Phase 3 Study of Adjuvant Radiotherapy Versus Wait and See in pT3 Prostate Cancer: Impact of Pathology Review on Analysis

Predicting prostate cancer grade reclassification on active surveillance using a deep learning-based grading algorithm

Deep learning (DL)-based algorithms to determine prostate cancer (PCa) Grade Group (GG) on biopsy slides have not been validated by comparison to clinical outcomes. We used a DL-based algorithm, AIRAProstate, to regrade initial prostate biopsies in 2 independent PCa active surveillance (AS) cohorts. In a cohort initially diagnosed with GG1 PCa using only systematic biopsies (n = 138), upgrading of the initial biopsy to ≥GG2 by AIRAProstate was associated with rapid or extreme grade reclassification on AS (odds ratio = 3.3, P = .04), whereas upgrading of the initial biopsy by contemporary uropathologist reviews was not associated with this outcome. In a contemporary validation cohort that underwent prostate magnetic resonance imaging before initial biopsy (n = 169), upgrading of the initial biopsy (all contemporary GG1 by uropathologist grading) by AIRAProstate was associated with grade reclassification on AS (hazard ratio = 1.7, P = .03). These results demonstrate the utility of a DL-based grading algorithm in PCa risk stratification for AS.

Comparison of Pathologist and Artificial Intelligence-based Grading for Prediction of Metastatic Outcomes After Radical Prostatectomy

Gleason grade group (GG) is the most powerful prognostic variable in localized prostate cancer; however, interobserver variability remains a challenge. Artificial intelligence algorithms applied to histopathologic images standardize grading, but most have been tested only for agreement with pathologist GG, without assessment of performance with respect to oncologic outcomes. We compared deep learning-based and pathologist-based GGs for an association with metastatic outcome in three surgical cohorts comprising 777 unique patients. A digitized whole slide image of the representative hematoxylin and eosin-stained slide of the dominant tumor nodule was assigned a GG by an artificial intelligence-based grading algorithm and was compared with the GG assigned by a contemporary pathologist or the original pathologist-assigned GG for the entire prostatectomy. Harrell's C-indices based on Cox models for time to metastasis were compared. In a combined analysis of all cohorts, the C-index for the artificial intelligence-assigned GG was 0.77 (95% confidence interval [CI]: 0.73-0.81), compared with 0.77 (95% CI: 0.73-0.81) for the pathologist-assigned GG. By comparison, the original pathologist-assigned GG for the entire case had a C-index of 0.78 (95% CI: 0.73-0.82). PATIENT SUMMARY: Artificial intelligence-enabled prostate cancer grading on a single slide was comparable with pathologist grading for predicting metastatic outcome in men treated by radical prostatectomy, enabling equal access to expert grading in lower resource settings.

DNA ploidy and PTEN as biomarkers for predicting aggressive disease in prostate cancer patients under active surveillance

BACKGROUND

Current risk stratification tools for prostate cancer patients under active surveillance (AS) may inadequately identify those needing treatment. We investigated DNA ploidy and PTEN as potential biomarkers to predict aggressive disease in AS patients.

METHODS

We assessed DNA ploidy by image cytometry and PTEN protein expression by immunohistochemistry in 3197 tumour-containing tissue blocks from 558 patients followed in AS at a Norwegian local hospital. The primary endpoint was treatment, with treatment failure (biochemical recurrence or initiation of salvage therapy) as the secondary endpoint.

RESULTS

The combined DNA ploidy and PTEN (DPP) status at diagnosis was associated with treatment-free survival in univariable- and multivariable analysis, with a HR for DPP-aberrant vs. DPP-normal tumours of 2.12 (p < 0.0001) and 1.94 (p < 0.0001), respectively. Integration of DNA ploidy and PTEN status with the Cancer of the Prostate Risk Assessment (CAPRA) score improved risk stratification (c-index difference = 0.025; p = 0.0033). Among the treated patients, those with DPP-aberrant tumours exhibited a significantly higher likelihood of treatment failure (HR 2.01; p = 0.027).

CONCLUSIONS

DNA ploidy and PTEN could serve as additional biomarkers to identify AS patients at increased risk of developing aggressive disease, enabling earlier intervention for nearly 50% of the patients that will eventually receive treatment with current protocol.

International Society of Urological Pathology (ISUP) Gleason Grade Groups stratify outcomes in the CHHiP Phase 3 prostate radiotherapy trial

OBJECTIVES

To compare the results of Gleason Grade Group (GGG) classification following central pathology review with previous local pathology assessment, and to examine the difference between using overall and worst GGG in a large patient cohort treated with radiotherapy and short-course hormone therapy.

PATIENTS AND METHODS

Patients with low- to high-risk localized prostate cancer were randomized into the multicentre CHHiP fractionation trial between 2002 and 2011. Patients received short-course hormone therapy (≤6 month) and radical intensity-modulated radiotherapy (IMRT). Of 2749 consented patients, 1875 had adequate diagnostic biopsy tissue for blinded central pathology review. The median follow-up was 9.3 years. Agreement between local pathology and central pathology-derived GGG and between central pathology-derived overall and worst GGG was assessed using kappa (κ) statistics. Multivariate Cox regression and Kaplan-Meier methods were used to compare the biochemical/clinical failure (BCF) and distant metastases (DM) outcomes of patients with GGG 1-5.

