Abstract
Quercetin is a kind of distinctive bioactive flavonoid that has potent anti-oxidant, anti-inflammatory and anti-diabetic properties. The present article was designed to check the effect of ...quercetin on diabetic retinopathy. Adult retinal pigment epithelial cell line (ARPE)-19 cells were pre-treated with quercetin and then stimulated by high glucose. Cell damage was evaluated by CCK-8 assay, flow cytometer, quantitative reverse transcriptase polymerase chain reaction (qRT-PCR), enzyme-linked immunosorbent assay, 2,7-dichlorofluorescein diacetate probe and western blot. The association between quercetin and miR-29b expression as well as the downstream pathways was studied by qRT-PCR and western blot. Pre-treating ARPE-19 cells with quercetin clearly attenuated high glucose-induced viability loss, apoptosis, MCP-1 and IL-6 overproduction and reactive oxygen species (ROS) generation. Quercetin down-regulated p53, Bax and cleaved-caspase-3 expression, while up-regulated CyclinD1, CDK4 and Bcl-2. miR-29b was low expressed in high glucose-treated cell, but quercetin elevated its expression. Moreover, the protective action of quercetin towards ARPE-19 cells was attenuated when miR-29b was suppressed. Also, quercetin promoted PTEN/AKT pathway, while inhibited NF-κB pathway via a miR-29b-dependent way. These data illustrated quercetin possibly possess the anti-diabetic retinopathy potential, as quercetin clearly attenuated high glucose-evoked damage in ARPE-19 cells. The protective action of quercetin may due to its regulation on miR-29b expression as well as PTEN/AKT and NF-κB pathways.
We established a CT-derived approach to achieve accurate progression-free survival (PFS) prediction to EGFR tyrosine kinase inhibitors (TKI) therapy in multicenter, stage IV
-mutated non-small cell ...lung cancer (NSCLC) patients.
A total of 1,032 CT-based phenotypic characteristics were extracted according to the intensity, shape, and texture of NSCLC pretherapy images. On the basis of these CT features extracted from 117 stage IV
-mutant NSCLC patients, a CT-based phenotypic signature was proposed using a Cox regression model with LASSO penalty for the survival risk stratification of EGFR-TKI therapy. The signature was validated using two independent cohorts (101 and 96 patients, respectively). The benefit of EGFR-TKIs in stratified patients was then compared with another stage-IV
-mutant NSCLC cohort only treated with standard chemotherapy (56 patients). Furthermore, an individualized prediction model incorporating the phenotypic signature and clinicopathologic risk characteristics was proposed for PFS prediction, and also validated by multicenter cohorts.
The signature consisted of 12 CT features demonstrated good accuracy for discriminating patients with rapid and slow progression to EGFR-TKI therapy in three cohorts (HR: 3.61, 3.77, and 3.67, respectively). Rapid progression patients received EGFR TKIs did not show significant difference with patients underwent chemotherapy for progression-free survival benefit (
= 0.682). Decision curve analysis revealed that the proposed model significantly improved the clinical benefit compared with the clinicopathologic-based characteristics model (
< 0.0001).
The proposed CT-based predictive strategy can achieve individualized prediction of PFS probability to EGFR-TKI therapy in NSCLCs, which holds promise of improving the pretherapy personalized management of TKIs.
.
Objectives
To distinguish squamous cell carcinoma (SCC) from lung adenocarcinoma (ADC) based on a radiomic signature
Methods
This study involved 129 patients with non-small cell lung cancer (NSCLC) ...(81 in the training cohort and 48 in the independent validation cohort). Approximately 485 features were extracted from a manually outlined tumor region. The LASSO logistic regression model selected the key features of a radiomic signature. Receiver operating characteristic curve and area under the curve (AUC) were used to evaluate the performance of the radiomic signature in the training and validation cohorts.
