Precise segmentation of lung field is a crucial step in chest radiographic computer-aided diagnosis system. With the development of deep learning, fully convolutional network based models for lung field segmentation have achieved great effect but are poor at accurate identification of the boundary and preserving lung field consistency. To solve this problem, this paper proposed a lung segmentation algorithm based on non-local attention and multi-task learning. Firstly, an encoder-decoder convolutional network based on residual connection was used to extract multi-scale context and predict the boundary of lung. Secondly, a non-local attention mechanism to capture the long-range dependencies between pixels in the boundary regions and global context was proposed to enrich feature of inconsistent region. Thirdly, a multi-task learning to predict lung field based on the enriched feature was conducted. Finally, experiments to evaluate this algorithm were performed on JSRT and Montgomery dataset. The maximum improvement of Dice coefficient and accuracy were 1.99% and 2.27%, respectively, comparing with other representative algorithms. Results show that by enhancing the attention of boundary, this algorithm can improve the accuracy and reduce false segmentation.
X-Rays ; Algorithms ; Diagnosis, Computer-Assisted ; Thorax/diagnostic imaging* ; Lung/diagnostic imaging* ; Image Processing, Computer-Assisted

Central lung cancer is a common disease in clinic which usually occurs above the segmental bronchus. It is commonly accompanied by bronchial stenosis or obstruction, which can easily lead to atelectasis. Accurately distinguishing lung cancer from atelectasis is important for tumor staging, delineating the radiotherapy target area, and evaluating treatment efficacy. This article reviews domestic and foreign literatures on how to define the boundary between central lung cancer and atelectasis based on multimodal images, aiming to summarize the experiences and propose the prospects.
Humans ; Lung Neoplasms/diagnostic imaging* ; Pulmonary Atelectasis/complications* ; Bronchi ; Constriction, Pathologic/complications* ; Multimodal Imaging

With the progress of targeted therapy and immunotherapy for lung cancer, the clinical demand for lung biopsy is increasing. An ideal biopsy specimen can be used not only for histopathological diagnosis, but also for biomarker detection. The ideal biopsy specimen should meet two requirements, including more than 60 mm2 of tumor tissue and containing more than 20% of tumor cells. In order to obtain ideal lung cancer biopsy specimens, advanced imaging techniques are needed to help. In this article, we reviewed the requirements for biopsy specimens based on biomarker detection, as well as the current status and research progress of using imaging techniques for preoperative planning and intraoperative real time guidance of lung cancer biopsy.
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Humans ; Lung Neoplasms/diagnostic imaging* ; Biopsy ; Biomarkers ; Immunotherapy

OBJECTIVE: To explore lung ultrasound radiomics features which related to extravascular lung water index (EVLWI), and to predict EVLWI in critically ill patients based on lung ultrasound radiomics combined with machine learning and validate its effectiveness. METHODS: A retrospective case-control study was conducted. The lung ultrasound videos and pulse indicated continuous cardiac output (PiCCO) monitoring results of critically ill patients admitted to the department of critical care medicine of the First Affiliated Hospital of Guangxi Medical University from November 2021 to October 2022 were collected, and randomly divided into training set and validation set at 8:2. The corresponding images from lung ultrasound videos were obtained to extract radiomics features. The EVLWI measured by PiCCO was regarded as the "gold standard", and the radiomics features of training set was filtered through statistical analysis and LASSO algorithm. Eight machine learning models were trained using filtered radiomics features including random forest (RF), extreme gradient boost (XGBoost), decision tree (DT), Naive Bayes (NB), multi-layer perceptron (MLP), K-nearest neighbor (KNN), support vector machine (SVM), and Logistic regression (LR). Receiver operator characteristic curve (ROC curve) was plotted to evaluate the predictive performance of models on EVLWI in the validation set. RESULTS: A total of 151 samples from 30 patients were enrolled (including 906 lung ultrasound videos and 151 PiCCO monitoring results), 120 in the training set, and 31 in the validation set. There were no statistically significant differences in main baseline data including gender, age, body mass index (BMI), mean arterial pressure (MAP), central venous pressure (CVP), heart rate (HR), cardiac index (CI), cardiac function index (CFI), stroke volume index (SVI), global end diastolic volume index (GEDVI), systemic vascular resistance index (SVRI), pulmonary vascular permeability index (PVPI) and EVLWI. The overall EVLWI range in 151 PiCCO monitoring results was 3.7-25.6 mL/kg. Layered analysis showed that both datasets had EVLWI in the 7-15 mL/kg interval, and there was no statistically significant difference in EVLWI distribution. Two radiomics features were selected by using LASSO algorithm, namely grayscale non-uniformity (weight was -0.006 464) and complexity (weight was -0.167 583), and they were used for modeling. ROC curve analysis showed that the MLP model had better predictive performance. The area under the ROC curve (AUC) of the prediction validation set EVLWI was higher than that of RF, XGBoost, DT, KNN, LR, SVM, NB models (0.682 vs. 0.658, 0.657, 0.614, 0.608, 0.596, 0.557, 0.472). CONCLUSIONS The gray level non-uniformity and complexity of lung ultrasound were the most correlated radiomics features with EVLWI monitored by PiCCO. The MLP model based on gray level non-uniformity and complexity of lung ultrasound can be used for semi-quantitative prediction of EVLWI in critically ill patients.
Humans ; Extravascular Lung Water/diagnostic imaging* ; Retrospective Studies ; Critical Illness ; Case-Control Studies ; Bayes Theorem ; China ; Lung/diagnostic imaging*

