Browsing Scholarly Projects 2016 by Authors
CT Textural Analysis (CTTA) of Metastatic Treatment‐Resistant Pancreatic Adenocarcinoma (PDAC): Identifying Biomarkers for Genetic Instability and Overall SurvivalCampbell, David; The University of Arizona College of Medicine - Phoenix; Korn, Ronald (The University of Arizona., 2016-03-23)Metastatic, treatment‐resistant pancreatic ductal adenocarcinoma (PDAC) is a rapidly fatal disease that typically carries a bleak prognosis. Contrast‐enhanced CT is the current standard of care tool for imaging evaluation, and repeat imaging is routinely performed in clinical trials. The availability of these imaging data render them exploitable for further analysis. CT texural analysis (CTTA), a quantitative tool for examining a region of interest on CT and generating statistical parameters based on gray‐level pixel data, is powerful technique that has been studied in other cancers and shown to correlate with features such as tumor grade, stage, and prognosis. However, the application of CTTA to PDAC has not been studied. Given the paucity of diagnostic tests to guide therapy, validated CTTA biomarkers could be immensely useful. Identifying PDAC variants that have a relative deficit in DNA repair might allow these cancers to be treated with targeted cytotoxic regimens sooner. Additionally, identifying prognostic CTTA parameters would be useful in gauging the severity of disease. We sought to perform quantitative textural analysis on CT imaging from a clinical trial cohort of patients with metastatic, treatment‐resistant PDAC. We aimed to correlate CTTA features to molecular profiling results (copy number variations obtained by array CGH) and clinical features (overall survival). Metastatic tumor sites from patients with treatment‐resistant PDAC were biopsied and molecularly profiled. Intrachromosal copy number were assessed by CGH in tumor specimens, and patients were treated based on these individual molecular profiling results. Pre‐biopsy portal‐venous phase and non‐contrast CT scans were obtained for retrospective analysis (n=15). CTTA was performed by drawing regions of interest around the primary pancreas adenocarcinoma and the normal pancreas tissue. CTTA parameters including mean positive pixels, entropy, kurtosis, and skewness were derived using the TexRAD platform at texture filtering densities of 0, 2, 3, 4, 5, and 6 pixels. CTTA values were then compared to intrachromosomal copy number variation (CNV) per tumor and overall survival (OS) post treatment using a Spearman’s rank correlation coefficient. Additional linear regression analysis was performed for positive correlations, and a Kaplan‐Meier statistic was generated for OS using median CTTA entropy. Multivariate analyses for CNV and OS were also performed. CNV were negatively correlated with the kurtosis value of the primary tumor mass using medium texture filtering (p=0.034, n=15). Linear regression revealed a significant negative correlation between kurtosis and CNV (p=0.038). Secondary analysis of the normal pancreas using coarse texture filtering revealed that increasing entropy was associated with decreased OS (p=0.0014, n=12). Using median entropy as a cutoff value (median: 4.165), median OS was greater in the entropy < 4.165 group versus the entropy > 4.165 group (179 days v 43 days; 95% CI 73.137 – 166.87; p=0.004, n=12). This exploratory study with admittedly limited sample size raises interesting questions about the use of CTTA parameters as diagnostic tools and/or biopsy adjuncts in assessing PDAC susceptibility to commercially available cytotoxics. Secondarily, entropy, a potential marker of heterogeneity and inflammation in the normal pancreas, represents an intriguing possibility for gauging prognosis.
Effectiveness of Using Texture Analysis in Evaluating Heterogeneity in Breast Tumor and in Predicting Tumor Aggressiveness in Breast Cancer PatientsHopp, Alix; The University of Arizona College of Medicine - Phoenix; Korn, Ronald (The University of Arizona., 2016-03-25)Objective and Hypothesis We hypothesize that tumor heterogeneity or tissue complexity, as measured by quantitative texture analysis (QTA) on mammogram, is a marker of tumor aggressiveness in breast cancer patients. Methods Tumor heterogeneity was assessed using QTA on digital mammograms of 64 patients with invasive ductal carcinoma (IDC). QTA generates six different values – Mean, standard deviation (SD), mean positive pixel value (MPPV), entropy, kurtosis, and skewness. Tumor aggressiveness was assessed using patients’ Oncotype DX® Recurrence Score (RS), a proven genomic assay score that correlates with the rate of remote breast cancer recurrence. RS and hormonal receptor status ‐ estrogen receptor (ER) and progesterone receptor (PR) ‐ were collected from pathology reports. Data were analyzed using statistical tools including Spearman rank correlation, linear regression, and logistic regression. Results Linear regression analysis showed that QTA parameter, SD, was a good predictor of RS (F=6.89, p=0.0108, R2=0.0870) at SSF=0.4. When PR status was included as a predictor, PR status and QTA parameter Skewness‐Diff, achieved linear model of greater fit (F=15.302, p<0.0001, R2=0.2988) at SSF=1. Among PR+ patients, Skewness‐Diff was a good linear predictor of RS (F=9.36, p=0.0034, R2=0.1320) at SSF=0.8. Logistic regression analysis showed that QTA parameters were good predictors of high risk RS probability, using different cutoffs of 30 and 25 for high risk RS; these QTA parameters were Entropy‐Diff for RS>30 (chi2=10.98, p=0.0009, AUC=0.8424, SE=0.0717) and Mean‐Total for RS>25 (chi2=9.98, p=0.0016, AUC=0.7437, SE=0.0612). When PR status was included, logistic models of higher log‐likelihood chi2 were found with SD‐Diff for RS>30 (chi2=18.69, p=0.0001, AUC=0.9409, SE=0.0322), and with Mean‐Total for RS>25 (chi2=25.56, p<0.0001, AUC=0.8443, SE=0.0591). For PR+ patients, good predictors were SD‐Diff for RS>30 (chi2=6.87, p=0.0087, AUC=0.9212, SE=0.0515), and MPP‐Diff and Skewness‐Diff for RS>25 (chi2=16.17, p=0.0003, AUC=0.9103, SE=0.0482). Significance Quantitative measurement of breast cancer tumor heterogeneity using QTA on digital mammograms may be used as predictors of RS and can potentially allow a non‐invasive and cost‐effective way to quickly assess the likelihood of RS and high risk RS.