Pathologic Interpretation Research Articles

Contemporary Diagnostic Reporting for Prostatic Adenocarcinoma: Morphologic Aspects, Molecular Correlates, and Management Perspectives.

The diagnosis and reporting of prostatic adenocarcinoma have evolved from the classic framework promulgated by Dr Donald Gleason in the 1960s into a complex and nuanced system of grading and reporting that nonetheless retains the essence of his remarkable observations. The criteria for the "Gleason patterns" originally proposed have been continually refined by consensuses in the field, and Gleason scores have been stratified into a patient-friendly set of prognostically validated and widely adopted Grade Groups. One product of this successful grading approach has been the opportunity for pathologists to report diagnoses that signal carefully personalized management, placing the surgical pathologist's interpretation at the center of patient care. At one end of the continuum of disease aggressiveness, personalized diagnostic care means to sub-stratify patients with more indolent disease for active surveillance, while at the other end of the continuum, reporting histologic markers signaling aggression allows sub-stratification of clinically significant disease. Whether contemporary reporting parameters represent deeper nuances of more established ones (eg, new criteria and/or quantitation of Gleason patterns 4 and 5) or represent additional features reported alongside grade (intraductal carcinoma, cribriform patterns of carcinoma), assessment and grading have become more complex and demanding. Herein, we explore these newer reporting parameters, highlighting the state of knowledge regarding morphologic, molecular, and management aspects. Emphasis is made on the increasing value and stakes of histopathologists' interpretations and reporting into current clinical risk stratification and treatment guidelines.

Rapid and Label-Free Histopathology of Oral Lesions Using Deep Learning Applied to Optical and Infrared Spectroscopic Imaging Data.

Oral potentially malignant disorders (OPMDs) are precursors to over 80% of oral cancers. Hematoxylin and eosin (H&E) staining, followed by pathologist interpretation of tissue and cellular morphology, is the current gold standard for diagnosis. However, this method is qualitative, can result in errors during the multi-step diagnostic process, and results may have significant inter-observer variability. Chemical imaging (CI) offers a promising alternative, wherein label-free imaging is used to record both the morphology and the composition of tissue and artificial intelligence (AI) is used to objectively assign histologic information. Here, we employ quantum cascade laser (QCL)-based discrete frequency infrared (DFIR) chemical imaging to record data from oral tissues. In this proof-of-concept study, we focused on achieving tissue segmentation into three classes (connective tissue, dysplastic epithelium, and normal epithelium) using a convolutional neural network (CNN) applied to three bands of label-free DFIR data with paired darkfield visible imaging. Using pathologist-annotated H&E images as the ground truth, we demonstrate results that are 94.5% accurate with the ground truth using combined information from IR and darkfield microscopy in a deep learning framework. This chemical-imaging-based workflow for OPMD classification has the potential to enhance the efficiency and accuracy of clinical oral precancer diagnosis.

Using Patient Navigation to Reduce Time to Diagnosis of Breast Cancer in Uganda

PurposeThe Ugandan Ministry of Health adopted BI-RADS as standard of care in 2016. The authors performed a medical audit of breast ultrasound practices at four tertiary-level hospitals to assess interpretive performance. The authors also determined the effect of a low-cost navigation program linking breast imaging and pathology on the percentage of patients completing diagnostic care. MethodsThe authors retrieved 966 consecutive diagnostic breast ultrasound reports, with complete data, for studies performed on women aged >18 years presenting with symptoms of breast cancer between 2018 and 2020 from participating hospitals. Ultrasound results were linked to tumor registries and patient follow-up. A medical audit was performed according to the ACR’s BI-RADS Atlas, fifth edition, and results were compared with those of a prior audit performed in 2013. At Mulago Hospital, an intervention was piloted on the basis of patient navigation, cost sharing, and same-day imaging, tissue sampling, and pathology. ResultsIn total, 888 breast ultrasound examinations (91.9%) were eligible for inclusion. Compared with 2013, the postintervention cancer detection rate increased from 38 to 148.7 cancers per 1,000 examinations, positive predictive value 2 from 29.6% to 48.9%, and positive predictive value 3 from 62.7% to 79.9%. Specificity decreased from 90.5% to 87.7% and sensitivity from 92.3% to 81.1%. The mean time from tissue sampling to receipt of a diagnosis decreased from 60 to 7 days. The intervention increased the percentage of patients completing diagnostic care from 0% to 100%. ConclusionsEfforts to establish a culture of continuous quality improvement in breast ultrasound require robust data collection that links imaging results to pathology and patient follow-up. Interpretive performance met BI-RADS benchmarks for palpable masses, except sensitivity. This resource-appropriate strategy linking imaging, tissue sampling, and pathology interpretation decreased time to diagnosis and rates of loss to follow-up and improved the precision of the audit.

