[Predictive value of hemoglobin decrease for necrotizing enterocolitis in preterm infants with late-onset sepsis]. / è¡€çº¢è›‹ç™½ä¸‹é™å€¼å¯¹æ™šå‘åž‹è´¥è¡€ç—‡æ—©äº§å„¿ç»§å‘åæ­»æ€§å°è‚ ç»“è‚ ç‚Žçš„é¢„æµ‹ä»·å€¼.

OBJECTIVES: To study the predictive value of hemoglobin (Hb) decrease for the occurrence of necrotizing enterocolitis (NEC) in preterm infants with late-onset sepsis (LOS) . METHODS: Clinical data of 93 LOS preterm infants were collected for retrospective analysis, among which 16 infants developed NEC while 77 infants did not. Based on the decrease in Hb levels from the most recent Hb measurement before LOS occurrence to the initial Hb levels during LOS, the infants were divided into three groups: no Hb decrease (n=15), mild Hb decrease (Hb decrease <15 g/L; n=35), and severe Hb decrease (Hb decrease ≥15 g/L; n=43). Multivariate logistic regression analysis was conducted to explore the predictive factors for NEC secondary to LOS, and the value of Hb decrease in predicting NEC secondary to LOS was evaluated through receiver operating characteristic curve analysis. RESULTS: The incidence of NEC in the severe Hb decrease group, mild Hb decrease group, and no Hb decrease group were 26%, 14%, and 0% (P<0.05), respectively. Multivariate logistic regression analysis revealed that a larger Hb decrease was an independent predictive factor for NEC in LOS preterm infants (OR=1.141, 95%CI: 1.061-1.277, P<0.001). Receiver operating characteristic curve analysis showed that the area under the curve for predicting NEC in preterm infants with LOS using Hb decrease (with a cut-off value of 20 g/L) was 0.803, with sensitivity and specificity of 0.69 and 0.78, respectively. CONCLUSIONS: Hb decrease can serve as an indicator for prediction of NEC in preterm infants with LOS.

Gut mycobiome as a potential non-invasive tool in early detection of lung adenocarcinoma: a cross-sectional study.

BACKGROUND: The gut mycobiome of patients with lung adenocarcinoma (LUAD) remains unexplored. This study aimed to characterize the gut mycobiome in patients with LUAD and evaluate the potential of gut fungi as non-invasive biomarkers for early diagnosis. METHODS: In total, 299 fecal samples from Beijing, Suzhou, and Hainan were collected prospectively. Using internal transcribed spacer 2 sequencing, we profiled the gut mycobiome. Five supervised machine learning algorithms were trained on fungal signatures to build an optimized prediction model for LUAD in a discovery cohort comprising 105 patients with LUAD and 61 healthy controls (HCs) from Beijing. Validation cohorts from Beijing, Suzhou, and Hainan comprising 44, 17, and 15 patients with LUAD and 26, 19, and 12 HCs, respectively, were used to evaluate efficacy. RESULTS: Fungal biodiversity and richness increased in patients with LUAD. At the phylum level, the abundance of Ascomycota decreased, while that of Basidiomycota increased in patients with LUAD. Candida and Saccharomyces were the dominant genera, with a reduction in Candida and an increase in Saccharomyces, Aspergillus, and Apiotrichum in patients with LUAD. Nineteen operational taxonomic unit markers were selected, and excellent performance in predicting LUAD was achieved (area under the curve (AUC) = 0.9350) using a random forest model with outcomes superior to those of four other algorithms. The AUCs of the Beijing, Suzhou, and Hainan validation cohorts were 0.9538, 0.9628, and 0.8833, respectively. CONCLUSIONS: For the first time, the gut fungal profiles of patients with LUAD were shown to represent potential non-invasive biomarkers for early-stage diagnosis.

Non-linear modifications enhance prediction of pathological response to pre-operative PD-1 blockade in lung cancer: A longitudinal hybrid radiological model.

