Improvement of diagnostic agreement among pathologists in resolving an “atypical glands suspicious for cancer” diagnosis in prostate biopsies using a novel “Disease-Focused Diagnostic Review” quality improvement process

Direct three-dimensional segmentation of prostate glands with nnU-Net

Significance

In recent years, we and others have developed non-destructive methods to obtain three-dimensional (3D) pathology datasets of clinical biopsies and surgical specimens. For prostate cancer risk stratification (prognostication), standard-of-care Gleason grading is based on examining the morphology of prostate glands in thin 2D sections. This motivates us to perform 3D segmentation of prostate glands in our 3D pathology datasets for the purposes of computational analysis of 3D glandular features that could offer improved prognostic performance.

Aim

To facilitate prostate cancer risk assessment, we developed a computationally efficient and accurate deep learning model for 3D gland segmentation based on open-top light-sheet microscopy datasets of human prostate biopsies stained with a fluorescent analog of hematoxylin and eosin (H&E).

Approach

For 3D gland segmentation based on our H&E-analog 3D pathology datasets, we previously developed a hybrid deep learning and computer vision-based pipeline, called image translation-assisted segmentation in 3D (ITAS3D), which required a complex two-stage procedure and tedious manual optimization of parameters. To simplify this procedure, we use the 3D gland-segmentation masks previously generated by ITAS3D as training datasets for a direct end-to-end deep learning-based segmentation model, nnU-Net. The inputs to this model are 3D pathology datasets of prostate biopsies rapidly stained with an inexpensive fluorescent analog of H&E and the outputs are 3D semantic segmentation masks of the gland epithelium, gland lumen, and surrounding stromal compartments within the tissue.

Results

nnU-Net demonstrates remarkable accuracy in 3D gland segmentations even with limited training data. Moreover, compared with the previous ITAS3D pipeline, nnU-Net operation is simpler and faster, and it can maintain good accuracy even with lower-resolution inputs.

Conclusions

Our trained DL-based 3D segmentation model will facilitate future studies to demonstrate the value of computational 3D pathology for guiding critical treatment decisions for patients with prostate cancer.

Artificial Intelligence for Clinical Diagnosis and Treatment of Prostate Cancer

As medical science and technology progress towards the era of "big data", a multi-dimensional dataset pertaining to medical diagnosis and treatment is becoming accessible for mathematical modelling. However, these datasets are frequently inconsistent, noisy, and often characterized by a significant degree of redundancy. Thus, extensive data processing is widely advised to clean the dataset before feeding it into the mathematical model. In this context, Artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL) algorithms based on artificial neural networks (ANNs) and their types, are being used to produce a precise and cross-sectional illustration of clinical data. For prostate cancer patients, datasets derived from the prostate-specific antigen (PSA), MRI-guided biopsies, genetic biomarkers, and the Gleason grading are primarily used for diagnosis, risk stratification, and patient monitoring. However, recording diagnoses and further stratifying risks based on such diagnostic data frequently involves much subjectivity. Thus, implementing an AI algorithm on a PC's diagnostic data can reduce the subjectivity of the process and assist in decision making. In addition, AI is used to cut down the processing time and help with early detection, which provides a superior outcome in critical cases of prostate cancer. Furthermore, this also facilitates offering the service at a lower cost by reducing the amount of human labor. Herein, the prime objective of this review is to provide a deep analysis encompassing the existing AI algorithms that are being deployed in the field of prostate cancer (PC) for diagnosis and treatment. Based on the available literature, AI-powered technology has the potential for extensive growth and penetration in PC diagnosis and treatment to ease and expedite the existing medical process.

