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.

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.

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.

Colocalized Particle Counting Platform for Zeptomole Level Multiplexed Quantification.

For multiplexed detection, it is important yet challenging to simultaneously meet the requirement of sensitivity, throughput, and implementation convenience for practical applications. Using the detection of DNAs and miRNAs for illustration, we present a colocalized particle counting platform that can realize the separation-free multiplexed detection of 6 nucleic acid targets with a zeptomole sensitivity and a dynamic range of up to 5 orders of magnitude. The presence of target induces the formation of a sandwich nanostructure via hybridization; thus, there is an occurrence of colocalization of two microbeads with two different colors. The sequence specific coding is realized by an arbitrary combination of two fluorescence channels with different emitting colors. The platform presents robustness in detecting multiple nucleic acid targets with a minimal cross talk and matrix effect as well as the ability to distinguish the specific miRNA from members of the same family. The results of simultaneous detection of 3 miRNAs in 3 different cell lines present straight consistency with that of the standard qRT-PCR. This platform can be adapted to other multiplexing designs such as the "turn-off" mode, in which the proportion of colocalized microbeads is decreased due to the strand-displacement reaction initiated by the specific target. This separation-free platform offers the possibility to achieve the on-site multiplexed detection with compatibility to different experimental designs and extensibility to other signal sources for enumeration.

Nanomaterial-based multiplex optical sensors.

The drive for a simultaneous analysis of multiple targets with excellent accuracy and efficiency, which is often required in both basic biomedical research and clinical applications, demands the development of multiplexed bioassays with desired throughput. With the development of nanotechnologies, innovative multiplex optical bioassays have been achieved. Nanomaterials exhibit unique physical and chemical properties such as easily tunable size, large surface-to-volume ratio, excellent catalysis and the desired signal transduction mechanism, which makes them excellent candidates for the fabrication of novel optical nanoprobes. This mini review summarizes nanomaterial-based optical multiplex sensors from the last 5 years. Specific optical techniques covered in this review are fluorescence, surface-enhanced Raman scattering (SERS), localized surface plasmon resonance (LSPR), chemiluminescence (CL), and the multimodality with fundamentals and examples.

Competitive aptasensor for the ultrasensitive multiplexed detection of cancer biomarkers by fluorescent nanoparticle counting.

Cancer biomarker quantification in human serum is of great importance for accurate patient diagnosis and informed clinical management. To date, ultrasensitive multiplexed detection of proteins without amplification is still a major challenge. Herein, we proposed a competitive aptasensor strategy for ultrasensitive multiplexed cancer biomarker detection by fluorescent nanoparticle (FNP) counting. The sequences are designed such that the binding abilities of linker DNA (L-DNA) with DNA-functionalized FNPs (DNA-FNPs) and aptamer are comparable. As long as one target binds with one molecule of aptamer, a signalling FNP forms a sandwich-structured nanocomposite, which was subsequently observed and enumerated with a fluorescence microscope. This 1 : 1 target-to-signal FNP production assured an improved sensitivity, benefiting from the reasonably good brightness and photostability of FNPs. For both singleplexed and multiplexed detection, this proposed strategy achieved an approximately 1000-fold improved limit of detection than the conventional method with the detection volume of 3.2 µL. Notably, the results for carcinoembryonic antigen (CEA) detection obtained directly from 9 human serum samples (colorectal/lung/healthy individuals) were consistent with that obtained by ELISA, showing potential application in clinical diagnosis.

OCT4&SOX2-specific cytotoxic T lymphocytes plus programmed cell death protein 1 inhibitor presented with synergistic effect on killing lung cancer stem-like cells in vitro and treating drug-resistant lung cancer mice in vivo.

