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Diagnostic and Interventional Imaging Oct 2016Adenocarcinoma is the most common histologic type of lung cancer. Recent lung adenocarcinoma classifications from the International Association for the Study of Lung... (Review)
Review
Adenocarcinoma is the most common histologic type of lung cancer. Recent lung adenocarcinoma classifications from the International Association for the Study of Lung cancer, the American Thoracic Society and the European Respiratory Society (IASLC/ETS/ERS, 2011) and World Health Organization (WHO, 2015) define a wide range of adenocarcinoma types and subtypes featuring different prognosis and management. This spectrum of lesions translates into various CT presentations and features, which generally show good correlation with histopathology, stressing the key role of the radiologist in the diagnosis and management of those patients. This review aims at helping radiologists to understand the basics of the up-to-date adenocarcinoma pathological classifications, radio-pathological correlations and how to use them in the clinical setting, as well as other imaging-related correlations (radiogenomics, quantitative analysis, PET-CT).
Topics: Adenocarcinoma; Diagnosis, Differential; Humans; Lung; Lung Neoplasms; Sensitivity and Specificity; Solitary Pulmonary Nodule; Statistics as Topic; Tomography, X-Ray Computed
PubMed: 27639313
DOI: 10.1016/j.diii.2016.06.021 -
Scientific Reports Oct 2022Lung CAD system can provide auxiliary third-party opinions for doctors, improve the accuracy of lung nodule recognition. The selection and fusion of nodule features and...
Lung CAD system can provide auxiliary third-party opinions for doctors, improve the accuracy of lung nodule recognition. The selection and fusion of nodule features and the advancement of recognition algorithms are crucial improving lung CAD systems. Based on the HDL model, this paper mainly focuses on the three key algorithms of feature extraction, feature fusion and nodule recognition of lung CAD system. First, CBAM is embedded into VGG16 and VGG19, and feature extraction models AE-VGG16 and AE-VGG19 are constructed, so that the network can pay more attention to the key feature information in nodule description. Then, feature dimensionality reduction based on PCA and feature fusion based on CCA are sequentially performed on the extracted depth features to obtain low-dimensional fusion features. Finally, the fusion features are input into the proposed MKL-SVM-IPSO model based on the improved Particle Swarm Optimization algorithm to speed up the training speed, get the global optimal parameter group. The public dataset LUNA16 was selected for the experiment. The results show that the accuracy of lung nodule recognition of the proposed lung CAD system can reach 99.56%, and the sensitivity and F1-score can reach 99.3% and 0.9965, respectively, which can reduce the possibility of false detection and missed detection of nodules.
Topics: Humans; Solitary Pulmonary Nodule; Support Vector Machine; Lung Neoplasms; Sensitivity and Specificity; Algorithms; Lung; Radiographic Image Interpretation, Computer-Assisted
PubMed: 36257988
DOI: 10.1038/s41598-022-22442-3 -
Computational and Mathematical Methods... 2022This study retrospectively analyzed the clinical diagnosis, treatment process, and laboratory test data of patients with pulmonary cryptococcosis to improve the...
OBJECTIVE
This study retrospectively analyzed the clinical diagnosis, treatment process, and laboratory test data of patients with pulmonary cryptococcosis to improve the understanding and diagnosis and treatment ability of the disease.
METHODS
Patients with pulmonary cryptococcosis diagnosed in the First Affiliated Hospital of Dalian Medical University from October 2003 to July 2021 were selected, and their medical records were consulted. The general data, clinical manifestations, laboratory examinations, imaging characteristics, diagnosis, and treatment methods were studied. The software SPSS 22 was used for statistical analysis.
RESULTS
A total of 50 patients with pulmonary cryptococcosis were included in the study. The ratio of male to female was 1 : 1. The average age was 53.56 ± 11.99 years with a range of 27-82 years. Grouping the patients by age, with 10 years as an age group, we found that 40-60 years was the high-incidence age group. Two patients (4%) had a history of bird contact, and 18 patients (36%) had at least one underlying conditions. Hypertension and cough were the most common underlying condition and clinical manifestation, respectively. The main admission diagnoses were lung shadow (19/50, 38%) and chest/lung mass (15/50, 30%). In the imaging findings, the most common type of lesions was nodule/nodule shadow (29/69, 42.03%). Lesion distribution in the lower lobe, single lobe, and right lung was more frequent than that in the upper lobe, multilobes, and left lung, respectively. Burr sign (12/43, 27.91%) was the most common concomitant sign. Pulmonary ventilatory defect was found in 7 cases. Laboratory test results were largely nonspecific. The pathological examination showed granuloma, with 47 cases (94%) confirmed by postoperative biopsy. Two cases (4%) were confirmed by serology. One case (2%) was diagnosed with smear. 43 cases (86%) were treated with simple surgical resection, 6 cases (12%) were treated with antifungal drugs, and 1 case (2%) was transferred to another hospital for suspicion of pulmonary tuberculosis.
