This study aims to explore the clinical value of the computer-aided diagnosis (CAD) system for early detection of the pulmonary nodules on digital chest X-ray. A total of 100 cases of digital chest radiographs with pulmonary nodules of 5-20 mm diameter were selected from Pictures Archiving and Communication System (PACS) database in West China Hospital of Sichuan University were enrolled into trial group, and other 200 chest radiographs without pulmonary nodules as control group. All cases were confirmed by CT examination. Firstly, these cases were diagnosed by 5 different-seniority doctors without CAD, and after three months, these cases were re-diagnosed by the 5 doctors with CAD. Subsequently, the diagnostic results were analyzed by using SPSS statistical methods. The results showed that the sensitivity and specificity for detecting pulmonary nodules tended to be improved by using the CAD system, especially for specificity, but there was no significant difference before and after using CAD system.
Objective To investigate the risk factors, diagnosis and treatment of solitary pulmonary nodule （diameter≤3cm）. Methods From Jan. 2001 to Dec. 2002, the clinical data of 297 patients with solitary pulmonary nodule were reviewed. Chi-square or t-test were used in univariate analysis of age, gender, symptom, smoking history, the size, location and radiological characteristics of nodule, and logistic regression in multivariate analysis. Results Univariate analysis revealed that malignancy was significantly associated with age （P=0. 000）, smoking history （P=0. 001）, the size （P=0. 000） and radiological characteristics （P=0. 000） of nodule. In multivariate analysis （logistic regression）, it was significantly associated with age （OR = 1. 096）, the size （OR = 2. 329） and radiological characteristics （OR=0. 167） of nodule. Conclusion Age and the size of nodule could be risk factors. Radiological findings could help distinguish from malignant nodules.
ObjectiveTo investigate the feasibility of using magnetic beads to locate small pulmonary nodules.MethodsTwelve rabbits were randomly divided into two groups, 6 in each group. One group underwent thoracotomy after anesthesia and the other group underwent percutaneous puncture under the guidance of X-ray. One and two cylindrical tracer magnets (magnetic beads) with a diameter of 1 mm and a height of 3 mm were injected adjacent to the imaginary pulmonary nodules in left lung in each group. The magnetic beads beside the imaginary nodules were attracted by a pursuit magnet with a diameter of 9 mm and a height of 19 mm. The effectiveness of localization by magnetic beads were determined by attraction between tracer and pursuit magnets.ResultsAll processes were uneven in 12 rabbits. There was micro hemorrhage and no hematoma in the lung tissue at the injection site of the magnetic beads. When tracked with the pursuit magnets, there was one bead divorce in cases that one bead was injected, but no migration or divorce of the magnetic beads in cases that two magnetic beads were simultaneously injected to localize the small pulmonary nodules.ConclusionThe feasibility of using magnetic beads to locate small pulmonary nodules has been preliminarily verified.
ObjectiveTo establish and internally validate a predictive model for poorly differentiated adenocarcinoma based on CT imaging and tumor marker results. MethodsPatients with solid and partially solid lung nodules who underwent lung nodule surgery at the Department of Thoracic Surgery, the Affiliated Brain Hospital of Nanjing Medical University in 2023 were selected and randomly divided into a training set and a validation set at a ratio of 7:3. Patients' CT features, including average density value, maximum diameter, pleural indentation sign, and bronchial inflation sign, as well as patient tumor marker results, were collected. Based on postoperative pathological results, patients were divided into a poorly differentiated adenocarcinoma group and a non-poorly differentiated adenocarcinoma group. Univariate analysis and logistic regression analysis were performed on the training set to establish the predictive model. The receiver operating characteristic (ROC) curve was used to evaluate the model's discriminability, the calibration curve to assess the model's consistency, and the decision curve to evaluate the clinical value of the model, which was then validated in the validation set. ResultsA total of 299 patients were included, with 103 males and 196 females, with a median age of 57.00 (51.00, 67.25) years. There were 211 patients in the training set and 88 patients in the validation set. Multivariate analysis showed that carcinoembryonic antigen (CEA) value [OR=1.476, 95%CI (1.184, 1.983), P=0.002], cytokeratin 19 fragment antigen (CYFRA21-1) value [OR=1.388, 95%CI (1.084, 1.993), P=0.035], maximum tumor diameter [OR=6.233, 95%CI (1.069, 15.415), P=0.017], and average density [OR=1.083, 95%CI (1.020, 1.194), P=0.040] were independent risk factors for solid and partially solid lung nodules as poorly differentiated adenocarcinoma. Based on this, a predictive model was constructed with an area under the ROC curve of 0.896 [95%CI (0.810, 0.982)], a maximum Youden index corresponding cut-off value of 0.103, sensitivity of 0.750, and specificity of 0.936. Using the Bootstrap method for 1000 samplings, the calibration curve predicted probability was consistent with actual risk. Decision curve analysis indicated positive benefits across all prediction probabilities, demonstrating good clinical value. ConclusionFor patients with solid and partially solid lung nodules, preoperative use of CT to measure tumor average density value and maximum diameter, combined with tumor markers CEA and CYFRA21-1 values, can effectively predict whether it is poorly differentiated adenocarcinoma, allowing for early intervention.