Integrating a self-directed ultrasound curriculum for the internal medicine clerkship

BackgroundIncorporating ultrasound into the clinical curriculum of undergraduate medical education has been limited by a need for faculty support. Without integration into the clinical learning environment, ultrasound skills become a stand-alone skill and may decline by the time of matriculation into residency. A less time intensive ultrasound curriculum is needed to preserve skills acquired in preclinical years. We aimed to create a self-directed ultrasound curriculum to support and assess students’ ability to acquire ultrasound images and to utilize ultrasound to inform clinical decision-making.MethodsThird year students completed the self-directed ultrasound curriculum during their required internal medicine clerkship. Students used Butterfly iQ+ portable ultrasound probes. The curriculum included online modules that focused on clinical application of ultrasound as well as image acquisition technique. Students were graded on image acquisition quality and setting, a patient write-up focused on clinical decision-making, and a multiple-choice quiz. Student feedback was gathered with an end-of-course survey. Faculty time was tracked.ResultsOne hundred and ten students participated. Students averaged 1.79 (scale 0–2; SD = 0.21) on image acquisition, 78% (SD = 15%) on the quiz, and all students passed the patient write-up. Most reported the curriculum improved their clinical reasoning (72%), learning of pathophysiology (69%), and patient care (55%). Faculty time to create the curriculum was approximately 45 h. Faculty time to grade student assignments was 38.5 h per year.ConclusionsStudents were able to demonstrate adequate image acquisition, use of ultrasound to aid in clinical decision-making, and interpretation of ultrasound pathology with no in-person faculty instruction. Additionally, students reported improved learning of pathophysiology, clinical reasoning, and rapport with patients. The self-directed curriculum required less faculty time than prior descriptions of ultrasound curricula in the clinical years and could be considered at institutions that have limited faculty support.

Immunohistochemical assessment of HER2 low breast cancer: interobserver reproducibility and correlation with digital image analysis.

The recent findings from the DESTINY-Breast04 trial highlighted the clinical importance of distinguishing between HER2 immunohistochemistry (IHC) scores 0 and 1 + in metastatic breast cancer (BC). However, pathologist interpretation of HER2 IHC scoring is subjective, and standardized methodology is needed. We evaluated the consistency of HER2 IHC scoring among pathologists and the accuracy of digital image analysis (DIA) in interpreting HER2 IHC staining in cases of HER2-low BC. Fifty whole-slide biopsies of BC with HER2 IHC staining were evaluated, comprising 25 cases originally reported as IHC score 0 and 25 as 1 +. These slides were digitally scanned. Six pathologists with breast expertise independently reviewed and scored the scanned images, and DIA was applied. Agreement among pathologists and concordance between pathologist scores and DIA results were statistically analyzed using Kendall coefficient of concordance (W) tests. Substantial agreement among at least five of the six pathologists was found for 18 of the score 0 cases (72%) and 15 of the score 1 + cases (60%), indicating excellent interobserver agreement (W = 0.828). DIA scores were highly concordant with pathologist scores in 96% of cases (47/49), indicating excellent concordance (W = 0.959). Although breast subspecialty pathologists were relatively consistent in evaluating BC with HER2 IHC scores of 0 and 1 +, DIA may be a reliable supplementary tool to enhance the standardization and quantification of HER2 IHC assessment, especially in challenging cases where results may be ambiguous (i.e., scores 0-1 +). These findings hold promise for improving the accuracy and consistency of HER2 testing.

