Pathology Imagebase-a reference image database for standardization of pathology

Histopathological evaluation and grading for prostate cancer: current issues and crucial aspects

A crucial aspect of prostate cancer grading, especially in low- and intermediate-risk cancer, is the accurate identification of Gleason pattern 4 glands, which includes ill-formed or fused glands. However, there is notable inconsistency among pathologists in recognizing these glands, especially when mixed with pattern 3 glands. This inconsistency has significant implications for patient management and treatment decisions. Conversely, the recognition of glomeruloid and cribriform architecture has shown higher reproducibility. Cribriform architecture, in particular, has been linked to the worst prognosis among pattern 4 subtypes. Intraductal carcinoma of the prostate (IDC-P) is also associated with high-grade cancer and poor prognosis. Accurate identification, classification, and tumor size evaluation by pathologists are vital for determining patient treatment. This review emphasizes the importance of prostate cancer grading, highlighting challenges like distinguishing between pattern 3 and pattern 4 and the prognostic implications of cribriform architecture and intraductal proliferations. It also addresses the inherent grading limitations due to interobserver variability and explores the potential of computational pathology to enhance pathologist accuracy and consistency.

Critical evaluation of artificial intelligence as a digital twin of pathologists for prostate cancer pathology

Prostate cancer pathology plays a crucial role in clinical management but is time-consuming. Artificial intelligence (AI) shows promise in detecting prostate cancer and grading patterns. We tested an AI-based digital twin of a pathologist, vPatho, on 2603 histological images of prostate tissue stained with hematoxylin and eosin. We analyzed various factors influencing tumor grade discordance between the vPatho system and six human pathologists. Our results demonstrated that vPatho achieved comparable performance in prostate cancer detection and tumor volume estimation, as reported in the literature. The concordance levels between vPatho and human pathologists were examined. Notably, moderate to substantial agreement was observed in identifying complementary histological features such as ductal, cribriform, nerve, blood vessel, and lymphocyte infiltration. However, concordance in tumor grading decreased when applied to prostatectomy specimens (κ = 0.44) compared to biopsy cores (κ = 0.70). Adjusting the decision threshold for the secondary Gleason pattern from 5 to 10% improved the concordance level between pathologists and vPatho for tumor grading on prostatectomy specimens (κ from 0.44 to 0.64). Potential causes of grade discordance included the vertical extent of tumors toward the prostate boundary and the proportions of slides with prostate cancer. Gleason pattern 4 was particularly associated with this population. Notably, the grade according to vPatho was not specific to any of the six pathologists involved in routine clinical grading. In conclusion, our study highlights the potential utility of AI in developing a digital twin for a pathologist. This approach can help uncover limitations in AI adoption and the practical application of the current grading system for prostate cancer pathology.

Use of the ISUP e-learning module improves interrater reliability in prostate cancer grading

AIMS

Prostate cancer (PCa) grading is an important prognostic parameter, but is subject to considerable observer variation. Previous studies have shown that interobserver variability decreases after participants were trained using an e-learning module. However, since the publication of these studies, grading of PCa has been enhanced by adopting the International Society of Urological Pathology (ISUP) 2014 grading classification. This study investigates the effect of training on interobserver variability of PCa grading, using the ISUP Education web e-learning on Gleason grading.

METHODS

The ISUP Education Prostate Test B Module was distributed among Dutch pathologists. The module uses images graded by the ISUP consensus panel consisting of 24 expert uropathologists. Participants graded the same 10 images before and after e-learning. We included those who completed the tests before and after training. We evaluated variation in PCa grading in a fully crossed study design, using linearly weighted kappa values for each pathologist, comparing them to other pathologists and to the ISUP consensus panel. We analysed the improvement in median weighted kappas before and after training, using Wilcoxon's signed rank-test.

RESULTS

We included 42 pathologists. Inter-rater reliability between pathologists improved from 0.70 before training to 0.74 after training (p=0.01). When compared with the ISUP consensus panel, five pathologists improved significantly, whereas the kappa of one pathologist was significantly lower after training. All pathologists who improved significantly, graded with less than substantial agreement before training.

