Empirical Driven Automatic Detection of Lobulation Imaging Signs in Lung CT

Malignancy risk stratification for solitary pulmonary nodule: A clinical practice guideline

CLINICAL QUESTION

The detection rate of the solitary pulmonary nodule (SPN) is increasing with the popularization of CT scanning. Malignancy risk stratification for SPN is a major clinical difficulty.

CURRENT PRACTICE

There have been several guidelines for SPN assessment. Inconsistency of these guidelines makes the clinical application difficult and confusing.

RECOMMENDATIONS

In this Rapid Recommendation, solid and subsolid SPNs are recommended to be evaluated respectively. Six factors, namely the combination of age with sex, smoking history, history of malignancy, family history of malignancy, and nodule size, are recommended for malignancy risk stratification for both kinds of SPNs; the border of nodules (spiculation and lobulation) is recommended for evaluating solid SPNs and the density of nodules (pure or mixed ground-glass nodule) is recommended for subsolid nodules. Among them, smoking history and radiologic features (nodule diameter, border, and density) are of relatively higher importance. A scoring system was proposed to assist malignancy risk stratification of SPNs, with a total score ranging from six points to 15 points (if solid) or 17 points (if subsolid). For each SPN, regardless of solid or subsolid in nature, a total score of ≤ 7 points suggested a low risk of being malignant, while 7 to 9 points suggested medium risk, and ≥ 9 points suggested high risk.

HOW THIS GUIDELINE WAS CREATED

This rapid recommendation was developed using the MAGIC (Making GRADE the Irresistible Choice) methodological framework. First, a clinical subcommittee identified the topic of recommendation and requested evidence. Then, an independent evidence synthesis subcommittee performed a comprehensive literature review and evaluated the evidence. Finally, based on findings from the systematic review and use of real-world data, the clinical subcommittee formulated recommendations, including the scoring system, through a consensus procedure.

THE EVIDENCE

A total of 13857 patients with SPNs were included in the meta-analysis and the association between 12 candidate factors and the risk of SPNs being malignant was studied. Eventually, seven factors were recommended for SPNs evaluation, and a scoring system was proposed.

UNDERSTANDING THE RECOMMENDATION

The parameters included are objective. Therefore, this recommendation is feasible in clinical practice. However, there are several uncertainties, such as a lack of further verification. It might be misclassified by the scoring system. Clinicians could choose the most suitable scheme according to the recommendation, along with their own experience in specific situations.

Nomogram model to predict pneumothorax after computed tomography-guided coaxial core needle lung biopsy

PURPOSE

To develop a predictive model to determine risk factors of pneumothorax in patients undergoing the computed tomography (CT)¹-guided coaxial core needle lung biopsy (CCNB).

METHODS

A total of 489 patients who underwent CCNBs with an 18-gauge coaxial core needle were retrospectively included. Patient characteristics, primary pulmonary disease, target lesion image characteristics and biopsy-related variables were evaluated as potential risk factors of pneumothorax which was determined on the chest X-ray and CT scans. Univariate and multivariate logistic regressions were used to identify the independent risk factors of pneumothorax and establish the predictive model, which was presented in the form of a nomogram. The discrimination and calibration of the model were evaluated as well.

RESULTS

The incidence of pneumothorax was 32.91 % and 31.42 % in the development and validation groups, respectively. Age, emphysema, pleural thickening, lesion location, lobulation sign, and size grade were identified independent risk factors of pneumothorax at the multivariate logistic regression model. The forming model produced an area under the curve of 0.718 (95 % CI = 0.660-0.776) and 0.722 (95 % CI = 0.638-0.805) in development and validation group, respectively. The calibration curve showed good agreement between predicted and actual probability.

CONCLUSIONS

The predictive model for pneumothorax after CCNBs had good discrimination and calibration, which could help in clinical practice.

A Novel Computer-Aided Diagnosis Scheme on Small Annotated Set: G2C-CAD

Purpose

Computer-aided diagnosis (CAD) can aid in improving diagnostic level; however, the main problem currently faced by CAD is that it cannot obtain sufficient labeled samples. To solve this problem, in this study, we adopt a generative adversarial network (GAN) approach and design a semisupervised learning algorithm, named G2C-CAD.

Methods

From the National Cancer Institute (NCI) Lung Image Database Consortium (LIDC) dataset, we extracted four types of pulmonary nodule sign images closely related to lung cancer: noncentral calcification, lobulation, spiculation, and nonsolid/ground-glass opacity (GGO) texture, obtaining a total of 3,196 samples. In addition, we randomly selected 2,000 non-lesion image blocks as negative samples. We split the data 90% for training and 10% for testing. We designed a DCGAN generative adversarial framework and trained it on the small sample set. We also trained our designed CNN-based fuzzy Co-forest on the labeled small sample set and obtained a preliminary classifier. Then, coupled with the simulated unlabeled samples generated by the trained DCGAN, we conducted iterative semisupervised learning, which continually improved the classification performance of the fuzzy Co-forest until the termination condition was reached. Finally, we tested the fuzzy Co-forest and compared its performance with that of a C4.5 random decision forest and the G2C-CAD system without the fuzzy scheme, using ROC and confusion matrix for evaluation.

Results

Four different types of lung cancer-related signs were used in the classification experiment: noncentral calcification, lobulation, spiculation, and nonsolid/ground-glass opacity (GGO) texture, along with negative image samples. For these five classes, the G2C-CAD system obtained AUCs of 0.946, 0.912, 0.908, 0.887, and 0.939, respectively. The average accuracy of G2C-CAD exceeded that of the C4.5 random decision tree by 14%. G2C-CAD also obtained promising test results on the LISS signs dataset; its AUCs for GGO, lobulation, spiculation, pleural indentation, and negative image samples were 0.972, 0.964, 0.941, 0.967, and 0.953, respectively.

Conclusion

The experimental results show that G2C-CAD is an appropriate method for addressing the problem of insufficient labeled samples in the medical image analysis field. Moreover, our system can be used to establish a training sample library for CAD classification diagnosis, which is important for future medical image analysis.