Automatic detection of pulmonary embolism on computed tomography pulmonary angiogram scan using a three-dimensional convolutional neural network.

OBJECTIVE: To propose a convolutional neural network (EmbNet) for automatic pulmonary embolism detection on computed tomography pulmonary angiogram (CTPA) scans and to assess its diagnostic performance. METHODS: 305 consecutive CTPA scans between January 2019 and December 2021 were enrolled in this study (142 for training, 163 for internal validation), and 250 CTPA scans from a public dataset were used for external validation. The framework comprised a preprocessing step to segment the pulmonary vessels and the EmbNet to detect emboli. Emboli were divided into three location-based subgroups for detailed evaluation: central arteries, lobar branches, and peripheral regions. Ground truth was established by three radiologists. RESULTS: The EmbNet's per-scan level sensitivity, specificity, positive predictive value (PPV), and negative predictive value were 90.9%, 75.4%, 48.4%, and 97.0% (internal validation) and 88.0%, 70.5%, 42.7%, and 95.9% (external validation). At the per-embolus level, the overall sensitivity and PPV of the EmbNet were 86.0% and 61.3% (internal validation), and 83.5% and 57.5% (external validation). The sensitivity and PPV of central emboli were 89.7% and 52.0% (internal validation), and 94.4% and 43.0% (external validation); of lobar emboli were 95.2% and 76.9% (internal validation), and 93.5% and 72.5% (external validation); and of peripheral emboli were 82.6% and 61.7% (internal validation), and 80.2% and 59.4% (external validation). The average false positive rate was 0.45 false emboli per scan (internal validation) and 0.69 false emboli per scan (external validation). CONCLUSION: The EmbNet provides high sensitivity across embolus locations, suggesting its potential utility for initial screening in clinical practice.

Identifying autism spectrum disorder from multi-modal data with privacy-preserving.

The application of deep learning models to precision medical diagnosis often requires the aggregation of large amounts of medical data to effectively train high-quality models. However, data privacy protection mechanisms make it difficult to perform medical data collection from different medical institutions. In autism spectrum disorder (ASD) diagnosis, automatic diagnosis using multimodal information from heterogeneous data has not yet achieved satisfactory performance. To address the privacy preservation issue as well as to improve ASD diagnosis, we propose a deep learning framework using multimodal feature fusion and hypergraph neural networks for disease prediction in federated learning (FedHNN). By introducing the federated learning strategy, each local model is trained and computed independently in a distributed manner without data sharing, allowing rapid scaling of medical datasets to achieve robust and scalable deep learning predictive models. To further improve the performance with privacy preservation, we improve the hypergraph model for multimodal fusion to make it suitable for autism spectrum disorder (ASD) diagnosis tasks by capturing the complementarity and correlation between modalities through a hypergraph fusion strategy. The results demonstrate that our proposed federated learning-based prediction model is superior to all local models and outperforms other deep learning models. Overall, our proposed FedHNN has good results in the work of using multi-site data to improve the performance of ASD identification.

Combination of Contrast-enhanced FlAIR and Contrast-enhanced T1WI: A Quick and Efficient Method in Detecting Brain Metastases of Lung Cancers.

BACKGROUND: Some patients with suspected brain metastases (BM) could not tolerate longer scanning examinations according to the standardized MRI protocol. OBJECTIVE: The purpose of this study was to evaluate the clinical value of contrast-enhanced fast fluid-attenuated inversion recovery (CE FLAIR) imaging in combination with contrast-enhanced T1 weighted imaging (CE T1WI) in detecting BM of lung cancer and explore a quick and effective MRI protocol. MATERIAL AND METHODS: In 201 patients with lung cancers and suspected BM, T1WI and FLAIR were performed before and after administration of gadopentetate dimeglumine. Two radiologists reviewed pre- and post-contrast images to determine the presence of abnormal contrast enhancement or signal intensity and decided whether it was metastatic or not on CE T1WI (Group 1) and CE FLAIR (Group 2). The number, locations and features of abnormal findings in two groups were recorded. Receiver Operating Characteristic (ROC) analyses were conducted in three groups: Group 1, 2 and 3(combination of CE FLAIR and CE T1WI). RESULTS: A total of 714 abnormal findings were revealed, of which 672 were considered as BM and 42 nonmetastatic. Superficial and small metastases(≤10mm) in parenchyma and ependyma, leptomeningeal and non-expansive skull metastases were typically better seen on CE FLAIR. The areas under ROC in the three groups were 0.720,0.887 and 0.973, respectively. Group 3 was significantly better in diagnostic efficiency of BMs than Group 1 (p<0.0001) or Group 2 (p=0.0006). CONCLUSION: The combination of CE T1WI and CE FLAIR promotes diagnostic performance and results in better observation and characterization of BM in patients with lung cancers. It provides a quick and efficient way of detecting BM.

