Gleason score concordance on biopsy-confirmed prostate cancer: is pathological re-evaluation necessary prior to radical prostatectomy?

Blood cancer diagnosis using hyperspectral imaging combined with the forward searching method and machine learning

Hyperspectral imaging (HSI), a widely used biosensing technique, has been applied to tumor detection. Rapid, accurate, and low-cost detection of blood cancer using hyperspectral technology remains a challenge. We developed a new method to discriminate blood cancer using hyperspectral imaging (HSI) and the forward searching method (FSM). Four commonly used classification models are applied for four types of blood cancer spectra recognition. The support vector machine (SVM) model with the highest recognition accuracy (94.5%) combined with HSI achieves high-precision tumor identification. For higher recognition accuracy and lower hardware barriers, based on the selection probabilities of spectral lines calculated by a multi-objective atomic orbital search method, the FSM is proposed for HSI feature selection. With the proposed method, the wavelength band range of the spectrum is reduced by at least 50%. Compared with the traditional dimensionality reduction methods, the FSM can obtain a higher accuracy rate with lower hardware requirements. These results show that our proposed method can achieve non-invasive rapid screening of blood cancers with lower hardware requirements. Therefore, HSI assisted with FSM and SVM hybrid models can be a powerful and promising tool for blood cancer detection.

The prognostic value of mean apparent diffusion coefficient measured with diffusion-weighted magnetic resonance image in patients with prostate cancer treated with definitive radiotherapy

PURPOSE

To assess the correlation between initial tumor apparent diffusion coefficient (ADC) values and clinicopathological parameters in prostate cancer (PCa) patients treated with definitive radiotherapy (RT). Additionally, the prognostic factors for freedom from biochemical failure (FFBF) and progression-free survival (PFS) in this patient cohort were analyzed.

MATERIALS AND METHODS

The clinical data of 503 patients with biopsy-confirmed PCa were evaluated retrospectively. All patients had clearly evident tumors on diffusion-weighted magnetic resonance imaging (DW-MRI) for ADC values. Univariable and multivariable analyses were used to determine prognostic factors for FFBF and PFS.

RESULTS

The median follow-up was 72.9 months. The 5-year FFBF and PFS rates were 93.2% and 86.2%, respectively. Significantly lower ADC values were found in patients with a high PSA level; advanced clinical stage; higher ISUP score, and higher risk group than their counterparts. Receiver operating characteristic (ROC) curve analysis revealed an ADC cut-off value of 0.737 × 10^-3 mm²/sec for tumor recurrence. Patients who progressed had a lower mean ADC value than those who did not (0.712±0.158 vs. 1.365±0.227 × 10^-3 mm²/sec; p<0.001). There was a significant difference in 5-year FFBF (96.3% vs. 90%; p<0.001) and PFSrates (83.8% vs. 73.5%; p=0.002) between patients with higher and lower mean ADC values. The FFBF and PFS were found to be correlated with tumor ADC value and ISUP grades in multivariable analysis. Additionally, older age was found to be a significant predictor of worse PFS.

CONCLUSIONS

Lower ADC values were found in patients with high-risk characteristics such as a high serum PSA level, stage or grade of tumor, or high-risk disease, implying that ADC values could be used to predict prognosis. Lower ADC values and higher ISUP grades were associated with an increased risk of BF and progression, implying that treatment intensification may be required in these patients.

Development of a highly sensitive digital PCR assay to quantify long non-coding RNA MYU in urine samples which exhibited great potential as an alternative diagnostic biomarker for prostate cancer

Background

The diagnostic methods of prostate cancer (PCa) present major drawbacks in that serum prostate specific antigen (PSA) testing lacks specificity for PCa and prostate needle biopsy is a painful and highly invasive procedure for patients. Thus, new alternative screening methods which are specific and non-invasive both in the early detection and in the clinical definitive diagnosis of PCa are in urgent need. Long non-coding RNA MYU has been shown to promote PCa cell proliferation and migration, and is significantly upregulated both at the cellular and tumor tissue level. Therefore, long non-coding RNA MYU may be a new potential diagnostic biomarker for PCa.

Methods

In the present study, we successfully developed a highly sensitive digital PCR assay to detect long non-coding RNA in clinical urine samples. dPCR was carried out using Qx200 ddPCR EvaGreen Supermix (Bio-Rad) according to the manufacturer’s instructions.