Results
Five features were selected to construct the radiomic signature for histologic subtype classification. The performance of the radiomic signature to distinguish between lung ADC and SCC in both training and validation cohorts was good, with an AUC of 0.905 (95% confidence interval CI: 0.838 to 0.971), sensitivity of 0.830, and specificity of 0.929. In the validation cohort, the radiomic signature showed an AUC of 0.893 (95% CI: 0.789 to 0.996), sensitivity of 0.828, and specificity of 0.900.
Conclusions
A unique radiomic signature was constructed for use as a diagnostic factor for discriminating lung ADC from SCC. Patients with NSCLC will benefit from the proposed radiomic signature.
Key points
• Machine learning can be used for auxiliary distinguish in lung cancer.
• Radiomic signature can discriminate lung ADC from SCC.
• Radiomics can help to achieve precision medical treatment.
Background
To date, published studies have shown that
18
F-FDG PET/CT and CT have limited value in differentiating benign and malignant solitary fibrous tumours of the pleura (SFTP). This study aimed ...to determine whether the metabolic and morphological characteristics of
18
F-FDG PET/CT can be a valuable addition to diagnostic tools for SFTPs.
Methods
From January 2016 to November 2021, we performed a retrospective review in 32 SFTPs patients who underwent
18
F-FDG PET/CT scan. All the SFTP diagnoses were confirmed by surgical resection or biopsy samples. The metabolic parameters (including SUVmax, SUVmean, MTV, TLG, and SULmax) were obtained from
18
F-FDG PET/CT images.
Results
Thirty-two patients with SFTP were consecutively identified. The malignant SFTPs have higher Ki-67 expression (
P
= 0.005). The study observed that tumour heterogeneity without contrast injection (
P
= 0.001) and intratumor blood vessels (
P
= 0.047) were morphological features associated with malignant SFTP. Malignant SFTP was more frequently observed with higher SUVmax values (
P
= 0.001), higher SUVmean values (
P
= 0.001), higher TLG values (
P
= 0.006), and higher SULmax values (
P
< 0.001). For predicting malignant SFTP, the AUC values of SUVmax, SUVmean, TLG, and SULmax obtained by the area under curve analysis were 0.970 (95% CI 0.907–1.000;
P
= 0.001), 0.874 (95% CI 0.675–1.000;
P
= 0.009), 0.807 (95% CI 0.654–0.961;
P
= 0.031), and 0.911 (95% CI 0.747–1.000;
P
= 0.004), respectively.
Conclusion
The study showed that metabolic and morphological features were useful for distinguishing malignant from benign SFTPs.
To develop a short-term follow-up CT-based radiomics approach to predict response to immunotherapy in advanced non-small-cell lung cancer (NSCLC) and investigate the prognostic value of radiomics ...features in predicting progression-free survival (PFS) and overall survival (OS). We first retrospectively collected 224 advanced NSCLC patients from two centers, and divided them into a primary cohort and two validation cohorts respectively. Then, we processed CT scans with a series of image preprocessing techniques namely, tumor segmentation, image resampling, feature extraction and normalization. To select the optimal features, we applied the feature ranking with recursive feature elimination method. After resampling the training dataset with a synthetic minority oversampling technique, we applied the support vector machine classifier to build a machine-learning-based classification model to predict response to immunotherapy. Finally, we used Kaplan-Meier (KM) survival analysis method to evaluate prognostic value of rad-score generated by CT-radiomics model. In two validation cohorts, the delta-radiomics model significantly improved the area under receiver operating characteristic curve from 0.64 and 0.52 to 0.82 and 0.87, respectively (P < .05). In sub-group analysis, pre- and delta-radiomics model yielded higher performance for adenocarcinoma (ADC) patients than squamous cell carcinoma (SCC) patients. Through the KM survival analysis, the rad-score of delta-radiomics model had a significant prognostic for PFS and OS in validation cohorts (P < .05). Our results demonstrated that (1) delta-radiomics model could improve the prediction performance, (2) radiomics model performed better on ADC patients than SCC patients, (3) delta-radiomics model had prognostic values in predicting PFS and OS of NSCLC patients.
