The first reported pulmonary nocardiosis caused by Nocardia gipuzkoensis resisted to TMP-SMZ in an immunocompetent patient.

BACKGROUND: . Nocardia gipuzkoensis was first described as a novel and distinct species in 2020 by Imen Nouioui and pulmonary nocardiosis associated with Nocardia gipuzkoensis was once reported in two bronchiectasis patients. Noteworthy, both reported Nocardia gipuzkoensis cases showed sensitivity to Trimethoprim/Sulfamethoxazol (TMP-SMZ), which are usually recommended for empirical therapy. METHODS: We reported the third case of Nocardia gipuzkoensis infection in a 16-year-old girl with chief complaints of cough, persistent chest and back pain. No underlying immuno-suppressive conditions and glucocorticoid use was revealed. Patchy lesions next to spine and located in the posterior basal segment of lower lobes of left lung were seen in thorax computed tomography (CT), but no pathogenic bacteria was detected according to routine laboratory testings. RESULTS: Metagenomic next-generation sequencing (mNGS) combined with Whole Genome Sequencing (WGS) was used to classified our isolate from bronchoalveolar lavage fluid (BALF) as a Nocardia gipuzkoensis. It is worth mentioning that drug susceptibility testing of our isolate showed resistance to TMP-SMZ, which was never reported before. The patient improved remarkably both clinically and radiographically according to the treatment with Imipenem-cilastatin infusion alone. CONCLUSION: mNGS and WGS showed excellent performance in identifying Nocardia genus to the species level and improving detection rate of Nocardia gipuzkoensis ignored by traditional culture. Different from previously reported cases, Nocardia gipuzkoensis infection case showed resistance to TMP-SMZ, which is an unprecedented finding and a crucial addition to our understanding of the antibacterial spectrum of Nocardia gipuzkoensis. The successful treatment with Imipenem-cilastatin infusion alone in this case is a testament to the importance of precise identification and tailored antibiotic therapy.

A training program for improving the capacity of infection high-throughput sequencing and diagnosis in China.

BACKGROUND: Infectious diseases are a serious threat to human especially since the COVID-19 outbreak has proved the importance and urgency of their diagnosis and treatment again. Metagenomic next-generation sequencing (mNGS) has been widely used and recognized in clinical and carried out localized testing in hospitals. Increasing the training of mNGS detection technicians can enhance their professional quality and more effectively realize the application value of the hospital platform. METHODS: Based on the initial theoretical understanding and practice of the mNGS platform for localization construction, we have designed a training program to enhance the ability of technicians to detect pathogens by utilizing mNGS, and hence to conduct training practices nationwide. RESULTS: Until August 30, 2022, the page views of online classes have reached 51,500 times and 6 of offline small-scale training courses have been conducted. A total of 67 trainees from 67 hospitals have participated in the training with a qualified rate of 100%. After the training course, the localization platform of 1 participating hospital has been put into use, 2 have added the mNGS localization platform for admission, among which 3 have expressed strong intention of localization. CONCLUSIONS: This study focuses on the training procedures and practical experience of the project which is the first systematic standardized program of mNGS in the world. It solves the training difficulties in the current industry, and effectively promotes the localization construction and application of mNGS in hospitals. It has great development potential in the future and is worth further promotion.

Risk factors, outcomes, and epidemiological and etiological study of hospitalized COVID-19 patients with bacterial co-infection and secondary infections.

BACKGROUND: As a common complication of viral respiratory tract infection, bacterial infection was associated with higher mortality and morbidity. Determining the prevalence, culprit pathogens, outcomes, and risk factors of co-infection and secondary infection occurring in hospitalized patients with coronavirus disease 2019 (COVID-19) will be beneficial for better antibiotic management. METHODS: In this retrospective cohort research, we assessed clinical characteristics, laboratory parameters, microbiologic results, and outcomes of laboratory-confirmed COVID-19 patients with bacterial co-infection and secondary infection in West China Hospital from 2022 December 2nd to 2023 March 15th. RESULTS: The incidence of bacterial co-infection and secondary infection, as defined by positive culture results of clinical specimens, was 16.3% (178/1091) and 10.1% (110/1091) respectively among 1091 patients. Acinetobacter, Klebsiella, and Pseudomonas were the most commonly identified bacteria in respiratory tract samples of COVID-19 patients. In-hospital mortality of COVID-19 patients with co-infection (17.4% vs 9.5%, p = 0.003) and secondary infection (28.2% vs 9.5%, p < 0.001) greatly exceeded that of COVID-19 patients without bacterial infection. Cardiovascular disease (1.847 (1.202-2.837), p = 0.005), severe COVID-19 (1.694 (1.033-2.778), p = 0.037), and critical COVID-19 (2.220 (1.196-4.121), p = 0.012) were proved to be risk factors for bacterial co-infection, while only critical COVID-19 (1.847 (1.202-2.837), p = 0.005) was closely related to secondary infection. CONCLUSIONS: Bacterial co-infection and secondary infection could aggravate the disease severity and worsen clinical outcomes of COVID-19 patients. Notably, only critical COVID-19 subtype was proved to be an independent risk factor for both co-infection and secondary infection. Therefore, standard empirical antibiotics was recommended for critically ill COVID-19 rather than all the inpatients according to our research.

