Contrast-enhanced CT and PET-CT characteristics of primary tracheal lymphoepithelioma-like carcinoma: case series.

Background: Primary tracheal lymphoepithelioma-like carcinoma (LELC) is extremely rare, with only a few cases reported so far, and few studies have focused on the radiological features. This study aimed to investigate contrast-enhanced computed tomography (CECT) and positron emission tomography-computed tomography (PET-CT) presentations of primary tracheal LELC to improve diagnosis. Methods: A retrospective analysis was conducted on the clinical and imaging data of 13 patients with confirmed primary tracheal LELC between December 2013 and August 2022. We analyzed the radiological profiles of lesions on the CECT and PET-CT images. Results: In 92.3% (12/13) of the cases, primary tracheal LELC lesions predominantly occurred in the thoracic segment. They manifested as singular, wide-based, eccentric, irregular nodules, or exhibited mass-like thickening of the tracheal wall with invasive growth both internally and externally along the wall. The thickest dimension of the lesion ranged from 9 to 28 mm, affecting a length of 30.8±13.5 mm. Luminal stenosis was evident in all patients, with the narrowest point reaching a stenosis rate of 85%. Lesion margins were clear in 69.2% (9/13), indistinct in 23.1% (3/13), and unclear in 7.7% (1/13) of all cases. Among the patients, 92.3% (12/13) exhibited a relatively uniform density on CT plain scans, with a CT value of 44.5±7.8 Hounsfield units (HU). Enhancement scans revealed moderate to marked enhancement in 75% (9/12) of cases. In 2 cases undergoing PET-CT examination, lesion standardized uptake values (SUVs) were 4.4 and 5.1, whereas enlarged lymph node SUVs were 7.7 and 6.3, respectively. Mediastinal lymph node enlargement was observed in 8 patients (61.5%, 8/13), with a maximum short axis of 11.1±5.5 mm. After treatment, 9 out of 12 patients (75%) showed no evidence of distant metastasis upon CT re-examination. Conclusions: Early detection of primary tracheal LELC allows for curative resection and may lead to a favorable prognosis. It presents with characteristic CT findings, and the utilization of PET-CT improves diagnosis and staging.

Accurate classification of pulmonary nodules by a combined model of clinical, imaging, and cell-free DNA methylation biomarkers: a model development and external validation study.

