Improved recovery of urinary small extracellular vesicles by differential ultracentrifugation.

Extracellular vesicles (EVs) are lipid-membrane enclosed structures that are associated with several diseases, including those of genitourinary tract. Urine contains EVs derived from urinary tract cells. Owing to its non-invasive collection, urine represents a promising source of biomarkers for genitourinary disorders, including cancer. The most used method for urinary EVs separation is differential ultracentrifugation (UC), but current protocols lead to a significant loss of EVs hampering its efficiency. Moreover, UC protocols are labor-intensive, further limiting clinical application. Herein, we sought to optimize an UC protocol, reducing the time spent and improving small EVs (SEVs) yield. By testing different ultracentrifugation times at 200,000g to pellet SEVs, we found that 48 min and 60 min enabled increased SEVs recovery compared to 25 min. A step for pelleting large EVs (LEVs) was also evaluated and compared with filtering of the urine supernatant. We found that urine supernatant filtering resulted in a 1.7-fold increase on SEVs recovery, whereas washing steps resulted in a 0.5 fold-decrease on SEVs yield. Globally, the optimized UC protocol was shown to be more time efficient, recovering higher numbers of SEVs than Exoquick-TC (EXO). Furthermore, the optimized UC protocol preserved RNA quality and quantity, while reducing SEVs separation time.

Cervical cytology and HPV distribution in Cape Verde: A snapshot of a country taken during its first HPV nation-wide vaccination campaign.

Cervical cancer ranks as the third most common female cancer in Cape Verde and is the leading cause of cancer-related deaths among women in the country. While Human Papillomavirus (HPV) vaccination, which started in 2021, is anticipated to significantly reduce disease incidence, cervical screening remains crucial for non-vaccinated women. We retrospectively reviewed gynecologic cytology exams and HPV tests performed in Cape Verde between 2017 and April 2023 and processed at IMP Diagnostics. For this study, we considered 13035 women with cytology examinations performed and, 2013 of these, also with an HPV molecular test. Cytology diagnostics comprised 83 % NILM cases; 12 % ASC-US; 2.7 % LSIL; 1.2 % ASC-H; 0.5 % HSIL and 0.1 % SCC. In 505 (25.1 %) high-risk HPV infection was detected. Prevalence of HPV infection varied with age, peaking at young ages - ≤24 years old (55.5 %) and 25-35-year-old women (31.5 %) - and the lowest after 66 years old (9.7 %). Herein we present a comprehensive study regarding Cape Verde's cervical cytology and HPV distribution, aiming to provide a snapshot of the country's cervical cytology results and HPV distribution in recent years. Moreover, these data may contribute to establish a baseline to assess, in the future, the vaccination impact in the country.

An interpretable machine learning system for colorectal cancer diagnosis from pathology slides.

Considering the profound transformation affecting pathology practice, we aimed to develop a scalable artificial intelligence (AI) system to diagnose colorectal cancer from whole-slide images (WSI). For this, we propose a deep learning (DL) system that learns from weak labels, a sampling strategy that reduces the number of training samples by a factor of six without compromising performance, an approach to leverage a small subset of fully annotated samples, and a prototype with explainable predictions, active learning features and parallelisation. Noting some problems in the literature, this study is conducted with one of the largest WSI colorectal samples dataset with approximately 10,500 WSIs. Of these samples, 900 are testing samples. Furthermore, the robustness of the proposed method is assessed with two additional external datasets (TCGA and PAIP) and a dataset of samples collected directly from the proposed prototype. Our proposed method predicts, for the patch-based tiles, a class based on the severity of the dysplasia and uses that information to classify the whole slide. It is trained with an interpretable mixed-supervision scheme to leverage the domain knowledge introduced by pathologists through spatial annotations. The mixed-supervision scheme allowed for an intelligent sampling strategy effectively evaluated in several different scenarios without compromising the performance. On the internal dataset, the method shows an accuracy of 93.44% and a sensitivity between positive (low-grade and high-grade dysplasia) and non-neoplastic samples of 0.996. On the external test samples varied with TCGA being the most challenging dataset with an overall accuracy of 84.91% and a sensitivity of 0.996.

Invisible for a few but essential for many: the role of Histotechnologists in the establishment of digital pathology.

