Histopathological Information Research Articles

OGC SO33 - Resection Margins in Gastrectomy Patients – Are Current Guidelines Fit for Purpose? An Observational Study from a Tertiary Centre

Abstract Background Positive resection margins in cancer surgery are associated with higher likelihood of locoregional recurrence and poor outcomes. The Association of Upper GI Surgeons (AUGIS) outlined a target of less than 5% margin positivity for gastric resections, a goal not met in repeated national audits (NOGCA). Our study aimed to evaluate the positive resection margin rate after a gastrectomy in a tertiary centre and identify associated factors to assess the current guideline's suitability. Our hypothesis is that positive longitudinal margins are more related to tumour factors, such as proximity to the intended margin, rather than poor surgical technique. Method All records of patients who underwent gastrectomy from June 2015 to September 2021 were retrospectively reviewed. Data collection included demographic details, type of gastrectomy, exposure to neoadjuvant chemotherapy, and post operative histopathological information such as tumour differentiation, presence of signet ring (SR) morphology and linitis-plastica (LP), and resection margin(RM) status. Chi square tests of association were utilised to determine associations between the resection margin status and the potential determinant factors. Results Our study included 144 subjects (100 males, mean age 66, range 27-87). Types of gastrectomy were subtotal (38.9%), proximal (4.2%), total (29.2%), extended total (ETG) (27.8%). Positive RMs were found in 13.2%, with 8.3% having positive longitudinal margins and 6.3% positive circumferential margins. Neoadjuvant-chemotherapy was completed by 73% of subjects. Poor tumour differentiation was seen in 59.7%, SR presence in 12.5%, and LP in 3.5%. Significant associations were noted between positive margins and LP (80%, p<0.001) and age (<65: 15%, >=65: 4%, p=0.028). ETGs had double the rate of positive margins (12.5%) than that of other gastrectomies (6.7%,p=0.314). Conclusion The overall positive longitudinal margin rate was higher than the national guideline, replicating NOGCA audits. Our hypothesis was not proved: although the longitudinal positive margin rate in ETGs was almost double that of other gastrectomies, this did not reach statistical significance. Positive margin rates were higher in younger patients, and those with LP. Further large-scale audit is urgently required to determine if the recommended margin rate is appropriate or whether it should be adjusted for factors such as tumour location, presence of LP and age when the risk of a positive margin is higher.

PZS‐Net: Incorporating of Frame Sequence and Multi‐Scale Priors for Prostate Zonal Segmentation in Transrectal Ultrasound

Transrectal ultrasound (TRUS) videos offer valuable histopathologic information about the prostate. Accurate prostate zonal segmentation in TRUS videos is vital for diagnosing prostate cancer and guiding surgery. However, TRUS videos are manually recorded by urologists, resulting in no standardized coordinate system, which limits direct prostate zonal segmentation in these videos. To overcome the limitation, a novel Prostate Zonal Segmentation Network (PZS‐Net), based on U‐Net, which learns critical cross‐frame information and multi‐scale features from sequential frames, is proposed. First, a sequential frame cross‐attention (SFCA) module is designed to capture remote information from sequential frames to enhance the feature representation of the current frame. The SFCA module is embedded at each skip connection layer to extract crucial cross‐frame information. Then, a multi‐scale fusion (MSF) module that utilizes three parallel branches with different atrous convolutions is designed. The MSF module is placed at the bottleneck layer to dynamically fuse multi‐scale context information from high‐level features. Extensive experiments on TRUS image datasets show that the PZS‐Net achieves higher accuracy in both the transitional zone (dice coefficient [Dice]: 68.90% ± 1.73%, mean intersection over union [mIoU]: 59.19% ± 2.09%, 95% Hausdorff distance [HD95]: 5.02 ± 0.83 mm) and the peripheral zone (Dice: 63.99% ± 3.16%, mIoU: 54.60% ± 3.35%, HD95: 5.28 ± 1.12 mm) and demonstrates the effectiveness and competitiveness of its key components via comprehensive ablation studies.

Diagnostic accuracy and safety of renal tumour biopsy in patients with small renal masses and its impact on treatment decisions.

