Invasion and Grading of Pulmonary Non-Mucinous Adenocarcinoma.

Lung adenocarcinoma staging and grading were recently updated to reflect the link between histologic growth patterns and outcomes. The lepidic growth pattern is regarded as "in-situ," whereas all other patterns are regarded as invasive, though with stratification. Solid, micropapillary, and complex glandular patterns are associated with worse prognosis than papillary and acinar patterns. These recent changes have improved prognostic stratification. However, multiple pitfalls exist in measuring invasive size and in classifying lung adenocarcinoma growth patterns. Awareness of these limitations and recommended practices will help the pathology community achieve consistent prognostic performance and potentially contribute to improved patient management.

Optimizing double-layered convolutional neural networks for efficient lung cancer classification through hyperparameter optimization and advanced image pre-processing techniques.

Lung cancer remains a leading cause of cancer-related mortality globally, with prognosis significantly dependent on early-stage detection. Traditional diagnostic methods, though effective, often face challenges regarding accuracy, early detection, and scalability, being invasive, time-consuming, and prone to ambiguous interpretations. This study proposes an advanced machine learning model designed to enhance lung cancer stage classification using CT scan images, aiming to overcome these limitations by offering a faster, non-invasive, and reliable diagnostic tool. Utilizing the IQ-OTHNCCD lung cancer dataset, comprising CT scans from various stages of lung cancer and healthy individuals, we performed extensive preprocessing including resizing, normalization, and Gaussian blurring. A Convolutional Neural Network (CNN) was then trained on this preprocessed data, and class imbalance was addressed using Synthetic Minority Over-sampling Technique (SMOTE). The model's performance was evaluated through metrics such as accuracy, precision, recall, F1-score, and ROC curve analysis. The results demonstrated a classification accuracy of 99.64%, with precision, recall, and F1-score values exceeding 98% across all categories. SMOTE significantly enhanced the model's ability to classify underrepresented classes, contributing to the robustness of the diagnostic tool. These findings underscore the potential of machine learning in transforming lung cancer diagnostics, providing high accuracy in stage classification, which could facilitate early detection and tailored treatment strategies, ultimately improving patient outcomes.

Precise and automated lung cancer cell classification using deep neural network with multiscale features and model distillation.

Lung diseases globally impose a significant pathological burden and mortality rate, particularly the differential diagnosis between adenocarcinoma, squamous cell carcinoma, and small cell lung carcinoma, which is paramount in determining optimal treatment strategies and improving clinical prognoses. Faced with the challenge of improving diagnostic precision and stability, this study has developed an innovative deep learning-based model. This model employs a Feature Pyramid Network (FPN) and Squeeze-and-Excitation (SE) modules combined with a Residual Network (ResNet18), to enhance the processing capabilities for complex images and conduct multi-scale analysis of each channel's importance in classifying lung cancer. Moreover, the performance of the model is further enhanced by employing knowledge distillation from larger teacher models to more compact student models. Subjected to rigorous five-fold cross-validation, our model outperforms existing models on all performance metrics, exhibiting exceptional diagnostic accuracy. Ablation studies on various model components have verified that each addition effectively improves model performance, achieving an average accuracy of 98.84% and a Matthews Correlation Coefficient (MCC) of 98.83%. Collectively, the results indicate that our model significantly improves the accuracy of disease diagnosis, providing physicians with more precise clinical decision-making support.

The Classification of VOCs Based on Sensor Images Using a Lightweight Neural Network for Lung Cancer Diagnosis.

The application of artificial intelligence to point-of-care testing (POCT) disease detection has become a hot research field, in which breath detection, which detects the patient's exhaled VOCs, combined with sensor arrays of convolutional neural network (CNN) algorithms as a new lung cancer detection is attracting more researchers' attention. However, the low accuracy, high-complexity computation and large number of parameters make the CNN algorithms difficult to transplant to the embedded system of POCT devices. A lightweight neural network (LTNet) in this work is proposed to deal with this problem, and meanwhile, achieve high-precision classification of acetone and ethanol gases, which are respiratory markers for lung cancer patients. Compared to currently popular lightweight CNN models, such as EfficientNet, LTNet has fewer parameters (32 K) and its training weight size is only 0.155 MB. LTNet achieved an overall classification accuracy of 99.06% and 99.14% in the own mixed gas dataset and the University of California (UCI) dataset, which are both higher than the scores of the six existing models, and it also offers the shortest training (844.38 s and 584.67 s) and inference times (23 s and 14 s) in the same validation sets. Compared to the existing CNN models, LTNet is more suitable for resource-limited POCT devices.

