The Computed Tomography Findings and Follow-up Course of Pulmonary Nocardiosis.

OBJECTIVE: Our study aimed to elucidate the computed tomography (CT) features and follow-up course of pulmonary nocardiosis patients to improve the understanding and diagnostic accuracy of this disease. METHODS: The chest CT findings and clinical data of patients diagnosed with pulmonary nocardiosis by culture or histopathological examination in our hospital between 2010 and 2019 were retrospectively analyzed. RESULTS: A total of 34 cases of pulmonary nocardiosis were included in our study. Thirteen patients were on long-term immunosuppressant therapy, among whom 6 had disseminated nocardiosis. Among the immunocompetent patients, 16 had chronic lung diseases or a history of trauma. Multiple or solitary nodules represented the most common CT feature (n = 32, 94.12%), followed by ground-glass opacities (n = 26, 76.47%), patchy consolidations (n = 25, 73.53%), cavitations (n = 18, 52.94%), and masses (n = 11, 32.35%). There were 20 cases (61.76%) with mediastinal and hilar lymphadenopathy, 18 (52.94%) with pleural thickening, 15 (44.12%) with bronchiectasis, and 13 (38.24%) with pleural effusion. Significantly higher rates of cavitations were observed among immunosuppressed patients (85% vs 29%, P = 0.005). At follow-up, 28 patients (82.35%) clinically improved with treatment, while 5 (14.71%) had disease progression, and 1 (2.94%) died. CONCLUSIONS: Chronic structural lung diseases and long-term immunosuppressant use were found as risk factors for pulmonary nocardiosis. While the CT manifestations were highly heterogeneous, clinical suspicion should be raised upon findings of coexisting nodules, patchy consolidations, and cavitations, particularly in the presence of extrapulmonary infections such as those of the brain and subcutaneous tissues. A significant incidence of cavitations may be observed among immunosuppressed patients.

Radiomics Analysis of Multiphasic Computed Tomography Images for Distinguishing High-Risk Thymic Epithelial Tumors From Low-Risk Thymic Epithelial Tumors.

OBJECTIVES: The objective of this study is to preoperatively investigate the value of multiphasic contrast-enhanced computed tomography (CT)-based radiomics signatures for distinguishing high-risk thymic epithelial tumors (HTET) from low-risk thymic epithelial tumors (LTET) compared with conventional CT signatures. MATERIALS AND METHODS: Pathologically confirmed 305 thymic epithelial tumors (TETs), including 147 LTET (Type A/AB/B1) and 158 HTET (Type B2/B3/C), were retrospectively analyzed, and were randomly divided into training (n = 214) and validation cohorts (n = 91). All patients underwent nonenhanced, arterial contrast-enhanced, and venous contrast-enhanced CT analysis. The least absolute shrinkage and selection operator regression with 10-fold cross-validation was performed for radiomic models building, and multivariate logistic regression analysis was performed for radiological and combined models building. The performance of the model was evaluated by the area under the receiver operating characteristic curve (AUC of ROC), and the AUCs were compared using the Delong test. Decision curve analysis was used to evaluate the clinical value of each model. Nomogram and calibration curves were plotted for the combined model. RESULTS: The AUCs for radiological model in the training and validation cohorts were 0.756 and 0.733, respectively. For nonenhanced, arterial contrast-enhanced, venous contrast-enhanced CT and 3-phase images combined radiomics models, the AUCs were 0.940, 0.946, 0.960, and 0.986, respectively, in the training cohort, whereas 0.859, 0.876, 0.930, and 0.923, respectively, in the validation cohort. The combined model, including CT morphology and radiomics signature, showed AUCs of 0.990 and 0.943 in the training and validation cohorts, respectively. Delong test and decision curve analysis showed that the predictive performance and clinical value of the 4 radiomics models and combined model were greater than the radiological model ( P < 0.05). CONCLUSIONS: The combined model, including CT morphology and radiomics signature, greatly improved the predictive performance for distinguishing HTET from LTET. Radiomics texture analysis can be used as a noninvasive method for preoperative prediction of the pathological subtypes of TET.

CLSSL-ResNet: Predicting malignancy of solitary pulmonary nodules from CT images by chimeric label with self-supervised learning.

