A novel integrative risk index of papillary thyroid cancer progression combining genomic alterations and clinical factors.

Although the majority of papillary thyroid cancer (PTC) is indolent, a subset of PTC behaves aggressively despite the best available treatment. A major clinical challenge is to reliably distinguish early on between those patients who need aggressive treatment from those who do not. Using a large cohort of PTC samples obtained from The Cancer Genome Atlas (TCGA), we analyzed the association between disease progression and multiple forms of genomic data, such as transcriptome, somatic mutations, and somatic copy number alterations, and found that genes related to FOXM1 signaling pathway were significantly associated with PTC progression. Integrative genomic modeling was performed, controlling for demographic and clinical characteristics, which included patient age, gender, TNM stages, histological subtypes, and history of other malignancy, using a leave-one-out elastic net model and 10-fold cross validation. For each subject, the model from the remaining subjects was used to determine the risk index, defined as a linear combination of the clinical and genomic variables from the elastic net model, and the stability of the risk index distribution was assessed through 2,000 bootstrap resampling. We developed a novel approach to combine genomic alterations and patient-related clinical factors that delineates the subset of patients who have more aggressive disease from those whose tumors are indolent and likely will require less aggressive treatment and surveillance (p = 4.62 × 10-10, log-rank test). Our results suggest that risk index modeling that combines genomic alterations with current staging systems provides an opportunity for more effective anticipation of disease prognosis and therefore enhanced precision management of PTC

Establishing Relevant ADC-based Texture Analysis Metrics for Quantifying Early Treatment-Induced Changes in Head and Neck Squamous Cell Carcinomas

Purpose: The purpose of this study is to identify which texture analysis metrics calculated from apparent diffusion coefficient (ADC) maps from patients with head and neck squamous cell carcinomas (HNSCC) provide quantifiable measures of tumor physiology changes. We discerned which imaging metrics were relevant using baseline agreement and variations during early treatment. Methods: For selective patients with stages II-IV HNSCC, ADC maps were generated from two baselines, taken 1 week apart, and one early treatment scan, obtained during the 2nd week of curative-intent chemoradiation therapy. Regions of interest (ROI), consisting of primary and nodal disease were drawn onto resampled ADC maps. Four 3D texture matrices describing local and regional relationships between voxel intensities in the ROIs were generated. From these, 38 texture metrics and 7 histogram features were calculated for each patient, including the mean and median ADC. Agreement between the two baseline measures was estimated with the intra-class correlation coefficient (ICC). For each metric with an ICC≥0.80, the Wilcoxon signed-rank test was used to test if the difference between the mean of the baselines and the early treatment was non-zero. Results: Texture analysis was implemented on nine patients that had both baselines and early treatment images. Due to baseline agreement, only 9 of the 45 metrics had an ICC ≥0.80, including ADC mean and median. Six of these 9 metrics had a p-value \u3c 0.05. Only 1 of the 9 metrics remained of interest, after applying the Holm correction to the alpha levels: the run length non-uniformity metric (p = 0.004) in the Gray Level Run Length Matrix. Conclusion: The feasibility of texture analysis is dependent on the baseline agreement of each metric, which disqualifies many texture characteristics. However, metrics with high ICC have potential to provide additional quantitative information for the assessment of early treatment changes for HNSCC

The Value of MRI in Distinguishing Subtypes of Lipomatous Extremity Tumors Needs Reassessment in the Era of MDM2 and CDK4 Testing

Introduction. Extremity lipomas and well-differentiated liposarcomas (WDLs) are difficult to distinguish on MR imaging. We sought to evaluate the accuracy of MRI interpretation using MDM2 amplification, via fluorescence in-situ hybridization (FISH), as the gold standard for pathologic diagnosis. Furthermore, we aimed to investigate the utility of a diagnostic formula proposed in the literature. Methods. We retrospectively collected 49 patients with lipomas or WDLs utilizing MDM2 for pathologic diagnosis. Four expert readers interpreted each patient\u27s MRI independently and provided a diagnosis. Additionally, a formula based on imaging characteristics (i.e. tumor depth, diameter, presence of septa, and internal cystic change) was used to predict the pathologic diagnosis. The accuracy and reliability of imaging-based diagnoses were then analyzed in comparison to the MDM2 pathologic diagnoses. Results. The accuracy of MRI readers was 73.5% (95% CI 61-86%) with substantial interobserver agreement (κ = 0.7022). The formula had an accuracy of 71%, which was not significantly different from the readers (p = 0.71). The formula and expert observers had similar sensitivity (83% versus 83%) and specificity (64.5% versus 67.7%; p = 0.659) for detecting WDLs. Conclusion. The accuracy of both our readers and the formula suggests that MRI remains unreliable for distinguishing between lipoma and WDLs