Classification of lung cancer subtypes on CT images with synthetic pathological priors.

The accurate diagnosis on pathological subtypes for lung cancer is of significant importance for the follow-up treatments and prognosis managements. In this paper, we propose self-generating hybrid feature network (SGHF-Net) for accurately classifying lung cancer subtypes on computed tomography (CT) images. Inspired by studies stating that cross-scale associations exist in the image patterns between the same case's CT images and its pathological images, we innovatively developed a pathological feature synthetic module (PFSM), which quantitatively maps cross-modality associations through deep neural networks, to derive the "gold standard" information contained in the corresponding pathological images from CT images. Additionally, we designed a radiological feature extraction module (RFEM) to directly acquire CT image information and integrated it with the pathological priors under an effective feature fusion framework, enabling the entire classification model to generate more indicative and specific pathologically related features and eventually output more accurate predictions. The superiority of the proposed model lies in its ability to self-generate hybrid features that contain multi-modality image information based on a single-modality input. To evaluate the effectiveness, adaptability, and generalization ability of our model, we performed extensive experiments on a large-scale multi-center dataset (i.e., 829 cases from three hospitals) to compare our model and a series of state-of-the-art (SOTA) classification models. The experimental results demonstrated the superiority of our model for lung cancer subtypes classification with significant accuracy improvements in terms of accuracy (ACC), area under the curve (AUC), positive predictive value (PPV) and F1-score.

ML3CNet: Non-local means-assisted automatic framework for lung cancer subtypes classification using histopathological images.

BACKGROUND AND OBJECTIVE: Lung cancer (LC) has a high fatality rate that continuously affects human lives all over the world. Early detection of LC prolongs human life and helps to prevent the disease. Histopathological inspection is a common method to diagnose LC. Visual inspection of histopathological diagnosis necessitates more inspection time, and the decision depends on the subjective perception of clinicians. Usually, machine learning techniques mostly depend on traditional feature extraction which is labor-intensive and may not be appropriate for enormous data. In this work, a convolutional neural network (CNN)-based architecture is proposed for the more effective classification of lung tissue subtypes using histopathological images. METHODS: Authors have utilized the first-time nonlocal mean (NLM) filter to suppress the effect of noise from histopathological images. NLM filter efficiently eliminated noise while preserving the edges of images. Then, the obtained denoised images are given as input to the proposed multi-headed lung cancer classification convolutional neural network (ML3CNet). Furthermore, the model quantization technique is utilized to reduce the size of the proposed model for the storage of the data. Reduction in model size requires less memory and speeds up data processing. RESULTS: The effectiveness of the proposed model is compared with the other existing state-of-the-art methods. The proposed ML3CNet achieved an average classification accuracy of 99.72%, sensitivity of 99.66%, precision of 99.64%, specificity of 99.84%, F-1 score of 0.9965, and area under the curve of 0.9978. The quantized accuracy of 98.92% is attained by the proposed model. To validate the applicability of the proposed ML3CNet, it has also been tested on the colon cancer dataset. CONCLUSION: The findings reveal that the proposed approach can be beneficial to automatically classify LC subtypes that might assist healthcare workers in making decisions more precisely. The proposed model can be implemented on the hardware using Raspberry Pi for practical realization.

A multi-instance tumor subtype classification method for small PET datasets using RA-DL attention module guided deep feature extraction with radiomics features.