BACKGROUND: Pulmonary granulomatous nodules (GN) with spiculation or lobulation have a similar morphological appearance to solid lung adenocarcinoma (SADC) under computed tomography (CT). However, these two kinds of solid pulmonary nodules (SPN) have different malignancies and are sometimes misdiagnosed. OBJECTIVE: This study aims to predict malignancies of SPNs by a deep learning model automatically. METHODS: A chimeric label with self-supervised learning (CLSSL) is proposed to pre-train a ResNet-based network (CLSSL-ResNet) for distinguishing isolated atypical GN from SADC in CT images. The malignancy, rotation, and morphology labels are integrated into a chimeric label and utilized to pre-train a ResNet50. The pre-trained ResNet50 is then transferred and fine-tuned to predict the malignancy of SPN. Two image datasets of 428 subjects (Dataset1, 307; Dataset2, 121) from different hospitals are collected. Dataset1 is divided into training, validation, and test data by a ratio of 7:1:2 to develop the model. Dataset2 is utilized as an external validation dataset. RESULTS: CLSSL-ResNet achieves an area under the ROC curve (AUC) of 0.944 and an accuracy (ACC) of 91.3%, which was much higher than that of the consensus of two experienced chest radiologists (77.3%). CLSSL-ResNet also outperforms other self-supervised learning models and many counterparts of other backbone networks. In Dataset2, AUC and ACC of CLSSL-ResNet are 0.923 and 89.3%, respectively. Additionally, the ablation experiment result indicates higher efficiency of the chimeric label. CONCLUSION: CLSSL with morphology labels can increase the ability of feature representation by deep networks. As a non-invasive method, CLSSL-ResNet can distinguish GN from SADC via CT images and may support clinical diagnoses after further validation.

Computed Tomography-Based Radiomics Signature: A Potential Indicator of Epidermal Growth Factor Receptor Mutation in Pulmonary Adenocarcinoma Appearing as a Subsolid Nodule.

BACKGROUND: Lung adenocarcinoma (LADC) with epidermal growth factor receptor (EGFR) mutation is considered a subgroup of lung cancer sensitive to EGFR-targeted tyrosine kinase inhibitors. We aimed to develop and validate a computed tomography (CT)-based radiomics signature for prediction of EGFR mutation status in LADC appearing as a subsolid nodule. MATERIALS AND METHODS: A total of 467 eligible patients were divided into training and validation cohorts (n = 306 and 161, respectively). Radiomics features were extracted from unenhanced CT images by using Pyradiomics. A CT-based radiomics signature for distinguishing EGFR mutation status was constructed using the random forest (RF) method in the training cohort and then tested in the validation cohort. A combination of the radiomics signature with a clinical factors model was also constructed using the RF method. The performance of the model was evaluated using the area under the curve (AUC) of a receiver operating characteristic curve. RESULTS: In this study, 64.2% (300/467) of the patients showed EGFR mutations. L858R mutation of exon 21 was the most common mutation type (185/301). We identified a CT-based radiomics signature that successfully discriminated between EGFR positive and EGFR negative in the training cohort (AUC = 0.831) and the validation cohort (AUC = 0.789). The radiomics signature combined with the clinical factors model was not superior to the simple radiomics signature in the two cohorts (p > .05). CONCLUSION: As a noninvasive method, the CT-based radiomics signature can be used to predict the EGFR mutation status of LADC appearing as a subsolid nodule. IMPLICATIONS FOR PRACTICE: Lung adenocarcinoma (LADC) with epidermal growth factor receptor (EGFR) mutation is considered a subgroup of lung cancer that is sensitive to EGFR-targeted tyrosine kinase inhibitors. However, some patients with inoperable subsolid LADC are unable to undergo tissue sampling by biopsy for molecular analysis in clinical practice. A computed tomography-based radiomics signature may serve as a noninvasive biomarker to predict the EGFR mutation status of subsolid LADCs when mutational profiling is not available or possible.

Uncontrolled asthma phenotypes defined from parameters using quantitative CT analysis.