Deep learning for malignancy risk estimation of incidental sub-centimeter pulmonary nodules on CT images.

OBJECTIVES: To establish deep learning models for malignancy risk estimation of sub-centimeter pulmonary nodules incidentally detected by chest CT and managed in clinical settings. MATERIALS AND METHODS: Four deep learning models were trained using CT images of sub-centimeter pulmonary nodules from West China Hospital, internally tested, and externally validated on three cohorts. The four models respectively learned 3D deep features from the baseline whole lung region, baseline image patch where the nodule located, baseline nodule box, and baseline plus follow-up nodule boxes. All regions of interest were automatically segmented except that the nodule boxes were additionally manually checked. The performance of models was compared with each other and that of three respiratory clinicians. RESULTS: There were 1822 nodules (981 malignant) in the training set, 806 (416 malignant) in the testing set, and 357 (253 malignant) totally in the external sets. The area under the curve (AUC) in the testing set was 0.754, 0.855, 0.928, and 0.942, respectively, for models derived from baseline whole lung, image patch, nodule box, and the baseline plus follow-up nodule boxes. When baseline models externally validated (follow-up images not available), the nodule-box model outperformed the other two with AUC being 0.808, 0.848, and 0.939 respectively in the three external datasets. The resident, junior, and senior clinicians achieved an accuracy of 67.0%, 82.5%, and 90.0%, respectively, in the testing set. The follow-up model performed comparably to the senior clinician. CONCLUSION: The deep learning algorithms solely mining nodule information can efficiently predict malignancy of incidental sub-centimeter pulmonary nodules. CLINICAL RELEVANCE STATEMENT: The established models may be valuable for supporting clinicians in routine clinical practice, potentially reducing the number of unnecessary examinations and also delays in diagnosis. KEY POINTS: • According to different regions of interest, four deep learning models were developed and compared to evaluate the malignancy of sub-centimeter pulmonary nodules by CT images. • The models derived from baseline nodule box or baseline plus follow-up nodule boxes demonstrated sufficient diagnostic accuracy (86.4% and 90.4% in the testing set), outperforming the respiratory resident (67.0%) and junior clinician (82.5%). • The proposed deep learning methods may aid clinicians in optimizing follow-up recommendations for sub-centimeter pulmonary nodules and may lead to fewer unnecessary diagnostic interventions.

Metatranscriptomic analysis revealed Prevotella as a potential biomarker of oropharyngeal microbiomes in SARS-CoV-2 infection.

Background and objectives: Disease severity and prognosis of coronavirus disease 2019 (COVID-19) disease with other viral infections can be affected by the oropharyngeal microbiome. However, limited research had been carried out to uncover how these diseases are differentially affected by the oropharyngeal microbiome of the patient. Here, we aimed to explore the characteristics of the oropharyngeal microbiota of COVID-19 patients and compare them with those of patients with similar symptoms. Methods: COVID-19 was diagnosed in patients through the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by quantitative reverse transcription polymerase chain reaction (RT-qPCR). Characterization of the oropharyngeal microbiome was performed by metatranscriptomic sequencing analyses of oropharyngeal swab specimens from 144 COVID-19 patients, 100 patients infected with other viruses, and 40 healthy volunteers. Results: The oropharyngeal microbiome diversity in patients with SARS-CoV-2 infection was different from that of patients with other infections. Prevotella and Aspergillus could play a role in the differentiation between patients with SARS-CoV-2 infection and patients with other infections. Prevotella could also influence the prognosis of COVID-19 through a mechanism that potentially involved the sphingolipid metabolism regulation pathway. Conclusion: The oropharyngeal microbiome characterization was different between SARS-CoV-2 infection and infections caused by other viruses. Prevotella could act as a biomarker for COVID-19 diagnosis and of host immune response evaluation in SARS-CoV-2 infection. In addition, the cross-talk among Prevotella, SARS-CoV-2, and sphingolipid metabolism pathways could provide a basis for the precise diagnosis, prevention, control, and treatment of COVID-19.

