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.

Suppression of T lymphocyte activation in simulated microgravity

Immune dysfunction is encountered during spaceflight. Various aspects of spaceflight, including microgravity, cosmic radiation, and both physiological and psychological stress, may perturb immune function. We sought to understand the impact of microgravity alone on the cellular mechanisms critical to immunity. Clinostatic RWV bioreactors that simulate aspects of microgravity were used to analyze the response of human PBMC to polyclonal and oligoclonal activation. PHA responsiveness in the RWV bioreactor was almost completely diminished. IL-2 and IFN-$\gamma$ secretion was reduced whereas IL-1$\beta$ and IL-6 secretion was increased, suggesting that monocytes may not be as adversely affected by simulated microgravity as T cells. Activation marker expression (CD25, CD69, CD71) was significantly reduced in RWV cultures. Furthermore, addition of exogenous IL-2 to these cultures did not restore proliferation. Antigen specific T cell activation, including the mixed-lymphocyte reaction, tetanus toxoid responsiveness, and Borrelia activation of a specific T cell line, was also suppressed in the RWV bioreactor.^ The role of altered culture conditions in the suppression of T cell activation were considered. Potential reduced cell-cell and cell-substratum interactions in the RWV bioreactor may play a role in the loss of PHA responsiveness. However, PHA activation in Teflon culture bags that limit cell-substratum interactions was not affected. Furthermore, increasing cell-population density, and therefore cell-cell interactions, in the RWV cultures did not help restore PHA activation. However, placing PBMC within small collagen beads did partially restore PHA responsiveness. Finally, activation of purified T cells with crosslinked CD2/CD28 or CD3/CD28 antibody pairs, which does not require costimulation through cell-cell contact, was completely suppressed in the RWV bioreactor suggesting a defect internal to the T cell.^ Activation of both PBMC and purified T cells with PMA and ionomycin was unaffected by RWV culture, indicating that signaling mechanisms downstream of PKC activation and calcium flux are not sensitive to simulated microgravity. Furthermore, sub-mitogenic doses of PMA alone but not ionomycin alone restored PHA responsiveness of PBMC in RWV culture. Thus, our data indicate that during polyclonal activation in simulated microgravity, there is a specific dysfunction within the T cell involving the signaling pathways upstream of PKC activation. ^

Comparison of multiple comparison methods for identifying differential gene expression in simulated and real papillary thyroid cancer microarray data

The difficulty of detecting differential gene expression in microarray data has existed for many years. Several correction procedures try to avoid the family-wise error rate in multiple comparison process, including the Bonferroni and Sidak single-step p-value adjustments, Holm's step-down correction method, and Benjamini and Hochberg's false discovery rate (FDR) correction procedure. Each multiple comparison technique has its advantages and weaknesses. We studied each multiple comparison method through numerical studies (simulations) and applied the methods to the real exploratory DNA microarray data, which detect of molecular signatures in papillary thyroid cancer (PTC) patients. According to our results of simulation studies, Benjamini and Hochberg step-up FDR controlling procedure is the best process among these multiple comparison methods and we discovered 1277 potential biomarkers among 54675 probe sets after applying the Benjamini and Hochberg's method to PTC microarray data.^