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Contains fulltext : 304958.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 305481.pdf (Publisher’s version ) (Closed access), Radboud University, 07 mei 2024, Promotores : Nagtegaal, I.D., Laak, J.A.W.M. van der, Lugli, A. Co-promotor : Simmer, F., 152 p.

Contains fulltext : 305495.pdf (Publisher’s version ) (Open Access), Programmed death-ligand 1 (PD-L1) expression is currently used in the clinic to assess eligibility for immune-checkpoint inhibitors via the tumor proportion score (TPS), but its efficacy is limited by high interobserver variability. Multiple papers have presented systems for the automatic quantification of TPS, but none report on the task of determining cell-level PD-L1 expression and often reserve their evaluation to a single PD-L1 monoclonal antibody or clinical center. In this paper, we report on a deep learning algorithm for detecting PD-L1 negative and positive tumor cells at a cellular level and evaluate it on a cell-level reference standard established by six readers on a multi-centric, multi PD-L1 assay dataset. This reference standard also provides for the first time a benchmark for computer vision algorithms. In addition, in line with other papers, we also evaluate our algorithm at slide-level by measuring the agreement between the algorithm and six pathologists on TPS quantification. We find a moderately low interobserver agreement at cell-level level (mean reader-reader F1 score = 0.68) which our algorithm sits slightly under (mean reader-AI F1 score = 0.55), especially for cases from the clinical center not included in the training set. Despite this, we find good AI-pathologist agreement on quantifying TPS compared to the interobserver agreement (mean reader-reader Cohen's kappa = 0.54, 95% CI 0.26-0.81, mean reader-AI kappa = 0.49, 95% CI 0.27-0.72). In conclusion, our deep learning algorithm demonstrates promise in detecting PD-L1 expression at a cellular level and exhibits favorable agreement with pathologists in quantifying the tumor proportion score (TPS). We publicly release our models for use via the Grand-Challenge platform.

Item does not contain fulltext, Even though entirely digitized microscopic tissue sections (whole slide images, WSIs) are increasingly being used in histopathology diagnostics, little data is still available on the effect of this technique on pathologists' reading time. This study aimed to compare the time required to perform the microscopic assessment by pathologists between a conventional workflow (an optical microscope) and digitized WSIs. WSI was used in primary diagnostics at the Laboratory for Pathology Eastern Netherlands for several years (LabPON, Hengelo, The Netherlands). Cases were read either in a traditional workflow, with the pathologist recording the time required for diagnostics and reporting, or entirely digitally. Reading times were extracted from image management system log files, and the digitized workflow was fully integrated into the laboratory information system. The digital workflow saved time in the majority of case categories, with prostate biopsies saving the most (68% time gain). Taking into account case distribution, the digital workflow produced an average gain of 12.3%. Using WSI instead of conventional microscopy significantly reduces pathologists' reading times. Pathologists must work in a fully integrated environment to fully reap the benefits of a digital workflow.

Contains fulltext : 291079.pdf (Publisher’s version ) (Open Access), BACKGROUND: The amount of stroma within the primary tumor is a prognostic parameter for colon cancer patients. This phenomenon can be assessed using the tumor-stroma ratio (TSR), which classifies tumors in stroma-low (≤50% stroma) and stroma-high (>50% stroma). Although the reproducibility for TSR determination is good, improvement might be expected from automation. The aim of this study was to investigate whether the scoring of the TSR in a semi- and fully automated method using deep learning algorithms is feasible. METHODS: A series of 75 colon cancer slides were selected from a trial series of the UNITED study. For the standard determination of the TSR, 3 observers scored the histological slides. Next, the slides were digitized, color normalized, and the stroma percentages were scored using semi- and fully automated deep learning algorithms. Correlations were determined using intraclass correlation coefficients (ICCs) and Spearman rank correlations. RESULTS: 37 (49%) cases were classified as stroma-low and 38 (51%) as stroma-high by visual estimation. A high level of concordance between the 3 observers was reached, with ICCs of 0.91, 0.89, and 0.94 (all P < .001). Between visual and semi-automated assessment the ICC was 0.78 (95% CI 0.23-0.91, P-value 0.005), with a Spearman correlation of 0.88 (P < .001). Spearman correlation coefficients above 0.70 (N=3) were observed for visual estimation versus the fully automated scoring procedures. CONCLUSION: Good correlations were observed between standard visual TSR determination and semi- and fully automated TSR scores. At this point, visual examination has the highest observer agreement, but semi-automated scoring could be helpful to support pathologists.