BACKGROUND: Positron emission tomography (PET) is extensively employed for diagnosing and staging various tumors, including liver cancer, lung cancer, and lymphoma. Accurate subtype classification of tumors plays a crucial role in formulating effective treatment plans for patients. Notably, lymphoma comprises subtypes like diffuse large B-cell lymphoma and Hodgkin's lymphoma, while lung cancer encompasses adenocarcinoma, small cell carcinoma, and squamous cell carcinoma. Similarly, liver cancer consists of subtypes such as cholangiocarcinoma and hepatocellular carcinoma. Consequently, the subtype classification of tumors based on PET images holds immense clinical significance. However, in clinical practice, the number of cases available for each subtype is often limited and imbalanced. Therefore, the primary challenge lies in achieving precise subtype classification using a small dataset. METHOD: This paper presents a novel approach for tumor subtype classification in small datasets using RA-DL (Radiomics-DeepLearning) attention. To address the limited sample size, Support Vector Machines (SVM) is employed as the classifier for tumor subtypes instead of deep learning methods. Emphasizing the importance of texture information in tumor subtype recognition, radiomics features are extracted from the tumor regions during the feature extraction stage. These features are compressed using an autoencoder to reduce redundancy. In addition to radiomics features, deep features are also extracted from the tumors to leverage the feature extraction capabilities of deep learning. In contrast to existing methods, our proposed approach utilizes the RA-DL-Attention mechanism to guide the deep network in extracting complementary deep features that enhance the expressive capacity of the final features while minimizing redundancy. To address the challenges of limited and imbalanced data, our method avoids using classification labels during deep feature extraction and instead incorporates 2D Region of Interest (ROI) segmentation and image reconstruction as auxiliary tasks. Subsequently, all lesion features of a single patient are aggregated into a feature vector using a multi-instance aggregation layer. RESULT: Validation experiments were conducted on three PET datasets, specifically the liver cancer dataset, lung cancer dataset, and lymphoma dataset. In the context of lung cancer, our proposed method achieved impressive performance with Area Under Curve (AUC) values of 0.82, 0.84, and 0.83 for the three-classification task. For the binary classification task of lymphoma, our method demonstrated notable results with AUC values of 0.95 and 0.75. Moreover, in the binary classification task of liver tumor, our method exhibited promising performance with AUC values of 0.84 and 0.86. CONCLUSION: The experimental results clearly indicate that our proposed method outperforms alternative approaches significantly. Through the extraction of complementary radiomics features and deep features, our method achieves a substantial improvement in tumor subtype classification performance using small PET datasets.

Correlation between Radiological Characteristics, PET-CT and Histological Subtypes of Primary Lung Adenocarcinoma-A 102 Case Series Analysis.

Background and Objectives: Lung cancer is the second most common form of cancer in the world for both men and women as well as the most common cause of cancer-related deaths worldwide. The aim of this study is to summarize the radiological characteristics between primary lung adenocarcinoma subtypes and to correlate them with FDG uptake on PET-CT. Materials and Methods: This retrospective study included 102 patients with pathohistologically confirmed lung adenocarcinoma. A PET-CT examination was performed on some of the patients and the values of SUVmax were also correlated with the histological and morphological characteristics of the masses in the lungs. Results: The results of this analysis showed that the mean size of AIS-MIA (adenocarcinoma in situ and minimally invasive adenocarcinoma) cancer was significantly lower than for all other cancer types, while the mean size of the acinar cancer was smaller than in the solid type of cancer. Metastases were significantly more frequent in solid adenocarcinoma than in acinar, lepidic, and AIS-MIA cancer subtypes. The maximum standardized FDG uptake was significantly lower in AIS-MIA than in all other cancer types and in the acinar predominant subtype compared to solid cancer. Papillary predominant adenocarcinoma had higher odds of developing contralateral lymph node involvement compared to other types. Solid adenocarcinoma was associated with higher odds of having metastases and with higher SUVmax. AIS-MIA was associated with lower odds of one unit increase in tumor size and ipsilateral lymph node involvement. Conclusions: The correlation between histopathological and radiological findings is crucial for accurate diagnosis and staging. By integrating both sets of data, clinicians can enhance diagnostic accuracy and determine the optimal treatment plan.

A dual data stream hybrid neural network for classifying pathological images of lung adenocarcinoma.

Lung cancer has seriously threatened human health due to its high lethality and morbidity. Lung adenocarcinoma, in particular, is one of the most common subtypes of lung cancer. Pathological diagnosis is regarded as the gold standard for cancer diagnosis. However, the traditional manual screening of lung cancer pathology images is time consuming and error prone. Computer-aided diagnostic systems have emerged to solve this problem. Current research methods are unable to fully exploit the beneficial features inherent within patches, and they are characterized by high model complexity and significant computational effort. In this study, a deep learning framework called Multi-Scale Network (MSNet) is proposed for the automatic detection of lung adenocarcinoma pathology images. MSNet is designed to efficiently harness the valuable features within data patches, while simultaneously reducing model complexity, computational demands, and storage space requirements. The MSNet framework employs a dual data stream input method. In this input method, MSNet combines Swin Transformer and MLP-Mixer models to address global information between patches and the local information within each patch. Subsequently, MSNet uses the Multilayer Perceptron (MLP) module to fuse local and global features and perform classification to output the final detection results. In addition, a dataset of lung adenocarcinoma pathology images containing three categories is created for training and testing the MSNet framework. Experimental results show that the diagnostic accuracy of MSNet for lung adenocarcinoma pathology images is 96.55 %. In summary, MSNet has high classification performance and shows effectiveness and potential in the classification of lung adenocarcinoma pathology images.

