BACKGROUND: Lung cancer is one of the most common malignant tumors worldwide and a major cause of cancer-related deaths. Early-stage lung cancer is often manifested as pulmonary nodules, and accurate assessment of the malignancy risk is crucial for prolonging survival and avoiding overtreatment. This study aims to construct a model based on image feature parameters automatically extracted by artificial intelligence (AI) to evaluate its effectiveness in predicting the malignancy of part-solid nodule (PSN). METHODS: This retrospective study analyzed 229 PSN from 222 patients who underwent pulmonary nodule resection at Lanzhou University Second Hospital between October 2020 and February 2025. According to pathological results, 45 cases of benign lesions and precursor glandular lesion were categorized into the non-malignant group, and 184 cases of pulmonary malignancies were categorized into the malignant group. All patients underwent preoperative chest computed tomography (CT), and AI software was used to extract imaging feature parameters. Univariate analysis was used to screen significant variables; variance inflation factor (VIF) was calculated to exclude highly collinear variables, and LASSO regression was further applied to identify key features. Multivariate Logistic regression was used to determine independent risk factors. Based on the selected variables, five models were constructed: Logistic regression, random forest, XGBoost, LightGBM, and support vector machine (SVM). Receiver operating characteristic (ROC) curves were used to assess the performance of the models. RESULTS: The independent risk factors for the malignancy of PSN include roughness (ngtdm), dependence variance (gldm), and short run low gray-level emphasis (glrlm). Logistic regression achieved area under the curves ( AUCs) of 0.86 and 0.89 in the training and testing sets, respectively, showing good performance. XGBoost had AUCs of 0.78 and 0.77, respectively, demonstrating relatively balanced performance, but with lower accuracy. SVM showed an AUC of 0.93 in the training set, which decreased to 0.80 in the testing set, indicating overfitting. LightGBM performed excellently in the training set with an AUC of 0.94, but its performance declined in the testing set, with an AUC of 0.88. In contrast, random forest demonstrated stable performance in both the training and testing sets, with AUCs of 0.89 and 0.91, respectively, exhibiting high stability and excellent generalizability. CONCLUSIONS The random forest model constructed based on independent risk factors demonstrated the best performance in predicting the malignancy of PSN and could provide effective auxiliary predictions for clinicians, supporting individualized treatment decisions.
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Humans ; Male ; Female ; Lung Neoplasms/pathology* ; Middle Aged ; Retrospective Studies ; Artificial Intelligence ; Aged ; Tomography, X-Ray Computed ; Adult ; Solitary Pulmonary Nodule/diagnostic imaging* ; ROC Curve

In recent years, the widespread application of chest computed tomography (CT) screening has led to a significant increase in the detection rate of pulmonary nodules. As a critical diagnostic tool for early-stage lung cancer, video-assisted thoracic surgery (VATS) has emerged as the preferred therapeutic approach for pulmonary nodules. Clinical evidence demonstrates that precise preoperative localization significantly enhances surgical success rates (reducing conversion to thoracotomy), minimizes complications, and shortens operation time. This comprehensive review systematically evaluates six cutting-edge localization techniques: percutaneous puncture-assisted localization, electromagnetic navigation bronchoscopy (ENB) localization, 3D-printed auxiliary localization, basin-analysis-based localization, robotic navigation system localization, and mixed reality (MR)-guided localization. By critically analyzing their operational principles, efficacy, safety profiles, and clinical applicability, this paper aims to provide evidence-based recommendations for optimizing clinical decision-making in pulmonary nodule management.
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Humans ; Lung Neoplasms/diagnosis* ; Solitary Pulmonary Nodule/diagnostic imaging* ; Thoracic Surgery, Video-Assisted/methods* ; Multiple Pulmonary Nodules/diagnostic imaging* ; Tomography, X-Ray Computed