RESULTS

There was poor agreement between local pathology- and central pathology-derived GGG (κ = 0.19) but good agreement between overall and worst GGG on central pathology review (κ = 0.89). Central pathology-derived GGG stratified BCF and DM outcomes better than local pathology, while overall and worst GGG on central pathology review performed similarly. GGG 3 segregated with GGG 4 for BCF, with BCF-free rates of 90%, 82%, 74%, 71% and 58% for GGGs 1-5, respectively, at 8 years when assessed using overall GGG. There was a progressive decrease in DM-free rates from 98%, 96%, 92%, 88% and 83% for GGGs 1-5, respectively, at 8 years with overall GGG. Patients (n = 57) who were upgraded from GGG 2-3 using worst GS had BCF-free and DM-free rates of 74% and 92% at 8 years. CHHiP eligibility criteria limit the interpretation of these results.

CONCLUSION

Contemporary review of International Society of Urological Pathology GGG successfully stratified patients treated with short-course hormone therapy and IMRT with regard to both BCF-free and DM-free outcomes. Patients upgraded from GGG 2 to GGG 3 using worst biopsy GS segregate with GGG 3 on long-term follow-up. We recommend that both overall and worst GS be used to derive GGG.

Multi-Stage Classification-Based Deep Learning for Gleason System Grading Using Histopathological Images

In this work, we introduced an automated diagnostic system for Gleason system grading and grade groups (GG) classification using whole slide images (WSIs) of digitized prostate biopsy specimens (PBSs). Our system first classifies the Gleason pattern (GP) from PBSs and then identifies the Gleason score (GS) and GG. We developed a comprehensive DL-based approach to develop a grading pipeline system for the digitized PBSs and consider GP as a classification problem (not segmentation) compared to current research studies (deals with as a segmentation problem). A multilevel binary classification was implemented to enhance the segmentation accuracy for GP. Also, we created three levels of analysis (pyramidal levels) to extract different types of features. Each level has four shallow binary CNN to classify five GP labels. A majority fusion is applied for each pixel that has a total of 39 labeled images to create the final output for GP. The proposed framework is trained, validated, and tested on 3080 WSIs of PBS. The overall diagnostic accuracy for each CNN is evaluated using several metrics: precision (PR), recall (RE), and accuracy, which are documented by the confusion matrices.The results proved our system's potential for classifying all five GP and, thus, GG. The overall accuracy for the GG is evaluated using two metrics, PR and RE. The grade GG results are between 50% to 92% for RE and 50% to 92% for PR. Also, a comparison between our CNN architecture and the standard CNN (ResNet50) highlights our system's advantage. Finally, our deep-learning system achieved an agreement with the consensus grade groups.

Postoperative Radiotherapy of Prostate Cancer: Adjuvant versus Early Salvage

Results of three randomized clinical trials (RCTs) comparing adjuvant radiotherapy (ART) and early salvage radiotherapy (eSRT) of prostate carcinoma and a subsequent meta-analysis of the individual patient data from these RCTs were recently published. The results suggest that early eSRT is as effective and potentially less toxic than ART. Therefore, eSRT should be considered the standard of care. However, due to limitations in the RCTs, ART remains a valid treatment option in patients with the combination of high-risk features such as Gleason Score (GS) 8–10, positive surgical margins (R1) and pathological T-stage 3 or 4 (pT3/4). This article provides a critical appraisal of the RCTs and the rationale for recommendations adopted in the current national guidelines regarding patients with high-risk features after radical prostatectomy (RP): ART should be offered in case of pT3/pT4 and R1 and Gleason Score 8–10; ART can be offered in case of pT3/pT4 and R0 and Gleason Score 8–10 as well as in case of multifocal R1 (including pT2) and Gleason Score 8–10. In any case, the alternative treatment option of eSRT in case of rising PSA should be discussed with the patient.

Discriminatory Gleason grade group signatures of prostate cancer: An application of machine learning methods

One of the most precise methods to detect prostate cancer is by evaluation of a stained biopsy by a pathologist under a microscope. Regions of the tissue are assessed and graded according to the observed histological pattern. However, this is not only laborious, but also relies on the experience of the pathologist and tends to suffer from the lack of reproducibility of biopsy outcomes across pathologists. As a result, computational approaches are being sought and machine learning has been gaining momentum in the prediction of the Gleason grade group. To date, machine learning literature has addressed this problem by using features from magnetic resonance imaging images, whole slide images, tissue microarrays, gene expression data, and clinical features. However, there is a gap with regards to predicting the Gleason grade group using DNA sequences as the only input source to the machine learning models. In this work, using whole genome sequence data from South African prostate cancer patients, an application of machine learning and biological experiments were combined to understand the challenges that are associated with the prediction of the Gleason grade group. A series of machine learning binary classifiers (XGBoost, LSTM, GRU, LR, RF) were created only relying on DNA sequences input features. All the models were not able to adequately discriminate between the DNA sequences of the studied Gleason grade groups (Gleason grade group 1 and 5). However, the models were further evaluated in the prediction of tumor DNA sequences from matched-normal DNA sequences, given DNA sequences as the only input source. In this new problem, the models performed acceptably better than before with the XGBoost model achieving the highest accuracy of 74 ± 01, F1 score of 79 ± 01, recall of 99 ± 0.0, and precision of 66 ± 0.1.

Narrative Review of the Post-Operative Management of Prostate Cancer Patients: Is It Really the End of Adjuvant Radiotherapy?