Results

Our results indicated that the digital PCR assay showed better linearity, repeatability, and reproducibility when compared with real-time quantitative PCR. In addition, we identified the normalized MYU level and used the digital PCR assay to measure it in 100 clinical urine samples. Our study showed that the normalized MYU level is a promising diagnostic biomarker for predicting and evaluating the malignancy of PCa.

Conclusions

Our findings presented a non-invasive liquid biopsy method to detect an alternative diagnostic parameter which can assist the diagnosis of PCa in clinical practice.

Reassessment of Prostate Biopsy Specimens for Patients Referred for Robot-assisted Radical Prostatectomy Rarely Influences Surgical Planning

Background

The minimum volume standard is 100 robot-assisted radical prostatectomy (RARP) procedures per hospital in the Netherlands, so patients have to be referred to high-volume surgical centers for RARP. During preoperative work-up, prostate biopsies taken elsewhere are reassessed, with upgrading or downgrading of the initial Gleason grade group a possible consequence.

Objective

To determine if prostate biopsy reassessment leads to adjustment of the surgical plan regarding a nerve-sparing approach and extended pelvic lymph node dissection (ePLND) during RARP.

Design, setting, and participants

For 125 men who were referred to the Prosper prostate center at Canisius Wilhelmina Hospital (CWH) in the Netherlands between 2013 and 2016, results for the initial assessment of prostate biopsy by a local uropathologist were compared to results for biopsy reassessment by dedicated uropathologists at CWH.

Results and limitations

The pathologists reached agreement in 80% of the cases. In cases for which there was disagreement (n = 25), biopsy revision involved upgrading of the initial grade group in 68% and downgrading in 32%. Biopsy reassessment led to a change in surgical plan in ten cases (8%). As a result of upgrading, ePLND was performed in three patients (2%). ePLND was omitted in one patient (1%) because of downgrading. For three patients (2%) a non–nerve-sparing procedure was planned after upgrading of the initial grade group. For four patients (3%), a unilateral nerve-sparing procedure was performed after downgrading.

Conclusions

This study shows that there is large interobserver agreement between uropathologists in the assessment of Gleason grade group in prostate biopsy specimens. Reassessment rarely leads to a change in surgical plan regarding the indication for a nerve-sparing approach and ePLND. Therefore, reassessment of prostate biopsy before radical prostatectomy can be omitted when the initial pathological assessment was performed by a dedicated uropathologist.

Patient summary

Reassessment of the initial prostate biopsy specimen for patients referred to a specialist center for robot-assisted removal of the prostate rarely influences surgical planning and can be omitted.

Applications of Topological Data Analysis in Oncology

The emergence of the information age in the last few decades brought with it an explosion of biomedical data. But with great power comes great responsibility: there is now a pressing need for new data analysis algorithms to be developed to make sense of the data and transform this information into knowledge which can be directly translated into the clinic. Topological data analysis (TDA) provides a promising path forward: using tools from the mathematical field of algebraic topology, TDA provides a framework to extract insights into the often high-dimensional, incomplete, and noisy nature of biomedical data. Nowhere is this more evident than in the field of oncology, where patient-specific data is routinely presented to clinicians in a variety of forms, from imaging to single cell genomic sequencing. In this review, we focus on applications involving persistent homology, one of the main tools of TDA. We describe some recent successes of TDA in oncology, specifically in predicting treatment responses and prognosis, tumor segmentation and computer-aided diagnosis, disease classification, and cellular architecture determination. We also provide suggestions on avenues for future research including utilizing TDA to analyze cancer time-series data such as gene expression changes during pathogenesis, investigation of the relation between angiogenic vessel structure and treatment efficacy from imaging data, and experimental confirmation that geometric and topological connectivity implies functional connectivity in the context of cancer.

A second opinion pathology review improves the diagnostic concordance between prostate cancer biopsy and radical prostatectomy specimens

Objectives:

The Gleason scoring system is an essential tool for determining the treatment strategy in prostate cancer (PCa). However, the Gleason grade group (GGG) often differs between needle-core biopsy (NCB) and radical prostatectomy (RP) specimens. We investigated the diagnostic value of a second opinion pathology review using NCB specimens in PCa.

Materials and Methods:

We retrospectively evaluated 882 patients who underwent robot-assisted RP from January 2012 to September 2019. Of these, patients whose original biopsy specimens were obtained from another hospital and reviewed by the urological pathology expert at our institution were included in the study. Patients who received neoadjuvant hormonal therapy were excluded from the study. Weighted kappa (k) coefficients were used to evaluate the diagnostic accuracy of each review.