To assess the safety and diagnostic accuracy of renal tumour biopsy (RTB) in patients with small renal masses (SRM) and to assess if RTB prevents overtreatment in patients with benign SRM. In a retrospective, single-centre study from Västmanland, Sweden, 195 adult patients (69 women and 126 men) with SRM ≤4 cm who had undergone RTB during 2010-2023 were included. The median age was 70 years (range 23-89). The sensitivity, specificity and predictive values of RTB were calculated using the final diagnosis as the reference standard. Treatment outcomes were recorded for a median 42-month follow-up. Complications following the biopsies were assessed according to the Clavien-Dindo system. The overall sensitivity of RTB was 95% (95% confidence interval [CI] 90% - 98%) and specificity was 100% (95% CI 95% - 100%). The positive predictive value was 100% and negative predictive value was 92%. The rate of agreement between RTB and the final diagnosis measured using kappa statistics was 0.92. Of the 195 patients, 62 underwent surgery and 48 were treated with ablation. The concordance rate between the RTB histology and final histology after surgery was 89%. Treatment was withheld in 67 of 195 patients with a benign or inconclusive RTB. No patients developed renal cell carcinoma or metastasis during follow-up. Complications occurred in two patients that were classified with Clavien-Dindo grades I and IV. Percutaneous renal tumour biopsy appears to be a safe diagnostic method that provides accurate histopathological information about small renal masses and reduces overtreatment of benign SRM.

Predicting lymph node recurrence in cT1-2N0 tongue squamous cell carcinoma: collaboration between artificial intelligence and pathologists.

Researchers have attempted to identify the factors involved in lymph node recurrence in cT1-2N0 tongue squamous cell carcinoma (SCC). However, studies combining histopathological and clinicopathological information in prediction models are limited. We aimed to develop a highly accurate lymph node recurrence prediction model for clinical stage T1-2, N0 (cT1-2N0) tongue SCC by integrating histopathological artificial intelligence (AI) with clinicopathological information. A dataset from 148 patients with cT1-2N0 tongue SCC was divided into training and test sets. The prediction models were constructed using AI-extracted information from whole slide images (WSIs), human-assessed clinicopathological information, and both combined. Weakly supervised learning and machine learning algorithms were used for WSIs and clinicopathological information, respectively. The combination model utilised both algorithms. Highly predictive patches from the model were analysed for histopathological features. In the test set, the areas under the receiver operating characteristic (ROC) curve for the model using WSI, clinicopathological information, and both combined were 0.826, 0.835, and 0.991, respectively. The highest area under the ROC curve was achieved with the model combining WSI and clinicopathological factors. Histopathological feature analysis showed that highly predicted patches extracted from recurrence cases exhibited significantly more tumour cells, inflammatory cells, and muscle content compared with non-recurrence cases. Moreover, patches with mixed inflammatory cells, tumour cells, and muscle were significantly more prevalent in recurrence versus non-recurrence cases. The model integrating AI-extracted histopathological and human-assessed clinicopathological information demonstrated high accuracy in predicting lymph node recurrence in patients with cT1-2N0 tongue SCC.

Clinical significance of transjugular liver biopsy in acute liver failure – a real-world analysis