Major pathologic remission (MPR, residual tumor <10%) is a promising clinical endpoint for prognosis analysis in patients with lung cancer receiving pre-operative PD-1 blockade therapy. Most of the current biomarkers for predicting MPR such as PD-L1 and tumor mutation burden (TMB) need to be obtained invasively. They cannot overcome the spatiotemporal heterogeneity or provide dynamic monitoring solutions. Radiomics and artificial intelligence (AI) models provide a practical tool enabling non-invasive follow-up observation of tumor structural information through high-throughput data analysis. Currently, AI-based models mainly focus on the single baseline scan or pipeline, namely sole radiomics or deep learning (DL). This work merged the delta-radiomics based on the slope of classic radiomics indexes within a time interval and the features extracted by deep networks from the subtraction between the baseline and follow-up images. The subtracted images describing the tumor changes were based on the transformation generated by registration. Stepwise optimization of components was performed by repeating experiments among various combinations of DL networks, registration methods, feature selection algorithms, and classifiers. The optimized model could predict MPR with a cross-validation AUC of 0.91 and an external validation AUC of 0.85. A core set of 27 features (eight classic radiomics, 15 delta-radiomics, one classic DL features, and three delta-DL features) was identified. The changes in delta-radiomics indexes during the treatment were fitted with mathematic models. The fitting results revealed that over half of the features were of non-linear dynamics. Therefore, non-linear modifications were made on eight features by replacing the original features with non-linear fitting parameters, and the modified model achieved an improved power. The dynamic hybrid model serves as a novel and promising tool to predict the response of lesions to PD-1 blockade, which implies the importance of introducing the non-linear dynamic effects and DL approaches to the original delta-radiomics in the future.

Interfacially Polymerized ß-Cyclodextrin Film for Rapid Removal of Organic Dyes.

Porous cyclodextrin-based polymers are widely used for the rapid removal of organic pollutants in water. Traditional cyclodextrin-based polymers are prepared in the organic phase, which is time consuming and costly. Herein, a novel cyanuricchloride (TCT) cross-linked porous ß-cyclodextrin-based thin-film composite membrane is designed in the aqueous phase by interfacial polymerization. A self-standing TCT-CDP film is formed instantly at the surface of water phase at room temperature. Several different water-soluble organic dyes such as Methylene Blue, Neutral Red, Auramine, Brilliant Green, and Crystal Violet are selected for rejection study with TCT-CDP membrane. The effective rejection of TCT-CDP membrane for typical dyes is up to 99%, indicating TCT-CDP membrane exhibit excellent selectivity for separation of organic dyes from water.

Long-term results of postoperative unsuspected small cell lung cancer on real-world data.

BACKGROUND: In traditional opinion, solid pulmonary nodule suspected lung cancer should be confirmed by pathology before the operation to exclude small cell lung cancer (SCLC), considering SCLC tends to be aggressive and surgical effect in the management of SCLC remains controversial. The aim of this study was to evaluate the survival result and risk factors of postoperative unsuspected SCLC. METHODS: A total of 120 patients with postoperative unsuspected SCLC who were confirmed by pathology and referred to Chinese PLA General Hospital between 2000 and 2021 were retrospectively analyzed (surgery group). Additionally, 120 patients with limited-stage SCLC who underwent chemotherapy and radiotherapy in the same period were enrolled in the chemoradiotherapy group.. Kaplan-Meier method was used to estimate survival; the Log-Rank test was used to compare survival rates between different groups; a COX stepwise regression model was used for multivariate analysis. RESULTS: Among 120 patients in the surgery group, 28 were with central type and other 92 with peripheral type. The median survival (OS) was 44.85 months, and the 5-year survival rate was 46%. The 5-year survival rates for stage I, II, and III were 52.1%, 45.4%, and 27.8%, respectively. The mean disease-free survival time (DFS) was 30.63 ± 4.38 months, and the 5-year DFS rate was 31.5%. In the chemoradiotherapy group, the mean OS was 21.4 ± 4.26 months, and the 5-year survival rate was 28.3%. The 5-year survival rates for clinical stage I, II, and III were 42.5%, 39.8%, and 20.5%, respectively. The mean progression-free survival (PFS) was 10.63 ± 3.6 months. In the surgery group, one-way ANOVA revealed that the gender, symptoms, smoking history, tumor location, and postoperative radiotherapy were not associated with OS (P ≥ 0.05), while age, surgical approach, surgical method, N stage, TNM stage, and vascular tumor thrombus were related to OS (P < 0.05). Multivariate analysis indicated that the N stage was associated with OS (HR = 1.86 P = 0.042). CONCLUSION: Surgery and adjuvant therapy were found to have encouraging outcomes in postoperative unsuspected SCLC. Patients with stage I, stage II and part of stage IIIA SCLC could benefit from surgery and the standard lobectomy, and systematic lymph node dissection, is also recommended for these patients.

