Radiomics of NSCLC: Quantitative CT Image Feature Characterization and Tumor Shrinkage Prediction

Radiomics is the high-throughput extraction and analysis of quantitative image features. For non-small cell lung cancer (NSCLC) patients, radiomics can be applied to standard of care computed tomography (CT) images to improve tumor diagnosis, staging, and response assessment. The first objective of this work was to show that CT image features extracted from pre-treatment NSCLC tumors could be used to predict tumor shrinkage in response to therapy. This is important since tumor shrinkage is an important cancer treatment endpoint that is correlated with probability of disease progression and overall survival. Accurate prediction of tumor shrinkage could also lead to individually customized treatment plans. To accomplish this objective, 64 stage NSCLC patients with similar treatments were all imaged using the same CT scanner and protocol. Quantitative image features were extracted and principal component regression with simulated annealing subset selection was used to predict shrinkage. Cross validation and permutation tests were used to validate the results. The optimal model gave a strong correlation between the observed and predicted shrinkages with . The second objective of this work was to identify sets of NSCLC CT image features that are reproducible, non-redundant, and informative across multiple machines. Feature sets with these qualities are needed for NSCLC radiomics models to be robust to machine variation and spurious correlation. To accomplish this objective, test-retest CT image pairs were obtained from 56 NSCLC patients imaged on three CT machines from two institutions. For each machine, quantitative image features with concordance correlation coefficient values greater than 0.90 were considered reproducible. Multi-machine reproducible feature sets were created by taking the intersection of individual machine reproducible feature sets. Redundant features were removed through hierarchical clustering. The findings showed that image feature reproducibility and redundancy depended on both the CT machine and the CT image type (average cine 4D-CT imaging vs. end-exhale cine 4D-CT imaging vs. helical inspiratory breath-hold 3D CT). For each image type, a set of cross-machine reproducible, non-redundant, and informative image features was identified. Compared to end-exhale 4D-CT and breath-hold 3D-CT, average 4D-CT derived image features showed superior multi-machine reproducibility and are the best candidates for clinical correlation

Advanced Imaging Analysis for Predicting Tumor Response and Improving Contour Delineation Uncertainty

ADVANCED IMAGING ANALYSIS FOR PREDICTING TUMOR RESPONSE AND IMPROVING CONTOUR DELINEATION UNCERTAINTY By Rebecca Nichole Mahon, MS A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at Virginia Commonwealth University. Virginia Commonwealth University, 2018 Major Director: Dr. Elisabeth Weiss, Professor, Department of Radiation Oncology Radiomics, an advanced form of imaging analysis, is a growing field of interest in medicine. Radiomics seeks to extract quantitative information from images through use of computer vision techniques to assist in improving treatment. Early prediction of treatment response is one way of improving overall patient care. This work seeks to explore the feasibility of building predictive models from radiomic texture features extracted from magnetic resonance (MR) and computed tomography (CT) images of lung cancer patients. First, repeatable primary tumor texture features from each imaging modality were identified to ensure a sufficient number of repeatable features existed for model development. Then a workflow was developed to build models to predict overall survival and local control using single modality and multi-modality radiomics features. The workflow was also applied to normal tissue contours as a control study. Multiple significant models were identified for the single modality MR- and CT-based models, while the multi-modality models were promising indicating exploration with a larger cohort is warranted. Another way advances in imaging analysis can be leveraged is in improving accuracy of contours. Unfortunately, the tumor can be close in appearance to normal tissue on medical images creating high uncertainty in the tumor boundary. As the entire defined target is treated, providing physicians with additional information when delineating the target volume can improve the accuracy of the contour and potentially reduce the amount of normal tissue incorporated into the contour. Convolution neural networks were developed and trained to identify the tumor interface with normal tissue and for one network to identify the tumor location. A mock tool was presented using the output of the network to provide the physician with the uncertainty in prediction of the interface type and the probability of the contour delineation uncertainty exceeding 5mm for the top three predictions