Beyond radiologist-level liver lesion detection on multi-phase contrast-enhanced CT images by deep learning.

Accurate detection of liver lesions from multi-phase contrast-enhanced CT (CECT) scans is a fundamental step for precise liver diagnosis and treatment. However, the analysis of multi-phase contexts is heavily challenged by the misalignment caused by respiration coupled with the movement of organs. Here, we proposed an AI system for multi-phase liver lesion segmentation (named MULLET) for precise and fully automatic segmentation of real-patient CECT images. MULLET enables effectively embedding the important ROIs of CECT images and exploring multi-phase contexts by introducing a transformer-based attention mechanism. Evaluated on 1,229 CECT scans from 1,197 patients, MULLET demonstrated significant performance gains in terms of Dice, Recall, and F2 score, which are 5.80%, 6.57%, and 5.87% higher than state of the arts, respectively. MULLET has been successfully deployed in real-world settings. The deployed AI web server provides a powerful system to boost clinical workflows of liver lesion diagnosis and could be straightforwardly extended to general CECT analyses.

A CT-based radiomics integrated model for discriminating pulmonary cryptococcosis granuloma from lung adenocarcinoma-a diagnostic test.

Background: Chest computed tomography (CT) is a critical tool in the diagnosis of pulmonary cryptococcosis as approximately 30% of normal immunity individuals may not exhibit any significant symptoms or laboratory findings. Pulmonary cryptococcosis granuloma and lung adenocarcinoma can appear similar on noncontrast chest CT. This study evaluates the use of an integrated model that was developed based on radiomic features combined with demographic and radiological features to differentiate pulmonary cryptococcosis nodules from lung adenocarcinomas. Methods: Preoperative chest CT images for 215 patients with solid pulmonary nodules with histopathologically confirmed lung adenocarcinoma and cryptococcosis infection were collected from two clinical centers (108 cases in the training set and 107 cases in the test set divided by the different hospitals). Radiomics models were constructed based on nodular lesion volume (LV), 5-mm extended lesion volume (ELV), and perilesion volume (PLV). A demoradiological model was constructed using logistic regression based on demographic information (age, sex) and 12 radiological features (location, number, shape and specific imaging signs). Both models were used to build an integrated model, the performance of which was assessed using the test set. A junior and a senior radiologist evaluated the nodules. Receiver operating characteristic (ROC) curve analysis was conducted, and areas under the curve (AUCs), sensitivity (SEN), and specificity (SPE) of the models were calculated and compared. Results: Among the radiomics models, AUCs of the LV, ELV, and PLV were 0.558, 0.757, and 0.470, respectively. Age, lesion number, and lobular sign were identified as independent discriminative features providing an AUC of 0.77 in the demoradiological model (SEN 0.815, SPE 0.642). The integrated model achieved the highest AUC of 0.801 (SEN 0.759, SPE 0.755), which was significantly higher than that obtained by a junior radiologist (AUC =0.689, P=0.024) but showed no significant difference from that of the senior radiologist (AUC =0.784, P=0.388). Conclusions: An integrated model with radiomics and demoradiological features improves discrimination of cryptococcosis granulomas from solid adenocarcinomas on noncontrast CT. This model may be an effective strategy for machine complementation to discrimination by radiologists, and whole-lung automated recognition methods might dominate in the future.