OBJECTIVE: In order to improve the accuracy of the current pulmonary nodule location detection method based on CT images, reduce the problem of missed detection or false detection, and effectively assist imaging doctors in the diagnosis of pulmonary nodules. METHODS: Propose a novel method for detecting the location of pulmonary nodules based on multiscale convolution. First, image preprocessing methods are used to eliminate the noise and artifacts in lung CT images. Second, multiple adjacent single-frame CT images are selected to be concatenate into multi-frame images, and the feature extraction is carried out through the artificial neural network model U-Net improved by multi-scale convolution to enhanced feature extraction capability for pulmonary nodules of different sizes and shapes, so as to improve the accuracy of feature extraction of pulmonary nodules. Finally, using point detection to improve the loss function of U-Net training process, the accuracy of pulmonary nodule location detection is improved. RESULTS: The accuracy of detecting pulmonary nodules equal or larger than 3 mm and smaller than 3 mm are 98.02% and 96.94% respectively. CONCLUSIONS This method can effectively improve the detection accuracy of pulmonary nodules on CT image sequence, and can better meet the diagnostic needs of pulmonary nodules.
Humans ; Lung Neoplasms/diagnostic imaging* ; Solitary Pulmonary Nodule/diagnostic imaging* ; Tomography, X-Ray Computed ; Neural Networks, Computer

Humans ; Multiple Pulmonary Nodules ; Lung ; Lung Neoplasms/surgery* ; Solitary Pulmonary Nodule/surgery* ; Bronchoscopy

Objective: To investigate the detection and diagnostic efficacy of chest radiographs for ≤30 mm pulmonary nodules and the factors affecting them, and to compare the level of consistency among readers. Methods: A total of 43 patients with asymptomatic pulmonary nodules who consulted in Cancer Hospital, Chinese Academy of Medical Sciences from 2012 to 2014 and had chest CT and X-ray chest radiographs during the same period were retrospectively selected, and one nodule ≤30 mm was visible on chest CT images in the whole group (total 43 nodules in the whole group). One senior radiologist with more than 20 years of experience in imaging diagnosis reviewed CT images and recording the size, morphology, location, and density of nodules was selected retrospectively. Six radiologists with different levels of experience (2 residents, 2 attending physicians and 2 associate chief physicians independently reviewed the chest images and recorded the time of review, nodule detection, and diagnostic opinion. The CT imaging characteristics of detected and undetected nodules on X images were compared, and the factors affecting the detection of nodules on X-ray images were analyzed. Detection sensitivity and diagnosis accuracy rate of 6 radiologists were calculated, and the level of consistency among them was compared to analyze the influence of radiologists' seniority and reading time on the diagnosis results. Results: The number of nodules detected by all 6 radiologists was 17, with a sensitivity of detection of 39.5%(17/43). The number of nodules detected by ≥5, ≥4, ≥3, ≥2, and ≥1 physicians was 20, 21, 23, 25, and 28 nodules, respectively, with detection sensitivities of 46.5%, 48.8%, 53.5%, 58.1%, and 65.1%, respectively. Reasons for false-negative result of detection on X-ray images included the size, location, density, and morphology of the nodule. The sensitivity of detecting ≤30 mm, ≤20 mm, ≤15 mm, and ≤10 mm nodules was 46.5%-58.1%, 45.9%-54.1%, 36.0%-44.0%, and 36.4% for the 6 radiologists, respectively; the diagnosis accuracy rate was 19.0%-85.0%, 16.7%-6.5%, 18.2%-80.0%, and 0%-75.0%, respectively. The consistency of nodule detection among 6 doctors was good (Kappa value: 0.629-0.907) and the consistency of diagnostic results among them was moderate or poor (Kappa value: 0.350-0.653). The higher the radiologist's seniority, the shorter the time required to read the images. The reading time and the seniority of the radiologists had no significant influence on the detection and diagnosis results (P>0.05). Conclusions: The ability of radiographs to detect lung nodules ≤30 mm is limited, and the ability to determine the nature of the nodules is not sufficient, and the increase in reading time and seniority of the radiologists will not improve the diagnostic accuracy. X-ray film exam alone is not suitable for lung cancer diagnosis.
Humans ; Retrospective Studies ; Solitary Pulmonary Nodule/diagnostic imaging* ; Radiography ; Multiple Pulmonary Nodules/diagnostic imaging* ; Tomography, X-Ray Computed/methods* ; Lung Neoplasms/diagnostic imaging* ; Sensitivity and Specificity ; Radiographic Image Interpretation, Computer-Assisted/methods*

