariable and multivariable regression analyses were performed.

RESULTS

Univariable analysis shows statistically significant difference (p < .05) between median age (nMRI-61 years vs. pMRI 63 years), race (higher incidence of nMRI in African American men), use of 5-alpha reductase inhibitors (higher rate in nMRI). While incidence rates of family history of PCa, suspicious digital rectal examination (DRE) findings, median PSA levels and 4Kscore, were lower in nMRI versus pMRI. Rates of positive surgical margins and BCR were comparable in nMRI versus pMRI. Biopsy ISUP Grades I and II upgraded by 51% and 12%, respectively in final pathology. African American race and no history of the prior negative biopsy were significant predictors for upgrading.

CONCLUSION

Men with nMRI pose diagnostic challenges as they tend to be younger patients with lower rates of suspicious DRE findings and lower 4K scores, yet comparable oncological outcomes in csPCa rates, positive surgical margins, and BCR rates.

Artificial Intelligence for solid tumor diagnosis in digital pathology

Tumor diagnosis relies on the visual examination of histological slides by pathologists through a microscope eyepiece. Digital pathology, the digitalization of histological slides at high magnification with slides scanners, has raised the opportunity to extract quantitative information thanks to image analysis. In the last decade, medical image analysis has made exceptional progress due to the development of artificial intelligence (AI) algorithms. AI has been successfully used in the field of medical imaging and more recently in digital pathology. The feasibility and usefulness of AI assisted pathology tasks have been demonstrated in the very last years and we can expect those developments to be applied on routine histopathology in the future. In this review, we will describe and illustrate this technique and present the most recent applications in the field of tumor histopathology.

MRI-based radiomics models to assess prostate cancer, extracapsular extension and positive surgical margins

Purpose

To investigate the performance of magnetic resonance imaging (MRI)-based radiomics models for benign and malignant prostate lesion discrimination and extracapsular extension (ECE) and positive surgical margins (PSM) prediction.

Methods and materials

In total, 459 patients who underwent multiparametric MRI (mpMRI) before prostate biopsy were included. Radiomic features were extracted from both T2-weighted imaging (T2WI) and the apparent diffusion coefficient (ADC). Patients were divided into different training sets and testing sets for different targets according to a ratio of 7:3. Radiomics signatures were built using radiomic features on the training set, and integrated models were built by adding clinical characteristics. The areas under the receiver operating characteristic curves (AUCs) were calculated to assess the classification performance on the testing sets.

Results

The radiomics signatures for benign and malignant lesion discrimination achieved AUCs of 0.775 (T2WI), 0.863 (ADC) and 0.855 (ADC + T2WI). The corresponding integrated models improved the AUC to 0.851/0.912/0.905, respectively. The radiomics signatures for ECE achieved the highest AUC of 0.625 (ADC), and the corresponding integrated model achieved the highest AUC (0.728). The radiomics signatures for PSM prediction achieved AUCs of 0.614 (T2WI) and 0.733 (ADC). The corresponding integrated models reached AUCs of 0.680 and 0.766, respectively.

Conclusions

The MRI-based radiomics models, which took advantage of radiomic features on ADC and T2WI scans, showed good performance in discriminating benign and malignant prostate lesions and predicting ECE and PSM. Combining radiomics signatures and clinical factors enhanced the performance of the models, which may contribute to clinical diagnosis and treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40644-021-00414-6.

Detection of Prostate Cancer in Whole-Slide Images Through End-to-End Training With Image-Level Labels

Prostate cancer is the most prevalent cancer among men in Western countries, with 1.1 million new diagnoses every year. The gold standard for the diagnosis of prostate cancer is a pathologists' evaluation of prostate tissue. To potentially assist pathologists deep / learning / based cancer detection systems have been developed. Many of the state-of-the-art models are patch / based convolutional neural networks, as the use of entire scanned slides is hampered by memory limitations on accelerator cards. Patch-based systems typically require detailed, pixel-level annotations for effective training. However, such annotations are seldom readily available, in contrast to the clinical reports of pathologists, which contain slide-level labels. As such, developing algorithms which do not require manual pixel-wise annotations, but can learn using only the clinical report would be a significant advancement for the field. In this paper, we propose to use a streaming implementation of convolutional layers, to train a modern CNN (ResNet / 34) with 21 million parameters end-to-end on 4712 prostate biopsies. The method enables the use of entire biopsy images at high-resolution directly by reducing the GPU memory requirements by 2.4 TB. We show that modern CNNs, trained using our streaming approach, can extract meaningful features from high-resolution images without additional heuristics, reaching similar performance as state-of-the-art patch-based and multiple-instance learning methods. By circumventing the need for manual annotations, this approach can function as a blueprint for other tasks in histopathological diagnosis. The source code to reproduce the streaming models is available at https://github.com/DIAGNijmegen/ pathology-streaming-pipeline.