Simple Summary

Among patients with prostate cancer who have been operated on, a subset harboring high-risk features will present with a biochemical recurrence (BCR). Adjuvant radiotherapy (aRT) was proven to significantly reduce the risk of BCR when compared to salvage radiotherapy (SRT) but suffered from several limitations: a lack of patient selection criteria, a higher treatment-related morbidity and an uncertain benefit for long-term clinical endpoints. In the same clinical setting, early SRT (eSRT) appears as non-inferior to aRT with a lower morbidity, replacing aRT as the preferred option. In this review, we insist on the need for multidisciplinary discussions to fully comprehend the individual characteristics of each patient and propose the best treatment strategy for every patient.

Abstract

Despite three randomized trials indicating a significant reduction in biochemical recurrence (BCR) in high-risk patients, adjuvant radiotherapy (aRT) was rarely performed, even in patients harboring high-risk features. aRT is associated with a higher risk of urinary incontinence and is often criticized for the lack of patient selection criteria. With a BCR rate reaching 30–70% in high-risk patients, a consensus between urologists and radiation oncologists was needed, leading to three different randomized trials challenging aRT with early salvage radiotherapy (eSRT). In these three different randomized trials with event-free survival as the primary outcome and a planned meta-analysis, eSRT appeared as non-inferior to aRT, answering, for some, this never-ending question. For many, however, the debate persists; these results raised several questions among urologists and radiation oncologists. BCR is thought to be a surrogate for clinically meaningful endpoints such as overall survival and cancer-specific survival but may be poorly efficient in comparison with metastasis-free survival. Imaging of rising prostate-specific antigen (PSA), post-operative persistent PSA and BCR was revolutionized by the broader use of MRI and nuclear imaging such as PET-PSMA; these imaging modalities were not analyzed in the previous randomized trials. A sub-group of very high-risk patients could possibly benefit from an adjuvant radiotherapy; but their usual risk factors such as high Gleason score or invaded surgical margins mean they are unable to be selected. More precise biomarkers of early BCR or even metastatic-relapse were developed in this setting and could be useful for the patients’ stratification. In this review, we insist on the need for multidisciplinary discussions to fully comprehend the individual characteristics of each patient and propose the best treatment strategy for every patient.

A Deep Learning Pipeline for Grade Groups Classification Using Digitized Prostate Biopsy Specimens

Prostate cancer is a significant cause of morbidity and mortality in the USA. In this paper, we develop a computer-aided diagnostic (CAD) system for automated grade groups (GG) classification using digitized prostate biopsy specimens (PBSs). Our CAD system aims to firstly classify the Gleason pattern (GP), and then identifies the Gleason score (GS) and GG. The GP classification pipeline is based on a pyramidal deep learning system that utilizes three convolution neural networks (CNN) to produce both patch- and pixel-wise classifications. The analysis starts with sequential preprocessing steps that include a histogram equalization step to adjust intensity values, followed by a PBSs' edge enhancement. The digitized PBSs are then divided into overlapping patches with the three sizes: 100 × 100 (CNNS), 150 × 150 (CNNM), and 200 × 200 (CNNL), pixels, and 75% overlap. Those three sizes of patches represent the three pyramidal levels. This pyramidal technique allows us to extract rich information, such as that the larger patches give more global information, while the small patches provide local details. After that, the patch-wise technique assigns each overlapped patch a label as GP categories (1 to 5). Then, the majority voting is the core approach for getting the pixel-wise classification that is used to get a single label for each overlapped pixel. The results after applying those techniques are three images of the same size as the original, and each pixel has a single label. We utilized the majority voting technique again on those three images to obtain only one. The proposed framework is trained, validated, and tested on 608 whole slide images (WSIs) of the digitized PBSs. The overall diagnostic accuracy is evaluated using several metrics: precision, recall, F1-score, accuracy, macro-averaged, and weighted-averaged. The (CNNL) has the best accuracy results for patch classification among the three CNNs, and its classification accuracy is 0.76. The macro-averaged and weighted-average metrics are found to be around 0.70-0.77. For GG, our CAD results are about 80% for precision, and between 60% to 80% for recall and F1-score, respectively. Also, it is around 94% for accuracy and NPV. To highlight our CAD systems' results, we used the standard ResNet50 and VGG-16 to compare our CNN's patch-wise classification results. As well, we compared the GG's results with that of the previous work.

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.

Predicting prostate cancer specific-mortality with artificial intelligence-based Gleason grading

Background

Gleason grading of prostate cancer is an important prognostic factor, but suffers from poor reproducibility, particularly among non-subspecialist pathologists. Although artificial intelligence (A.I.) tools have demonstrated Gleason grading on-par with expert pathologists, it remains an open question whether and to what extent A.I. grading translates to better prognostication.

Methods

In this study, we developed a system to predict prostate cancer-specific mortality via A.I.-based Gleason grading and subsequently evaluated its ability to risk-stratify patients on an independent retrospective cohort of 2807 prostatectomy cases from a single European center with 5–25 years of follow-up (median: 13, interquartile range 9–17).

Results

Here, we show that the A.I.’s risk scores produced a C-index of 0.84 (95% CI 0.80–0.87) for prostate cancer-specific mortality. Upon discretizing these risk scores into risk groups analogous to pathologist Grade Groups (GG), the A.I. has a C-index of 0.82 (95% CI 0.78–0.85). On the subset of cases with a GG provided in the original pathology report (n = 1517), the A.I.’s C-indices are 0.87 and 0.85 for continuous and discrete grading, respectively, compared to 0.79 (95% CI 0.71–0.86) for GG obtained from the reports. These represent improvements of 0.08 (95% CI 0.01–0.15) and 0.07 (95% CI 0.00–0.14), respectively.

