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.

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.

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.

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.

The Asian Thyroid Working Group, from 2017 to 2023.

The Asian Thyroid Working Group was founded in 2017 at the 12th Asia Oceania Thyroid Association (AOTA) Congress in Busan, Korea. This group activity aims to characterize Asian thyroid nodule practice and establish strict diagnostic criteria for thyroid carcinomas, a reporting system for thyroid fine needle aspiration cytology without the aid of gene panel tests, and new clinical guidelines appropriate to conservative Asian thyroid nodule practice based on scientific evidence obtained from Asian patient cohorts. Asian thyroid nodule practice is usually designed for patient-centered clinical practice, which is based on the Hippocratic Oath, "First do not harm patients," and an oriental filial piety "Do not harm one's own body because it is a precious gift from parents," which is remote from defensive medical practice in the West where physicians, including pathologists, suffer from severe malpractice climate. Furthermore, Asian practice emphasizes the importance of resource management in navigating the overdiagnosis of low-risk thyroid carcinomas. This article summarizes the Asian Thyroid Working Group activities in the past 7 years, from 2017 to 2023, highlighting the diversity of thyroid nodule practice between Asia and the West and the background reasons why Asian clinicians and pathologists modified Western systems significantly.

Real-World Implementation of Digital Pathology: Results From an Intercontinental Survey.

The past 70 years have been characterized by rapid advancements in computer technology, and the health care system has not been immune to this trend. However, anatomical pathology has remained largely an analog discipline. In recent years, this has been changing with the growing adoption of digital pathology, partly driven by the potential of computer-aided diagnosis. As part of an international collaboration, we conducted a comprehensive survey to gain a deeper understanding of the status of digital pathology implementation in Europe and Asia. A total of 127 anatomical pathology laboratories participated in the survey, including 75 from Europe and 52 from Asia, with 72 laboratories having established digital pathology workflow and 55 without digital pathology. Laboratories using digital pathology for diagnostic (n = 29) and nondiagnostic (n = 43) purposes were thoroughly questioned about their implementation strategies and institutional experiences, including details on equipment, storage, integration with laboratory information system, computer-aided diagnosis, and the costs of going digital. The impact of the digital pathology workflow was also evaluated, focusing on turnaround time, specimen traceability, quality control, and overall satisfaction. Laboratories without access to digital pathology were asked to provide insights into their perceptions of the technology, expectations, barriers to adoption, and potential facilitators. Our findings indicate that although digital pathology is still the future for many, it is already the present for some. This decade may be a time when anatomical pathology finally embraces digital revolution on a larger scale.

Artificial Intelligence-Based Tool for Tumor Detection and Quantitative Tissue Analysis in Colorectal Specimens.

Digital pathology adoption allows for applying computational algorithms to routine pathology tasks. Our study aimed to develop a clinical-grade artificial intelligence (AI) tool for precise multiclass tissue segmentation in colorectal specimens (resections and biopsies) and clinically validate the tool for tumor detection in biopsy specimens. The training data set included 241 precisely manually annotated whole-slide images (WSIs) from multiple institutions. The algorithm was trained for semantic segmentation of 11 tissue classes with an additional module for biopsy WSI classification. Six case cohorts from 5 pathology departments (4 countries) were used for formal and clinical validation, digitized by 4 different scanning systems. The developed algorithm showed high precision of segmentation of different tissue classes in colorectal specimens with composite multiclass Dice score of up to 0.895 and pixel-wise tumor detection specificity and sensitivity of up to 0.958 and 0.987, respectively. In the clinical validation study on multiple external cohorts, the AI tool reached sensitivity of 1.0 and specificity of up to 0.969 for tumor detection in biopsy WSI. The AI tool analyzes most biopsy cases in less than 1 minute, allowing effective integration into clinical routine. We developed and extensively validated a highly accurate, clinical-grade tool for assistive diagnostic processing of colorectal specimens. This tool allows for quantitative deciphering of colorectal cancer tissue for development of prognostic and predictive biomarkers and personalization of oncologic care. This study is a foundation for a SemiCOL computational challenge. We open-source multiple manually annotated and weakly labeled test data sets, representing a significant contribution to the colorectal cancer computational pathology field.

Standardized Classification of Lung Adenocarcinoma Subtypes and Improvement of Grading Assessment Through Deep Learning.