Combining weakly and strongly supervised learning improves strong supervision in Gleason pattern classification

BACKGROUND

One challenge to train deep convolutional neural network (CNNs) models with whole slide images (WSIs) is providing the required large number of costly, manually annotated image regions. Strategies to alleviate the scarcity of annotated data include: using transfer learning, data augmentation and training the models with less expensive image-level annotations (weakly-supervised learning). However, it is not clear how to combine the use of transfer learning in a CNN model when different data sources are available for training or how to leverage from the combination of large amounts of weakly annotated images with a set of local region annotations. This paper aims to evaluate CNN training strategies based on transfer learning to leverage the combination of weak and strong annotations in heterogeneous data sources. The trade-off between classification performance and annotation effort is explored by evaluating a CNN that learns from strong labels (region annotations) and is later fine-tuned on a dataset with less expensive weak (image-level) labels.

RESULTS

As expected, the model performance on strongly annotated data steadily increases as the percentage of strong annotations that are used increases, reaching a performance comparable to pathologists ([Formula: see text]). Nevertheless, the performance sharply decreases when applied for the WSI classification scenario with [Formula: see text]. Moreover, it only provides a lower performance regardless of the number of annotations used. The model performance increases when fine-tuning the model for the task of Gleason scoring with the weak WSI labels [Formula: see text].

CONCLUSION

Combining weak and strong supervision improves strong supervision in classification of Gleason patterns using tissue microarrays (TMA) and WSI regions. Our results contribute very good strategies for training CNN models combining few annotated data and heterogeneous data sources. The performance increases in the controlled TMA scenario with the number of annotations used to train the model. Nevertheless, the performance is hindered when the trained TMA model is applied directly to the more challenging WSI classification problem. This demonstrates that a good pre-trained model for prostate cancer TMA image classification may lead to the best downstream model if fine-tuned on the WSI target dataset. We have made available the source code repository for reproducing the experiments in the paper: https://github.com/ilmaro8/Digital_Pathology_Transfer_Learning.

Deep learning approach to predict lymph node metastasis directly from primary tumour histology in prostate cancer

OBJECTIVE

To develop a new digital biomarker based on the analysis of primary tumour tissue by a convolutional neural network (CNN) to predict lymph node metastasis (LNM) in a cohort matched for already established risk factors.

PATIENTS AND METHODS

Haematoxylin and eosin (H&E) stained primary tumour slides from 218 patients (102 N+; 116 N0), matched for Gleason score, tumour size, venous invasion, perineural invasion and age, who underwent radical prostatectomy were selected to train a CNN and evaluate its ability to predict LN status.

RESULTS

With 10 models trained with the same data, a mean area under the receiver operating characteristic curve (AUROC) of 0.68 (95% confidence interval [CI] 0.678-0.682) and a mean balanced accuracy of 61.37% (95% CI 60.05-62.69%) was achieved. The mean sensitivity and specificity was 53.09% (95% CI 49.77-56.41%) and 69.65% (95% CI 68.21-71.1%), respectively. These results were confirmed via cross-validation. The probability score for LNM prediction was significantly higher on image sections from N+ samples (mean [SD] N+ probability score 0.58 [0.17] vs 0.47 [0.15] N0 probability score, P = 0.002). In multivariable analysis, the probability score of the CNN (odds ratio [OR] 1.04 per percentage probability, 95% CI 1.02-1.08; P = 0.04) and lymphovascular invasion (OR 11.73, 95% CI 3.96-35.7; P < 0.001) proved to be independent predictors for LNM.

CONCLUSION

In our present study, CNN-based image analyses showed promising results as a potential novel low-cost method to extract relevant prognostic information directly from H&E histology to predict the LN status of patients with prostate cancer. Our ubiquitously available technique might contribute to an improved LN status prediction.