Abstract： Objective To explore the approach of clinical diagnosis and treatment strategy for patients with small pulmonary nodules （SPN）≤ 1.0 cm in size on CT. Methods We retrospectively analyzed the clinical records of 39 patients with SPN less than 1.0 cm in size who underwent lung resection at Nanjing Drum Tower Hospital from January 2005 to June 2011. There were 23 males and 16 females. Their age ranged from 31-74 （51.0±7.4） years. Nine patients had cough and sputum and other patients had no symptom. All the patients were found to have SPN less than 1.0（0.8±0.1）cm in size but not associated with hilum and mediastinal lymphadenectasis in chest CT and X-ray. The results of their sputum cytology and electronic bronchoscope were all negative. All the patients had no histologic evidence and underwent pulmonary function test prior to operation. Eleven patients had positron emission tomography/computer tomography （PET/CT）or single-photon emission computed tomography （SPECT）which was all negative. Thirteen patients underwent video-assisted minithoracotomy（VAMT） and 26 patients underwent video-assisted thoracoscopic surgery （VATS）. Results The average operation time was 121.0±48.0 min. Patients after partial lung resection were discharged 4～5 d postoperatively, and patients after lobectomy were discharged 7 d postoperatively. All the patients had no postoperative complications. Twenty one patients were identified as lung malignancy by postoperative pathology, including 9 patients with adenocarcinoma, 7 patients with bronchioloalveolar carcinoma, 1 patient with small cell lung carcinoma, and 4 patients with pulmonary metastasis. Eighteen patients had benign lesions including 4 patients with sclerosing hemangioma, 4 patients with inflammatory pseudotumor, 2 patients with pneumonia, 3 patients with granuloma, 2 patients with tuberculosis, and 3 patients with pulmonary lymph node hyperplasia. The SPN were located in left upper lobe in 11 patients, left lower lobe in 6 patients, right upper lobe in 14 patients, right middle lobe in 1 patient, and right lower lobe in 7 patients. Conclusion The diagnosis of SPN ≤1.0 cm in size on CT should consider malignance in the first step to avoid treatment delay. Patients may have a 3-month observation period to receive selective antibiotic treatment, chest CT and X-ray review after 2 to 4 weeks. CT- guided hook-wire fixation is useful to help in precise lesion localization for surgical resection. VATS and VAMT are common and effective methods for the diagnosis and treatment for SPN.
ObjectiveTo investigate the predictive value of volatile organic compounds (VOCs) on pulmonary nodules in people aged less than 50 years.MethodsThe 147 patients with pulmonary nodules and aged less than 50 years who were treated in the Department of Thoracic Surgery of Sichuan Cancer Hospital from August 1, 2019 to January 15, 2020 were divided into a lung cancer group and a lung benign disease group. The lung cancer group included 36 males and 68 females, with the age of 27-49 (43.54±5.73) years. The benign lung disease group included 23 males and 20 females, with the age of 22-49 (42.49±6.83) years. Clinical data and exhaled breath samples were collected prospectively from the two groups. Exhaled breath VOCs were analyzed by gas chromatography mass spectrometry. Binary logistic regression analysis was used to select variables and establish a prediction model. The sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve of the prediction model were calculated.ResultsThere were statistically significant differences in sex (P=0.034), smoking history (P=0.047), cyclopentane (P=0.002), 3-methyl pentane (P=0.043) and ethylbenzene (P=0.009) between the two groups. The sensitivity, specificity and area under the ROC curve of the prediction model with gender, cyclopentane, 3-methyl pentane, ethylbenzene and N,N-dimethylformamide as variables were 80.8%, 60.5% and 0.781, respectively.ConclusionThe combination of VOCs and clinical characteristics has a certain predictive value for the benign and malignant pulmonary nodules in people aged less than 50 years.