BACKGROUND: There is an unmet clinical need for accurate non-invasive tests to facilitate the early diagnosis of lung cancer. We propose a combined model of clinical, imaging, and cell-free DNA methylation biomarkers that aims to improve the classification of pulmonary nodules. METHODS: We conducted a prospective specimen collection and retrospective masked evaluation study. We recruited participants with a solitary pulmonary nodule sized 5-30 mm from 24 hospitals across 20 cities in China. Participants who were aged 18 years or older and had been referred with 5-30 mm non-calcified and solitary pulmonary nodules, including solid nodules, part solid nodules, and pure ground-glass nodules, were included. We developed a combined clinical and imaging biomarkers (CIBM) model by machine learning for the classification of malignant and benign pulmonary nodules in a cohort (n=839) and validated it in two cohorts (n=258 in the first cohort and n=283 in the second cohort). We then integrated the CIBM model with our previously established circulating tumour DNA methylation model (PulmoSeek) to create a new combined model, PulmoSeek Plus (n=258), and verified it in an independent cohort (n=283). The clinical utility of the models was evaluated using decision curve analysis. A low cutoff (0·65) for high sensitivity and a high cutoff (0·89) for high specificity were applied simultaneously to stratify pulmonary nodules into low-risk, medium-risk, and high-risk groups. The primary outcome was the diagnostic performance of the CIBM, PulmoSeek, and PulmoSeek Plus models. Participants in this study were drawn from two prospective clinical studies that were registered (NCT03181490 and NCT03651986), the first of which was completed, and the second of which is ongoing because 25% of participants have not yet finished the required 3-year follow-up. FINDINGS: We recruited a total of 1380 participants. 1097 participants were enrolled from July 7, 2017, to Feb 12, 2019; 839 participants were used for the CIBM model training set, and the rest (n=258) for the first CIBM validation set and the PulmoSeek Plus training set. 283 participants were enrolled from Oct 26, 2018, to March 20, 2020, as an independent validation set for the PulmoSeek Plus model and the second validation set for the CIBM model. The CIBM model validation cohorts had area under the curves (AUCs) of 0·85 (95% CI 0·80-0·89) and 0·85 (0·81-0·89). The PulmoSeek Plus model had better discrimination capacity compared with the CIBM and PulmoSeek models with an increase of 0·05 in AUC (PulmoSeek Plus vs CIBM, 95% CI 0·022-0·087, p=0·001; and PulmoSeek Plus vs PulmoSeek, 0·018-0·083, p=0·002). The overall sensitivity of the PulmoSeek Plus model was 0·98 (0·97-0·99) at a fixed specificity of 0·50 for ruling out lung cancer. A high sensitivity of 0·98 (0·96-0·99) was maintained in early-stage lung cancer (stages 0 and I) and 0·99 (0·96-1·00) in 5-10 mm nodules. The decision curve showed that if an invasive intervention, such as surgical resection or biopsy, was deemed necessary at more than the risk threshold score of 0·54, the PulmoSeek Plus model would provide a standardised net benefit of 82·38% (76·06-86·79%), equivalent to correctly identifying approximately 83 of 100 people with lung cancer. Using the PulmoSeek Plus model to classify pulmonary nodules with two cutoffs (0·65 and 0·89) would have reduced 89% (105/118) of unnecessary surgeries and 73% (308/423) of delayed treatments. INTERPRETATION: The PulmoSeek Plus Model combining clinical, imaging, and cell-free DNA methylation biomarkers aids the early diagnosis of pulmonary nodules, with potential application in clinical decision making for the management of pulmonary nodules. FUNDING: The China National Science Foundation, the Key Project of Guangzhou Scientific Research Project, the High-Level University Construction Project of Guangzhou Medical University, the National Key Research & Development Programme, the Guangdong High Level Hospital Construction "Reaching Peak" Plan, the Guangdong Basic and Applied Basic Research Foundation, the National Natural Science Foundation of China, The Leading Projects of Guangzhou Municipal Health Sciences Foundation, the Key Research and Development Plan of Shaanxi Province of China, the Scheme of Guangzhou Economic and Technological Development District for Leading Talents in Innovation and Entrepreneurship, the Scheme of Guangzhou for Leading Talents in Innovation and Entrepreneurship, the Scheme of Guangzhou for Leading Team in Innovation, the Guangzhou Development Zone International Science and Technology Cooperation Project, and the Science and Technology Planning Project of Guangzhou.

Semi-Supervised Segmentation of Interstitial Lung Disease Patterns from CT Images via Self-Training with Selective Re-Training.

Accurate segmentation of interstitial lung disease (ILD) patterns from computed tomography (CT) images is an essential prerequisite to treatment and follow-up. However, it is highly time-consuming for radiologists to pixel-by-pixel segment ILD patterns from CT scans with hundreds of slices. Consequently, it is hard to obtain large amounts of well-annotated data, which poses a huge challenge for data-driven deep learning-based methods. To alleviate this problem, we propose an end-to-end semi-supervised learning framework for the segmentation of ILD patterns (ESSegILD) from CT images via self-training with selective re-training. The proposed ESSegILD model is trained using a large CT dataset with slice-wise sparse annotations, i.e., only labeling a few slices in each CT volume with ILD patterns. Specifically, we adopt a popular semi-supervised framework, i.e., Mean-Teacher, that consists of a teacher model and a student model and uses consistency regularization to encourage consistent outputs from the two models under different perturbations. Furthermore, we propose introducing the latest self-training technique with a selective re-training strategy to select reliable pseudo-labels generated by the teacher model, which are used to expand training samples to promote the student model during iterative training. By leveraging consistency regularization and self-training with selective re-training, our proposed ESSegILD can effectively utilize unlabeled data from a partially annotated dataset to progressively improve the segmentation performance. Experiments are conducted on a dataset of 67 pneumonia patients with incomplete annotations containing over 11,000 CT images with eight different lung patterns of ILDs, with the results indicating that our proposed method is superior to the state-of-the-art methods.

Artificial intelligence for stepwise diagnosis and monitoring of COVID-19.

