Objective The management of pulmonary nodules is a common clinical problem, and this study constructed a nomogram model based on FUT7 methylation combined with CT imaging features to predict the risk of adenocarcinoma in patients with pulmonary nodules. Methods The clinical data of 219 patients with pulmonary nodules diagnosed by histopathology at the First Affiliated Hospital of Zhengzhou University from 2021 to 2022 were retrospectively analyzed. The FUT7 methylation level in peripheral blood were detected, and the patients were randomly divided into training set (n=154) and validation set (n=65) according to proportion of 7:3. They were divided into a lung adenocarcinoma group and a benign nodule group according to pathological results. Single-factor analysis and multi-factor logistic regression analysis were used to construct a prediction model in the training set and verified in the validation set. The receiver operating characteristic (ROC) curve was used to evaluate the discrimination of the model, the calibration curve was used to evaluate the consistency of the model, and the clinical decision curve analysis (DCA) was used to evaluate the clinical application value of the model. The applicability of the model was further evaluated in the subgroup of high-risk CT signs (located in the upper lobe, vascular sign, and pleural sign). Results Multivariate logistic regression analysis showed that female, age, FUT7_CpG_4, FUT7_CpG_6, sub-solid nodules, lobular sign and burr sign were independent risk factors for lung adenocarcinoma (P<0.05). A column-line graph prediction model was constructed based on the results of the multifactorial analysis, and the area under the ROC curve was 0.925 (95%CI 0.877 - 0.972 ), and the maximum approximate entry index corresponded to a critical value of 0.562, at which time the sensitivity was 89.25%, the specificity was 86.89%, the positive predictive value was 91.21%, and the negative predictive value was 84.13%. The calibration plot predicted the risk of adenocarcinoma of pulmonary nodules was highly consistent with the risk of actual occurrence. The DCA curve showed a good clinical net benefit value when the threshold probability of the model was 0.02 - 0.80, which showed a good clinical net benefit value. In the upper lobe, vascular sign and pleural sign groups, the area under the ROC curve was 0.903 (95%CI 0.847 - 0.959), 0.897 (95%CI 0.848 - 0.945), and 0.894 (95%CI 0.831 - 0.956). Conclusions This study developed a nomogram model to predict the risk of lung adenocarcinoma in patients with pulmonary nodules. The nomogram has high predictive performance and clinical application value, and can provide a theoretical basis for the diagnosis and subsequent clinical management of pulmonary nodules.
Objective To investigate the diagnostic value of tumor marker combining the probability of malignancy model in pulmonary nodules. Methods A total of 117 patients with pulmonary nodules diagnosed between January 2013 and January 2016 were retrospectively analyzed. Seventy-six cases of the patients diagnosed with cancer were selected as a lung cancer group. Forty-one cases of the patients diagnosed with benign lesions were selected as a benign group. Tumor markers were detected and the probability of malignancy were calculated. Results The positive rate of carcinoembryonic antigen (CEA), cancer antigen 125 (CA125), neuron-specific enolase (NSE), cytokeratin marker (CYFRA21-1), and the probability of malignancy in the lung caner group were significantly higher than those of the benign group. The sensitivity, specificity, and accuracy of CEA, CA125, NSE, CYFRA21-1 combined detection were 72.37%, 73.17%, and 72.65%, respectively. Using the probability of malignancy model to calculate each pulmonary nodules, the area under ROC curve was 0.743 which was higher than 0.7; and 28.5% was selected as cut-off value based on clinical practice and ROC curve. The sensitivity, specificity, and accuracy of the probability of malignancy model were 63.16%, 78.05%, and 68.68%, respectively. The sensitivity, specificity, and accuracy of tumor marker combining the probability of malignancy model were 93.42%, 68.29%, and 92.31%, respectively. The sensitivity and accuracy of tumor marker combining the probability of malignancy model were significantly improved compared with tumor markers or the probability of malignancy model single detection (P<0.01). Conclusion The tumor marker combining the probability of malignancy model can improve the sensitivity and accuracy in diagnosis of pulmonary nodules.