Results:

A total of 497 patients were included in this study. Substantial agreement (weighted k = 0.783) in the GGG between initial- and second-opinion diagnoses based on NCB specimens was observed in 310 cases (62.4%). Although diagnoses based on a single opinion showed moderate agreement with the GGG of RP specimens (initial: 35.2%, weighted k = 0.522; second opinion; 38.8%, weighted k = 0.560), matching initial and second opinion diagnoses improved the concordance (42.9%, 133/310 cases) to substantial agreement (weighted k = 0.626).

Conclusions:

A second opinion of PCa pathology helps to improve the diagnostic accuracy of NCB specimens. However, over half of diagnoses that matched between the initial and second opinions differed from the diagnosis of RP specimens.

An artificial intelligence algorithm for prostate cancer diagnosis in whole slide images of core needle biopsies: a blinded clinical validation and deployment study

BACKGROUND

There is high demand to develop computer-assisted diagnostic tools to evaluate prostate core needle biopsies (CNBs), but little clinical validation and a lack of clinical deployment of such tools. We report here on a blinded clinical validation study and deployment of an artificial intelligence (AI)-based algorithm in a pathology laboratory for routine clinical use to aid prostate diagnosis.

METHODS

An AI-based algorithm was developed using haematoxylin and eosin (H&E)-stained slides of prostate CNBs digitised with a Philips scanner, which were divided into training (1 357 480 image patches from 549 H&E-stained slides) and internal test (2501 H&E-stained slides) datasets. The algorithm provided slide-level scores for probability of cancer, Gleason score 7-10 (vs Gleason score 6 or atypical small acinar proliferation [ASAP]), Gleason pattern 5, and perineural invasion and calculation of cancer percentage present in CNB material. The algorithm was subsequently validated on an external dataset of 100 consecutive cases (1627 H&E-stained slides) digitised on an Aperio AT2 scanner. In addition, the AI tool was implemented in a pathology laboratory within routine clinical workflow as a second read system to review all prostate CNBs. Algorithm performance was assessed with area under the receiver operating characteristic curve (AUC), specificity, and sensitivity, as well as Pearson's correlation coefficient (Pearson's r) for cancer percentage.

FINDINGS

The algorithm achieved an AUC of 0·997 (95% CI 0·995 to 0·998) for cancer detection in the internal test set and 0·991 (0·979 to 1·00) in the external validation set. The AUC for distinguishing between a low-grade (Gleason score 6 or ASAP) and high-grade (Gleason score 7-10) cancer diagnosis was 0·941 (0·905 to 0·977) and the AUC for detecting Gleason pattern 5 was 0·971 (0·943 to 0·998) in the external validation set. Cancer percentage calculated by pathologists and the algorithm showed good agreement (r=0·882, 95% CI 0·834 to 0·915; p<0·0001) with a mean bias of -4·14% (-6·36 to -1·91). The algorithm achieved an AUC of 0·957 (0·930 to 0·985) for perineural invasion. In routine practice, the algorithm was used to assess 11 429 H&E-stained slides pertaining to 941 cases leading to 90 Gleason score 7-10 alerts and 560 cancer alerts. 51 (9%) cancer alerts led to additional cuts or stains being ordered, two (4%) of which led to a third opinion request. We report on the first case of missed cancer that was detected by the algorithm.

INTERPRETATION

This study reports the successful development, external clinical validation, and deployment in clinical practice of an AI-based algorithm to accurately detect, grade, and evaluate clinically relevant findings in digitised slides of prostate CNBs.

FUNDING

Ibex Medical Analytics.

Blood cancer diagnosis using ensemble learning based on a random subspace method in laser-induced breakdown spectroscopy

There are two main challenges in the diagnosis of blood cancer. The first is to diagnose cancer from healthy control, and the second is to identify the types of blood cancer. The chemometrics method combined with laser-induced breakdown spectroscopy (LIBS) can be used for cancer detection. However, chemometrics methods were easily influenced by the spectral feature redundancy and noise, resulting in low accuracy rate because of their simple structure. We proposed an approach using LIBS combined with the ensemble learning based on the random subspace method (RSM). The serum samples were dripped onto a boric acid substrate for LIBS spectrum collection. The complete blood cancer sample set include leukemia [acute myeloid leukemia (AML) and chronic myelogenous leukemia (CML)], multiple myeloma (MM), and lymphoma. The results showed that the accuracy rates using k nearest neighbors (kNN) and linear discriminant analysis (LDA) only were 88.14% and 94.45%, respectively, while using RSM with LDA (RSM-LDA), the average accuracy rate was improved from 94.45% to 98.34%. Furthermore, the variable importance of spectral lines (Na, K, Mg, Ca, H, O, N, C-N) were evaluated by the RSM-LDA model, which can improve the recognition ability of blood cancer types. Comparing the RSM-LDA model and only with LDA, the results showed that the average accuracy rate for cancer type identification was improved from 80.4% to 91.0%. These results demonstrate that LIBS combined with the RSM-LDA model can discriminate the blood cancer from the health control, as well as the recognition the types for blood cancers.