BACKGROUND: The localization of pulmonary nodules is related to whether the lesions can be found and removed accurately and quickly. It is an important link for the success of minimally invasive video-assisted thoracic surgery (VATS). This study investigated the feasibility of medical glue localization under VATS video-assisted thoracoscopic computed tomography (CT) guidance for single pulmonary nodule and more than two pulmonary nodules, and compared with the accuracy and safety of single nodule localization. METHODS: A retrospective analysis of the clinical data of patients who underwent unilateral CT-guided medical glue localization before VATS from November 2018 to March 2021 were performed, the patients was divided into multiple pulmonary nodules group (localized nodules ≥2) and single pulmonary nodule group according to the number of localized nodules. The localization time, success rate and complication rate of the two groups were compared. RESULTS: There were 126 nodules in the two groups, including 62 in single pulmonary nodule group and 64 in multiple pulmonary nodules group. The average single nodule localization time was (13.23±4.5) min in single pulmonary nodule group and (10.52±2.8) min in multiple pulmonary nodules group, the difference between the two groups is statistically significant (P<0.05). The localization success rate of single pulmonary nodule group and multiple pulmonary nodules group were 100% and 98.4% separately, the difference between the two groups was not statistically significant (P>0.05). All VATS were successfully completed after localization. The incidence of pneumothorax was higher in multiple pulmonary nodules group than in single pulmonary nodule group (P=0.07). CONCLUSIONS Compared with localization of single lung nodule, unilateral CT-guided medical glue localization for multiple pulmonary nodules before VATS is also feasible and accuracy, it is worthy of clinical application. But the higher rate of pneumothorax should be paid attention to.
Humans ; Lung Neoplasms/surgery* ; Multiple Pulmonary Nodules/surgery* ; Pneumothorax ; Retrospective Studies ; Solitary Pulmonary Nodule/surgery* ; Thoracic Surgery, Video-Assisted ; Tomography, X-Ray Computed

The incidence and mortality of lung cancer rank first among all malignant tumors in China. With the popularization of high resolution computed tomography (CT) in clinic, chest CT has become an important means of clinical screening for early lung cancer and reducing the mortality of lung cancer. Imaging findings of early lung adenocarcinoma often show partial solid nodules with ground glass components. With the development of imaging, the relationship between the imaging features of some solid nodules and their prognosis has attracted more and more attention. At the same time, with the development of 3D-reconstruction technology, clinicians can improve the accuracy of diagnosis and treatment of such nodules.This article focuses on the traditional imaging analysis of partial solid nodules and the imaging analysis based on 3D reconstruction, and systematically expounds the advantages and disadvantages of both.
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Adenocarcinoma of Lung/pathology* ; Humans ; Image Processing, Computer-Assisted ; Lung Neoplasms/pathology* ; Solitary Pulmonary Nodule/pathology* ; Tomography, X-Ray Computed