Cryptogenic Organizing Pneumonia Presenting as a Solitary Mass: Clinical, Imaging, and Pathologic Features.

BACKGROUND Cryptogenic organizing pneumonia (COP), with a variety of radiologic findings, is a clinical pathological entity characterized by the presence of granulation tissue composed of fibroblasts/myofibroblasts and loose connective tissue in the alveoli and/or the distal bronchioles. Nevertheless, the presence of a solitary mass in COP is relatively rare. This study investigated the clinical, imaging, and pathologic features of COP with solitary mass form. MATERIAL AND METHODS This retrospective analysis included 12 patients (9 men and 3 women; age range 36-78 years; mean age 60±9 years) with surgery- or biopsy-proven COP with a solitary lung mass, diagnosed between June 2012 and December 2017 at the Department of Radiology in our hospital. RESULTS All patients experienced cough with expectoration and 8 patients had hemoptysis. All lesions were adjacent to the pleura. Mean size of the lesions was 4.2±0.9 cm (range, 3.2-6.1 cm). The upper left lobe was the site of the lesion in 4 patients. Six masses had heterogeneous density; among these, 4 had cavities and distal obstructive inflammation. The mass caused pleural indentation in 4 patients. Lymphadenopathy was seen in 7 patients. All specimens showed buds of granulation tissue within the lumen of the distal pulmonary airspaces, with significant increase in interstitial lymphocytes in 4 specimens. CONCLUSIONS Patients with COP with solitary mass form are more susceptible to hemoptysis and the mass is prone to necrosis. Vascular bundles, exudation around the mass, interstitial lymphocyte infiltration, and mediastinal lymph node enlargement are common features.

A new turn-on fluorescent probe for fast detection of diabetic biomarker beta-hydroxybutyrate in vitro.

Beta-hydroxybutyrate (ß-HB) serve as a valuable diagnostic biomarker for Diabetic Ketoacidosis (DKA). Here, a new Schiff base fluorescent probe T was designed and synthesized to detect ß-HB level in aqueous solution in vitro. The probe T can detect ß-HB sensitively and selectively in DMF solution (5.0 × 10-5 M) among other interfering species (cations, anions, amino acids, biomarkers). The detection limit of probe T for ß-HB was calculated to be 0.154 µM. These results demonstrate that the probe T may provide a convenient method for rapid detection of ß-HB to diagnose diabetic ketoacidosis.

Development of a CT-Based comprehensive model combining clinical, radiomics with deep learning for differentiating pulmonary metastases from noncalcified pulmonary hamartomas, a retrospective cohort study.

BACKGROUND: Clinical differentiation between pulmonary metastases and noncalcified pulmonary hamartomas (NCPH) often presents challenges, leading to potential misdiagnosis. However, the efficacy of a comprehensive model that integrates clinical features, radiomics, and deep learning (CRDL) for differential diagnosis of these two diseases remains uncertain. OBJECTIVE: This study evaluated the diagnostic efficacy of a Clinical Features, Radiomics, and Deep Learning (CRDL) model in differentiating pulmonary metastases from noncalcified pulmonary hamartomas (NCPH). METHODS: We retrospectively analyzed the clinical and imaging data of 256 patients from Hospital A and 85 patients from Hospital B, who were pathologically confirmed pulmonary hamartomas or pulmonary metastases after thoracic surgery. Employing Python 3.7 software suites, we extracted radiomic features and deep learning attributes from patient datasets. The cohort was divided into training set, internal validation set, and external validation set. The diagnostic performance of the constructed models was evaluated using receiver operating characteristic (ROC) curve analysis to determine their effectiveness in differentiating between pulmonary metastases and NCPH. RESULTS: Clinical features such as white blood cell count (WBC), platelet count (PLT), history of cancer, carcinoembryonic antigen (CEA) level, tumor marker status, lesion margin characteristics (smooth or blurred) and maximum diameter were found to have diagnostic value in differentiating between the two diseases. In the domains of radiomics and deep learning. Of the 1,130 radiomics features and 512 deep learning features, 24 and 7, respectively, were selected for model development. The area under the ROC curve (AUC) values for the four groups were 0.980, 0.979, 0.999, and 0.985 in the training set, 0.947, 0.816, 0.934, and 0.952 in the internal validation set, and 0.890, 0.904, 0.923, and 0.938 in the external validation set. This demonstrated that the CRDL model showed the greatest efficacy. CONCLUSIONS: The comprehensive model incorporating clinical features, radiomics, and deep learning shows promise for aiding in the differentiation between pulmonary metastases and hamartomas.

