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Modern Pathology : An Official Journal... Jan 2022The clinical and pathologic diagnosis of hypersensitivity pneumonitis has been confounded by conflicting definitions, with two recent guidelines suggesting that... (Review)
Review
The clinical and pathologic diagnosis of hypersensitivity pneumonitis has been confounded by conflicting definitions, with two recent guidelines suggesting that hypersensitivity pneumonitis simply be diagnosed as nonfibrotic or fibrotic. Nonfibrotic hypersensitivity pneumonitis is usually characterized by a bronchiolocentric chronic interstitial inflammatory infiltrate, frequently but by no means always with associated granulomas or giant cells. Fibrotic hypersensitivity pneumonitis may take the form of interstitial fibrosis confined to the peribronchiolar regions, or fibrotic nonspecific interstitial pneumonia, or a process similar to and sometimes indistinguishable from usual interstitial pneumonia/idiopathic interstitial fibrosis, but the exact pathologic features that favor a diagnosis of fibrotic hypersensitivity pneumonitis are disputed. Granulomas/giant cells are much less frequent in fibrotic compared to nonfibrotic hypersensitivity pneumonitis. Extensive peribronchiolar metaplasia, particularly peribronchiolar metaplasia affecting more than half the bronchioles, supports a diagnosis of fibrotic hypersensitivity pneumonitis over usual interstitial pneumonia, as does the presence of predominantly peribronchiolar disease with relative subpleural sparing. Clinical and CT features are crucial to the diagnosis of hypersensitivity pneumonitis: sparing of the lung bases, centrilobular nodules, air-trapping, or the triple density sign with fibrosis favor a diagnosis of fibrotic hypersensitivity pneumonitis. At this point there are no molecular tests that reliably separate fibrotic hypersensitivity pneumonitis from other forms of interstitial lung disease. Currently the separation of fibrotic hypersensitivity pneumonitis from usual interstitial pneumonia is crucial to treatment (immunosuppressives for the former, anti-fibrotics for the latter) but this approach is changing and all progressive fibrosing interstitial pneumonias will probably be treated with antifibrotics in the future.
Topics: Alveolitis, Extrinsic Allergic; Biopsy; Diagnosis, Differential; Humans; Idiopathic Pulmonary Fibrosis; Lung; Lung Diseases, Interstitial
PubMed: 34531525
DOI: 10.1038/s41379-021-00866-y -
Archives of Pathology & Laboratory... Jan 2022Because granulomas are represented in almost every disease category, the number of clinically and pathologically important granulomatous pulmonary diseases is large.... (Review)
Review
CONTEXT.—
Because granulomas are represented in almost every disease category, the number of clinically and pathologically important granulomatous pulmonary diseases is large. Their diagnosis by pathologists is particularly challenging because of their nonspecificity. A specific diagnosis can be achieved only when a granuloma-inciting agent(s) (eg, acid-fast bacilli, fungi, foreign bodies, etc) are identified microscopically or by culture; this does not occur in most cases. Furthermore, a specific diagnosis cannot be reached in a high percentage of cases. Although sarcoidosis and infectious diseases account for approximately half of pulmonary granulomatous diseases worldwide, there is significant geographic variation in their prevalence.
OBJECTIVES.—
To present updated information to serve as a guide to pathologic diagnosis of pulmonary granulomatous diseases, to address some commonly held misconceptions and to stress the importance of multidisciplinary coordination. Presentation of basic aspects of granulomas is followed by discussion of specific disease entities, such as tuberculous and nontuberculous Mycobacterial infections, fungal, bacterial, and parasitic infections, sarcoidosis, necrotizing sarcoid granulomatosis, berylliosis, hypersensitivity pneumonitis, hot tub lung, rheumatoid nodule, bronchocentric granulomatosis, aspirated, inhaled, and embolized foreign bodies, drug-induced granulomas, chronic granulomatous disease, common variable immunodeficiency, and granulomatous lesions associated with various types of cancer.
DATA SOURCES.—
Review of pertinent medical literature using the PubMed search engine and the author's practical experience.
CONCLUSIONS.—
Although the diagnosis of granulomatous lung diseases continues to present significant challenges to pathologists, the information presented in this review can be helpful in overcoming them. The importance of multidisciplinary coordination in cases where morphologic diagnosis is not possible cannot be overstated.