Low-dose computed tomography (CT) for lung cancer screening has been proven to reduce lung cancer deaths in the screening group compared with the control group. The increasing number of pulmonary nodules being detected by CT scans significantly increase the workload of the radiologists for scan interpretation. Artificial intelligence (AI) has the potential to increase the efficiency of pulmonary nodule discrimination and has been tested in preliminary studies for nodule management. As more and more artificial AI products are commercialized, the consensus statement has been organized in a collaborative effort by Thoracic Surgery Committee, Department of Simulated Medicine, Wu Jieping Medical Foundation to aid clinicians in the application of AI-assisted management for pulmonary nodules.
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Artificial Intelligence ; China ; Early Detection of Cancer ; Humans ; Lung Neoplasms/diagnostic imaging* ; Multiple Pulmonary Nodules ; Sensitivity and Specificity ; Solitary Pulmonary Nodule

With the wide application of high-resolution chest CT in health check-up, the ground glass nodule(GGN) has been increasingly detected. GGNs have a complex etiology and image features, which can develop fast or very slowly. Therefore, whether to follow up or to resect it is usually very difficult to be determined. Overdiagnosis or overtreatment frequently happens. According to the development of GGNs, the process can be clinically divided into four stages: biological onset stage (pre-detection stage), observational stage, clinical treatment stage and postoperative follow-up stage. This review summarizes the progress on the natural development process, imaging monitoring and differentiation, the optimal time of surgical treatment for GGNs based on the decision of multidisciplinary team. This revie wmay be helpful for clinicians to understand the rule of GGN development in the follow-up, and find an optimal time to give surgical intervention for improving the prognosis of and life quality of the GGN patients.
Follow-Up Studies ; Humans ; Lung Neoplasms/surgery* ; Multiple Pulmonary Nodules/surgery* ; Retrospective Studies ; Solitary Pulmonary Nodule/surgery*

BACKGROUND: Computed tomography images are easy to misjudge because of their complexity, especially images of solitary pulmonary nodules, of which diagnosis as benign or malignant is extremely important in lung cancer treatment. Therefore, there is an urgent need for a more effective strategy in lung cancer diagnosis. In our study, we aimed to externally validate and revise the Mayo model, and a new model was established. METHODS: A total of 1450 patients from three centers with solitary pulmonary nodules who underwent surgery were included in the study and were divided into training, internal validation, and external validation sets (n = 849, 365, and 236, respectively). External verification and recalibration of the Mayo model and establishment of new logistic regression model were performed on the training set. Overall performance of each model was evaluated using area under receiver operating characteristic curve (AUC). Finally, the model validation was completed on the validation data set. RESULTS: The AUC of the Mayo model on the training set was 0.653 (95% confidence interval [CI]: 0.613-0.694). After re-estimation of the coefficients of all covariates included in the original Mayo model, the revised Mayo model achieved an AUC of 0.671 (95% CI: 0.635-0.706). We then developed a new model that achieved a higher AUC of 0.891 (95% CI: 0.865-0.917). It had an AUC of 0.888 (95% CI: 0.842-0.934) on the internal validation set, which was significantly higher than that of the revised Mayo model (AUC: 0.577, 95% CI: 0.509-0.646) and the Mayo model (AUC: 0.609, 95% CI, 0.544-0.675) (P < 0.001). The AUC of the new model was 0.876 (95% CI: 0.831-0.920) on the external verification set, which was higher than the corresponding value of the Mayo model (AUC: 0.705, 95% CI: 0.639-0.772) and revised Mayo model (AUC: 0.706, 95% CI: 0.640-0.772) (P < 0.001). Then the prediction model was presented as a nomogram, which is easier to generalize. CONCLUSIONS After external verification and recalibration of the Mayo model, the results show that they are not suitable for the prediction of malignant pulmonary nodules in the Chinese population. Therefore, a new model was established by a backward stepwise process. The new model was constructed to rapidly discriminate benign from malignant pulmonary nodules, which could achieve accurate diagnosis of potential patients with lung cancer.
Humans ; Lung ; Lung Neoplasms/diagnostic imaging* ; Multiple Pulmonary Nodules ; Risk Assessment ; Solitary Pulmonary Nodule/diagnostic imaging* ; Tomography, X-Ray Computed