Long-term prognosis in patients with thymoma combined with myasthenia gravis: a propensity score-matching analysis.

Introduction: We aimed to assess the impact of myasthenia gravis (MG) on the long-term prognosis in patients with thymoma after surgery and identify related prognostic factors or predictors. Methods: This retrospective observational study included 509 patients with thymoma (thymoma combined with MG [MG group] and thymoma alone [non-MG group]). Propensity score matching was performed to obtain comparable subsets of 96 patients in each group. A comparative analysis was conducted on various parameters. Results: Before matching, the 10-year survival and recurrence-free survival rates in both groups were 93.8 and 98.4%, and 85.9 and 93.4%, respectively, with no statistically significant difference observed in the survival curves between the groups (p > 0.05). After propensity score matching, 96 matched pairs of patients from both groups were created. The 10-year survival and recurrence-free survival rates in these matched pairs were 96.9 and 97.7%, and 86.9 and 91.1%, respectively, with no statistical significance in the survival curves between the groups (p > 0.05). Univariate analysis of patients with thymoma postoperatively revealed that the World Health Organization histopathological classification, Masaoka-Koga stage, Tumor Node Metastasis stage, resection status, and postoperative adjuvant therapy were potentially associated with tumor recurrence after thymoma surgery. Multivariate analysis demonstrated that the Masaoka-Koga stage and postoperative adjuvant therapy independently predicted the risk of recurrence in patients with thymoma after surgery. Conclusion: There was no difference in prognosis in patients with thymoma with or without MG. The Masaoka-Koga stage has emerged as an independent prognostic factor affecting recurrence-free survival in patients with thymoma, while postoperative adjuvant therapy represents a poor prognostic factor.

Development and validation of a predictive model for early diagnosis of neonatal acute respiratory distress syndrome based on the Montreux definition.

Objective: Based on the Montreux definition, we aim to develop and validate a predictive model for the early diagnosis of neonatal acute respiratory distress syndrome (ARDS). Methods: A retrospective analysis of clinical data on 198 neonates with respiratory distress from January 2018 to January 2022 was conducted. Neonates meeting Montreux definition were classified as ARDS group (n = 79), while the rest were non-ARDS group (n = 119). Univariate analysis identified indicators for neonatal ARDS, followed by logistic regression to construct a predictive model for early diagnosis. The ability of predictors and models to predict neonatal ARDS was evaluated using area under the curve (AUC), and model performance was estimated through bootstrap resampling. Results: Maternal prenatal fever, abnormal fetal heart beat, meconium-stained amniotic fluid (MSAF), white blood cell (WBC), absolute neutrophil count (ANC), neutrophil percentage (NE%), platelet count (PLT), C-reactive protein (CRP), procalcitonin (PCT), creatine kinase (CK), activated partial thromboplastin time (APTT), serum calcium (Ca) and sodium (Na)exhibited significant differences between the ARDS group and the non-ARDS group (P < 0.05). MSAF (OR=5.037; 95% CI: 1.523-16.657; P < 0.05), ANC (OR = 1.324; 95% CI: 1.172-1.495; P < 0.05), PLT (OR = 0.979; 95% CI: 0.971-0.986; P < 0.05), Ca (OR = 0.020; 95% CI: 0.004-0.088; P < 0.05) emerged as independent risk factors for the development of ARDS. The respective AUC values for MSAF, ANC, PLT, Ca, and the combined prediction models were 0.606, 0.691, 0.808, 0.761 and 0.931. Internal validation showed that the C-index for the model was 0.931. Conclusions: Early application of the model combining MSAF, ANC, PLT and Ca may have a good predictive effect on the early diagnosis of neonatal ARDS.

