PD-L1 Expression and Its Modulating Factors in Anaplastic Thyroid Carcinoma: A Multi-institutional Study.

Anti-PD immunotherapy is currently under investigation in anaplastic thyroid carcinoma (ATC). Tumor cell surface PD-L1 expression is considered predictive of therapeutic response. Although papillary thyroid carcinoma has been widely studied for PD-L1 expression, there are limited data on ATC. In this retrospective multi-institutional study involving 9 centers across Asia, 179 ATCs were assessed for PD-L1 expression using the SP263 (Ventana) clone. A tumor proportion score (TPS) ≥1% was required to consider a case PD-L1-positive. PD-L1 expression was compared with the histological patterns, the type of specimen (small or large), tumor molecular profile (BRAF V600E and TERT promoter mutation status), and patient outcome. PD-L1 expression in any co-existent differentiated thyroid carcinoma (DTC) was evaluated separately and compared with ATC. Most ATCs (73.2%) were PD-L1-positive. The median TPS among positive cases was 36% (IQR 11% to 75%; range 1% to 99%). A high expression (TPS ≥ 50%) was noted in 30.7%. PD-L1-negative cases were more likely to be small specimens (P=0.01). A negative result on small samples, hence, may not preclude expression elsewhere. ATCs having epithelioid and pleomorphic histological patterns were more likely to be PD-L1-positive with higher TPS than sarcomatoid (P<0.01). DTCs were more frequently negative and had lower TPS than ATC (P<0.01). Such PD-L1 conversion from DTC-negative to ATC-positive was documented in 71% of cases with co-existent DTC. BRAF V600E, but not TERT promoter mutations, correlated significantly with PD-L1-positivity rate (P=0.039), reinforcing the potential of combining anti-PD and anti-BRAF V600E drugs. PD-L1 expression, however, did not impact the patient outcome.

A survey analysis of the adoption of large language models among pathologists.

OBJECTIVES: We sought to investigate the adoption and perception of large language model (LLM) applications among pathologists. METHODS: A cross-sectional survey was conducted, gathering data from pathologists on their usage and views concerning LLM tools. The survey, distributed globally through various digital platforms, included quantitative and qualitative questions. Patterns in the respondents' adoption and perspectives on these artificial intelligence tools were analyzed. RESULTS: Of 215 respondents, 100 (46.5%) reported using LLMs, particularly ChatGPT (OpenAI), for professional purposes, predominantly for information retrieval, proofreading, academic writing, and drafting pathology reports, highlighting a significant time-saving benefit. Academic pathologists demonstrated a better level of understanding of LLMs than their peers. Although chatbots sometimes provided incorrect general domain information, they were considered moderately proficient concerning pathology-specific knowledge. The technology was mainly used for drafting educational materials and programming tasks. The most sought-after feature in LLMs was their image analysis capabilities. Participants expressed concerns about information accuracy, privacy, and the need for regulatory approval. CONCLUSIONS: Large language model applications are gaining notable acceptance among pathologists, with nearly half of respondents indicating adoption less than a year after the tools' introduction to the market. They see the benefits but are also worried about these tools' reliability, ethical implications, and security.

Seven years of Non-invasive Follicular Thyroid Neoplasm with Papillary-like Nuclear Features (NIFTP): Rate of Acceptance and Variation of Diagnostic Approaches Across Different Continents.

CONTEXT: Noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) was introduced as a new entity replacing the diagnosis of noninvasive encapsulated follicular variant of papillary thyroid carcinoma (PTC). Significant variability in the incidence of NIFTP diagnosed in different world regions has been reported. OBJECTIVE: To investigate the rate of adoption of NIFTP, change in practice patterns, and uniformity in applying diagnostic criteria among pathologists practicing in different regions. METHODS: Two surveys distributed to pathologists of the International Endocrine Pathology Discussion Group with multiple-choice questions on NIFTP adoption into pathology practice and whole slide images of 5 tumors to collect information on nuclear score and diagnosis. Forty-eight endocrine pathologists, including 24 from North America, 8 from Europe, and 16 from Asia/Oceania completed the first survey and 38 the second survey. RESULTS: A 94% adoption rate of NIFTP by the pathologists was found. Yet, the frequency of rendering NIFTP diagnosis was significantly higher in North America than in other regions (P = .009). While the highest concordance was found in diagnosing lesions with mildly or well-developed PTC-like nuclei, there was significant variability in nuclear scoring and diagnosing NIFTP for tumors with moderate nuclear changes (nuclear score 2) (case 2, P < .05). Pathologists practicing in North America and Europe showed a tendency for lower thresholds for PTC-like nuclei and NIFTP than those practicing in Asia/Oceania. CONCLUSION: Despite a high adoption rate of NIFTP across geographic regions, NIFTP is diagnosed more often by pathologists in North America. Significant differences remain in diagnosing intermediate PTC-like nuclei and respectively NIFTP, with more conservative nuclear scoring in Asia/Oceania, which may explain the geographic differences in NIFTP incidence.

