Pediatric Feeding Disorder and the School-Based Speech-Language Pathologist: An Evidence-Based Update for Clinical Practice.

PURPOSE: This prologue introduces the forum "Pediatric Feeding Disorder and the School-Based SLP: An Evidence-Based Update for Clinical Practice" and informs the reader of the scope of articles presented. METHOD: The guest prologue author provides a brief history of pediatric feeding and swallowing services in the public-school setting, including previous forums on swallowing and feeding services in the schools (Logemann & O'Toole, 2000; McNeilly & Sheppard, 2008). The concepts that have been learned since the 2008 forum are shared. The contributing authors in the forum are introduced, and a summary is provided for each of the articles. CONCLUSIONS: The articles provide evidence-based information on topics that are uniquely of interest to school-based speech-language pathologists managing pediatric feeding and swallowing in their districts. The topics shared in this forum range from relevant information on anatomy, physiology, developmental milestones, and differential diagnosis to therapeutic practice when identifying and treating pediatric feeding and swallowing in the school setting. The forum also includes focused articles on the necessity of collaboration with families during the treatment process, current information on legal parameters dealing with school-based pediatric feeding disorder services, and a framework for assessment and treating pediatric feeding disorder in the school setting.

[Analysis of the workload in medical time according to the degree of complexity of the samples and proposal of an algorithm for the equitable distribution of the workload in an anatomic pathology department]. / Análisis de la carga laboral en tiempo médico en función del grado de complejidad de las muestras y propuesta de algoritmo para la distribución equitativa del trabajo asistencial en un servicio de anatomía patológica.

INTRODUCTION: In a pathological anatomy service, the workload in medical time is analyzed based on the complexity of the samples received and its distribution among pathologists is assessed, presenting a new computer algorithm that favors an equitable distribution. METHODS: Following the second edition of the Spanish guidelines for the estimation of workload in cytopathology and histopathology (medical time) according to the Spanish Pathology Society-International Academy of Pathology (SEAP-IAP) catalog of samples and procedures, we determined the workload units (UCL) per pathologist and the overall UCL of the service, the average workload of the service (MU factor), the time dedicated by each pathologist to healthcare activity and the optimal number of pathologists according to the workload of the service. RESULTS: We determined 12 197 total annual UCL for the chief pathologist, as well as 14 702 and 13 842 UCL for associate pathologists, with an overall of 40 742 UCL for the whole service. The calculated MU factor is 4.97. The chief pathologist devoted 72.25% of his working day to healthcare activity while associate pathologists dedicated 87.09% and 82.01% of their working hours. The optimal number of pathologists for the service is found to be 3.55. CONCLUSIONS: The results demonstrate medical work overload and a non-equitable distribution of UCLs among pathologists. We propose a computer algorithm capable of distributing the workload in an equitable manner. It would be associated with the laboratory information system and take into account the type of specimen, its complexity and the dedication of each pathologist to healthcare activity.

Multidisciplinary Approach to the Diagnosis of Idiopathic Interstitial Pneumonias: Focus on the Pathologist's Key Role.

Idiopathic Interstitial Pneumonias (IIPs) are a heterogeneous group of the broader category of Interstitial Lung Diseases (ILDs), pathologically characterized by the distortion of lung parenchyma by interstitial inflammation and/or fibrosis. The American Thoracic Society (ATS)/European Respiratory Society (ERS) international multidisciplinary consensus classification of the IIPs was published in 2002 and then updated in 2013, with the authors emphasizing the need for a multidisciplinary approach to the diagnosis of IIPs. The histological evaluation of IIPs is challenging, and different types of IIPs are classically associated with specific histopathological patterns. However, morphological overlaps can be observed, and the same histopathological features can be seen in totally different clinical settings. Therefore, the pathologist's aim is to recognize the pathologic-morphologic pattern of disease in this clinical setting, and only after multi-disciplinary evaluation, if there is concordance between clinical and radiological findings, a definitive diagnosis of specific IIP can be established, allowing the optimal clinical-therapeutic management of the patient.

The role of teledermatology in Mohs micrographic surgery: a review.