Clinicopathological and computed tomography features of patients with early-stage non-small-cell lung cancer harboring ALK rearrangement.

BACKGROUND: Although some studies have assessed the correlation between computed tomography (CT) features and anaplastic lymphoma kinase (ALK) rearrangement in patients with non-small-cell lung cancer (NSCLC), few have focused on early-stage patients. The results of some previous studies are inconsistent and contradictory. Therefore, this study aimed to analyze the clinicopathological and CT features of patients with early-stage NSCLC harboring ALK rearrangement. METHODS: This retrospective analysis included 65 patients with ALK rearrangement and 629 ALK-negative patients. All patients had surgically resected NSCLC and were diagnosed with stage IA or stage IIB NSCLC. Clinicopathological features and CT signs, including tumor size and density, consolidation tumor ratio (CTR), lesion location, round or irregular shape, lobulated or spiculated margins, air bronchograms, bubble-like lucency or cavities, and pleural retraction, were investigated according to different genotypes. RESULTS: The prevalence of ALK rearrangement in patients with early-stage NSCLC was 9.3% (65/694). Patients with ALK rearrangement were significantly younger than those without ALK rearrangement (P = 0.033). The frequency of moderate cell differentiation was significantly lower in tumors with ALK rearrangement than in those without ALK rearrangement (46.2% vs. 59.8%, P = 0.034). The frequency of the mucinous subtype was significantly higher in the ALK-positive group than in the ALK-negative group (13.8% vs. 5.4%, P = 0.007). No significant differences were found in any CT signs between the ALK-positive and ALK-negative groups. CONCLUSIONS: Patients with ALK-positive lung cancer may have specific clinicopathological features, including younger age, lower frequency of moderate cell differentiation, and higher frequency of the mucinous type. CT features may not correlate with ALK rearrangement in early-stage lung cancer. Immunohistochemistry or next-generation sequencing is needed to further clarify the genomic mutation status.

Combined large cell neuroendocrine carcinoma, lung adenocarcinoma, and squamous cell carcinoma: a case report and review of the literature.

BACKGROUND: Combined large cell neuroendocrine carcinoma (C-LCNEC) has a poor prognosis and there is no consensus about the treatment regimen for both LCNEC and C-LCNEC patients. CASE PRESENTATION: The patient was a 47-year-old female who received surgical resection. The postoperative histology and staging of the tumor suggested C-LCNEC with adenocarcinoma and squamous cell carcinoma and T2aN0M0 stage IB. Next-generation sequencing test showed KIF5B/RET fusion mutation without EGFR, ALK, RB1, and TP53 alterations. Adjuvant chemotherapy with 4-cycle docetaxel plus carboplatin was given and brain metastasis occurred after 10 months. CONCLUSIONS: C-LCNEC with adenocarcinoma and squamous cell carcinoma is rare and highly aggressive cancer. Surgical resection and adjuvant chemotherapy with SCLC regimen may improve the disease-free survival and overall survival. The accumulation of similar cases will clarify the profile and management of the disease.

Clinicopathological and survival outcomes of 4L lymph node dissection in left lung adenocarcinoma and squamous cell carcinoma.

Background: Whether 4L lymph node dissection (LND) should be performed remains unclear and controversial. Prior studies have found that station 4L metastasis was not rare and that 4L LND may provide survival benefits. The objective of this study was to analyze the clinicopathological and survival outcomes of 4L LND from the perspective of histology. Methods: This retrospective study included 74 patients with squamous cell carcinoma (SCC) and 84 patients diagnosed with lung adenocarcinoma (ADC) between January 2008 and October 2020. All patients underwent pulmonary resection with station 4L LND and were staged as T1-4N0-2M0. Clinicopathological features and survival outcomes were investigated based on histology. The study endpoints were disease-free survival (DFS) and overall survival (OS). Results: The incidence rate of station 4L metastasis was 17.1% (27/158) in the entire cohort, with 8.1% in the SCC group, and 25.0% in the ADC group. No statistical differences in the 5-year DFS rates (67.1% vs. 61.7%, P=0.812) and 5-year OS rates (68.6% vs. 59.3%, P=0.100) were observed between the ADC group and the SCC group. Multivariate logistic analysis revealed that histology (SCC vs. ADC: OR, 0.185; 95% CI, 0.049-0.706; P=0.013) was independently associated with 4L metastasis. Multivariate survival analysis showed that the status of 4L metastasis was an independent factor for DFS (HR, 2.563; 95% CI, 1.282-5.123; P=0.008) but not for OS (HR, 1.597; 95% CI, 0.749-3.402; P=0.225). Conclusion: Station 4L metastasis is not rare in left lung cancer. Patients with ADC have a greater predilection for station 4L metastasis and may benefit more from performing 4L LND.