Squamous metaplasia is an indicator of acute exacerbation in patients with usual interstitial pneumonia / idiopathic pulmonary fibrosis.

BACKGROUND: Acute exacerbation (AE) is a potentially lethal event in patients with usual interstitial pneumonia/idiopathic pulmonary fibrosis (UIP/IPF). However, to date, no pathological predictors of AE have been identified. This retrospective study aimed to elucidate the pathological features that could predict AE in patients with UIP. METHODS: We reviewed the pathological findings of 91 patients with UIP/IPF and correlated these findings with AE events. Thirteen histological variables related to acute lung injury were evaluated by three independent observers and classified as positive or negative. The patients' clinical data during follow-up were collected and reviewed for AE. A recursive partition using the Gini index for the prediction of AE was performed, with each pathological finding as a candidate for branching. RESULTS: Twenty patients (22%) developed AE during the median follow-up duration of 40 months. Thirty-eight patients died (15 due to AE and 23 for other reasons). The median time interval from surgical lung biopsy to AE onset was 497 (interquartile range: 901-1657) days. Histologically, squamous metaplasia was positively associated with AE (odds ratio: 4.7, P = 0.015) and worse event-free survival in patients with UIP (P = 0.04). Leaf scoring based on the Gini index for recursive partition, including five positive findings (squamous metaplasia, neutrophilic infiltration, septal widening, Kuhn's hyaline, and fibrin), showed a sensitivity of 90% with a specificity of 74.7% (area under curve: 0.89). CONCLUSIONS: We found that squamous metaplasia is an important histopathological finding that predicts AE events and tends to unfavorable outcome in patients with UIP/IPF.

Presence of focal usual interstitial pneumonia is a key prognostic factor in progressive pulmonary fibrosis.

AIMS: Progressive pulmonary fibrosis (PPF) is a newly recognised clinical phenotype of interstitial lung diseases in the 2022 interstitial pulmonary fibrosis (IPF) guidelines. This category is based entirely on clinical and radiological factors, and the background histopathology is unknown. Our objective was to investigate the histopathological characteristics of PPF and to examine the correlation between usual interstitial pneumonia (UIP) and prognosis in this new disease type. We hypothesised that the presence of UIP-like fibrosis predicts patients' survival in PPF cases. METHODS AND RESULTS: We selected 201 cases fulfilling the clinical criteria of PPF from case archives. Cases diagnosed as IPF by a multidisciplinary team were excluded. Whole slide images were evaluated by three pathologists who were blinded to clinical and radiological data. We measured areas of UIP-like fibrosis and calculated what percentage of the total lesion area they occupied. The presence of focal UIP-like fibrosis amounting to 10% or more of the lesion area was seen in 148 (73.6%), 168 (83.6%) and 165 (82.1%) cases for each pathologist, respectively. Agreement of the recognition of UIP-like fibrosis in PPF cases was above κ = 0.6 between all pairs. Survival analysis showed that the presence of focal UIP-like fibrosis correlated with worsened survival under all parameters tested (P < 0.001). CONCLUSIONS: The presence of UIP-like fibrosis is a core pathological feature of clinical PPF, and its presence within diseased areas is associated with poorer prognosis. This study highlights the importance of considering the presence of focal UIP-like fibrosis in the evaluation and management of PPF.

Fast-Track Development and Multi-Institutional Clinical Validation of an Artificial Intelligence Algorithm for Detection of Lymph Node Metastasis in Colorectal Cancer.