Prognostic Models for Patients With Gleason Score 9 Prostate Cancer: A Population-Based Study

Purpose: Gleason score (GS) system is one of the most widely used histological grading methods for prostate cancer (PCa) all over the world. GS can be obtained by adding the primary Gleason pattern (GP) and secondary GP. Different proportions of GP 4 and GP 5 in prostate specimens can both lead to GS 9. In this study, we explored whether GP 5 + 4 or GP 4 + 5 was associated with different prognoses among patients with GS 9 PCa.

Materials and methods: A retrospective population-based study was conducted on 10,124 subjects diagnosed with GS 9 PCa between 2004 and 2009 from the Surveillance, Epidemiology, and End Results program. A 1:1 propensity-score matching (PSM) was performed to balance the baseline characteristics between the GP 4 + 5 and 5 + 4 groups and to compare the prognoses between the two groups. Cox regression analysis and Fine-Gray competing risk regression models were adopted to screen the covariates significantly associated with all-cause mortality (ACM) and cancer-specific mortality (CAM).

Results: GP 5 + 4 was associated with higher risks of ACM and CSM before or after PSM than GP 4 + 5. In the original cohort, there were eight independent predictors for ACM, which were age at diagnosis, race, AJCC NM stage, PSA levels, treatments, GP, and marital status, confirmed by the Cox analysis; and nine independent predictors for CSM, which were age at diagnosis, race, AJCC TNM stage, PSA levels, treatments, GP, and marital status, confirmed by the competing-risk model.

Conclusion: GP 5 + 4 was associated with a poorer overall survival and cancer-specific survival compared with GP 4 + 5.

Expression of ERBB Family Members as Predictive Markers of Prostate Cancer Progression and Mortality

BACKGROUND

EGFR, ERBB2, ERBB3, and ERBB4 are growth receptors of the ERBB family implicated in the development of epithelial cancers. Studies have suggested a role for EGFR and ERBB3 in the development of prostate cancer (PC), while the involvement of ERBB2 and ERBB4 remains unclear. In this study, we evaluated the expression of all members of the ERBB family in PC tissue from a large cohort and determined their contribution, alone or in combination, as prognostic markers.

METHODS

Using immunofluorescence coupled with digital image analyses, we quantified the expression of EGFR, ERBB2, ERBB3, and ERBB4 on radical prostatectomy specimens (n = 285) arrayed on six tissue microarrays. By combining EGFR, ERBB2, and ERBB3 protein expression in a decision tree model, we identified an association with biochemical recurrence (log rank = 25.295, p < 0.001), development of bone metastases (log rank = 23.228, p < 0.001), and cancer-specific mortality (log rank = 24.586, p < 0.001).

CONCLUSIONS

Our study revealed that specific protein expression patterns of ERBB family members are associated with an increased risk of PC progression and mortality.

Grading Evolution and Contemporary Prognostic Biomarkers of Clinically Significant Prostate Cancer

Simple Summary

Prostate cancer treatment decisions are based on clinical stage and histological diagnosis, including Gleason grading assessed by a pathologist, in biopsies. Prior to staging and grading, serum or blood prostate-specific antigen (PSA) levels are measured and often trigger diagnostic examinations. However, PSA is best suited as a marker of cancer relapse after initial treatment. In this review, we first narratively describe the evolution of histological grading, the current status of Gleason pattern-based diagnostics and glance into future methodology of risk assessment by histological examination. In the second part, we systematically review the biomarkers that have been shown, independent from clinical characteristics, to correlate with clinically relevant end-points, i.e., occurrence of metastases, disease-specific mortality and overall survival after initial treatment of localized prostate cancer.

Abstract

Gleason grading remains the strongest prognostic parameter in localized prostate adenocarcinoma. We have here outlined the evolution and contemporary practices in pathological evaluation of prostate tissue samples for Gleason score and Grade group. The state of more observer-independent grading methods with the aid of artificial intelligence is also reviewed. Additionally, we conducted a systematic review of biomarkers that hold promise in adding independent prognostic or predictive value on top of clinical parameters, Grade group and PSA. We especially focused on hard end points during the follow-up, i.e., occurrence of metastasis, disease-specific mortality and overall mortality. In peripheral blood, biopsy-detected prostate cancer or in surgical specimens, we can conclude that there are more than sixty biomarkers that have been shown to have independent prognostic significance when adjusted to conventional risk assessment or grouping. Our search brought up some known putative markers and panels, as expected. Also, the synthesis in the systematic review indicated markers that ought to be further studied as part of prospective trials and in well characterized patient cohorts in order to increase the resolution of the current clinico-pathological prognostic factors.