Thin-slice computed tomography enables to classify pulmonary subsolid nodules into pre-invasive lesion/minimally invasive adenocarcinoma and invasive adenocarcinoma: a retrospective study.

The aim was to investigate the ability of thin-slice computed tomography (TSCT) to differentiate invasive pulmonary adenocarcinomas (IACs) from pre-invasive/minimally invasive adenocarcinoma (AAH-MIAs), manifesting as subsolid nodules (SSNs) of diameter less than 30 mm. The CT findings of 810 patients with single subsolid nodules diagnosed by pathology of resection specimens were analyzed (atypical adenomatous hyperplasia, n = 13; adenocarcinoma in situ, n = 175; minimally invasive adenocarcinoma, n = 285; and invasive adenocarcinoma, n = 337). According to the classification of lung adenocarcinoma published by WHO classification of thoracic tumors in 2015, TSCT features of 368 pure ground-glass nodules (pGGN) and 442 part-solid nodules (PSNs) were compared AAH-MIAs with IACs. Logistic regression and receiver operating characteristic (ROC) curve analyses were performed. In pGGNs, multivariate analysis of factors found to be significant by univariate analysis revealed that higher mean-CT values (p = 0.006, OR 1.006, 95% CI 1.002-1.010), larger tumor size (p < 0.001, OR 1.483, 95% CI 1.304-1.688) with air bronchogram and non-smooth margins were significantly associated with IACs. The optimal cut-off tumor diameter for AAH-MIAs lesions was less than 10.75 mm (sensitivity, 82.8%; specificity, 80.6%) and optimal cut-off mean-CT value - 629HU (sensitivity, 78.1%; specificity, 50.7%). In PSNs, multivariate analysis of factors found to be significant by univariate analysis revealed that smaller tumor diameter (p < 0.001, OR 0.647, 95% CI 0.481-0.871), smaller size of solid component (p = 0.001, OR 83.175, 95% CI 16.748-413.079),and lower mean-CT value of solid component (p < 0.001, OR 1.009, 95% CI 1.004-1.014) were significantly associated with AAH-MIAs (p < 0.05). The optimal cut-off tumor diameter, size of solid component, and mean-CT value of solid component for AAH-MIAs lesions were less than 14.595 mm (sensitivity, 71.1%; specificity, 83.4%), 4.995 mm (sensitivity, 97.8%; specificity, 92.3%) and - 227HU (sensitivity, 65.6%; specificity, 76.3%), respectively. In subsolid nodules, whether pGGN or PSNs, the characteristics of TSCT can help in distinguishing IACs from AAH-MIAs.

Clinical efficacy and safety of adjuvant EGFR-TKIs for resected stage IB lung adenocarcinoma: A real-world study based on propensity score matching.