Nodule-CLIP: Lung nodule classification based on multi-modal contrastive learning.

The latest developments in deep learning have demonstrated the importance of CT medical imaging for the classification of pulmonary nodules. However, challenges remain in fully leveraging the relevant medical annotations of pulmonary nodules and distinguishing between the benign and malignant labels of adjacent nodules. Therefore, this paper proposes the Nodule-CLIP model, which deeply mines the potential relationship between CT images, complex attributes of lung nodules, and benign and malignant attributes of lung nodules through a comparative learning method, and optimizes the model in the image feature extraction network by using its similarities and differences to improve its ability to distinguish similar lung nodules. Firstly, we segment the 3D lung nodule information by U-Net to reduce the interference caused by the background of lung nodules and focus on the lung nodule images. Secondly, the image features, class features, and complex attribute features are aligned by contrastive learning and loss function in Nodule-CLIP to achieve lung nodule image optimization and improve classification ability. A series of testing and ablation experiments were conducted on the public dataset LIDC-IDRI, and the final benign and malignant classification rate was 90.6%, and the recall rate was 92.81%. The experimental results show the advantages of this method in terms of lung nodule classification as well as interpretability.

The International Association for the Study of Lung Cancer Staging Project for Lung Cancer: Proposals for the Revision of the M Descriptors in the Forthcoming Ninth Edition of the TNM Classification for Lung Cancer.

INTRODUCTION: This study analyzed all metastatic categories of the current TNM classification of NSCLC to propose modifications of the M component in the next edition (ninth) of the classification. METHODS: A database of 124,581 patients diagnosed between 2011 and 2019 was established; of these, 14,937 with NSCLC in stages IVA to IVB were available for this analysis. Overall survival was calculated using the Kaplan-Meier method, and prognosis was assessed using multivariable-adjusted Cox proportional hazards regression. RESULTS: The eighth edition M categories revealed good discrimination in the ninth edition data set. Assessments revealed that an increasing number of metastatic lesions were associated with decreasing prognosis; because this seems to be a continuum and adjustment for confounders was not possible, no specific lesion number was deemed appropriate for stage classification. Among tumors involving multiple metastases, decreasing prognosis was found with an increasing number of organ systems involved. Multiple assessments, including after adjustment for potential confounders, revealed that M1c patients who had metastases to a single extrathoracic organ system were prognostically distinct from M1c patients who had involvement of multiple extrathoracic organ systems. CONCLUSIONS: These data validate the eighth edition M1a and M1b categories, which are recommended to be maintained. We propose the M1c category be divided into M1c1 (involvement of a single extrathoracic organ system) and M1c2 (involvement of multiple extrathoracic organ systems).

Classification of benign and malignant pulmonary nodule based on local-global hybrid network.

BACKGROUND: The accurate classification of pulmonary nodules has great application value in assisting doctors in diagnosing conditions and meeting clinical needs. However, the complexity and heterogeneity of pulmonary nodules make it difficult to extract valuable characteristics of pulmonary nodules, so it is still challenging to achieve high-accuracy classification of pulmonary nodules. OBJECTIVE: In this paper, we propose a local-global hybrid network (LGHNet) to jointly model local and global information to improve the classification ability of benign and malignant pulmonary nodules. METHODS: First, we introduce the multi-scale local (MSL) block, which splits the input tensor into multiple channel groups, utilizing dilated convolutions with different dilation rates and efficient channel attention to extract fine-grained local information at different scales. Secondly, we design the hybrid attention (HA) block to capture long-range dependencies in spatial and channel dimensions to enhance the representation of global features. RESULTS: Experiments are carried out on the publicly available LIDC-IDRI and LUNGx datasets, and the accuracy, sensitivity, precision, specificity, and area under the curve (AUC) of the LIDC-IDRI dataset are 94.42%, 94.25%, 93.05%, 92.87%, and 97.26%, respectively. The AUC on the LUNGx dataset was 79.26%. CONCLUSION: The above classification results are superior to the state-of-the-art methods, indicating that the network has better classification performance and generalization ability.