Digital pathology (DP) is indisputably the future for histopathology laboratories. The process of digital implementation requires deep workflow reorganisation which involves an interdisciplinary team. This transformation may have the greatest impact on the Histotechnologist (HTL) profession. Our review of the literature has clearly revealed that the role of HTLs in the establishment of DP is being unnoticed and guidance is limited. This article aims to bring HTLs from behind-the-scenes into the spotlight. Our objective is to provide them guidance and practical recommendations to successfully contribute to the implementation of a new digital workflow. Furthermore, it also intends to contribute for improvement of study programs, ensuring the role of HTL in DP is addressed as part of graduate and post-graduate education. In our review, we report on the differences encountered between workflow schemes and the limitations observed in this process. The authors propose a digital workflow to achieve its limitless potential, focusing on the HTL's role. This article explores the novel responsibilities of HTLs during specimen gross dissection, embedding, microtomy, staining, digital scanning, and whole slide image quality control. Furthermore, we highlight the benefits and challenges that DP implementation might bring the HTLs career. HTLs have an important role in the digital workflow: the responsibility of achieving the perfect glass slide.

A CAD system for automatic dysplasia grading on H&E cervical whole-slide images.

Cervical cancer is the fourth most common female cancer worldwide and the fourth leading cause of cancer-related death in women. Nonetheless, it is also among the most successfully preventable and treatable types of cancer, provided it is early identified and properly managed. As such, the detection of pre-cancerous lesions is crucial. These lesions are detected in the squamous epithelium of the uterine cervix and are graded as low- or high-grade intraepithelial squamous lesions, known as LSIL and HSIL, respectively. Due to their complex nature, this classification can become very subjective. Therefore, the development of machine learning models, particularly directly on whole-slide images (WSI), can assist pathologists in this task. In this work, we propose a weakly-supervised methodology for grading cervical dysplasia, using different levels of training supervision, in an effort to gather a bigger dataset without the need of having all samples fully annotated. The framework comprises an epithelium segmentation step followed by a dysplasia classifier (non-neoplastic, LSIL, HSIL), making the slide assessment completely automatic, without the need for manual identification of epithelial areas. The proposed classification approach achieved a balanced accuracy of 71.07% and sensitivity of 72.18%, at the slide-level testing on 600 independent samples, which are publicly available upon reasonable request.

Annotating for Artificial Intelligence Applications in Digital Pathology: A Practical Guide for Pathologists and Researchers.

Training machine learning models for artificial intelligence (AI) applications in pathology often requires extensive annotation by human experts, but there is little guidance on the subject. In this work, we aimed to describe our experience and provide a simple, useful, and practical guide addressing annotation strategies for AI development in computational pathology. Annotation methodology will vary significantly depending on the specific study's objectives, but common difficulties will be present across different settings. We summarize key aspects and issue guiding principles regarding team interaction, ground-truth quality assessment, different annotation types, and available software and hardware options and address common difficulties while annotating. This guide was specifically designed for pathology annotation, intending to help pathologists, other researchers, and AI developers with this process.

Vimentin epigenetic deregulation in Bladder Cancer associates with acquisition of invasive and metastatic phenotype through epithelial-to-mesenchymal transition.

Bladder cancer (BlCa) is the ninth most common cancer worldwide, associated with significant morbidity and mortality. Thus, understand the biological mechanisms underlying tumour progression is of great clinical significance. Vimentin (VIM) is (over)expressed in several carcinomas, putatively in association with EMT. We have previously found that VIM promoter methylation accurately identified BlCa and VIM expression associated with unfavourable prognosis. Herein, we sought to investigate VIM expression regulation and its role in malignant transformation of BlCa. Analysis of tissue samples disclosed higher VIM transcript, protein, and methylation levels in BlCa compared with normal urothelium. VIM protein and transcript levels significantly increased from non-muscle invasive (NMIBC) to muscle-invasive (MIBC) cases and to BlCa metastases. Inverse correlation between epithelial CDH1 and VIM, and a positive correlation between mesenchymal CDH2 and VIM were also observed. In BlCa cell lines, exposure to demethylating agent increased VIM protein, with concomitant decrease in VIM methylation. Moreover, exposure to histone deacetylases pan-inhibitor increased the deposit of active post-translational marks (PTMs) across VIM promoter. In primary normal urothelium cells, lower levels of active PTMs with concomitant higher levels of repressive marks deposit were observed. Finally, VIM knockdown in UMUC3 cell line increased epithelial-like features and decreased migration and invasion in vitro, decreasing tumour size and angiogenesis in vivo. We demonstrated that VIM promoter is epigenetically regulated in normal and neoplastic urothelium, which determine a VIM switch associated with EMT and acquisition of invasive and metastatic properties. These findings might allow for development of new, epigenetic-based, therapeutic strategies for BlCa.