Conclusions

Our results suggest that A.I.-based Gleason grading can lead to effective risk stratification, and warrants further evaluation for improving disease management.

Gleason grading is the process by which pathologists assess the morphology of prostate tumors. The assigned Grade Group tells us about the likely clinical course of people with prostate cancer and helps doctors to make decisions on treatment. The process is complex and subjective, with frequent disagreement amongst pathologists. In this study, we develop and evaluate an approach to Gleason grading based on artificial intelligence, rather than pathologists’ assessment, to predict risk of dying of prostate cancer. Looking back at tumors and data from 2,807 people diagnosed with prostate cancer, we find that our approach is better at predicting outcomes compared to grading by pathologists alone. These findings suggest that artificial intelligence might help doctors to accurately determine the probable clinical course of people with prostate cancer, which, in turn, will guide treatment.

Wulczyn et al. utilise a deep learning-based Gleason grading model to predict prostate cancer-specific mortality in a retrospective cohort of radical prostatectomy patients. Their model enables improved risk stratification compared to pathologists’ grading and demonstrates the potential for computational pathology in the management of prostate cancer.

Development and Validation of a Deep Learning Algorithm for Gleason Grading of Prostate Cancer From Biopsy Specimens

Question

How does a deep learning system for assessing prostate biopsy specimens compare with interpretations determined by specialists in urologic pathology and by general pathologists?

Findings

In a validation data set of 752 biopsy specimens obtained from 2 independent medical laboratories and a tertiary teaching hospital, this study found that rate of agreement with subspecialists was significantly higher for the deep learning system than it was for a cohort of general pathologists.

Meaning

The deep learning system warrants evaluation as an assistive tool for improving prostate cancer diagnosis and treatment decisions, especially where subspecialist expertise is unavailable.

Importance

For prostate cancer, Gleason grading of the biopsy specimen plays a pivotal role in determining case management. However, Gleason grading is associated with substantial interobserver variability, resulting in a need for decision support tools to improve the reproducibility of Gleason grading in routine clinical practice.

Objective

To evaluate the ability of a deep learning system (DLS) to grade diagnostic prostate biopsy specimens.

Design, Setting, and Participants

The DLS was evaluated using 752 deidentified digitized images of formalin-fixed paraffin-embedded prostate needle core biopsy specimens obtained from 3 institutions in the United States, including 1 institution not used for DLS development. To obtain the Gleason grade group (GG), each specimen was first reviewed by 2 expert urologic subspecialists from a multi-institutional panel of 6 individuals (years of experience: mean, 25 years; range, 18-34 years). A third subspecialist reviewed discordant cases to arrive at a majority opinion. To reduce diagnostic uncertainty, all subspecialists had access to an immunohistochemical-stained section and 3 histologic sections for every biopsied specimen. Their review was conducted from December 2018 to June 2019.

Main Outcomes and Measures

The frequency of the exact agreement of the DLS with the majority opinion of the subspecialists in categorizing each tumor-containing specimen as 1 of 5 categories: nontumor, GG1, GG2, GG3, or GG4-5. For comparison, the rate of agreement of 19 general pathologists’ opinions with the subspecialists’ majority opinions was also evaluated.

Results

For grading tumor-containing biopsy specimens in the validation set (n = 498), the rate of agreement with subspecialists was significantly higher for the DLS (71.7%; 95% CI, 67.9%-75.3%) than for general pathologists (58.0%; 95% CI, 54.5%-61.4%) (P < .001). In subanalyses of biopsy specimens from an external validation set (n = 322), the Gleason grading performance of the DLS remained similar. For distinguishing nontumor from tumor-containing biopsy specimens (n = 752), the rate of agreement with subspecialists was 94.3% (95% CI, 92.4%-95.9%) for the DLS and similar at 94.7% (95% CI, 92.8%-96.3%) for general pathologists (P = .58).

Conclusions and Relevance

In this study, the DLS showed higher proficiency than general pathologists at Gleason grading prostate needle core biopsy specimens and generalized to an independent institution. Future research is necessary to evaluate the potential utility of using the DLS as a decision support tool in clinical workflows and to improve the quality of prostate cancer grading for therapy decisions.

Evaluation of the Use of Combined Artificial Intelligence and Pathologist Assessment to Review and Grade Prostate Biopsies

IMPORTANCE

Expert-level artificial intelligence (AI) algorithms for prostate biopsy grading have recently been developed. However, the potential impact of integrating such algorithms into pathologist workflows remains largely unexplored.

OBJECTIVE

To evaluate an expert-level AI-based assistive tool when used by pathologists for the grading of prostate biopsies.

DESIGN, SETTING, AND PARTICIPANTS

This diagnostic study used a fully crossed multiple-reader, multiple-case design to evaluate an AI-based assistive tool for prostate biopsy grading. Retrospective grading of prostate core needle biopsies from 2 independent medical laboratories in the US was performed between October 2019 and January 2020. A total of 20 general pathologists reviewed 240 prostate core needle biopsies from 240 patients. Each pathologist was randomized to 1 of 2 study cohorts. The 2 cohorts reviewed every case in the opposite modality (with AI assistance vs without AI assistance) to each other, with the modality switching after every 10 cases. After a minimum 4-week washout period for each batch, the pathologists reviewed the cases for a second time using the opposite modality. The pathologist-provided grade group for each biopsy was compared with the majority opinion of urologic pathology subspecialists.