ObjectiveTo evaluate the clinical feasibility and safety of CT-guided percutaneous microwave ablation for peripheral solitary pulmonary nodules.MethodsThe imaging and clinical data of 33 patients with pulmonary nodule less than 3 cm in diameter treated by CT-guided microwave ablation treatment (PMAT) in our hospital from July 2018 to December 2019 were retrospectively analyzed. There were 21 males and 12 females aged 38-90 (67.6±13.4) years. Among them, 26 patients were confirmed with lung cancer by biopsy and 7 patients were clinically considered as partial malignant lesions. The average diameter of 33 nodules was 0.6-3.0 (1.8±0.6) cm. The 3- and 6-month follow-up CT was performed to evaluate the therapy method by comparing the diameter and enhancement degree of lesions with 1-month CT manifestation. Short-term treatment analysis including complete response (CR), partial response (PR), stable disease (SD) and progressive disease (PD) was calculated according to the WHO modified response evaluation criteria in solid tumor (mRECIST) for short-term efficacy evaluation. Eventually the result of response rate (RR) was calculated. Progression-free survival was obtained by Kaplan–Meier analysis.ResultsCT-guided percutaneous microwave ablation was successfully conducted in all patients. Three patients suffered slight pneumothorax. There were 18 (54.5%) patients who achieved CR, 9 (27.3%) patients PR, 4 (12.1%) patients SD and 2 (6.1%) patients PD. The short-term follow-up effective rate was 81.8%. Logistic analysis demonstrated that primary and metastatic pulmonary nodules had no difference in progression-free time (log-rank P=0.624).ConclusionPMAT is of high success rate for the treatment of solitary pulmonary nodules without severe complications, which can be used as an effective alternative treatment for nonsurgical candidates.
ObjectiveBy combining biological detection and imaging evaluation, a clinical prediction model is constructed based on a large cohort to improve the accuracy of distinguishing between benign and malignant pulmonary nodules. MethodsA retrospective analysis was conducted on the clinical data of the 32 627 patients with pulmonary nodules who underwent chest CT and testing for 7 types of lung cancer-related serum autoantibodies (7-AABs) at our hospital from January 2020 to April 2024. The univariate and multivariate logistic regression models were performed to screen independent risk factors for benign and malignant pulmonary nodules, based on which a nomogram model was established. The performance of the model was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). ResultsA total of 1 017 patients with pulmonary nodules were included in the study. The training set consisted of 712 patients, including 291 males and 421 females, with a mean age of (58±12) years. The validation set included 305 patients, comprising 129 males and 176 females, with a mean age of (58±13) years. Univariate ROC curve analysis indicated that the combination of CT and 7-AABs testing achieved the highest area under the curve (AUC) value (0.794), surpassing the diagnostic efficacy of CT alone (AUC=0.667) or 7-AABs alone (AUC=0.514). Multivariate logistic regression analysis showed that radiological nodule diameter, nodule nature, and CT combined with 7-AABs detection were independent predictors, which were used to construct a nomogram prediction model. The AUC values for this model were 0.826 and 0.862 in the training and validation sets, respectively, demonstrating excellent performance in DCA. ConclusionThe combination of 7-AABs with CT significantly enhances the accuracy of distinguishing between benign and malignant pulmonary nodules. The developed predictive model provides strong support for clinical decision-making and contributes to achieving precise diagnosis and treatment of pulmonary nodules.
ObjectiveTo evaluate the feasibility and clinical value of robot-assisted lung segmentectomy through anterior approach.MethodsWe retrospectively analyzed the clinical data of 77 patients who underwent robotic lung segmentectomy through anterior approach in our hospital between June 2018 to October 2019. There were 22 males and 55 females, aged 53 (30-71) years. Patients' symptoms, general conditions, preoperative imaging data, distribution of resected lung segments, operation time, bleeding volume, number of lymph node dissected, postoperative duration of chest tube insertion, drainage volume, postoperative hospital stay, postoperative complications, perioperative death and other indicators were analyzed.ResultsAll operations were successfully completed. There was no conversion to thoracotomy, serious complications or perioperative death. The postoperative pathology revealed early lung cancer in 48 patients, and benign tumors in 29 patients. The mean clinical parameters were following: the robot Docking time 1-30 (M=4) min, the operation time 30-170 (M=76) min, the blood loss 20-400 (M=30) mL, the drainage tube time 2-15 (M=4) days, the drainage fluid volume 200-3 980 (M=780) mL and the postoperative hospital time 3-19 (M=7) days.ConclusionRobotic lung segmentectomy through anterior approach is a safe and convenient operation method for pulmonary nodules.
Lung cancer, as one of the malignant tumors with the fastest increasing morbidity and mortality in the world, has a serious impact on people's health. With the continuous advancement of medical technology, more and more medical methods are applied to lung cancer screening, which has gradually increased the detection rate of early lung cancer. At present, the standard operation for the treatment of early non-small cell lung cancer (NSCLC) is still lobectomy and mediastinal lymph node dissection. There is a growing trend to use segmentectomy for the treatment of early stage lung cancer. Anatomical segmentectomy not only removes the lesions to the maximum extent, but also preserves the lung function to the greatest extent, and its advantages are also obvious. This article reviews the progress of anatomical segmentectomy in the treatment of early NSCLC.
