The Use of Low-Dose CT Intra- and Extra-Nodular Image Texture Features to Improve Small Lung Nodule Diagnosis in Lung Cancer Screening

TLDR: The CAD framework incorporating the clinical reading with the texture features extracted from LDCT increased the PPV and reduced the false positive (FP) rate in the early diagnosis of lung cancer.

Abstract: –Standard computed tomography (CT) scan is performed on lung cancer patients for progression and lesion classification. However, low-dose CT (LDCT) is commonly used in lung cancer screening for high-risk people. Extensive studies have shown that computer-aided diagnosis (CAD) using standard CT could greatly improve the diagnostic accuracy of early lung cancer. Unlike standard CT imaging, the application of radiological texture features extracted by radiologists on LDCT imaging is not well established due to lower resolution and higher variations. The purpose of this study is to investigate possible diagnosis value of texture features by comparing the classification performance of radiologic reading with radiologic reading combined with computer-aided texture features. A total of 186 biopsy-confirmed control and lung cancer cases were obtained from the National Lung Screening Trial (NLST). Cases were matched by various clinical parameters including age, gender, smoking status, chronic obstructive pulmonary disease (COPD) status, body mass index (BMI) and image appearances. We compared the subjective diagnosis of benign/malignant with the consensus readings of three radiologists. We then developed a CAD framework that imports radiologic reading features and extracts CAD features for heterogeneity quantification and data analysis. A total of 1342 CAD features were extracted. After eliminating highly correlated and redundant features, the remaining 458 features were given to a random forest algorithm, and a predicted probability of malignancy score (Pm) was calculated. Patients were grouped into 140 training (70 biopsypositive for cancer and 70 negatives) and 46 testing (20 positives and 26 negatives) sets, and a threshold value over Pm (0.5) was then used to classify the test set into cancer and non-cancer. Clinical accuracy [sensitivity, specificity, positive predictive value (PPV), and negative predictive value (PV)] were [0.95, 0.88, 0.86, 0.96] and [0.70, 0.69, 0.64, 0.75] for the CAD and radiologic reading, respectively. The CAD framework incorporating the clinical reading with the texture features extracted from LDCT increased the PPV and reduced the false positive (FP) rate in the early diagnosis of lung cancer. This approach shows promise for improving the accuracy of lung cancer diagnosis in the clinical environment, especially in patients with well-established risk factors.