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01 januari 2023, Item does not contain fulltext, Machine learning model deployment in clinical practice demands real-time risk assessment to identify situations in which the model is uncertain. Once deployed, models should be accurate for classes seen during training while providing informative estimates of uncertainty to flag abnormalities and unseen classes for further analysis. Although recent developments in uncertainty estimation have resulted in an increasing number of methods, a rigorous empirical evaluation of their performance on large-scale digital pathology datasets is lacking. This work provides a benchmark for evaluating prevalent methods on multiple datasets by comparing the uncertainty estimates on both in-distribution and realistic near and far out-of-distribution (OOD) data on a whole-slide level. To this end, we aggregate uncertainty values from patch-based classifiers to whole-slide level uncertainty scores. We show that results found in classical computer vision benchmarks do not always translate to the medical imaging setting. Specifically, we demonstrate that deep ensembles perform best at detecting far-OOD data but can be outperformed on a more challenging near-OOD detection task by multi-head ensembles trained for optimal ensemble diversity. Furthermore, we demonstrate the harmful impact OOD data can have on the performance of deployed machine learning models. Overall, we show that uncertainty estimates can be used to discriminate in-distribution from OOD data with high AUC scores. Still, model deployment might require careful tuning based on prior knowledge of prospective OOD data.

01 februari 2023, Item does not contain fulltext, BACKGROUND: Artificial intelligence (AI) is rapidly fuelling a fundamental transformation in the practice of pathology. However, clinical integration remains challenging, with no AI algorithms to date in routine adoption within typical anatomic pathology (AP) laboratories. This survey gathered current expert perspectives and expectations regarding the role of AI in AP from those with first-hand computational pathology and AI experience. METHODS: Perspectives were solicited using the Delphi method from 24 subject matter experts between December 2020 and February 2021 regarding the anticipated role of AI in pathology by the year 2030. The study consisted of three consecutive rounds: 1) an open-ended, free response questionnaire generating a list of survey items; 2) a Likert-scale survey scored by experts and analysed for consensus; and 3) a repeat survey of items not reaching consensus to obtain further expert consensus. FINDINGS: Consensus opinions were reached on 141 of 180 survey items (78.3%). Experts agreed that AI would be routinely and impactfully used within AP laboratory and pathologist clinical workflows by 2030. High consensus was reached on 100 items across nine categories encompassing the impact of AI on (1) pathology key performance indicators (KPIs) and (2) the pathology workforce and specific tasks performed by (3) pathologists and (4) AP lab technicians, as well as (5) specific AI applications and their likelihood of routine use by 2030, (6) AI's role in integrated diagnostics, (7) pathology tasks likely to be fully automated using AI, and (8) regulatory/legal and (9) ethical aspects of AI integration in pathology. INTERPRETATION: This systematic consensus study details the expected short-to-mid-term impact of AI on pathology practice. These findings provide timely and relevant information regarding future care delivery in pathology and raise key practical, ethical, and legal challenges that must be addressed prior to AI's successful clinical implemen

Contains fulltext : 292740.pdf (Publisher’s version ) (Open Access), (1) Background: Histopathological assessment of Wilms tumors (WT) is crucial for risk group classification to guide postoperative stratification in chemotherapy pre-treated WT cases. However, due to the heterogeneous nature of the tumor, significant interobserver variation between pathologists in WT diagnosis has been observed, potentially leading to misclassification and suboptimal treatment. We investigated whether artificial intelligence (AI) can contribute to accurate and reproducible histopathological assessment of WT through recognition of individual histopathological tumor components. (2) Methods: We assessed the performance of a deep learning-based AI system in quantifying WT components in hematoxylin and eosin-stained slides by calculating the Sørensen-Dice coefficient for fifteen predefined renal tissue components, including six tumor-related components. We trained the AI system using multiclass annotations from 72 whole-slide images of patients diagnosed with WT. (3) Results: The overall Dice coefficient for all fifteen tissue components was 0.85 and for the six tumor-related components was 0.79. Tumor segmentation worked best to reliably identify necrosis (Dice coefficient 0.98) and blastema (Dice coefficient 0.82). (4) Conclusions: Accurate histopathological classification of WT may be feasible using a digital pathology-based AI system in a national cohort of WT patients.