Topics: Alveolitis, Extrinsic Allergic; Granuloma; Humans; Lung; Lung Diseases; Sarcoidosis
PubMed: 33905479
DOI: 10.5858/arpa.2020-0543-RA -
Diagnostic and Interventional Imaging Jan 2023Artificial intelligence (AI) is a broad concept that usually refers to computer programs that can learn from data and perform certain specific tasks. In the recent... (Review)
Review
Artificial intelligence (AI) is a broad concept that usually refers to computer programs that can learn from data and perform certain specific tasks. In the recent years, the growth of deep learning, a successful technique for computer vision tasks that does not require explicit programming, coupled with the availability of large imaging databases fostered the development of multiple applications in the medical imaging field, especially for lung nodules and lung cancer, mostly through convolutional neural networks (CNN). Some of the first applications of AI is this field were dedicated to automated detection of lung nodules on X-ray and computed tomography (CT) examinations, with performances now reaching or exceeding those of radiologists. For lung nodule segmentation, CNN-based algorithms applied to CT images show excellent spatial overlap index with manual segmentation, even for irregular and ground glass nodules. A third application of AI is the classification of lung nodules between malignant and benign, which could limit the number of follow-up CT examinations for less suspicious lesions. Several algorithms have demonstrated excellent capabilities for the prediction of the malignancy risk when a nodule is discovered. These different applications of AI for lung nodules are particularly appealing in the context of lung cancer screening. In the field of lung cancer, AI tools applied to lung imaging have been investigated for distinct aims. First, they could play a role for the non-invasive characterization of tumors, especially for histological subtype and somatic mutation predictions, with a potential therapeutic impact. Additionally, they could help predict the patient prognosis, in combination to clinical data. Despite these encouraging perspectives, clinical implementation of AI tools is only beginning because of the lack of generalizability of published studies, of an inner obscure working and because of limited data about the impact of such tools on the radiologists' decision and on the patient outcome. Radiologists must be active participants in the process of evaluating AI tools, as such tools could support their daily work and offer them more time for high added value tasks.
Topics: Humans; Lung Neoplasms; Artificial Intelligence; Early Detection of Cancer; Neural Networks, Computer; Lung; Solitary Pulmonary Nodule
PubMed: 36513593
DOI: 10.1016/j.diii.2022.11.007 -
The Journal of Pathology Jul 2022Lung diseases carry a significant burden of morbidity and mortality worldwide. The advent of digital pathology (DP) and an increase in computational power have led to... (Review)
Review
Lung diseases carry a significant burden of morbidity and mortality worldwide. The advent of digital pathology (DP) and an increase in computational power have led to the development of artificial intelligence (AI)-based tools that can assist pathologists and pulmonologists in improving clinical workflow and patient management. While previous works have explored the advances in computational approaches for breast, prostate, and head and neck cancers, there has been a growing interest in applying these technologies to lung diseases as well. The application of AI tools on radiology images for better characterization of indeterminate lung nodules, fibrotic lung disease, and lung cancer risk stratification has been well documented. In this article, we discuss methodologies used to build AI tools in lung DP, describing the various hand-crafted and deep learning-based unsupervised feature approaches. Next, we review AI tools across a wide spectrum of lung diseases including cancer, tuberculosis, idiopathic pulmonary fibrosis, and COVID-19. We discuss the utility of novel imaging biomarkers for different types of clinical problems including quantification of biomarkers like PD-L1, lung disease diagnosis, risk stratification, and prediction of response to treatments such as immune checkpoint inhibitors. We also look briefly at some emerging applications of AI tools in lung DP such as multimodal data analysis, 3D pathology, and transplant rejection. Lastly, we discuss the future of DP-based AI tools, describing the challenges with regulatory approval, developing reimbursement models, planning clinical deployment, and addressing AI biases. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
Topics: Artificial Intelligence; COVID-19; Humans; Lung; Lung Neoplasms; Pathologists
PubMed: 35579955
DOI: 10.1002/path.5966 -
Thoracic Cancer Mar 2022Screening with low-dose computed tomography (LDCT) is an efficient way to detect lung cancer at an earlier stage, but has a high false-positive rate. Several pulmonary... (Review)
Review
BACKGROUND
Screening with low-dose computed tomography (LDCT) is an efficient way to detect lung cancer at an earlier stage, but has a high false-positive rate. Several pulmonary nodules risk prediction models were developed to solve the problem. This systematic review aimed to compare the quality and accuracy of these models.