This paper explores the role of teledermatology (TD) in Mohs micrographic surgery (MMS) at various stages of patient care. The study aims to assess the benefits, limitations, and patient experiences surrounding TD integration into MMS practices. We conducted a PubMed search using keywords related to TD and MMS, categorizing selected articles into pre-operative, intra-operative, and post-operative stages of MMS. TD reduced waiting times (26.10 days for TD compared to 60.57 days for face-to-face [FTF]) and consultation failure rates (6% for TD vs. 17% for FTF) for MMS preoperative consultations. It also shortened time to treatment by two weeks and led to notable travel savings (162.7 min, 144.5 miles, and $60.00 per person). Telepathology facilitated communication and decision-making during MMS, improving accuracy and efficiency, especially in challenging cases requiring collaboration where physical presence of another surgeon or pathologist is not feasible. Telepathology definitively diagnosed benign lesions and malignant tumors in 81.8% of cases (18/22). Additionally, there was a 95% agreement between conventional light microscopy diagnosis and telepathology in tumors (19/20), and 100% agreement for all 20 Mohs frozen section consultations. For post-operative follow-up, telephone follow-up (TFU) and text messaging proved effective, cost-efficient alternatives with high patient satisfaction (94% in New Zealand and 96% in the U.K.) and early complication identification. This study underscores TD's multifaceted benefits in MMS: enhanced patient experience preoperatively, improved communication during surgery, and cost-effective postoperative follow-up. Limitations include the financial expense and technical issues that can arise with TD (connectivity problems, delays in video/audio transmission, etc.). Further studies are needed to explore emerging TD modalities in post-operative patient management. The integration of TD into MMS signifies a progressive step in dermatological care, offering convenient, cost-effective, and better solutions with the potential to enhance patient experiences and outcomes.

Are We Providing Enough Information to the Pathologists? An Audit of Filling of Histopathology Request Forms for Surgically Resected Tumours.

OBJECTIVE: To analyse quantitatively the adequacy of demographics of clinical information and highlight specific areas of neglect, by assessing the adequacy of filling out histopathology request forms. STUDY DESIGN: Clinical Audit. Place and Duration of the Study: Department of Pathology, Dow University of Health Sciences (DUHS), Karachi, Pakistan, from January to September 2021. METHODOLOGY: A retrospective audit was carried out on the request forms of surgically resected tumours and biopsies. The recorded details of the patients' demographics and biopsy, clinical history and examination, and intraoperative findings were analysed. RESULTS: Out of 175 forms, patients' names were written in 174 (99.4%) while medical record numbers were written in 113 (64.6%). The doctors' names were given in 172 (98.3%) forms and phone numbers in 34 (19.4%). A clinical diagnosis was provided in 164 (93.7%) forms, while 152 (86.9%) forms correctly entered the biopsy site. Sixty-seven (38.3%) forms included the correct nature of the biopsy. Relevant operative details were provided in half of the forms. Symptoms and their duration were mentioned in 144 (82.3%) and 100 (57.1%), respectively. The form-filling rate was the same for both benign and malignant tumours. CONCLUSION: This study shows that in a significant proportion of cases, complete relevant information is not provided to the histopathologists on request forms for logistics. KEY WORDS: Histopathology, Request forms, Tumours, Audit.

AI-guided histopathology predicts brain metastasis in lung cancer patients.

Brain metastases can occur in nearly half of patients with early and locally advanced (stage I-III) non-small cell lung cancer (NSCLC). There are no reliable histopathologic or molecular means to identify those who are likely to develop brain metastases. We sought to determine if deep learning (DL) could be applied to routine H&E-stained primary tumor tissue sections from stage I-III NSCLC patients to predict the development of brain metastasis. Diagnostic slides from 158 patients with stage I-III NSCLC followed for at least 5 years for the development of brain metastases (Met+, 65 patients) versus no progression (Met-, 93 patients) were subjected to whole-slide imaging. Three separate iterations were performed by first selecting 118 cases (45 Met+, 73 Met-) to train and validate the DL algorithm, while 40 separate cases (20 Met+, 20 Met-) were used as the test set. The DL algorithm results were compared to a blinded review by four expert pathologists. The DL-based algorithm was able to distinguish the eventual development of brain metastases with an accuracy of 87% (p < 0.0001) compared with an average of 57.3% by the four pathologists and appears to be particularly useful in predicting brain metastases in stage I patients. The DL algorithm appears to focus on a complex set of histologic features. DL-based algorithms using routine H&E-stained slides may identify patients who are likely to develop brain metastases from those who will remain disease free over extended (>5 year) follow-up and may thus be spared systemic therapy. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

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.

Closing the loop - the role of pathologists in digital and computational pathology research.