Risk factors for pulmonary embolism in lung cancer patients with lower limb deep venous thrombosis: a case-control study.

Background: There is growing evidence that misdiagnosis contributes to the high mortality rate in lung cancer patients complicated with pulmonary embolism (PE). This current study analyzed predictors of PE in lung cancer patients with lower extremity deep venous thrombosis (DVT) with the aim of personalizing the treatment and management of patients with PE. Methods: This retrospective case-control study included lung cancer patients with DVT at the emergency department of Shanghai Chest Hospital from January 2018 to December 2019. Patients were classified as having DVT with or without PE. The following characteristics were examined, including age, gender, smoking, hypertension, surgical trauma, hyperlipidemia, long-term bedridden status, calf swelling, coronary heart disease, chronic pulmonary disease, DVT location, DVT type, prothrombin time (PT), international normalized ratio (INR), activated partial thromboplastin time (APTT), thrombin time (TT), fibrinogen, and D-dimer, and univariate and multivariate analyses were performed. Results: A total of 90 patients with lung cancer and DVT were analyzed, of whom 60% (54/90) had PE. Those variables independently associated to PE were hypertension [odds ratio (OR): 7.883, 95% confidence interval (CI): 2.038-30.495, P=0.003], long-term bedridden status (OR: 4.166, 95% CI: 1.236-14.044, P=0.021), and D-dimer levels (OR: 2.123, 95% CI: 1.476-3.053, P=0.000) were identified as independent risk factors for PE. The cut-off value of the receiver operating characteristic (ROC) curve for predicting PE by presented scoring system according to the risk factors was 1.5 and the area under the curve (AUC) was 0.84 (P<0.001). Conclusions: Hypertension, being bedridden for an extended period, and elevated serum D-dimer levels were independent risk factors of PE in lung cancer patients with lower extremity DVT. Novel strategies for patient management should be developed to decrease the risk of PE.

Clinicopathological features and survival of rare primary pulmonary lymphoepithelial carcinoma: A cohort from a single center.

BACKGROUND: Primary pulmonary lymphoepithelial carcinoma (PLEC) is a rare subtype of nonsmall cell lung cancer. This study aimed to investigate the clinicopathological and prognostic characteristics of resected primary PLEC. MATERIALS AND METHODS: In this retrospective study, 95 consecutive patients with primary PLEC, who received radical surgical resection treatment, were examined from October 2009 to January 2022. The clinicopathological features and their association with survival outcomes were analyzed. RESULTS: Primary PLEC predominated in relatively younger patients and nonsmokers, who lacked driver mutations and were always positive for immunohistochemical markers of the squamous cell lineage. Further, 21.1% of patients had abnormally elevated preoperative serum marker fragments of cytokeratin 19 (Cyfra21-1). The median follow-up time was 43.5 months. The 1-, 3-, and 5-year recurrence-free survival (RFS) rates were 96.5%, 81.8%, and 64.3%, respectively. The median RFS time was not reached. Cox univariate survival analysis showed that patients with positive lymph nodes had significantly worse RFS than those with negative ones (p = 0.017). The patients with open surgery experienced significantly worse RFS than those with video-assisted thoracoscopic surgery (p = 0.038). The multivariate survival analysis confirmed that only lymph node involvement (hazard ratio: 2.769; 95% confidence interval: 1.171-6.548, p = 0.020) was an independent prognostic factor. CONCLUSIONS: Primary PLEC is a rare type of lung cancer with a favorable outcome, more common in young and nonsmoking Asian populations. Driver gene mutations are rare. Regional lymph node metastasis is an independent prognostic factor for RFS after radical surgical resection.

Impact of surgery and adjuvant chemotherapy on the survival of stage I lung adenocarcinoma patients with tumor spread through air spaces.