OBJECTIVE: Asthma is a heterogeneous disease with diverse clinical phenotypes that have been identified via cluster analyses. However, the classification of phenotypes based on quantitative CT (qCT) is poorly understood. The study was conducted to investigate CT determination of uncontrolled asthma phenotypes. METHODS: Sixty-five patients with uncontrolled asthma (37 with severe asthma, 28 with non-severe asthma) underwent detailed clinical, laboratory, and pulmonary function tests, as well as qCT analysis. Twenty-five healthy subjects were also included in this study and underwent clinical physical examinations, pulmonary function tests, and low-dose CT scans. RESULTS: The mean lumen area/body surface area ratio was smaller in patients with severe uncontrolled asthma compared with that in healthy subjects (9.84 mm2 [SD, 2.57 mm2], 11.96 mm2 [SD, 3.09 mm2]; p = 0.026). However, the percentage of mean wall area (WA) was greater (64.39% [SD, 2.55%], 62.09% [SD, 3.81%], p = 0.011). Air trapping (measured based on mean lung density and VI-856 [%] on expiratory scan) was greater in patients with severe uncontrolled asthma than in those with non-severe uncontrolled asthma and was higher in all patients with uncontrolled asthma than that in healthy subjects (all p < 0.001). Three CT-determined uncontrolled asthma phenotypes were identified. Cluster 1 had mild air trapping with or without proximal airway remodeling. Cluster 2 had moderate air trapping with or without proximal airway remodeling. Cluster 3 had severe air trapping with proximal airway remodeling. CONCLUSIONS: There was obvious air trapping and proximal airway remodeling in patients with severe uncontrolled asthma. The three CT-determined uncontrolled asthma phenotypes might reflect underlying mechanisms of disease in patient stratification and in the different stages of disease development. KEY POINTS: • Obvious air trapping and proximal airway remodeling were present in patients with severe uncontrolled asthma. • CT air trapping indices showed a good correlation with disease duration, total IgE, atopy, and OCS and ICS doses, and were even more strongly correlated with clinical lung function. • Three CT-determined uncontrolled asthma phenotypes were identified, which might reflect underlying mechanisms of disease in patient stratification and in the different stages of disease development.

Quantitative Low-Dose Computed Tomography of the Lung Parenchyma and Airways for the Differentiation between Chronic Obstructive Pulmonary Disease and Asthma Patients.

BACKGROUND: It is difficult to differentiate between chronic obstructive pulmonary disease (COPD) and asthma in clinics; therefore, for diagnostic purposes, imaging-based measurements could be beneficial to differentiate between the two diseases. OBJECTIVES: We aim to analyze quantitative measurements of the lung and bronchial parameters that are provided by low-dose computed tomography (CT) to differentiate COPD and asthma from an imaging perspective. MATERIALS AND METHODS: 69 COPD patients, 52 asthma patients, and 20 healthy subjects were recruited to participate in CT imaging and pulmonary function tests (PFTs). Comparative analysis was performed to identify differences between COPD and asthma in CT measurements. PFT measurements enabled validation of the differentiation between COPD and asthma patients. RESULTS: There were significant differences among the COPD, asthma, and healthy control groups. The differences were more significant among the following: inspiratory emphysema index (EI)-950 (%), expiratory lung volume, expiratory mean lung density (MLD), and expiratory EI-950 (%) and EI-850 (%). The COPD group had a significantly higher EI-950 (%) than the asthma group (p = 0.008). There were significant differences among the three groups in lumen area (LA), wall area (WA), total area, and Pi10WA. The asthma group had significantly higher WA%/WV% than both the COPD (p = 0.002) and the control group (p = 0.012). There was high sensitivity in EI-950 (%), EI-850 (%) and expiratory MLD in the parenchyma and high sensitivity in LA and Pi10WA in small airways in the differential diagnosis of COPD and asthma. CONCLUSION: To aid the diagnosis, CT can provide quantitative measurements to differentiate between COPD and asthma patients.

Computed Tomography Manifestations and Excision Cross-Complementation Group 1 Expression of Stage I Non-Small-Cell Lung Cancer and Their Correlation With Prognosis.

OBJECTIVE: The aim of this study was to evaluate the computed tomography (CT) manifestations and expression of the excision cross-complementation group 1 (ERCC1) and their correlation with prognosis in stage I non-small-cell lung cancer (NSCLC). MATERIALS AND METHODS: A total of 133 patients with stage I NSCLC with complete 3- and 5-year disease-free survival (DFS) and overall survival (OS) data, who underwent thoracic CT and pathological examination, were included. Expression of ERCC1 in tumor samples was evaluated using semiquantitative immunohistochemical analysis. RESULTS: The 3- and 5-year DFS rates for the 133 patients were 72.2% and 60.9%, respectively, and the 3- and 5-year OS rates were 89.5% and 82.0%, respectively. Significant differences in the 3- and 5-year DFS occurred (P = 0.003 and P = 0.001, respectively), whereas no significant differences in the 3- and 5-year OS were found (P = 0.099 and P = 0.062, respectively) between high and low ERCC1 protein expression. Patients with high expression of ERCC1 had a better prognosis. There was a significant correlation between tumors with an irregular edge and signs of spiculation on CT and low expression of ERCC1 evaluated using logistic regression analysis (P < 0.05). CONCLUSIONS: It was concluded that patients with stage I NSCLC with high ERCC1 expression had superior survival rates relative to those with low ERCC1 expression. Tumors with an irregular edge and signs of spiculation on CT were significantly correlated with low expression of ERCC1.

Correlation of pulmonary function indexes determined by low-dose MDCT with spirometric pulmonary function tests in patients with chronic obstructive pulmonary disease.

