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.
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 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 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.
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 develop an innovative recognition algorithm that aids physicians in the identification of pulmonary nodules. MethodsPatients with pulmonary nodules who underwent thoracoscopic surgery at the Department of Thoracic Surgery, Affiliated Drum Tower Hospital of Nanjing University Medical School in December 2023, were enrolled in the study. Chest surface exploration data were collected at a rate of 60 frames per second and a resolution of 1 920×1 080. Frame images were saved at regular intervals for subsequent block processing. An algorithm database for lung nodule recognition was developed using the collected data. ResultsA total of 16 patients were enrolled, including 9 males and 7 females, with an average age of (54.9±14.9) years. In the optimized multi-topology convolutional network model, the test results demonstrated an accuracy rate of 94.39% for recognition tasks. Furthermore, the integration of micro-variation amplification technology into the convolutional network model enhanced the accuracy of lung nodule identification to 96.90%. A comprehensive evaluation of the performance of these two models yielded an overall recognition accuracy of 95.59%. Based on these findings, we conclude that the proposed network model is well-suited for the task of lung nodule recognition, with the convolutional network incorporating micro-variation amplification technology exhibiting superior accuracy. Conclusion Compared to traditional methods, our proposed technique significantly enhances the accuracy of lung nodule identification and localization, aiding surgeons in locating lung nodules during thoracoscopic surgery.
ObjectiveTo explore the influencing factors for Hook-wire precise positioning under CT guidance, determine the best positioning management strategy, and develop Nomogram prediction model. Methods Patients who underwent CT-guided Hook-wire puncture positioning in our hospital from July 2018 to November 2022 were selected. They were randomly divided into a training set and a validation set with a ratio of 7 : 3. Clinical data of the patients were analyzed, and the logistic analysis was used to screen out the risk factors that affected CT-guided Hook-wire precise positioning for the training set. The Nomogram prediction model was constructed according to the risk factors, and the goodness of fit test and clinical decision curve analysis were performed. ResultsA total of 199 patients with CT-guided Hook-wire puncture were included in this study, including 72 males and 127 females, aged 25-83 years. There were 139 patients in the training set and 60 patients in the validation set. In the training set, 70 patients were accurately located, with an incidence of 50.36%. Logistic regression analysis showed that height [OR=3.46, 95%CI (1.44, 8.35), P=0.006], locating needle perpendicular to the horizontal plane [OR=3.40, 95%CI (1.37, 8.43), P=0.008], locating needle perpendicular to the tangent line of skin surface [OR=6.01, 95%CI (2.38, 15.20), P＜0.001], CT scanning times [OR=3.03, 95%CI (1.25, 7.33), P=0.014], occlusion [OR=10.56, 95%CI (1.98, 56.48), P=0.006] were independent risk factors for CT-guided Hook-wire precise localization. The verification results of the Nomogram prediction model based on these independent risk factors showed that the area under the receiver operating characteristic curve (AUC) was 0.843 [95%CI (0.776, 0.910)], and the predicted value of the correction curve was basically consistent with the measured value. The AUC of the model in the validation set was 0.854 [95%CI (0.759, 0.950)]. The decision curves showed that when the threshold probability was within the range of 8%-85% in the training set and 18%-99% in the validation set, there was a high net benefit value. Conclusion Height, the locating needle perpendicular to the horizontal plane, the locating needle perpendicular to the tangent line of skin surface, number of CT scans, and occlusion are independent risk factors for CT-guided Hook-wire accurate localization. The Nomogram model established based on the above risk factors can accurately assess and quantify the risk of CT-guided Hook-wire accurate localization.
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.
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.
ObjectiveTo explore the efficiency of Ki-67 expression and CT imaging features in predicting the degree of invasion of lung adenocarcinoma. MethodsThe clinical data of 217 patients with pulmonary nodules, who were diagnosed as suspicious lung cancer by multi-disciplinary treatment clinic of pulmonary nodules in our hospital from September 2017 to August 2021, were retrospectively analyzed. There were 84 males and 133 females, aged 52 (25-84) years. The patients were divided into two groups according to the infiltration degree, including an adenocarcinoma in situ and microinvasive adenocarcinoma group (n=145) and an invasive adenocarcinoma group (n=72). ResultsThere was no statistical difference in the age and gender between the two groups (P>0.05). The univariate analysis showed that CK-7, P63, P40 and CK56 expressions were not different between the two groups (P=0.172, 0.468, 0.827, 0.313), while Napsin A, TTF-1 and Ki-67 expressions were statistically different (P=0.002, 0.020, <0.001). The multivariate analysis showed that Ki-67 expression was statistically different between the two groups (P<0.001). Ki-67 was positively correlated with malignant features of CT images and the degree of lung adenocarcinoma invasion (P<0.05). Ki-67 and CT imaging features alone could predict the degree of lung adenocarcinoma invasion, but their sensitivity and specificity were not high. Ki-67 combined with CT imaging features could achieve a higher prediction efficiency.ConclusionCompared with Ki-67 or CT imaging features alone, the combined prediction of Ki-67 and imaging features is more effective, which is of great significance for clinicians to select the appropriate operation occasion.