[A new one in the lung and pleura neoplasms classification (WHO, 2021, 5th edition)]. / Novoe v Klassifikatsii novoobrazovanii legkikh i plevry (VOZ, 2021 g., 5-e izdanie).

The article contains an overview of the new WHO classification of thoracic tumors (2021). As in the previous edition of 2015, considerable attention is paid to neoplasms of the lungs and pleura. The article presents current data on molecular genetic features and morphological manifestations of a number of new lung tumors, with a detailed histological and immunohistochemical data. Thoracis undifferentiated tumor with SMARCA4 deficiency and bronchiolar adenoma are described. Emphasis is placed on the algorithms of morphological diagnostics, including a complete description of the tumor and facilitating the study in the practice of a pathologist. The main morphological criteria of mesothelial tumors of the pleura are given; describes in detail the procedure for assessing the degree of malignancy of diffuse epithelioid pleural mesothelioma and non-mucinous lung adenocarcinomas.

The International Association for the Study of Lung Cancer Thymic Epithelial Tumor Staging Project: Unresolved Issues to be Addressed for the Next Ninth Edition of the TNM Classification of Malignant Tumors.

Thymic epithelial tumors are presently staged using a consistent TNM classification developed by the International Association for the Study of Lung Cancer (IASLC) and approved by the Union for International Cancer Control and the American Joint Committee on Cancer. The stage classification is incorporated in the eight edition of the TNM classification of thoracic malignancies. The IASLC Staging and Prognostic Factors Committee (SPFC)-Thymic Domain (TD) is in charge for the next (ninth) edition expected in 2024. The present article represents the midterm report of the SPFC-TD: in particular, it describes the unresolved issues identified by the group in the current stage classification which are worth being addressed and discussed for the ninth edition of the TNM classification on the basis of the available data collected in the central thymic database which will be managed and analyzed by Cancer Research And Biostatistics. These issues are grouped into issues of general importance and those specifically related to T, N, and M categories. Each issue is described in reference to the most recent reports on the subject, and the priority assigned by the IASLC SPFC-TD for the discussion of the ninth edition is provided.

The classification of neuroendocrine neoplasms of the lung and digestive system according to WHO, 5th edition: similarities, differences, challenges, and unmet needs.

Neuroendocrine neoplasms (NENs) are a group of disease entities sharing common morphological, ultrastructural and immunophenotypical features, yet with distinct biological behavior and clinical outcome, ranging from benign to frankly malignant. Accordingly, a spectrum of therapeutic options for each single entity is available, including somatostatin analogues (SSA), mTOR-inhibitors, peptide receptor radionuclide therapy (PRRT), non-platinum and platinum chemotherapy. In the last few decades, several attempts have been made to better stratify these lesions refining the pathological classifications, so as to obtain an optimal correspondence between the scientific terminology and, the predictive and prognostic features of each disease subtype, and achieve a global Classification encompassing NENs arising at different anatomical sites. The aim of this review was to analyze, compare and discuss the main features and issues of the latest WHO Classifications of NENs of the lung and the digestive system, in order to point out the strengths and limitations of our current understanding of these complex diseases.

Pulmonary "Inflammatory Leiomyosarcomas" Are Indolent Tumors With Diploid Genomes and No Convincing Rhabdomyoblastic Differentiation.

Inflammatory leiomyosarcoma is a rare myogenic tumor with striking inflammatory infiltrates and a specific genomic pattern of near-haploidization despite exception(s). Recent studies demonstrated that inflammatory leiomyosarcoma shares substantially overlapping features with histiocyte-rich rhabdomyoblastic tumor, including expression of rhabdomyoblastic markers such as myogenin, MyoD1, and PAX7 and a high prevalence of genomic near-haploidization, suggesting that they represent a unifying entity, for which the term inflammatory rhabdomyoblastic tumor was coined. In this study, we identified 4 pulmonary tumors histologically typical of inflammatory leiomyosarcomas, all in men (aged 26 to 49), presented as slow-growing well-defined nodules ranging from 1.4 to 3.5 cm, and following uneventful postoperative courses. All tumors were positive for desmin immunostaining, while only 1 and 2 were focally positive for smooth muscle actin and smooth muscle myosin heavy chain, respectively. They showed no expression of myogenin, MyoD1, or PAX7 by immunohistochemistry or RNA sequencing. Copy number analyses by whole-exome sequencing (N=1), OncoScan single-nucleotide polymorphism array (2), and fluorescence in situ hybridization (1) revealed/suggested diploid genomes. Together with a previously reported case, all these pulmonary "inflammatory leiomyosarcomas" seemed clinically, pathologically, and genomically alike. Despite a superficial resemblance to conventional inflammatory leiomyosarcoma in somatic soft tissues (now preferably termed inflammatory rhabdomyoblastic tumor), they differ in the lack of convincing rhabdomyoblastic differentiation and genomic near-haploidization. Therefore, we propose that these pulmonary tumors probably represent a distinct entity, for which the exact line of differentiation, and perhaps the most suitable terminology to better reflect its nature, remains to be determined. The term inflammatory rhabdomyoblastic tumor seems inappropriate for this group of tumors.