OBJECTIVE: The objective of our study was to evaluate the correlation between pulmonary function indexes determined by low-dose MDCT and those obtained from routine spirometric pulmonary function tests (PFTs) in patients with chronic obstructive pulmonary disease (COPD). MATERIALS AND METHODS: Lung function of patients with COPD stages 0-III was evaluated by both MDCT and spirometric PFTs. Scanning was performed at maximum end-inspiration and maximum end-expiration. RESULTS: A very strong correlation was found between extrapolated expiratory lung volume (LVex) and COPD stage (r = 0.802, p < 0.001) and between extrapolated LVex and the ratio of forced expiratory volume in 1 second and percentage forced vital capacity (FEV1/FVC%) (r = -0.831, p < 0.001). Moreover, strong positive correlations were found between inspiratory lung volume (LVin) and total lung capacity (TLC) (r = 0.658, p < 0.001), LVex and residual volume (RV) (r = 0.683, p < 0.001), extrapolated LVex and RV (r = 0.640, p < 0.001), LVex and RV/TLC (r = 0.602, p < 0.001), LVex/LVin and RV/TLC (r = 0.622, p < 0.001), extrapolated LVex and RV/TLC (r = 0.663, p < 0.001), and LVex and COPD stage (r = 0.697, p < 0.001). CONCLUSION: Low-dose MDCT lung function indexes correlate well with spirometric PFT results, and the highest correlation is at end-expiration. Low-dose MDCT may be useful for evaluating lung function in patients with COPD.

Primary small cell neuroendocrine carcinoma of the mediastinum: computed tomography and histopathological correlation.

OBJECTIVES: The objectives of this study were to describe the computed tomographic (CT) and histopathological characteristics of neuroendocrine carcinomas of the mediastinum and to improve the diagnostic accuracy for these tumors. MATERIALS AND METHODS: We retrospectively analyzed 9 patients with histopathologically confirmed primary small cell neuroendocrine carcinoma of the mediastinum. RESULTS: Of the 9 tumors, 6 (67%) were located in the anterior-middle mediastinum; 2, in the anterior-middle-posterior mediastinum; and 1, in the middle-mediastinum. Eight tumors appeared inhomogeneous on CT, with large areas of necrosis, whereas 1 tumor had a uniform density. Four tumors (44%) had scattered punctate calcifications. Moderate, heterogeneous enhancement (range, 21-34 Hounsfield units) was present in 8 patients. All tumors compressed or invaded the adjacent mediastinal structures. Hematoxylin and eosin staining results revealed small tumor cells, with little cytoplasm, pale chromatin, and inconspicuous or absent nucleoli. Immunohistochemical analysis results showed that the tumor cells were positive for chromogranin A, synaptophysin, and neuron-specific enolase. CONCLUSIONS: A large tumor located in the anterior-middle mediastinum, showing scattered punctate calcifications and compressing or invading the adjacent mediastinal structures, should arouse suspicion for a small cell neuroendocrine carcinoma. However, the diagnosis of such tumors requires a combination of pathological and immunohistochemical examination.

Deep radiomic model based on the sphere-shell partition for predicting treatment response to chemotherapy in lung cancer.

BACKGROUND: The prognosis of chemotherapy is important in clinical decision-making for non-small cell lung cancer (NSCLC) patients. OBJECTIVES: To develop a model for predicting treatment response to chemotherapy in NSCLC patients from pre-chemotherapy CT images. MATERIALS AND METHODS: This retrospective multicenter study enrolled 485 patients with NSCLC who received chemotherapy alone as a first-line treatment. Two integrated models were developed using radiomic and deep-learning-based features. First, we partitioned pre-chemotherapy CT images into spheres and shells with different radii around the tumor (0-3, 3-6, 6-9, 9-12, 12-15 mm) containing intratumoral and peritumoral regions. Second, we extracted radiomic and deep-learning-based features from each partition. Third, using radiomic features, five sphere-shell models, one feature fusion model, and one image fusion model were developed. Finally, the model with the best performance was validated in two cohorts. RESULTS: Among the five partitions, the model of 9-12 mm achieved the highest area under the curve (AUC) of 0.87 (95% confidence interval: 0.77-0.94). The AUC was 0.94 (0.85-0.98) for the feature fusion model and 0.91 (0.82-0.97) for the image fusion model. For the model integrating radiomic and deep-learning-based features, the AUC was 0.96 (0.88-0.99) for the feature fusion method and 0.94 (0.85-0.98) for the image fusion method. The best-performing model had an AUC of 0.91 (0.81-0.97) and 0.89 (0.79-0.93) in two validation sets, respectively. CONCLUSIONS: This integrated model can predict the response to chemotherapy in NSCLC patients and assist physicians in clinical decision-making.