Objective: To investigate the detection and diagnostic efficacy of chest radiographs for ≤30 mm pulmonary nodules and the factors affecting them, and to compare the level of consistency among readers. Methods: A total of 43 patients with asymptomatic pulmonary nodules who consulted in Cancer Hospital, Chinese Academy of Medical Sciences from 2012 to 2014 and had chest CT and X-ray chest radiographs during the same period were retrospectively selected, and one nodule ≤30 mm was visible on chest CT images in the whole group (total 43 nodules in the whole group). One senior radiologist with more than 20 years of experience in imaging diagnosis reviewed CT images and recording the size, morphology, location, and density of nodules was selected retrospectively. Six radiologists with different levels of experience (2 residents, 2 attending physicians and 2 associate chief physicians independently reviewed the chest images and recorded the time of review, nodule detection, and diagnostic opinion. The CT imaging characteristics of detected and undetected nodules on X images were compared, and the factors affecting the detection of nodules on X-ray images were analyzed. Detection sensitivity and diagnosis accuracy rate of 6 radiologists were calculated, and the level of consistency among them was compared to analyze the influence of radiologists' seniority and reading time on the diagnosis results. Results: The number of nodules detected by all 6 radiologists was 17, with a sensitivity of detection of 39.5%(17/43). The number of nodules detected by ≥5, ≥4, ≥3, ≥2, and ≥1 physicians was 20, 21, 23, 25, and 28 nodules, respectively, with detection sensitivities of 46.5%, 48.8%, 53.5%, 58.1%, and 65.1%, respectively. Reasons for false-negative result of detection on X-ray images included the size, location, density, and morphology of the nodule. The sensitivity of detecting ≤30 mm, ≤20 mm, ≤15 mm, and ≤10 mm nodules was 46.5%-58.1%, 45.9%-54.1%, 36.0%-44.0%, and 36.4% for the 6 radiologists, respectively; the diagnosis accuracy rate was 19.0%-85.0%, 16.7%-6.5%, 18.2%-80.0%, and 0%-75.0%, respectively. The consistency of nodule detection among 6 doctors was good (Kappa value: 0.629-0.907) and the consistency of diagnostic results among them was moderate or poor (Kappa value: 0.350-0.653). The higher the radiologist's seniority, the shorter the time required to read the images. The reading time and the seniority of the radiologists had no significant influence on the detection and diagnosis results (P>0.05). Conclusions: The ability of radiographs to detect lung nodules ≤30 mm is limited, and the ability to determine the nature of the nodules is not sufficient, and the increase in reading time and seniority of the radiologists will not improve the diagnostic accuracy. X-ray film exam alone is not suitable for lung cancer diagnosis.
Humans ; Retrospective Studies ; Solitary Pulmonary Nodule/diagnostic imaging* ; Radiography ; Multiple Pulmonary Nodules/diagnostic imaging* ; Tomography, X-Ray Computed/methods* ; Lung Neoplasms/diagnostic imaging* ; Sensitivity and Specificity ; Radiographic Image Interpretation, Computer-Assisted/methods*