BACKGROUND: Main challenges for COVID-19 include the lack of a rapid diagnostic test, a suitable tool to monitor and predict a patient's clinical course and an efficient way for data sharing among multicenters. We thus developed a novel artificial intelligence system based on deep learning (DL) and federated learning (FL) for the diagnosis, monitoring, and prediction of a patient's clinical course. METHODS: CT imaging derived from 6 different multicenter cohorts were used for stepwise diagnostic algorithm to diagnose COVID-19, with or without clinical data. Patients with more than 3 consecutive CT images were trained for the monitoring algorithm. FL has been applied for decentralized refinement of independently built DL models. RESULTS: A total of 1,552,988 CT slices from 4804 patients were used. The model can diagnose COVID-19 based on CT alone with the AUC being 0.98 (95% CI 0.97-0.99), and outperforms the radiologist's assessment. We have also successfully tested the incorporation of the DL diagnostic model with the FL framework. Its auto-segmentation analyses co-related well with those by radiologists and achieved a high Dice's coefficient of 0.77. It can produce a predictive curve of a patient's clinical course if serial CT assessments are available. INTERPRETATION: The system has high consistency in diagnosing COVID-19 based on CT, with or without clinical data. Alternatively, it can be implemented on a FL platform, which would potentially encourage the data sharing in the future. It also can produce an objective predictive curve of a patient's clinical course for visualization. KEY POINTS: • CoviDet could diagnose COVID-19 based on chest CT with high consistency; this outperformed the radiologist's assessment. Its auto-segmentation analyses co-related well with those by radiologists and could potentially monitor and predict a patient's clinical course if serial CT assessments are available. It can be integrated into the federated learning framework. • CoviDet can be used as an adjunct to aid clinicians with the CT diagnosis of COVID-19 and can potentially be used for disease monitoring; federated learning can potentially open opportunities for global collaboration.

An Exploratory Study on the Stable Radiomics Features of Metastatic Small Pulmonary Nodules in Colorectal Cancer Patients.

OBJECTIVES: To identify the relatively invariable radiomics features as essential characteristics during the growth process of metastatic pulmonary nodules with a diameter of 1 cm or smaller from colorectal cancer (CRC). METHODS: Three hundred and twenty lung nodules were enrolled in this study (200 CRC metastatic nodules in the training cohort, 60 benign nodules in the verification cohort 1, 60 CRC metastatic nodules in the verification cohort 2). All the nodules were divided into four groups according to the maximum diameter: 0 to 0.25 cm, 0.26 to 0.50 cm, 0.51 to 0.75 cm, 0.76 to 1.0 cm. These pulmonary nodules were manually outlined in computed tomography (CT) images with ITK-SNAP software, and 1724 radiomics features were extracted. Kruskal-Wallis test was performed to compare the four different levels of nodules. Cross-validation was used to verify the results. The Spearman rank correlation coefficient is calculated to evaluate the correlation between features. RESULTS: In training cohort, 90 features remained stable during the growth process of metastasis nodules. In verification cohort 1, 293 features remained stable during the growth process of benign nodules. In verification cohort 2, 118 features remained stable during the growth process of metastasis nodules. It is concluded that 20 features remained stable in metastatic nodules (training cohort and verification cohort 2) but not stable in benign nodules (verification cohort 1). Through the cross-validation (n=100), 11 features remained stable more than 90 times. CONCLUSIONS: This study suggests that a small number of radiomics features from CRC metastatic pulmonary nodules remain relatively stable from small to large, and they do not remain stable in benign nodules. These stable features may reflect the essential characteristics of metastatic nodules and become a valuable point for identifying metastatic pulmonary nodules from benign nodules.

CT Quantification of COVID-19 Pneumonia at Admission Can Predict Progression to Critical Illness: A Retrospective Multicenter Cohort Study.