Computed tomography appearance of inflammatory myofibroblastic tumor in the mediastinum.

OBJECTIVE: This study aimed to improve the diagnosis of inflammatory myofibroblastic tumor (IMT) in the mediastinum by analysis of computed tomographic (CT) images. MATERIALS AND METHODS: Clinical data, CT, and pathological findings of 6 patients diagnosed with IMT in the mediastinum were retrospectively analyzed. RESULTS: Of the 6 patients, 5 were women, and mean age at diagnosis was 34 years. All the lesions were solid soft tissue masses and ranged in maximum diameter from 5.0 to 8.5 cm, which were located in the anterior (n = 1), middle (n = 2), and posterior mediastinum (n = 3). The anterior mediastinal tumor had a clear boundary. The tumors in the middle mediastinum had indistinct boundaries: one was invading the right wall of the trachea and the other was invading the esophageal wall. A tumor located in the right posterior mediastinum caused osteolysis of the adjacent ribs. A small amount of calcification was seen in the tumor in the right posterior-inferior mediastinum. After administration of contrast, all tumors showed varying degrees of contrast enhancement (range, 17-47 HU) on chest CT scan. Recurrence occurred in only 1 case. CONCLUSIONS: The common CT appearance of IMT in the mediastinum is as a soft tissue mass with uniform density. All tumors show varying degrees of contrast enhancement. Some lesions have clear boundaries; others do not. Computed tomography examination can help to determine the areas involved by lesions and their relationships with adjacent tissues, which facilitates the prediction of the likely surgical requirements.

The efficacy of the inhalation of an aerosolized Group A streptococcal preparation in the treatment of lung cancer.

OBJECTIVE: To observe the efficacy of the inhalation of an aerosolized group A streptococcal (GAS) preparation in treating orthotopic lung cancer in mouse models and assess the feasibility, safety, and effectiveness of this administration mode for lung cancer. METHODS: Lewis lung carcinoma (LLC) cell strains were administered via intrathoracic injection to establish orthotopic lung cancer mouse models. After the tumor-bearing models were successfully established, as confirmed by computed tomography, the mice were administered by inhalation with an aerosolized GAS preparation (GAS group) or aerosolized normal saline (control group). The anti-tumor effect of the aerosolized GAS preparation was evaluated histologically; meanwhile, the survival and quality of life were compared between these two groups. RESULTS: The aerosolized GAS preparation showed remarkably anti-tumor effect, causing the necrosis of the orthotopic lung cancer cells in tumor-bearing mice. Furthermore, mice in the GAS group had significantly better quality of life and longer survival than those in control group. CONCLUSIONS: The inhalation of aerosolized GAS preparation may be a feasible, safe and effective solution for lung cancer.

Multi-scale segmentation squeeze-and-excitation UNet with conditional random field for segmenting lung tumor from CT images.

BACKGROUND AND OBJECTIVE: Lung cancer counts among diseases with the highest global morbidity and mortality rates. The automatic segmentation of lung tumors from CT images is of vast significance. However, the segmentation faces several challenges, including variable shapes and different sizes, as well as complicated surrounding tissues. METHODS: We propose a multi-scale segmentation squeeze-and-excitation UNet with a conditional random field (M-SegSEUNet-CRF) to automatically segment lung tumors from CT images. M-SegSEUNet-CRF employs a multi-scale strategy to solve the problem of variable tumor size. Through the spatially adaptive attention mechanism, the segmentation SE blocks embedded in 3D UNet are utilized to highlight tumor regions. The dense connected CRF framework is further added to delineate tumor boundaries at a detailed level. In total, 759 CT scans of patients with lung cancer were used to train and evaluate the M-SegSEUNet-CRF model (456 for training, 152 for validation, and 151 for test). Meanwhile, the public NSCLC-Radiomics and LIDC datasets have been utilized to validate the generalization of the proposed method. The role of different modules in the M-SegSEUNet-CRF model is analyzed by the ablation experiments, and the performance is compared with that of UNet, its variants and other state-of-the-art models. RESULTS: M-SegSEUNet-CRF can achieve a Dice coefficient of 0.851 ± 0.071, intersection over union (IoU) of 0.747 ± 0.102, sensitivity of 0.827 ± 0.108, and positive predictive value (PPV) of 0.900 ± 0.107. Without a multi-scale strategy, the Dice coefficient drops to 0.820 ± 0.115; without CRF, it drops to 0.842 ± 0.082, and without both, it drops to 0.806 ± 0.120. M-SegSEUNet-CRF presented a higher Dice coefficient than 3D UNet (0.782 ± 0.115) and its variants (ResUNet, 0.797 ± 0.132; DenseUNet, 0.792 ± 0.111, and UNETR, 0.794 ± 0.130). Although the performance slightly declines with the decrease in tumor volume, M-SegSEUNet-CRF exhibits more obvious advantages than the other comparative models. CONCLUSIONS: Our M-SegSEUNet-CRF model improves the segmentation ability of UNet through the multi-scale strategy and spatially adaptive attention mechanism. The CRF enables a more precise delineation of tumor boundaries. The M-SegSEUNet-CRF model integrates these characteristics and demonstrates outstanding performance in the task of lung tumor segmentation. It can furthermore be extended to deal with other segmentation problems in the medical imaging field.