Prostate cancer

Prostate cancer is a complex disease that affects millions of men globally, predominantly in high human development index regions. Patients with localized disease at a low to intermediate risk of recurrence generally have a favourable outcome of 99% overall survival for 10 years if the disease is detected and treated at an early stage. Key genetic alterations include fusions of TMPRSS2 with ETS family genes, amplification of the MYC oncogene, deletion and/or mutation of PTEN and TP53 and, in advanced disease, amplification and/or mutation of the androgen receptor (AR). Prostate cancer is usually diagnosed by prostate biopsy prompted by a blood test to measure prostate-specific antigen levels and/or digital rectal examination. Treatment for localized disease includes active surveillance, radical prostatectomy or ablative radiotherapy as curative approaches. Men whose disease relapses after prostatectomy are treated with salvage radiotherapy and/or androgen deprivation therapy (ADT) for local relapse, or with ADT combined with chemotherapy or novel androgen signalling-targeted agents for systemic relapse. Advanced prostate cancer often progresses despite androgen ablation and is then considered castration-resistant and incurable. Current treatment options include AR-targeted agents, chemotherapy, radionuclides and the poly(ADP-ribose) inhibitor olaparib. Current research aims to improve prostate cancer detection, management and outcomes, including understanding the fundamental biology at all stages of the disease.

Prostate cancer

Prostate cancer is a complex disease that affects millions of men globally, predominantly in high human development index regions. Patients with localized disease at a low to intermediate risk of recurrence generally have a favourable outcome of 99% overall survival for 10 years if the disease is detected and treated at an early stage. Key genetic alterations include fusions of TMPRSS2 with ETS family genes, amplification of the MYC oncogene, deletion and/or mutation of PTEN and TP53 and, in advanced disease, amplification and/or mutation of the androgen receptor (AR). Prostate cancer is usually diagnosed by prostate biopsy prompted by a blood test to measure prostate-specific antigen levels and/or digital rectal examination. Treatment for localized disease includes active surveillance, radical prostatectomy or ablative radiotherapy as curative approaches. Men whose disease relapses after prostatectomy are treated with salvage radiotherapy and/or androgen deprivation therapy (ADT) for local relapse, or with ADT combined with chemotherapy or novel androgen signalling-targeted agents for systemic relapse. Advanced prostate cancer often progresses despite androgen ablation and is then considered castration-resistant and incurable. Current treatment options include AR-targeted agents, chemotherapy, radionuclides and the poly(ADP-ribose) inhibitor olaparib. Current research aims to improve prostate cancer detection, management and outcomes, including understanding the fundamental biology at all stages of the disease.

Artificial intelligence assistance significantly improves Gleason grading of prostate biopsies by pathologists

The Gleason score is the most important prognostic marker for prostate cancer patients, but it suffers from significant observer variability. Artificial intelligence (AI) systems based on deep learning can achieve pathologist-level performance at Gleason grading. However, the performance of such systems can degrade in the presence of artifacts, foreign tissue, or other anomalies. Pathologists integrating their expertise with feedback from an AI system could result in a synergy that outperforms both the individual pathologist and the system. Despite the hype around AI assistance, existing literature on this topic within the pathology domain is limited. We investigated the value of AI assistance for grading prostate biopsies. A panel of 14 observers graded 160 biopsies with and without AI assistance. Using AI, the agreement of the panel with an expert reference standard increased significantly (quadratically weighted Cohen's kappa, 0.799 vs. 0.872; p = 0.019). On an external validation set of 87 cases, the panel showed a significant increase in agreement with a panel of international experts in prostate pathology (quadratically weighted Cohen's kappa, 0.733 vs. 0.786; p = 0.003). In both experiments, on a group-level, AI-assisted pathologists outperformed the unassisted pathologists and the standalone AI system. Our results show the potential of AI systems for Gleason grading, but more importantly, show the benefits of pathologist-AI synergy.