CONCLUSIONS

ISUP Prostate Test B e-learning reduces variability in PCa grading. E-learning is a cost-effective method for standardisation of pathology.

Web-grading-a tool to test personal grading of renal and prostate cancer

Only a few pathologists have the opportunity to verify their personal grading through objective assessment. This study introduces a web-based grading platform to facilitate and validate the grading of renal cell carcinoma and prostate cancer. Two representative images of two clinically annotated cohorts of 100 cases each of prostate and renal cell carcinoma were used. Each participant was asked to grade a tumor series utilizing a three tiered grading system. Finally, a Kaplan-Meier curve was drawn, and the log-rank test was used for statistical testing of the p-value. The grading of 22 participants (68%) achieved prognostic significance. Further analysis highlighted that only two pathologists were able to reliably separate low- and high-grade tumors from intermediate grades. The limitations of this study are the low number of participants in each of the cohorts and the potential selection bias of the tumor images. This web-based grading portal facilitates the assessment of the validity of grading by individual pathologists. The observation that most participants can only successfully identify high- or low-grade tumors but cannot discriminate between more subtle intermediate grades does indicate that there is a need for the development of more formal training programs for tumor grading.

Estimating the Irreducible Uncertainty in Visual Diagnosis: Statistical Modeling of Skill Using Response Models

BACKGROUND

For the representative problem of prostate cancer grading, we sought to simultaneously model both the continuous nature of the case spectrum and the decision thresholds of individual pathologists, allowing quantitative comparison of how they handle cases at the borderline between diagnostic categories.

METHODS

Experts and pathology residents each rated a standardized set of prostate cancer histopathological images on the International Society of Urological Pathologists (ISUP) scale used in clinical practice. They diagnosed 50 histologic cases with a range of malignancy, including intermediate cases in which clear distinction was difficult. We report a statistical model showing the degree to which each individual participant can separate the cases along the latent decision spectrum.

RESULTS

The slides were rated by 36 physicians in total: 23 ISUP pathologists and 13 residents. As anticipated, the cases showed a full continuous range of diagnostic severity. Cases ranged along a logit scale consistent with the consensus rating (Consensus ISUP 1: mean -0.93 [95% confidence interval {CI} -1.10 to -0.78], ISUP 2: -0.19 logits [-0.27 to -0.12]; ISUP 3: 0.56 logits [0.06-1.06]; ISUP 4 1.24 logits [1.10-1.38]; ISUP 5: 1.92 [1.80-2.04]). The best raters were able to meaningfully discriminate between all 5 ISUP categories, showing intercategory thresholds that were quantifiably precise and meaningful.

CONCLUSIONS

We present a method that allows simultaneous quantification of both the confusability of a particular case and the skill with which raters can distinguish the cases.

IMPLICATIONS

The technique generalizes beyond the current example to other clinical situations in which a diagnostician must impose an ordinal rating on a biological spectrum.

HIGHLIGHTS

Question: How can we quantify skill in visual diagnosis for cases that sit at the border between 2 ordinal categories-cases that are inherently difficult to diagnose?Findings: In this analysis of pathologists and residents rating prostate biopsy specimens, decision-aligned response models are calculated that show how pathologists would be likely to classify any given case on the diagnostic spectrum. Decision thresholds are shown to vary in their location and precision.Significance: Improving on traditional measures such as kappa and receiver-operating characteristic curves, this specialization of item response models allows better individual feedback to both trainees and pathologists, including better quantification of acceptable decision variation.

Interobserver reproducibility of cribriform cancer in prostate needle biopsies and validation of International Society of Urological Pathology criteria

AIMS

There is strong evidence that cribriform morphology indicates a worse prognosis of prostatic adenocarcinoma. Our aim was to investigate its interobserver reproducibility in prostate needle biopsies.