EXPOSURE

An AI-based assistive tool for Gleason grading of prostate biopsies.

MAIN OUTCOMES AND MEASURES

Agreement between pathologists and subspecialists with and without the use of an AI-based assistive tool for the grading of all prostate biopsies and Gleason grade group 1 biopsies.

RESULTS

Biopsies from 240 patients (median age, 67 years; range, 39-91 years) with a median prostate-specific antigen level of 6.5 ng/mL (range, 0.6-97.0 ng/mL) were included in the analyses. Artificial intelligence-assisted review by pathologists was associated with a 5.6% increase (95% CI, 3.2%-7.9%; P < .001) in agreement with subspecialists (from 69.7% for unassisted reviews to 75.3% for assisted reviews) across all biopsies and a 6.2% increase (95% CI, 2.7%-9.8%; P = .001) in agreement with subspecialists (from 72.3% for unassisted reviews to 78.5% for assisted reviews) for grade group 1 biopsies. A secondary analysis indicated that AI assistance was also associated with improvements in tumor detection, mean review time, mean self-reported confidence, and interpathologist agreement.

CONCLUSIONS AND RELEVANCE

In this study, the use of an AI-based assistive tool for the review of prostate biopsies was associated with improvements in the quality, efficiency, and consistency of cancer detection and grading.

Prostate cancer: more effective use of underutilized postoperative radiation therapy

Introduction: Adverse pathological features at radical prostatectomy such as extracapsular extension, seminal-vesicle involvement, positive surgical margins and/or lymph node invasion define a particular subgroup of patients that might benefit from additional treatment after surgery, in particular radiation therapy.Areas covered: Post-prostatectomy radiation is intended as adjuvant, early-salvage or salvage depending on the timing and PSA levels at the treatment. After providing the most used definitions, the high-level evidence supporting adjuvant radiation is reviewed together with the limitations affecting its utilization. In recent years early-salvage radiation was hypothesized to be a non-inferior alternative based on good-quality retrospective data. Recently, preliminary results of ongoing trials provide additional evidence. In light of the need to identify patients that will truly benefit from adjuvant radiation, clinically based and molecular tools available for this purpose are reviewed.Expert opinion: In order to tailor treatment for the patient after radical prostatectomy, there is a need for a tool that could both improve the oncological outcomes and be cost-effective. To date, genomic testing provides the most promising results that will be reasonably improved in the coming years.

Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer

For prostate cancer patients, the Gleason score is one of the most important prognostic factors, potentially determining treatment independent of the stage. However, Gleason scoring is based on subjective microscopic examination of tumor morphology and suffers from poor reproducibility. Here we present a deep learning system (DLS) for Gleason scoring whole-slide images of prostatectomies. Our system was developed using 112 million pathologist-annotated image patches from 1226 slides, and evaluated on an independent validation dataset of 331 slides. Compared to a reference standard provided by genitourinary pathology experts, the mean accuracy among 29 general pathologists was 0.61 on the validation set. The DLS achieved a significantly higher diagnostic accuracy of 0.70 (p = 0.002) and trended towards better patient risk stratification in correlations to clinical follow-up data. Our approach could improve the accuracy of Gleason scoring and subsequent therapy decisions, particularly where specialist expertise is unavailable. The DLS also goes beyond the current Gleason system to more finely characterize and quantitate tumor morphology, providing opportunities for refinement of the Gleason system itself.

Postoperative radiotherapy for prostate cancer : Morbidity of local-only or local-plus-pelvic radiotherapy

PURPOSE

The aim of this work was to characterise actuarial incidence and prevalence of early and late side effects of local versus pelvic three-dimensional conformal postoperative radiotherapy for prostate cancer.

MATERIALS AND METHODS

Based on a risk-adapted protocol, 575 patients received either local (n = 447) or local-plus-pelvic (n = 128) radiotherapy. Gastrointestinal (GI) and genitourinary (GU) side effects (≥grade 2 RTOG/EORTC criteria) were prospectively assessed. Maximum morbidity, actuarial incidence rate, and prevalence rates were compared between the two groups.

RESULTS

For local radiotherapy, median follow-up was 68 months, and the mean dose was 66.7 Gy. In pelvic radiotherapy, the median follow-up was 49 months, and the mean local and pelvic doses were 66.9 and 48.3 Gy respectively. Early GI side effects ≥ G2 were detected in 26% and 42% of patients respectively (p < 0.001). Late GI adverse events were detected in 14% in both groups (p = 0.77). The 5‑year actuarial incidence rates were 14% and 14%, while the prevalence rates were 2% and 0% respectively. Early GU ≥ G2 side effects were detected in 15% and 16% (p = 0.96), while late GU morbidity was detected in 18% and 24% (p = 0.001). The 5‑year actuarial incidence rates were 16% and 35% (p = 0.001), while the respective prevalence rates were 6% and 8%.

CONCLUSIONS

Despite the low prevalence of side effects, postoperative pelvic radiotherapy results in significant increases in the actuarial incidence of early GI and late GU morbidity using a conventional 4‑field box radiotherapy technique. Advanced treatment techniques like intensity-modulated radiotherapy (IMRT) or volumetric modulated arc radiotherapy (VMAT) should therefore be considered in pelvic radiotherapy to potentially reduce these side effects.