Concordance Between Biopsy and Radical Prostatectomy Gleason Scores: Evaluation of Determinants in a Large-Scale Study of Patients Undergoing RARP in Belgium

To determine whether Gleason scores were concordant between prostate biopsies (bGS) and the definitive resection specimen (pGS) excised with robot-assisted radical prostatectomy (RARP); to identify clinical and pathological factors that might predict upgrading; and to evaluate how upgrading affected outcome. Between 2009 and 2016, 25 Belgian centers participated in collecting prospective data for patients that underwent RARP. We analyzed the concordance rate between the bGS and the pGS in 8021 patients with kappa statistics, and we compared concordance rates from different centers. We assessed the effect of several clinical and pathological factors on the concordance rate with logistic regression analysis. The concordance rate for the entire population was 62.9%. Upgrading from bGS to pGS occurred in 27.3% of patients. The number of biopsies was significantly associated with concordance. Older age (>60 y), a higher clinical T stage (≥cT2), a higher PSA value at the time of biopsy (>10 ng/ml), and more time between the biopsy and the radical prostatectomy were significantly associated with a higher risk of upgrading. Positive margins and PSA relapse occurred more frequently in upgraded patients. Center size did not significantly affect the concordance rate (p = 0.40).This prospective, nationwide analysis demonstrated a Gleason score concordance rate of 62.9%. Upgrading was most frequently observed in the non-concordant group. We identified clinical and pathological factors associated with (non)-concordance. Upgrading was associated with a worse oncological outcome. Center volume was not associated with pathological accuracy.

Retrospective correlation of 68ga-psma uptake with clinical parameters in prostate cancer patients undergoing definitive radiotherapy

OBJECTIVE

The aim of the study is to investigate the correlation between the intensity of prostate-specific membrane antigen (PSMA) uptake in primary tumor and clinico-pathological characteristics of non-metastatic prostate cancer patients treated with definitive radiotherapy (RT).

METHODS

Using the clinical data of 201 prostate cancer patients who were referred for ⁶⁸ Ga-PSMA-positron emission tomography (PET/CT) for staging and RT planning, we analyzed the correlations among intermediate- or high-risk disease based on Gleason score (GS), prostate-specific antigen (PSA) level, D'Amico risk group classification, and maximum standardized uptake (SUV_max) of primary tumor.

RESULTS

Primary tumor was visualized via ⁶⁸ Ga-PSMA-PET/CT scan in 192 patients (95.5%). The median SUV_max of primary tumor and metastatic lymph node were 13.2 (range 3.3-83.7) and 11.4 (range 3.6-64.5), respectively. A significant moderate correlation was observed between PSA level and median tumor SUV_max as measured by ⁶⁸ Ga-PSMA-PET/CT (Spearman = 0.425; p < 0.001). Patients with serum PSA > 10 ng/mL, GS > 7, D'Amico high-risk group classification, and pelvic lymph node metastasis had significantly higher tracer uptake in primary tumor than their counterparts. The median SUV_max of primary tumor was highest in patients with GS 9. The primary tumor detection rates of ⁶⁸ Ga-PSMA-PET/CT were 83%, 92%, and 99% for patients with serum PSA ≤ 5.0 ng/mL (14 patients, 7%), PSA 5.1-10.0 ng/mL (45 patients, 22%), and PSA > 10 ng/mL (142 patients, 71%), respectively.

CONCLUSIONS

We demonstrated a correlation between prostate tumor characteristics and PSMA tracer uptake. Patients with serum PSA > 10 ng/mL, GS > 7, D'Amico high-risk group classification, and pelvic lymph node metastasis had significantly higher SUV than their counterparts. In addition, the primary tumor detection rate was higher in patients with serum PSA > 10 ng/mL and GS > 7.

