Objective To explore the diagnostic and treatment value of computed tomography (CT)-guided embolization coil localization of pulmonary nodules accurately resected under the thoracoscope. Methods Between October 2015 and October 2016, 40 patients with undiagnosed nodules of 15 mm or less were randomly divided into a no localization group (n=20, 11 males and 9 females with an average age of 60.50±8.27 years) or preoperative coil localization group (n=20, 12 males and 8 females with an average age of 61.35±8.47 years). Coils were placed with the distal end deep to the nodule and the superficial end coiled on the visceral pleural surface with subsequent visualization by video-assisted thoracoscopic (VATS). Nodules were removed by VATS wedge excision using endo staplers. The tissue was sent for rapid pathological examination, and the pulmonary nodules with definitive pathology found at the first time could be defined as the exact excision. Results The age, sex, forced expiratory volume in the first second of expiration, nodule size/depth were similar between two groups. The coil group had a higher rate of accurate resection (100.00% vs. 70.00%, P=0.008), less operation time to nodule excision (35.65±3.38 minvs. 44.38±11.53 min,P=0.003), and reduced stapler firings (3.25±0.85vs. 4.44±1.26,P=0.002) with no difference in total costs. Conclusion Preoperative CT-guided coil localization increases the rate of accurate resection.
ObjectiveTo assess the prognostic significance of the Controlling Nutritional Status (CONUT) score in patients with non-small cell lung cancer (NSCLC) and its association with clinicopathological characteristics. MethodsThe relevant studies investigating the association between CONUT score and prognosis of NSCLC patients were systematically searched in the PubMed, Web of Science, EMbase, Cochrane Library, CNKI, Wanfang Database and other databases from their inception to July 2023. Two independent researchers screened the references according to predefined inclusion and exclusion criteria, extracted data and conducted quality assessment. The quality of included references was evaluated using New Castle-Ottawa Scale (NOS). The meta-analysis was performed using Stata 17.0 software, and a combined hazard ratio (HR) or odds ratio (OR) and 95% confidence interval (CI) were calculated to assess the association of CONUT score with prognosis and clinicopathological characteristics in NSCLC patients. ResultsA total of 17 cohort studies, comprising 5182 NSCLC patients with stage Ⅰ-Ⅳ, were included in this analysis. All studies had a NOS≥6 points. The meta-analysis showed that there was a significant correlation between CONUT score and overall survival (OS) as well as disease-free survival (DFS) among NSCLC patients: the higher the score, the shorter the OS [HR=1.87, 95%CI (1.58, 2.21), P<0.001] and DFS [HR=1.91, 95%CI (1.63, 2.24), P<0.001]. These differences were statistically significant. Furthermore, CONUT score was significantly associated with age, smoking status, tumor stage, and N stage (P<0.05). ConclusionA higher CONUT score is associated with a poorer OS and DFS in patients with NSCLC, and CONUT score can be used as a potential predictor of NSCLC prognosis.
Objective To establish a patient-derived tumor xenograft (PDTX) model and to observe the latency and rate of tumor formation, tumor size, tumor invasion and metastasis of transplanted tumors. Methods Seven patients with chest tumor in Drum Tower Hospital from April to December 2015 were chosen. There were 5 males and 2 females with age ranging from 61-71 years, including 4 patients of esophageal tumor and 3 patients of lung tumor. PDTX model was established by surgical removal of fresh tumor tissues of these patients and transplantation in NOD-Prkdcem26Il2rgem26Nju subcutaneous (NCG) mice. The latency and rate of tumor formation, tumor size, tumor invasion and metastasis of transplanted tumors were observed, and pathology of HE staining and immunohistochemical testing results were compared between PDTX model and the patients. Results PDTX model was successfully established in 4 patients, and the success rate was 66.7%, including 2 patients of esophageal cancer. The PDTX model retained the differentiation, morphological and structural characteristics of original tumors. Conclusion Pathology and molecular biology characteristics of PDTX model are consistent with the original tumor, which can be an " avatar” of tumor patients for clinical pharmacodynamics screening and new drug research and development.
ObjectiveTo summarize and explore the application of machine learning models to survival data with non-proportional hazards (NPH), and to provide a methodological reference for large-scale, high-dimensional survival data. MethodsFirst, the concept of NPH and related testing methods were outlined. Then the advantages and disadvantages of machine learning algorithm-based NPH survival analysis methods were summarized based on the relevant literature. Finally, using real-world clinical data, a case study was conducted with two ensemble machine learning models and two deep learning models in survival data with NPH: a study of the risk of death within 30 days in stroke patients in the ICU. ResultsEight commonly used machine learning model-based NPH survival analyses were identified, including five traditional machine learning models such as random survival forest and three deep learning models based on artificial neural networks (e.g., DeepHit). The case study found that the random survival forest model performed the best (C-index=0.773, IBS=0.151), and the permutation importance-based algorithm found that age was the most important characteristic affecting the risk of death in stroke patients. ConclusionSurvival big data in the era of precision medicine presenting NPH are common, and machine learning model-based survival analysis can be used when faced with more complex survival data and higher survival analysis needs.