Prostate Cancer Risk Stratification via Nondestructive 3D Pathology with Deep Learning-Assisted Gland Analysis

Prostate cancer treatment planning is largely dependent upon examination of core-needle biopsies. The microscopic architecture of the prostate glands forms the basis for prognostic grading by pathologists. Interpretation of these convoluted three-dimensional (3D) glandular structures via visual inspection of a limited number of two-dimensional (2D) histology sections is often unreliable, which contributes to the under- and overtreatment of patients. To improve risk assessment and treatment decisions, we have developed a workflow for nondestructive 3D pathology and computational analysis of whole prostate biopsies labeled with a rapid and inexpensive fluorescent analogue of standard hematoxylin and eosin (H&E) staining. This analysis is based on interpretable glandular features and is facilitated by the development of image translation-assisted segmentation in 3D (ITAS3D). ITAS3D is a generalizable deep learning-based strategy that enables tissue microstructures to be volumetrically segmented in an annotation-free and objective (biomarker-based) manner without requiring immunolabeling. As a preliminary demonstration of the translational value of a computational 3D versus a computational 2D pathology approach, we imaged 300 ex vivo biopsies extracted from 50 archived radical prostatectomy specimens, of which, 118 biopsies contained cancer. The 3D glandular features in cancer biopsies were superior to corresponding 2D features for risk stratification of patients with low- to intermediate-risk prostate cancer based on their clinical biochemical recurrence outcomes. The results of this study support the use of computational 3D pathology for guiding the clinical management of prostate cancer. SIGNIFICANCE: An end-to-end pipeline for deep learning-assisted computational 3D histology analysis of whole prostate biopsies shows that nondestructive 3D pathology has the potential to enable superior prognostic stratification of patients with prostate cancer.

Diagnosing 12 prostate needle cores within an hour of biopsy via open-top light-sheet microscopy

SIGNIFICANCE

Processing and diagnosing a set of 12 prostate biopsies using conventional histology methods typically take at least one day. A rapid and accurate process performed while the patient is still on-site could significantly improve the patient's quality of life.

AIM

We develop and assess the feasibility of a one-hour-to-diagnosis (1Hr2Dx) method for processing and providing a preliminary diagnosis of a set of 12 prostate biopsies.

APPROACH

We developed a fluorescence staining, optical clearing, and 3D open-top light-sheet microscopy workflow to enable 12 prostate needle core biopsies to be processed and diagnosed within an hour of receipt. We analyzed 44 biopsies by the 1Hr2Dx method, which does not consume tissue. The biopsies were then processed for routine, slide-based 2D histology. Three pathologists independently evaluated the 3D 1Hr2Dx and 2D slide-based datasets in a blinded, randomized fashion. Turnaround times were recorded, and the accuracy of our method was compared with gold-standard slide-based histology.

RESULTS

The average turnaround time for tissue processing, imaging, and diagnosis was 44.5 min. The sensitivity and specificity of 1Hr2Dx in diagnosing cancer were both >90  %  .

CONCLUSIONS

The 1Hr2Dx method has the potential to improve patient care by providing an accurate preliminary diagnosis within an hour of biopsy.

Significant reduction of indeterminate (atypical) diagnosis after implementation of The Paris System for Reporting Urinary Cytology: A single-institution study of more than 27,000 cases

BACKGROUND

Urinary cytology is a noninvasive and cost-effective diagnostic and surveillance test in the clinical management of urothelial carcinoma (UC). The Paris System for Reporting Urinary Cytology (TPS), published in 2016, introduced definite diagnostic criteria aimed at improving performance in detecting high-grade UC (HGUC) and decreasing the indeterminate (atypical) diagnosis.

METHODS

The authors retrospectively reviewed and compared urinary cytology diagnoses reported between January 2013 and December 2014 (pre-TPS, 7658 cases) and between May 2016 and April 2018 (post-TPS, 20,026 cases) to assess the influence of TPS in their practice. The time in between was used as a learning period. Follow-up information and correlation with the UroVysion fluorescence in situ hybridization test were obtained when available.

RESULTS

Urinary cytology diagnoses pre-TPS included negative for UC (NUC) (n = 5293; 69.2%), atypical urothelial cells (AUC) (n = 2227; 29%), and suspicious/positive for HGUC (SHGUC/HGUC) (n = 138; 1.8%). Diagnoses post-TPS included negative for HGUC (NHGUC) (n = 18,507; 92.4%), AUC (n = 1237; 6.2%), and SHGUC/HGUC (n = 282; 1.4%). Comparing the pre-TPS and post-TPS periods, AUC diagnoses decreased from 29% to 6.2% (P < .00001), and the specificity and positive predictive value of AUC to detect HGUC significantly improved from 49% to 86% (P < .00001) and from 9% to 39% (P = .002), respectively. The correlation of an AUC diagnosis with a positive UroVysion test improved from 17% to 38% (P < .00001), whereas overall use of the UroVysion test was decreased.