OBJECTIVES: Tumor spread through air spaces (STAS) is a unique mechanism of lung cancer metastasis; however, its clinical value for stage I lung adenocarcinoma (ADC) remains unclear at present. We investigated the (1) prognosis of patients after sublobar resection compared with lobectomy for stage I lung adenocarcinoma with STAS; and (2) potential benefits of adjuvant chemotherapy (ACT) for patients with stage I ADC and STAS. METHODS: A total of 3328 consecutive patients with stage I ADC were retrospectively identified between 2014 and 2018 at our institution; among them, 600 were diagnosed with STAS. Kaplan-Meier analysis and Cox proportional hazard regression models were used to evaluate the impact of STAS on overall survival (OS) and recurrence-free survival (RFS). RESULTS: Among stage IA patients with STAS, there was no significant difference between those who underwent sublobar resection and lobectomy in OS (P = 0.919) and RFS (P = 0.066). Multivariate analysis confirmed this result (sublobar resection versus lobectomy, OS: HR = 0.523, 95 % CI, 0.056-18.458, P = 0.714; RFS, HR = 0.360, 95 % CI, 0.115-1.565, P = 0.897). ACT did not improve the prognosis of stage IA patients but did improve the RFS of stage IB patients with high-risk recurrence factors, including poorly differentiated tumors, lymphovascular invasion and visceral pleural invasion (P = 0.046). CONCLUSIONS: Sublobar and lobectomy resection provided a comparable prognosis for stage IA ADC patients with STAS. When STAS was confirmed postoperatively, ACT should be considered for patients with stage IB with high-risk recurrence factors but not for those with stage IA disease.

An Interpretable Deep Embedding Model for Few and Imbalanced Biomedical Data.

In healthcare, training examples are usually hard to obtain (e.g., cases of a rare disease), or the cost of labelling data is high. With a large number of features ( p) be measured in a relatively small number of samples ( N), the "big p, small N" problem is an important subject in healthcare studies, especially on the genomic data. Another major challenge of effectively analyzing medical data is the skewed class distribution caused by the imbalance between different class labels. In addition, feature importance and interpretability play a crucial role in the success of solving medical problems. Therefore, in this paper, we present an interpretable deep embedding model (IDEM) to classify new data having seen only a few training examples with highly skewed class distribution. IDEM model consists of a feature attention layer to learn the informative features, a feature embedding layer to directly deal with both numerical and categorical features, a siamese network with contrastive loss to compare the similarity between learned embeddings of two input samples. Experiments on both synthetic data and real-world medical data demonstrate that our IDEM model has better generalization power than conventional approaches with few and imbalanced training medical samples, and it is able to identify which features contribute to the classifier in distinguishing case and control.

Prognostic and predictive value of radiomic signature in stage I lung adenocarcinomas following complete lobectomy.

BACKGROUND: The overall survival (OS) of stage I operable lung cancer is relatively low, and not all patients can benefit from adjuvant chemotherapy. This study aimed to develop and validate a radiomic signature (RS) for prediction of OS and adjuvant chemotherapy candidates in stage I lung adenocarcinoma. METHODS: A total of 474 patients from 2 centers were divided into 1 training (n = 287), 1 internal validation (n = 122), and 1 external validation (n = 65) cohorts. We extracted 1218 radiomic features from preoperative CT images and constructed RS. We further investigated the prognostic value of the RS in survival analysis. Interaction between treatment and RS was assessed to evaluate its predictive value. Propensity score matching (PSM) was conducted. RESULTS: Overall, 474 eligible patients with stage I lung adenocarcinoma (214 men [45.1%]; median age, 60 years) were identified. The RS was significantly associated with OS in the training and two validation cohorts (hazard ratios [HRs] > = 3.22). In multivariable analysis, the RS remained an independent prognostic factor adjusting for clinicopathologic variables (adjusted HRs > = 2.63). The prognostic value of RS was also confirmed in PSM analysis. In stage I patients, the interaction between RS status and adjuvant chemotherapy was significant (interaction P = 0.020). Within the stratified analysis, good chemotherapy efficacy was only observed for patients with stage IB disease (interaction P < 0.001). CONCLUSIONS: Our results suggested that the radiomic signature was associated with overall survival in patients with stage I lung adenocarcinoma and might predict adjuvant chemotherapy benefit, especially in stage IB patients. The potential of radiomic signature as a noninvasive predictor needed to be confirmed in future studies.