Factors predicting pathological upgrading after prostatectomy in patients with Gleason grade group 1 prostate cancer based on opinion-matched biopsy specimens

The present study investigated the concordance between Gleason scores assigned to prostate biopsy specimens by outside pathologists and a urological pathology expert, and determined the risk of upgrading between opinion-matched Gleason grade group (GGG) 1 biopsy specimens and radical prostatectomy specimens. Between January 2012 and May 2018, 733 patients underwent robot-assisted radical prostatectomy. Patients whose original biopsy specimens from outside hospitals were reviewed by a urological pathology expert Okayama University Hospital were included. Patients who had received neoadjuvant hormonal therapy were excluded. Logistic regression analysis was used to identify predictors of upgrading among GGG 1 diagnoses. A total of 403 patients were included in the present study. Agreement in GGG between initial and second-opinion diagnoses was present in 256 cases (63.5%). Although opinion-matched cases improved concordance between biopsy and prostatectomy specimen GGG compared with single-opinion cases (initial, 35.2%; second-opinion, 36.5%; matched, 41.4%), 71% (56/79) of cases classified as GGG 1 were upgraded after prostatectomy. Multivariate analysis revealed that prostate-specific antigen density and Prostate Imaging Reporting and Data System version 2 score were significant predictors of upgrading (odds ratio, 1.10; P=0.01; and odds ratio, 1.88; P=0.03, respectively). In conclusion, the GGG concordance rate between needle-core biopsy and radical prostatectomy specimens was higher in opinion-matched cases; however, 71% of opinion-matched GGG1 cases were upgraded after robot-assisted radical prostatectomy. Urologists should propose treatment strategies or further biopsy rather than active surveillance for patients with GGG1 and a high PSAD and/or PI-RADS score.

Automated approach for estimation of grade groups for prostate cancer based on histological image feature analysis

BACKGROUND

There is a low reproducibility of the Gleason scores that determine the grade group of prostate cancer given the intra- and interobserver variability among pathologists. This study aimed to develop an automated approach for estimating prostate cancer grade groups based on features obtained from histological image analysis.

METHODS

Fifty-nine patients who underwent radical prostatectomy were selected under the approval of the institutional review board of our university hospital. For estimation, we followed the grade group criteria provided by the International Society of Urological Pathology in 2014. One hundred eight specimen slides obtained from the patients were digitized to extract 110 regions of interest (ROI) from hematoxylin and eosin-stained histological images using a digital whole slide scanner at ×20 magnification with a pixel size of 0.4 μm. Each color pixel value in the ROI was decomposed into six intensities corresponding to the RGB (red, green, and blue) and HSV (hue, saturation, and value) color models. Image features were extracted by histological image analysis, obtaining 54 features from the ROI based on histogram and texture analyses in the six types of decomposed histological images. Then, 40 representative features were selected from the 324 histological image features based on statistically significant differences (P < .05) between the mean image feature values for high (≥3, Gleason score ≥4 + 3) and low (≤2, Gleason score ≤3 + 4) grade groups. The relationship between grade groups and the most representative image feature (ie, complexity) was approximated using regression to estimate real-number grade groups defined by continuous numerical grading. Finally, the grade groups were expressed as the conventional grade groups (ie, integers from 1 to 5) using a piecewise step function.

RESULTS

The grade groups were correctly estimated by the proposed approach without errors on training (70 ROIs) and validation (40 ROIs) data.

CONCLUSIONS

Our results suggest that the proposed approach may support pathologists during the evaluation of grade groups for prostate cancer, thus mitigating intra- and interobserver variability.

The clinical predictive factors and postoperative histopathological parameters associated with upgrading after radical prostatectomy: A contemporary analysis with grade groups

BACKGROUND AND AIM

Upgrading after radical prostatectomy (RP) is an ongoing problem since first description of Gleason score. In this retrospective study, our aim is to investigate upgrading after RP in grade groups (GG) and clinical predictive, and postoperative histopathological factors associated with GG upgrading (GGU).

PATIENTS AND METHODS

A total of 753 patients undergoing RP between January 2006 and June 2019 at our institution were investigated. Overall cohort were divided into two groups according to GGU status after RP as nonupgrading and upgrading. Retrospectively documented preoperative clinical and postoperative histopathological parameters were compared between two groups. Furthermore, we investigated a subgroup of institutional cohort (n = 398) whose prostate biopsy (Pbx) and RP were performed in our institution and we also divided this cohort into two groups according to GGU status. χ² and Mann-Whitney U tests were used for comparative analyses. The independent preoperative predictive and postoperative histopathological factors associated with GGU were investigated using multivariate logistic regression analysis.