ObjectivesTo investigate the influence of scanning parameters (tube voltages and tube currents) on image quality and corresponding radiation doses with simulated lung nodules in chest CT.MethodsThe anthropomorphic chest phantoms with 12 simulated, randomly placed nodules of different diameters and densities in the chest were scanned by different scanning parameters. The detection rate, degree of nodular deformation, image quality (with both subjective and objective evaluation) and the corresponding radiation doses were recorded and evaluated, and the correlation between nodule detection rate, degree of nodular deformation, radiation dose and image quality using different scanning parameters was analyzed.ResultsThe image quality improved with the increase of tube voltage and tube current (P<0.05). When the tube current was constant, the CT values of the vertebral decreased gradually with the increase of tube voltages (P<0.05); however, significant difference was not detected in CT values of the lung field (P>0.05). When the tube current was 100 mAs, the lung nodules with CT values of +100 HU and −630 HU showed statistical difference when using different tube voltage (P<0.05); but there was no significant difference in nodules of −800 HU (P=0.57). When tube voltage was 100 kV and 120 kV each, it was possible to detect all lung nodules with a detection rate of 100%. The detection rate was 33% and 66% in 3 mm diameter when the tube current was 80 kV/15 mA and 80 kV/20 mA, respectively. The nodules deformation in nodules with a CT value of −630 HU and diameter less than 5 mm was the most prominent (P<0.05). After analyzing the relationship between image quality and radiation doses using different tube voltages, we established a system of correlation equations: 80 kV: Y=2.625X+0.038; 100 kV: Y=14.66X+0.158; 120 kV: Y=18.59X+0.093.ConclusionsThe image quality improves with the increase of tube current and tube voltage, as well as the corresponding radiation doses. By reducing the tube voltage and increasing the tube current appropriately, the radiation doses can be reduced. Follow-up CT examination of pulmonary ground glass nodules should apply the same tube voltage imaging parameters, so as to effectively reduce the measurement error of nodule density and evaluate the change of nodules more accurately.
The precise localization of pulmonary nodules has become an important technical key point in the treatment of pulmonary nodules by thoracoscopic surgery, which is a guarantee for safe margin and avoiding removal of too much normal lung parenchyma. With the development of medical technology and equipment, the methods of locating pulmonary nodules are also becoming less trauma and convenience. There are currently a number of methods applied to the preoperative or intraoperative localization of pulmonary nodules, including preoperative percutaneous puncture localization, preoperative transbronchial localization, intraoperative palpation localization, intraoperative ultrasound localization, and localization according to anatomy. The most appropriate localization method should be selected according to the location of the nodule, available equipment, and surgeon’s experience. According to the published literatures, we have sorted out a variety of different theories and methods of localization of pulmonary nodules in this article, summarizing their advantages and disadvantages for references.
Lung cancer has brought tough challenges to human health due to its high incidence and mortality rate in the current practice. Nowadays, computed tomography (CT) imaging is still the most preferred diagnostic tool for early screening of lung cancer. However, a great challenge brought from accumulative CT imaging data can not meet the demand of the current clinical practice. As a novel kind of artificial intelligence technique aimed to deal with medical images, a computer-aided diagnosis has been found to provide useful auxiliary information, attenuate the workload of doctors, and significantly improve the efficiency and accuracy for clinical diagnosis of lung cancer. Therefore, an effective combination of computer-aided techniques and CT imaging has increasingly become an active area of investigation in early diagnosis of lung cancer. This review aims to summarize the latest progress on the diagnostic value of computer-aided technology with regard to early stage lung cancer from the perspectives of machine learning and deep learning.
Objective To explore the efficacy of a novel detection technique of circulating tumor cells (CTCs) to identify benign and malignant lung nodules. Methods Nanomagnetic CTC detection based on polypeptide with epithelial cell adhesion molecule (EpCAM)-specific recognition was performed on enrolled patients with pulmonary nodules. There were 73 patients including 48 patients with malignant lesions as a malignant group and 25 patients with benign lesion as a benign group. There were 13 males and 35 females at age of 57.0±11.9 years in the malignant group and 11 males and 14 females at age of 53.1±13.2 years in the benign group. e calculated the differential diagnostic efficacy of CTC count, and conducted subgroup analysis according to the consolidation-tumor ratio, while compared with PET/CT on the efficacy. Results CTC count of the malignant group was significantly higher than that of the benign group (0.50/ml vs. 0.00/ml, P<0.05). Subgroup analysis according to consolidation tumor ratio (CTR) revealed that the difference was statistically significant in pure ground glass (pGGO) nodules 1.00/mlvs. 0.00/ml, P<0.05), but not in part-solid or pure solid nodules. For pGGO nodules, the area under the receiver operating characteristic (ROC) curve of CTC count was 0.833, which was significantly higher than that of maximum of standardized uptake value (SUVmax) (P<0.001). Its sensitivity and specificity was 80.0% and 83.3%, respectively. Conclusion The peptide-based nanomagnetic CTC detection system can differentiate malignant tumor and benign lesions in pulmonary nodules presented as pGGO. It is of great clinical potential as a noninvasive, nonradiating method to identify malignancies in pulmonary nodules.