Contains fulltext : 293170.pdf (Publisher’s version ) (Open Access), In colorectal cancer (CRC), artificial intelligence (AI) can alleviate the laborious task of characterization and reporting on resected biopsies, including polyps, the numbers of which are increasing as a result of CRC population screening programs ongoing in many countries all around the globe. Here, we present an approach to address two major challenges in the automated assessment of CRC histopathology whole-slide images. We present an AI-based method to segment multiple ([Formula: see text]) tissue compartments in the H &E-stained whole-slide image, which provides a different, more perceptible picture of tissue morphology and composition. We test and compare a panel of state-of-the-art loss functions available for segmentation models, and provide indications about their use in histopathology image segmentation, based on the analysis of (a) a multi-centric cohort of CRC cases from five medical centers in the Netherlands and Germany, and (b) two publicly available datasets on segmentation in CRC. We used the best performing AI model as the basis for a computer-aided diagnosis system that classifies colon biopsies into four main categories that are relevant pathologically. We report the performance of this system on an independent cohort of more than 1000 patients. The results show that with a good segmentation network as a base, a tool can be developed which can support pathologists in the risk stratification of colorectal cancer patients, among other possible uses. We have made the segmentation model available for research use on https://grand-challenge.org/algorithms/colon-tissue-segmentation/ .

01 juli 2023, Item does not contain fulltext, AIM: Reliably diagnosing or safely excluding serous tubal intraepithelial carcinoma (STIC), a precursor lesion of tubo-ovarian high-grade serous carcinoma (HGSC), is crucial for individual patient care, for better understanding the oncogenesis of HGSC, and for safely investigating novel strategies to prevent tubo-ovarian carcinoma. To optimize STIC diagnosis and increase its reproducibility, we set up a three-round Delphi study. METHODS AND RESULTS: In round 1, an international expert panel of 34 gynecologic pathologists, from 11 countries, was assembled to provide input regarding STIC diagnosis, which was used to develop a set of statements. In round 2, the panel rated their level of agreement with those statements on a 9-point Likert scale. In round 3, statements without previous consensus were rated again by the panel while anonymously disclosing the responses of the other panel members. Finally, each expert was asked to approve or disapprove the complete set of consensus statements. The panel indicated their level of agreement with 64 statements. A total of 27 statements (42%) reached consensus after three rounds. These statements reflect the entire diagnostic work-up for pathologists, regarding processing and macroscopy (three statements); microscopy (eight statements); immunohistochemistry (nine statements); interpretation and reporting (four statements); and miscellaneous (three statements). The final set of consensus statements was approved by 85%. CONCLUSION: This study provides an overview of current clinical practice regarding STIC diagnosis amongst expert gynecopathologists. The experts' consensus statements form the basis for a set of recommendations, which may help towards more consistent STIC diagnosis.

Item does not contain fulltext, Tumor budding is a histopathological biomarker associated with metastases and adverse survival outcomes in colorectal carcinoma (CRC) patients. It is characterized by the presence of single tumor cells or small clusters of cells within the tumor or at the tumor-invasion front. In order to obtain a tumor budding score for a patient, the region with the highest tumor bud density must first be visually identified by a pathologist, after which buds will be counted in the chosen hotspot field. The automation of this process will expectedly increase efficiency and reproducibility. Here, we present a deep learning convolutional neural network model that automates the above procedure. For model training, we used a semi-supervised learning method, to maximize the detection performance despite the limited amount of labeled training data. The model was tested on an independent dataset in which human- and machine-selected hotspots were mapped in relation to each other and manual and machine detected tumor bud numbers in the manually selected fields were compared. We report the results of the proposed method in comparison with visual assessment by pathologists. We show that the automated tumor bud count achieves a prognostic value comparable with visual estimation, while based on an objective and reproducible quantification. We also explore novel metrics to quantify buds such as density and dispersion and report their prognostic value. We have made the model available for research use on the grand-challenge platform.