BackgroundHistopathological characterization obtained by transjugular liver biopsy (TJLB) may theoretically contribute to clarification of the exact aetiology of acute liver failure (ALF). It's unclear whether the histopathological information from TJLB, due to the small specimen size, significantly contributes to diagnosing ALF causes, guiding therapy decisions, or predicting overall prognosis. This retrospective study aimed to analyse safety and clinical significance of TJLB in patients with ALF.MethodsThis retrospective, monocentric study investigated safety and efficacy of TJLB in patients with ALF over a ten-year period at a tertiary care transplant-center. The predictive value of various clinical and laboratory characteristics as well as histopathological findings obtained by TJLB on 28-day liver-transplant-free survival were evaluated by calculating uni- and multivariate Cox-proportional hazard regression models. Additional univariate logistic regression analyses were performed to explore the influence of degree of intrahepatic necrosis on the secondary endpoints intensive-care-unit (ICU) admission, need for endotracheal intubation, renal replacement therapy and high-urgency listing for LTX.ResultsA total of 43 patients with ALF receiving TJLB were included into the study. In most cases (n = 39/43 cases) TJLB confirmed the initially already clinically presumed ALF aetiology and the therapeutic approach was unchanged by additional histological examination in the majority of patients (36/43 cases). However, in patients with a high suspicion for aetiologies potentially treatable by medical immunosuppression (e.g. AIH, GvHD), TJLB significantly influenced further treatment planning and/or adjustment. While the degree of intrahepatic necrosis showed significance in the univariate analysis (p = 0.04), it did not demonstrate a significant predictive effect on liver transplant-free survival in the multivariate analysis (p = 0.1). Only consecutive ICU admission was more likely with higher extent of intrahepatic necrosis (Odds ratio (OR) 1.04 (95% CI 1–1.08), p = 0.046).ConclusionsPerformance of TJLB in ALF led to a change in suspected diagnosis and to a significant change in therapeutic measures only in those patients with a presumed high risk for aetiologies potentially responsive to immunosuppressive therapy. Clinical assessment alone was accurate enough, with additional histopathological examination adding no significant value, to predict overall prognosis of patients with ALF.

What the Interventional Radiologist Needs to Know about the Genetics of Vascular Anomalies.

The purpose of this article is to familiarize the reader with the basic genetics and vascular biology behind the array of vascular anomalies they may encounter in their practice. Individuals with vascular malformations are often referred to multidisciplinary clinics composed of diverse specialists all with the same goal: how to provide the best care possible. The team is typically composed of physicians, nurses, social workers, and technical staff from multiple specialties including diagnostic and interventional radiology, dermatology, hematology/oncology, otolaryngology, plastic surgery, and several additional subspecialties. Imaging plays a crucial role in diagnosis and treatment planning, but increasingly biopsies are needed for more accurate histopathological and genetic information to inform the plan of treatment, as well as for counseling patients and their families on the natural history, heritability, and long-term prognosis of the condition. Understanding the molecular mechanism that gives rise to vascular anomalies is crucial for arriving at the proper diagnosis and choosing among treatment options. As oncological medications are being increasingly repurposed to treat vascular malformations, it is vital for those caring for patients with vascular anomalies to understand how these anomalies develop, and which drug may be appropriate to repurpose for this benign disease.

Spatial omics technologies for understanding molecular status associated with cancer progression.

Cancer cells are generally exposed to numerous extrinsic stimulations in the tumor microenvironment. In this environment, cancer cells change their expression profiles to fight against circumstantial stresses, allowing their progression in the challenging tissue space. Technological advancements of spatial omics have had substantial influence on cancer genomics. This technical progress, especially that occurring in the spatial transcriptome, has been drastic and rapid. Here, we describe the latest spatial analytical technologies that have allowed omics feature characterization to retain their spatial and histopathological information in cancer tissues. Several spatial omics platforms have been launched, and the latest platforms finally attained single-cell level or even higher subcellular level resolution. We discuss several key papers elucidating the initial utility of the spatial analysis. In fact, spatial transcriptome analyses reveal comprehensive omics characteristics not only in cancer cells but also their surrounding cells, such as tumor infiltrating immune cells and cancer-associated fibroblasts. We also introduce several spatial omics platforms. We describe our own attempts to investigate molecular events associated with cancer progression. Furthermore, we discuss the next challenges in analyzing the multiomics status of cells, including their morphology and location. These novel technologies, in conjunction with spatial transcriptome analysis and, more importantly, with histopathology, will elucidate even novel key aspects of the intratumor heterogeneity of cancers. Such enhanced knowledge is expected to open a new path for overcoming therapeutic resistance and eventually to precisely stratify patients.

Shear Wave Elastography in Breast Cancer: Unveiling Correlations With Histopathological Grades and Subtypes.