METHODS
The keywords "lung cancer," "lung neoplasms," "lung tumor," "risk," "lung carcinoma" "risk," "predict," "assessment," and "nodule" were used to identify relevant articles published before February 2021. All studies with multivariate risk models developed and validated on human LDCT data were included. Informal publications or studies with incomplete procedures were excluded. Information was extracted from each publication and assessed.
RESULTS
A total of 41 articles and 43 models were included. External validation was performed for 23.2% (10/43) models. Deep learning algorithms were applied in 62.8% (27/43) models; 60.0% (15/25) deep learning based researches compared their algorithms with traditional methods, and received better discrimination. Models based on Asian and Chinese populations were usually built on single-center or small sample retrospective studies, and the majority of the Asian models (12/15, 80.0%) were not validated using external datasets.
CONCLUSION
The existing models showed good discrimination for identifying high-risk pulmonary nodules, but lacked external validation. Deep learning algorithms are increasingly being used with good performance. More researches are required to improve the quality of deep learning models, particularly for the Asian population.
Topics: Early Detection of Cancer; Humans; Lung; Lung Neoplasms; Multiple Pulmonary Nodules; Retrospective Studies
PubMed: 35137543
DOI: 10.1111/1759-7714.14333 -
The Oncologist Jan 2023Patients with interstitial lung disease (ILD), especially those with pulmonary fibrosis, are at increased risk of developing lung cancer. Management of lung cancer in... (Review)
Review
Patients with interstitial lung disease (ILD), especially those with pulmonary fibrosis, are at increased risk of developing lung cancer. Management of lung cancer in patients with ILD is particularly challenging. Diagnosis can be complicated by difficulty differentiating lung nodules from areas of focal fibrosis, and percutaneous biopsy approaches confer an increased risk of complications in those with pulmonary fibrosis. Lung cancer treatment in these patients pose several specific considerations. The degree of lung function impairment may preclude lobectomy or surgical resection of any type. Surgical resection can trigger an acute exacerbation of the underlying ILD. The presence of ILD confers an increased risk of pneumonitis with radiotherapy, and many of the systemic therapies also carry an increased risk of pneumonitis in this population. The safety of immunotherapy in the setting of ILD remains to be fully elucidated and concerns remain as to triggering pneumonitis. The purpose of this review is to summarize the evidence regarding consideration for tissue diagnosis, chemotherapy and immunotherapy, radiotherapy, and surgery, in this patient population and discuss emerging areas of research. We also propose a multidisciplinary approach and practical considerations for monitoring for ILD progression during lung cancer treatment.
Topics: Humans; Pulmonary Fibrosis; Lung Neoplasms; Lung Diseases, Interstitial; Lung; Pneumonia
PubMed: 36426803
DOI: 10.1093/oncolo/oyac226 -
Respiration; International Review of... 2020With the advent of lung cancer screening, and the increasingly frequent use of computed tomography (CT) scanning for investigating non-pulmonary pathology (for example... (Review)
Review
With the advent of lung cancer screening, and the increasingly frequent use of computed tomography (CT) scanning for investigating non-pulmonary pathology (for example CT coronary angiogram), the number of pulmonary nodules requiring further investigation has risen significantly. Most of these nodules are found in the lung periphery, which presents challenges to biopsy, and many centers rely on trans-thoracic needle biopsy performed under image guidance by radiologists. However, the desire to minimize complications is driving the development of increasingly accurate navigation bronchoscopy platforms, something that will be crucial in the new era of bronchoscopic therapeutics for lung cancer. This review describes these platforms, summarizes the current evidence for their use, and takes a look at future developments.
