Objective To explore the diagnostic value and safety of CT-guided percutaneous lung biopsy (CT-PLB) for pulmonary nodules. Methods A total of 438 patients with pulmonary nodules underwent CT-PLB for further diagnosis. Results The CT-PLB was performed successfully in all 438 patients. The positive biopsy rate at the first puncture was 94.9%, and 100.0% at the second puncture. The pathology results revealed 379 (86.5%) cases of malignant lesions, 37 cases of benign lesions, and 22 cases with uncertainty. The sensitivity, specificity and accuracy of CT-PLB were 97.9% (376/384), 94.4% (51/54), and 97.4% (427/438), respectively. The first puncture induced complications included pneumothorax in 33 (7.5%) cases, blood in phlegm in 62 (14.2%) cases, pleural reaction in 7 (1.6%) cases, and bleeding at the site of puncture in 6 (1.4%) cases. There was no occurrence of neoplasm needle track implantation. The second puncture induced complications included pneumothorax in 7 (46.6%) cases and blood in phlegm in 11 (73.3%) cases. The incidences of pneumothorax and blood in phlegm were significantly higher in the patients with chronic obstructive pulmonary disease (COPD), with pulmonary lesion size＜3 cm, or with penetration depth ≥5 cm (P＜0.05). Conclusions CT-PLB is a safe method with a relatively small trauma and has good diagnostic value for pulmonary nodules. The incidence of complications increases in patients with smaller pulmonary lesions, deeper puncture, or COPD.
The widespread application of low-dose computed tomography (LDCT) has significantly increased the detection of pulmonary small nodules, while accurate prediction of their growth patterns is crucial to avoid overdiagnosis or underdiagnosis. This article reviews recent research advances in predicting pulmonary nodule growth based on CT imaging, with a focus on summarizing key factors influencing nodule growth, such as baseline morphological parameters, dynamic indicators, and clinical characteristics, traditional prediction models (exponential and Gompertzian models), and the applications and limitations of radiomics-based and deep learning models. Although existing studies have achieved certain progress in predicting nodule growth, challenges such as small sample sizes and lack of external validation persist. Future research should prioritize the development of personalized and visualized prediction models integrated with larger-scale datasets to enhance predictive accuracy and clinical applicability.
With the widespread adoption of low-dose CT screening and the extensive application of high-resolution CT, the detection rate of sub-centimeter lung nodules has significantly increased. How to scientifically manage these nodules while avoiding overtreatment and diagnostic delays has become an important clinical issue. Among them, lung nodules with a consolidation tumor ratio less than 0.25, dominated by ground-glass shadows, are particularly worthy of attention. The therapeutic challenge for this group is how to achieve precise and complete resection of nodules during surgery while maximizing the preservation of the patient's lung function. The "watershed topography map" is a new technology based on big data and artificial intelligence algorithms. This method uses Dicom data from conventional dose CT scans, combined with microscopic (22-24 levels) capillary network anatomical watershed features, to generate high-precision simulated natural segmentation planes of lung sub-segments through specific textures and forms. This technology forms fluorescent watershed boundaries on the lung surface, which highly fit the actual lung anatomical structure. By analyzing the adjacent relationship between the nodule and the watershed boundary, real-time, visually accurate positioning of the nodule can be achieved. This innovative technology provides a new solution for the intraoperative positioning and resection of lung nodules. This consensus was led by four major domestic societies, jointly with expert teams in related fields, oriented to clinical practical needs, referring to domestic and foreign guidelines and consensus, and finally formed after multiple rounds of consultation, discussion, and voting. The main content covers the theoretical basis of the "watershed topography map" technology, indications, operation procedures, surgical planning details, and postoperative evaluation standards, aiming to provide scientific guidance and exploration directions for clinical peers who are currently or plan to carry out lung nodule resection using the fluorescent microscope watershed analysis method.
Surgical resection is the only radical method for the treatment of early-stage non-small cell lung cancer. Intraoperative frozen section (FS) has the advantages of high accuracy, wide applicability, few complications and real-time diagnosis of pulmonary nodules. It is one of the main means to guide surgical strategies for pulmonary nodules. Therefore, we searched PubMed, Web of Science, CNKI, Wanfang and other databases for nearly 30 years of relevant literature and research data, held 3 conferences, and formulated this consensus by using the Delphi method. A total of 6 consensus contents were proposed: (1) Rapid intraoperative FS diagnosis of benign and malignant diseases; (2) Diagnosis of lung cancer types including adenocarcinoma, squamous cell carcinoma, others, etc; (3) Diagnosis of lung adenocarcinoma infiltration degree; (4) Histological subtype diagnosis of invasive adenocarcinoma; (5) The treatment strategy of lung adenocarcinoma with inconsistent diagnosis on degree of invasion between intraoperative FS and postoperative paraffin diagnosis; (6) Intraoperative FS diagnosis of tumor spread through air space, visceral pleural invasion and lymphovascular invasion. Finally, we gave 11 recommendations in the above 6 consensus contents to provide a reference for diagnosis of pulmonary nodules and guiding surgical decision-making for peripheral non-small cell lung cancer using FS, and to further improve the level of individualized and precise diagnosis and treatment of early-stage lung cancer.
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.
ObjectiveTo explore and analyze the risk factors of pleural invasion in patients with small nodular type stage ⅠA pulmonary adenocarcinoma.MethodsFrom June 2016 to December 2017, 168 patients with small nodular type stage ⅠA pulmonary adenocarcinoma underwent surgical resection in the First Affiliated Hospital of Nanjing Medical University. There were 59 males and 109 females aged 58.7±11.5 years ranging from 28 to 83 years. The clinical data were analyzed retrospectively. Single factor Chi-square test and multivariate logistic regression were used to analyze the independent risk factors of pleural invasion.ResultsAmong 168 patients, 20 (11.9%) were pathologically confirmed with pleural invasion and 148 (88.1%) with no pleural invasion. Single factor analysis revealed significant differences (P<0.05) in nodule size, nodule status, pathological type, relation of lesion to pleura (RLP), distance of lesion to pleura (DLP), epidermal growth factor receptor (EGFR) mutation between patients with and without pleural invasion in stage ⅠA pulmonary adenocarcinoma. Logistic multivariate regression analysis showed that significant differences of nodule size, nodule status, RLP, DLP and EGFR mutation existed between the two groups (P<0.05), which were independent risk factors for pleural invasion.ConclusionImageological-pathological-biological characteristics of patients with small nodular type stage ⅠA pulmonary adenocarcinoma are closely related to pleural invasion. The possibility of pleural invasion should be evaluated by combining these parameters in clinical diagnosis and treatment.
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.
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 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.
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.