Stiffness reduction and collagenase resistance of aging lungs measured using scanning acoustic microscopy.

Lung tissue stiffness is altered with aging. Quantitatively evaluating lung function is difficult using a light microscope (LM) alone. Scanning acoustic microscope (SAM) calculates the speed-of-sound (SOS) using sections to obtain histological images by plotting SOS values on the screen. As SOS is positively correlated with stiffness, SAM has a superior characteristic of simultaneously evaluating tissue stiffness and structure. SOS images of healthy bronchioles, arterioles, and alveoli were compared among young, middle-aged, and old lung sections. Formalin-fixed, paraffin-embedded (FFPE) sections consistently exhibited relatively higher SOS values than fresh-frozen sections, indicating that FFPE became stiffer but retained the relative stiffness reflecting fresh samples. All lung components exhibited gradually declining SOS values with aging and were associated with structural alterations such as loss of smooth muscles, collagen, and elastic fibers. Moreover, reaction to collagenase digestion resulted in decreased SOS values. SOS values of all components were significantly reduced in young and middle-aged groups, whereas no significant reduction was observed in the old group. Protease damage in the absence of regeneration or loss of elastic components was present in old lungs, which exbited dilated bronchioles and alveoli. Aging lungs gradually lose stiffness with decreasing structural components without exposure to specific insults such as inflammation.

Classification of subtypes including LCNEC in lung cancer biopsy slides using convolutional neural network from scratch.

Identifying the lung carcinoma subtype in small biopsy specimens is an important part of determining a suitable treatment plan but is often challenging without the help of special and/or immunohistochemical stains. Pathology image analysis that tackles this issue would be helpful for diagnoses and subtyping of lung carcinoma. In this study, we developed AI models to classify multinomial patterns of lung carcinoma; ADC, LCNEC, SCC, SCLC, and non-neoplastic lung tissue based on convolutional neural networks (CNN or ConvNet). Four CNNs that were pre-trained using transfer learning and one CNN built from scratch were used to classify patch images from pathology whole-slide images (WSIs). We first evaluated the diagnostic performance of each model in the test sets. The Xception model and the CNN built from scratch both achieved the highest performance with a macro average AUC of 0.90. The CNN built from scratch model obtained a macro average AUC of 0.97 on the dataset of four classes excluding LCNEC, and 0.95 on the dataset of three subtypes of lung carcinomas; NSCLC, SCLC, and non-tumor, respectively. Of particular note is that the relatively simple CNN built from scratch may be an approach for pathological image analysis.

Elastin in pulmonary pathology: relevance in tumours with a lepidic or papillary appearance. A comprehensive understanding from a morphological viewpoint.

Elastin and collagen are the main components of the lung connective tissue network, and together provide the lung with elasticity and tensile strength. In pulmonary pathology, elastin staining is used to variable extents in different countries. These uses include evaluation of the pleura in staging, and the distinction of invasion from collapse of alveoli after surgery (iatrogenic collapse). In the latter, elastin staining is used to highlight distorted but pre-existing alveolar architecture from true invasion. In addition to variable levels of use and experience, the interpretation of elastin staining in some adenocarcinomas leads to interpretative differences between collapsed lepidic patterns and true papillary patterns. This review aims to summarise the existing data on the use of elastin staining in pulmonary pathology, on the basis of literature data and morphological characteristics. The effect of iatrogenic collapse and the interpretation of elastin staining in pulmonary adenocarcinomas is discussed in detail, especially for the distinction between lepidic patterns and papillary carcinoma.