Non-small cell lung cancer (NSCLC) can be detected with enlarged lymph nodes on imaging, but their benignity and malignancy are difficult to determine directly, making it difficult to stage the tumor and design radiotherapy target volumes. The clinical diagnosis of malignant lymph nodes is often based on the short diameter of lymph nodes ≥1 cm or the maximum standard uptake value ≥2.5, but the sensitivity and specificity of these criteria are too low to meet the clinical needs. In recent years, many advances have been made in diagnosing benign and malignant lymph nodes using other imaging parameters, and with the development of radiomics, deep learning and other technologies, models of mining the image information of enlarged lymph node regions further improve the diagnostic accuracy. The purpose of this paper is to review recent advances in imaging-based diagnosis of benign and malignant enlarged lymph nodes in NSCLC for more accurate and noninvasive assessment of lymph node status in clinical practice.
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Humans ; Carcinoma, Non-Small-Cell Lung/pathology* ; Diagnostic Imaging ; Lung Neoplasms/pathology* ; Lymph Nodes/pathology* ; Sensitivity and Specificity

OBJECTIVE: In order to improve the accuracy of the current pulmonary nodule location detection method based on CT images, reduce the problem of missed detection or false detection, and effectively assist imaging doctors in the diagnosis of pulmonary nodules. METHODS: Propose a novel method for detecting the location of pulmonary nodules based on multiscale convolution. First, image preprocessing methods are used to eliminate the noise and artifacts in lung CT images. Second, multiple adjacent single-frame CT images are selected to be concatenate into multi-frame images, and the feature extraction is carried out through the artificial neural network model U-Net improved by multi-scale convolution to enhanced feature extraction capability for pulmonary nodules of different sizes and shapes, so as to improve the accuracy of feature extraction of pulmonary nodules. Finally, using point detection to improve the loss function of U-Net training process, the accuracy of pulmonary nodule location detection is improved. RESULTS: The accuracy of detecting pulmonary nodules equal or larger than 3 mm and smaller than 3 mm are 98.02% and 96.94% respectively. CONCLUSIONS This method can effectively improve the detection accuracy of pulmonary nodules on CT image sequence, and can better meet the diagnostic needs of pulmonary nodules.
Humans ; Lung Neoplasms/diagnostic imaging* ; Solitary Pulmonary Nodule/diagnostic imaging* ; Tomography, X-Ray Computed ; Neural Networks, Computer

Interstitial lung abnormalities (ILAs) refer to the subtle or mild signs of ILAs pulmonary parenchyma on chest HRCT scans, which are not yet sufficient to diagnose a certain interstitial lung disease, may be potentially compatible an early stage of the diseases. The signs of ILAs usually includes ground-glass opacities, reticular abnormakicies, honeycombing, traction bronchiectasis or non-emphysematous cysts. This article reviews the research progreses in the definition and classification, risk factors, prognosis, comorbidities and management of ILAs in combination with domestic and foreign literatures.
Humans ; Lung/diagnostic imaging* ; Tomography, X-Ray Computed ; Lung Diseases, Interstitial/diagnosis* ; Prognosis ; Diagnosis, Differential

BACKGROUND: Screening using low-dose computed tomography (LDCT) is a more effective approach and has the potential to detect lung cancer more accurately. We aimed to conduct a meta-analysis to estimate the accuracy of population-based screening studies primarily assessing baseline LDCT screening for lung cancer. METHODS: MEDLINE, Excerpta Medica Database, and Web of Science were searched for articles published up to April 10, 2022. According to the inclusion and exclusion criteria, the data of true positives, false-positives, false negatives, and true negatives in the screening test were extracted. Quality Assessment of Diagnostic Accuracy Studies-2 was used to evaluate the quality of the literature. A bivariate random effects model was used to estimate pooled sensitivity and specificity. The area under the curve (AUC) was calculated by using hierarchical summary receiver-operating characteristics analysis. Heterogeneity between studies was measured using the Higgins I2 statistic, and publication bias was evaluated using a Deeks' funnel plot and linear regression test. RESULTS: A total of 49 studies with 157,762 individuals were identified for the final qualitative synthesis; most of them were from Europe and America (38 studies), ten were from Asia, and one was from Oceania. The recruitment period was 1992 to 2018, and most of the subjects were 40 to 75 years old. The analysis showed that the AUC of lung cancer screening by LDCT was 0.98 (95% CI: 0.96-0.99), and the overall sensitivity and specificity were 0.97 (95% CI: 0.94-0.98) and 0.87 (95% CI: 0.82-0.91), respectively. The funnel plot and test results showed that there was no significant publication bias among the included studies. CONCLUSIONS Baseline LDCT has high sensitivity and specificity as a screening technique for lung cancer. However, long-term follow-up of the whole study population (including those with a negative baseline screening result) should be performed to enhance the accuracy of LDCT screening.
Humans ; Adult ; Middle Aged ; Aged ; Lung Neoplasms/diagnostic imaging* ; Early Detection of Cancer ; Sensitivity and Specificity ; Mass Screening ; Tomography, X-Ray Computed

