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Aging Jun 2024A deep understanding of the biological mechanisms of lung cancer offers more precise treatment options for patients. In our study, we integrated data from the Gene...
A deep understanding of the biological mechanisms of lung cancer offers more precise treatment options for patients. In our study, we integrated data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) to investigate lung adenocarcinoma. Analyzing 538 lung cancer samples and 31 normal samples, we focused on 3076 autophagy-related genes. Using Seurat, dplyr, tidyverse, and ggplot2, we conducted single-cell data analysis, assessing the quality and performing Principal Component Analysis (PCA) and t-SNE analyses. Differential analysis of TCGA data using the "Limma" package, followed by immune infiltration analysis using the CIBERSORT algorithm, led us to identify seven key genes. These genes underwent further scrutiny through consensus clustering and gene set variation analysis (GSVA). We developed a prognostic model using Lasso Cox regression and multivariable Cox analysis, which was then validated with a nomogram, predicting survival rates for lung adenocarcinoma. The model's accuracy and universality were corroborated by ROC curves. Additionally, we explored the relationship between immune checkpoint genes and immune cell infiltration and identified two key genes, HLA-DQB1 and OLR1. This highlighted their potential as therapeutic targets. Our comprehensive approach sheds light on the molecular landscape of lung adenocarcinoma and offers insights into potential treatment strategies, emphasizing the importance of integrating single-cell and genomic data in cancer research.
PubMed: 38942606
DOI: 10.18632/aging.205973 -
Sarcoidosis, Vasculitis, and Diffuse... Jun 2024
PubMed: 38940709
DOI: 10.36141/svdld.v41i2.15524 -
Journal of Cardiothoracic Surgery Jun 2024In recent years, with the widespread use of chest CT, the detection rate of pulmonary nodules has significantly increased (Abtin and Brown, J Clin Oncol 31:1002-8,... (Review)
Review
In recent years, with the widespread use of chest CT, the detection rate of pulmonary nodules has significantly increased (Abtin and Brown, J Clin Oncol 31:1002-8, 2013). Video-assisted thoracoscopic surgery (VATS) is the most commonly used method for suspected malignant nodules. However, for nodules with a diameter less than 1 cm, or located more than 1.5 cm from the pleural edge, especially ground-glass nodules, it is challenging to achieve precise intraoperative localization by manual palpation (Ciriaco et al., Eur J Cardiothorac Surg 25:429-33, 2004). Therefore, preoperative accurate localization of such nodules becomes a necessary condition for precise resection. This article provides a comprehensive review and analysis of the research progress in pulmonary nodule localization, focusing on four major localization techniques: Percutaneous puncture-assisted localization, Bronchoscopic preoperative pulmonary nodule localization, 3D Printing-Assisted Localization, and intraoperative ultrasound-guided pulmonary nodule localization.
Topics: Humans; Lung Neoplasms; Solitary Pulmonary Nodule; Thoracic Surgery, Video-Assisted; Multiple Pulmonary Nodules; Bronchoscopy; Tomography, X-Ray Computed; Printing, Three-Dimensional
PubMed: 38937797
DOI: 10.1186/s13019-024-02911-8 -
Journal of Cardiothoracic Surgery Jun 2024Currently, the differentiation between benign and malignant cystic pulmonary nodules poses a significant challenge for clinicians. The objective of this retrospective...
BACKGROUND
Currently, the differentiation between benign and malignant cystic pulmonary nodules poses a significant challenge for clinicians. The objective of this retrospective study was to construct a predictive model for determining the likelihood of malignancy in patients with cystic pulmonary nodules.
METHODS
The current study involved 129 patients diagnosed with cystic pulmonary nodules between January 2017 and June 2023 at the Neijiang First People's Hospital. The study gathered the clinical data, preoperative imaging features of chest CT, and postoperative histopathological results for both cohorts. Univariate and multivariate logistic regression analyses were employed to identify independent risk factors, from which a prediction model and nomogram were developed. In addition, The model's performance was assessed through receiver operating characteristic (ROC) curve analysis, calibration curve analysis, and decision curve analysis (DCA).