Performance of radiomics models derived from different CT reconstruction parameters for lung cancer risk prediction.

BACKGROUND: This study analysed the performance of radiomics features extracted from computed tomography (CT) images with different reconstruction parameters in differentiating malignant and benign pulmonary nodules. METHODS: We evaluated routine chest CT images acquired from 148 participants with pulmonary nodules, which were pathologically diagnosed during surgery in West China Hospital, including a 5 mm unenhanced lung window, a 5 mm unenhanced mediastinal window, a 5 mm contrast-enhanced mediastinal window and a 1 mm unenhanced lung window. The pulmonary nodules were segmented, and 1409 radiomics features were extracted for each window. Then, we created 15 cohorts consisting of single windows or multiple windows. Univariate correlation analysis and principal component analysis were performed to select the features, and logistic regression analysis was performed to establish models for each cohort. The area under the curve (AUC) was applied to compare model performance. RESULTS: There were 75 benign and 73 malignant pulmonary nodules, with mean diameters of 18.63 and 19.86 mm, respectively. For the single-window setting, the AUCs of the radiomics model from the 5 mm unenhanced lung window, 5 mm unenhanced mediastinal window, 5 mm contrast-enhanced mediastinal window and 1 mm unenhanced lung window were 0.771, 0.808, 0.750, and 0.771 in the training set and 0.711, 0.709, 0.684, and 0.674 in the test set, respectively. Regarding the multiple-window setting, the radiomics model based on all four windows showed an AUC of 0.825 in the training set and 0.743 in the test set. Statistically, the 15 models demonstrated comparable performances (P > 0.05). CONCLUSION: A single chest CT window was acceptable in predicting the malignancy of pulmonary nodules, and additional windows did not statistically improve the performance of the radiomics models. In addition, slice thickness and contrast enhancement did not affect the diagnostic performance.

The diagnostic and prognostic value of radiomics and deep learning technologies for patients with solid pulmonary nodules in chest CT images.

BACKGROUND: Solid pulmonary nodules are different from subsolid nodules and the diagnosis is much more challenging. We intended to evaluate the diagnostic and prognostic value of radiomics and deep learning technologies for solid pulmonary nodules. METHODS: Retrospectively enroll patients with pathologically-confirmed solid pulmonary nodules and collect clinical data. Obtain pre-treatment high-resolution thoracic CT and manually delineate the nodule in 3D. Then, all patients were randomly divided into training and testing sets at a ratio of 7:3, and convolutional neural networks (CNN) models and random forest (RF) models were established. Survival analyses were performed for patients with solid adenocarcinomas. RESULTS: Totally 720 solid pulmonary nodules were enrolled, 348 benign and 372 malignant. The CNN model with clinical features achieved the highest AUC [0.819, 95% confidence interval (CI): 0.760-0.877] with a sensitivity of 0.778, specificity of 0.788 and accuracy of 0.783. No significant differences were observed between the CNN and radiomics models. There were 295 solid adenocarcinomas in survival analysis. Different disease-free survival was observed between the low-risk and high-risk groups divided according to the radiomics Rad-score. However, the groups based on deep learning signatures showed similar survival. Cox regression analysis indicated that the radiomics Rad-score (hazard ratio: 5.08, 95% CI: 2.61-9.90) was an independent predictor of recurrence. CONCLUSIONS: The radiomics and deep learning models can well predict the malignancy of solid pulmonary nodules. Radiomics signatures also demonstrate prognostic value in solid adenocarcinomas.

Factors associated with concurrent malignancy risk among patients with incidental solitary pulmonary nodule: A systematic review taskforce for developing rapid recommendations.