METHODS AND RESULTS

A panel of nine prostate pathology experts from five continents independently reviewed 304 digitised biopsies for cribriform cancer according to recent International Society of Urological Pathology criteria. The biopsies were collected from a series of 702 biopsies that were reviewed by one of the panellists for enrichment of high-grade cancer and potentially cribriform structures. A 2/3 consensus diagnosis of cribriform and noncribriform cancer was reached in 90% (272/304) of the biopsies with a mean kappa value of 0.56 (95% confidence interval 0.52-0.61). The prevalence of consensus cribriform cancers was estimated to 4%, 12%, 21%, and 20% of Gleason scores 7 (3 + 4), 7 (4 + 3), 8, and 9-10, respectively. More than two cribriform structures per level or a largest cribriform mass with ≥9 lumina or a diameter of ≥0.5 mm predicted a consensus diagnosis of cribriform cancer in 88% (70/80), 84% (87/103), and 90% (56/62), respectively, and noncribriform cancer in 3% (2/80), 5% (5/103), and 2% (1/62), respectively (all P < 0.01).

CONCLUSION

Cribriform prostate cancer was seen in a minority of needle biopsies with high-grade cancer. Stringent diagnostic criteria enabled the identification of cribriform patterns and the generation of a large set of consensus cases for standardisation.

Virtual Microscopy Goes Global: The Images Are Virtual and the Problems Are Real

For the last two centuries, the scholarly education of histology and pathology has been based on technology, initially on the availability of low-cost, high-quality light microscopes, and more recently on the introduction of computers and e-learning approaches to biomedical education. Consequently, virtual microscopy (VM) is replacing glass slides and the traditional light microscope as the main instruments of instruction in histology and pathology laboratories. However, as with most educational changes, there are advantages and disadvantages associated with a new technology. The use of VM for the teaching of histology and pathology requires an extensive infrastructure and the availability of computing devices to all learners, both posing a considerable financial strain on schools and students. Furthermore, there may be valid reasons for practicing healthcare professionals to maintain competency in using light microscopes. In addition, some educators may be reluctant to embrace new technologies. These are some of the reasons why the introduction of VM as an integral part of histology and pathology instruction has been globally uneven. This paper compares the teaching of histology and pathology using traditional or VM in five different countries and their adjacent regions, representing developed, as well as developing areas of the globe. We identify general and local roadblocks to the introduction of this still-emerging didactic technology and outline solutions for overcoming these barriers.

Identification of areas of grading difficulties in prostate cancer and comparison with artificial intelligence assisted grading

The International Society of Urological Pathology (ISUP) hosts a reference image database supervised by experts with the purpose of establishing an international standard in prostate cancer grading. Here, we aimed to identify areas of grading difficulties and compare the results with those obtained from an artificial intelligence system trained in grading. In a series of 87 needle biopsies of cancers selected to include problematic cases, experts failed to reach a 2/3 consensus in 41.4% (36/87). Among consensus and non-consensus cases, the weighted kappa was 0.77 (range 0.68-0.84) and 0.50 (range 0.40-0.57), respectively. Among the non-consensus cases, four main causes of disagreement were identified: the distinction between Gleason score 3 + 3 with tangential cutting artifacts vs. Gleason score 3 + 4 with poorly formed or fused glands (13 cases), Gleason score 3 + 4 vs. 4 + 3 (7 cases), Gleason score 4 + 3 vs. 4 + 4 (8 cases) and the identification of a small component of Gleason pattern 5 (6 cases). The AI system obtained a weighted kappa value of 0.53 among the non-consensus cases, placing it as the observer with the sixth best reproducibility out of a total of 24. AI may serve as a decision support and decrease inter-observer variability by its ability to make consistent decisions. The grading of these cancer patterns that best predicts outcome and guides treatment warrants further clinical and genetic studies. Results of such investigations should be used to improve calibration of AI systems.