Longitudinal prediction of lung nodule invasiveness by sequential modelling with common clinical computed tomography (CT) measurements: a prediction accuracy study.

Background: Accurate preoperative prediction of the invasiveness of lung nodules on computed tomography (CT) can avoid unnecessary invasive procedures and costs for low-risk patients. While previous studies approached this task using cross-sectional data, this study aimed to utilize the commonly available longitudinal data of lung nodules through sequential modelling based on long short-term memory (LSTM) networks. Methods: We retrospectively included 171 patients with lung nodules that were followed-up at least once and pathologically diagnosed with adenocarcinoma for model development. Pathological diagnosis was the gold standard for deciding lung nodule invasiveness. For each nodule, a handful of semantic features, including size intensity and interval since first discovery, were obtained from an arbitrary number of CT scans available to individual patients and used as input variables to pre-operatively predict nodule invasiveness. The LSTM-based classifier was optimized by extensive experiments and compared to logistic regression (LR) as baseline with five-fold cross-validation. Results: The best LSTM-based classifier, capable of receiving data from an arbitrary number of time points, achieved better preoperative prediction of lung nodule invasiveness [area under the curve (AUC), 0.982; accuracy, 0.924; sensitivity, 0.946; specificity, 0.881] than the best LR (AUC, 0.947; accuracy, 0.906; sensitivity, 0.938; specificity, 0.847) classifier. Conclusions: The longitudinal data of lung nodules, though unevenly spaced and varying in length, can be well modeled by the LSTM, allowing for the accurate prediction of nodule invasiveness. Given that the input variables of the sequential modelling consist of a few semantic features that are easily obtained and interpreted by clinicians, our approach is worthy further investigation for the optimal management of lung nodules.

Clinical, radiologic, and physiologic features of idiopathic pulmonary fibrosis (IPF) with and without emphysema.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) can combine with emphysema, a condition termed as IPF with emphysema (IPFE). We compared the clinical, radiologic, and physiologic features of IPF and IPFE. RESEARCH DESIGN AND METHODS: Newly diagnosed IPF    and IPFE    patients were recruited between January 2018 and September 2020. Symptoms, high resolution computed tomography (HRCT), pulmonary function test (PFT) data, composite physiologic index (CPI), gender-age-physiology (GAP) scores, and follow-up data were obtained. RESULTS: The IPFE group had greater proportion of male smokers, and of lung cancer cases. The IPFE group had higher VC, FVC FEV1, and lower FEV1/FVC and DLCO and lower percent fibrosis on HRCT. Both groups had similar symptoms and mortality. Mortality rate was associated with inability to perform PFT, CPI, GAP scores, percent fibrosis, VC, FVC, FEV1, and DLCO, serum SCC-Ag and CA125, and anti-fibrotic therapy (≥12 months) in IPF, while it was associated with inability to perform PFT, CPI, percent fibrosis, DLCO, serum CEA, CYFRA21-1 and CA125, and anti-fibrotic therapy (≥12 months) in IPFE. CONCLUSION: IPF and IPFE patients are different in smoking history, physiologic indices, HRCT patterns and prognostic factors, however, they have similar mortality. Anti-fibrotic therapy could improve the survival rate in both IPF and IPFE.

A Comprehensive Review of Artificial Intelligence in Prevention and Treatment of COVID-19 Pandemic.

The unprecedented outbreak of the Corona Virus Disease 2019 (COVID-19) pandemic has seriously affected numerous countries in the world from various aspects such as education, economy, social security, public health, etc. Most governments have made great efforts to control the spread of COVID-19, e.g., locking down hard-hit cities and advocating masks for the population. However, some countries and regions have relatively poor medical conditions in terms of insufficient medical equipment, hospital capacity overload, personnel shortage, and other problems, resulting in the large-scale spread of the epidemic. With the unique advantages of Artificial Intelligence (AI), it plays an extremely important role in medical imaging, clinical data, drug development, epidemic prediction, and telemedicine. Therefore, AI is a powerful tool that can help humans solve complex problems, especially in the fight against COVID-19. This study aims to analyze past research results and interpret the role of Artificial Intelligence in the prevention and treatment of COVID-19 from five aspects. In this paper, we also discuss the future development directions in different fields and prove the validity of the models through experiments, which will help researchers develop more efficient models to control the spread of COVID-19.