CONCLUSIONS
Pulmonary cryptococcosis is more common in the middle-aged and elderly, and the clinical specificity is low. It can occur in people with normal or impaired immune function. The main clinical and imaging manifestation is cough and pulmonary nodules, which are very easy 5to be misdiagnosed. Surgical resection is the primary treatment.
Topics: Adult; Aged; Aged, 80 and over; Child; Cough; Cryptococcosis; Female; Humans; Lung; Lung Diseases, Fungal; Male; Middle Aged; Retrospective Studies
PubMed: 35924106
DOI: 10.1155/2022/7981472 -
BMC Cancer Aug 2023This project aimed to research the significance of THRIL in the diagnosis of benign and malignant solitary pulmonary nodules (SPNs) and to investigate the role of...
BACKGROUND
This project aimed to research the significance of THRIL in the diagnosis of benign and malignant solitary pulmonary nodules (SPNs) and to investigate the role of THRIL/miR-99a in malignant SPNs.
METHODS
The study groups consisted of 169 patients with SPN and 74 healthy subjects. The differences in THRIL levels were compared between the two groups and the healthy group. The receiver operating characteristic curve (ROC) was utilized to analyze the THRIL's significance in detecting benign and malignant SPN. Pearson correlation and binary regression coefficients represented the association between THRIL and SPN. CCK-8 assay, Transwell assay, and flow cytometry were utilized to detect the regulatory effect of THRIL silencing. The interaction between THRIL, miR-99a, and IGF1R was confirmed by the double luciferase reporter gene.
RESULTS
There were differences in THRIL expression in the healthy group, benign SPN group, and malignant SPN group. High accuracy of THRIL in the diagnosis of benign SPN and malignant SPN was observed. THRIL was associated with the development of SPN. The expression of THRIL was upregulated and miR-99a was downregulated in lung cancer cells. The double luciferase report experiment confirmed the connections between THRIL/miR-99a/IGF1R. Silencing THRIL could suppress cell proliferation, migration, and invasion and promote cell apoptosis by binding miR-99a.
CONCLUSION
The detection of THRIL in serum is useful for the assessment of malignant SPN. THRIL can regulate the expression of IGF1R through miR-99a, thereby promoting the growth of lung cancer cells and inhibiting apoptosis.
Topics: Humans; RNA, Long Noncoding; Lung Neoplasms; Lung; Solitary Pulmonary Nodule; Multiple Pulmonary Nodules; MicroRNAs
PubMed: 37582734
DOI: 10.1186/s12885-023-11264-9 -
The Indian Journal of Medical Research Aug 2012Ever since the discovery of the first indigenous case in 1981, paragonimiasis has gained recognition as a significant food borne parasitic zoonosis in India. The data... (Review)
Review
Ever since the discovery of the first indigenous case in 1981, paragonimiasis has gained recognition as a significant food borne parasitic zoonosis in India. The data available on the occurrence of paragonimiasis, until today, may be just the tip of an iceberg as the study areas covered were restricted to Northeast Indian States. Nevertheless, the results of research on paragonimiasis in India have revealed valuable information in epidemiology, life cycle, pathobiology and speciation of Indian Paragonimus. Potamiscus manipurensis, Alcomon superciliosum and Maydelliathelphusa lugubris were identified as the crab hosts of Paragonimus. Paragonimus miyazakii manipurinus n. sub sp., P. hueit'ungensis, P. skrjabini, P. heterotremus, P. compactus, and P. westermani have been described from India. P. heterotremus was found as the causative agent of human paragonimiasis. Ingestion of undercooked crabs and raw crab extract was the major mode of infection. Pulmonary paragonimiasis was the commonest clinical manifestation while pleural effusion and subcutaneous nodules were the common extra-pulmonary forms. Clinico-radiological features of pulmonary paragonimiasis simulated pulmonary tuberculosis. Intradermal test, ELISA and Dot-immunogold filtration assay (DIGFA) were used for diagnosis and epidemiological survey of paragonimiasis. Phylogenitically, Indian Paragonimus species, although nested within the respective clade were distantly related to others within the clade.
Topics: Animals; Humans; India; Life Cycle Stages; Lung; Paragonimiasis; Paragonimus; Phylogeography; Radiography; Sputum
PubMed: 22960885
DOI: No ID Found -
Archives of Pathology & Laboratory... Dec 2005Frozen section of lung tissue is performed to guide the surgeon in subsequent therapy. (Review)
Review
CONTEXT
Frozen section of lung tissue is performed to guide the surgeon in subsequent therapy.