Glycosylation in Indolent, Significant and Aggressive Prostate Cancer by Automated High-Throughput N-Glycan Profiling

The diagnosis and treatment of prostate cancer (PCa) is a major health-care concern worldwide. This cancer can manifest itself in many distinct forms and the transition from clinically indolent PCa to the more invasive aggressive form remains poorly understood. It is now universally accepted that glycan expression patterns change with the cellular modifications that accompany the onset of tumorigenesis. The aim of this study was to investigate if differential glycosylation patterns could distinguish between indolent, significant, and aggressive PCa. Whole serum N-glycan profiling was carried out on 117 prostate cancer patients’ serum using our automated, high-throughput analysis platform for glycan-profiling which utilizes ultra-performance liquid chromatography (UPLC) to obtain high resolution separation of N-linked glycans released from the serum glycoproteins. We observed increases in hybrid, oligomannose, and biantennary digalactosylated monosialylated glycans (M5A1G1S1, M8, and A2G2S1), bisecting glycans (A2B, A2(6)BG1) and monoantennary glycans (A1), and decreases in triantennary trigalactosylated trisialylated glycans with and without core fucose (A3G3S3 and FA3G3S3) with PCa progression from indolent through significant and aggressive disease. These changes give us an insight into the disease pathogenesis and identify potential biomarkers for monitoring the PCa progression, however these need further confirmation studies.

Ductal Prostate Cancers Demonstrate Poor Outcomes with Conventional Therapies

BACKGROUND

Ductal prostate adenocarcinoma (DAC) is a rare, aggressive, histologic variant of prostate cancer that is treated with conventional therapies, similar to high-risk prostate adenocarcinoma (PAC).

OBJECTIVE

To assess the outcomes of men undergoing definitive therapy for DAC or high-risk PAC and to explore the effects of androgen deprivation therapy (ADT) in improving the outcomes of DAC.

DESIGN, SETTING, AND PARTICIPANTS

A single-center retrospective review of all patients with cT1-4/N0-1 DAC from 2005 to 2018 was performed. Those undergoing radical prostatectomy (RP) or radiotherapy (RTx) for DAC were compared with cohorts of high-risk PAC patients.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Metastasis-free survival (MFS) and overall survival (OS) rates were analyzed using Kaplan-Meier and Cox regression models.

RESULTS AND LIMITATIONS

A total of 228 men with DAC were identified; 163 underwent RP, 34 underwent RTx, and 31 had neoadjuvant therapy prior to RP. In this study, 163 DAC patients and 155 PAC patients undergoing RP were compared. Similarly, 34 DAC patients and 74 PAC patients undergoing RTx were compared. DAC patients undergoing RP or RTx had worse 5-yr MFS (75% vs 95% and 62% vs 93%, respectively, p < 0.001) and 5-yr OS (88% vs 97% and 82% vs 100%, respectively, p < 0.05) compared with PAC patients. In the 76 men who received adjuvant/salvage ADT after RP, DAC also had worse MFS and OS than PAC (p < 0.01). A genomic analysis revealed that 10/11 (91%) DACs treated with ADT had intrinsic upregulation of androgen-resistant pathways. Further, none of the DAC patients (0/15) who received only neoadjuvant ADT prior to RP had any pathologic downgrading. The retrospective nature was a limitation.

CONCLUSIONS

Men undergoing RP or RTx for DAC had worse outcomes than PAC patients, regardless of the treatment modality. Upregulation of several intrinsic resistance pathways in DAC rendered ADT less effective. Further evaluation of the underlying biology of DAC with clinical trials is needed.

PATIENT SUMMARY

This study demonstrated worse outcomes among patients with ductal adenocarcinoma of the prostate than among high-grade prostate adenocarcinoma patients, regardless of the treatment modality.

Quality of life among patients after cystoprostatectomy as the treatment for locally advanced prostate cancer with bladder invasion

PURPOSE

This study aimed to evaluate the changes of patients' quality of life (QoL) after cystoprostatectomy as a treatment for locally advanced prostate cancer (LAPC) with the bladder invasion and to determine risk factors for postoperative poor QoL.

MATERIALS AND METHODS

Between Jan 2012 and December 2015, 27 patients who received cystoprostatectomy for LAPC with the bladder invasion were retrospectively included. QoL was assessed with the functional assessment of cancer therapy-prostate (FACT-P) questionnaire scores. Determinants for postoperative poor QoL were investigated using univariate and multivariate regression analysis.

RESULTS

Three-year overall survival, biochemical progression-free survival, and clinical progress-free survival were 88.89%, 62.96% and 77.78%, respectively. Preoperative symptoms of hematuria, urinary frequency, and dysuria were well alleviated after cystoprostatectomy. Moreover, FACT-P questionnaire scores at 6 months and 1 year after cystoprostatectomy were significantly higher than preoperative scores. Univariate and multivariable analysis (p < .05) showed that postoperative complication was the independent risk factor for the loss of postoperative QoL.