Prediction of future imagery of lung nodule as growth modeling with follow-up computed tomography scans using deep learning: a retrospective cohort study.

Background: Risk prediction models of lung nodules have been built to alleviate the heavy interpretative burden on clinicians. However, the malignancy scores output by those models can be difficult to interpret in a clinically meaningful manner. In contrast, the modeling of lung nodule growth may be more readily useful. This study developed a CT-based visual forecasting system that can visualize and quantify a nodule in three dimensions (3D) in any future time point using follow-up CT scans. Methods: We retrospectively included 246 patients with 313 lung nodules with at least 1 follow-up CT scan. For the manually segmented nodules, we calculated geometric properties including CT value, diameter, volume, and mass, as well as growth properties including volume doubling time (VDT), and consolidation-to-tumor ratio (CTR) at follow-ups. These nodules were divided into growth and non-growth groups by thresholding their VDTs. We then developed a convolutional neural network (CNN) to model the imagery change of the nodules from baseline CT image (combined with the nodule mask) to follow-up CT image with a particular time interval. The model was evaluated on the geometric and radiological properties using either logistic regression or receiver operating characteristic (ROC) curve. Results: The lung nodules consisted of 115 ground glass nodules (GGN) and 198 solid nodules and were followed up for an average of 354 days with 2 to 11 scans. The 2 groups differed significantly in most properties. The prediction of our forecasting system was highly correlated with the ground truth with small relative errors regarding the four geometric properties. The prediction-derived VDTs had an area under the curve (AUC) of 0.857 and 0.843 in differentiating growth and non-growth nodules for GGN and solid nodules, respectively. The prediction-derived CTRs had an AUC of 0.892 in classifying high- and low-risk nodules. Conclusions: This proof-of-concept study demonstrated that the deep learning-based model can accurately forecast the imagery of a nodule in a given future for both GGNs and solid nodules and is worthy of further investigation. With a larger dataset and more validation, such a system has the potential to become a prognostication tool for assessing lung nodules.

Computed tomography versus frozen sections for distinguishing lung adenocarcinoma: A cohort study of concordance rate.

BACKGROUND: Computed tomography (CT) imaging can help to predict the pathological invasiveness of early-stage lung adenocarcinoma and guide surgical resection. This retrospective study investigated whether CT imaging could distinguish pre-invasive lung adenocarcinoma from IAC. It also compared final pathology prediction accuracy between CT imaging and intraoperative frozen section analysis. METHODS: This study included 2093 patients with early-stage peripheral lung adenocarcinoma who underwent CT imaging and intraoperative frozen section analysis between March 2013 and November 2014. Nodules were classified as ground-glass (GGNs), part-solid (PSNs), and solid nodules according to CT findings; they were classified as pre-IAC and IAC according to final pathology. Univariate, multivariate, and receiver operating characteristic (ROC) curve analyses were performed to evaluate whether CT imaging could distinguish pre-IAC from IAC. The concordance rates of CT imaging and intraoperative frozen section analyses with final pathology were also compared to determine their accuracies. RESULTS: Multivariate analysis identified tumor size as an independent distinguishing factor. ROC curve analyses showed that the optimal cut-off sizes for distinguishing pre-IAC from IAC for GGNs, PSNs, and solid nodules were 10.79, 11.48, and 11.45 mm, respectively. The concordance rate of CT imaging with final pathology was significantly greater than the concordance rate of intraoperative frozen section analysis with final pathology (P = 0.041). CONCLUSION: CT imaging could distinguish pre-IAC from IAC in patients with early-stage lung adenocarcinoma. Because of its accuracy in predicting final pathology, CT imaging could contribute to decisions associated with surgical extent. Multicenter standardized trials are needed to confirm the findings in this study.

