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.
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.
Lung cancer is the malignant tumor with the highest incidence and mortality rate in China. Early diagnosis and treatment are key to improving the survival rate and reducing the mortality rate for lung cancer patients. This article introduces the integrated management model for patients with pulmonary nodules/lung cancer developed by West China Hospital of Sichuan University based on “internet plus” and health service team of treatment, nursing, and care. The Integrated Care Management Center has established a multidisciplinary team, using internet platforms and artificial intelligence tools to develop a whole life cycle health service system for patients with pulmonary nodules/lung cancer, which is from the screening of high-risk population for lung cancer, the intelligent risk stratification and follow-up management of pulmonary nodules, the subsequent standardized diagnosis and treatment of lung cancer and comorbidity management, until the patient’s demise. After the implementation of this model, the malignancy rate in surgically treated patients with pulmonary nodules reached 85.08%, and the patient satisfaction score was 95.76. This model provides a new idea and reference for the innovation of chronic disease service model and the management of pulmonary nodules and lung cancer.
ObjectiveTo explore the key points and difficulties of intraoperative frozen section diagnosis of pulmonary diseases. MethodsThe intraoperative frozen section and postoperative paraffin section results of pulmonary nodule patients in Beijing Chaoyang Hospital, Capital Medical University from January 2021 to January 2022 were collected. The main causes of misdiagnosis in frozen section diagnosis were analyzed, and the main points of diagnosis and differential diagnosis were summarized. ResultsAccording to the inclusion criteria, a total of 1 263 frozen section diagnosis results of 1 178 patients were included in the study, including 475 males and 703 females, with an average age of 58.7 (23-86) years. In 1 263 frozen section diagnosis results, the correct diagnosis rate was 95.65%, and the misdiagnosis rate was 4.35%. There were 55 misdiagnoses, including 18 (3.44%) invasive adenocarcinoma, 17 (5.82%) adenocarcinoma in situ, 7 (35.00%) mucinous adenocarcinoma, 4 (2.09%) minimally invasive adenocarcinoma, 3 (100.00%) IgG4 related diseases, 2 (66.67%) mucinous adenocarcinoma in situ, 1 (16.67%) atypical adenomatous hyperplasia, 1 (14.29%) sclerosing pulmonary cell tumor, 1 (33.33%) bronchiolar adenoma, and 1 (100.00%) papillary adenoma. ConclusionIntraoperative frozen section diagnosis still has its limitations. Clinicians need to make a comprehensive judgment based on imaging examination and clinical experience.
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.
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.
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.
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.
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.
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.