The histopathologic distinction of lung adenocarcinoma (LADC) subtypes is subject to high interobserver variability, which can compromise the optimal assessment of patient prognosis. Therefore, this study developed convolutional neural networks capable of distinguishing LADC subtypes and predicting disease-specific survival, according to the recently established LADC tumor grades. Consensus LADC histopathologic images were obtained from 17 expert pulmonary pathologists and one pathologist in training. Two deep learning models (AI-1 and AI-2) were trained to predict eight different LADC classes. Furthermore, the trained models were tested on an independent cohort of 133 patients. The models achieved high precision, recall, and F1 scores exceeding 0.90 for most of the LADC classes. Clear stratification of the three LADC grades was reached in predicting the disease-specific survival by the two models, with both Kaplan-Meier curves showing significance (P = 0.0017 and 0.0003). Moreover, both trained models showed high stability in the segmentation of each pair of predicted grades with low variation in the hazard ratio across 200 bootstrapped samples. These findings indicate that the trained convolutional neural networks improve the diagnostic accuracy of the pathologist and refine LADC grade assessment. Thus, the trained models are promising tools that may assist in the routine evaluation of LADC subtypes and grades in clinical practice.

Not choosing pathology: An essay-based survey of first-year clinical residents.

OBJECTIVES: Health care systems worldwide are facing a shortage in the pathology workforce that could negatively affect health care services, particularly oncology. To address this potential shortfall, the pathology community must identify the reasons why graduating students are not choosing pathology as a specialty. METHODS: We analyzed 226 free-text essay responses submitted by nonpathology residents at a teaching hospital. We used a general inductive approach and quantitative analysis to identify the barriers that prevent medical college graduates from choosing pathology as a specialty. RESULTS: Residents at our institution view pathology similar to residents from other countries, with the main obstacles to choosing pathology as a specialty being a perception that it lacks practical application to patient care or provides "no real help" and has a lack of patient interaction. In Russia specifically, there is a focus on the perceived negative aspects of autopsy as a barrier to selecting pathology. Less significant factors that may be based more on stereotypes than reality include the expectation that the work is not engaging, the emotional burden, and the occupational risks. CONCLUSIONS: For medical students who place less importance on patient interaction, addressing these secondary factors could help select potential pathologists during students' undergraduate years.

Effect of the 2020 hypersensitivity pneumonitis guideline on the pathologic diagnosis of interstitial pneumonia.

It was reported that the 2020 guideline for hypersensitivity pneumonitis (HP) might result in the overdiagnosis of fibrotic HP (fHP). fHP and other types of interstitial pneumonias have several overlapping characteristics, and a high diagnostic concordance rate of fHP is rarely obtained. Therefore, we investigated the impact of the 2020 HP guideline on the pathological diagnosis of cases previously diagnosed as interstitial pneumonia. We identified 289 fibrotic interstitial pneumonia cases from 2014 to 2019 and classified them into four categories according to the 2020 HP guideline: typical, probable, and indeterminate for fHP and alternative diagnosis. The original pathological diagnosis of 217 cases were compared to their classification as either typical, probable, or indeterminate for fHP according to the 2020 guideline. The clinical data, including serum data and pulmonary function tests, were compared among the groups. Diagnoses changed from non-fHP to fHP for 54 (25%) of the 217 cases, of which, 8 were typical fHP and 46 were probable fHP. The ratio of typical and probable fHP cases to the total number of VATS cases was significantly lower when using transbronchial lung cryobiopsy (p < 0.001). The clinical data of these cases bore a more remarkable resemblance to those diagnosed as indeterminate for fHP than to those diagnosed as typical or probable. The pathological criteria in the new HP guidelines increase the diagnosis of fHP. However, it is unclear whether this increase leads to overdiagnosis, and requires further investigation. Transbronchial lung cryobiopsy may not be helpful when using the new criteria to impart findings for fHP diagnosis.

Learning at a distance: results of an international survey on the adoption of virtual conferences and whole slide imaging by pathologists.

AIMS: This study presents the findings of a global survey of pathologists' views of online conferences and digital pathology. METHODS: An online anonymous survey consisting of 11 questions focusing on pathologists' perceptions of virtual conferences and digital slides was distributed to practising pathologists and trainees across the globe using the authors' social media accounts and professional society connections. Participants were asked to rank their preference for various aspects of pathology meetings on a 5-point Likert scale. RESULTS: There were 562 respondents from 79 countries. Several advantages of virtual meetings were recognised, including that they are less expensive to attend than in-person meetings (mean 4.4), more convenient to attend remotely (mean 4.3) and more efficient due to no loss of time for travel (mean 4.3). The lack of networking was reported as the main disadvantage of virtual conferences (mean 4.0). Most respondents (n=450, 80.1%) preferred hybrid or virtual meetings. About two-thirds (n=356, 63.3%) had no concern regarding the use of virtual slides for educational purposes and viewed them as an acceptable substitute for glass slides. CONCLUSIONS: Online meetings and whole slide imaging are viewed as valuable tools in pathology education. Virtual conferences allow affordable registration fees and flexibility for participants. However, networking opportunities are limited, meaning in-person meetings cannot be entirely replaced by virtual conferences. Hybrid meetings may be a solution to maximise the benefits of both virtual and in-person meetings.