Accurate prediction of epidermal growth factor receptor mutation status in early-stage lung adenocarcinoma, using radiomics and clinical features.

OBJECTIVES: To develop a nomogram based on CT radiomics and clinical features to predict the epidermal growth factor receptor (EGFR) mutations in early-stage lung adenocarcinomas. METHODS: A retrospective analysis of postoperative patients with pathologically confirmed lung adenocarcinoma, which had been tested for EGFR mutations was performed from January 2015 to December 2015. Patients were randomly assigned to training and validation cohorts. A total of 1,078 radiomics features were extracted. least absolute shrinkage and selection operator (LASSO) regression analysis was applied to select clinical and radiomics features, and to establish predictive models. The radiomics score (rad-score) of each patient was calculated. The discrimination of the model was evaluated with area under the curve. RESULTS: 1092 patients (444 men and 648 women; mean age: 59.59±9.6) were enrolled. The radiomics signature consisted of 28 radiomics features and emphysema. The mean validation cohort result of the rad-score for patients with EGFR mutations (0.814±0.988) was significantly higher than those with EGFR wild-type (0.315±1.237; p = 0.001). When combined with clinical features, LASSO regression analysis revealed four radiomics features, emphysema, and three clinical features including sex, age, and histologic subtype as associated with to EGFR mutation status. The nomogram that combined radiomics and clinical features significantly improved the predictive discrimination (AUC: 0.723), which is better than that of the radiomics signature alone (AUC: 0.646). CONCLUSION: A relationship between selected radiomics features and EGFR mutant lung adenocarcinomas is demonstrated. A nomogram, combining radiomics features and clinical features for EGFR prediction in early-stage lung adenocarcinomas, has shown a moderate discriminatory efficiency and high sensitivity, providing additional information for clinicians.

Universal Nanoparticle Counting Platform for Tetraplexed Biomarkers by Integrating Immunorecognition and Nucleic Acid Hybridization in One Assay.

The development of a simple and universal strategy for simultaneous quantification of proteins and nucleic acid biomarkers in one assay is valuable, particularly for disease diagnosis and pathogenesis studies. Herein, a universal and amplification-free quantum dot-doped nanoparticle counting platform was developed by integrating immunorecognition and nucleic acid hybridization in one assay. The assay can be performed at room temperature, which is friendly for routine analysis. Multiplexed biomarkers associated with Alzheimer's disease (AD) including proteins and nucleic acids were detected. For simultaneous detection of tetraplex biomarkers, the assay for amyloid ß 1-42 (Aß42), tau protein, miR-146a, and miR-138 presented limit of detection values of 250 pg/mL, 55.7 pg/mL, 52.5 pM, and 0.62 pM, respectively. By spiking all the above four biomarkers in one artificial cerebrospinal fluid sample, the recoveries were found to be 94.7-117.2%. Using tau protein as the model, four measurements in 88 days presented a coefficient of variance of 7.5%. The proposed platform for the multiplexed assay of proteins and nucleic acids presents the universality, reasonable sensitivity, and repeatability, which may open a new door for early diagnosis and pathogenesis research for AD and other diseases.

Contrast-enhanced CT longitudinal tail sign as a marker of positive resection margins in adenoid cystic carcinoma of the central airway.

BACKGROUND: Due to submucosal infiltration's biological nature along the airway, adenoid cystic carcinoma (ACC) frequently leaves positive surgical margins. This study evaluated the clinicopathologic, and computed tomography (CT) features for predicting surgical margin status in central airway ACC. METHODS: We retrospectively analyzed the files of 71 patients with ACC of the central airway proven by histopathology and surgery who had presented between January 2010 and December 2018. All patients were classified into positive and negative surgical margin groups according to margin status. Univariate analysis and multivariable logistic regression models were then performed to compare demography, histopathology, and CT characteristics between ACC patients with positive and negative margins. RESULTS: After surgical resection, 59 (83.1%) patients had positive margins, and 12 (16.9%) had negative margins. The contrast-enhanced CT (CECT) longitudinal tail sign (LTS) was identified in 55 of 59 (93.2%) patients with positive margins and was the only feature that had a significant association with positive margins (odds ratio 41.250, 95% CI: 7.886-215.767; P<0.001). Moreover, positive margins in upper or/and lower directions were associated with the LTS in corresponding directions (P<0.001). CONCLUSIONS: Most central airway ACC patients exhibited positive margins following surgery. The appearance of the LTS on CECT was significantly associated with positive margins and could help preoperatively predict the submucosal invasion of ACC.