An increasing number of manuscripts related to digital and computational pathology are being submitted to The Journal of Pathology: Clinical Research as part of the continuous evolution from digital imaging and algorithm-based digital pathology to computational pathology and artificial intelligence. However, despite these technological advances, tissue analysis still relies heavily on pathologists' annotations. There are three crucial elements to the pathologist's role during annotation tasks: granularity, time constraints, and responsibility for the interpretation of computational results. Granularity involves detailed annotations, including case level, regional, and cellular features; and integration of attributions from different sources. Time constraints due to pathologist shortages have led to the development of techniques to expedite annotation tasks from cell-level attributions up to so-called unsupervised learning. The impact of pathologists may seem diminished, but their role is crucial in providing ground truth and connecting pathological knowledge generation with computational advancements. Measures to display results back to pathologists and reflections about correctly applied diagnostic criteria are mandatory to maintain fidelity during human-machine interactions. Collaboration and iterative processes, such as human-in-the-loop machine learning are key for continuous improvement, ensuring the pathologist's involvement in evaluating computational results and closing the loop for clinical applicability. The journal is interested particularly in the clinical diagnostic application of computational pathology and invites submissions that address the issues raised in this editorial.

DiagSet: a dataset for prostate cancer histopathological image classification.

Cancer diseases constitute one of the most significant societal challenges. In this paper, we introduce a novel histopathological dataset for prostate cancer detection. The proposed dataset, consisting of over 2.6 million tissue patches extracted from 430 fully annotated scans, 4675 scans with assigned binary diagnoses, and 46 scans with diagnoses independently provided by a group of histopathologists can be found at https://github.com/michalkoziarski/DiagSet . Furthermore, we propose a machine learning framework for detection of cancerous tissue regions and prediction of scan-level diagnosis, utilizing thresholding to abstain from the decision in uncertain cases. The proposed approach, composed of ensembles of deep neural networks operating on the histopathological scans at different scales, achieves 94.6% accuracy in patch-level recognition and is compared in a scan-level diagnosis with 9 human histopathologists showing high statistical agreement.

Comparing deep learning and pathologist quantification of cell-level PD-L1 expression in non-small cell lung cancer whole-slide images.

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

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

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

[Interpretation on genetic tumour syndromes in the 5th WHO classification of paediatric tumours: part â ¡].

WHO firstly published the classification of paediatric tumours, in which genetic tumour syndromes were introduced as a separate chapter, covering the clinicopathological features, molecular genetic alterations, and diagnostic criteria of various tumor susceptibility syndromes common in children. This article briefly introduces and interprets 5 hotspot genetic tumour syndromes (neurofibromatosis type 1, naevoid basal cell carcinoma syndrome, von Hippel-Lindau syndrome, familial adenomatous polyposis and xeroderma pigmentosum) based on relevant literature, in order to bring new perspectives and insights to pathologists and clinicians.

Training pathologists to assess stromal tumour-infiltrating lymphocytes in breast cancer synergises efforts in clinical care and scientific research.

A growing body of research supports stromal tumour-infiltrating lymphocyte (TIL) density in breast cancer to be a robust prognostic and predicive biomarker. The gold standard for stromal TIL density quantitation in breast cancer is pathologist visual assessment using haematoxylin and eosin-stained slides. Artificial intelligence/machine-learning algorithms are in development to automate the stromal TIL scoring process, and must be validated against a reference standard such as pathologist visual assessment. Visual TIL assessment may suffer from significant interobserver variability. To improve interobserver agreement, regulatory science experts at the US Food and Drug Administration partnered with academic pathologists internationally to create a freely available online continuing medical education (CME) course to train pathologists in assessing breast cancer stromal TILs using an interactive format with expert commentary. Here we describe and provide a user guide to this CME course, whose content was designed to improve pathologist accuracy in scoring breast cancer TILs. We also suggest subsequent steps to translate knowledge into clinical practice with proficiency testing.

Genomic testing and targeted therapy of non-small cell lung cancer in China: a nationwide survey of physicians and clinical pathologists.