OBJECTIVE: To assess the association between prespecified factors and the malignancy risk of solitary pulmonary nodules (SPNs) to support the development of rapid recommendations for daily use in the Chinese setting. METHODS: The expert panel for the rapid recommendations voted for 12 candidate factors based on published guidelines, selected publications, and clinical experiences. We then searched Medline, Embase, and Web of Science up to October 17, 2021, for studies investigating the association between these factors and the diagnosis of malignant SPNs in patients with CT-identified SPNs through multivariable regression analysis. The risk of bias was assessed using the Agency for Healthcare Research and Quality (AHRQ) Checklist. We pooled adjusted odds ratios (aOR) between candidate factors and the diagnosis of the malignant SPNs. RESULTS: A total of 32 cross-sectional studies were included. Nine factors were statistically associated with malignant SPNs: age (aOR 1.06, 95% confidence interval [CI]: 1.05-1.07), smoking history (2.83, 1.84-4.36), history of extrathoracic malignancy (5.66, 2.80-11.46), history of malignancy (4.64, 3.37-6.39), family history of malignancy (3.11, 1.66-5.83), nodule diameter (1.23, 1.17-1.31), spiculation (3.41, 2.64-4.41), lobulation (3.85, 2.47-6.01), and mixed ground-glass opacity (mGGO) density of the nodule (5.56, 2.47-12.52). No statistical association was found between family history of lung cancer, emphysema, nodule border, and malignant SPNs. CONCLUSION: Nine prespecified factors were associated with the concurrent malignancy risk among patients with SPNs. Risk stratification for SPNs is warranted in clinical practice.

Deep Learning Empowers Lung Cancer Screening Based on Mobile Low-Dose Computed Tomography in Resource-Constrained Sites.

BACKGROUND: Existing challenges of lung cancer screening included non-accessibility of computed tomography (CT) scanners and inter-reader variability, especially in resource-limited areas. The combination of mobile CT and deep learning technique has inspired innovations in the routine clinical practice. METHODS: This study recruited participants prospectively in two rural sites of western China. A deep learning system was developed to assist clinicians to identify the nodules and evaluate the malignancy with state-of-the-art performance assessed by recall, free-response receiver operating characteristic curve (FROC), accuracy (ACC), area under the receiver operating characteristic curve (AUC). RESULTS: This study enrolled 12,360 participants scanned by mobile CT vehicle, and detected 9511 (76.95%) patients with pulmonary nodules. Majority of participants were female (8169, 66.09%), and never-smokers (9784, 79.16%). After 1-year follow-up, 86 patients were diagnosed with lung cancer, with 80 (93.03%) of adenocarcinoma, and 73 (84.88%) at stage I. This deep learning system was developed to detect nodules (recall of 0.9507; FROC of 0.6470) and stratify the risk (ACC of 0.8696; macro-AUC of 0.8516) automatically. CONCLUSIONS: A novel model for lung cancer screening, the integration mobile CT with deep learning, was proposed. It enabled specialists to increase the accuracy and consistency of workflow and has potential to assist clinicians in detecting early-stage lung cancer effectively.

Malignancy risk stratification for solitary pulmonary nodule: A clinical practice guideline.

CLINICAL QUESTION: The detection rate of the solitary pulmonary nodule (SPN) is increasing with the popularization of CT scanning. Malignancy risk stratification for SPN is a major clinical difficulty. CURRENT PRACTICE: There have been several guidelines for SPN assessment. Inconsistency of these guidelines makes the clinical application difficult and confusing. RECOMMENDATIONS: In this Rapid Recommendation, solid and subsolid SPNs are recommended to be evaluated respectively. Six factors, namely the combination of age with sex, smoking history, history of malignancy, family history of malignancy, and nodule size, are recommended for malignancy risk stratification for both kinds of SPNs; the border of nodules (spiculation and lobulation) is recommended for evaluating solid SPNs and the density of nodules (pure or mixed ground-glass nodule) is recommended for subsolid nodules. Among them, smoking history and radiologic features (nodule diameter, border, and density) are of relatively higher importance. A scoring system was proposed to assist malignancy risk stratification of SPNs, with a total score ranging from six points to 15 points (if solid) or 17 points (if subsolid). For each SPN, regardless of solid or subsolid in nature, a total score of ≤ 7 points suggested a low risk of being malignant, while 7 to 9 points suggested medium risk, and ≥ 9 points suggested high risk. HOW THIS GUIDELINE WAS CREATED: This rapid recommendation was developed using the MAGIC (Making GRADE the Irresistible Choice) methodological framework. First, a clinical subcommittee identified the topic of recommendation and requested evidence. Then, an independent evidence synthesis subcommittee performed a comprehensive literature review and evaluated the evidence. Finally, based on findings from the systematic review and use of real-world data, the clinical subcommittee formulated recommendations, including the scoring system, through a consensus procedure. THE EVIDENCE: A total of 13857 patients with SPNs were included in the meta-analysis and the association between 12 candidate factors and the risk of SPNs being malignant was studied. Eventually, seven factors were recommended for SPNs evaluation, and a scoring system was proposed. UNDERSTANDING THE RECOMMENDATION: The parameters included are objective. Therefore, this recommendation is feasible in clinical practice. However, there are several uncertainties, such as a lack of further verification. It might be misclassified by the scoring system. Clinicians could choose the most suitable scheme according to the recommendation, along with their own experience in specific situations.