CONCLUSIONS

Implementation of TPS resulted in a significant reduction in AUC diagnoses that had a superior correlation with a subsequent biopsy and a UroVysion test, resulting in potential reductions in test use and medical cost.

Application of a novel targeting nanoparticle contrast agent combined with magnetic resonance imaging in the diagnosis of intraductal papillary mucinous neoplasm

Intraductal papillary mucinous neoplasm (IPMN) is a severe disease with macroscopic visible mucin secretion that primarily occurs in biliary tracts or pancreatic ducts. In comparison with standard diagnostic imaging, probing the molecular abnormalities associated with the initial stages of diseases rather than imaging the end effects markedly improves the accuracy of diagnosis. In the present study, magnetic resonance imaging (MRI) in combination with the contrast agent PEGylated magnetoliposome consisting of gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) and target molecules of IPMN were investigated in the diagnosis of patients with suspected IPMN. The present investigation indicated that the novel targeting nanoparticle contrast agent targeted platelet-derived growth factor receptor-β and RET, and maintained a high affinity with tumor markers located on the IPMN surface. The novel targeting nanoparticle contrast agent combined with MRI exhibited increased sensitivity in diagnosing early-stage patients with IPMN. Furthermore, image quality was improved following the use of the novel targeting nanoparticle contrast agent combined with MRI compared with standard MRI. The targeting nanoparticle contrast agent retained sufficient affinity and was present for an adequate amount of time to observe the tumor mass in papillae using MRI. Notably, the targeting nanoparticle contrast agent was metabolized at 12 h post-injection. In conclusion, these outcomes indicate that the novel targeting nanoparticle contrast agent combined with MRI improved image quality and sensitivity compared with standard MRI, which suggests that this approach may be promising for clinical detection in patients with suspected IPMN.

Impact of the Pathologist on Prostate Biopsy Diagnosis and Immunohistochemical Stain Usage Within a Single Institution

OBJECTIVES

To determine whether pathologists in a tertiary care institution vary in diagnosis and immunohistochemical stain usage in prostate biopsy specimens.

METHODS

Men who underwent prostate needle biopsies between 2008 and 2013 were included.

RESULTS

In total, 1,777 prostate biopsy specimens diagnosed by nine pathologists showed variation in diagnostic reporting (atypical small acinar proliferation, 2.0%-8.0%; high-grade prostatic intraepithelial neoplasia, 2.0%-8.5%; nonneoplastic, 30.2%-48.3%; adenocarcinoma, 46.2%-55.3%; P < .001). Variation in Gleason scoring was observed (P < .001), with the 4 + 3 = 7 category having the greatest variability (6.9%-30.3%). A blinded review from the most outlying pathologist in this category revealed 45% grading discrepancies. The mean number of immunostains performed per case (0.3-1.2) differed between pathologists (P < .001), and one pathologist used immunostains at twice the rate of the remaining cohort.

CONCLUSIONS

Case pathologist significantly affects prostate biopsy diagnosis and immunohistochemical workup. We recommend evaluation for outlying practice patterns to provide consistent and efficient patient care.

Application of novel targeting nanoparticles contrast agent combined with contrast-enhanced computed tomography during screening for early-phase gastric carcinoma