Pathologists’ perspective on the study design, analysis, and interpretation of proliferative lesions in a lifetime rodent carcinogenicity bioassay of sucralose

Sucralose, a sugar substitute first approved for use in 1991, is a non-caloric sweetener regulated globally as a food additive. Based on numerous experimental animal studies (dating to the 1980s) and human epidemiology studies, international health agencies have determined that sucralose is safe when consumed as intended. A single lifetime rodent carcinogenicity bioassay conducted by the Ramazzini Institute (RI) reported that mice fed diets containing sucralose develop hematopoietic neoplasia, but controversy continues regarding the validity and relevance of these data for predicting health effects in humans. The present paper addresses the controversy by providing the perspective of experienced pathologists on sucralose-related animal toxicity and carcinogenicity data generally, and the RI carcinogenicity bioassay findings specifically, using results from publicly available papers and international regulatory authority decisions. In the authors’ view, flaws in the design, methodology, data evaluation, and reporting of the RI carcinogenicity bioassay for sucralose diminish the value of the data as evidence that this agent represents a carcinogenic hazard to humans. This limitation will remain until the RI bioassay is repeated under Good Laboratory Practices and the design, data, and accuracy of the pathology diagnoses and interpretations are reviewed by qualified pathologists with experience in evaluating potential chemically-induced carcinogenic hazards.

P153 Artificial Intelligence to predict interleukin-23 signalling activity from H&E stained whole slide images of Inflammatory Bowel Disease

Abstract Background Interleukin23 (IL-23) is a cytokine that plays a crucial role in the pathogenesis of inflammatory bowel disease (IBD), making it a highly validated therapeutic target. Understanding the role of IL-23 in IBD at the histopathological level is crucial for determining effective treatment strategies, providing insights into IBD patients who fail to respond to targeted therapies, or predicting those who are likely to lose response. In this context, there is a surge in utilizing artificial intelligence (AI) for histopathological data in IBD and other disease indications. Here, we present an automated computer vision approach to predict IL-23 signalling activity directly from routinely stained Hematoxylin and Eosin (H&E) images Methods A total of 1502 samples with matched clinical data and H&E biopsy images were included from 991 Crohn’s disease (CD) and 511 ulcerative colitis (UC) samples. IL-23 signalling activity was calculated using gene set variation analysis on RNA-seq data collected from the same tissue biopsies. The data were obtained from the IBD Plexus program of the Crohn’s & Colitis Foundation. The proposed approach is based on vision transformers (ViTs) which is a type of deep learning model. ViTs divide the input image into fixed-size patches, transform it into linear embedding, and analyze it with the self-attention mechanism. This enables ViTs to incorporate relationships between different patches of the input image to identify regions predictive of IL-23. Our approach was trained in a weakly supervised manner to automatically identify tissue regions that correlate with IL-23 signaling activity. The model produces interpretable heatmaps to interrogate model predictions and allow clinicians to visualize and interpret the significance of different tissue regions predictive of IL-23 Results We performed 5-fold cross-validation on the splits obtained at the patient level, retaining the data distribution of IL-23 signaling activity, biopsy location, and diagnosis. We separately validated the performance of the proposed model on both disease categories, including CD and UC. The proposed approach achieved an area under the curve (AUC) of 0.82 ± 0.04 on unseen data from CD and an AUC of 0.80 ± 0.02 for UC. The 5-fold results for both disease categories are shown below Conclusion The presented results highlight the significance of computational pathology algorithms to identify IL-23 signalling activity from H&E images. Pathological interpretation from the heatmaps may help understand disease pathomechanism and optimize the treatment options for IBD patients by timely identification of IL-23 status. We are further validating the clinical utility of such heatmaps and expanding the use of H&E to predict other patient-centric endpoints