Diagnostic study on clinical feasibility of an AI-based diagnostic system as a second reader on mobile CT images: a preliminary result.

Background: Artificial intelligence (AI) has breathed new life into the lung nodules detection and diagnosis. However, whether the output information from AI will translate into benefits for clinical workflow or patient outcomes in a real-world setting remains unknown. This study was to demonstrate the feasibility of an AI-based diagnostic system deployed as a second reader in imaging interpretation for patients screened for pulmonary abnormalities in a clinical setting. Methods: The study included patients from a lung cancer screening program conducted in Sichuan Province, China using a mobile computed tomography (CT) scanner which traveled to medium-size cities between July 10th, 2020 and September 10th, 2020. Cases that were suspected to have malignant nodules by junior radiologists, senior radiologists or AI were labeled a high risk (HR) tag as HR-junior, HR-senior and HR-AI, respectively, and included into final analysis. The diagnosis efficacy of the AI was evaluated by calculating negative predictive value and positive predictive value when referring to the senior readers' final results as the gold standard. Besides, characteristics of the lesions were compared among cases with different HR labels. Results: In total, 251/3,872 patients (6.48%, male/female: 91/160, median age, 66 years) with HR lung nodules were included. The AI algorithm achieved a negative predictive value of 88.2% [95% confidence interval (CI): 62.2-98.0%] and a positive predictive value of 55.6% (95% CI: 49.0-62.0%). The diagnostic duration was significantly reduced when AI was used as a second reader (223±145.6 vs. 270±143.17 s, P<0.001). The information yielded by AI affected the radiologist's decision-making in 35/145 cases. Lesions of HR cases had a higher volume [309.9 (214.9-732.5) vs. 141.3 (79.3-380.8) mm3, P<0.001], lower average CT number [-511.0 (-576.5 to -100.5) vs. -191.5 (-487.3 to 22.5), P=0.010], and pure ground glass opacity rather than solid. Conclusions: The AI algorithm had high negative predictive value but low positive predictive value in diagnosing HR lung lesions in a clinical setting. Deploying AI as a second reader could help avoid missed diagnoses, reduce diagnostic duration, and strengthen diagnostic confidence for radiologists.

Mining whole-lung information by artificial intelligence for predicting EGFR genotype and targeted therapy response in lung cancer: a multicohort study.

BACKGROUND: Epidermal growth factor receptor (EGFR) genotype is crucial for treatment decision making in lung cancer, but it can be affected by tumour heterogeneity and invasive biopsy during gene sequencing. Importantly, not all patients with an EGFR mutation have good prognosis with EGFR-tyrosine kinase inhibitors (TKIs), indicating the necessity of stratifying for EGFR-mutant genotype. In this study, we proposed a fully automated artificial intelligence system (FAIS) that mines whole-lung information from CT images to predict EGFR genotype and prognosis with EGFR-TKI treatment. METHODS: We included 18 232 patients with lung cancer with CT imaging and EGFR gene sequencing from nine cohorts in China and the USA, including a prospective cohort in an Asian population (n=891) and The Cancer Imaging Archive cohort in a White population. These cohorts were divided into thick CT group and thin CT group. The FAIS was built for predicting EGFR genotype and progression-free survival of patients receiving EGFR-TKIs, and it was evaluated by area under the curve (AUC) and Kaplan-Meier analysis. We further built two tumour-based deep learning models as comparison with the FAIS, and we explored the value of combining FAIS and clinical factors (the FAIS-C model). Additionally, we included 891 patients with 56-panel next-generation sequencing and 87 patients with RNA sequencing data to explore the biological mechanisms of FAIS. FINDINGS: FAIS achieved AUCs ranging from 0·748 to 0·813 in the six retrospective and prospective testing cohorts, outperforming the commonly used tumour-based deep learning model. Genotype predicted by the FAIS-C model was significantly associated with prognosis to EGFR-TKIs treatment (log-rank p<0·05), an important complement to gene sequencing. Moreover, we found 29 prognostic deep learning features in FAIS that were able to identify patients with an EGFR mutation at high risk of TKI resistance. These features showed strong associations with multiple genotypes (p<0·05, t test or Wilcoxon test) and gene pathways linked to drug resistance and cancer progression mechanisms. INTERPRETATION: FAIS provides a non-invasive method to detect EGFR genotype and identify patients with an EGFR mutation at high risk of TKI resistance. The superior performance of FAIS over tumour-based deep learning methods suggests that genotype and prognostic information could be obtained from the whole lung instead of only tumour tissues. FUNDING: National Natural Science Foundation of China.