Computer-aided biomarker discovery for precision medicine: data resources, models and applications

Biomarkers are a class of measurable and evaluable indicators with the potential to predict disease initiation and progression. In contrast to disease-associated factors, biomarkers hold the promise to capture the changeable signatures of biological states. With methodological advances, computer-aided biomarker discovery has now become a burgeoning paradigm in the field of biomedical science. In recent years, the 'big data' term has accumulated for the systematical investigation of complex biological phenomena and promoted the flourishing of computational methods for systems-level biomarker screening. Compared with routine wet-lab experiments, bioinformatics approaches are more efficient to decode disease pathogenesis under a holistic framework, which is propitious to identify biomarkers ranging from single molecules to molecular networks for disease diagnosis, prognosis and therapy. In this review, the concept and characteristics of typical biomarker types, e.g. single molecular biomarkers, module/network biomarkers, cross-level biomarkers, etc., are explicated on the guidance of systems biology. Then, publicly available data resources together with some well-constructed biomarker databases and knowledge bases are introduced. Biomarker identification models using mathematical, network and machine learning theories are sequentially discussed. Based on network substructural and functional evidences, a novel bioinformatics model is particularly highlighted for microRNA biomarker discovery. This article aims to give deep insights into the advantages and challenges of current computational approaches for biomarker detection, and to light up the future wisdom toward precision medicine and nation-wide healthcare.

Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study

BACKGROUND

An increasing volume of prostate biopsies and a worldwide shortage of urological pathologists puts a strain on pathology departments. Additionally, the high intra-observer and inter-observer variability in grading can result in overtreatment and undertreatment of prostate cancer. To alleviate these problems, we aimed to develop an artificial intelligence (AI) system with clinically acceptable accuracy for prostate cancer detection, localisation, and Gleason grading.

METHODS

We digitised 6682 slides from needle core biopsies from 976 randomly selected participants aged 50-69 in the Swedish prospective and population-based STHLM3 diagnostic study done between May 28, 2012, and Dec 30, 2014 (ISRCTN84445406), and another 271 from 93 men from outside the study. The resulting images were used to train deep neural networks for assessment of prostate biopsies. The networks were evaluated by predicting the presence, extent, and Gleason grade of malignant tissue for an independent test dataset comprising 1631 biopsies from 246 men from STHLM3 and an external validation dataset of 330 biopsies from 73 men. We also evaluated grading performance on 87 biopsies individually graded by 23 experienced urological pathologists from the International Society of Urological Pathology. We assessed discriminatory performance by receiver operating characteristics and tumour extent predictions by correlating predicted cancer length against measurements by the reporting pathologist. We quantified the concordance between grades assigned by the AI system and the expert urological pathologists using Cohen's kappa.

FINDINGS

The AI achieved an area under the receiver operating characteristics curve of 0·997 (95% CI 0·994-0·999) for distinguishing between benign (n=910) and malignant (n=721) biopsy cores on the independent test dataset and 0·986 (0·972-0·996) on the external validation dataset (benign n=108, malignant n=222). The correlation between cancer length predicted by the AI and assigned by the reporting pathologist was 0·96 (95% CI 0·95-0·97) for the independent test dataset and 0·87 (0·84-0·90) for the external validation dataset. For assigning Gleason grades, the AI achieved a mean pairwise kappa of 0·62, which was within the range of the corresponding values for the expert pathologists (0·60-0·73).

INTERPRETATION

An AI system can be trained to detect and grade cancer in prostate needle biopsy samples at a ranking comparable to that of international experts in prostate pathology. Clinical application could reduce pathology workload by reducing the assessment of benign biopsies and by automating the task of measuring cancer length in positive biopsy cores. An AI system with expert-level grading performance might contribute a second opinion, aid in standardising grading, and provide pathology expertise in parts of the world where it does not exist.

FUNDING

Swedish Research Council, Swedish Cancer Society, Swedish eScience Research Center, EIT Health.