Predictors of urinary and rectal toxicity after external conformed radiation therapy in prostate cancer: Correlation between clinical, tumour and dosimetric parameters and radical and postoperative radiation therapy

OBJECTIVE

To determine rectal and urinary toxicity after external beam radiation therapy (EBRT), assessing the results of patients who undergo radical or postoperative therapy for prostate cancer (pancreatic cancer) and their correlation with potential risk factors.

METHOD

A total of 333 patients were treated with EBRT. Of these, 285 underwent radical therapy and 48 underwent postoperative therapy (39 cases of rescue and 9 of adjuvant therapy). We collected clinical, tumour and dosimetric variable to correlate with toxicity parameters. We developed decision trees based on the degree of statistical significance.

RESULTS

The rate of severe acute toxicity, both urinary and rectal, was 5.4% and 1.5%, respectively. The rate of chronic toxicity was 4.5% and 2.7%, respectively. Twenty-seven patients presented haematuria, and 9 presented haemorrhagic rectitis. Twenty-five patients (7.5%) presented permanent limiting sequela. The patients with lower urinary tract symptoms prior to the radiation therapy presented poorer tolerance, with greater acute bladder toxicity (P=0.041). In terms of acute rectal toxicity, 63% of the patients with mean rectal doses >45Gy and anticoagulant/antiplatelet therapy developed mild toxicity compared with 37% of the patients with mean rectal doses <45 Gy and without anticoagulant therapy. We were unable to establish predictors of chronic toxicity in the multivariate analysis. The long-term sequelae were greater in the patients who underwent urological operations prior to the radiation therapy and who were undergoing anticoagulant therapy.

CONCLUSIONS

The tolerance to EBRT was good, and severe toxicity was uncommon. Baseline urinary symptoms constitute the predictor that most influenced the acute urinary toxicity. Rectal toxicity is related to the mean rectal dose and with anticoagulant/antiplatelet therapy. There were no significant differences in severe toxicity between radical versus postoperative radiation therapy.

Gland segmentation in prostate histopathological images

Glandular structural features are important for the tumor pathologist in the assessment of cancer malignancy of prostate tissue slides. The varying shapes and sizes of glands combined with the tedious manual observation task can result in inaccurate assessment. There are also discrepancies and low-level agreement among pathologists, especially in cases of Gleason pattern 3 and pattern 4 prostate adenocarcinoma. An automated gland segmentation system can highlight various glandular shapes and structures for further analysis by the pathologist. These objective highlighted patterns can help reduce the assessment variability. We propose an automated gland segmentation system. Forty-three hematoxylin and eosin-stained images were acquired from prostate cancer tissue slides and were manually annotated for gland, lumen, periacinar retraction clefting, and stroma regions. Our automated gland segmentation system was trained using these manual annotations. It identifies these regions using a combination of pixel and object-level classifiers by incorporating local and spatial information for consolidating pixel-level classification results into object-level segmentation. Experimental results show that our method outperforms various texture and gland structure-based gland segmentation algorithms in the literature. Our method has good performance and can be a promising tool to help decrease interobserver variability among pathologists.

Adjuvant and Salvage Radiotherapy after Radical Prostatectomy in Prostate Cancer Patients

CONTEXT

Prostate cancer (PCa) patients found to have adverse pathologic features following radical prostatectomy (RP) are less likely to be cured with surgery alone.

OBJECTIVE

To analyze the role of postoperative radiotherapy (RT) in patients with aggressive PCa.

EVIDENCE ACQUISITION

We performed a systematic literature review of the Medline and EMBASE databases. The search strategy included the terms radical prostatectomy, adjuvant radiotherapy, and salvage radiotherapy, alone or in combination. We limited our search to studies published between January 2009 and August 2016.

EVIDENCE SYNTHESIS

Three randomized trials demonstrated that immediate RT after RP reduces the risk of recurrence in patients with aggressive PCa. However, immediate postoperative RT is associated with an increased risk of acute and late side effects ranging from 15% to 35% and 2% to 8%, respectively. Retrospective studies support the oncologic efficacy of initial observation followed by salvage RT administered at the first sign of recurrence; however, the impact of this delay on long-term control remains uncertain. Hopefully, ongoing randomized trials will shed light on the role of adjuvant RT versus observation±salvage RT in individuals with adverse features at RP. Accurate patient selection based on clinical characteristics and molecular profile is crucial. Dose escalation, whole-pelvis RT, novel techniques, and the use of hormonal therapy might improve the outcomes of postoperative RT.

CONCLUSIONS

Immediate RT reduces the risk of recurrence after RP in patients with aggressive disease. However, this approach is associated with an increase in the incidence of short- and long-term side effects. Observation followed by salvage RT administered at the first sign of recurrence might be associated with durable cancer control, but prospective randomized comparison with adjuvant RT is still awaited. Dose escalation, refinements in the technique, and the concomitant use of hormonal therapies might improve outcomes of patients undergoing postoperative RT.

PATIENT SUMMARY

Postoperative radiotherapy has an impact on oncologic outcomes in patients with aggressive disease characteristics. Salvage radiotherapy administered at the first sign of recurrence might be associated with durable cancer control in selected patients but might compromise cure in others.

Chromatin changes predict recurrence after radical prostatectomy

Background:

Pathological evaluations give the best prognostic markers for prostate cancer patients after radical prostatectomy, but the observer variance is substantial. These risk assessments should be supported and supplemented by objective methods for identifying patients at increased risk of recurrence. Markers of epigenetic aberrations have shown promising results in several cancer types and can be assessed by automatic analysis of chromatin organisation in tumour cell nuclei.