Objective This study explores the correlation between shear wave elastography (SWE) features and histopathological grades and subtypes in breast cancer, aiming to enhance diagnostic accuracy andpersonalized treatment strategies. Methods The study retrospectively analyzed 59 consecutive women with breast cancer who underwent breast ultrasound with SWE. SWE parameters and histopathologic information, including histological type and grade, were recorded. Qualitative and quantitative SWE findings were analyzed, and B-mode findings wereevaluated. Sociodemographic and clinical factors and B-mode findings were assessed as predictors of elastography stiffness using logistic regression analysis. Results Of the 59 participants diagnosed with breast cancer,invasive ductal carcinoma of no special type (IDC-NST) was predominantly found in 50 (84.7%) cases, followed by invasive medullary carcinoma in 5 (8.5%) cases. The majority of participants belonged to the 50-59 age group, comprising 19 (32.2%) patients. Histopathological grading revealed grade II tumors in 27 (45.8%) cases and grade III tumors in 24 (40.7%) cases. Notably, grade III tumors exhibited higher tissue stiffness compared to grade II tumors. Out of 36 stiff lesions, 30 (83.3%%) were IDC-NST while 3 (8.3%) wereinvasive medullary carcinoma. A significant association was observed between higher histopathological grade (grade III) and increased tissue stiffness (p < 0.05). Furthermore, among participants with stiff lesions, 21 (58.3%) exhibited color defectswhile 4 (23.5%) cases with soft lesions also displayed color defects Conclusion The correlation between SWE findings and histopathological grades and subtypes underscores the potential of SWE as a valuable tool for predicting tumor aggressiveness and characterizing specific subtypes.SWE enhances diagnostic accuracy and complements traditional imaging modalities, holding promise for personalized treatment strategies.

REAL-Colon: A dataset for developing real-world AI applications in colonoscopy

Detection and diagnosis of colon polyps are key to preventing colorectal cancer. Recent evidence suggests that AI-based computer-aided detection (CADe) and computer-aided diagnosis (CADx) systems can enhance endoscopists' performance and boost colonoscopy effectiveness. However, most available public datasets primarily consist of still images or video clips, often at a down-sampled resolution, and do not accurately represent real-world colonoscopy procedures. We introduce the REAL-Colon (Real-world multi-center Endoscopy Annotated video Library) dataset: a compilation of 2.7 M native video frames from sixty full-resolution, real-world colonoscopy recordings across multiple centers. The dataset contains 350k bounding-box annotations, each created under the supervision of expert gastroenterologists. Comprehensive patient clinical data, colonoscopy acquisition information, and polyp histopathological information are also included in each video. With its unprecedented size, quality, and heterogeneity, the REAL-Colon dataset is a unique resource for researchers and developers aiming to advance AI research in colonoscopy. Its openness and transparency facilitate rigorous and reproducible research, fostering the development and benchmarking of more accurate and reliable colonoscopy-related algorithms and models.

Digital histological staining of tissue slide images from optical coherence microscopy.

The convergence of staining-free optical imaging and digital staining technologies has become a central focus in digital pathology, presenting significant advantages in streamlining specimen preparation and expediting the rapid acquisition of histopathological information. Despite the inherent merits of optical coherence microscopy (OCM) as a staining-free technique, its widespread application in observing histopathological slides has been constrained. This study introduces a novel approach by combining wide-field OCM with digital staining technology for the imaging of histopathological slides. Through the optimization of the histology slide production process satisfying the ground growth for digital staining as well as pronounced contrast for OCM imaging, successful imaging of various mouse tissues was achieved. Comparative analyses with conventional staining-based bright field images were executed to evaluate the proposed methodology's efficacy. Moreover, the study investigates the generalization of digital staining color appearance to ensure consistent histopathology, considering tissue-specific and thickness-dependent variations.

Hyperspectral dark-field microscopy of human breast lumpectomy samples for tumor margin detection in breast-conserving surgery.