Annotations, Ontologies, and Whole Slide Images - Development of an Annotated Ontology-Driven Whole Slide Image Library of Normal and Abnormal Human Tissue

Objective:

Digital pathology is today a widely used technology, and the digitalization of microscopic slides into whole slide images (WSIs) allows the use of machine learning algorithms as a tool in the diagnostic process. In recent years, “deep learning” algorithms for image analysis have been applied to digital pathology with great success. The training of these algorithms requires a large volume of high-quality images and image annotations. These large image collections are a potent source of information, and to use and share the information, standardization of the content through a consistent terminology is essential. The aim of this project was to develop a pilot dataset of exhaustive annotated WSI of normal and abnormal human tissue and link the annotations to appropriate ontological information.

Materials and Methods:

Several biomedical ontologies and controlled vocabularies were investigated with the aim of selecting the most suitable ontology for this project. The selection criteria required an ontology that covered anatomical locations, histological subcompartments, histopathologic diagnoses, histopathologic terms, and generic terms such as normal, abnormal, and artifact. WSIs of normal and abnormal tissue from 50 colon resections and 69 skin excisions, diagnosed 2015-2016 at the Department of Clinical Pathology in Linköping, were randomly collected. These images were manually and exhaustively annotated at the level of major subcompartments, including normal or abnormal findings and artifacts.

Results:

Systemized nomenclature of medicine clinical terms (SNOMED CT) was chosen, and the annotations were linked to its codes and terms. Two hundred WSI were collected and annotated, resulting in 17,497 annotations, covering a total area of 302.19 cm², equivalent to 107,7 gigapixels. Ninety-five unique SNOMED CT codes were used. The time taken to annotate a WSI varied from 45 s to over 360 min, a total time of approximately 360 h.

Conclusion:

This work resulted in a dataset of 200 exhaustive annotated WSIs of normal and abnormal tissue from the colon and skin, and it has informed plans to build a comprehensive library of annotated WSIs. SNOMED CT was found to be the best ontology for annotation labeling. This project also demonstrates the need for future development of annotation tools in order to make the annotation process more efficient.

The International Society of Urological Pathology Education web-a web-based system for training and testing of pathologists

Pathology training resources remain scarce in many parts of the world. With rapid economic development comes the need to educate new pathologists to meet the medical care demands. Our aim was to set up a cost-effective system for training and testing the diagnostic skills of pathologists. Pathologists in nine countries in Asia and South America were invited by the International Society of Urological Pathology (ISUP) to participate in a prostate pathology education course combining image-based tests with lectures and on-line tutorials. The tests and tutorials are available free of charge at the ISUP education website www.edu.isupweb.org . A total of 603 pathologists registered on the website. Of these, 224 completed pre- and post-lecture assessments (tests 1 and 2). Replies were classified as correct/acceptable, when a lesion was accurately classified into clinically relevant categories (benign, cancer, high-grade prostatic intraepithelial neoplasia, intraductal carcinoma of the prostate). The rate of correct/acceptable replies increased from 60.7 to 72.3% in Tests 1 and 2, respectively. In Test 1, pathologists from upper middle, lower middle, and low resource countries gave a correct/acceptable diagnosis in 65.8%, 61.0%, and 47.4%, respectively. Their results improved in Test 2 to 76.4%, 72.5%, and 62.8%, respectively. The greatest improvement in diagnostic ability was achieved in pathologists from the low resource group of countries. The use of web-based testing and training, combined with lectures, is an efficient method for improving diagnostic skills of pathologists in low to middle resource countries.

Evolution, controversies and the future of prostate cancer grading

Histological grading of prostate cancer is one of the most important tissue-based parameters for prediction of outcome and treatment response. Gleason grading remains the foundation of prostate cancer grading, but has undergone a series of changes in the past 30 years, often initiated by consensus conference decisions. This review summarizes the most important modifications that were introduced by the 2005 and 2014 International Society of Urological Pathology (ISUP) revisions of Gleason grading and discusses the impact that these have had on current grading practices. A considerable inflation in Gleason scores has been observed, especially following the ISUP 2005 revision, and the effects of this are discussed. ISUP 2014 grading recommendations are described, including the reporting of ISUP grades 1-5. Controversial issues include methods for reporting of grades on needle biopsies, reporting of percent Gleason grades 4/5 and grading of cribriform and intraductal carcinoma of the prostate. Educational programs developed recently to promote standardization of grading are described and their results assessed.