01 augustus 2023, Contains fulltext : 296181.pdf (Publisher’s version ) (Open Access), The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in-depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple-negative breast cancer. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

01 augustus 2023, Contains fulltext : 296131.pdf (Publisher’s version ) (Closed access), Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector-based). We then provide a compendium of spatial immune cell metrics that have been reported in the literature, summarizing prognostic associations in the context of a variety of cancers. We conclude by discussing two well-described clinical biomarkers, the breast cancer stromal tumor infiltrating lymphocytes score and the colon cancer Immunoscore, and describe investigative opportunities to improve clinical utility of these spatial biomarkers. © 2023 The Pathological Society of Great Britain and Ireland.

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01 september 2023, Contains fulltext : 296777.pdf (Publisher’s version ) (Open Access), Computational pathology (CPath) algorithms detect, segment or classify cancer in whole slide images, approaching or even exceeding the accuracy of pathologists. Challenges have to be overcome before these algorithms can be used in practice. We therefore aim to explore international perspectives on the future role of CPath in oncological pathology by focusing on opinions and first experiences regarding barriers and facilitators. We conducted an international explorative eSurvey and semi-structured interviews with pathologists utilizing an implementation framework to classify potential influencing factors. The eSurvey results showed remarkable variation in opinions regarding attitude, understandability and validation of CPath. Interview results showed that barriers focused on the quality of available evidence, while most facilitators concerned strengths of CPath. A lack of consensus was present for multiple factors, such as the determination of sufficient validation using CPath, the preferred function of CPath within the digital workflow and the timing of CPath introduction in pathology education. The diversity in opinions illustrates variety in influencing factors in CPath adoption. A next step would be to quantitatively determine important factors for adoption and initiate validation studies. Both should include clear case descriptions and be conducted among a more homogenous panel of pathologists based on sub specialization.

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Item does not contain fulltext, Digital health technologies have been in use for many years in a wide spectrum of healthcare scenarios. This narrative review outlines the current use and the future strategies and significance of digital health technologies in modern healthcare applications. It covers the current state of the scientific field (delineating major strengths, limitations, and applications) and envisions the future impact of relevant emerging key technologies. Furthermore, we attempt to provide recommendations for innovative approaches that would accelerate and benefit the research, translation and utilization of digital health technologies.

Item does not contain fulltext, BACKGROUND: Invasive breast cancer patients are increasingly being treated with neoadjuvant chemotherapy; however, only a fraction of the patients respond to it completely. To prevent overtreatment, there is an urgent need for biomarkers to predict treatment response before administering the therapy. METHODS: In this retrospective study, we developed hypothesis-driven interpretable biomarkers based on deep learning, to predict the pathological complete response (pCR, i.e., the absence of tumor cells in the surgical resection specimens) to neoadjuvant chemotherapy solely using digital pathology H&E images of pre-treatment breast biopsies. Our approach consists of two steps: First, we use deep learning to characterize aspects of the tumor micro-environment by detecting mitoses and segmenting tissue into several morphology compartments including tumor, lymphocytes and stroma. Second, we derive computational biomarkers from the segmentation and detection output to encode slide-level relationships of components of the tumor microenvironment, such as tumor and mitoses, stroma, and tumor infiltrating lymphocytes (TILs). RESULTS: We developed and evaluated our method on slides from n = 721 patients from three European medical centers with triple-negative and Luminal B breast cancers and performed external independent validation on n = 126 patients from a public dataset. We report the predictive value of the investigated biomarkers for predicting pCR with areas under the receiver operating characteristic curve between 0.66 and 0.88 across the tested cohorts. CONCLUSION: The proposed computational biomarkers predict pCR, but will require more evaluation and finetuning for clinical application. Our results further corroborate the potential role of deep learning to automate TILs quantification, and their predictive value in breast cancer neoadjuvant treatment planning, along with automated mitoses quantification. We made our method publicly available to extract segmentation-bas