Semi-Supervised Deep Transfer Learning for Benign-Malignant Diagnosis of Pulmonary Nodules in Chest CT Images.

Lung cancer is the leading cause of cancer deaths worldwide. Accurately diagnosing the malignancy of suspected lung nodules is of paramount clinical importance. However, to date, the pathologically-proven lung nodule dataset is largely limited and is highly imbalanced in benign and malignant distributions. In this study, we proposed a Semi-supervised Deep Transfer Learning (SDTL) framework for benign-malignant pulmonary nodule diagnosis. First, we utilize a transfer learning strategy by adopting a pre-trained classification network that is used to differentiate pulmonary nodules from nodule-like tissues. Second, since the size of samples with pathological-proven is small, an iterated feature-matching-based semi-supervised method is proposed to take advantage of a large available dataset with no pathological results. Specifically, a similarity metric function is adopted in the network semantic representation space for gradually including a small subset of samples with no pathological results to iteratively optimize the classification network. In this study, a total of 3,038 pulmonary nodules (from 2,853 subjects) with pathologically-proven benign or malignant labels and 14,735 unlabeled nodules (from 4,391 subjects) were retrospectively collected. Experimental results demonstrate that our proposed SDTL framework achieves superior diagnosis performance, with accuracy = 88.3%, AUC = 91.0% in the main dataset, and accuracy = 74.5%, AUC = 79.5% in the independent testing dataset. Furthermore, ablation study shows that the use of transfer learning provides 2% accuracy improvement, and the use of semi-supervised learning further contributes 2.9% accuracy improvement. Results implicate that our proposed classification network could provide an effective diagnostic tool for suspected lung nodules, and might have a promising application in clinical practice.

Metatranscriptomic Analysis Reveals Disordered Alterations in Oropharyngeal Microbiome during the Infection and Clearance Processes of SARS-CoV-2: A Warning for Secondary Infections.

This study was conducted to investigate oropharyngeal microbiota alterations during the progression of coronavirus disease 2019 (COVID-19) by analyzing these alterations during the infection and clearance processes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The diagnosis of COVID-19 was confirmed by using positive SARS-CoV-2 quantitative reverse transcription polymerase chain reaction (RT-qPCR). The alterations in abundance, diversity, and potential function of the oropharyngeal microbiome were identified using metatranscriptomic sequencing analyses of oropharyngeal swab specimens from 47 patients with COVID-19 (within a week after diagnosis and within two months after recovery from COVID-19) and 40 healthy individuals. As a result, in the infection process of SARS-CoV-2, compared to the healthy individuals, the relative abundances of Prevotella, Aspergillus, and Epstein-Barr virus were elevated; the alpha diversity was decreased; the beta diversity was disordered; the relative abundance of Gram-negative bacteria was increased; and the relative abundance of Gram-positive bacteria was decreased. After the clearance of SARS-CoV-2, compared to the healthy individuals and patients with COVID-19, the above disordered alterations persisted in the patients who had recovered from COVID-19 and did not return to the normal level observed in the healthy individuals. Additionally, the expressions of several antibiotic resistance genes (especially multi-drug resistance, glycopeptide, and tetracycline) in the patients with COVID-19 were higher than those in the healthy individuals. After SARS-CoV-2 was cleared, the expressions of these genes in the patients who had recovered from COVID-19 were lower than those in the patients with COVID-19, and they were different from those in the healthy individuals. In conclusion, our findings provide evidence that potential secondary infections with oropharyngeal bacteria, fungi, and viruses in patients who have recovered from COVID-19 should not be ignored; this evidence also highlights the clinical significance of the oropharyngeal microbiome in the early prevention of potential secondary infections of COVID-19 and suggests that it is imperative to choose appropriate antibiotics for subsequent bacterial secondary infection in patients with COVID-19.