Objective: Early identification of coronavirus disease 2019 (COVID-19) patients with worse outcomes may benefit clinical management of patients. We aimed to quantify pneumonia findings on CT at admission to predict progression to critical illness in COVID-19 patients. Methods: This retrospective study included laboratory-confirmed adult patients with COVID-19. All patients underwent a thin-section chest computed tomography (CT) scans showing evidence of pneumonia. CT images with severe moving artifacts were excluded from analysis. Patients' clinical and laboratory data were collected from medical records. Three quantitative CT features of pneumonia lesions were automatically calculated using a care.ai Intelligent Multi-disciplinary Imaging Diagnosis Platform Intelligent Evaluation System of Chest CT for COVID-19, denoting the percentage of pneumonia volume (PPV), ground-glass opacity volume (PGV), and consolidation volume (PCV). According to Chinese COVID-19 guidelines (trial version 7), patients were divided into noncritical and critical groups. Critical illness was defined as a composite of admission to the intensive care unit, respiratory failure requiring mechanical ventilation, shock, or death. The performance of PPV, PGV, and PCV in discrimination of critical illness was assessed. The correlations between PPV and laboratory variables were assessed by Pearson correlation analysis. Results: A total of 140 patients were included, with mean age of 58.6 years, and 85 (60.7%) were male. Thirty-two (22.9%) patients were critical. Using a cutoff value of 22.6%, the PPV had the highest performance in predicting critical illness, with an area under the curve of 0.868, sensitivity of 81.3%, and specificity of 80.6%. The PPV had moderately positive correlation with neutrophil (%) (r = 0.535, p < 0.001), erythrocyte sedimentation rate (r = 0.567, p < 0.001), d-Dimer (r = 0.444, p < 0.001), high-sensitivity C-reactive protein (r = 0.495, p < 0.001), aspartate aminotransferase (r = 0.410, p < 0.001), lactate dehydrogenase (r = 0.644, p < 0.001), and urea nitrogen (r = 0.439, p < 0.001), whereas the PPV had moderately negative correlation with lymphocyte (%) (r = -0.535, p < 0.001). Conclusions: Pneumonia volume quantified on initial CT can non-invasively predict the progression to critical illness in advance, which serve as a prognostic marker of COVID-19.

Retrospective comparison of high-resolution computed tomography of eosinophilic granulomatosis with polyangiitis with severe asthma.

BACKGROUND: Eosinophilic granulomatosis with polyangiitis (EGPA) is often misdiagnosed as severe asthma due to their similar clinical presentations. We compared the pulmonary radiologic features of EGPA to those of severe asthma by high-resolution computed tomography (HRCT) in order to early diagnose EGPA. METHODS: We retrospectively reviewed clinical records and HRCT findings of 96 patients with EGPA and 82 patients with severe asthma who were seen at our hospital from 2011 to 2017. We used a semi-quantitative grading system to evaluate radiological findings. A radiological only and a clinical-radiological model were used to differentiate EGPA from severe asthma. RESULTS: Bronchial wall thickening, air trapping, tree-in-bud opacities, bronchial mucus plugging, bronchiectasis, diffuse ground-glass opacities (GGOs), consolidation, and increased small vascular markings were more common in EGPA patients than in severe asthmatics (P<0.05). The gradings of GGO (grade 2 vs. grade 1) and tree-in-bud opacities (grade 2 vs. grade 0) were higher in EGPA patients than in severe asthmatics. The total image score of EGPA patients was significantly higher than that of severe asthmatics (P<0.05). In the radiological only and the clinical-radiological model, the area under the receiver operating characteristic (ROC) curves (AUCs) for the identification of EGPA and severe asthma were 0.904 [95% confidence interval (CI): 0.860 to 0.948] and 0.974 (95% CI: 0.955 to 0.993), respectively. CONCLUSIONS: Lung HRCT scan is useful in differentiating EGPA from severe asthma. In patients with difficult-to-treat asthma, an HRCT scan of the thorax should be performed should there be features that raise the suspicion of EGPA.

Machine learning models for predicting critical illness risk in hospitalized patients with COVID-19 pneumonia.

BACKGROUND: To develop machine learning classifiers at admission for predicting which patients with coronavirus disease 2019 (COVID-19) who will progress to critical illness. METHODS: A total of 158 patients with laboratory-confirmed COVID-19 admitted to three designated hospitals between December 31, 2019 and March 31, 2020 were retrospectively collected. 27 clinical and laboratory variables of COVID-19 patients were collected from the medical records. A total of 201 quantitative CT features of COVID-19 pneumonia were extracted by using an artificial intelligence software. The critically ill cases were defined according to the COVID-19 guidelines. The least absolute shrinkage and selection operator (LASSO) logistic regression was used to select the predictors of critical illness from clinical and radiological features, respectively. Accordingly, we developed clinical and radiological models using the following machine learning classifiers, including naive bayes (NB), linear regression (LR), random forest (RF), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), K-nearest neighbor (KNN), kernel support vector machine (k-SVM), and back propagation neural networks (BPNN). The combined model incorporating the selected clinical and radiological factors was also developed using the eight above-mentioned classifiers. The predictive efficiency of the models is validated using a 5-fold cross-validation method. The performance of the models was compared by the area under the receiver operating characteristic curve (AUC). RESULTS: The mean age of all patients was 58.9±13.9 years and 89 (56.3%) were males. 35 (22.2%) patients deteriorated to critical illness. After LASSO analysis, four clinical features including lymphocyte percentage, lactic dehydrogenase, neutrophil count, and D-dimer and four quantitative CT features were selected. The XGBoost-based clinical model yielded the highest AUC of 0.960 [95% confidence interval (CI): 0.913-1.000)]. The XGBoost-based radiological model achieved an AUC of 0.890 (95% CI: 0.757-1.000). However, the predictive efficacy of XGBoost-based combined model was very close to that of the XGBoost-based clinical model, with an AUC of 0.955 (95% CI: 0.906-1.000). CONCLUSIONS: A XGBoost-based based clinical model on admission might be used as an effective tool to identify patients at high risk of critical illness.