Contains fulltext : 299840.pdf (Publisher’s version ) (Open Access), The Banff Digital Pathology Working Group (DPWG) was established with the goal to establish a digital pathology repository; develop, validate, and share models for image analysis; and foster collaborations using regular videoconferencing. During the calls, a variety of artificial intelligence (AI)-based support systems for transplantation pathology were presented. Potential collaborations in a competition/trial on AI applied to kidney transplant specimens, including the DIAGGRAFT challenge (staining of biopsies at multiple institutions, pathologists' visual assessment, and development and validation of new and pre-existing Banff scoring algorithms), were also discussed. To determine the next steps, a survey was conducted, primarily focusing on the feasibility of establishing a digital pathology repository and identifying potential hosts. Sixteen of the 35 respondents (46%) had access to a server hosting a digital pathology repository, with 2 respondents that could serve as a potential host at no cost to the DPWG. The 16 digital pathology repositories collected specimens from various organs, with the largest constituent being kidney (n = 12,870 specimens). A DPWG pilot digital pathology repository was established, and there are plans for a competition/trial with the DIAGGRAFT project. Utilizing existing resources and previously established models, the Banff DPWG is establishing new resources for the Banff community.

Radboud University, 28 januari 2022, Promotores : Laak, J.A.W.M. van der, Ginneken, B. van Co-promotores : Litjens, G.J.S., Hulsbergen-van de Kaa, C.A., Contains fulltext : 241550.pdf (Publisher’s version ) (Open Access)

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Item does not contain fulltext, In the last decade, the field of computational pathology has advanced at a rapid pace because of the availability of deep neural networks, which achieved their first successes in computer vision tasks in 2012. An important driver for the progress of the field were public competitions, so called 'Grand Challenges', in which increasingly large data sets were offered to the public to solve clinically relevant tasks. Going from the first Pathology challenges, which had data obtained from 23 patients, to current challenges sharing data of thousands of patients, performance of developed deep learning solutions has reached (and sometimes surpassed) the level of experienced pathologists for specific tasks. We expect future challenges to broaden the horizon, for instance by combining data from radiology, pathology and tumor genetics, and to extract prognostic and predictive information independent of currently used grading schemes.

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Contains fulltext : 283491.pdf (Publisher’s version ) (Open Access), BACKGROUND: Transplant nephropathology is a highly specialized field of pathology comprising both the evaluation of organ donor biopsy for organ allocation and post-transplant graft biopsy for assessment of rejection or graft damage. The introduction of digital pathology with whole-slide imaging (WSI) in clinical research, trials and practice has catalyzed the application of artificial intelligence (AI) for histopathology, with development of novel machine-learning models for tissue interrogation and discovery. We aimed to review the literature for studies specifically applying AI algorithms to WSI-digitized pre-implantation kidney biopsy. METHODS: A systematic search was carried out in the electronic databases PubMed-MEDLINE and Embase until 25th September, 2021 with a combination of the key terms "kidney", "biopsy", "transplantation" and "artificial intelligence" and their aliases. Studies dealing with the application of AI algorithms coupled with WSI in pre-implantation kidney biopsies were included. The main theme addressed was detection and quantification of tissue components. Extracted data were: author, year and country of the study, type of biopsy features investigated, number of cases, type of algorithm deployed, main results of the study in terms of diagnostic outcome, and the main limitations of the study. RESULTS: Of 5761 retrieved articles, 7 met our inclusion criteria. All studies focused largely on AI-based detection and classification of glomerular structures and to a lesser extent on tubular and vascular structures. Performance of AI algorithms was excellent and promising. CONCLUSION: All studies highlighted the importance of expert pathologist annotation to reliably train models and the need to acknowledge clinical nuances of the pre-implantation setting. Close cooperation between computer scientists and practicing as well as expert renal pathologists is needed, helping to refine the performance of AI-based models for routine pre-implantation kidne