Hyperspectral dark-field microscopy (HSDFM) and data cube analysis algorithms demonstrate successful detection and classification of various tissue types, including carcinoma regions in human post-lumpectomy breast tissues excised during breast-conserving surgeries. We expand the application of HSDFM to the classification of tissue types and tumor subtypes in pre-histopathology human breast lumpectomy samples. Breast tissues excised during breast-conserving surgeries were imaged by the HSDFM and analyzed. The performance of the HSDFM is evaluated by comparing the backscattering intensity spectra of polystyrene microbead solutions with the Monte Carlo simulation of the experimental data. For classification algorithms, two analysis approaches, a supervised technique based on the spectral angle mapper (SAM) algorithm and an unsupervised technique based on the -means algorithm are applied to classify various tissue types including carcinoma subtypes. In the supervised technique, the SAM algorithm with manually extracted endmembers guided by H&E annotations is used as reference spectra, allowing for segmentation maps with classified tissue types including carcinoma subtypes. The manually extracted endmembers of known tissue types and their corresponding threshold spectral correlation angles for classification make a good reference library that validates endmembers computed by the unsupervised -means algorithm. The unsupervised -means algorithm, with no a priori information, produces abundance maps with dominant endmembers of various tissue types, including carcinoma subtypes of invasive ductal carcinoma and invasive mucinous carcinoma. The two carcinomas' unique endmembers produced by the two methods agree with each other within residual error margin. Our report demonstrates a robust procedure for the validation of an unsupervised algorithm with the essential set of parameters based on the ground truth, histopathological information. We have demonstrated that a trained library of the histopathology-guided endmembers and associated threshold spectral correlation angles computed against well-defined reference data cubes serve such parameters. Two classification algorithms, supervised and unsupervised algorithms, are employed to identify regions with carcinoma subtypes of invasive ductal carcinoma and invasive mucinous carcinoma present in the tissues. The two carcinomas' unique endmembers used by the two methods agree to residual error margin. This library of high quality and collected under an environment with no ambient background may be instrumental to develop or validate more advanced unsupervised data cube analysis algorithms, such as effective neural networks for efficient subtype classification.

Advantage of whole-mount histopathology in prostate cancer: current applications and future prospects

BackgroundWhole-mount histopathology (WMH) has been a powerful tool to investigate the characteristics of prostate cancer. However, the latest advancement of WMH was yet under summarization. In this review, we offer a comprehensive exposition of current research utilizing WMH in diagnosing and treating prostate cancer (PCa), and summarize the clinical advantages of WMH and outlines potential on future prospects.MethodsAn extensive PubMed search was conducted until February 26, 2023, with the search term “prostate”, “whole-mount”, “large format histology”, which was limited to the last 4 years. Publications included were restricted to those in English. Other papers were also cited to contribute a better understanding.ResultsWMH exhibits an enhanced legibility for pathologists, which improved the efficacy of pathologic examination and provide educational value. It simplifies the histopathological registration with medical images, which serves as a convincing reference standard for imaging indicator investigation and medical image-based artificial intelligence (AI). Additionally, WMH provides comprehensive histopathological information for tumor volume estimation, post-treatment evaluation, and provides direct pathological data for AI readers. It also offers complete spatial context for the location estimation of both intraprostatic and extraprostatic cancerous region.ConclusionsWMH provides unique benefits in several aspects of clinical diagnosis and treatment of PCa. The utilization of WMH technique facilitates the development and refinement of various clinical technologies. We believe that WMH will play an important role in future clinical applications.

Lung Cancer Diagnosis on Virtual Histologically Stained Tissue Using Weakly Supervised Learning

Lung adenocarcinoma (LUAD) is the most common primary lung cancer and accounts for 40% of all lung cancer cases. The current gold standard for lung cancer analysis is based on the pathologists’ interpretation of hematoxylin and eosin (H&E)-stained tissue slices viewed under a brightfield microscope or a digital slide scanner. Computational pathology using deep learning has been proposed to detect lung cancer on histology images. However, the histological staining workflow to acquire the H&E-stained images and the subsequent cancer diagnosis procedures are labor-intensive and time-consuming with tedious sample preparation steps and repetitive manual interpretation, respectively. In this work, we propose a weakly supervised learning method for LUAD classification on label-free tissue slices with virtual histological staining. The autofluorescence images of label-free tissue with histopathological information can be converted into virtual H&E-stained images by a weakly supervised deep generative model. For the downstream LUAD classification task, we trained the attention-based multiple-instance learning model with different settings on the open-source LUAD H&E-stained whole-slide images (WSIs) dataset from the Cancer Genome Atlas (TCGA). The model was validated on the 150 H&E-stained WSIs collected from patients in Queen Mary Hospital and Prince of Wales Hospital with an average area under the curve (AUC) of 0.961. The model also achieved an average AUC of 0.973 on 58 virtual H&E-stained WSIs, comparable to the results on 58 standard H&E-stained WSIs with an average AUC of 0.977. The attention heatmaps of virtual H&E-stained WSIs and ground-truth H&E-stained WSIs can indicate tumor regions of LUAD tissue slices. In conclusion, the proposed diagnostic workflow on virtual H&E-stained WSIs of label-free tissue is a rapid, cost effective, and interpretable approach to assist clinicians in postoperative pathological examinations. The method could serve as a blueprint for other label-free imaging modalities and disease contexts.