Deep learning in interstitial lung disease: classification and prognostic insights

Interstitial lung disease (ILD) comprises diverse parenchymal lung disorders, and are an important cause of morbidity and mortality among lung diseases. Disagreement is frequently observed among radiologic reads, pathologic interpretations, and multidisciplinary discussion consensus. Therefore, establishing a definitive diagnosis of ILD by using current techniques and criteria poses a considerable challenge. High-resolution computed tomography (HRCT) plays a crucial role in characterizing imaging patterns and predicting ILD prognosis. However, the substantial overlap in radiographic findings hinders accurate diagnosis of ILD in HRCT, even by experienced radiologists. Recently, deep learning (DL), a strategy that can automatically learn important characteristic features and patterns within CT images, has shown great potential in classifying and predicting ILD prognosis. This review summarizes current DL applications in ILD classification and prognosis evaluation; discusses challenges in clinical implementation; and presents insights for advancing this field. In conclusion, advanced DL can enhance diagnostic accuracy and enable more personalized treatment, thus providing new perspectives for managing ILD in the future.

Lymphoma Diagnosis and Classification Using Next Generation Sequencing of 30 CD Markers and Machine Learning As an Alternative to Immunohistochemistry

Introduction: Lymphoma diagnosis and classification requires pathologist interpretation of morphology and large numbers of immunohistochemistry (IHC) stains of various CD markers. This process is subjective and requires a significant amount of tissue. In contrast, RNA quantification of the same CD markers used in IHC using next generation sequencing (NGS) requires little tissue and is less influenced by the antigen retrieval process used in IHC. However, IHC staining and microscopic examination allows evaluation of the expression in various subpopulations and makes diagnosis possible. In contrast, when total RNA is evaluated by NGS, distinguishing between subpopulations is lost. Machine learning algorithms are capable of multi-marker normalizing and compensate for the loss of subpopulation analysis. To confirm this, we explored the capability of using RNA quantification of 30 CD markers by NGS from FFPE tissue along with machine learning in the clinical diagnosis and classification of various types of lymphoma. Methods: Formalin-fixed paraffin-embedded (FFPE) tissue from 130 diffuse large B-cell lymphoma (DLBCL), 70 mantle cell lymphoma, 92 T-cell lymphoma, 48 follicular lymphoma, 36 Hodgkin lymphoma, and 52 marginal zone lymphoma samples were used for extracting mRNA. The studied samples were consecutive without selection and included mainly lymph node excisional biopsies or core biopsies. RNA sequencing was performed using a targeted hybrid capture panel that included CD1A, CD2, CD3D, CD3E, CD3G, CD4, CD5, CD7, CD8A, CD8B, CD10, CD14, CD19, CD20, CD22, CD33, CD34, CD38, CD40, CD44, CD47, CD68, CD70, CD74, CD79A, CD79B, CD81, CD138, CD200, and CD274 genes. Salmon v1.4.0 software was used for expression quantification (TPM). Random forest machine learning algorithm was used for predicting diagnosis. Randomly selected two thirds of samples were used for training and one third was used for testing. Results: In some cases, diagnosis can be made by simply inspecting the RNA levels of various CD markers. However, machine learning shows remarkably high sensitivity and specificity in the diagnosis of most lymphoma subclasses. Area under the curve (AUC) was at 1.00 (95% CI: 1.000-1.00) for DLBCL vs. T-cell lymphoma, Hodgkin vs. T-cell, Hodgkin vs. DLBCL, mantle vs. DLBCL, and Follicular lymphoma vs. marginal lymphoma with 100% sensitivity and specificity in the testing set. AUC was at 0.974 (95% CI: 0.920-1.000) for marginal lymphoma vs. mantle cell lymphoma with sensitivity of 88% and specificity of 100%. The AUC was at 0.887 (95% CI: 0.776-0.999) for follicular lymphoma vs. DLBCL with sensitivity of 81.3% and specificity of 83.7%. Conclusions: This data demonstrates that NGS quantification of RNA from 30 CD markers when combined with machine learning is adequate for reliable classification of various types of lymphoma. This approach can provide valuable information to distinguish between difficult diagnoses, and if trained adequately has the potential to expand to more borderline cases. More importantly, this technology can be automated and less susceptible to human errors. RNA quantification using NGS has the potential to replace the need for IHC and can be applied when samples are limited such as in needle aspiration or core biopsies.