The Value of MRI in Distinguishing Subtypes of Lipomatous Extremity Tumors Needs Reassessment in the Era of MDM2 and CDK4 Testing

Introduction. Extremity lipomas and well-differentiated liposarcomas (WDLs) are difficult to distinguish on MR imaging. We sought to evaluate the accuracy of MRI interpretation using MDM2 amplification, via fluorescence in-situ hybridization (FISH), as the gold standard for pathologic diagnosis. Furthermore, we aimed to investigate the utility of a diagnostic formula proposed in the literature. Methods. We retrospectively collected 49 patients with lipomas or WDLs utilizing MDM2 for pathologic diagnosis. Four expert readers interpreted each patient’s MRI independently and provided a diagnosis. Additionally, a formula based on imaging characteristics (i.e. tumor depth, diameter, presence of septa, and internal cystic change) was used to predict the pathologic diagnosis. The accuracy and reliability of imaging-based diagnoses were then analyzed in comparison to the MDM2 pathologic diagnoses. Results. The accuracy of MRI readers was 73.5% (95% CI 61–86%) with substantial interobserver agreement (κ=0.7022). The formula had an accuracy of 71%, which was not significantly different from the readers (p=0.71). The formula and expert observers had similar sensitivity (83% versus 83%) and specificity (64.5% versus 67.7%; p=0.659) for detecting WDLs. Conclusion. The accuracy of both our readers and the formula suggests that MRI remains unreliable for distinguishing between lipoma and WDLs

A novel integrative risk index of papillary thyroid cancer progression combining genomic alterations and clinical factors.

Although the majority of papillary thyroid cancer (PTC) is indolent, a subset of PTC behaves aggressively despite the best available treatment. A major clinical challenge is to reliably distinguish early on between those patients who need aggressive treatment from those who do not. Using a large cohort of PTC samples obtained from The Cancer Genome Atlas (TCGA), we analyzed the association between disease progression and multiple forms of genomic data, such as transcriptome, somatic mutations, and somatic copy number alterations, and found that genes related to FOXM1 signaling pathway were significantly associated with PTC progression. Integrative genomic modeling was performed, controlling for demographic and clinical characteristics, which included patient age, gender, TNM stages, histological subtypes, and history of other malignancy, using a leave-one-out elastic net model and 10-fold cross validation. For each subject, the model from the remaining subjects was used to determine the risk index, defined as a linear combination of the clinical and genomic variables from the elastic net model, and the stability of the risk index distribution was assessed through 2,000 bootstrap resampling. We developed a novel approach to combine genomic alterations and patient-related clinical factors that delineates the subset of patients who have more aggressive disease from those whose tumors are indolent and likely will require less aggressive treatment and surveillance (p = 4.62 × 10-10, log-rank test). Our results suggest that risk index modeling that combines genomic alterations with current staging systems provides an opportunity for more effective anticipation of disease prognosis and therefore enhanced precision management of PTC

A Superpixel-by-Superpixel Clustering Framework for Hyperspectral Change Detection

Hyperspectral image change detection (HSI-CD) is an interesting task in the Earth’s remote sensing community. However, current HSI-CD methods are feeble at detecting subtle changes from bitemporal HSIs, because the decision boundary is partially stretched by strong changes so that subtle changes are ignored. In this paper, we propose a superpixel-by-superpixel clustering framework (SSCF), which avoids the confusion of different changes and thus reduces the impact on decision boundaries. Wherein the simple linear iterative clustering (SLIC) is employed to spatially segment the different images (DI) of the bitemporal HSIs into superpixels. Meanwhile, the Gaussian mixture model (GMM) extracts uncertain pixels from the DI as a rough threshold for clustering. The final CD results are obtained by passing the determined superpixels and uncertain pixels through K-means. The experimental results of two spaceborne bitemporal HSIs datasets demonstrate competitive efficiency and accuracy in the proposed SSCF

A Superpixel-by-Superpixel Clustering Framework for Hyperspectral Change Detection

Hyperspectral image change detection (HSI-CD) is an interesting task in the Earth’s remote sensing community. However, current HSI-CD methods are feeble at detecting subtle changes from bitemporal HSIs, because the decision boundary is partially stretched by strong changes so that subtle changes are ignored. In this paper, we propose a superpixel-by-superpixel clustering framework (SSCF), which avoids the confusion of different changes and thus reduces the impact on decision boundaries. Wherein the simple linear iterative clustering (SLIC) is employed to spatially segment the different images (DI) of the bitemporal HSIs into superpixels. Meanwhile, the Gaussian mixture model (GMM) extracts uncertain pixels from the DI as a rough threshold for clustering. The final CD results are obtained by passing the determined superpixels and uncertain pixels through K-means. The experimental results of two spaceborne bitemporal HSIs datasets demonstrate competitive efficiency and accuracy in the proposed SSCF