ObjectiveTo evaluate the predictive ability and clinical application value of artificial intelligence (AI) systems in the benign and malignant differentiation and pathological type of pulmonary nodules, and to summarize clinical application experience. MethodsA retrospective analysis was conducted on the clinical data of patients with pulmonary nodules admitted to the Department of Thoracic Surgery, Second Hospital of Lanzhou University, from February 2016 to February 2025. Firstly, pulmonary nodules were divided into benign and non-benign groups, and the discriminative abilities of AI systems and clinicians were compared. Subsequently, lung nodules reported as precursor glandular lesions (PGL), microinvasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) in postoperative pathological results were analyzed, comparing the efficacy of AI systems and clinicians in predicting the pathological type of pulmonary nodules. ResultsIn the analysis of benign/non-benign pulmonary nodules, clinical data from a total of 638 patients with pulmonary nodules were included, of which there were 257 males (10 patients and 1 patient of double and triple primary lesions, respectively) and 381 females (18 patients and 1 patient of double and triple primary lesions, respectively), with a median age of 55.0 (47.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis of the two groups of variables showed that, except for nodule location, the differences in the remaining variables were statistically significant (P<0.05). Multivariate logistic regression analysis showed that age, nodule type (subsolid pulmonary nodule), average density, spicule sign, and vascular convergence sign were independent influencing factors for non-benign pulmonary nodules, among which age, nodule type (subsolid pulmonary nodule), spicule sign, and vascular convergence sign were positively correlated with non-benign pulmonary nodules, while average density was negatively correlated with the occurrence of non-benign pulmonary nodules. The area under the receiver operating characteristic curve (AUC) of the malignancy risk value given by the AI system in predicting non-benign pulmonary nodules was 0.811, slightly lower than the 0.898 predicted by clinicians. In the PGL/MIA/IAC analysis, clinical data from a total of 411 patients with pulmonary nodules were included, of which there were 149 males (8 patients of double primary lesions) and 262 females (17 patients of double primary lesions), with a median age of 56.0 (50.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis results showed that, except for gender, nodule location, and vascular convergence sign, the differences in the remaining variables among the three groups of PGL, MIA, and IAC patients were statistically significant (P<0.05). Multinomial multivariate logistic regression analysis showed that the differences between the parameters in the PGL group and the MIA group were not statistically significant (P>0.05), and the maximum diameter and average density of the nodules were statistically different between the PGL and IAC groups (P<0.05), and were positively correlated with the occurrence of IAC as independent risk factors. The average AUC value, accuracy, recall rate, and F1 score of the AI system in predicting lung nodule pathological type were 0.807, 74.3%, 73.2%, and 68.5%, respectively, all better than the clinical physicians’ prediction of lung nodule pathological type indicators (0.782, 70.9%, 66.2%, and 63.7% respectively). The AUC value of the AI system in predicting IAC was 0.853, and the sensitivity, specificity, and optimal cutoff value were 0.643, 0.943, and 50.0%, respectively. ConclusionThis AI system has demonstrated high clinical value in predicting the benign and malignant nature and pathological type of lung nodules, especially in predicting lung nodule pathological type, its ability has surpassed that of clinical physicians. With the optimization of algorithms and the adequate integration of multimodal data, it can better assist clinical physicians in formulating individualized diagnostic and treatment plans for patients with lung nodules.