To swab or not to swab? A qualitative study of pathology testing, interpretation, and value in diabetes-related foot ulceration

BackgroundDiagnostic testing has been proposed as a key strategy to tackle escalating antimicrobial resistance (AMR). However, effectiveness of testing is limited by the complexities of the hospital environment, including human factors. ObjectivesTo examine swab-testing in diabetes-related foot infections as a case study of the factors impacting microbiology testing use, efficacy, and antimicrobial resistance. MethodsSeventeen clinicians involved in the management of diabetes-related foot infections, including podiatrists, nurses, and doctors, participated in in-depth individual interviews conducted by a qualitative researcher on the investigation and management of diabetes-related foot infections. Thematic analysis was performed. ResultsThe multilayered and evolving features of the human-diagnostic interface were described by participants as potential barriers to effective swab-testing in clinical care, including diagnostic training and interpretation deficits; communication difficulties; interpretation deficits and diagnostic assumptions; the influence of inter-professional dynamics; and flow-on consequences for patient decisions and care. ConclusionsSwab-testing has been used for over 100 years, and yet there remain substantial factors that limit their effective use in clinical practice as demonstrated by this study. A focus on upscaling diagnostic testing, particularly with escalating AMR, without considering complex implementation and human factors is likely to have limited impact on practice improvement. This study identified vulnerability points in the human-diagnostic interaction which should be considered in the implementation of other microbiological tests. This study on the simple wound swab has implications for future diagnostic upscaling and investment, including its role in address antimicrobial resistance.

A multimodal pipeline for image correction and registration of mass spectrometry imaging with microscopy

The integration of matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) and histology plays a pivotal role in advancing our understanding of complex heterogeneous tissues, which provides a comprehensive description of biological tissue with both wide molecule coverage and high lateral resolution. Herein, we proposed a novel strategy for the correction and registration of MALDI MSI data with hematoxylin & eosin (H&E) staining images. To overcome the challenges of discrepancies in spatial resolution towards the unification of the two imaging modalities, a deep learning-based interpolation algorithm for MALDI MSI data was constructed, which enables spatial coherence and the following orientation matching between images. Coupled with the affine transformation (AT) and the subsequent moving least squares algorithm, the two types of images from one rat brain tissue section were aligned automatically with high accuracy. Moreover, we demonstrated the practicality of the developed pipeline by projecting it to a rat cerebral ischemia-reperfusion injury model, which would help decipher the link between molecular metabolism and pathological interpretation towards microregion. This new approach offers the chance for other types of bioimaging to boost the field of multimodal image fusion.

The OCDA-Net: a 3D convolutional neural network-based system for classification and staging of ovarian cancer patients using [18F]FDG PET/CT examinations.