Revolution spectral CT for urinary stone with a single/mixed composition in vivo: a large sample analysis.

PURPOSE: To analyze various compositions of urinary stones using revolution spectral CT (rapid kV switching dual-energy CT) in vivo. METHODS: 202 patients with urinary stones underwent spectral CT before surgery. Zeff peak, overall scope and CT values were detected. Moreover, water/iodine attenuating material images were obtained. Removed stones were subjected to infrared spectroscopy after surgery. The results of infrared spectroscopy were compared with CT. RESULTS: 28 stones (14.08%) with single composition, 165 stones with two mixed compositions (81.68%), and 9 stones with three mixed compositions (4.46%) were observed. When Zeff peaks of stones with single/mixed compositions were summarized together, 146 peaks of calcium oxalate monohydrate, 119 peaks of calcium oxalate dihydrate, 55 peaks of carbapatite, 38 peaks of urate, 16 peaks of struvite, and 11 peaks of brushite were totally observed. 93.8% of calcium oxalate monohydrate had Zeff peaks between 13.3 and 14.0. 91.6% of calcium oxalate dihydrate had peaks between 12.0 and 13.3. For carbapatite, 90.9% of stones had peaks from 14.0 to 15.0. A total of 94.8% of urate had peaks between 7.0 and 11.0. 93.8% of struvite had peaks between 11.0 and 13.0, and 90.9% of brushite had peaks between 12.0 and 14.0. Moreover, densities of urate, struvite and brushite were low density in iodine-based images and high-density in water-based images. CONCLUSION: The in-vivo analysis of spectral CT in urinary stone revealed characteristics of different compositions, especially mixed compositions. An in-vivo predictive model may be constructed to distinguish stone compositions.

Early radiologic and bronchoscopic changes after bronchial thermoplasty in patients with severe asthma.

Bronchial thermoplasty (BT) is a treatment to reduce the airway smooth muscle mass by delivering radiofrequency thermal energy to the airways. BT is used in patients with severe asthma. The present study reported on cases of pneumothorax directly after BT and retrospectively analyzed early radiologic and bronchoscopic modifications after BT. The clinical data and radiologic and bronchoscopic findings of 12 patients with severe asthma who were subjected to BT between July 2014 and October 2017 were analyzed. A total of 33 chest radiographs were collected within 18-24 h after BT. Radiological abnormalities were observed in 32 radiographs as atelectasis (53.1%), peribronchial consolidations (84.4%), pleural effusion (18.8%), effusion in oblique fissures (3.1%), pleural thickening (6.3%) and pneumothorax (3.1%). Of note, one patient suffered pneumothorax after the third BT session and underwent chest drain insertion, followed by mechanical ventilation at the intensive care unit and multiple bronchoscopic interventions, which revealed extensive phlegm plugs. A total of six patients with worsened symptoms and lobar atelectasis also required bronchoscopic intervention, which revealed that phlegm plugs occluded the bronchus in the treated lobe. No bronchoscopic intervention was required in the remaining five patients. During 16-30 days of follow-up, 95.7% of the findings on chest radiography were resolved. To the best of our knowledge, the present study reported the first case of pneumothorax following BT. Early radiologic modifications such as atelectasis and peribronchial consolidations appear common after BT. However, whether bronchoscopic intervention is required for atelectasis following BT warrants further investigation. Of note, BT should be audited and recorded in detail to ideally contribute to a framework of clinical trials to improve risk-benefit evaluations and the selection of patients likely to benefit from treatment.

[Value of anti-neutrophil cytoplasmic antibody in assessing the severity of bronchiolitis obliterans in children].