WHO/ISUP classification, grading and pathological staging of renal cell carcinoma: standards and controversies

PURPOSE

Pathological parameters assessed on biopsies and resection specimens have a pivotal role in the diagnosis, prognosis and management of patients with renal cell carcinoma (RCC).

METHODS

A non-systematic literature search was performed, updated to January 2018, to identify key standards and controversies in the pathological classification, grading and staging of RCC.

RESULTS

Although most RCCs exhibit characteristic morphology that enables easy categorisation, RCCs show considerable morphological heterogeneity and it is not uncommon for there to be difficulty in assigning a tumour type, especially with rarer tumour subtypes. The differentiation between benign and malignant oncocytic tumours remains a particular challenge. The development of additional immunohistochemical and molecular tests is needed to facilitate tumour typing, because of the prognostic and therapeutic implications, and to enable more reliable identification of poorly differentiated metastatic tumours as being of renal origin. Any new tests need to be applicable to small biopsy samples, to overcome the heterogeneity of renal tumours. There is also a need to facilitate identification of tumour types that have genetic implications, to allow referral and management at specialist centres. Digital pathology has a potential role in such referral practice.

CONCLUSION

Much has been done to standardise pathological assessment of renal cell carcinomas in recent years, but there still remain areas of difficulty in classification and grading of these heterogeneous tumours.

Harmonization in laboratory medicine: more than clinical chemistry?

The goal of harmonizing laboratory information is to contribute to quality in patient care, ultimately improving upon patient outcomes and safety. The main focus of harmonization and standardization initiatives has been on analytical processes within the laboratory walls, clinical chemistry tests in particular. However, two major evidences obtained in recent years show that harmonization should be promoted not only in the analytical phase but also in all steps of the testing process, encompassing the entire field of laboratory medicine, including innovative areas (e.g. “omics”) rather than just conventional clinical chemistry tests. A large body of evidence demonstrates the vulnerability of the extra-analytical phases of the testing cycle. Because only “good biological samples” can assure good analytical quality, a closer interconnection between the different phases of the cycle is needed. In order to provide reliable and accurate laboratory information, harmonization activities should cover all steps of the cycle from the “pre-pre-analytical” phase (right choice of test at right time for right patient) through the analytical steps (right results with right report) to the “post-post-analytical” steps (right and timely acknowledgment of laboratory information, right interpretation and utilization with any necessary advice as to what to do next with the information provided). In addition, modern clinical laboratories are performing a broad menu of hundreds of tests, covering both traditional and innovative subspecialties of the discipline. In addition, according to a centered viewpoint, harmonization initiatives should not be addressed exclusively to clinical chemistry tests but should also include all areas of laboratory medicine.

Utility of Pathology Imagebase for standardisation of prostate cancer grading

AIMS

Despite efforts to standardise grading of prostate cancer, even among experts there is still a considerable variation in grading practices. In this study we describe the use of Pathology Imagebase, a novel reference image library, for setting an international standard in prostate cancer grading.

METHODS AND RESULTS

The International Society of Urological Pathology (ISUP) recently launched a reference image database supervised by experts. A panel of 24 international experts in prostate pathology reviewed independently microphotographs of 90 cases of prostate needle biopsies with cancer. A linear weighted kappa of 0.67 (95% confidence interval = 0.62-0.72) and consensus was reached in 50 cases. The interobserver weighted kappa varied from 0.48 to 0.89. The highest level of agreement was seen for Gleason score (GS) 3 + 3 = 6 (ISUP grade 1), while higher grades and particularly GS 4 + 3 = 7 (ISUP grade 3) showed considerable disagreement. Once a two-thirds majority was reached, images were moved automatically into a public database available for all ISUP members at www.isupweb.org. Non-members are able to access a limited number of cases.

CONCLUSIONS

It is anticipated that the database will assist pathologists to calibrate their grading and, hence, decrease interobserver variability. It will also help to identify instances where definitions of grades need to be clarified.