Objective: To explore the characteristics of pulmonary blood flow perfusion imaging of single photo emission computer tomography/computer tomography (SPECT/CT) in chronic pulmonary vascular Stenosis (CPVS) caused by different etiological factors. Methods: This is a retropective study. Present study screened 50 consecutive cases diagnosed with chronic pulmonary vascular stenosis from January 2019 to January 2020 in the department of cardiology of Gansu Provincial Hospital and underwent SPECT/CT pulmonary blood flow perfusion examination. Thirteen patients were excluded because of pulmonary vascular lesions with a disease course of less than 3 months and poor image quality. According to the etiology, patients were divided into fibrosing mediastinitis (FM) group, Takyasu's arteritis (PTA) group, and chronic thromboembolic pulmonary hypertension/chronic thromboembolic pulmonary disease (CTEPH/CTED) group. The severity of pulmonary blood flow perfusion was evaluated in accordance with the Begic scoring principle in the three groups. The overall Begic score, lung lobe scores among three groups were compared. CT signs of lung SPECT/CT, such as enlargement of hilar lymph node, atelectasis, bronchial stenosis, were also analyzed in three groups. Results: A total of 37 patients with chronic pulmonary vascular stenosis were finally enrolled (18 in the FM group, 5 in the PTA group, and 14 in the CTEPH/CTED group). The total Begic score of pulmonary perfusions was similar among the three groups (F=0.657,P>0.05). There was a statistically significant difference in the left upper lobe Begic score among the three groups (H=4.081, P<0.05). The left upper lobe Begic score was higher in the FM group than in the PTA group (3.44±2.50 vs. 1.60±0.55, P<0.05). As compared to other two groups, patients in FM group were featured with CT signs of higher percent of hilar enlargement (FM group vs. PTA group: 16/18 vs. 1/5, P=0.008; FM group vs. CTEPH/CTED group: 16/18 vs. 3/14, P=0.000 2), enlargement of the pulmonary hilum lymph nodes (FM group vs. PTA group: 14/18 vs. 1/5, P=0.033; FM group vs. CTEPH/CTED group: 14/18 vs. 2/14, P=0.001), and calcification of mediastinal soft tissue (FM group vs. PTA group: 11/18 to 0/5, P=0.037; FM group vs. CTEPH/CTED group: 11/18 vs. 1/14, P=0.003). The proportion of CT signs of bronchial stenosis (9/18 vs. 0/14, P=0.002) and atelectasis (9/18 vs. 1/14, P=0.002) was also higher in the FM group than in the CTEPH/CTED group. In case of abnormal pulmonary blood flow perfusion, the diagnostic accuracy of CT signs hilar enlargement, hilar lymph node enlargement, mediastinal soft tissue calcification, bronchial stenosis, and atelectasis for the diagnosis of FM were 81.1%, 83.8%, 78.4%, 75.7%, and 73.0%, respectively. Conclusion: There is no significant difference in the Begic score of SPECT/CT pulmonary blood flow perfusion imagines among the three groups of patients. Impaired pulmonary blood flow perfusion combined with typical CT signs is useful for identifying patients with FM.
Humans ; Constriction, Pathologic/diagnostic imaging* ; Perfusion ; Pulmonary Atelectasis ; Mediastinitis ; Calcinosis ; Lung/diagnostic imaging* ; Tomography, Emission-Computed, Single-Photon ; Tomography, X-Ray Computed