Quantitative image analysis of peripheral nerves in whiplash injury patients

The research in this thesis has examined the use of texture and shape analysis to characterise Magnetic Resonance (MR) images of peripheral nerves in order to provide a potential quantitative tool for better diagnosis and treatments. Texture and shape can be considered as inherent properties of all surfaces and have the potential to provide sensitive information which cannot be quantitatively perceived by human vision. Texture analysis has been successfully used in image classification of aerial and satellite imagery and the diagnosis and prognosis of several types of cancer. However, to date, it has never been used in investigating peripheral nerve damage. In this thesis, we study the application of texture and shape analysis to the peripheral nerves in the upper extremities of patients suffering from Whiplash Associated Disorders (WAD). Specifically, quantitative texture analysis was performed on MR images of the carpal tunnel which contains the median nerve. The median nerve was studied to identify differences in textural patterns. Texture methods such as: first order features; co-occurrence matrices; run-length matrices and autocorrelation function were applied and their performance was assessed. Texture analysis was also performed to investigate nerve damage in the MR images of the brachial plexus, both in controls and patients. Further, spatial domain shape metrics were used to quantify and study the morphological differences of the median nerve in controls and patients. This highlighted that some significant differences exist between groups and thus could potentially be reliably used in combination with clinical scale metrics to identify possible nerve damage. As MR images contain noise, locating the median nerve accurately to perform image analysis is very important. Therefore, we further investigated the application of an enhanced correlation filtering method that could be trained on images of the median nerve and then applied to detect the median nerve in test images. The Optimal Trade-off Maximum Average Correlation Height (OT-MACH) filter includes the expected distortions in the target in the construction of the filter reference function. The OT-MACH filter was tuned in a bandpass to maximize the correlation peak and thereby successfully locate the position of the median nerve in the carpal tunnel. This study has successfully demonstrated that texture and shape analysis can be used to investigate possible peripheral nerve damage. Further research is required using larger datasets to establish a quantitative image analysis tool to support clinical decision making and thereby improve patient care and treatment outcome

QUANTITATIVE IMAGING FOR PRECISION MEDICINE IN HEAD AND NECK CANCER PATIENTS

The purpose of this work was to determine if prediction models using quantitative imaging measures in head and neck squamous cell carcinoma (HNSCC) patients could be improved when noise due to imaging was reduced. This was investigated separately for salivary gland function using dynamic contrast enhanced magnetic resonance imaging (DCE-MRI), overall survival using computed tomography (CT)-based radiomics, and overall survival using positron emission tomography (PET)-based radiomics. From DCE-MRI, where T1-weighted images are serially acquired after injection of contrast, quantitative measures of diffusion can be obtained from the series of images. Radiomics is the study of the relationship of voxels to one another providing measures of texture from the area of interest. Quantitative information obtained from imaging could help in radiation treatment planning by providing quantifiable spatial information with computational models for assigning dose to regions to improve patient outcome, both survival and quality of life. By reducing the noise within the quantitative data, the prediction accuracy could improve to move this type of work closer to clinical practice. For each imaging modality sources of noise that could impact the patient analysis were identified, quantified, and if possible minimized during the patient analysis. In MRI, a large potential source of uncertainty was the image registration. To evaluate this, both physical and synthetic phantoms were used, which showed that registration of MR images was high, with all root mean square errors below 3 mm. Then, 15 HNSCC patients with pre-, mid-, and post-treatment DCE-MRI scans were evaluated. However, differences in algorithm output were found to be a large source of noise as different algorithms could not consistently rank patients as above or below the median for quantitative metrics from DCE-MRI. Therefore, further analysis using this modality was not pursued. In CT, a large potential source of noise that could impact patient analysis was the inter-scanner variability. To investigate this a controlled protocol was designed and used to image, along with the local head and chest protocols, a radiomics phantom on 100 CT scanners. This demonstrated that the inter-scanner variability could be reduced by over 50% using a controlled protocol compared to local protocols. Additionally, it was shown that the reconstruction parameters impact feature values while most acquisition parameters do not, therefore, most of this benefit can be achieved using a radiomics reconstruction with no additional dose to the patient. Then to evaluate this impact in patient studies, 726 HNSCC patients with CT images were used to create and test a Cox proportional hazards model for overall survival. Those patients with the same imaging protocol were subset and a new Cox proportional hazards model was created and tested in order to determine if the reduction in noise due to controlling the imaging protocol translated into improved prediction. However, noise between patient populations from different institutions was shown to be larger than the reduction in noise due to a controlled imaging protocol. In PET, a large potential source of noise that could impact patient analysis was the imaging protocol. A phantom scanned on three different scanners and vendors demonstrated that on a single vendor, imaging parameter choices did not affect radiomics feature values, but inter-scanner variances could be large. Then, 686 HNSCC patients with PET images were used to create and test a Cox proportional hazards model for overall survival. Those patients with the same imaging protocol were subset and a new Cox proportional hazards model was created and tested in order to determine if the reduction in noise due to controlling the imaging protocol on a vendor translated into improved prediction. However, no predictive radiomics signature could be determined for any subset of the patient cohort that resulted in significant stratification of patients into high and low risk. This study demonstrated that the imaging variability could be quantified and controlled for in each modality. However, for each modality there were larger sources of noise identified that did not allow for improvement in prediction modeling of salivary gland function or overall survival using quantitative imaging metrics for MRI, CT, or PET