Deep multiple instance learning for predicting chemotherapy response in non-small cell lung cancer using pretreatment CT images.

The individual prognosis of chemotherapy is quite different in non-small cell lung cancer (NSCLC). There is an urgent need to precisely predict and assess the treatment response. To develop a deep multiple-instance learning (DMIL) based model for predicting chemotherapy response in NSCLC in pretreatment CT images. Two datasets of NSCLC patients treated with chemotherapy as the first-line treatment were collected from two hospitals. Dataset 1 (163 response and 138 nonresponse) was used to train, validate, and test the DMIL model and dataset 2 (22 response and 20 nonresponse) was used as the external validation cohort. Five backbone networks in the feature extraction module and three pooling methods were compared. The DMIL with a pre-trained VGG16 backbone and an attention mechanism pooling performed the best, with an accuracy of 0.883 and area under the curve (AUC) of 0.982 on Dataset 1. While using max pooling and convolutional pooling, the AUC was 0.958 and 0.931, respectively. In Dataset 2, the best DMIL model produced an accuracy of 0.833 and AUC of 0.940. Deep learning models based on the MIL can predict chemotherapy response in NSCLC using pretreatment CT images and the pre-trained VGG16 with attention mechanism pooling yielded better predictions.

Predicting chemotherapy response in non-small-cell lung cancer via computed tomography radiomic features: Peritumoral, intratumoral, or combined?

Purpose: This study aims to evaluate the ability of peritumoral, intratumoral, or combined computed tomography (CT) radiomic features to predict chemotherapy response in non-small cell lung cancer (NSCLC). Methods: After excluding subjects with incomplete data or other types of treatments, 272 (Dataset 1) and 43 (Dataset 2, external validation) NSCLC patients who were only treated with chemotherapy as the first-line treatment were enrolled between 2015 and 2019. All patients were divided into response and nonresponse based on the response evaluation criteria in solid tumors, version 1.1. By using 3D slicer and morphological operations in python, the intra- and peritumoral regions of lung tumors were segmented from pre-treatment CT images (unenhanced) and confirmed by two experienced radiologists. Then radiomic features (the first order, texture, shape, et al.) were extracted from the above regions of interest. The models were trained and tested in Dataset 1 and further validated in Dataset 2. The performance of models was compared using the area under curve (AUC), confusion matrix, accuracy, precision, recall, and F1-score. Results: The radiomic model using features from the peritumoral region of 0-3 mm outperformed that using features from 3-6, 6-9, 9-12 mm peritumoral region, and intratumoral region (AUC: 0.95 versus 0.87, 0.86, 0.85, and 0.88). By the fusion of features from 0-3 and 3-6 mm peritumoral regions, the logistic regression model achieved the best performance, with an AUC of 0.97. This model achieved an AUC of 0.85 in the external cohort. Moreover, among the 20 selected features, seven features differed significantly between the two groups (p < 0.05). Conclusions: CT radiomic features from both the peri- and intratumoral regions can predict chemotherapy response in NSCLC using machine learning models. Combined features from two peritumoral regions yielded better predictions.

Pulmonary mucosa-associated lymphoid tissue lymphoma: computed tomography and ¹8F fluorodeoxyglucose-positron emission tomography/computed tomography imaging findings and follow-up.

