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.
Diagnosis and treatment of solitary pulmonary nodule (SPN, less than 30 mm in diameter) has been a formidable problem in clinical work. It is often detected in medical examination or other disease examinations by chance. There are no corresponding signs and symptoms of SPN except those on the imaging, so it is difficult to make a correct diagnosis as early as possible. Literature shows that there is a certain probability of malignant SPN, so early correct diagnosis is the key factor in deciding the prognosis and appropriate treatment. With the accumulation of clinical experiences, the development of new fiberoptic bronchoscopy, highresolution CT, and videoassisted thoracoscopic surgery, as well as the evolution of some invasive examination technologies, it is less difficult in distinguishing benign from malignant SPN than ever before. In this article, we will make a comprehensive review on the development in the aspect of differential diagnosis of SPN.
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.
This paper presents a probability segmentation algorithm for lung nodules based on three-dimensional features. Firstly, we computed intensity and texture features in region of interest (ROI) pixel by pixel to get their feature vector, and then classified all the pixels based on their feature vector. At last, we carried region growing on the classified result, and got the final segmentation result. Using the public Lung Imaging Database Consortium (LIDC) lung nodule datasets, we verified the performance of proposed method by comparing the probability map within LIDC datasets, which was drawn by four radiology doctors separately. The experimental results showed that the segmentation algorithm using three-dimensional intensity and texture features would be effective.
Computer-aided detection (CAD) of pulmonary nodule technology can effectively assist the radiologist to enhance lung nodule detection efficiency and accuracy rate, so it can lay the foundation for the early diagnosis of lung cancer. In order to provide reference for the scholars and to develop the CAD technology, we in this paper review the technology research and development of CAD of the pulmonary nodules which is based on CT image in recent years both home and abroad. At the same time, we also analyse the advantages and shortcomings of different methods. Then we present the improvement direction for reference. According to the literature in recent years, there still has been large development space in CAD technology for pulmonary nodules. The establishment and improvement of the CAD system in each step would be of great scientific value.
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.
The possibility of solitary pulmonary nodules tending to lung cancer is very high in the middle and late stage. In order to detect the middle and late solitary pulmonary nodules, we present a new computer-aided diagnosis method based on the geometric features. The new algorithm can overcome the disadvantage of the traditional algorithm which can't eliminate the interference of vascular cross section. The proposed algorithm was implemented by multiple clustering of the extracted geometric features of region of interest (ROI) through K-means algorithm, including degree of slenderness, similar degree of circle, degree of compactness and discrete degree. The 232 lung CT images were selected from Lung Image Database Consortium (LIDC) database to do contrast experiment. Compared with the traditional algorithm, the detection rate of the new algorithm was 92.3%, and the error rate was 14.8%. At the same time, the detection rate of the traditional algorithm was only 83.9%, and the error rate was 78.2%. The results show that the proposed algorithm can mark the solitary pulmonary nodules more accurately and reduce the error rate due to precluding the disturbance of vessel section.
ObjectiveTo elucidate the relationship between clinical characteristics and pathology findings of solitary pulmonary nodules (SPN). MethodsA retrospective cohort study was carried out on 231 SPN patients pathologically confirmed between January 2009 and December 2013 in Nanjing General Hospital of Fuzhou Military Command and Fuzhou Second Hospital. Using pathological results as reference standard, the sex, age, smoking history, smoking amount, quit smoking history, and extrapulmonary malignant tumor history were compared between the SPN patients with different pathological type. ResultsFemale and age were positively correlated with the probability of malignancy in SPN with correlation coefficients as 1.090 and 0.063 respectively. Extrapulmonary malignant tumor history, smoking history, smoking amount, quit smoking history did not show significant relationship. Gender was a factor that affects pathological types of SPN. Female patients were in higher risk than male patients to have precancerous lesions, pulmonary aspergillosis, pulmonary sclerosing hemangioma, adenocarcinoma. Male patients had higher risk suffering from pulmonary tuberculosis, pulmonary cryptococcosis, squamous cell carcinoma, adenosquamous carcinoma, inflammatory pseudotumor and metastases. Distribution of SPN pathologic types in each age group was similar. Most patients who had precancerous lesions, pulmonary hamartoma, pulmonary aspergillosis, pulmonary sclerosing hemangioma, adenocarcinoma and inflammatory pseudotumor were not smokers. ConclusionsGender and age are valuable in distinguishing benign SPN from malignant SPN. Pathologic types of SPN are related to patients' gender and smoking history.
With widespread utilization of multi-slice helical computed tomography (CT) and low-dose CT in lung cancer screening, significantly greater incidence of patients with solitary pulmonary nodules (SPN) has been found. Once SPN is discovered, it is very difficult to immediately determine whether it is benign or malignant in clinical practice. In this review, SPN etiology, epidemiological characteristics of SPN patients, nodule size, morphology, location and growth rate, mathematical models for predicting malignancy of SPN, and diagnostic value of positron emission tomography (PET) and positron emission tomography-computed tomography (PET/CT) are summarized to provide reference for differential diagnosis of SPN. Current management strategies for SPN are also discussed in this review. According to whether SPN diameter is greater than 8 mm, whether SPN patients are advanced aged, have smoking or malignancy history, different follow-up and treatment strategies can be chosen. The diagnostic and treatment value of video-assisted thoracoscopic surgery for SPN is also discussed.