This study aimed to investigate the synergistic effect of octamer-binding transcription factor 4 and sex determining region Y-box 2 (OCT4&SOX2)-specific cytotoxic T lymphocytes (CTLs) and programmed cell death protein 1 (PD-1) inhibitor on killing lung cancer stem-like cells (LCSCs) and their efficacy in treating drug-resistant lung cancer (DRLC) mice. OCT4&SOX2-specific CTLs and PD-1 inhibitor with differed doses were applied to treat PC9 cells and PC9 LCSCs. Cell counting kit-8 (CCK8) assay and flow cytometry (FCM) assay with carboxyfluorescein diacetate/succinimidyl ester staining target cells before treatment and propidium iodide (PI) staining dead cells after treatment were conducted to detect the cytotoxic activity. DRLC mice were constructed by injection of PC9 LCSCs suspension and Matrigel into left lung of SD mice. DRLC mice were randomly divided into five groups: control group, cytomegalovirus (CMV) pp65 CTLs group, OCT4&SOX2 CTLs group, PD-1 inhibitor group, and OCT4&SOX2 CTLs + PD-1 inhibitor group. In vitro, both CCK8 assay and FCM assay disclosed that OCT4&SOX2-specific CTLs plus PD-1 inhibitor presented with elevated cytotoxic activity on PC9 cells and PC9 LCSCs. In vivo, tumor volume and tumor weight were decreased, while tumor necrosis and tumor apoptosis were increased in OCT4&SOX2 CTLs group than CMV pp65 CTLs group and control group, and in OCT4&SOX2 CTLs + PD-1 inhibitor group than OCT4&SOX2 CTLs group and PD-1 inhibitor group. In addition, CD8 expression was increased while OCT4&SOX2 expressions were decreased in OCT4&SOX2 CTLs + PD-1 inhibitor group than OCT4&SOX2 CTLs group and PD-1 inhibitor group. In conclusion, OCT4&SOX2-specific CTLs and PD-1 inhibitor presented with the synergistic effect on killing LCSCs in vitro and treating DRLC mice in vivo.

Prediction of pathologic stage in non-small cell lung cancer using machine learning algorithm based on CT image feature analysis.

PURPOSE: To explore imaging biomarkers that can be used for diagnosis and prediction of pathologic stage in non-small cell lung cancer (NSCLC) using multiple machine learning algorithms based on CT image feature analysis. METHODS: Patients with stage IA to IV NSCLC were included, and the whole dataset was divided into training and testing sets and an external validation set. To tackle imbalanced datasets in NSCLC, we generated a new dataset and achieved equilibrium of class distribution by using SMOTE algorithm. The datasets were randomly split up into a training/testing set. We calculated the importance value of CT image features by means of mean decrease gini impurity generated by random forest algorithm and selected optimal features according to feature importance (mean decrease gini impurity > 0.005). The performance of prediction model in training and testing sets were evaluated from the perspectives of classification accuracy, average precision (AP) score and precision-recall curve. The predictive accuracy of the model was externally validated using lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) samples from TCGA database. RESULTS: The prediction model that incorporated nine image features exhibited a high classification accuracy, precision and recall scores in the training and testing sets. In the external validation, the predictive accuracy of the model in LUAD outperformed that in LUSC. CONCLUSIONS: The pathologic stage of patients with NSCLC can be accurately predicted based on CT image features, especially for LUAD. Our findings extend the application of machine learning algorithms in CT image feature prediction for pathologic staging and identify potential imaging biomarkers that can be used for diagnosis of pathologic stage in NSCLC patients.

An on-site bacterial detection strategy based on broad-spectrum antibacterial ε-polylysine functionalized magnetic nanoparticles combined with a portable fluorometer.

A sensitive on-site bacterial detection strategy is presented that integrates the broad-spectrum capturing feature of ε-polylysine-functionalized magnetic nanoparticles with an in-house built portable fluorometer. Based on the electrostatic interaction, the functionalized magnetic nanoparticles (ε-PL-MNPs) were prepared for Gram-positive and Gram-negative bacterial separation and subsequent viable release. ε-PL-MNPs show a broad reactivity towards bacteria with the high capture efficiency from real-world sample media. They also enable controlled viable bacterial release with pH adjustment. Detection of bacteria is based on a combination of broad-spectrum capture with colorimetric and fluorimetric immunoassays. A portable fluorometer is built to enhance the applicability for sensitive on-site detection. A limit of detection of 98 CFU·mL-1 is achieved that is comparable to that of a known spectrofluorometric method for E. coli DH5α. Graphical abstract Schematic presentation of bacterial capture using cationic polymer functionalized magnetic nanoparticles and general fluorometric immunoassay with portable fluorometer. The limit of detection is 98 CFU·mL-1 for E. coli DH5α.

Ultraspecific Multiplexed Detection of Low-Abundance Single-Nucleotide Variants by Combining a Masking Tactic with Fluorescent Nanoparticle Counting.

