Browsing by Author "Lafata, Kyle J"

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.

Purpose: To characterize the effect of different discretization techniques (methods and values) on radiomic features extracted from positron emission tomography (PET) images of patients undergoing definitive radiotherapy for oropharyngeal cancer (OPC) and determine if there are optimal binning techniques associated with the computed texture and histogram measurements.Methods: 71 patients were enrolled in a prospective clinical trial to receive definitive radiotherapy (70Gy) for OPC. PET/CT images were acquired both prior to treatment and two weeks into treatment (i.e., after 20 Gy). All patients were scanned on the same PET/CT imaging system. The gross tumor volume at the primary tumor site was manually segmented on CT and transferred to PET, from which 74 quantitative radiomic features were extracted as potential imaging biomarkers. The sensitivity of feature extraction to common discretization techniques (fixed bin number vs. fixed bin size) was systematically evaluated by measuring radiomic feature values at monotonically increasing bin numbers (32, 64, 128, 256) and bin sizes (0.1, 0.5, 1.0, 5.0). Disparities in radiomics data parameterized by these different discretization settings were quantified based on t-tests of individual features and cross-correlation of matrix-level feature spaces. A discretization invariance score (DIS) was defined as an aggregation of each unique probability of rejecting the null hypothesis that any two discretization techniques produce the same feature value. To evaluate the generalization of these characteristics during treatment, DIS values were compared between pre- and intra-treatment imaging. Results: Only 50% of radiomic features were robust (DIS > 0.7) to changes in bin number, compared to 66% of features when varying bin size. Regardless of discretization technique, grey level variance (DIS=0.0) and high grey level size emphasis (DIS=0.21) were the most sensitive to binning perturbations, while skewness (DIS=1.0) and kurtosis (DIS=1.0) were nearly invariant. The cross-correlation between discretization-specific feature spaces was maximized for fixed bin number and minimized for fixed bin size. Ranked DIS measurements were comparable between pre-treatment and intra-treatment imaging, implying that feature sensitivity is invariant to changes in the absolute feature value over time. Conclusion: The impact of discretization is largely feature-dependent. Individual features demonstrated a non-linear response to systematic changes in discretization parameters, which was captured by our DIS metric. DIS values can be used to optimize downstream radiomic biomarkers, where the prognostic value of individual features may depend on feature-specific discretization.

In 2020, gastric cancer was the fifth most common cancer worldwide, and esophageal cancer was the eighth most common cancer worldwide (Global Cancer Observatory). Strategies for treatment of both cancers range from curative to palliative. There is a growing evidence linking patient body composition with treatment tolerance (Hay et al., 2019; Miller et al., 2014). Therefore, the goal of this research was to find potentially significant biomarkers in predicting treatment tolerance and survival. This was done by investigating two aims. The first aim was to investigate the potential association between semantic radiomic features, clinical features, and demographic features with treatment outcome. The second aim was to investigate the potential association between agnostic radiomic features, clinical features, and demographic features with treatment outcome

We retrospectively identified 142 patients with gastric and esophageal cancer treated with neoadjuvant chemotherapy with some patients receiving radiation. Study outcomes were measures of treatment tolerance and survival. An existing segmentation model based on the nnU-Net architecture was used to derive cross-sectional masks from subcutaneous fat, skeletal muscle, and visceral fat at the L3 vertebral body level. Morphological and texture radiomic features were then extracted from the segmented cross-sections, and relationships between imaging-derived features and study outcomes were assessed.

On univariate analysis, skeletal muscle area was associated with a decrease in therapy break, and a decrease in Emergency Department admissions (p = 0.0072 and 0.0314, respectively). Increases in visceral fat area (p = 0.0285) and the ratio of visceral to subcutaneous fat areas (p = 0.0363) were both associated with chemotherapy dose reductions. An increase in skeletal muscle index (p = 0.0044) was associated with a decrease in therapy breaks. A combination of body mass index and skeletal muscle index was significant in predicting survival. Also, obesity increased the survival risk factor. Combining radiomic morphological features with clinical data increases the performance of a regularized logistic regression model in predicting the likelihood of surgical resection after neoadjuvant treatment.

The purpose of this work was to develop a new radiomics paradigm for sparse, time-series imaging data, where features are extracted from a spatial-temporal manifold modeling the time evolution between images, and to assess the prognostic value on patients with oropharyngeal cancer (OPC).To accomplish this, we developed an algorithm to mathematically describe the relationship between two images acquired at time t=0 and t>0. These images serve as boundary conditions of a partial differential equation describing the transition from one image to the other. To solve this equation, we propagate the position and momentum of each voxel according to Fokker-Planck dynamics (i.e., a technique common in statistical mechanics). This transformation is driven by an underlying potential force uniquely determined by the equilibrium image. The solution generates a spatial-temporal manifold (3 spatial dimensions + time) from which we define dynamic radiomic features. First, our approach was numerically verified by stochastically sampling dynamic Gaussian processes of monotonically decreasing noise. The transformation from high to low noise was compared between our Fokker-Planck estimation and simulated ground-truth. To demonstrate feasibility and clinical impact, we applied our approach to 18F-FDG-PET images to estimate early metabolic response of patients (n=57) undergoing definitive (chemo)radiation for OPC. Images were acquired pre-treatment and two-weeks intra-treatment (after 20 Gy). Dynamic radiomic features capturing changes in texture and morphology were then extracted. Patients were partitioned into two groups based on similar dynamic radiomic feature expression via k-means clustering and compared by Kaplan-Meier analyses with log-rank tests (p<0.05). These results were compared to conventional delta radiomics to test the added value of our approach. Numerical results confirmed our technique can recover image noise characteristics given sparse input data as boundary conditions. Our technique was able to model tumor shrinkage and metabolic response. While no delta radiomics features proved prognostic, Kaplan-Meier analyses identified nine significant dynamic radiomic features. The most significant feature was Gray-Level-Size-Zone-Matrix gray-level variance (p=0.011), which demonstrated prognostic improvement over its corresponding delta radiomic feature (p=0.722). We developed, verified, and demonstrated the prognostic value of a novel, physics-based radiomics approach over conventional delta radiomics via data assimilation of quantitative imaging and differential equations.