Abstract
Occult nodal metastasis (ONM) plays a significant role in comprehensive treatments of non-small cell lung cancer (NSCLC). This study aims to develop a deep learning signature based on ...positron emission tomography/computed tomography to predict ONM of clinical stage N0 NSCLC. An internal cohort (n = 1911) is included to construct the deep learning nodal metastasis signature (DLNMS). Subsequently, an external cohort (n = 355) and a prospective cohort (n = 999) are utilized to fully validate the predictive performances of the DLNMS. Here, we show areas under the receiver operating characteristic curve of the DLNMS for occult N1 prediction are 0.958, 0.879 and 0.914 in the validation set, external cohort and prospective cohort, respectively, and for occult N2 prediction are 0.942, 0.875 and 0.919, respectively, which are significantly better than the single-modal deep learning models, clinical model and physicians. This study demonstrates that the DLNMS harbors the potential to predict ONM of clinical stage N0 NSCLC.
To identify a computed tomography (CT)-based radiomic signature for predicting progression-free survival (PFS) in stage IV anaplastic lymphoma kinase (
)-positive non-small-cell lung cancer (NSCLC) ...patients treated with tyrosine kinase inhibitor (TKI) crizotinib.
This retrospective proof-of-concept study included a cohort of 63 stage IV
-positive NSCLC patients who had received TKI crizotinib therapy for model construction and validation. Another independent cohort including 105 stage IV
-positive NSCLC patients was also used for external validation in
-TKI treatment. We initially extracted 481 quantitative three-dimensional features derived from manually segmented tumor volumes of interest. Pearson's correlation analysis along with the least absolute shrinkage and selection operator (LASSO) penalized Cox proportional hazards regression was successively performed to select critical radiomic features. A CT-based radiomic signature for PFS prediction was obtained using multivariate Cox regression. The performance evaluation of the radiomic signature was conducted using the concordance index (C-index), time-dependent receiver operating characteristic (ROC) analysis, and Kaplan-Meier survival analysis.
A radiomic signature containing three features showed significant prognostic performance for
-positive NSCLC patients in both the training cohort (C-index, 0.744; time-dependent AUC, 0.895) and the validation cohort (C-index, 0.717; time-dependent AUC, 0.824). The radiomic signature could significantly risk-stratify
-positive NSCLC patients (hazard ratio, 2.181;
< 0.001) and outperformed other prognostic factors. However, no significant association with PFS was captured for the radiomic signature in the
-positive NSCLC cohort (log-rank tests,
= 0.41).
The CT-based radiomic features can capture valuable information regarding the tumor phenotype. The proposed radiomic signature was found to be an effective prognostic factor in stage IV
mutated nonsynchronous nodules in NSCLC patients treated with a TKI.
The aim of the study was to investigate clinical features of patients with AIDS having respiratory symptoms as initial manifestations and help in the early diagnosis. Eighty-eight patients admitted ...to the Shanghai Pulmonary Hospital were included in the study. General data, clinical manifestations, laboratory tests, chest computed tomography (CT) imaging features, treatments, and prognosis were analyzed. Peripheral leukopenia, lymphopenia, hypoxemia, and reduced percentage of CD4+ T lymphocytes were found in 25.6%, 43.6%, 27.5%, and 94.9% of the patients, respectively. Pneumocystis jirovecii pneumonia (PCP) was the most frequent cause of opportunistic pulmonary infection. Patients with PCP had more bilateral lung involvement and ground-glass shadow in CT manifestations. A follow-up of the 43 patients transferred to the Public Health Center showed improvement in 27 (62.8%), stabilization in 4 (9.3%), worsening in 1 (2.3%), and death in 11 (25.6%) patients. Detailed medical history recording, screening of human immunodeficiency virus antibody, and flow cytometry would improve the diagnostic efficiency of AIDS in patients with diffuse ground-glass shadow in chest CT. Early and empirical treatment could improve the prognosis.
Highlights • TSCT features can help distinguish IAs from AAH-MIAs both in pure and mixed GGNs. • TSCT features can help identify AIS from AAH in pure GGNs. • Imaging features will be helpful to guide ...the therapeutic choice for patients with GGNs.
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