Contains fulltext : 251830.pdf (Publisher’s version ) (Open Access), Our understanding of the oncogenesis of high-grade serous cancer of the ovary and its precursor lesions, such as serous tubal intraepithelial carcinoma (STIC), has significantly increased over the last decades. Adequate and reproducible diagnosis of these precursor lesions is important. Diagnosing STIC can have prognostic consequences and is an absolute requirement for safely offering alternative risk reducing strategies, such as risk reducing salpingectomy with delayed oophorectomy. However, diagnosing STIC is a challenging task, possessing only moderate reproducibility. In this review and meta-analysis, we look at how pathologists come to a diagnosis of STIC. We performed a literature search identifying 39 studies on risk reducing salpingo-oophorectomy in women with a known BRCA1/2 PV, collectively reporting on 6833 patients. We found a pooled estimated proportion of STIC of 2.8% (95% CI, 2.0-3.7). We focused on reported grossing protocols, morphological criteria, level of pathologist training, and the use of immunohistochemistry. The most commonly mentioned morphological characteristics of STIC are (1) loss of cell polarity, (2) nuclear pleomorphism, (3) high nuclear to cytoplasmic ratio, (4) mitotic activity, (5) pseudostratification, and (6) prominent nucleoli. The difference in reported incidence of STIC between studies who totally embedded all specimens and those who did not was 3.2% (95% CI, 2.3-4.2) versus 1.7% (95% CI, 0.0-6.2) (p 0.24). We provide an overview of diagnostic features and present a framework for arriving at an adequate diagnosis, consisting of the use of the SEE-FIM grossing protocol, evaluation by a subspecialized gynecopathologist, rational use of immunohistochemical staining, and obtaining a second opinion from a colleague.

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Item does not contain fulltext, In kidney transplant biopsies, both inflammation and chronic changes are important features that predict long-term graft survival. Quantitative scoring of these features is important for transplant diagnostics and kidney research. However, visual scoring is poorly reproducible and labor intensive. The goal of this study was to investigate the potential of convolutional neural networks (CNNs) to quantify inflammation and chronic features in kidney transplant biopsies. A structure segmentation CNN and a lymphocyte detection CNN were applied on 125 whole-slide image pairs of periodic acid-Schiff- and CD3-stained slides. The CNN results were used to quantify healthy and sclerotic glomeruli, interstitial fibrosis, tubular atrophy, and inflammation within both nonatrophic and atrophic tubuli, and in areas of interstitial fibrosis. The computed tissue features showed high correlation with Banff lesion scores of five pathologists (A.A., A.Dend., J.H.B., J.K., and T.N.). Analyses on a small subset showed a moderate correlation toward higher CD3(+) cell density within scarred regions and higher CD3(+) cell count inside atrophic tubuli correlated with long-term change of estimated glomerular filtration rate. The presented CNNs are valid tools to yield objective quantitative information on glomeruli number, fibrotic tissue, and inflammation within scarred and non-scarred kidney parenchyma in a reproducible manner. CNNs have the potential to improve kidney transplant diagnostics and will benefit the community as a novel method to generate surrogate end points for large-scale clinical studies.

Item does not contain fulltext, Artificial Intelligence (AI) has become of increasing interest over the past decade. While digital image analysis (DIA) is already being used in radiology, it is still in its infancy in pathology. One of the reasons is that large-scale digitization of glass slides has only recently become available. With the advent of digital slide scanners, that digitize glass slides into whole slide images, many labs are now in a transition phase towards digital pathology. However, only few departments worldwide are currently fully digital. Digital pathology provides the ability to annotate large datasets and train computers to develop and validate robust algorithms, similar to radiology. In this opinionated overview, we will give a brief introduction into AI in pathology, discuss the potential positive and negative implications and speculate about the future role of AI in the field of pediatric pathology.

