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.
Early diagnosis of lung cancer is difficult because of it’s lacking in distinctive clinical characteristics. With the development of CT technology for chest, the detection rate of pulmonary nodules is increasing year by year and acquires extensive attention. Therefore, the accurate clinical diagnosis to identify the character of solitary pulmonary nodules is urgently needed. However, the current clinical applications of different diagnosis have pluses and minuses. In this paper, we mainly review the diagnosis, management strategies and the existing problems of solitary pulmonary nodules based on the cancer-screening guidelines of Fleischner Society, American College of Chest Physicians, National Comprehensive Cancer Network, Evaluation of Pulmonary Nodules: Clinical Practice Consensus Guidelines for Asia, and Chinese Consensus on Pulmonary Nodules, and clinical research progress of 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.
Objective To develop an innovative recognition algorithm that aids physicians in the identification of pulmonary nodules. MethodsPatients with pulmonary nodules who underwent thoracoscopic surgery at the Department of Thoracic Surgery, Affiliated Drum Tower Hospital of Nanjing University Medical School in December 2023, were enrolled in the study. Chest surface exploration data were collected at a rate of 60 frames per second and a resolution of 1 920×1 080. Frame images were saved at regular intervals for subsequent block processing. An algorithm database for lung nodule recognition was developed using the collected data. ResultsA total of 16 patients were enrolled, including 9 males and 7 females, with an average age of (54.9±14.9) years. In the optimized multi-topology convolutional network model, the test results demonstrated an accuracy rate of 94.39% for recognition tasks. Furthermore, the integration of micro-variation amplification technology into the convolutional network model enhanced the accuracy of lung nodule identification to 96.90%. A comprehensive evaluation of the performance of these two models yielded an overall recognition accuracy of 95.59%. Based on these findings, we conclude that the proposed network model is well-suited for the task of lung nodule recognition, with the convolutional network incorporating micro-variation amplification technology exhibiting superior accuracy. Conclusion Compared to traditional methods, our proposed technique significantly enhances the accuracy of lung nodule identification and localization, aiding surgeons in locating lung nodules during thoracoscopic surgery.
ObjectiveTo reveal the scientific output and trends in pulmonary nodules/early-stage lung cancer prediction models. MethodsPublications on predictive models of pulmonary nodules/early lung cancer between January 1, 2002 and June 3, 2023 were retrieved and extracted from CNKI, Wanfang, VIP and Web of Science database. CiteSpace 6.1.R3 and VOSviewer 1.6.18 were used to analyze the hotspots and theme trends. ResultsA marked increase in the number of publications related to pulmonary nodules/early-stage lung cancer prediction models was observed. A total of 12581 authors from 2711 institutions in 64 countries/regions published 2139 documents in 566 academic journals in English. A total of 282 articles from 1256 authors were published in 176 journals in Chinese. The Chinese and English journals which published the most pulmonary nodules/early-stage lung cancer prediction model-related papers were Journal of Clinical Radiology and Frontiers in Oncology, respectively. Chest was the most frequently cited journal. China and the United States were the leading countries in the field of pulmonary nodules/early-stage lung cancer prediction models. The institutions represented by Fudan University had significant academic influence in the field. Analysis of keywords revealed that multi-omics, nomogram, machine learning and artificial intelligence were the current focus of research. ConclusionOver the last two decades, research on risk-prediction models for pulmonary nodules/early-stage lung cancer has attracted increasing attention. Prognosis, machine learning, artificial intelligence, nomogram, and multi-omics technologies are both current hotspots and future trends in this field. In the future, in-depth explorations using different omics should increase the sensitivity and accuracy of pulmonary nodules/early-stage lung cancer prediction models. More high-quality future studies should be conducted to validate the efficacy and safety of pulmonary nodules/early-stage lung cancer prediction models further and reduce the global burden of lung cancer.
ObjectiveTo evaluate the value of imaging quantification parameters in artificial intelligence (AI) assisted diagnosis systems in clinical decision-making for lung nodules≤2 cm and the diagnostic efficacy of AI. MethodsLung nodule patients admitted to Affiliated Zhongshan Hospital of Dalian University from 2020 to 2023 were included. Imaging parameters of lung nodules were extracted using AI assisted diagnosis systems. Multifactor analysis was used to screen predictors for distinguishing benign and malignant nodules and high-risk predictors for recurrent invasive adenocarcinoma, and a diagnostic model was established and its performance evaluated. The diagnostic efficacy of the AI system was judged according to pathological results. ResultsA total of 594 patients with lung nodules were included, including 202 males and 392 females, with an average age of (58.75±11.55) years. Volume, average CT value, and 3D maximum diameter of non-solid nodules were independent predictors of malignant nodules, with thresholds of 287.4 mm3, ?491 HU, and 12.0 mm, respectively. The area under the curve (AUC) for diagnostic efficacy was ranked from high to low as combined model (0.802), volume (0.783), average CT value (0.749), and 3D maximum diameter (0.714). The average CT value and 3D long diameter of solid nodules were independent predictors of malignant nodules, with thresholds of ?81 HU and 17.5 mm, respectively, and AUC values of 0.874 and 0.686, respectively, with the combined prediction AUC of 0.957. The mass of cystic nodules was an independent predictor of malignancy when the mass>180.7 mg. Independent predictors of high recurrence risk of invasive adenocarcinoma in non-solid nodules were consolidation-tumor ratio (CTR), average CT value, 3D long diameter, and volume, with thresholds of 0.14, ?386 HU, 15.6 mm, and 1018.9 mm3, respectively, and diagnostic efficacy was ranked from high to low as combined model (0.788), 3D long diameter (0.735), volume (0.725), average CT value (0.720), and CTR (0.697). The accuracy of AI in predicting benign and malignant target nodules was 87.4%, with positive predictive value of 96.6% and negative predictive value of 58.9%. ConclusionIn clinical decision-making for lung nodules ≤2 cm, AI assisted diagnosis systems have high application value.