RESULTS

The total GGU was 55.8% in overall cohort and 45.2% in institutional cohort. The GGU was found as the most common in bioptic GG1 group in both overall (64.0%), and institutional (54.5%) cohorts. In multivariate analyses, the noninstitutional Pbx (odds ratio [OR] = 2.56; 95% confidence interval [CI]: 1.86-3.51; P < .001), tumor positive core numbers in Pbx (OR = 1.11; 95%CI: 1.04-1.19; P = .003), increased prostate specific antigen (PSA) density (OR = 3.59; 95%CI: 1.03-12.52, P = .045) and age (OR = 1.03; 95%CI: 1.00-1.05, P = .046) were independent clinical predictors of GGU in overall cohort whereas only increased PSA density (OR = 5.94; 95%CI: 1.28-27.50; P = .023) was independent predictor in institutional cohort. Among postoperative histopathological factors, perineural invasion (OR = 1.57; 95%CI: 1.70-3.87; P < .001 and OR = 2.53; 95%CI: 1.46-4.40; P = .001, respectively), increased maximum tumor diameter (OR = 1.46; 95%CI: 1.23-1.73; P < .001 and OR = 1.33; 95%CI: 1.07-1.66; P = .010, respectively), and high-grade prostatic intraepithelial neoplasia (HGPIN) existence at tumor surrounding tissue (OR = 1.96; 95%CI: 1.32-2.90; P = .001 and OR = 1.87; 95%CI: 1.10-3.21; P = .022, respectively) were independently associated with GGU after RP, in both of overall and institutional cohorts.

CONCLUSIONS

Noninstitutional prostate biopsy, increased PSA density, higher tumor positive cores in Pbx and older age are the clinical predictors of upgrading after RP in contemporary GG. Perineural invasion, increased maximum tumor diameter, and HGPIN existence at tumor surrounding tissue are postoperative histopathological factors associated with GGU.

Persistent Homology for the Quantitative Evaluation of Architectural Features in Prostate Cancer Histology

The current system for evaluating prostate cancer architecture is the Gleason grading system which divides the morphology of cancer into five distinct architectural patterns, labeled 1 to 5 in increasing levels of cancer aggressiveness, and generates a score by summing the labels of the two most dominant patterns. The Gleason score is currently the most powerful prognostic predictor of patient outcomes; however, it suffers from problems in reproducibility and consistency due to the high intra-observer and inter-observer variability amongst pathologists. In addition, the Gleason system lacks the granularity to address potentially prognostic architectural features beyond Gleason patterns. We evaluate prostate cancer for architectural subtypes using techniques from topological data analysis applied to prostate cancer glandular architecture. In this work we demonstrate the use of persistent homology to capture architectural features independently of Gleason patterns. Specifically, using persistent homology, we compute topological representations of purely graded prostate cancer histopathology images of Gleason patterns 3,4 and 5, and show that persistent homology is capable of clustering prostate cancer histology into architectural groups through a ranked persistence vector. Our results indicate the ability of persistent homology to cluster prostate cancer histopathology images into unique groups with dominant architectural patterns consistent with the continuum of Gleason patterns. In addition, of particular interest, is the sensitivity of persistent homology to identify specific sub-architectural groups within single Gleason patterns, suggesting that persistent homology could represent a robust quantification method for prostate cancer architecture with higher granularity than the existing semi-quantitative measures. The capability of these topological representations to segregate prostate cancer by architecture makes them an ideal candidate for use as inputs to future machine learning approaches with the intent of augmenting traditional approaches with topological features for improved diagnosis and prognosis.

Temporal changes in the pathologic assessment of prostate cancer

Thirty years have witnessed dramatic changes in the manner in which we diagnose and manage prostate cancer. With prostate-specific antigen screening, there was a shift towards smaller, clinically localized tumors. Tumors are often multifocal and display phenotypic and molecular heterogeneity. Pathologic evaluation of tissue obtained by needle biopsy remains the gold standard for the diagnosis and risk assessment of prostate cancer. Years of experience with grading, along with changes in the amount of biopsy tissue obtained and diagnostic tools available, have produced shifts in grading practices among genitourinary pathologists. Trends in Gleason grading and advances in pathological risk assessment are reviewed with particular emphasis on recent Gleason grading modifications of the International Society of Urologic Pathology. Efforts to maximize the amount of information from pathological specimens, whether it be morphometric, histochemical, or molecular, may improve predictive accuracy of prostate biopsies. New diagnostic techniques are needed to optimize management decisions.