RESULTS
A cohort of 129 patients presenting with cystic pulmonary nodules, consisting of 92 malignant and 37 benign lesions, was examined. Logistic data analysis identified a cystic airspace with a mural nodule, spiculation, mural morphology, and the number of cystic cavities as significant independent predictors for discriminating between benign and malignant cystic lung nodules. The nomogram prediction model demonstrated a high level of predictive accuracy, as evidenced by an area under the ROC curve (AUC) of 0.874 (95% CI: 0.804-0.944). Furthermore, the calibration curve of the model displayed satisfactory calibration. DCA proved that the prediction model was useful for clinical application.
CONCLUSION
In summary, the risk prediction model for benign and malignant cystic pulmonary nodules has the potential to assist clinicians in the diagnosis of such nodules and enhance clinical decision-making processes.
Topics: Humans; Nomograms; Male; Female; Retrospective Studies; Tomography, X-Ray Computed; Middle Aged; Lung Neoplasms; Diagnosis, Differential; Multiple Pulmonary Nodules; Aged; Solitary Pulmonary Nodule; ROC Curve; Adult; Radiomics
PubMed: 38937772
DOI: 10.1186/s13019-024-02936-z -
Radiologie (Heidelberg, Germany) Jun 2024Cystic and nodular lung diseases encompass a broad spectrum of diseases with different etiologies and clinicoradiological presentations. Their differentiation is... (Review)
Review
BACKGROUND
Cystic and nodular lung diseases encompass a broad spectrum of diseases with different etiologies and clinicoradiological presentations. Their differentiation is crucial for patient management but can be complex due to diseases with features of both categories and overlapping radiological patterns.
OBJECTIVE
This study aims to describe the imaging features of cystic and nodular lung diseases in high-resolution computed tomography (CT) in detail-primarily based on their etiology-in order to allow a more accurate differential diagnosis of these diseases.
MATERIALS AND METHODS
A narrative review based on current literature on the topic was conducted from a clinicoradiological perspective.
RESULTS
This paper systematically categorizes the differential diagnosis of cystic and nodular lung disease and provides insights into their radiological patterns and etiologies. It highlights the role of CT in the diagnosis of these diseases and emphasizes the importance of multidisciplinary panels combining expertise from radiology, pulmonology, rheumatology, and pathology.
CONCLUSION
Reliable differential diagnosis of cystic and nodular lung diseases, particularly based on their radiological features alone, remains difficult due to their overlapping and dynamic nature. Multidisciplinary boards should be the clinical standard for accurate work-up of these diseases, as they combine the medical history, symptoms, radiological findings, and, if necessary, histopathological examinations, thus providing a more robust framework for diagnosis and management.
PubMed: 38937303
DOI: 10.1007/s00117-024-01341-w -
The American Journal of the Medical... Jun 2024Some patients with pulmonary tuberculosis (PTB) do not display typical clinical features, leading to delays in diagnosis and treatment.
PURPOSE
Some patients with pulmonary tuberculosis (PTB) do not display typical clinical features, leading to delays in diagnosis and treatment.
METHODS
We retrospectively analyzed PTB patients admitted to the Second Affiliated Hospital of Chongqing Medical University between 2017 and 2020. They are divided into pathological group (diagnosed through pathological biopsy) and control group (diagnosed via sputum or lavage fluid). Clinical data of both groups were compared. Based on radiographic features, the pathological group was further divided into the inflammation group, peripheral nodule group, and central occupancy group. We then statistically analyzed the computed tomography (CT) signs, bronchoscopic manifestations and results of pathological biopsy for each subgroup.
RESULTS
The pathological group consisted of 75 patients, while the control group had 338 patients. Multivariate logistic regression analysis showed that the pathological group had more diabetes (OR=3.266, 95%CI=1.609-6.630, P=0.001), lower ESR (OR=0.984, 95%CI=0.971-0.998, P=0.022), and lower CRP (OR=0.990, 95%CI=0.980-0.999, P=0.036). In the three subgroups, the exudative lesions in the inflammation group were mostly located in atypical areas of PTB. The lobulation sign and spiculation sign were frequently observed in the peripheral nodule group. All presented with significant hilar mediastinal lymphadenopathy in the central occupancy group. In the pathological group, bronchoscopic manifestations typically included mucosal edema and bronchial stenosis.