To create the 3D convolutional neural network (CNN)-based system that can use whole-body [18F]FDG PET for recurrence/post-therapy surveillance in ovarian cancer (OC). In this study, 1224 image sets from OC patients who underwent whole-body [18F]FDG PET/CT at Kowsar Hospital between April 2019 and May 2022 were investigated. For recurrence/post-therapy surveillance, diagnostic classification as cancerous, and non-cancerous and staging as stage III, and stage IV were determined by pathological diagnosis and specialists' interpretation. New deep neural network algorithms, the OCDAc-Net, and the OCDAs-Net were developed for diagnostic classification and staging of OC patients using [18F]FDG PET/CT images. Examinations were divided into independent training (75%), validation (10%), and testing (15%) subsets. This study included 37 women (mean age 56.3years; age range 36-83years). Data augmentation techniques were applied to the images in two phases. There were 1224 image sets for diagnostic classification and staging. For the test set, 170 image sets were considered for diagnostic classification and staging. The OCDAc-Net areas under the receiver operating characteristic curve (AUCs) and overall accuracy for diagnostic classification were 0.990 and 0.92, respectively. The OCDAs-Net achieved areas under the receiver operating characteristic curve (AUCs) of 0.995 and overall accuracy of 0.94 for staging. The proposed 3D CNN-based models provide potential tools for recurrence/post-therapy surveillance in OC. The OCDAc-Net and the OCDAs-Net model provide a new prognostic analysis method that can utilize PET images without pathological findings for diagnostic classification and staging.

Multidisciplinary Inflammatory Bowel Disease Conference: The Impact of the Expert Pathologist on Patient Care.

Multidisciplinary teams (MDT) aid the diagnosis and management of patients with inflammatory bowel disease (IBD) and improve patient outcomes. The direct impact of a gastrointestinal expert pathologist on MDT care of IBD patients is unknown. A retrospective chart review was conducted evaluating all cases (N = 289) discussed at the IBD MDT conference at Carilion Roanoke Memorial Hospital from June 1, 2013, through December 31, 2019. Cases were discussed between 1 and 6 times at the conference. Data collected included demographics, diagnosis before and after conference, reason for diagnostic change, endoscopy findings, medications, surgeries, and clinical follow-up. Approximately 15% to 42% of patients had a change in diagnosis after the first 3 conferences. The majority of diagnostic changes after the first (84%), second (73%), and third (67%) conferences were due to expert pathologist interpretation. Indeterminate colitis was the most frequently changed diagnosis, and Crohn's disease was the most common new diagnosis after conference. Among patients with a diagnostic change, 28.6% to 38.5% of patients had a change in their IBD medication regimen, and 7.7% to 10.9% had a surgical intervention after the first 2 conferences. Approximately 54.2% to 60% of patients reported clinical improvement or remission within 6 months of the first 3 conferences. The majority of diagnostic changes made at the multidisciplinary IBD conference were due to histopathologic re-interpretation. A change in diagnosis at times led to significant modifications in medical or surgical management. An expert gastrointestinal pathologist is an essential MDT member for IBD management.

Recurrent cervical adenofibroma progressing to adenosarcoma: a rare case report.

Cervical adenofibroma is a rare form of mixed mesodermal tumor that can present as cervical polyps with a tendency for local recurrence and progression. Few cases progressing to adenosarcoma have previously been reported. We report a case of cervical adenofibroma progressing to adenosarcoma, and we seek to remind clinicians of the method and importance of the differential diagnosis of this disease. A fertile woman was admitted in our department for the eighth recurrence of a cervical polypoidal mass which for the past 10 years. Recurrence of cervical adenofibroma was confirmed by ultrasound and MRI. A wide local excision under hysteroscopy was performed due to her strong desire to preserve the uterus. Surgical pathology and immunohistochemical interpretation revealed cervical adenosarcoma. A hysterectomy with conservation of the ovaries was recommended, with regular follow-ups for evidence of disease recurrence. Differential diagnoses of cervical adenofibroma are hard to prove. Adenosarcoma should be ruled out, especially in women presenting with recurrent cervical polypoidal masses. A combined histological/immunohistochemical investigation is mandatory.