Item does not contain fulltext, PURPOSE: Breast terminal duct lobular units (TDLUs) are the main source of breast cancer (BC) precursors. Higher serum concentrations of hormones and growth factors have been linked to increased TDLU numbers and to elevated BC risk, with variable effects by menopausal status. We assessed associations of circulating factors with breast histology among premenopausal women using artificial intelligence (AI) and preliminarily tested whether parity modifies associations. METHODS: Pathology AI analysis was performed on 316 digital images of H&E-stained sections of normal breast tissues from Komen Tissue Bank donors ages ≤ 45 years to assess 11 quantitative metrics. Associations of circulating factors with AI metrics were assessed using regression analyses, with inclusion of interaction terms to assess effect modification. RESULTS: Higher prolactin levels were related to larger TDLU area (p < 0.001) and increased presence of adipose tissue proximate to TDLUs (p < 0.001), with less significant positive associations for acini counts (p = 0.012), dilated acini (p = 0.043), capillary area (p = 0.014), epithelial area (p = 0.007), and mononuclear cell counts (p = 0.017). Testosterone levels were associated with increased TDLU counts (p < 0.001), irrespective of parity, but associations differed by adipose tissue content. AI data for TDLU counts generally agreed with prior visual assessments. CONCLUSION: Among premenopausal women, serum hormone levels linked to BC risk were also associated with quantitative features of normal breast tissue. These relationships were suggestively modified by parity status and tissue composition. We conclude that the microanatomic features of normal breast tissue may represent a marker of BC risk.

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Contains fulltext : 252016.pdf (Publisher’s version ) (Open Access), Background: The first sign of metastatic prostate cancer after radical prostatectomy is rising PSA levels in the blood, termed biochemical recurrence. The prediction of recurrence relies mainly on the morphological assessment of prostate cancer using the Gleason grading system. However, in this system, within-grade morphological patterns and subtle histopathological features are currently omitted, leaving a significant amount of prognostic potential unexplored. Methods: To discover additional prognostic information using artificial intelligence, we trained a deep learning system to predict biochemical recurrence from tissue in H&E-stained microarray cores directly. We developed a morphological biomarker using convolutional neural networks leveraging a nested case-control study of 685 patients and validated on an independent cohort of 204 patients. We use concept-based explainability methods to interpret the learned tissue patterns. Results: The biomarker provides a strong correlation with biochemical recurrence in two sets (n = 182 and n = 204) from separate institutions. Concept-based explanations provided tissue patterns interpretable by pathologists. Conclusions: These results show that the model finds predictive power in the tissue beyond the morphological ISUP grading.

Item does not contain fulltext, The convergence of digital pathology and computer vision is increasingly enabling computers to perform tasks performed by humans. As a result, artificial intelligence (AI) is having an astoundingly positive effect on the field of pathology, including breast pathology. Research using machine learning and the development of algorithms that learn patterns from labeled digital data based on "deep learning" neural networks and feature-engineered approaches to analyze histology images have recently provided promising results. Thus far, image analysis and more complex AI-based tools have demonstrated excellent success performing tasks such as the quantification of breast biomarkers and Ki67, mitosis detection, lymph node metastasis recognition, tissue segmentation for diagnosing breast carcinoma, prognostication, computational assessment of tumor-infiltrating lymphocytes, and prediction of molecular expression as well as treatment response and benefit of therapy from routine H&E images. This review critically examines the literature regarding these applications of AI in the area of breast pathology.

Contains fulltext : 245191.pdf (Publisher’s version ) (Open Access), Tumor budding is a long-established independent adverse prognostic marker in colorectal cancer, yet methods for its assessment have varied widely. In an effort to standardize its reporting, a group of experts met in Bern, Switzerland, in 2016 to reach consensus on a single, international, evidence-based method for tumor budding assessment and reporting (International Tumor Budding Consensus Conference [ITBCC]). Tumor budding assessment using the ITBCC criteria has been validated in large cohorts of cancer patients and incorporated into several international colorectal cancer pathology and clinical guidelines. With the wider reporting of tumor budding, new issues have emerged that require further clarification. To better inform researchers and health-care professionals on these issues, an international group of experts in gastrointestinal pathology participated in a modified Delphi process to generate consensus and highlight areas requiring further research. This effort serves to re-affirm the importance of tumor budding in colorectal cancer and support its continued use in routine clinical practice.