OBJECTIVE: To study the value of anti-neutrophil cytoplasmic antibody (ANCA) in assessing the severity of bronchiolitis obliterans (BO) in children. METHODS: A prospective analysis was performed on 59 children who were diagnosed with BO from June 2009 to October 2014. ELISA was used to measure the concentrations of myeloperoxidase (MPO)-ANCA and proteinase 3 (PR3)-ANCA in serum. According to the results of ELISA, the children were divided into three groups: double-negative ANCA (n=22), single-positive ANCA (n=17), and double-positive ANCA (n=20). The three groups were compared in terms of the scores of BO risk factors, clinical symptoms, chest high-resolution computed tomography (HRCT), and lung pathology on admission, as well as the changes in the expression level of ANCA and the scores of clinical symptoms and chest HRCT over time. RESULTS: Compared with the double-negative ANCA group, the double-positive ANCA group had a significantly higher score of BO risk factors (P<0.05), and the single-positive ANCA group and the double-positive ANCA group had significantly higher scores of clinical symptoms, chest HRCT, and lung pathology (P<0.05). The children were followed up for 6 months after discharge, and there were significant reductions in MPO-ANCA and PR3-ANCA titers from admission and discharge to the end of follow-up (P<0.05), as well as a significant reduction in the score of clinical symptoms from admission to the end of follow-up (P<0.05), while there was no significant change in the score of chest HRCT from admission to the end of follow-up (P>0.05). The single-positive ANCA and double-positive ANCA groups still had a significantly higher score of clinical symptoms than the double-negative ANCA group (P<0.05). CONCLUSIONS: The expression level of ANCA is correlated with the severity of BO in children and thus has certain clinical significance in disease evaluation.

The development and validation of a radiomic nomogram for the preoperative prediction of lung adenocarcinoma.

BACKGROUND: Accurate diagnosis of early lung cancer from small pulmonary nodules (SPN) is challenging in clinical setting. We aimed to develop a radiomic nomogram to differentiate lung adenocarcinoma from benign SPN. METHODS: This retrospective study included a total of 210 pathologically confirmed SPN (≤ 10 mm) from 197 patients, which were randomly divided into a training dataset (n = 147; malignant nodules, n = 94) and a validation dataset (n = 63; malignant nodules, n = 39). Radiomic features were extracted from the cancerous volumes of interest on contrast-enhanced CT images. The least absolute shrinkage and selection operator (LASSO) regression was used for data dimension reduction, feature selection, and radiomic signature building. Using multivariable logistic regression analysis, a radiomic nomogram was developed incorporating the radiomic signature and the conventional CT signs observed by radiologists. Discrimination and calibration of the radiomic nomogram were evaluated. RESULTS: The radiomic signature consisting of five radiomic features achieved an AUC of 0.853 (95% confidence interval [CI]: 0.735-0.970), accuracy of 81.0%, sensitivity of 82.9%, and specificity of 77.3%. The two conventional CT signs achieved an AUC of 0.833 (95% CI: 0.707-0.958), accuracy of 65.1%, sensitivity of 53.7%, and specificity of 86.4%. The radiomic nomogram incorporating the radiomic signature and conventional CT signs showed an improved AUC of 0.857 (95% CI: 0.723-0.991), accuracy of 84.1%, sensitivity of 85.4%, and specificity of 81.8%. The radiomic nomogram had good calibration power. CONCLUSION: The radiomic nomogram might has the potential to be used as a non-invasive tool for individual prediction of SPN preoperatively. It might facilitate decision-making and improve the management of SPN in the clinical setting.

Computed Tomography-Based Radiomic Features for Diagnosis of Indeterminate Small Pulmonary Nodules.

OBJECTIVE: This study aimed to determine the potential of radiomic features extracted from preoperative computed tomography to discriminate malignant from benign indeterminate small (≤10 mm) pulmonary nodules. METHODS: A total of 197 patients with 210 nodules who underwent surgical resections between January 2011 and March 2017 were analyzed. Three hundred eighty-five radiomic features were extracted from the computed tomographic images. Feature selection and data dimension reduction were performed using the Kruskal-Wallis test, Spearman correlation analysis, and principal component analysis. The random forest was used for radiomic signature building. The receiver operating characteristic curve analysis was used to evaluate the model performance. RESULTS: Fifteen principal component features were selected for modeling. The area under the curve, sensitivity, specificity, and accuracy of the prediction model were 0.877 (95% confidence interval [CI], 0.795-0.959), 81.8% (95% CI, 72.0%-90.9%), 77.4% (95% CI, 63.9%-89.3%), and 80.0% (95% CI, 72.0%-86.7%) in the validation cohort, respectively. CONCLUSIONS: Computed tomography-based radiomic features showed good discriminative power for benign and malignant indeterminate small pulmonary nodules.