DESIGN
Practical experience in frozen section of the lung was reviewed in the medical literature and from the records of several academic hospitals.
RESULTS
Most frozen sections of the lung are performed for evaluation of a solitary nodule, a mass, or the surgical margins of a resection. Frozen section may also be used to assess the adequacy of a lung wedge biopsy taken for later diagnosis of a condition.
CONCLUSION
The pathologic evaluation of intraoperative pulmonary lesions is indicated for the differential diagnosis of pulmonary nodules and masses, both neoplastic and nonneoplastic, surgical resection margins, and mediastinal lymph nodes. The most worrisome pitfalls involve differentiating benign reactive atypia from malignancy on frozen section.
Topics: Diagnosis, Differential; Frozen Sections; Humans; Intraoperative Period; Lung; Lung Diseases; Lymph Nodes; Mediastinum; Pathology, Surgical
PubMed: 16329732
DOI: 10.5858/2005-129-1602-FSOLS -
BMC Medical Imaging Feb 2023Medical image processing has proven to be effective and feasible for assisting oncologists in diagnosing lung, thyroid, and other cancers, especially at early stage....
Medical image processing has proven to be effective and feasible for assisting oncologists in diagnosing lung, thyroid, and other cancers, especially at early stage. However, there is no reliable method for the recognition, screening, classification, and detection of nodules, and even deep learning-based methods have limitations. In this study, we mainly explored the automatic pre-diagnosis of lung nodules with the aim of accurately identifying nodules in chest CT images, regardless of the benign and malignant nodules, and the insertion path planning of suspected malignant nodules, used for further diagnosis by robotic-based biopsy puncture. The overall process included lung parenchyma segmentation, classification and pre-diagnosis, 3-D reconstruction and path planning, and experimental verification. First, accurate lung parenchyma segmentation in chest CT images was achieved using digital image processing technologies, such as adaptive gray threshold, connected area labeling, and mathematical morphological boundary repair. Multi-feature weight assignment was then adopted to establish a multi-level classification criterion to complete the classification and pre-diagnosis of pulmonary nodules. Next, 3-D reconstruction of lung regions was performed using voxelization, and on its basis, a feasible local optimal insertion path with an insertion point could be found by avoiding sternums and/or key tissues in terms of the needle-inserting path. Finally, CT images of 900 patients from Lung Image Database Consortium and Image Database Resource Initiative were chosen to verify the validity of pulmonary nodule diagnosis. Our previously designed surgical robotic system and a custom thoracic model were used to validate the effectiveness of the insertion path. This work can not only assist doctors in completing the pre-diagnosis of pulmonary nodules but also provide a reference for clinical biopsy puncture of suspected malignant nodules considered by doctors.
Topics: Humans; Lung Neoplasms; Solitary Pulmonary Nodule; Tomography, X-Ray Computed; Lung; Multiple Pulmonary Nodules; Radiographic Image Interpretation, Computer-Assisted
PubMed: 36737717
DOI: 10.1186/s12880-023-00973-z -
Systematic Reviews Dec 2021Solitary pulmonary nodule (SPN) is a common finding in routine clinical practice when performing chest imaging tests. The vast majority of these nodules are benign, and...
BACKGROUND
Solitary pulmonary nodule (SPN) is a common finding in routine clinical practice when performing chest imaging tests. The vast majority of these nodules are benign, and only a small proportion are malignant. The application of predictive models of nodule malignancy in routine clinical practice would help to achieve better diagnostic management of SPN. The present systematic review was carried out with the purpose of critically assessing studies aimed at developing predictive models of solitary pulmonary nodule (SPN) malignancy from SPN incidentally detected in routine clinical practice.
METHODS
We performed a search of available scientific literature until October 2020 in Pubmed, SCOPUS and Cochrane Central databases. The inclusion criteria were observational studies carried out in low-risk population from 35 years old onwards aimed at constructing predictive models of malignancy of pulmonary solitary nodule detected incidentally in routine clinical practice. Studies had to be published in peer-reviewed journals, either in Spanish, Portuguese or English. Exclusion criteria were non-human studies, or predictive models based in high-risk populations, or models based on computational approaches. Exclusion criteria were non-human studies, or predictive models based in high-risk populations, or models based on computational approaches (such as radiomics). We used The Transparent Reporting of a multivariable Prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, to describe the type of predictive model included in each study, and The Prediction model Risk Of Bias ASsessment Tool (PROBAST) to evaluate the quality of the selected articles.
RESULTS
A total of 186 references were retrieved, and after applying the exclusion/inclusion criteria, 15 articles remained for the final review. All studies analysed clinical and radiological variables. The most frequent independent predictors of SPN malignancy were, in order of frequency, age, diameter, spiculated edge, calcification and smoking history. Variables such as race, SPN growth rate, emphysema, fibrosis, apical scarring and exposure to asbestos, uranium and radon were not analysed by the majority of the studies. All studies were classified as high risk of bias due to inadequate study designs, selection bias, insufficient population follow-up and lack of external validation, compromising their applicability for clinical practice.