Methods:

A consecutive series of 317 prostate cancer patients treated with radical prostatectomy at a national hospital between 1987 and 2005 were followed for a median of 10 years (interquartile range, 7–14). On average three tumour block samples from each patient were included to account for tumour heterogeneity. We developed a novel marker, termed Nucleotyping, based on automatic assessment of disordered chromatin organisation, and validated its ability to predict recurrence after radical prostatectomy.

Results:

Nucleotyping predicted recurrence with a hazard ratio (HR) of 3.3 (95% confidence interval (CI), 2.1–5.1). With adjustment for clinical and pathological characteristics, the HR was 2.5 (95% CI, 1.5–4.1). An updated stratification into three risk groups significantly improved the concordance with patient outcome compared with a state-of-the-art risk-stratification tool (P<0.001). The prognostic impact was most evident for the patients who were high-risk by clinical and pathological characteristics and for patients with Gleason score 7.

Conclusion:

A novel assessment of epigenetic aberrations was capable of improving risk stratification after radical prostatectomy.

Variation between specialist uropatholgists in reporting extraprostatic extension after radical prostatectomy

Aims

Extraprostatic extension of prostate cancer in radical prostatectomy specimens significantly affects patient management. We evaluated the degree of interobserver variation between uropathologists at a tertiary referral teaching hospital in assessing the extraprostatic extension of prostate cancer in radical prostatectomy specimens.

Methods

Histopathological data from a consecutive series of 293 radical prostatectomy specimens (January 2007–December 2012) were reviewed. A subset of 50 consecutive radical prostatectomy cases originally staged as tumours confined to the prostate (pT2) or tumours extending into periprostatic tissue (pT3a) during this period were reviewed by four specialist uropathologists.

Results

Five consultant histopathologists reported these specimens with significant differences in the reported stage (p=0.0164) between pathologists. Double-blind review by 4 uropathologists of 50 consecutive radical prostatectomy cases showed a lack of consensus in 16/50 (32%) cases (κ score 0.58, moderate agreement). A consensus meeting was held, but consensus could still not be reached in 9/16 cases.

Conclusions

Our findings highlight variability in the reporting of pT stage in radical prostatectomy specimens even by specialist uropathologists. Assessment of extraprostatic extension has important implications for patient management and there is a need for more precise guidance.

Reexamining treatment of high-grade T1 bladder cancer according to depth of lamina propria invasion: a prospective trial of 200 patients

BACKGROUND

Management of high-grade T1 (HGT1) bladder cancer represents a major challenge. We studied a treatment strategy according to substaging by depth of lamina propria invasion.

METHODS

In this prospective observational cohort study, patients received initial transurethral resection (TUR), mitomycin-C, and BCG. Subjects with shallower lamina propria invasion (HGT1a) were followed without further surgery, whereas subjects with HGT1b received a second TUR. Association of clinical and histological features with outcomes (primary: progression; secondary: recurrence and cancer-specific survival) was assessed using Cox regression.

RESULTS

Median age was 71 years; 89.5% were males, with 89 (44.5%) cases T1a and 111 (55.5%) T1b. At median follow-up of 71 months, disease progression was observed in 31 (15.5%) and in univariate analysis, substaging, carcinoma in situ, tumour size, and tumour pattern predicted progression. On multivariate analysis only substaging, associated carcinoma in situ, and tumour size remained significant for progression.

CONCLUSIONS

In HGT1 bladder cancer, the strategy of performing a second TUR only in T1b cases results in a global low progression rate of 15.5%. Tumours deeply invading the lamina propria (HGT1b) showed a three-fold increase in risk of progression. Substaging should be routinely evaluated, with HGT1b cases being thoroughly evaluated for cystectomy. Inclusion in the TNM system should also be carefully considered.

Achievements and perspectives in prostate cancer phase 3 trials from genitourinary research groups in Europe: introducing the Prostate Cancer Consortium in Europe

CONTEXT

Phase 3 trials have made major contributions to advances in prostate cancer (PCa). However, funding limitations and excess bureaucracy are now making it difficult to conduct trials.

OBJECTIVE

To describe the collaborative groups in Europe and their academic phase 3 PCa trials.

EVIDENCE ACQUISITION

Leaders of collaborative groups from Scandinavia, the European Organisation for Research and Treatment of Cancer (EORTC), France, Spain, the United Kingdom, Germany, Switzerland, The Netherlands, and Ireland were asked to provide information.

EVIDENCE SYNTHESIS

Approximately 40 academic European phase 3 trials focussing on PCa have been completed, and about 10 are accruing patients. Cross-border trials have been successfully conducted led by EORTC (11), Scandinavian Prostate Cancer Group (9), European Association of Urology (1), Systemic Therapy in Advancing or Metastatic Prostate Cancer: Evaluation of Drug Efficiency (STAMPEDE) (1), and the French Genito-Urinary Tumor Group (1). Among these studies were practise-changing trials showing the superiority of prostatectomy over watchful waiting in patients <65 yr of age, the benefits of combining androgen-deprivation therapy (ADT) with radiation therapy (RXT) in high-risk localised disease, the superiority of long-term versus short-term ADT, the benefit of RXT in men treated with ADT, and the role of adjuvant RXT. To bridge the numbers gap for phase 3 studies, the Prostate Cancer Consortium in Europe (PEACE) is a recently established initiative that aims to favour cross-border networks of investigators. PEACE 1 is testing the addition of abiraterone and that of RXT directed at the primary cancer in patients with de novo metastatic PCa treated with ADT. PEACE 2 is testing the addition of cabazitaxel and that of pelvic irradiation in patients with at least two criteria for high-risk localised PCa.