Developing of risk models for small solid and subsolid pulmonary nodules based on clinical and quantitative radiomics features.

BACKGROUND: Accurate evaluation of pulmonary nodule malignancy is important for lung cancer management. This current study aimed to develop risk models for small solid and subsolid pulmonary nodules based on clinical and quantitative radiomics features. METHODS: This study enrolled 5-20 mm pulmonary nodules detected on thoracic high-resolution computed tomography (HRCT), which were all confirmed pathologically. There were 548 solid nodules (242 malignant vs. 306 benign) and 623 subsolid nodules (SSNs 519 malignant vs. 104 benign). Relevant clinical characteristics were recorded. The CT image prior to the initial treatment was chosen for manual segmentation of the targeted nodule using the ITK-SNAP software. Subsequently, the marked image was processed to quantitatively extract 1218 radiomics features using PyRadiomics. We performed five-fold cross-validation to select potential predictors from clinical and radiomics features using the LASSO method and to evaluate the performance of the established models. In total, four types of models were tried: random forest, XGBOOST, SVM, and logistic models. The established models were compared with the Mayo model. RESULTS: Lung cancer risk models were developed among four nodule groups: all nodules (410 benign vs. 761 malignant; 1:1.86), nodules ≤10 mm (185 benign vs. 224 malignant; 1:1.21), solid nodules (306 benign vs. 242 malignant; 1.26:1), and SSNs (104 benign vs. 104 malignant; 1:1 matched). Significant clinical and radiomics predictors were selected for each group. The accuracy, area under the ROC curve, sensitivity, and specificity of the best model on validation dataset was 0.86, 0.91, 0.93, 0.73 for all nodules (XGBOOST), 0.82, 0.90, 0.86, 0.76 for nodules ≤10 mm (XGBOOST), 0.80, 0.89, 0.78, 0.82 for solid nodules (XGBOOST) and 0.70, 0.73, 0.73, 0.67 for SSNs (Random Forest). Except for the SSN models, the established clinical-radiomics models were superior to the Mayo model. CONCLUSIONS: Predictive models based on both clinical and radiomics features can be used to assess the malignancy of small solid and subsolid pulmonary nodules, even for nodules that are 10 mm or smaller.

Metastatic patterns and prognosis of young lung cancer patients: a population-based study by age.

BACKGROUND: We aimed to examine the different metastatic patterns and corresponding survival outcomes between all ages of young (aged <60 years) and elderly lung cancer patients. METHODS: Lung cancer patients from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015 were divided into a young and elderly group. The young group was subdivided into four consecutive subgroups. Baseline characteristics were analyzed by the Chi-square test. Survival differences were evaluated by Kaplan-Meier curves and Cox proportional hazards models. RESULTS: Of the total 200,362 lung cancer patients, 155,348 were elderly patients and 45,014 were young patients, including 3,461 aged <45 years, 5,697 aged 45-49 years, 13,645 aged 50-54 years, and 22,211 aged 55-59 years. Compared with elderly lung cancer patients, extrathoracic metastases were significantly more frequent in each younger group, irrespective of the site and number of extrathoracic metastatic organs. Regardless of metastasis patterns, young ages were independent prognostic factors of lung cancer-specific survival (LCSS) [<45 years: hazard ratio (HR): 0.70; 45-49 years: HR: 0.87; 50-54 years: HR: 0.90; 55-59 years: HR: 0.93, all P values were <0.001]. In each age subgroup, patients with multi-organ extrathoracic metastasis had the worst LCSS. CONCLUSIONS: Young lung cancer patients across all ages were at increased risk of extrathoracic metastasis, especially multi-organ patterns, but had a reduced risk of lung cancer-related death compared to elderly patients. Regular and meticulous monitoring of potential metastasized organs is required in young lung cancer patients throughout the follow-up period.

Increased microRNA-30a levels in bronchoalveolar lavage fluid as a diagnostic biomarker for lung cancer.

