Analysis of Rare Events and Multi-Object Radiomics in Medical Imaging

Introduction: Medical imaging is essential in oncology for detecting, diagnosing, and treating cancer, and monitoring treatment effectiveness. Radiomics and machine learning are techniques that use computer algorithms to extract and analyze a vast number of quantitative features from medical images, which can lead to more accurate diagnoses and treatment plans. However, technical challenges, such as rare events and multi-object radiomics need to be addressed to fully realize the potential of these techniques in medical imaging and improve patient outcomes. Two examples of technical challenges in medical imaging are (1) the rare occurrence of a positive cancer diagnosis relative to the screened population, and (2) the difficulty of applying radiomics to multiple tumors in the same image, as seen in cases of multiple brain metastases.

Methods: (1) To evaluate the diagnostic performance of lung cancer screening (LCS) on low dose CT (LDCT), We retrospectively enrolled patients who received LCS via LDCT within our healthcare system between 1/1/2015-6/30/20. Our LCS program is a high-volume, ACR-recognized LCS program that houses a structured reporting registry of Lung-RADS scores. Using data from the electronic health record, we defined a malignant pulmonary nodule (i.e., lung cancer) as a pathology-proven diagnosis of lung cancer (via tissue obtained from a needle biopsy, bronchoscopy, or surgical biopsy). We determined the rate of screen-detected lung cancers, as well as all lung cancers diagnosed within one year after a LCS exam. The diagnostic performance of LCS was determined based on receiver operating characteristic analysis. Relevant clinical and demographic characteristics were analyzed as potential confounding factors, including age, sex, race/ethnicity, and smoking history. Predictive modeling on support vector machine (SVM) was performed and compared to standard-of-care Lung-RADS. (2) To explore radiomic feature aggregation methods in patients with metastatic brain cancer, seventy-eight relevant radiomic features were extracted from 449 unique metastases from 159 unique patients treated with stereotactic radiosurgery (SRS) using SPGR or T1+c MRI scans. MRI scans were normalized and discretized into 64 gray levels. Three different aggregation techniques were evaluated to compare radiomic feature results: (1) simple average, (2) weighted average by tumor volume, and (3) weighted average of the three largest metastases by volume. Univariate Kaplan-Meier analysis was performed based on the median value of each feature for three distinct clinical endpoints: overall survival, intracranial progression-free survival (ICPFS), and extracranial progression-free survival (ECPFS). In addition, this study considered molecular drivers (including EGFR, ALK, BRAF, KRAS, PD-L1, ROS1) and some clinical/demographic factors (age at SRS, KPS, number of metastases and NSCLC type) as potential confounding variables, evaluated for radiogenomic association based on Fisher's Exact Test.

Results: (1) 5,150 LCS exams were performed on 3,326 unique patients. The average age at LCS was 65.4±6.2 years, with 51.4% (1709/3,326) being male. The sensitivity and specificity of LCS were 93.1% and 83.8% respectively. Patients with positive Lung-RADs scores and patients who were current smokers had a higher likelihood of screen-detected lung cancer than former smokers (p<0.001 and p=0.017 respectively). The sensitivity plus specificity of one-class training on SVM outperformed standard-of-care Lung-RADS alone. (2) Radiomic texture features Small Zone Emphasis (p=0.014) and Correlation (p=0.018) demonstrated a significant association with ICPFS and ECPFS, respectively, regardless of the feature aggregation technique. The radiomic morphological feature Compactness was also significant for these endpoints, suggesting that both tumor shape and volume-corrected texture provide complementary prognostic value. The EGFR mutation was found to be associated with 11 prognostically-relevant radiomic features for ECPFS, with the strongest association for the feature of Correlation (p=0.010).Conclusions: (1) LCS has high sensitivity, modest specificity, and relatively low PPV, the latter suggesting a need for improvements in classification of "positive" LCS results. Screen-detected lung cancers were likely in currently smoking patients. (2) This exploratory study identified several associations between radiomic features and clinical endpoints, providing insight into their potential prognostic value. Molecular drivers were also identified as confounding variables, emphasizing the importance of further radiogenomic analyses in brain metastases.