ObjectiveTo explore the influencing factors for Hook-wire precise positioning under CT guidance, determine the best positioning management strategy, and develop Nomogram prediction model. Methods Patients who underwent CT-guided Hook-wire puncture positioning in our hospital from July 2018 to November 2022 were selected. They were randomly divided into a training set and a validation set with a ratio of 7 : 3. Clinical data of the patients were analyzed, and the logistic analysis was used to screen out the risk factors that affected CT-guided Hook-wire precise positioning for the training set. The Nomogram prediction model was constructed according to the risk factors, and the goodness of fit test and clinical decision curve analysis were performed. ResultsA total of 199 patients with CT-guided Hook-wire puncture were included in this study, including 72 males and 127 females, aged 25-83 years. There were 139 patients in the training set and 60 patients in the validation set. In the training set, 70 patients were accurately located, with an incidence of 50.36%. Logistic regression analysis showed that height [OR=3.46, 95%CI (1.44, 8.35), P=0.006], locating needle perpendicular to the horizontal plane [OR=3.40, 95%CI (1.37, 8.43), P=0.008], locating needle perpendicular to the tangent line of skin surface [OR=6.01, 95%CI (2.38, 15.20), P＜0.001], CT scanning times [OR=3.03, 95%CI (1.25, 7.33), P=0.014], occlusion [OR=10.56, 95%CI (1.98, 56.48), P=0.006] were independent risk factors for CT-guided Hook-wire precise localization. The verification results of the Nomogram prediction model based on these independent risk factors showed that the area under the receiver operating characteristic curve (AUC) was 0.843 [95%CI (0.776, 0.910)], and the predicted value of the correction curve was basically consistent with the measured value. The AUC of the model in the validation set was 0.854 [95%CI (0.759, 0.950)]. The decision curves showed that when the threshold probability was within the range of 8%-85% in the training set and 18%-99% in the validation set, there was a high net benefit value. Conclusion Height, the locating needle perpendicular to the horizontal plane, the locating needle perpendicular to the tangent line of skin surface, number of CT scans, and occlusion are independent risk factors for CT-guided Hook-wire accurate localization. The Nomogram model established based on the above risk factors can accurately assess and quantify the risk of CT-guided Hook-wire accurate localization.
With the increasing application of low-dose computed tomography and the rising public health awareness, the early detection of pulmonary nodules has become more prevalent. Pulmonary nodules, especially those with a diameter≤2 cm, pose a critical challenge in clinical practice due to the potential risk of progressing into malignant lung lesions. Guided by the principles of "avoiding both over-treatment and mistreatment", the goal is to standardize the clinical management of pulmonary nodules. The "Chinese thoracic surgery expert consensus on rational diagnosis and treatment of pulmonary nodules with a diameter≤2 cm (2024)" was developed after extensive consultation with nearly one hundred thoracic surgery experts in China, relying on large-scale clinical study data and referencing national and international guidelines and consensus. The consensus includes 29 recommendations, focusing on specific attributes such as the size, composition, and anatomical positioning of the nodules. It proposes targeted guidelines for screening, follow-up, diagnostic criteria, and recommendations for personalized treatment, surgical approaches, and protocols for rapid postoperative recovery.
Objective To explore the recognition capabilities of electronic nose combined with machine learning in identifying the breath odor map of benign and malignant pulmonary nodules and Traditional Chinese Medicine (TCM) syndrome elements. MethodsThe study design was a single-center observational study. General data and four diagnostic information were collected from 108 patients with pulmonary nodules admitted to the Department of Cardiothoracic Surgery of Hospital of Chengdu University of TCM from April 2023 to March 2024. The patients' TCM disease location and nature distribution characteristics were analyzed using the syndrome differentiation method. The Cyranose 320 electronic nose was used to collect the odor profiles of oral exhalation, and five machine learning algorithms including random forest (RF), K-nearest neighbor (KNN), logistic regression (LR), support vector machine (SVM), and eXtreme gradient boosting (XGBoost) were employed to identify the exhaled breath profiles of benign and malignant pulmonary nodules and different TCM syndromes. Results(1) The common disease locations in pulmonary nodules were ranked in descending order as liver, lung, and kidney; the common disease natures were ranked in descending order as Yin deficiency, phlegm, dampness, Qi stagnation, and blood deficiency. (2) The electronic nose combined with the RF algorithm had the best efficacy in identifying the exhaled breath profiles of benign and malignant pulmonary nodules, with an AUC of 0.91, accuracy of 86.36%, specificity of 75.00%, and sensitivity of 92.85%. (3) The electronic nose combined with RF, LR, or XGBoost algorithms could effectively identify the different TCM disease locations and natures of pulmonary nodules, with classification accuracy, specificity, and sensitivity generally exceeding 80.00%.ConclusionElectronic nose combined with machine learning not only has the potential capabilities to differentiate the benign and malignant pulmonary nodules, but also provides new technologies and methods for the objective diagnosis of TCM syndromes in pulmonary nodules.
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.