Pre-Immunotherapy Contrast-Enhanced CT Texture-Based Classification: A Useful Approach to Non-Small Cell Lung Cancer Immunotherapy Efficacy Prediction.

Objective: To investigate the utility of the pre-immunotherapy contrast-enhanced CT-based texture classification in predicting response to non-small cell lung cancer (NSCLC) immunotherapy treatment. Methods: Sixty-three patients with 72 lesions who received immunotherapy were enrolled in this study. We extracted textures including histogram, absolute gradient, run-length matrix, gray-level co-occurrence matrix, autoregressive model, and wavelet transform from pre-immunotherapy contrast-enhanced CT by using Mazda software. Three different methods, namely, Fisher coefficient, mutual information measure (MI), and minimization of classification error probability combined average correlation coefficients (POE + ACC), were performed to select 10 optimal texture feature sets, respectively. The patients were divided into non-progressive disease (non-PD) and progressive disease (PD) groups. t-test or Mann-Whitney U-test was performed to test the differences in each texture feature set between the above two groups. Each texture feature set was analyzed by principal component analysis (PCA), linear discriminant analysis (LDA), and non-linear discriminant analysis (NDA). The area under the curve (AUC) was used to quantify the predictive accuracy of the above three analysis models for each texture feature set, and the sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were also calculated, respectively. Results: Among the three texture feature sets, the texture parameter differences of kurtosis (2.12 ± 3.92 vs. 0.78 ± 1.10, p = 0.047), "S(2,2)SumEntrp" (1.14 ± 0.31 vs. 1.24 ± 0.12, p = 0.036), and "S(1,0)SumEntrp" (1.18 ± 0.27 vs. 1.28 ± 0.11, p = 0.046) between the non-PD and PD group were statistically significant (all p < 0.05). The classification result of texture feature set selected by POE + ACC and analyzed by NDA was identified as the best model (AUC = 0.812, 95% CI: 0.706-0.919) with a sensitivity, specificity, accuracy, PPV, and NPV of 88.2, 76.3, 81.9, 76.9, and 87.9%, respectively. Conclusion: Pre-immunotherapy contrast-enhanced CT-based texture provides a new method for clinical evaluation of the NSCLC immunotherapy efficacy prediction.

Nonamplification Multiplexed Assay of Endonucleases and DNA Methyltransferases by Colocalized Particle Counting.

Restriction endonucleases (ENases) and DNA methyltransferases (MTases) are important enzymes in biological processes, and detection of ENases/MTases activity is significant for biological and pharmaceutical studies. However, available nonamplification methods with a versatile design, desirable sensitivity, and signal production mode of unbiased quantification toward multiple nucleases are rare. By combining deliberately designed hairpin DNA probes with the colocalized particle counting technique, we present a nonamplification, separation-free method for multiplexed detection of ENases and MTases. In the presence of target ENases, the hairpin DNA is cleaved and the resulting DNA sequence forms a sandwich structure to tie two different-colored fluorescent microbeads together to generate a colocalization signal that can be easily detected using a standard fluorescence microscope. The multiplexed assay is realized via different color combinations. For the assay of methyltransferase, methylation by MTases prevents cleavage of the hairpin by the corresponding ENase, leading to decreased colocalization events. Three ENases can be simultaneously detected with high selectivity, minimal cross-talk, and detection limits of (4.1-6.4) × 10-4 U/mL, and the corresponding MTase activity can be measured without a change of the probe design. The potential for practical application is evaluated with human serum samples and different ENase and MTase inhibitors with satisfactory results. The proposed method is separation-free, unbiased toward multiple targets, and easy to implement, and the strategy has the potential to be extended to other targets.