To be able to detect simultaneously multiple single-nucleotide variants (SNVs) with both ultrahigh specificity and low-abundance sensitivity is of pivotal importance for molecular diagnostics and biological research. In this contribution, we for the first time developed a multiplex SNV detection method that combines the masking tactic with fluorescent nanoparticle (FNP) counting based on the sandwich design. The method presents a rivaling performance due to its advantageous features: the masking reagent was designed to hybridize with an extremely large amount of the wild-type sequence to render the assay with high specificity; FNP counting provides a sensitive multiplexed SNV detection; the sandwich design facilitates an easy separation to make the detection free of interferences from the matrix. For single SNV target discrimination, including the 6 most frequently occurring DNA KRAS gene mutations and 2 possible RNA KRAS gene mutations as well as 11 artificial mutations, the discrimination factor ranged from 204 to 1177 with the median being 545. Among the tested 19 SNVs, abundances as low as 0.05% were successfully identified in 14 cases, and an abundance as low as 0.1% was identified for the remaining 5 cases. For multiplexed detection of SNVs in the KRAS gene, abundances as low as 0.05-0.1% were achieved for multiple SNVs occurring at the same and different codons. As low as 0.05% low-abundance detection sensitivity was also achieved for PCR amplicons of human genomic DNA extracted from cell samples. This proposed method presents the potential for ultrahigh specific multiplexed detection of SNVs with low-abundance detection capability, which may be applied to practical applications.

Constructing a Robust Fluorescent DNA-Stabilized Silver Nanocluster Probe Module by Attaching a Duplex Moiety.

Fluorescent DNA-templated silver nanoclusters (DNA-Ag NCs) have served as excellent luminescent probes and operation units in various applications. However, the fluorescence property of DNA-Ag NCs is very sensitive to elongation or modification of the DNA template, limiting the breadth of applications. In this work, we propose a strategy for constructing a robust fluorescent DNA-Ag NCs probe module by attaching a duplex moiety to the nanocluster-bearing sequence. The fluorescence intensity of the DNA-Ag NCs can be enhanced 90-fold upon hybridization of the elongated moiety. Adenine in the linker sequence has a further enhancing effect on the fluorescence intensity, whereas thymine has a quenching effect. The transformation from a non-fluorescent species to fluorescent nanoclusters is responsible for the fluorescence enhancement with duplex formation of the elongated moiety. We hope that this design will aid future diversification of experimental designs to facilitate more applications that are currently limited by the aforementioned problems.

A universal and enzyme-free immunoassay platform for biomarker detection based on gold nanoparticle enumeration with a dark-field microscope.

Developing an enzyme-free, non-amplification strategy for biomarker detection with universality and easy implementation is of central importance in clinical diagnosis and therapeutic monitoring. Herein, we report for the first time a universal and enzyme-free magnetic bead-based sandwich-format immunoassay platform for biomarker detection by combining secondary antibody functionalized AuNPs and automatic AuNP counting readout. For the prostate specific antigen (PSA), the detection limit is found to be 1 ng mL-1, and the spike recoveries (n = 3) with 10% fetal bovine serum are 113.5% for 2 ng mL-1 and 107.7% for 10 ng mL-1. The assay also presents reasonable repeatability as indicated by the coefficient of variance of 13.1% with 5 measurements in 60 days. This strategy has been successfully applied to the determination of carcinoembryonic antigen (CEA) and alpha-fetoprotein (AFP), demonstrating the universality of this strategy. Our proposed non-amplification platform presents sensitivity comparable to that of the enzyme-linked immunosorbent assay (ELISA) with better repeatability; and more importantly, our method has better simplicity than most of the amplification-based methods, and thus is more suitable for routine analysis. The highlights of our work suggest that it is a promising method and would be potentially an alternative for ELISA in laboratories where routine analyses are intensively performed.

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.

DeepASD: a deep adversarial-regularized graph learning method for ASD diagnosis with multimodal data.

Autism Spectrum Disorder (ASD) is a prevalent neurological condition with multiple co-occurring comorbidities that seriously affect mental health. Precisely diagnosis of ASD is crucial to intervention and rehabilitation. A single modality may not fully reflect the complex mechanisms underlying ASD, and combining multiple modalities enables a more comprehensive understanding. Here, we propose, DeepASD, an end-to-end trainable regularized graph learning method for ASD prediction, which incorporates heterogeneous multimodal data and latent inter-patient relationships to better understand the pathogenesis of ASD. DeepASD first learns cross-modal feature representations through a multimodal adversarial-regularized encoder, and then constructs adaptive patient similarity networks by leveraging the representations of each modality. DeepASD exploits inter-patient relationships to boost the ASD diagnosis that is implemented by a classifier compositing of graph neural networks. We apply DeepASD to the benchmarking Autism Brain Imaging Data Exchange (ABIDE) data with four modalities. Experimental results show that the proposed DeepASD outperforms eight state-of-the-art baselines on the benchmarking ABIDE data, showing an improvement of 13.25% in accuracy, 7.69% in AUC-ROC, and 17.10% in specificity. DeepASD holds promise for a more comprehensive insight of the complex mechanisms of ASD, leading to improved diagnosis performance.

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.

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.

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.

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.

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.

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.