BACKGROUND: Adjuvant therapy for stage IB non-small cell lung cancer remains debatable. In this real-world study, we evaluate the efficacy and safety of adjuvant epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) for resected stage IB lung adenocarcinoma. METHODS: This real-world study recruited 249 patients diagnosed with stage IB disease after surgical resection between January 2013 and September 2021. Sixty-six (26.5%) patients received adjuvant targeted therapy (TKIs group), and 183 (73.5%) were enrolled in the clinical observation (CO) group. Propensity scores were matched to minimize the observed confounder effects between the two groups, and 59 patient pairs were matched. The primary endpoint was disease-free survival (DFS). RESULTS: In the TKI group, 38 (64.4%) patients chose to receive icotinib, 27.1% (16/59) received gefitinib, and 5 patients (8.5%) chose osimertinib. The median follow-up time was 30.8 months (range: 7-107 months). Two (3.4%) patients in the TKI group and 10 (16.9%) in the CO group experienced disease relapse. The 3-year DFS rates were 98.3% in the TKI group and 83.0% in the CO group (HR: 0.10; 95% CI: 0.01-0.78; p = 0.008). DFS differences were found in the entire cohort (p = 0.005) and the matched cohort (p = 0.024) between the two groups. Multivariate analysis showed that adjuvant EGFR-TKIs was an independent factor for DFS (HR: 0.211; 95% CI: 0.045-0.979; p = 0.047), along with poor cell differentiation (HR: 5.256; 95% CI: 1.648-16.769; p = 0.005), and spread through air spaces (HR: 5.612; 95% CI: 1.137-27.700; p = 0.034). None of the patients discontinued EGFR-TKIs owing to the low occurrence rate of treatment-related serious adverse events. CONCLUSION: Adjuvant EGFR-TKIs could significantly improve DFS among patients with stage IB lung adenocarcinoma compared with CO, with a safe and tolerable profile.

Improving the prediction of Spreading Through Air Spaces (STAS) in primary lung cancer with a dynamic dual-delta hybrid machine learning model: a multicenter cohort study.

BACKGROUND: Reliable pre-surgical prediction of spreading through air spaces (STAS) in primary lung cancer is essential for precision treatment and surgical decision-making. We aimed to develop and validate a dual-delta deep-learning and radiomics model based on pretreatment computed tomography (CT) image series to predict the STAS in patients with lung cancer. METHOD: Six hundred seventy-four patients with pre-surgery CT follow-up scans (with a minimum interval of two weeks) and primary lung cancer diagnosed by surgery were retrospectively recruited from three Chinese hospitals. The training cohort and internal validation cohort, comprising 509 and 76 patients respectively, were selected from Shanghai Chest Hospital; the external validation cohorts comprised 36 and 53 patients from two other centers, respectively. Four imaging signatures (classic radiomics features and deep learning [DL] features, delta-radiomics and delta-DL features) reflecting the STAS status were constructed from the pretreatment CT images by comprehensive methods including handcrafting, 3D views extraction, image registration and subtraction. A stepwise optimized three-step procedure, including feature extraction (by DL and time-base radiomics slope), feature selection (by reproducibility check and 45 selection algorithms), and classification (32 classifiers considered), was applied for signature building and methodology optimization. The interpretability of the proposed model was further assessed with Grad-CAM for DL-features and feature ranking for radiomics features. RESULTS: The dual-delta model showed satisfactory discrimination between STAS and non-STAS and yielded the areas under the receiver operating curve (AUCs) of 0.94 (95% CI, 0.92-0.96), 0.84 (95% CI, 0.82-0.86), and 0.84 (95% CI, 0.83-0.85) in the internal and two external validation cohorts, respectively, with interpretable core feature sets and feature maps. CONCLUSION: The coupling of delta-DL model with delta-radiomics features enriches information such as anisotropy of tumor growth and heterogeneous changes within the tumor during the radiological follow-up, which could provide valuable information for STAS prediction in primary lung cancer.

Risk Factors and Predictive Model for Dermatomyositis Associated with Rapidly Progressive Interstitial Lung Disease.

Background: Rapidly progressive interstitial lung disease (RP-ILD) is a significant complication that determines the prognosis of dermatomyositis (DM). Early RP-ILD diagnosis can improve screening and diagnostic efficiency and provide meaningful guidance to carry out early and aggressive treatment. Methods: A retrospective screening of 284 patients with DM was performed. Clinical and laboratory characteristics of the patients were recorded. The risk factors of RP-ILD in DM patients were screened by logistic regression (LR) and machine learning methods, and the prediction models of RP-ILD were developed by machine learning methods, namely least absolute shrinkage and selection operator (LASSO), random forest (RF), and extreme gradient boosting (XGBoost). Results: According to the result of univariate LR, disease duration is a protective factor for RP-ILD, and ESR, CRP, anti-Ro-52 antibody and anti-MDA5 antibody are risk factors for RP-ILD. The top 10 important variables of the 3 machine learning models were intersected to obtain common important variables, and there were 5 common important variables, namely disease duration, LDH, CRP, anti-Ro-52 antibody and anti-MDA5 antibody. The AUC of LASSO, RF and XGBoost test set were 0.661, 0.667 and 0.867, respectively. We further validated the performance of these three models on validation set, and the results showed that, the AUC of LASSO, RF and XGBoost were 0.764, 0.727 and 0.909, respectively. Based on the results of the models, XGBoost is the optimal model in this study. Conclusion: Disease duration, LDH, CRP, anti-Ro-52 antibody and anti-MDA5 antibody are vital risk factors for RP-ILD in DM. The prediction model constructed using XGBoost can be used for risk identification and early intervention in DM patients with RP-ILD and practical application.