Clinical Utility of a Nomogram for Predicting 30-Days Poor Outcome in Hospitalized Patients With COVID-19: Multicenter External Validation and Decision Curve Analysis.

Aim: Early detection of coronavirus disease 2019 (COVID-19) patients who are likely to develop worse outcomes is of great importance, which may help select patients at risk of rapid deterioration who should require high-level monitoring and more aggressive treatment. We aimed to develop and validate a nomogram for predicting 30-days poor outcome of patients with COVID-19. Methods: The prediction model was developed in a primary cohort consisting of 233 patients with laboratory-confirmed COVID-19, and data were collected from January 3 to March 20, 2020. We identified and integrated significant prognostic factors for 30-days poor outcome to construct a nomogram. The model was subjected to internal validation and to external validation with two separate cohorts of 110 and 118 cases, respectively. The performance of the nomogram was assessed with respect to its predictive accuracy, discriminative ability, and clinical usefulness. Results: In the primary cohort, the mean age of patients was 55.4 years and 129 (55.4%) were male. Prognostic factors contained in the clinical nomogram were age, lactic dehydrogenase, aspartate aminotransferase, prothrombin time, serum creatinine, serum sodium, fasting blood glucose, and D-dimer. The model was externally validated in two cohorts achieving an AUC of 0.946 and 0.878, sensitivity of 100 and 79%, and specificity of 76.5 and 83.8%, respectively. Although adding CT score to the clinical nomogram (clinical-CT nomogram) did not yield better predictive performance, decision curve analysis showed that the clinical-CT nomogram provided better clinical utility than the clinical nomogram. Conclusions: We established and validated a nomogram that can provide an individual prediction of 30-days poor outcome for COVID-19 patients. This practical prognostic model may help clinicians in decision making and reduce mortality.

A retrospective study analyzing missed diagnosis of lung metastases at their early stages on computed tomography.

BACKGROUND: Lungs are one of the target organs of metastases of primary lung, breast, liver, colorectal, and esophageal cancer. While computed tomography (CT) is the most widely used modality for detecting lung metastases, it is still very challenging to detect them at the earlier stages. If lung metastases could be found on CT scans at the earliest time points, patients would benefit by beginning treatment earlier. The objective of this study was to demonstrate that CT can reveal lung metastases in many cases at even earlier stages than current radiological practice may find. METHODS: One hundred patients with lung metastases were randomly selected and their surveillance CT scans were analyzed retrospectively. The patients had primary cancer in the breasts, lungs, esophagus, colorectum, and liver. All patients had multiple CT examinations of the lungs and their metastases, if any, were confirmed by subsequent CT scans. The earliest CT scans were examined to determine whether lung metastases at the same locations had been diagnosed or missed. Missed lung metastases, categorized by type of the primary cancer and adjacency to nearby blood vessels, were statistically analyzed. RESULTS: There were 36/100 (36%) cases of missed lung metastases, including 15 cases of single metastasis and 21 cases of multiple metastases. There were a total of 174 missed loci of lung metastases. Where metastases were missed, there was a statistically significant difference (P<0.001) in their distribution within the sub-regions of the lungs. Adjacency to blood vessels appeared to be a significant factor in metastases being missed during diagnosis (P<0.001). CONCLUSIONS: There was a considerable percentage of early lung metastases that were missed by radiologists but actually appeared on CT scans. The capability of CT to reveal such early metastases opens up an opportunity to move up the time points of detecting lung metastases through clinical and training improvement and technology development such as computer-aided detection.

Preoperative localisation of pulmonary ground-glass opacity using medical adhesive before thoracoscopic resection.