BACKGROUND: Genomic diagnostic testing is necessary to guide optimal treatment for non-small cell lung cancer (NSCLC) patients. The proportion of NSCLC patients whose treatment was selected based on genomic testing is still unknown in many countries or needs further improvement. This survey aimed to assess perception of genomic testing and targeted therapy for NSCLC in clinical pathologists and physicians across China. METHODS: The web-based survey was conducted with 150 clinical pathologists and 450 physicians from oncology, respiratory and thoracic surgery departments from May to September 2020, across 135 cities in China. The participants had >5 years of clinical experience in genomic testing, diagnosis or treatment of NSCLC. RESULTS: Clinical pathologists reported capability of epidermal growth factor receptor (EGFR), anaplastic lymphoma kinase (ALK), and ROS proto-oncogene 1 (ROS-1) testing as 95.3%, 94.7%, and 84.7%, respectively, but only 81.9%, 75.5%, and 65.6% of physicians believed that the pathology department of the hospital is capable of performing the testing. The proportions of sending out specimens for testing were 21.0% and 49.7% as reported from clinical pathologists and physicians, respectively. Testing for EGFR mutation was recommended by physicians most often, followed by ALK and ROS-1 rearrangement. As first-line treatment, among the newly diagnosed patients with EGFR mutation, 77% received tyrosine kinase inhibitors (TKIs) therapy (49% treated with gefitinib); among patients with ALK rearrangement, 71% received TKI (64% treated with crizotinib); among patients with ROS-1 fusion, 65% received TKI (88% treated with crizotinib). CONCLUSIONS: The improvement of the non-tertiary hospital pathology departments' detection capabilities and the physicians' awareness are needed for enhancing the rate of genomic testing and targeted therapy in NSCLC patients in China.

Advances in radiology and pathology of prostate cancer: a review for the pathologist.

Multiparametric magnetic resonance imaging (mpMRI) has improved systematic prostate biopsy procedures in the diagnosis of clinically significant prostate cancer (csPCa) by reducing the number of unnecessary biopsies; numerous level one evidence studies have confirmed the accuracy of MRI-targeted biopsy, but, still today, systematic prostate biopsy is recommended to reduce the 15-20% false negative rate of mpMRI. New advanced imaging has been proposed to detect suspicious lesions and perform targeted biopsies especially when mpMRI cannot be performed. Transrectal ultrasound (TRUS) modalities are emerging as methods with greater sensitivity and specificity for the detection of PCa compared to the traditional TRUS; these techniques include elastography and contrast-enhanced ultrasound, as well as improved B-mode and Doppler techniques. These modalities can be combined to define a novel ultrasound approach: multiparametric ultrasound (mpUS). More recently, micro-ultrasound (MicroUS) and prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) have demonstrated to be sensitive for the detection of primary prostatic lesions resulting highly correlated with the aggressiveness of the primary prostatic tumor. In parallel, artificial intelligence is advancing and is set out to deeply change both radiology and pathology. In this study we address the role, advantages and shortcomings of novel imaging techniques for Pca, and discuss future directions including the applications of artificial intelligence-based techniques to imaging as well as histology. The significance of these findings for the practicing pathologist is discussed.

[Papillary renal neoplasm with reverse polarity]. / Papillyarnaya opukhol' pochki s reversivnoi polyarnost'yu yader.

Papillary renal neoplasm with reverse polarity is a rare subtype of papillary renal cell tumors with unique morphology, specific molecular features and good prognosis. The article presents literature data and describes our own observation of a papillary kidney tumor with reverse nuclear polarity in a 73-year-old patient. The difficulties of preoperative diagnosis of a tumor are shown, histological and immunohistochemical criteria for diagnosis and differential diagnosis of this tumor with other kidney tumors are presented. This rare case is of interest for both pathologists and clinicians.

Extracting interpretable features for pathologists using weakly supervised learning to predict p16 expression in oropharyngeal cancer.

One drawback of existing artificial intelligence (AI)-based histopathological prediction models is the lack of interpretability. The objective of this study is to extract p16-positive oropharyngeal squamous cell carcinoma (OPSCC) features in a form that can be interpreted by pathologists using AI model. We constructed a model for predicting p16 expression using a dataset of whole-slide images from 114 OPSCC biopsy cases. We used the clustering-constrained attention-based multiple-instance learning (CLAM) model, a weakly supervised learning approach. To improve performance, we incorporated tumor annotation into the model (Annot-CLAM) and achieved the mean area under the receiver operating characteristic curve of 0.905. Utilizing the image patches on which the model focused, we examined the features of model interest via histopathologic morphological analysis and cycle-consistent adversarial network (CycleGAN) image translation. The histopathologic morphological analysis evaluated the histopathological characteristics of image patches, revealing significant differences in the numbers of nuclei, the perimeters of the nuclei, and the intercellular bridges between p16-negative and p16-positive image patches. By using the CycleGAN-converted images, we confirmed that the sizes and densities of nuclei are significantly converted. This novel approach improves interpretability in histopathological morphology-based AI models and contributes to the advancement of clinically valuable histopathological morphological features.