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.
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.
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 investigate the diagnostic value of tumor marker combining the probability of malignancy model in pulmonary nodules. Methods A total of 117 patients with pulmonary nodules diagnosed between January 2013 and January 2016 were retrospectively analyzed. Seventy-six cases of the patients diagnosed with cancer were selected as a lung cancer group. Forty-one cases of the patients diagnosed with benign lesions were selected as a benign group. Tumor markers were detected and the probability of malignancy were calculated. Results The positive rate of carcinoembryonic antigen (CEA), cancer antigen 125 (CA125), neuron-specific enolase (NSE), cytokeratin marker (CYFRA21-1), and the probability of malignancy in the lung caner group were significantly higher than those of the benign group. The sensitivity, specificity, and accuracy of CEA, CA125, NSE, CYFRA21-1 combined detection were 72.37%, 73.17%, and 72.65%, respectively. Using the probability of malignancy model to calculate each pulmonary nodules, the area under ROC curve was 0.743 which was higher than 0.7; and 28.5% was selected as cut-off value based on clinical practice and ROC curve. The sensitivity, specificity, and accuracy of the probability of malignancy model were 63.16%, 78.05%, and 68.68%, respectively. The sensitivity, specificity, and accuracy of tumor marker combining the probability of malignancy model were 93.42%, 68.29%, and 92.31%, respectively. The sensitivity and accuracy of tumor marker combining the probability of malignancy model were significantly improved compared with tumor markers or the probability of malignancy model single detection (P<0.01). Conclusion The tumor marker combining the probability of malignancy model can improve the sensitivity and accuracy in diagnosis of pulmonary nodules.
Lung cancer is a disease with high incidence rate and high mortality rate worldwide. Its diagnosis and treatment mode is developing constantly. Among them, multi-disciplinary team (MDT) can provide more accurate diagnosis and more individualized treatment, which can not only benefit more early patients, but also prolong the survival time of late patients. However, MDT diagnosis and treatment mode is only carried out more in provincial and municipal tertiary hospitals and has not been popularized. This article intends to introduce MDT mode and its advantages, hoping that MDT mode can be popularized and applied.
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.
Objective To explore the efficacy of a novel detection technique of circulating tumor cells (CTCs) to identify benign and malignant lung nodules. Methods Nanomagnetic CTC detection based on polypeptide with epithelial cell adhesion molecule (EpCAM)-specific recognition was performed on enrolled patients with pulmonary nodules. There were 73 patients including 48 patients with malignant lesions as a malignant group and 25 patients with benign lesion as a benign group. There were 13 males and 35 females at age of 57.0±11.9 years in the malignant group and 11 males and 14 females at age of 53.1±13.2 years in the benign group. e calculated the differential diagnostic efficacy of CTC count, and conducted subgroup analysis according to the consolidation-tumor ratio, while compared with PET/CT on the efficacy. Results CTC count of the malignant group was significantly higher than that of the benign group (0.50/ml vs. 0.00/ml, P<0.05). Subgroup analysis according to consolidation tumor ratio (CTR) revealed that the difference was statistically significant in pure ground glass (pGGO) nodules 1.00/mlvs. 0.00/ml, P<0.05), but not in part-solid or pure solid nodules. For pGGO nodules, the area under the receiver operating characteristic (ROC) curve of CTC count was 0.833, which was significantly higher than that of maximum of standardized uptake value (SUVmax) (P<0.001). Its sensitivity and specificity was 80.0% and 83.3%, respectively. Conclusion The peptide-based nanomagnetic CTC detection system can differentiate malignant tumor and benign lesions in pulmonary nodules presented as pGGO. It is of great clinical potential as a noninvasive, nonradiating method to identify malignancies in pulmonary nodules.
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.
ObjectiveBy combining biological detection and imaging evaluation, a clinical prediction model is constructed based on a large cohort to improve the accuracy of distinguishing between benign and malignant pulmonary nodules. MethodsA retrospective analysis was conducted on the clinical data of the 32 627 patients with pulmonary nodules who underwent chest CT and testing for 7 types of lung cancer-related serum autoantibodies (7-AABs) at our hospital from January 2020 to April 2024. The univariate and multivariate logistic regression models were performed to screen independent risk factors for benign and malignant pulmonary nodules, based on which a nomogram model was established. The performance of the model was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). ResultsA total of 1 017 patients with pulmonary nodules were included in the study. The training set consisted of 712 patients, including 291 males and 421 females, with a mean age of (58±12) years. The validation set included 305 patients, comprising 129 males and 176 females, with a mean age of (58±13) years. Univariate ROC curve analysis indicated that the combination of CT and 7-AABs testing achieved the highest area under the curve (AUC) value (0.794), surpassing the diagnostic efficacy of CT alone (AUC=0.667) or 7-AABs alone (AUC=0.514). Multivariate logistic regression analysis showed that radiological nodule diameter, nodule nature, and CT combined with 7-AABs detection were independent predictors, which were used to construct a nomogram prediction model. The AUC values for this model were 0.826 and 0.862 in the training and validation sets, respectively, demonstrating excellent performance in DCA. ConclusionThe combination of 7-AABs with CT significantly enhances the accuracy of distinguishing between benign and malignant pulmonary nodules. The developed predictive model provides strong support for clinical decision-making and contributes to achieving precise diagnosis and treatment 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.