BACKGROUND: MicroRNA-30a (miRNA-30a) levels have been shown to increase in the plasma of lung cancer patients. Herein, we evaluated the miRNA-30a levels in the bronchoalveolar lavage fluid (BALF) of lung cancer patients as a potential biomarker for lung cancer diagnosis. METHODS: BALF miRNA-30a expression of 174 subjects was quantified using quantitative real-time reverse transcription-polymerase chain reaction and compared between lung cancer patients and control patients with benign lung diseases. Moreover, its diagnostic value was evaluated by performing receiver operating characteristic (ROC) curve analysis. RESULTS: The relative BALF miRNA-30a expression was significantly higher in the lung cancer patients than in the controls (0.74 ±  0.55 versus 0.07 ±  0.48, respectively, p < 0.001) as well as in lung cancer patients with stage I-IIA disease than in those with stage IIB-IV disease (0.98 ±  0.64 versus 0.66 ±  0.54, respectively, p < 0.05). Additionally, miRNA-30a distinguished benign lung diseases from lung cancers, with an area under the ROC curve (AUC) of 0.822. ROC analysis also revealed an AUC of 0.875 for the Youden index-based optimal cut-off points for stage I-IIA adenocarcinoma. Thus, increased miRNA-30a levels in BALF may be a useful biomarker for non-small-cell lung cancer diagnosis.

Immune Checkpoint Blockade Therapy May Be a Feasible Option for Primary Pulmonary Lymphoepithelioma-like Carcinoma.

Primary pulmonary lymphoepithelioma-like carcinoma (PPLELC) is a rare subtype of non-small cell lung cancer (NSCLC) for which there is currently no recognized treatment. Recently, favorable immune checkpoint blockade responses have been observed in PPLELC. This study aimed to review the effects of this regimen in patients with advanced PPLELC. PPLELC patients treated with immune checkpoint inhibitors at West China Hospital between January 2008 and December 2019 were retrospectively identified. Demographic parameters and antitumor treatment details were retrieved and reviewed. Among 128 patients diagnosed with PPLELC, 5 who received immune checkpoint inhibitors at advanced stages were included in the analysis. All of these patients were female nonsmokers with a median age of 55.6 (range 53-58) years at diagnosis. Their median PD-L1 expression was 40% (range, 30-80%). Although the patients underwent surgeries, chemotherapy and radiotherapy, all the treatments failed. Immune checkpoint inhibitors were administered palliatively, and three patients responded favorably, with the best overall response being partial remission (PR). Thus, immune checkpoint inhibitors may be a promising treatment for advanced PPLELC, and large clinical trials are warranted to obtain more evidence regarding this regimen.

Molecular characteristics of primary pulmonary lymphoepithelioma-like carcinoma based on integrated genomic analyses.

Primary pulmonary lymphoepithelioma-like carcinoma (pLELC) is a rare non-small cell lung cancer (NSCLC) subtype. Clinical features have been described in our previous report, but molecular characteristics remain unclear. Herein, pLELC genomic features were explored. Among 41,574 lung cancers, 128 pLELCs and 162 non-pLELC NSCLCs were enrolled. Programmed cell death ligand 1 (PD-L1) and protein 53 (p53) expression was detected in 47 surgically resected pLELC samples by immunohistochemical assays. Multiomics genomic analyses, including whole-genome sequencing (WGS), RNA whole-transcriptome sequencing (RNA-seq), and Epstein-Barr virus (EBV) integration analyses, were performed on eight frozen pLELC tissues and compared with 50 lung adenocarcinomas (LUADs) and 50 lung squamous cell carcinomas (LUSCs) from The Cancer Genome Atlas (TCGA) and another 26 EBV-positive nasopharynx cancers (EBV+-NPCs). Progression-free survival (PFS) and overall survival (OS) of pLELC patients were better than those of non-pLELC patients. High PD-L1 or p53 expression was associated with extended disease-free survival (DFS). pLELC had 14 frequently mutated genes (FMGs). Somatically mutated genes and enrichment of genetic lesions were found, which differed from observations in LUAD, LUSC, and EBV+-nasopharyngeal carcinoma (NPC). Three tumor-associated genes, zinc finger and BTB domain-containing 16 (ZBTB16), peroxisome proliferator activated receptor gamma (PPARG), and transforming growth factor beta receptor 2 (TGFBR2), were downregulated with copy number variation (CNV) loss. EBV was prone to integrating into intergenic and intronic regions with two upregulated miR-BamH1-A rightward transcripts (BARTs), BART5-3P and BART20-3P. Our findings reveal that pLELC has a distinct genomic signature. Three tumor-associated genes with CNV loss and two miR-BARTs might be involved in pLELC tumorigenesis.