CONCLUSIONS
The studies included have been shown to have methodological weaknesses compromising the clinical applicability of the evaluated SPN malignancy predictive models and their potential influence on clinical decision-making for the SPN diagnostic management.
SYSTEMATIC REVIEW REGISTRATION
PROSPERO CRD42020161559.
Topics: Humans; Lung; Lung Neoplasms; Prognosis; Risk Factors; Solitary Pulmonary Nodule
PubMed: 34872592
DOI: 10.1186/s13643-021-01856-6 -
The British Journal of Radiology Sep 2022To investigate the improvement of two denoising models with different learning targets (Dir and Res) of generative adversarial network (GAN) on image quality and lung...
OBJECTIVES
To investigate the improvement of two denoising models with different learning targets (Dir and Res) of generative adversarial network (GAN) on image quality and lung nodule detectability in chest low-dose CT (LDCT).
METHODS
In training phase, by using LDCT images simulated from standard dose CT (SDCT) of 200 participants, Dir model was trained targeting SDCT images, while Res model targeting the residual between SDCT and LDCT images. In testing phase, a phantom and 95 chest LDCT, exclusively with training data, were included for evaluation of imaging quality and pulmonary nodules detectability.
RESULTS
For phantom images, structural similarity, peak signal-to-noise ratio of both Res and Dir models were higher than that of LDCT. Standard deviation of Res model was the lowest. For patient images, image noise and quality of both two models, were better than that of LDCT. Artifacts of Res model was less than that of LDCT. The diagnostic sensitivity of lung nodule by two readers for LDCT, Res and Dir model, were 72/77%, 79/83% and 72/79% respectively.
CONCLUSION
Two GAN denoising models, including Res and Dir trained with different targets, could effectively reduce image noise of chest LDCT. The image quality evaluation scoring and nodule detectability of Res denoising model was better than that of Dir denoising model and that of hybrid IR images.
ADVANCES IN KNOWLEDGE
The GAN-trained model, which learned the residual between SDCT and LDCT images, reduced image noise and increased the lung nodule detectability by radiologists on chest LDCT. This demonstrates the potential for clinical benefit.
Topics: Algorithms; Humans; Image Processing, Computer-Assisted; Lung; Radiation Dosage; Radiographic Image Interpretation, Computer-Assisted; Signal-To-Noise Ratio; Tomography, X-Ray Computed
PubMed: 35994298
DOI: 10.1259/bjr.20210125 -
Journal of Healthcare Engineering 2021The objective of this study was to perform segmentation and extraction of CT images of pulmonary nodules based on convolutional neural networks (CNNs). The Mask-RCNN...
The objective of this study was to perform segmentation and extraction of CT images of pulmonary nodules based on convolutional neural networks (CNNs). The Mask-RCNN algorithm model is a typical end-to-end image segmentation model, which uses the R-FCN structure for nodule detection. The effect of applying the two algorithm models to the computed tomography (CT) diagnosis of pulmonary nodules was analyzed, and different indexes of pulmonary nodule CT images in lung function examination after algorithm optimization were compared. A total of 56 patients diagnosed with pulmonary nodules by surgery or puncture were taken as the research objects. Based on the Mask-RCNN algorithm, a model for CT image segmentation processing of pulmonary nodules was proposed. Subsequently, the 3D Faster-RCNN model was used to label the nodules in the pulmonary nodules. The experimental results showed that the trained Mask-RCNN algorithm model can effectively complete the segmentation task of lung CT images, but there was a little jitter at the boundary. The speed of R-FCN algorithm for nodular detection was 0.172 seconds/picture, and the accuracy was 88.9%. CT scans were performed on the 56 patients based on a deep learning algorithm. The results showed that 30 cases of malignant pulmonary nodules were confirmed, and the diagnostic accuracy was 93.75%. There were 22 benign lesions, the diagnostic accuracy was 91.67%, and the overall diagnostic accuracy was 92.85%. This study effectively improved the diagnostic efficiency of CT images of pulmonary nodules, and the accuracy of CT images in the diagnosis of pulmonary nodules was analyzed and evaluated. It provided theoretical support for the follow-up diagnosis of pulmonary nodules and the treatment of lung cancer. It also significantly improved the diagnostic effect and detection efficiency of pulmonary nodules.
Topics: Algorithms; Deep Learning; Humans; Lung; Neural Networks, Computer; Radiographic Image Interpretation, Computer-Assisted; Tomography, X-Ray Computed
PubMed: 34721823
DOI: 10.1155/2021/3417285