Gastric cancer is one of the most common human tumors worldwide. The biggest bottleneck is a lack of advanced and sensitive protocols for the diagnosis of patients with early-stage gastric cancer. Therefore, more sensitive methods of diagnosing gastric cancer are urgently required to improve survival rates. In this clinical study, contrast-enhanced computed tomography (CECT) with targeting nanoparticles contrast agent (CECT-TNCA) was used to diagnose early-stage gastric cancer. The specific-targeted tyrosine kinase inhibitors of gastric cancer, including platelet-derived growth factor receptor-β, Ret and Kit, were used as TNCAs. A total of 484 patients with suspected gastric cancer were voluntarily recruited to investigate the efficacy of CECT-TNCA in the diagnosis of patients with early-stage gastric cancer. Patients with suspected gastric cancer were subjected CT and CECT-TNCA to detect whether gastric tumors existed. TNCA was orally administered before CT and CECT-TNCA (20 min). Our diagnostic data revealed that CECT-TNCA improved sensitivity and provided a new protocol to diagnose tumors in patients with suspected gastric cancer at the early stage. In addition, imaging using CECT-TNCA enabled the visualization of tiny nodules in the gastric area. CECT-TNCA diagnosed 182 patients with suspected gastric cancer as tumor-free. CECT-TNCA confirmed gastric cancer in 302 patients. Our novel diagnosis indicated significantly (P<0.01) differential signal enhancement in the gastric nodules via CECT-TNCA compared with CT, suggesting higher accuracy and the accumulation of TNCA in tumor nodules in the stomach. Furthermore, survival rates of patients detected by early-diagnosis of CECT-TNCA were significantly higher than the mean five-year survival (P<0.01). In conclusion, our investigations demonstrate that the sensibility and accuracy of CT is improved through combination with liposome-encapsulated nanoparticle contrast agent for the diagnosis of early stage gastric cancer when compared with single CT detection. CECT-TNCA improves the accuracy of CT and diagnostic confidence in assessing mural enhancement in patients with suspected gastric cancer.

Typing the atypical: Diagnostic issues and predictive markers in suspicious prostate lesions

As much as 5% of prostate biopsies yield findings equivocal for malignancy even for skilled uropathologist; such "grey zone" lesions have been addressed in many ways, although the acronym ASAP (atypical small acinar proliferation) is the most widely used when referring to an atypical focus suspicious, but not diagnostic, for malignancy. Since the introduction of this diagnostic category more than 20 years ago, debate has ensued over its histological characterization and clinical significance. Pathology reporting of ASAP, commonly based on strict morphological criteria and traditional immunohistochemical markers such as basal cell antibodies, has been improved by recent availability of novel immunohistochemical markers such as AMACR and ERG. Further pathological issues, such as the role of pre-analytical variables, number of tissue levels, interobserver variability, and association with prostatic intraepithelial neoplasia also play a role in the optimal assessment of ASAP. Apart from diagnostic issues, a major issue is ASAP predictive value for prostate cancer on repeat biopsy. Therefore, attempts have been made to identify clinical and biological parameters that could predict subsequent diagnosis of malignancy as well as define time and modality of repeat biopsy. Finally, pathological features of cancers detected after a previous ASAP diagnosis are compared with those diagnosed at first prostate biopsy.

Immunohistochemistry in the workup of prostate biopsies: Frequency, variation and appropriateness of use among pathologists practicing at an academic center

OBJECTIVES

We studied the frequency, inter-pathologist variation, appropriateness and utility of immunohistochemistry (IHC) performed on prostate biopsies (PB) to determine the significance of foci of suspicious glands/atypical small acinar proliferations (ASAP).

METHODS

We calculated the rate of IHC use and diagnostic rate of ASAP and adenocarcinoma in PB from 01/01/2008 to 06/30/2015 for individual pathologists working in a tertiary academic institution, and correlated them with the pathologists' experience, subspecialization and PB volume with the aim of determining the interpathologist variation and appropriateness of use of IHC according to recently published recommendations, and the usefulness of IHC to resolve foci of ASAP as either benign or adenocarcinoma.

RESULTS

IHC was used in 966/2652 (36.4%, 95% CI 33.4-39.4%) PB cases and 1915 of 16,359 (11.7%, 95% CI 11.2%-12.2%) of PB blocks and allowed definitive diagnosis of either benign or malignant in 75.8% (95% CI 73.9-77.7%) of blocks. By pathologist, IHC use rates varied more than twofold (22.8-50.5%); higher use was found for pathologists with genitourinary pathology specialization, higher PB volume and more experience, and correlated with higher rates of both ASAP and adenocarcinoma diagnoses. The use of IHC stains was considered appropriate in 822/966 (85.1%, 95% CI 82.9-87.4%) cases.

CONCLUSIONS

Despite the fact that the use of IHC stains was considered useful and deemed appropriate in the majority of cases, it showed wide variation between pathologists, suggesting monitoring of IHC use rates may be useful to standardize its use.