Topics: Bronchoscopy; Humans; Image-Guided Biopsy; Lung; Lung Neoplasms; Solitary Pulmonary Nodule; Surgery, Computer-Assisted; Surgical Navigation Systems; Tomography, X-Ray Computed
PubMed: 31600761
DOI: 10.1159/000503329 -
Clinics in Chest Medicine Sep 2019Immunoglobulin G4 (IgG4)-Related Disease (IgG4-RD) can cause fibroinflammatory lesions in nearly any organ and lead to organ dysfunction and irreversible damage. In... (Review)
Review
Immunoglobulin G4 (IgG4)-Related Disease (IgG4-RD) can cause fibroinflammatory lesions in nearly any organ and lead to organ dysfunction and irreversible damage. In addition to frequent involvement of the salivary glands, lacrimal glands, and/or pancreas, IgG4-RD often affects the chest. Thoracic manifestations include lung nodules and consolidations, pleural thickening, aortitis, and lymphadenopathy. The diagnosis is made after careful clinicopathologic correlation because there is no single diagnostic test with excellent sensitivity or specificity. Biopsy of pulmonary lesions can be useful for distinguishing IgG4-RD from common mimickers. Immunosuppressive regimens, such as glucocorticoids and/or glucocorticoid-sparing agents, form the cornerstone of treatment.
Topics: Humans; Immunoglobulin G4-Related Disease; Lung
PubMed: 31376893
DOI: 10.1016/j.ccm.2019.05.005 -
Ugeskrift For Laeger Apr 2024Lung cancer is the leading cause of cancer-related death in Denmark and the world. The increase in CT examinations has led to an increase in detection of pulmonary... (Review)
Review
Lung cancer is the leading cause of cancer-related death in Denmark and the world. The increase in CT examinations has led to an increase in detection of pulmonary nodules divided into solid and subsolid (including ground glass and part solid). Risk factors for malignancy include age, smoking, female gender, and specific ethnicities. Nodule traits like size, spiculation, upper-lobe location, and emphysema correlate with higher malignancy risk. Managing these potentially malignant nodules relies on evidence-based guidelines and risk stratification. These risk stratification models can standardize the approach for the management of incidental pulmonary findings, as argued in this review.
Topics: Humans; Female; Tomography, X-Ray Computed; Solitary Pulmonary Nodule; Multiple Pulmonary Nodules; Lung Neoplasms; Lung
PubMed: 38606710
DOI: 10.61409/V09230595 -
EBioMedicine Nov 2021Lung biopsy tissue samples can be used for infection detection and cancer diagnosis. Metagenomic next-generation sequencing (mNGS) has the potential to further improve...
BACKGROUND
Lung biopsy tissue samples can be used for infection detection and cancer diagnosis. Metagenomic next-generation sequencing (mNGS) has the potential to further improve diagnosis.
METHODS
From July 2018 to May 2020, lung biopsy samples of 133 patients with suspected pulmonary infection or abnormal imaging findings were collected and subjected to clinical microbiological testing, Illumina and Nanopore sequencing to identify pathogens. The neural networks were pretrained by extracting features of human reads from 2,095 metagenomic next-generation sequencing results, and the human reads of lung biopsy samples were entered into the validated pipeline to predict the risk of cancer.
FINDINGS
Based on the pathogen-cancer detection pipeline, the Illumina platform showed 77·6% sensitivity and 97·6% specificity compared to the composite reference standard for infection diagnosis. However, the Nanopore platform showed 34·7% sensitivity and 98·7% specificity. mNGS identified more fungi, which was confirmed by subsequent pathological examination. M. tuberculosis complex was weakly detected. For cancer detection, compared with histology, the Illumina platform showed 83·7% sensitivity and 97·6% specificity, diagnosing an additional 36 cancer patients, of whom half had abnormal imaging findings (pulmonary shadow, space-occupying lesions, or nodules).
INTERPRETATION
For the first time, we have established a pipeline to simultaneously detect pathogens and cancer based on Illumina sequencing of lung biopsy tissue. This pipeline efficiently diagnosed cancer in patients with abnormal imaging findings.
FUNDING
This work was supported by the National Key Research and Development Program of China and National Natural Science Foundation of China.
Topics: Adult; Aged; Biopsy; Disease Management; Disease Susceptibility; Female; Genomic Instability; High-Throughput Nucleotide Sequencing; Humans; Lung; Lung Diseases; Male; Metagenome; Metagenomics; Middle Aged; Neoplasms; Young Adult
PubMed: 34700283
DOI: 10.1016/j.ebiom.2021.103639