Quality Control in Digital Pathology: Automatic Fragment Detection and Counting.

Manual assessment of fragments during the pro-cessing of pathology specimens is critical to ensure that the material available for slide analysis matches that captured during grossing without losing valuable material during this process. However, this step is still performed manually, resulting in lost time and delays in making the complete case available for evaluation by the pathologist. To overcome this limitation, we developed an autonomous system that can detect and count the number of fragments contained on each slide. We applied and compared two different methods: conventional machine learning methods and deep convolutional network methods. For conventional machine learning methods, we tested a two-stage approach with a supervised classifier followed by unsupervised hierarchical clustering. In addition, Fast R-CNN and YOLOv5, two state-of-the-art deep learning models for detection, were used and compared. All experiments were performed on a dataset comprising 1276 images of colorec-tal biopsy and polypectomy specimens manually labeled for fragment/set detection. The best results were obtained with the YOLOv5 architecture with a map@0.5 of 0.977 for fragment/set detection.

iMIL4PATH: A Semi-Supervised Interpretable Approach for Colorectal Whole-Slide Images.

Colorectal cancer (CRC) diagnosis is based on samples obtained from biopsies, assessed in pathology laboratories. Due to population growth and ageing, as well as better screening programs, the CRC incidence rate has been increasing, leading to a higher workload for pathologists. In this sense, the application of AI for automatic CRC diagnosis, particularly on whole-slide images (WSI), is of utmost relevance, in order to assist professionals in case triage and case review. In this work, we propose an interpretable semi-supervised approach to detect lesions in colorectal biopsies with high sensitivity, based on multiple-instance learning and feature aggregation methods. The model was developed on an extended version of the recent, publicly available CRC dataset (the CRC+ dataset with 4433 WSI), using 3424 slides for training and 1009 slides for evaluation. The proposed method attained 90.19% classification ACC, 98.8% sensitivity, 85.7% specificity, and a quadratic weighted kappa of 0.888 at slide-based evaluation. Its generalisation capabilities are also studied on two publicly available external datasets.

Digital Pathology Implementation in Private Practice: Specific Challenges and Opportunities.

Digital pathology (DP) is being deployed in many pathology laboratories, but most reported experiences refer to public health facilities. In this paper, we report our experience in DP transition at a high-volume private laboratory, addressing the main challenges in DP implementation in a private practice setting and how to overcome these issues. We started our implementation in 2020 and we are currently scanning 100% of our histology cases. Pre-existing sample tracking infrastructure facilitated this process. We are currently using two high-capacity scanners (Aperio GT450DX) to digitize all histology slides at 40×. Aperio eSlide Manager WebViewer viewing software is bidirectionally linked with the laboratory information system. Scanning error rate, during the test phase, was 2.1% (errors detected by the scanners) and 3.5% (manual quality control). Pre-scanning phase optimizations and vendor feedback and collaboration were crucial to improve WSI quality and are ongoing processes. Regarding pathologists' validation, we followed the Royal College of Pathologists recommendations for DP implementation (adapted to our practice). Although private sector implementation of DP is not without its challenges, it will ultimately benefit from DP safety and quality-associated features. Furthermore, DP deployment lays the foundation for artificial intelligence tools integration, which will ultimately contribute to improving patient care.

CAD systems for colorectal cancer from WSI are still not ready for clinical acceptance.

Most oncological cases can be detected by imaging techniques, but diagnosis is based on pathological assessment of tissue samples. In recent years, the pathology field has evolved to a digital era where tissue samples are digitised and evaluated on screen. As a result, digital pathology opened up many research opportunities, allowing the development of more advanced image processing techniques, as well as artificial intelligence (AI) methodologies. Nevertheless, despite colorectal cancer (CRC) being the second deadliest cancer type worldwide, with increasing incidence rates, the application of AI for CRC diagnosis, particularly on whole-slide images (WSI), is still a young field. In this review, we analyse some relevant works published on this particular task and highlight the limitations that hinder the application of these works in clinical practice. We also empirically investigate the feasibility of using weakly annotated datasets to support the development of computer-aided diagnosis systems for CRC from WSI. Our study underscores the need for large datasets in this field and the use of an appropriate learning methodology to gain the most benefit from partially annotated datasets. The CRC WSI dataset used in this study, containing 1,133 colorectal biopsy and polypectomy samples, is available upon reasonable request.

A Multiplex Test Assessing MiR663ame and VIMme in Urine Accurately Discriminates Bladder Cancer from Inflammatory Conditions.