Integrating cytology into routine digital pathology workflow: a 5-year journey.

Despite recent advances in digital imaging, the adoption of digital cytology is challenging due to technical limitations. This study describes our 5-year institutional experience with the implementation of digital cytology. The routine cytology workflow included conventional two-step screening by cytotechnologists, followed by sign out by pathologists. We introduced sign out of cytologic cases using a microscopic digital imaging platform operated by cytotechnologists, which allowed for remote review of slides by cytopathologists via video streaming. We also provided cytologic correlation to support the virtual slide-based sign out of histopathological specimens and for a weekly pathology-radiology conference. In addition, positive cytology cases were archived for integration into the laboratory information system and for prospective computational pathology studies. We also summarized lessons learned over the years and outlined our vision for future developments. This unique experience may serve as a role model for other institutions.

Generalizable biomarker prediction from cancer pathology slides with self-supervised deep learning: A retrospective multi-centric study.

Deep learning (DL) can predict microsatellite instability (MSI) from routine histopathology slides of colorectal cancer (CRC). However, it is unclear whether DL can also predict other biomarkers with high performance and whether DL predictions generalize to external patient populations. Here, we acquire CRC tissue samples from two large multi-centric studies. We systematically compare six different state-of-the-art DL architectures to predict biomarkers from pathology slides, including MSI and mutations in BRAF, KRAS, NRAS, and PIK3CA. Using a large external validation cohort to provide a realistic evaluation setting, we show that models using self-supervised, attention-based multiple-instance learning consistently outperform previous approaches while offering explainable visualizations of the indicative regions and morphologies. While the prediction of MSI and BRAF mutations reaches a clinical-grade performance, mutation prediction of PIK3CA, KRAS, and NRAS was clinically insufficient.

Update on C-Cell Neuroendocrine Neoplasm: Prognostic and Predictive Histopathologic and Molecular Features of Medullary Thyroid Carcinoma.

Medullary thyroid carcinoma (MTC) is a C-cell-derived epithelial neuroendocrine neoplasm. With the exception of rare examples, most are well-differentiated epithelial neuroendocrine neoplasms (also known as neuroendocrine tumors in the taxonomy of the International Agency for Research on Cancer [IARC] of the World Health Organization [WHO]). This review provides an overview and recent evidence-based data on the molecular genetics, disease risk stratification based on clinicopathologic variables including molecular profiling and histopathologic variables, and targeted molecular therapies in patients with advanced MTC. While MTC is not the only neuroendocrine neoplasm in the thyroid gland, other neuroendocrine neoplasms in the thyroid include intrathyroidal thymic neuroendocrine neoplasms, intrathyroidal parathyroid neoplasms, and primary thyroid paragangliomas as well as metastatic neuroendocrine neoplasms. Therefore, the first responsibility of a pathologist is to distinguish MTC from other mimics using appropriate biomarkers. The second responsibility includes meticulous assessment of the status of angioinvasion (defined as tumor cells invading through a vessel wall and forming tumor-fibrin complexes, or intravascular tumor cells admixed with fibrin/thrombus), tumor necrosis, proliferative rate (mitotic count and Ki67 labeling index), and tumor grade (low- or high-grade) along with the tumor stage and the resection margins. Given the morphologic and proliferative heterogeneity in these neoplasms, an exhaustive sampling is strongly recommended. Routine molecular testing for pathogenic germline RET variants is typically performed in all patients with a diagnosis of MTC; however, multifocal C-cell hyperplasia in association with at least a single focus of MTC and/or multifocal C-cell neoplasia are morphological harbingers of germline RET alterations. It is of interest to assess the status of pathogenic molecular alterations involving genes other than RET like the MET variants in MTC families with no pathogenic germline RET variants. Furthermore, the status of somatic RET alterations should be determined in all advanced/progressive or metastatic diseases, especially when selective RET inhibitor therapy (e.g., selpercatinib or pralsetinib) is considered. While the role of routine SSTR2/5 immunohistochemistry remains to be further clarified, evidence suggests that patients with somatostatin receptor (SSTR)-avid metastatic disease may also benefit from the option of 177Lu-DOTATATE peptide radionuclide receptor therapy. Finally, the authors of this review make a call to support the nomenclature change of MTC to C-cell neuroendocrine neoplasm to align this entity with the IARC/WHO taxonomy since MTCs represent epithelial neuroendocrine neoplasms of endoderm-derived C-cells.

Computational pathology in 2030: a Delphi study forecasting the role of AI in pathology within the next decade.