CONCLUSIONS

Patients' QoL can be improved after cystoprostatectomy as the treatment for LAPC with the bladder invasion, which is associated with ameliorative urinary symptoms after the surgery. Besides, surgical complication is identified to be a risk factor for postoperative poor QoL.

Comparison of diagnostic performance between diffusion kurtosis imaging parameters and mono-exponential ADC for determination of clinically significant cancer in patients with prostate cancer

PURPOSE

To compare the diagnostic performance between diffusion kurtosis imaging (DKI) parameters and mono-exponential apparent diffusion coefficient (ADC) for determination of clinically significant cancer (CSC, Gleason score (GS) ≥ 7) in patients with histologically proven prostate cancer (PCa).

METHODS

A total of 92 patients (mean age: 71.5 years, range: 47-89 years) who had been diagnosed as PCa and undergone 3 T-MRI including DWI (b values, 0, 100, 1000, 2000s/mm²) were included in this study. The DKI parameters, namely apparent diffusion for non-Gaussian distribution (D_app) and apparent kurtosis coefficient (K_app), were calculated by dedicated software using mono-exponential and diffusion kurtosis models for quantitation. The measurement was performed for a whole tumor after segmentation, and pathologic topographic maps or systemic biopsy results served as the reference standard for segmentation. To compare the diagnostic performance of each parameter for determination of CSC, pair-wise comparison of receiver operating characteristic (ROC) curves was performed.

RESULTS

The study population consisted of GS 6 (n = 18), GS 7 (n = 31), GS 8 (n = 25), GS 9 (n = 15) and GS 10 (n = 3) patients. The area under the ROC curve of K_app (0.707, 95% CI 0.603-0.798) for discriminating CSC from non-CSC was not significantly different from those of mono-exponential ADC (0.725, 0.622-0.813, P = 0.2175) or D_app (0.726, 0.623-0.814, P = 0.9628). Diagnostic predictive values of K_app were estimated to a maximum accuracy of 78%, a sensitivity of 86%, and a specificity of 47%, while those of mono-exponential ADC were 75, 81, and 53%, respectively.

CONCLUSION

The DKI parameters showed a diagnostic performance comparable to mono-exponential ADC for determination of CSC in patients with PCa.

Impact of treatment modality on overall survival in localized ductal prostate adenocarcinoma: A national cancer database analysis

PURPOSE

Ductal adenocarcinoma is considered a rare histological variant of prostate adenocarcinoma (PCa). Given the rarity of this subtype, optimal treatment strategies for men with nonmetastatic ductal PCa is largely unknown. We aimed to describe the impact of surgery, radiotherapy, systemic therapy, and observation on overall survival (OS) in men with nonmetastatic ductal PCa.

MATERIALS AND METHODS

We selected 1,656 cases of nonmetastatic ductal PCa, diagnosed between 2004 and 2015, within the National Cancer Database. Covariates included age, race, Charlson comorbidity score, clinical T stage, clinical lymph node stage, serum prostate specific antigen (PSA), income, hospital type, insurance status, year of diagnosis, and location of residence. Cox regression analysis tested the impact of treatment (surgery, radiotherapy, systemic therapy, and observation) on OS.

RESULTS

In men with nonmetastatic ductal PCa, median (interquartile range [IQR]) age and PSA were 67 (60-73) years and 6.2 (4.2-10.7) ng/ml, respectively. Advanced local stage (≥cT3a) was most frequently observed in patients initially treated with systemic therapy (34.8%), followed by those treated with radiotherapy (18.1%), surgery (7.1%) and observation (6.4%, P< 0.001). Serum PSA at presentation was highest in the systemic therapy cohort (median 16.0 ng/ml, IQR: 4.9-37.7), followed by the radiotherapy cohort (median 7.2 ng/ml, IQR: 4.1-12.2), observation cohort (median 7.0 ng/ml, IQR: 4.3-13.3) and surgery cohort (median 5.9 ng/ml, IQR: 4.3-9.2, P< 0.001). Multivariable analysis showed that in comparison to men treated surgically, OS was significantly lower for patients receiving radiotherapy (HR 2.2; 95% CI: 1.5-3.2), under observation (HR 4.6; 95% CI: 2.8-7.6) and receiving systemic therapy (HR 5.2; 95% CI: 3.0-9.1) as an initial course of treatment.

CONCLUSIONS

While limited by its retrospective nature, our study shows that starting treatment with surgery is associated with more favorable long-term OS outcomes than radiotherapy, systemic therapy or observation.