Lymph node metastasis (LNM) detection can be automated using artificial intelligence (AI)-based diagnostic tools. Only limited studies have addressed this task for colorectal cancer (CRC). This study aimed to develop of a clinical-grade digital pathology tool for LNM detection in CRC using the original fast-track framework. The training cohort included 432 slides from one department. A segmentation algorithm detecting 8 relevant tissue classes was trained. The test cohorts consisted of materials from 5 pathology departments digitized by 4 different scanning systems. A high-quality, large training data set was generated within 7 days and a minimal amount of annotation work using fast-track principles. The AI tool showed very high accuracy for LNM detection in all cohorts, with sensitivity, negative predictive value, and specificity ranges of 0.980 to 1.000, 0.997 to 1.000, and 0.913 to 0.990, correspondingly. Only 5 of 14,460 analyzed test slides with tumor cells over all cohorts were classified as false negative (3/5 representing clusters of tumor cells in lymphatic vessels). A clinical-grade tool was trained in a short time using fast-track development principles and validated using the largest international, multi-institutional, multiscanner cohort of cases to date, showing very high precision for LNM detection in CRC. We are releasing a part of the test data sets to facilitate academic research.

What's new in thyroid pathology 2024: updates from the new WHO classification and Bethesda system.

In line with the release of the 5th edition WHO Classification of Tumors of Endocrine Organs (2022) and the 3rd edition of the Bethesda System for Reporting Thyroid Cytopathology (2023), the field of thyroid pathology and cytopathology has witnessed key transformations. This digest brings to the fore the refined terminologies, newly introduced categories, and contentious methodological considerations pivotal to the updated classification.

Machine-Learning-Based Classification Model to Address Diagnostic Challenges in Transbronchial Lung Biopsy.

BACKGROUND: When obtaining specimens from pulmonary nodules in TBLB, distinguishing between benign samples and mis-sampling from a tumor presents a challenge. Our objective is to develop a machine-learning-based classifier for TBLB specimens. METHODS: Three pathologists assessed six pathological findings, including interface bronchitis/bronchiolitis (IB/B), plasma cell infiltration (PLC), eosinophil infiltration (Eo), lymphoid aggregation (Ly), fibroelastosis (FE), and organizing pneumonia (OP), as potential histologic markers to distinguish between benign and malignant conditions. A total of 251 TBLB cases with defined benign and malignant outcomes based on clinical follow-up were collected and a gradient-boosted decision-tree-based machine learning model (XGBoost) was trained and tested on randomly split training and test sets. RESULTS: Five pathological changes showed independent, mild-to-moderate associations (AUC ranging from 0.58 to 0.75) with benign conditions, with IB/B being the strongest predictor. On the other hand, FE emerged to be the sole indicator of malignant conditions with a mild association (AUC = 0.66). Our model was trained on 200 cases and tested on 51 cases, achieving an AUC of 0.78 for the binary classification of benign vs. malignant on the test set. CONCLUSION: The machine-learning model developed has the potential to distinguish between benign and malignant conditions in TBLB samples excluding the presence or absence of tumor cells, thereby improving diagnostic accuracy and reducing the burden of repeated sampling procedures for patients.

Differentiating BRAF V600E- and RAS-like alterations in encapsulated follicular patterned tumors through histologic features: a validation study.

Differentiating BRAF V600E- and RAS-altered encapsulated follicular-patterned thyroid tumors based on morphology remains challenging. This study aimed to validate an 8-score scale nuclear scoring system and investigate the importance of nuclear pseudoinclusions (NPIs) in aiding this differentiation. A cohort of 44 encapsulated follicular-patterned tumors with varying degrees of nuclear atypia and confirmed BRAF V600E or RAS alterations was studied. Nuclear parameters (area, diameter, and optical density) were analyzed using a deep learning model. Twelve pathologists from eight Asian countries visually assessed 22 cases after excluding the cases with any papillae. Eight nuclear features were applied, yielding a semi-quantitative score from 0 to 24. A threshold score of 14 was used to distinguish between RAS- and BRAF V600E-altered tumors. BRAF V600E-altered tumors typically demonstrated higher nuclear scores and notable morphometric alterations. Specifically, the nuclear area and diameter were significantly larger, and nuclear optical density was much lower compared to RAS-altered tumors. Observer accuracy varied, with two pathologists correctly identifying genotype of all cases. Observers were categorized into proficiency groups, with the highest group maintaining consistent accuracy across both evaluation methods. The lower group showed a significant improvement in accuracy upon utilizing the 8-score scale nuclear scoring system, with notably increased sensitivity and negative predictive value in BRAF V600E tumor detection. BRAF V600E-altered tumors had higher median total nuclear scores. Detailed reevaluation revealed NPIs in all BRAF V600E-altered cases, but in only 2 of 14 RAS-altered cases. These results could significantly assist pathologists, particularly those not specializing in thyroid pathology, in making a more accurate diagnosis.