CONCLUSION
Diabetes is an independent risk factor for atypical PTB. Expression of erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) in atypical PTB is low. Radiologically, it is most easily misdiagnosed when presented as peripheral solid nodules or masses, so a biopsy is recommended.
PubMed: 38936510
DOI: 10.1016/j.amjms.2024.06.023 -
European Journal of Cardio-thoracic... Jun 2024Thoracoscopic segmentectomy is the recommended treatment option for small peripheral pulmonary nodules. To assess the ability of preoperative 3D reconstruction CT to...
OBJECTIVES
Thoracoscopic segmentectomy is the recommended treatment option for small peripheral pulmonary nodules. To assess the ability of preoperative 3D reconstruction CT to shorten the operative time and improve perioperative outcomes in thoracoscopic segmentectomy compared with standard chest CT, we conducted this randomized controlled trial.
METHODS
The DRIVATS study was a multicentre, randomized controlled trial conducted in three hospitals between July 2019 and November 2023. Patients with small peripheral pulmonary nodules not reaching segments borders were randomized in a 1:1 ratio to receive either 3D reconstruction CT or standard chest CT before thoracoscopic segmentectomy. The primary end-point was operative time. The secondary end-points included incidence of postoperative complications, intraoperative blood loss and operative accident event.
RESULTS
A total of 191 patients were enrolled in this study: 95 in the 3D reconstruction CT group and 96 in the standard chest CT group. All patients underwent thoracoscopic segmentectomy except for one patient in the standard chest CT group who received a wedge resection. There is no significant difference in operative time between the 3D reconstruction CT group (median, 100 min [IQR, 85-120]) and the standard chest CT group (median, 100 min [IQR, 81-140]) (P = 0.82). Only one intraoperative complication occurred in the standard chest CT group. No significant difference was observed in the incidence of postoperative complications between the two groups (P = 0.52). Other perioperative outcomes were also similar.
CONCLUSIONS
In patients with small peripheral pulmonary nodules not reaching segments borders, the use of 3D reconstruction CT in thoracoscopic segmentectomy was feasible, but it did not result in significant differences in operative time or perioperative outcomes compared to standard chest CT.
PubMed: 38936342
DOI: 10.1093/ejcts/ezae250 -
Frontiers in Endocrinology 2024The growing incidence of differentiated thyroid cancer (DTC) have been linked to insulin resistance and metabolic syndrome. The imperative need for developing effective...
OBJECTIVES
The growing incidence of differentiated thyroid cancer (DTC) have been linked to insulin resistance and metabolic syndrome. The imperative need for developing effective diagnostic imaging tools to predict the non-iodine-avid status of lung metastasis (LMs) in differentiated thyroid cancer (DTC) patients is underscored to prevent unnecessary radioactive iodine treatment (RAI).
METHODS
Primary cohort consisted 1962 pretreated LMs of 496 consecutive DTC patients with pretreated initially diagnosed LMs who underwent chest CT and subsequent post-treatment radioiodine SPECT. After automatic lesion segmentation by SE V-Net, SE Net deep learning was trained to predict non-iodine-avid status of LMs. External validation cohort contained 123 pretreated LMs of 24 consecutive patients from other two hospitals. Stepwise validation was further performed according to the nodule's largest diameter.
RESULTS
The SE-Net deep learning network yielded area under the receiver operating characteristic curve (AUC) values of 0.879 (95% confidence interval: 0.852-0.906) and 0.713 (95% confidence interval: 0.613-0.813) for internal and external validation. With the LM diameter decreasing from ≥10mm to ≤4mm, the AUCs remained relatively stable, for smallest nodules (≤4mm), the model yielded an AUC of 0.783. Decision curve analysis showed that most patients benefited using deep learning to decide radioactive I treatment.