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Contains fulltext : 238916.pdf (Publisher’s version ) (Open Access), Delayed graft function (DGF) is a strong risk factor for development of interstitial fibrosis and tubular atrophy (IFTA) in kidney transplants. Quantitative assessment of inflammatory infiltrates in kidney biopsies of DGF patients can reveal predictive markers for IFTA development. In this study, we combined multiplex tyramide signal amplification (mTSA) and convolutional neural networks (CNNs) to assess the inflammatory microenvironment in kidney biopsies of DGF patients (n = 22) taken at 6 weeks post-transplantation. Patients were stratified for IFTA development (<10% versus ≥10%) from 6 weeks to 6 months post-transplantation, based on histopathological assessment by three kidney pathologists. One mTSA panel was developed for visualization of capillaries, T- and B-lymphocytes and macrophages and a second mTSA panel for T-helper cell and macrophage subsets. The slides were multi spectrally imaged and custom-made python scripts enabled conversion to artificial brightfield whole-slide images (WSI). We used an existing CNN for the detection of lymphocytes with cytoplasmatic staining patterns in immunohistochemistry and developed two new CNNs for the detection of macrophages and nuclear-stained lymphocytes. F1-scores were 0.77 (nuclear-stained lymphocytes), 0.81 (cytoplasmatic-stained lymphocytes), and 0.82 (macrophages) on a test set of artificial brightfield WSI. The CNNs were used to detect inflammatory cells, after which we assessed the peritubular capillary extent, cell density, cell ratios, and cell distance in the two patient groups. In this cohort, distance of macrophages to other immune cells and peritubular capillary extent did not vary significantly at 6 weeks post-transplantation between patient groups. CD163(+) cell density was higher in patients with ≥10% IFTA development 6 months post-transplantation (p < 0.05). CD3(+)CD8(-)/CD3(+)CD8(+) ratios were higher in patients with <10% IFTA development (p < 0.05). We observed a high correlation between CD163(+)

Contains fulltext : 232872.pdf (Publisher’s version ) (Open Access), The Gleason score is the most important prognostic marker for prostate cancer patients, but it suffers from significant observer variability. Artificial intelligence (AI) systems based on deep learning can achieve pathologist-level performance at Gleason grading. However, the performance of such systems can degrade in the presence of artifacts, foreign tissue, or other anomalies. Pathologists integrating their expertise with feedback from an AI system could result in a synergy that outperforms both the individual pathologist and the system. Despite the hype around AI assistance, existing literature on this topic within the pathology domain is limited. We investigated the value of AI assistance for grading prostate biopsies. A panel of 14 observers graded 160 biopsies with and without AI assistance. Using AI, the agreement of the panel with an expert reference standard increased significantly (quadratically weighted Cohen's kappa, 0.799 vs. 0.872; p = 0.019). On an external validation set of 87 cases, the panel showed a significant increase in agreement with a panel of international experts in prostate pathology (quadratically weighted Cohen's kappa, 0.733 vs. 0.786; p = 0.003). In both experiments, on a group-level, AI-assisted pathologists outperformed the unassisted pathologists and the standalone AI system. Our results show the potential of AI systems for Gleason grading, but more importantly, show the benefits of pathologist-AI synergy.

Contains fulltext : 231951.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 231089.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 235736.pdf (Publisher’s version ) (Open Access), Machine learning techniques have great potential to improve medical diagnostics, offering ways to improve accuracy, reproducibility and speed, and to ease workloads for clinicians. In the field of histopathology, deep learning algorithms have been developed that perform similarly to trained pathologists for tasks such as tumor detection and grading. However, despite these promising results, very few algorithms have reached clinical implementation, challenging the balance between hope and hype for these new techniques. This Review provides an overview of the current state of the field, as well as describing the challenges that still need to be addressed before artificial intelligence in histopathology can achieve clinical value.

Contains fulltext : 235767.pdf (Publisher’s version ) (Open Access), To address the challenges posed by large-scale development, validation, and adoption of artificial intelligence (AI) in pathology, we have constituted a consortium of academics, small enterprises, and pharmaceutical companies and proposed the BIGPICTURE project to the Innovative Medicines Initiative. Our vision is to become the catalyst in the digital transformation of pathology by creating the first European, ethically compliant, and quality-controlled whole slide imaging platform, in which both large-scale data and AI algorithms will exist. Our mission is to develop this platform in a sustainable and inclusive way, by connecting the community of pathologists, researchers, AI developers, patients, and industry parties based on creating value and reciprocity in use based on a community model as the mechanism for ensuring sustainability of the platform.

Contains fulltext : 230096.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 236904.pdf (Publisher’s version ) (Open Access)