BACKGROUND: Artificial intelligence (AI) is rapidly fuelling a fundamental transformation in the practice of pathology. However, clinical integration remains challenging, with no AI algorithms to date in routine adoption within typical anatomic pathology (AP) laboratories. This survey gathered current expert perspectives and expectations regarding the role of AI in AP from those with first-hand computational pathology and AI experience. METHODS: Perspectives were solicited using the Delphi method from 24 subject matter experts between December 2020 and February 2021 regarding the anticipated role of AI in pathology by the year 2030. The study consisted of three consecutive rounds: 1) an open-ended, free response questionnaire generating a list of survey items; 2) a Likert-scale survey scored by experts and analysed for consensus; and 3) a repeat survey of items not reaching consensus to obtain further expert consensus. FINDINGS: Consensus opinions were reached on 141 of 180 survey items (78.3%). Experts agreed that AI would be routinely and impactfully used within AP laboratory and pathologist clinical workflows by 2030. High consensus was reached on 100 items across nine categories encompassing the impact of AI on (1) pathology key performance indicators (KPIs) and (2) the pathology workforce and specific tasks performed by (3) pathologists and (4) AP lab technicians, as well as (5) specific AI applications and their likelihood of routine use by 2030, (6) AI's role in integrated diagnostics, (7) pathology tasks likely to be fully automated using AI, and (8) regulatory/legal and (9) ethical aspects of AI integration in pathology. INTERPRETATION: This systematic consensus study details the expected short-to-mid-term impact of AI on pathology practice. These findings provide timely and relevant information regarding future care delivery in pathology and raise key practical, ethical, and legal challenges that must be addressed prior to AI's successful clinical implementation. FUNDING: No specific funding was provided for this study.

Cytological evaluation of thyroid nodules in children and young adults: a multi-institutional experience.

PURPOSE: The utility of the Bethesda system for reporting thyroid cytopathology is debatable in determining the risk of malignancy in pediatric patients. Moreover, the upper age limit for defining the pediatric group has varied across different studies. The aim of this study is to compare the risk of malignancy (ROM) and risk of neoplasia (RON) across different Bethesda categories between the pediatric, young adult, and adult patients. METHODS: This is a retrospective multi-institutional study performed in three Indian hospitals. ROM was calculated and compared across Bethesda categories in adult (>18 years) and pediatric age groups (≤18 years), with a subgroup analysis in young adults (19-21 years). RESULTS: Thyroid nodules from a total of 5958 patients were subjected to fine needle aspiration. Of these 199 were pediatric (3.3%) and follow-up histology was available in 2276. The ROM and RON rates, including overall ROM/RON, were significantly higher in pediatric age group as compared to adults. Overall ROM of suspicious for malignancy and malignant categories was higher in children as compared to adults. The overall surgical resection rates were also higher in pediatric patients (45.2% vs. 35.7%; p < 0.01). The similar trend of increased ROM, RON and resection rates was seen among young adults as compared to adult age group. CONCLUSION: Thyroid nodules presenting in children are more likely to be malignant than those in adults. Importantly, the young adult group behaved in a similar manner with regard to surgical resection rates, ROM and RON to pediatric.

Overcoming the Interobserver Variability in Lung Adenocarcinoma Subtyping: A Clustering Approach to Establish a Ground Truth for Downstream Applications.

CONTEXT.­: The accurate identification of different lung adenocarcinoma histologic subtypes is important for determining prognosis but can be challenging because of overlaps in the diagnostic features, leading to considerable interobserver variability. OBJECTIVE.­: To provide an overview of the diagnostic agreement for lung adenocarcinoma subtypes among pathologists and to create a ground truth using the clustering approach for downstream computational applications. DESIGN.­: Three sets of lung adenocarcinoma histologic images with different evaluation levels (small patches, areas with relatively uniform histology, and whole slide images) were reviewed by 17 international expert lung pathologists and 1 pathologist in training. Each image was classified into one or several lung adenocarcinoma subtypes. RESULTS.­: Among the 4702 patches of the first set, 1742 (37%) had an overall consensus among all pathologists. The overall Fleiss κ score for the agreement of all subtypes was 0.58. Using cluster analysis, pathologists were hierarchically grouped into 2 clusters, with κ scores of 0.588 and 0.563 in clusters 1 and 2, respectively. Similar results were obtained for the second and third sets, with fair-to-moderate agreements. Patches from the first 2 sets that obtained the consensus of the 18 pathologists were retrieved to form consensus patches and were regarded as the ground truth of lung adenocarcinoma subtypes. CONCLUSIONS.­: Our observations highlight discrepancies among experts when assessing lung adenocarcinoma subtypes. However, a subsequent number of consensus patches could be retrieved from each cluster, which can be used as ground truth for the downstream computational pathology applications, with minimal influence from interobserver variability.