CONCLUSIONS

European academic phase 3 trials have contributed to establishing the current standard treatment of PCa. The PEACE consortium was recently tasked with the goal of addressing unanswered questions and specific biology-related issues more efficiently.

PATIENT SUMMARY

The Prostate Cancer Consortium in Europe was established to conduct comparative trials aiming at assessing new treatments for prostate cancer patients.

Cancer of the Prostate Risk Assessment (CAPRA) Preoperative Score Versus Postoperative Score (CAPRA-S): ability to predict cancer progression and decision-making regarding adjuvant therapy after radical prostatectomy

The University of California, San Francisco, announced in 2011 Cancer of the Prostate Risk Assessment Postsurgical (CAPRA-S) score which included pathologic data, but there were no results for comparing preoperative predictors with the CAPRA-S score. We evaluated the validation of the CAPRA-S score in our institution and compare the result with the preoperative progression predictor, CAPRA score. Data of 130 patients were reviewed who underwent radical prostatectomy for localized prostate cancer from 2008 to 2013. Performance of CAPRA-S score in predicting progression free probabilities was assessed through Kaplan Meier analysis and Cox proportional hazards regression test. Additionally, prediction probability was compared with preoperative CAPRA score by logistic regression analysis. Comparing CAPRA score, the CAPRA-S score showed improved prediction ability for 5 yr progression free survival (concordance index 0.80, P = 0.04). After risk group stratification, 3 group model of CAPRA-S was superior than 3 group model of CAPRA for 3-yr progression free survival and 5-yr progression free survival (concordance index 0.74 vs. 0.70, 0.77 vs. 0.71, P < 0.001). Finally the CAPRA-S score was the more ideal predictor concerned with adjuvant therapy than the CAPRA score through decision curve analysis. The CPARA-S score is a useful predictor for disease progression after radical prostatectomy.

Adjuvant radiotherapy versus wait-and-see after radical prostatectomy: 10-year follow-up of the ARO 96-02/AUO AP 09/95 trial

BACKGROUND

Local failure after radical prostatectomy (RP) is common in patients with cancer extending beyond the capsule. Three prospectively randomized trials demonstrated an advantage for adjuvant radiotherapy (ART) compared with a wait-and-see (WS) policy.

OBJECTIVE

To determine the efficiency of ART after a 10-yr follow-up in the ARO 96-02 study.

DESIGN, SETTING, AND PARTICIPANTS

After RP, 388 patients with pT3 pN0 prostate cancer (PCa) were randomized to WS or three-dimensional conformal ART with 60 Gy. The present analysis focuses on intent-to-treat patients who achieved an undetectable prostate-specific antigen after RP (ITT2 population)--that is, 159 WS plus 148 ART men.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The primary end point of the study was progression-free survival (PFS) (events: biochemical recurrence, clinical recurrence, or death). Outcomes were compared by log-rank test. Cox regression analysis served to identify variables influencing the course of disease.

RESULTS AND LIMITATIONS

The median follow-up was 111 mo for ART and 113 mo for WS. At 10 yr, PFS was 56% for ART and 35% for WS (p<0.0001). In pT3b and R1 patients, the rates for WS even dropped to 28% and 27%, respectively. Of all 307 ITT2 patients, 15 died from PCa, and 28 died for other or unknown reasons. Neither metastasis-free survival nor overall survival was significantly improved by ART. However, the study was underpowered for these end points. The worst late sequelae in the ART cohort were one grade 3 and three grade 2 cases of bladder toxicity and two grade 2 cases of rectum toxicity. No grade 4 events occurred.

CONCLUSIONS

Compared with WS, ART reduced the risk of (biochemical) progression with a hazard ratio of 0.51 in pT3 PCa. With only one grade 3 case of late toxicity, ART was safe.

PATIENT SUMMARY

Precautionary radiotherapy counteracts relapse after surgery for prostate cancer with specific risk factors.

Margin status of the vas deferens in radical prostatectomy specimens: relevant or waste of time?

AIMS

Positive surgical margins (PSM) after radical prostatectomy are of great interest, but investigation of the vas deferens (VD) is not recommended. This study examined the VD margins in radical prostatectomy patients to report the incidence of PSM and their clinical staging.

METHODS AND RESULTS

A total of 2701 consecutive specimens (1995-2009) were reviewed for tumour infiltration of the VD margin and correlated with clinicopathological data. Forty-one of 2701 cases (1.5%) had a positive VD margin. Thirteen cases had bilateral infiltration. All tumours were locally advanced [pT3a (n = 1), pT3b (n = 34), pT4 (n = 6)]; 15 (37%) had lymph node metastases. While Gleason scores ranged from 7 to 9, mean PSA was 22.3 ng/ml (1.68-127 ng/ml). In all cases with seminal vesicle infiltration (40 of 41) the PSM of the VD was seen ipsilaterally. In 11 of 15 patients (73%) with pN1 status, seminal vesicle infiltration and PSM of the VD were seen on the same side. In 16 cases (39%) the VD was the only PSM.

CONCLUSIONS

A PSM of the VD is an infrequent finding, but might appear as the only PSM. Histological evaluation of the VD therefore seems reasonable, especially as biochemical recurrence has been reported with positive VD margins, and awareness of them might assist in making clinical decisions for adjuvant therapy.