A clinicopathological analysis of 151 odontogenic tumors based on new WHO classification 2022: A retrospective cross-sectional study

Background: Odontogenic tumors are a diverse group of lesions with a variety of clinical behavior and histopathologic subtypes, from hamartomatous and benign to malignant. The study aimed to examine the clinical and pathological features of odontogenic tumors in Baghdad over the last 11 years (2011–2021). Materials and Methods: The present retrospective study analyzed all formalin-fixed, paraffin-embedded tissue blocks of patients diagnosed with an odontogenic tumor that were retrieved from archives at a teaching hospital/College of Dentistry in Baghdad University, Iraq, between 2011 and 2021. The diagnosis of each case was confirmed by examining the hematoxylin and eosin stained sections by two expert pathologists. Data from patients' case sheets were collected, including age, gender, location, and histopathological information. The type of lesions was evaluated based on the World Health Organization's most recent classification (March 2022). Results: There were 151 odontogenic tumor during this period. The most common type (39.1%) was Solid ameloblastoma. The mandibular tumors (76.8%) were more than the maxillary tumors (23.2%). The female to male ratio was 1.1:1. The most cases are found between the 2nd and 5th decades of life. Conclusions: Solid ameloblastoma was the most common odontogenic tumor, while primordial odontogenic tumor was the rarest, Odontogenic tumors were slightly more common in females than in males, the most common cases occur in the mandible., the outcome of the study gives valuable information regarding the patients' profile and type of odontogenic tumors over 11 years, which could aid in the early diagnosis and enhance the intervention.

Deep Learning to Differentiate Benign and Malignant Vertebral Fractures at Multidetector CT.

Background Differentiating between benign and malignant vertebral fractures poses diagnostic challenges. Purpose To investigate the reliability of CT-based deep learning models to differentiate between benign and malignant vertebral fractures. Materials and Methods CT scans acquired in patients with benign or malignant vertebral fractures from June 2005 to December 2022 at two university hospitals were retrospectively identified based on a composite reference standard that included histopathologic and radiologic information. An internal test set was randomly selected, and an external test set was obtained from an additional hospital. Models used a three-dimensional U-Net encoder-classifier architecture and applied data augmentation during training. Performance was evaluated using the area under the receiver operating characteristic curve (AUC) and compared with that of two residents and one fellowship-trained radiologist using the DeLong test. Results The training set included 381 patients (mean age, 69.9 years ± 11.4 [SD]; 193 male) with 1307 vertebrae (378 benign fractures, 447 malignant fractures, 482 malignant lesions). Internal and external test sets included 86 (mean age, 66.9 years ± 12; 45 male) and 65 (mean age, 68.8 years ± 12.5; 39 female) patients, respectively. The better-performing model of two training approaches achieved AUCs of 0.85 (95% CI: 0.77, 0.92) in the internal and 0.75 (95% CI: 0.64, 0.85) in the external test sets. Including an uncertainty category further improved performance to AUCs of 0.91 (95% CI: 0.83, 0.97) in the internal test set and 0.76 (95% CI: 0.64, 0.88) in the external test set. The AUC values of residents were lower than that of the best-performing model in the internal test set (AUC, 0.69 [95% CI: 0.59, 0.78] and 0.71 [95% CI: 0.61, 0.80]) and external test set (AUC, 0.70 [95% CI: 0.58, 0.80] and 0.71 [95% CI: 0.60, 0.82]), with significant differences only for the internal test set (P < .001). The AUCs of the fellowship-trained radiologist were similar to those of the best-performing model (internal test set, 0.86 [95% CI: 0.78, 0.93; P = .39]; external test set, 0.71 [95% CI: 0.60, 0.82; P = .46]). Conclusion Developed models showed a high discriminatory power to differentiate between benign and malignant vertebral fractures, surpassing or matching the performance of radiology residents and matching that of a fellowship-trained radiologist. © RSNA, 2024 See also the editorial by Booz and D'Angelo in this issue.

