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: 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*

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

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

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

Computer-aided diagnosis based on computed tomography (CT) image can realize the detection and classification of pulmonary nodules, and improve the survival rate of early lung cancer, which has important clinical significance. In recent years, with the rapid development of medical big data and artificial intelligence technology, the auxiliary diagnosis of lung cancer based on deep learning has gradually become one of the most active research directions in this field. In order to promote the deep learning in the detection and classification of pulmonary nodules, we reviewed the research progress in this field based on the relevant literatures published at domestic and overseas in recent years. This paper begins with a brief introduction of two widely used lung CT image databases: lung image database consortium and image database resource initiative (LIDC-IDRI) and Data Science Bowl 2017. Then, the detection and classification of pulmonary nodules based on different network structures are introduced in detail. Finally, some problems of deep learning in lung CT image nodule detection and classification are discussed and conclusions are given. The development prospect is also forecasted, which provides reference for future application research in this field.
Deep Learning ; Humans ; Lung Neoplasms ; diagnostic imaging ; Radiographic Image Interpretation, Computer-Assisted ; Reproducibility of Results ; Solitary Pulmonary Nodule ; diagnostic imaging ; Tomography, X-Ray Computed

Due to emphasis on early screening for lung cancer, the detection rate of multiple ground glass opacities (GGOs) on computed tomography (CT) image increases in recent years, and research on multifocal adenocarcinomas presented by GGOs has been thriving. It is more common in women and non-smokers and has excellent prognosis both in patients with natural history and after surgery. These clinical features suggest that it is likely to be a distinct disease entity. From the perspective of molecular genetics, lesions in the same individual are likely to have distinct clonal features. Therefore, genetic heterogeneity is the most prominent feature of multifocal pulmonary adenocarcinomas with GGOs. The genetic heterogeneity is expected to assist the diagnosis of multifocal pulmonary adenocarcinoma and intrapulmonary metastasis, and also suggests that genetic testing of the GGO lesions is of great therapeutic significance. Some GGO lesions may harvest the similar clonal feature, which provide new evidence for the theory of spread through air spaces (STAS).
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Adenocarcinoma ; diagnostic imaging ; genetics ; pathology ; Adenocarcinoma of Lung ; Humans ; Lung Neoplasms ; diagnostic imaging ; genetics ; pathology ; Retrospective Studies ; Solitary Pulmonary Nodule ; diagnostic imaging ; genetics ; pathology ; Tomography, X-Ray Computed

Pulmonary ground glass nodule (GGN) is a term of radiological manifestation, which may be malignant or benign. The management for pulmonary GGN remains controversial. Both Fleischner society and National Comprehensive Cancer Network (NCCN) panel updated the guideline for the management of GGN in 2017. Compared with previous versions, the indication for surgery or biopsy is stricter, and the recommended follow-up interval is prolonged. In clinical practice, the size of GGN component, the size of consolidation component, dynamic change during follow-up and computed tomography (CT) value are the four factors that help surgeons to decide the timing of surgery. There are some misunderstandings for the management of GGN, such as the administration of antibiotics, the use of positron emission tomography-computed tomography (PET-CT), pure GGN adjacent to visceral pleura, and GGN with penetrating vessel. In conclusion, GGN is a kind of slowly growing lesion, which can be followed up safely.
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Humans ; Positron Emission Tomography Computed Tomography ; Retrospective Studies ; Solitary Pulmonary Nodule ; diagnosis ; diagnostic imaging ; surgery

Background and objective As computed tomography (CT) screening for lung cancer becomes more common in China, so too does detection of pulmonary ground-glass nodules (GGNs). Although anumber of national or international guidelines about pulmonary GGNs have been published,most of these guidelines are produced by respiratory, oncology or radiology physicians, who might not fully understand the progress of modern minimal invasive thoracic surgery, and these current guidelines may overlook or underestimate the value of thoracic surgery in the management of pulmonary GGNs. In addition, the management for pre-invasive adenocarcinoma is still controversial. Based onthe available literature and experience from Shanghai Pulmonary Hospital, we composed this consensus about diagnosis and treatment of pulmonary GGNs. For lesions which are considered as adenocarcinoma in situ, chest thin layer CT scan follow-up is recommended and resection can only be adopt in some specific cases and excision should not exceed single segment resection. For lesions which are considered as minimal invasive adenocarcinoma, limited pulmonary resection or lobectomy is recommended. For lesions which are considered as early stage invasive adenocarcinoma, pulmonary resection is recommend and optimal surgical methods depend on whether ground glass component exist, location, volume and number of the lesions and physical status of patients. Principle of management of multiple pulmonary nodules is that primary lesions should be handled with priority, with secondary lesions taking into account.
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Adenocarcinoma ; diagnosis ; diagnostic imaging ; surgery ; Adenocarcinoma of Lung ; China ; Consensus ; Hospitals ; Humans ; Lung Neoplasms ; diagnosis ; diagnostic imaging ; surgery ; Physicians ; psychology ; Positron Emission Tomography Computed Tomography ; Practice Guidelines as Topic ; Retrospective Studies ; Solitary Pulmonary Nodule ; diagnosis ; diagnostic imaging ; surgery ; Tomography, X-Ray Computed