CONCLUSION
This study presents a noninvasive, less radioactive and fully automatic approach that can facilitate suitable DTC patient selection for RAI therapy of LMs. Further prospective multicenter studies with larger study cohorts and related metabolic factors should address the possibility of comprehensive clinical transformation.
Topics: Humans; Thyroid Neoplasms; Iodine Radioisotopes; Lung Neoplasms; Female; Male; Middle Aged; Adult; Aged; Deep Learning; Retrospective Studies; Tomography, Emission-Computed, Single-Photon; Cohort Studies
PubMed: 38933823
DOI: 10.3389/fendo.2024.1429115 -
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi =... Jun 2024Automatic detection of pulmonary nodule based on computer tomography (CT) images can significantly improve the diagnosis and treatment of lung cancer. However, there is...
Automatic detection of pulmonary nodule based on computer tomography (CT) images can significantly improve the diagnosis and treatment of lung cancer. However, there is a lack of effective interactive tools to record the marked results of radiologists in real time and feed them back to the algorithm model for iterative optimization. This paper designed and developed an online interactive review system supporting the assisted diagnosis of lung nodules in CT images. Lung nodules were detected by the preset model and presented to doctors, who marked or corrected the lung nodules detected by the system with their professional knowledge, and then iteratively optimized the AI model with active learning strategy according to the marked results of radiologists to continuously improve the accuracy of the model. The subset 5-9 dataset of the lung nodule analysis 2016(LUNA16) was used for iteration experiments. The precision, F1-score and MioU indexes were steadily improved with the increase of the number of iterations, and the precision increased from 0.213 9 to 0.565 6. The results in this paper show that the system not only uses deep segmentation model to assist radiologists, but also optimizes the model by using radiologists' feedback information to the maximum extent, iteratively improving the accuracy of the model and better assisting radiologists.
Topics: Humans; Lung Neoplasms; Diagnosis, Computer-Assisted; Tomography, X-Ray Computed; Algorithms; Solitary Pulmonary Nodule; Multiple Pulmonary Nodules; Radiographic Image Interpretation, Computer-Assisted; Machine Learning
PubMed: 38932536
DOI: 10.7507/1001-5515.202310044 -
Nutrients Jun 2024Saikosaponin D (SSD), derived from L., has various pharmacological properties, including immunoregulatory, anti-inflammatory, and anti-allergic effects. Several studies...
Saikosaponin D (SSD), derived from L., has various pharmacological properties, including immunoregulatory, anti-inflammatory, and anti-allergic effects. Several studies have investigated the anti-tumor effects of SSD on cancer in multiple organs. However, its role in colorectal cancer (CRC) remains unclear. Therefore, this study aimed to elucidate the suppressive effects of SSD on CRC cell survival and metastasis. SSD reduced the survival and colony formation ability of CRC cells. SSD-induced autophagy and apoptosis in CRC cells were measured using flow cytometry. SSD treatment increased LC3B and p62 autophagic factor levels in CRC cells. Moreover, SSD-induced apoptosis occurred through the cleavage of caspase-9, caspase-3, and PARP, along with the downregulation of the Bcl-2 family. In the in vivo experiment, a reduction in the number of metastatic tumor nodules in the lungs was observed after the oral administration of SSD. Based on these results, SSD inhibits the metastasis of CRC cells to the lungs by inducing autophagy and apoptosis. In conclusion, SSD suppressed the proliferation and metastasis of CRC cells, suggesting its potential as a novel substance for the metastatic CRC treatment.
Topics: Saponins; Oleanolic Acid; Autophagy; Colorectal Neoplasms; Apoptosis; Humans; Lung Neoplasms; Animals; Cell Line, Tumor; Cell Proliferation; Mice; Mice, Inbred BALB C; Antineoplastic Agents, Phytogenic; Xenograft Model Antitumor Assays; Cell Survival; Mice, Nude
PubMed: 38931199
DOI: 10.3390/nu16121844