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.

A randomized single-center controlled trial of synchronized intermittent mandatory ventilation with heliox in newborn infants with meconium aspiration syndrome.

OBJECTIVE: This study aimed to investigate the beneficial effects of synchronized intermittent mandatory ventilation (SIMV) with heliox in newborn infants with meconium aspiration syndrome (MAS). METHODS: Seventy-one newborn infants with MAS in the neonatal intensive care unit (NICU) of Daping Hospital of Army Medical University were enrolled in the trial. Infants treated with SIMV were randomized and divided into the heliox group (n = 35) and control group (n = 36). The heliox group received heliox for 6 h followed by air-oxygen mixed gas, and the control group received air-oxygen mixed gas. The primary outcome measures were PaO2 /FiO2 (P/F) and the extubation time. The secondary outcome measures were the incidence of mechanical ventilation complications, hospital length of stay in the NICU, blood gas analysis, and inflammation markers. RESULTS: The P/F in the heliox group was significantly better than that in the control group (p < .001). The extubation time and hospital length of stay in the NICU in the heliox group were shorter than those in the control group (p < .001). The inflammation markers at 6 h and myocardial injury markers at 24 h were decreased compared with those at 0 h, and those in the heliox group were more significantly decreased than those in the control group ([interleukin {IL}-6/IL-8/tumor necrosis factor α] p < .001, [C-reaction protein] p = .012; [creatine kinase] p < .001, [CK-MB] p = .041). CONCLUSION: Heliox appears to be more effective in reducing the length of ventilation and increasing carbon dioxide eliminations than an air-oxygen mixture in infants with MAS under the support of SIMV.

[Clinicopathological characteristics and risk factors of station 4L lymph node metastasis of left non-small cell lung cancer].

OBJECTIVE: To analyze the clinicopathological characteristics and risk factors of 4L lymph node metastasis in left non-small cell lung cancer. METHODS: We retrospectively analyzed the data of 134 patients undergoing surgical resection of left non-small cell lung cancer and 4L lymph node dissection, including 60 patients with squamous cell carcinoma (SCC) and 74 with lung adenocarcinoma (ADC). The clinicopathological characteristics of the patients were analyzed, and logistic regression analysis was used to identify the predictors of station 4L metastasis. RESULTS: Of these patients, 16.4% (22/134) presented with station 4L metastasis. The patients with SCC and ADC showed significant differences in age, gender, smoking history, neoadjuvant chemotherapy, tumor size, tumor location and type, visceral pleural invasion, Ki-67 index, 4L metastasis and pathological TNM stage (stage Ⅱ). The rate of station 4L metastasis was significantly lower in SCC group than in ADC group. Univariate analysis revealed that pathological types (SCC or ADC), visceral pleural invasion, lymphovascular invasion, tumor markerabnormality, and station 5 to 10 metastasis were all high-risk factors for station 4L metastasis. Multivariate analysis suggested that the pathological type (OR=0.120, P=0.025), station 5 metastasis (OR=18.784, P=0.007) and station 10 metastasis (OR=5.233, P=0.044) were independent risk factors for 4L metastasis in patients with left non-small cell lung cancer. CONCLUSIONS: In patients with left non-small cell lung cancer, station 4L metastasis is not rare and is more likely to occur in patients with lung adenocarcinoma. Dissection of the 4L lymph nodes should be performed in cases with low risk of damages of the adjacent tissues and high risk of station 4L metastasis.