ObjectiveTo compare solitary pulmonary nodule resection via thoracoscopic 3D mode or 2D mode and to further evaluate the clinical application value of thoracoscopic 3D mode. MethodsWe retrospectively analyzed the clinical data of 120 patients with solitary pulmonary nodule between March 2013 and March 2014 in the First Hospital Affiliated to Xiamen University. The patients were allocated into two groups including a 3D-VATS group (50 patients) and a 2D-VATS group (70 patients). Pulmonary partial resection was performed firstly. Pulmonary lobectomy would be conducted or not on the basis of intra operative rapid pathological results. ResultsTwenty three patients were performed 3D-VATS in the 3D-VATS group. Twenty-nine patients were diagnosed as pathological malignancy underwent lobectomy plus partial dissection. There were statistical differences between the 3D-VATS group and the 2D-VATS group in operative time (t=1.967, P<0.05), intra operative blood loss (t=7.85, P<0.05), drainage volume 24 h after operation (t=6.18, P<0.05), postoperative chest tube retention time (t=7.1, P<0.05), and postoperative hospital stay (t=2.35, P<0.05). Following-up time in the 3D-VATS group was 6.3 (2-12) months. Complications occurred in 3 patients, including 2 patients with postoperative pneumonia and 1 patient with paroxysmal atrial fibrillation in the 3D-VATS group. The following-up time in the 2D-VATS group was 8.2 (2-15) months. Complications occurred in 4 patients, including 1 patient with chylous hydrothorax, 2 patients with pneumothorax, 1 patient with delayed pulling up the chest closed drainage in the 2D-VATS group. The patients in both groups with complications were cured by appropriate treatment. Conclusion3D-VAST for SPN is a new operation mode choice. It is safe and feasible with low incidence of postoperative complications. Surgery vision, stereo feeling, the operation of adaptation, and postoperative recovery have certain advantages. It is worthy popularizing.
Objective To analyze the imaging features of solitary pulmonary nodules ( SPNs) , and compare the two types of lung cancer prediction models in distinguishing malignancy of SPNs.Methods A retrospective study was performed on the patients admitted to Ruijin Hospital between 2002 and 2009 with newly discovered SPNs. The patients all received pathological diagnosis. The clinical and imaging characteristics were analyzed. Then the diagnostic accuracy of two lung cancer prediction models for distinguishing malignancy of SPNs was evaluated and compared.Results A total of 90 patients were enrolled, of which 32 cases were with benign SPNs, 58 cases were with malignant SPNs. The SPNs could be identified between benign and maligant by the SPN edge features of lobulation ( P lt;0. 05) . The area under ROC curve of VA model was 0. 712 ( 95% CI 0. 606 to 0. 821) . The area under ROC curve of Mayo Clinic model was 0. 753 ( 95% CI 0. 652 to 0. 843) , which was superior to VA model. Conclusions It is meaningful for the identification of benign and maligant SPNs by the obulation sign in CT scan. We can integrate the clinical features and the lung cancer predicting models to guide clinical work.
ObjectiveTo investigate differential diagnosis between benign and malignant of solitary pulmonary nodules (SPN)and surgical strategies. MethodsWe retrospectively analyzed clinical and pathological data of 151 SPN patients who underwent surgical resection in Provincial Hospital Affiliated to Shandong University between November 2010 and March 2012. There were 89 male and 62 female patients with their age of 30-80 (57.99±0.86)years. Differential factors between benign and malignant SPN were analyzed. ResultsThere were 29 patients with benign SPN and 122 patients with malignant SPN. Among the 122 malignant SPN patients, there were 58 patients in stage ⅠA, 30 patients in stage ⅠB, 7 patients in stage ⅡA, 25 patients in stage ⅢA and 2 patients in stage Ⅳ. Mean diameter of malignant SPN was significantly larger than that of benign SPN (2.03 cm vs 1.77 cm, P=0.039). Malignant rate of SPN larger than 2 cm was significantly higher than that of SPN smaller than 2 cm (90.3% vs. 74.2%, P=0.013). Patients with malignant SPN was significantly older than patients with benign SPN (60.39 years vs. 47.90 years, P < 0.01). Malignant rate of patients over 45 years was significantly higher than that of patients younger than 45 years (86.4% vs. 38.9%, P < 0.01).There was no statistical difference in malignant rate between male and female, with and without clinical symptoms, smoking and nonsmo-king, smoking index≤400 and > 400 and among different lobes. Conclusions Differential factors of SPN include patients' medical history, age, diameter and shape of nodules, which should be considered comprehensively and dynamically. Gender, clinical symptoms, smoking history, smoking index and SPN location are not helpful for differential diagnosis of SPN.