OBJECTIVES: To evaluate the safety and efficiency of computed tomography (CT)-guided medical adhesive, α-cyanoacrylate, for preoperative localisation of pulmonary ground-glass opacity (GGO) used for guiding the video-assisted thoracoscopic surgical (VATS) excision METHODS: The procedure was performed on 188 consecutive patients with solitary GGO (pure GGO = 90 cases; mixed GGO = 98 cases) prior to the thoracoscopic procedure. The complications and efficacy of this method were analysed. The resected GGO was analysed pathologically. RESULTS: The mean duration of the procedure was 16.3 ± 5.2 min. The preoperative localisation was 100% successful. All GGOs were successfully resected by VATS. Asymptomatic pneumothorax was developed in 16/188 patients (8.5%) and mild pulmonary haemorrhage occurred in 15 cases (7.9%) post-localisation. None of the patients required any further treatment for the complications. CONCLUSION: Preoperative localisation using CT-guided medical adhesive, α-cyanoacrylate, is a safe and short-duration procedure, with high accuracy and success rates with respect to VATS resection of GGO. KEY POINTS: • Preoperative localisation is crucial for successful resection of GGO by VATS. • Preoperative adhesive localisation provides an up to 100% successful localisation rate with few complications. • Preoperative adhesive localisation enabled VATS resection in 100% of the GGO. • Preoperative adhesive localisation is safe and effective for VATS resection of GGO.

Cardiovascular parameters of chest CT scan in estimating pulmonary arterial pressure in patients with pulmonary hypertension.

OBJECTIVE: To develop a formula to compute mean pulmonary arterial pressure (MPAP) by chest computerized tomography (CT), and to verify its accuracy and reliability. METHODS: Eighty-five patients who had taken chest CT and right heart catheterization (RHC) were recruited. The pulmonary arterial systolic pressure (PASP), pulmonary arterial diastolic pressure (PADP), and MPAP of each subject were measured and recorded by RHC. The diameters of the ascending aorta (dAA), descending aorta (dDA) and main pulmonary artery (dMPA), Cobb angle, diameters of right ventricle (dRV), diameters of left ventricle (dLV) were measured by means of chest CT scans. Systolic blood pressure (SBP) was measured by using electronic sphygmomanometer. A linear regression equation was generated in 56 patients to estimate PAP based on chest CT values, 29 patients were used to test the accuracy of the formula. RESULTS: The computed equation for analyzing MPAP is: MPAP = 9.011 + 34.195 × dMPA/dAA - 0.319 × SBP + 0.402 × Cobb angle. AUC of equation with three variables (dMPA/dAA, SBP, and Cobb angle) was 0.923 with 95% CI (0.863-0.982). The mean ± SD of predicted values and RHC values had no statistical difference. CONCLUSIONS: Ratio of dMAP/dAA, Cobb angle, and SBP can be reliably used to estimate MPAP and predict severity of PH.

CT features and differential diagnosis of primary pulmonary mucoepidermoid carcinoma and pulmonary adenoid cystic carcinoma.

BACKGROUND: The differential diagnosis of primary pulmonary mucoepidermoid carcinoma (PMEC) and pulmonary adenoid cystic carcinoma (PACC) is difficult, because both tumors could be similar in terms of certain characteristics on CT. METHODS: The CT findings from 24 cases of PMEC and 30 cases of PACC were retrospectively analyzed. According to the position of the lesion in airway, we divided these cases into three types: central, hilar, and peripheral. RESULTS: In PMEC, there were 7 cases of central type, 14 cases of hilar type, and 3 cases of peripheral type. And, 57.1% PMEC cases of the hilar type were accompanied by distal bronchial dilatation with mucoid impaction. Patchy areas of low density were observed in 79.2% cases of PMEC. The solid part of most lesions showed moderate (37.5%) or severe enhancement (45.8%). However, in PACC, there were 24 cases of central type, 3 cases of hilar type, and 3 cases of peripheral type. PACC had more cases of central type than PMEC. Moreover, longitudinal extent greater than 3 cm was observed in 62.5% PACC cases of the central type, while infiltration of the luminal perimeter more than 1/2 perimeter was observed in 95.8% PACC cases of the central type. Patchy areas of low density were observed in 26.7% cases of PMEC. In PACC cases, the solid part of 76.7% lesions showed slight enhancement. Cavities could be observed in PMEC, but not in PACC. CONCLUSIONS: PMEC and PACC have different CT features in various airway locations. PMEC is usually the hilar type, accompanied by distal bronchial dilatation with mucoid impaction. However, PACC is usually the central type, with longitudinal extent greater than 3 cm and infiltration of the luminal wall more than 1/2 perimeter. Patchy areas of low density and moderate or severe enhancement are more prominent in PMEC. However, slight enhancement is more common in PACC.