PURPOSE: The aim of the study was to present the computed tomography (CT) and fluorine 18 (F) fluorodeoxyglucose-positron emission tomography (FDG-PET)/CT imaging findings of pulmonary mucosa-associated lymphoid tissue (MALT) lymphoma and evaluate their roles in the follow-up of this tumor. METHODS: Computed tomography and FDG-PET/CT imaging findings of 18 cases of pathologically proven pulmonary MALT lymphoma were reviewed retrospectively. RESULTS: Multiple and solitary lesions were detected in 15 and 3 patients, respectively. Of those patients with multiple pulmonary lesions, 12 were bilateral, and 3 were unilateral. A total of 51 pulmonary lesions were identified in 18 patients, which included lesions with consolidation (31/51), mass and nodule (12/51), and ground-glass attenuation (8/51). F fluorodeoxyglucose-PET/CT imaging (n = 8) revealed increased FDG uptake in all lesions in 8 cases. At follow-up, 3 patients experienced complete remission, 10 had partial remission, and 2 remained stable. CONCLUSIONS: Computed tomography and FDG-PET/CT images of the pulmonary MALT lymphoma usually reveal multiple, bilateral consolidations, masses, or nodules with air bronchogram and increased FDG uptake. Computed tomography and FDG-PET/CT imaging play important roles in the diagnosis and follow-up of patients with pulmonary MALT lymphoma.

The Assessment of Prostate Cancer Aggressiveness Using a Combination of Quantitative Diffusion-Weighted Imaging and Dynamic Contrast-Enhanced Magnetic Resonance Imaging.

OBJECTIVE: To explore the value of combining dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) quantitative parameters with apparent diffusion coefficient (ADC) values in the diagnosis of prostate cancer. METHODS: The clinical data of 146 patients with prostate lesions, including 87 patients with prostate cancer (PCa) and 59 with benign prostatic hyperplasia (BPH), were collected. After DCE-MRI and diffusion-weighted imaging (DWI) prostate scans, the magnitude of the DCE-MRI transfer constant (Ktrans ), rate constant (kep ), the volume of the extravascular extracellular space (ve ), and the ADC between the groups were compared, and the correlations between the DCE-MRI parameters and Gleason scores were analyzed. The diagnostic efficacy of these quantitative parameters was assessed by the area under the receiver operating characteristic (ROC) curve. RESULTS: The DCE-MRI parameters Ktrans and kep were significantly greater in the PCa group than in the BPH group (p < 0.05). The ROC curve showed the area under the Ktrans, kep , and ADC curves to be 0.665, 0.658, and 0.782, respectively. When all three quantitative indicators were combined, the area under the ROC curve was 0.904, with sensitivity and specificity rates of 83.6% and 93.7%, respectively. The Gleason scores were positively correlated with the Ktrans, kep , and ve (r = 0.39, 0.572, 0.30, respectively; p < 0.05) and negatively correlated with the ADC (r = -0.525; p < 0.05). CONCLUSION: The DCE-MRI quantitative parameters Ktrans and kep , as well as the ADC value, provided effective references for the differential diagnosis of PCa and BPH, as well as more precise and reliable quantitative parameters for grading the aggressiveness of PCa.

Deep CNN Model Using CT Radiomics Feature Mapping Recognizes EGFR Gene Mutation Status of Lung Adenocarcinoma.

To recognize the epidermal growth factor receptor (EGFR) gene mutation status in lung adenocarcinoma (LADC) has become a prerequisite of deciding whether EGFR-tyrosine kinase inhibitor (EGFR-TKI) medicine can be used. Polymerase chain reaction assay or gene sequencing is for measuring EGFR status, however, the tissue samples by surgery or biopsy are required. We propose to develop deep learning models to recognize EGFR status by using radiomics features extracted from non-invasive CT images. Preoperative CT images, EGFR mutation status and clinical data have been collected in a cohort of 709 patients (the primary cohort) and an independent cohort of 205 patients. After 1,037 CT-based radiomics features are extracted from each lesion region, 784 discriminative features are selected for analysis and construct a feature mapping. One Squeeze-and-Excitation (SE) Convolutional Neural Network (SE-CNN) has been designed and trained to recognize EGFR status from the radiomics feature mapping. SE-CNN model is trained and validated by using 638 patients from the primary cohort, tested by using the rest 71 patients (the internal test cohort), and further tested by using the independent 205 patients (the external test cohort). Furthermore, SE-CNN model is compared with machine learning (ML) models using radiomics features, clinical features, and both features. EGFR(-) patients show the smaller age, higher odds of female, larger lesion volumes, and lower odds of subtype of acinar predominant adenocarcinoma (APA), compared with EGFR(+). The most discriminative features are for texture (614, 78.3%) and the features of first order of intensity (158, 20.1%) and the shape features (12, 1.5%) follow. SE-CNN model can recognize EGFR mutation status with an AUC of 0.910 and 0.841 for the internal and external test cohorts, respectively. It outperforms the CNN model without SE, the fine-tuned VGG16 and VGG19, three ML models, and the state-of-art models. Utilizing radiomics feature mapping extracted from non-invasive CT images, SE-CNN can precisely recognize EGFR mutation status of LADC patients. The proposed method combining radiomics features and deep leaning is superior to ML methods and can be expanded to other medical applications. The proposed SE-CNN model may help make decision on usage of EGFR-TKI medicine.