Digital pathology-based artificial intelligence models for differential diagnosis and prognosis of sporadic odontogenic keratocysts

Odontogenic keratocyst (OKC) is a common jaw cyst with a high recurrence rate. OKC combined with basal cell carcinoma as well as skeletal and other developmental abnormalities is thought to be associated with Gorlin syndrome. Moreover, OKC needs to be differentiated from orthokeratinized odontogenic cyst and other jaw cysts. Because of the different prognosis, differential diagnosis of several cysts can contribute to clinical management. We collected 519 cases, comprising a total of 2 157 hematoxylin and eosin-stained images, to develop digital pathology-based artificial intelligence (AI) models for the diagnosis and prognosis of OKC. The Inception_v3 neural network was utilized to train and test models developed from patch-level images. Finally, whole slide image-level AI models were developed by integrating deep learning-generated pathology features with several machine learning algorithms. The AI models showed great performance in the diagnosis (AUC = 0.935, 95% CI: 0.898–0.973) and prognosis (AUC = 0.840, 95%CI: 0.751–0.930) of OKC. The advantages of multiple slides model for integrating of histopathological information are demonstrated through a comparison with the single slide model. Furthermore, the study investigates the correlation between AI features generated by deep learning and pathological findings, highlighting the interpretative potential of AI models in the pathology. Here, we have developed the robust diagnostic and prognostic models for OKC. The AI model that is based on digital pathology shows promise potential for applications in odontogenic diseases of the jaw.

Molecular prognostication in grade 3 meningiomas and p16/MTAP immunohistochemistry for predicting CDKN2A/B status.

The World Health Organization 2021 classification introduces molecular grading criteria for anaplastic meningiomas, including TERT promoter (TERTp) mutations and CDKN2A/B homozygous deletion. Additional adverse prognostic factors include H3K27me3 and BAP1 loss. The aim of this study was to explore whether these molecular alterations stratified clinical outcomes in a single-center cohort of grade 3 meningiomas. Additionally, we examined whether p16 and MTAP immunohistochemistry can predict CDKN2A/B status. Clinical and histopathological information was obtained from the electronic medical records of grade 3 meningiomas resected at a tertiary center between 2007 and 2020. Molecular testing for TERTp mutations and CDKN2A/B copy-number status, methylation profiling, and immunohistochemistry for H3K27me3, BAP1, p16, and methylthioadenosine phosphorylase (MTAP) were performed. Predictors of survival were identified by Cox regression. Eight of 15 cases demonstrated elevated mitotic index (≥20 mitoses per 10 consecutive high-power fields), 1 tumor exhibited BAP1 loss, 4 harbored TERTp mutations, and 3 demonstrated CDKN2A/B homozygous deletion. Meningiomas with TERTp mutations and/or CDKN2A/B homozygous deletion showed significantly reduced survival compared to anaplastic meningiomas with elevated mitotic index alone. Immunohistochemical loss of p16 and MTAP demonstrated high sensitivity (67% and 100%, respectively) and specificity (100% and 100%, respectively) for predicting CDKN2A/B status. Molecular alterations of grade 3 meningiomas stratify clinical outcomes more so than histologic features alone. Immunohistochemical loss of p16 and MTAP show promise in predicting CDKN2A/B status.