Bladder cancer (BlCa) is a common malignancy with significant morbidity and mortality. Current diagnostic methods are invasive and costly, showing the need for newer biomarkers. Although several epigenetic-based biomarkers have been proposed, their ability to discriminate BlCa from common benign conditions of the urinary tract, especially inflammatory diseases, has not been adequately explored. Herein, we sought to determine whether VIMme and miR663ame might accurately discriminate those two conditions, using a multiplex test. Performance of VIMme and miR663ame in tissue samples and urines in testing set confirmed previous results (96.3% sensitivity, 88.2% specificity, area under de curve (AUC) 0.98 and 92.6% sensitivity, 75% specificity, AUC 0.83, respectively). In the validation sets, VIMme-miR663ame multiplex test in urine discriminated BlCa patients from healthy donors or patients with inflammatory conditions, with 87% sensitivity, 86% specificity and 80% sensitivity, 75% specificity, respectively. Furthermore, positive likelihood ratio (LR) of 2.41 and negative LR of 0.21 were also disclosed. Compared to urinary cytology, VIMme-miR663ame multiplex panel correctly detected 87% of the analysed cases, whereas cytology only forecasted 41%. Furthermore, high miR663ame independently predicted worse clinical outcome, especially in patients with invasive BlCa. We concluded that the implementation of this panel might better stratify patients for confirmatory, invasive examinations, ultimately improving the cost-effectiveness of BlCa diagnosis and management. Moreover, miR663ame analysis might provide relevant information for patient monitoring, identifying patients at higher risk for cancer progression.

Variants of Papillary Thyroid Carcinoma: An Algorithmic Cytomorphology-Based Approach to Cytology Specimens.

BACKGROUND: Thyroid cancer accounts for 1% of cancer cases in developed countries, in which papillary thyroid carcinoma (PTC) is the most common type. There are multiple variants of PTC described to date, some of them with aggressive behavior and poor clinical outcome. These variants are well described and accepted in recent guidelines of many international societies, and the prognostic and management implications are well laid out. Due to their established clinical importance and to guide appropriate surgical management, it is now imperative in clinical practice, including cytopathology, to differentiate aggressive variants from nonaggressive ones. This review aims to describe the variants of PTC and to provide a practical algorithmic approach to facilitate the cytological diagnosis of these variants. SUMMARY: Subtyping PTC variants on fine needle aspiration cytology (FNAC) is challenging even for the most experienced cytopathologist. To facilitate a correct subtyping on FNAC, we propose a stepwise approach that is mainly designed for conventional smear methodology. This approach requires first to stratify the lesions into oncocytic and nononcocytic features before analyzing further details in cell morphology and pattern. Key Messages: (1) Subtyping in PTC is possible on cytopathology. (2) The main aim of the cytopathologist is to differentiate aggressive from nonaggressive variants. (3) The subtyping of PTC can help in the surgical management of the patients.

Breast Fine Needle Aspiration Biopsy Cytology Using the Newly Proposed IAC Yokohama System for Reporting Breast Cytopathology: The Experience of a Single Institution.

OBJECTIVE: Recently the International Academy of Cytology (IAC) proposed a new reporting system for breast fine needle aspiration biopsy (FNAB) cytology. We aimed to categorize our samples according to this classification and to assess the risk of malignancy (ROM) for each category as well as the diagnostic yield of breast FNAB. STUDY DESIGN: Breast FNAB specimens obtained between January 2007 and December 2017 were reclassified according to the newly proposed IAC Yokohama reporting system. The ROM for each category was determined. Diagnostic yield was evaluated based on a three-category approach, benign versus malignant. RESULTS: The samples were distributed as follows: insufficient material 5.77%, benign 73.38%, atypical 13.74%, suspicious for malignancy 1.57%, and malignant 5.54%. Of the 3,625 cases collected, 776 (21.4%) had corresponding histology. The respective ROM for each category was 4.8% for category 1 (insufficient material), 1.4% for category 2 (benign), 13% for category 3 (atypical), 97.1% for category 4 (suspicious for malignancy), and 100% for category 5 (malignant). When only malignant cases were considered positive tests, the sensitivity, specificity, and diagnostic accuracy were 97.56, 100, and 99.11%, respectively. CONCLUSIONS: Our study is the first to categorize breast FNAB cytology samples according to the proposed IAC reporting system and to evaluate patient outcomes based on this categorization.

"The other side of the coin": understanding noninvasive follicular tumor with papillary-like nuclear features in unifocal and multifocal settings.