An antibiotic stewardship programme to reduce inappropriate antibiotic prescribing for acute respiratory infections in rural Chinese primary care facilities: study protocol for a clustered randomised controlled trial.

BACKGROUND: Inappropriate prescribing of antibiotics for acute respiratory infections at the primary care level represents the major source of antibiotic misuse in healthcare, and is a major driver for antimicrobial resistance worldwide. In this study we will develop, pilot and evaluate the effectiveness of a comprehensive antibiotic stewardship programme in China's primary care hospitals to reduce inappropriate prescribing of antibiotics for acute respiratory infections among all ages. METHODS: We will use a parallel-group, cluster-randomised, controlled, superiority trial with blinded outcome evaluation but unblinded treatment (providers and patients). We will randomise 34 primary care hospitals from two counties within Guangdong province into the intervention and control arm (1:1 overall ratio) stratified by county (8:9 within-county ratio). In the control arm, antibiotic prescribing and management will continue through usual care. In the intervention arm, we will implement an antibiotic stewardship programme targeting family physicians and patients/caregivers. The family physician components include: (1) training using new operational guidelines, (2) improved management and peer-review of antibiotic prescribing, (3) improved electronic medical records and smart phone app facilitation. The patient/caregiver component involves patient education via family physicians, leaflets and videos. The primary outcome is the proportion of prescriptions for acute respiratory infections (excluding pneumonia) that contain any antibiotic(s). Secondary outcomes will address how frequently specific classes of antibiotics are prescribed, how frequently key non-antibiotic alternatives are prescribed and the costs of consultations. We will conduct a qualitative process evaluation to explore operational questions regarding acceptability, cultural appropriateness and burden of technology use, as well as a cost-effectiveness analysis and a long-term benefit evaluation. The duration of the intervention will be 12 months, with another 24 months' post-trial long-term follow-up. DISCUSSION: Our study is one of the first trials to evaluate the effect of an antibiotic stewardship programme in primary care settings in a low- or middle-income country (LMIC). All interventional activities will be designed to be embedded into routine primary care with strong local ownership. Through the trial we intend to impact on clinical practice and national policy in antibiotic prescription for primary care facilities in rural China and other LMICs. TRIAL REGISTRATION: ISRCTN, ID: ISRCTN96892547. Registered on 18 August 2019.

Gemcitabine plus cisplatin chemotherapy with concurrent para-toluenesulfonamide local injection therapy for peripherally advanced nonsmall cell lung cancer larger than 3 cm in the greatest dimension.

Para-toluenesulfonamide (PTS), active ingredient being PTS, is a new anticancer drug applied through local intratumoral injection. The aim of this phase II clinical trial was to investigate the response and toxicity of standard gemcitabine (GEM) plus cisplatin (CIS) chemotherapy with concurrent intratumoral injection of PTS in peripherally advanced nonsmall cell lung cancer. Patients received 1250 mg/m of GEM on day 1, 8, and 75 mg/m of CIS on day 1, every 21 days for four cycles. PTS was injected intratumorally through percutaneous injection under computed tomography guidance on days 5, 12, 15, and 18 of cycle 1, and repeated on days 5 and 12 of cycle 2 if a less than 50% necrotic area was achieved after the first cycle according to the computed tomography scan. Twelve (46.2%) patients had metastatic disease, whereas 14 (53.8%) patients had stage IIIB disease. All 26 patients were assessable for response. Overall response rate by intention-to-treat was 53.8% (95% confidence interval: 34.6-73.0%). Median progression-free survival and overall survival were 6.5 months (95% confidence interval: 3.8-10.2 months) and 14.5 months (10.0-18.0 months), respectively. One-year and 2-year survivals were 57.7 and 22.4%, respectively. The grade 3-4 hematologic adverse events were neutropenia in six patients (23.1%), anemia in three (11.5%), and thrombocytopenia in four patients (15.4%). Nonhematologic toxicities were generally mild and usually not dose-limiting. Although grade 1-2 emesis occurred in nine patients (34.6%), only one had grade 3 vomiting. Grade 1-2 cough, local pain, and peripheral neurotoxocity developed in 12 (46.2%), three (11.5%), and five (19.2%) patients, respectively. There were no treatment-related deaths. GEM/CIS chemotherapy with concurrent PTS local injection therapy is a well-tolerated modality with potential activity in previously untreated peripheral advanced nonsmall cell lung cancer patients.