The newest IC3D classification of corneal dystrophies

In the year 2015, the International Committee for the Classification of Corneal Dystrophies (IC3D) incorporated new clinical, histopathologic, and genetic information into the former 2008 classification. To this end, the IC3D sighted and evaluated worldwide peer‐reviewed articles for new information on corneal dystrophies published between 2008 and 2014. Using this information, corneal dystrophy templates including categories 1 to 4 and anatomic classification were updated. New clinical, histopathologic, and confocal photographs were added.On the basis of revisiting the cellular origin of corneal dystrophies, a modified anatomic classification was proposed consisting of (1) epithelial and subepithelial dystrophies, (2) epithelial‐stromal TGFBI dystrophies, (3) stromal dystrophies, and (4) endothelial dystrophies. The entity “Epithelial recurrent erosion dystrophies ERED” actually includes a number of potentially distinct epithelial dystrophies (Franceschetti corneal dystrophy, Dystrophia Smolandiensis, and Dystrophia Helsinglandica) but must be differentiated from dystrophies such as TGFBI‐induced dystrophies, which are also often associated with recurrent epithelial erosions. The chromosome locus of Thiel‐Behnke corneal dystrophy is only located on 5q31. The entity previously designated as a variant of Thiel‐Behnke corneal dystrophy on chromosome 10q24 may represent a novel corneal dystrophy. Congenital hereditary endothelial dystrophy (CHED, formerly CHED2) is most likely only an autosomal recessive disorder. The so‐called autosomal dominant inherited CHED (formerly CHED1) is insufficiently distinct to continue to be considered a unique corneal dystrophy. On review of almost all of the published cases, the description appeared most similar to a type of posterior polymorphous corneal dystrophy linked to the same chromosome 20 locus (PPCD1). Anterior segment OCT has emerged as a helpful tool to reveal in vivo features of several corneal dystrophies that previously required histopathologic examination in order to definitively diagnose them.This revision of the IC3D classification includes an updated anatomic classification of corneal dystrophies that more accurately classifies TGFBI dystrophies affecting multiple layers rather than being confined to one corneal layer. At this time, a 3rd edition of the IC3D classification of corneal dystrophies is under way.

Surgical and health related quality of life outcomes following treatment with zygomatic implant perforated (ZIP) flaps.

The zygomatic implant perforated (ZIP) flap is a novel approach to the challenge of reconstructing the maxilla. We report on our experience using the ZIP flap technique for patients undergoing infrastructure maxillectomy at Chris O'Brien Lifehouse, Sydney, Australia. Thirteen patients who underwent a ZIP flap reconstruction between August 2019 and August 2021 were identified. Demographic, surgical, and histopathological information was collected. Health Related Quality of Life (HRQOL) was assessed using the FACE-Q Head and Neck Cancer module, the M.D. Anderson Dysphagia Inventory, and the Speech Handicap Index. A total of 44 zygomatic implants were placed, of which 42 (95%) survived. The median time from surgery to dental rehabilitation was 35 days. HRQOL data was available for nine patients over 24 months, demonstrating improved speech and swallowing outcomes over the follow up period. The ZIP flap is a reproducible surgical technique that facilitates rapid dental rehabilitation post infrastructure maxillectomy.

The Predictive Role of the Histopathological Scoring System in Adipose Tumors-Lipoma, Atypical Lipomatous Tumor, and Liposarcoma.

Lipomatous tumors are the most frequent soft tissue neoplasms. Sometimes their differential diagnosis is difficult to perform only by microscopic analysis. This study aims to create a histopathological scoring system and highlight the impact of intratumoral microvascular density. This study was conducted over 10 years. We analyzed the main pathogenic pathways (MDM2 and CDK4), as well as the tumor microvascularization (CD31 and CD34) by immunohistochemical tests. We also analyzed the status of the MDM2 gene by CISH. These data, together with the clinical and histopathological information, were statistically analyzed by appropriate tests. We identified 112 eligible cases, with most of the patients being in their sixth decade of life, with a slight predominance of the female sex. We found important associations like tumor location linked to nuclear pleomorphism severity and microvascularization density correlated with atypia severity. Also, we observed that a maximum diameter of a tumor of at least 69 mm is associated with the presence of tumor necrosis. The score designed in this study shows an increased sensitivity and specificity for the diagnosis of lipomas (100%, respectively, 97%), atypical lipomatous tumors (93.8%, respectively, 82.3%), and liposarcomas (100%, respectively, 90.5%). This present study enhances the present data by bringing to attention the histopathological score with a role in differential diagnosis, as well as in the prediction of immunohistochemical and genetic tests. Also, we highlighted the importance of microvascular density, especially in the diagnosis of liposarcomas.