Ultraviolet metasurface-assisted photoacoustic microscopy with great enhancement in DOF for fast histology imaging

Pathology interpretations of tissue rely on the gold standard of histology imaging, potentially hampering timely access to critical information for diagnosis and management of neoplasms because of tedious sample preparations. Slide-free capture of cell nuclei in unprocessed specimens without staining is preferable; however, inevitable irregular surfaces in fresh tissues results in limitations. An ultraviolet metasurface with the ability to generate an ultraviolet optical focus maintaining < 1.1-µm in lateral resolution and ∼290 µm in depth of field (DOF) is proposed for fast, high resolution, label-free photoacoustic histological imaging of unprocessed tissues with uneven surfaces. Microanatomical characteristics of the cell nuclei can be observed, as demonstrated by the mouse brain samples that were cut by hand and a ∼3 × 3-mm2 field of view was imaged in ∼27 min. Therefore, ultraviolet metasurface-assisted photoacoustic microscopy is anticipated to benefit intraoperative pathological assessments and basic scientific research by alleviating laborious tissue preparations.

Scientific and Regulatory Policy Committee Points to Consider: Sampling, Processing, Evaluation, Interpretation, and Reporting of Test Article-Related Ganglion Pathology for Nonclinical Toxicity Studies.

Certain biopharmaceutical products consistently affect dorsal root ganglia, trigeminal ganglia, and/or autonomic ganglia. Product classes targeting ganglia include antineoplastic chemotherapeutics, adeno-associated virus-based gene therapies, antisense oligonucleotides, and anti-nerve growth factor agents. This article outlines "points to consider" for sample collection, processing, evaluation, interpretation, and reporting of ganglion findings; these points are consistent with published best practices for peripheral nervous system evaluation in nonclinical toxicity studies. Ganglion findings often occur as a combination of neuronal injury (e.g., degeneration, necrosis, and/or loss) and/or glial effects (e.g., increased satellite glial cell cellularity) with leukocyte accumulation (e.g., mononuclear cell infiltration or inflammation). Nerve fiber degeneration and/or glial reactions may be seen in nerves, dorsal spinal nerve roots, spinal cord, and occasionally brainstem. Interpretation of test article (TA)-associated effects may be confounded by incidental background changes or experimental procedure-related changes and limited historical control data. Reports should describe findings at these sites, any TA relationship, and the criteria used for assigning severity grades. Contextualizing adversity of ganglia findings can require a weight-of-evidence approach because morphologic changes of variable severity occur in ganglia but often are not accompanied by observable overt in-life functional alterations detectable by conventional behavioral and neurological testing techniques.

Development and interobserver reliability of a rating scale for lung ultrasound pathology in lower respiratory tract infection

ObjectivesThe purpose of this study was to develop a severity rating scale for lung ultrasound pathology in lower respiratory tract infection based on multicenter expert consensus, and to test inter-rater reliability. MethodsTen point-of-care ultrasound experts from three academic institutions developed the scale iteratively through literature review, expert opinion, and pilot testing. Clips were prospectively collected from adults suspected of COVID-19 using a 14-zone scanning protocol. Blinded reviewers independently rated four data subsets. The rating scale was refined through eight consensus-building discussions reviewing challenging cases from the first three subsets. The final scale consisted of a set of ordinal scores from 0 to 4, for five sonographic findings: B-lines, pleural line abnormalities, consolidations, pleural effusions, and overall lung aeration. Ratings from the fourth subset were analyzed to determine reliability based on intraclass correlation coefficient (ICC). A total of 11,126 cine clips from 220 patients were acquired. DiscussionAfter excluding uninterpretable clips, the test dataset contained 81 clips and yielded an average ICC of 0.70 across the five sonographic findings (0.76 for B-lines, 0.52 for pleural line abnormalities, 0.71 for consolidations, 0.80 for pleural effusions, and 0.70 for overall lung aeration). Improvements in agreement were observed with each successive review session and dataset rating. ConclusionThe lung ultrasound severity scale established by multicenter expert consensus achieved moderate to good inter-rater reliability. The scale could be used clinically to standardize assessment of lower respiratory tract infection and in future studies to develop methods for automated interpretation of lung ultrasound pathology.