The recent description of noninvasive follicular tumor with papillary-like nuclear features (NIFTP) creates several diagnostic and therapeutic challenges for both the pathologist and the attending clinician. Given the concern about overtreatment of these neoplasms, the best way to manage the patients by a surgical procedure and postsurgical follow-up is still under discussion. We aimed to identify predictors of synchronous disease (eg, bilateral cancers) that can influence the appropriate type of surgery and long-term surveillance. We reevaluated the original diagnosis and the presence of contralateral lesions in 94 cases retrieved from our archives that were seen between 2010 and 2017. In 74 cases, the diagnosis was NIFTP, and in 20 cases, the diagnosis was infiltrative follicular variant of papillary thyroid carcinoma (IFVPTC). Bilateral disease was found in 17% of the cases. In 13 (18%) of those cases, NIFTP was the primary lesion, and in 3 (15%), it was IFVPTC. The contralateral disease was predominantly invasive: 6 cases of micropapillary carcinoma, 5 of papillary thyroid carcinoma, 3 of IFVPTC, and 2 of NIFTP. Despite the higher frequency of contralateral disease in NIFTP, there was no statistically significant difference with IFVPTC. In the patients with multifocal NIFTP, 2 (15%) of the contralateral malignancies showed microscopic extrathyroidal extension (P < .05). We conclude that close monitoring of the contralateral lobe is appropriate in patients with FVPTC, particularly NIFTP, if they are not submitted to total thyroidectomy.

A comparison study of the reporting systems for salivary gland fine needle aspirations: Are they really different?

BACKGROUND: Recently a new system for reporting salivary gland fine-needle aspiration (FNA) cytology was proposed, the Milan System for Reporting Salivary Gland Cytopathology (MSRSGC). Herein, we evaluated diagnostic accuracy of salivary gland FNA, comparing the system previously used in our hospital with the Milan system. METHODS: Salivary gland specimens obtained between 2011 and 2017 were reclassified according to MSRSGC. Risk of malignancy for each diagnostic category was determined. Diagnostic yield of both classifications was evaluated. RESULTS: The cases (n = 388) were classified according to the old system: nondiagnostic (n = 28), benign (n = 246), atypical (n = 36), neoplastic (n = 57), suspicious for malignancy (n = 7) and malignant (n = 14). The lesions were distributed according to the MSRSGC: nondiagnostic (n = 28), non-neoplastic (n = 89), atypia of undetermined significance (n = 39), benign neoplasm (n = 156), neoplasm of uncertain malignant potential (n = 55), suspicious for malignancy (n = 7) and malignant (n = 14). When considering only benign and malignant cases, both classifications showed the same sensitivity (62.5%), specificity (100%) and similar accuracy (95.8%). Comparison between the two systems showed no significant difference. CONCLUSIONS: Salivary gland FNA has high diagnostic accuracy and assists clinical management independently of the reporting system used, however, in some cases, the use of Milan system could be beneficial, since it allows an enhanced category stratification.

The new guidelines of Papanicolaou Society of Cytopathology for respiratory specimens: Assessment of risk of malignancy and diagnostic yield in different cytological modalities.

BACKGROUND: In 2016, the Papanicolaou Society of Cytopathology (PSC) issued a new classification scheme for respiratory cytology. We aim to evaluate our samples according to this classification and to assess risk of malignancy and diagnostic yield of different cytological modalities. METHODS: Respiratory specimens (sputum, bronchial wash/brush, BAL and FNA) obtained between 2007 and 2016 were reclassified according to PSC guidelines. Risk of malignancy for each diagnostic category was determined. Diagnostic yield was evaluated based on three-categorical approach. RESULTS: One thousand, two hundred and ninety respiratory specimens were retrieved, of which 280 had histologic follow-up. Samples were reclassified as nondiagnostic 16%, negative for malignancy 53%, atypical 5.4%, neoplastic (benign neoplasm/low-grade carcinoma) 0.4%, suspicious for malignancy 2.1% and malignant 23.1%. Risk of malignancy for each category was 64.01% for ND, 48.27% for NM, 59.09% for A, 100% for N-B-LG; 90% for SM and 89.74% for M. When only malignant cases were considered positive tests, cytology sensitivity was 55% and specificity 88%. CONCLUSION: Our results were in line with PSC guidelines, but the use of multiple cytological techniques may cause some discrepancies in overall diagnostic yield and in estimated risks of malignancy, which is important due to the widespread utilization of different cytological procedures.