Structured Case-Based Ethics Discussion for Trainees and Faculty on Dermatopathology.

Ethical and professional dilemmas are part of the day-to-day practice of medicine, including within dermatopathology (e.g., ethical implications of self-referring skin biopsies for pathology interpretation). There is a need for teaching aids that dermatology educators can easily access to help provide ethics education. We held an hour-long, faculty-facilitated, interactive, virtual discussion about ethical issues in dermatopathology. The session followed a structured, case-based format. We administered anonymous online feedback surveys after the session and used the Wilcoxon signed rank test to compare participants' before and after responses. Seventy-two individuals from two academic institutions participated in the session. We collected 35 total responses (49%) from dermatology residents (n = 15), dermatology faculty (n = 14), medical students (n = 2), and other providers and learners (n = 4). Feedback was largely positive, with 21 attendees (60%) indicating they learned a few things and 11 (31%) indicating they learned a great deal. Additionally, 32 participants (91%) indicated they would recommend the session to a colleague. Our analysis showed that attendees had a greater self-perceived level of achievement for each of our three objectives after the session. This dermatoethics session is structured so as to be easily shared, deployed, and built on by other institutions. We hope that other institutions will use our materials and results to improve upon the foundation presented here and that this framework will be used by other medical specialties seeking to foster ethics education in their training programs.

Comparison of the Interpretations of Brain Computed Tomography of Emergency Medicine Specialists and Neurosurgeons

Aim: We aimed to examine the similarity in the brain computed tomography (CT) reports of emergency medicine and neurosurgery specialists. Material and Methods: Patients who applied to the emergency department and underwent cranial CT in traumatic and non-traumatic conditions were analyzed retrospectively. The reports of emergency medicine and neurosurgeon specialists were reviewed. The radiologist's report was set as the gold standard. All CT examinations were performed independently of the radiologist by physicians with at least 5 years of experience. Results: Emergency medicine and neurosurgery specialists were found to be highly compatible in detecting pathologies in CT reports. There was significant similarity in the diagnosis of bone defects and maxillofacial trauma in the CT reporting of emergency medicine specialists and neurosurgeons. Conclusion: It was observed that emergency medicine specialists were as successful as neurosurgeons in the interpretation of brain CT pathologies of traumas. Since their knowledge and experience in non-traumatic pathologies are insufficient, they should receive support from radiology in residency training.

Systematic review of benefits and practical challenges for application of Mohs surgery for oral tumors.

Mohs micrographic surgery (MMS) may be an effective treatment modality for oral cavity cancers (OCC) due to possibility of more effective visualization of tumor margins and greater preservation of benign tissue. The objective of this study is to review the existing literature on the use of MMS for the treatment of OCC and categorize its uses and limitations. A systematic review was performed in accordance with Assessing the Methodological Quality of Systematic Reviews (AMSTAR) guidelines. PubMed, Scopus, and Google Scholar from inception of databases to January 20, 2023 identified all published studies on the use of MMS for OCC. Nine studies met inclusion criteria. Seventy-seven patients were treated with MMS for OCC, 74 of which (96%) were treated for squamous cell carcinoma (SCC). The tongue was the most common site (n = 57). Six out of seven studies showed no recurrence of disease during the follow-up periods, which ranged from 8 to 42months, and one study reported significantly lower loco-regional recurrence over a 2-years follow-up period (10.5% vs 25.7%). Mohs technique did not cause a statistically significant increase in operating time. Applicability of MMS is limited by operator comfort with surgical technique and pathological interpretation of specimens in the oral cavity. The main limitation was that various studies did not report specific patient characteristics. In conclusion, MMS may be an effective treatment for OCC, especially for squamous cell carcinomas, and tumors involving the tongue.