Bing-Shen Huang for his pertinent comments on unirradiated liver volume to standard liver volume ratio calculation.

This study analyzed magnetic resonance imaging (MRI) scans of Glioblastoma (GB) patients to develop an imaging-derived predictive model for assessing the extent of intratumoral CD3 T-cell infiltration. Pre-surgical T1-weighted post-contrast and T2-weighted Fluid-Attenuated-Inversion-Recovery (FLAIR) MRI scans, with corresponding mRNA expression of CD3D/E/G were obtained through The Cancer Genome Atlas (TCGA) for 79 GB patients. The tumor region was contoured and 86 image-derived features were extracted across the T1-post contrast and FLAIR images. Six imaging features-kurtosis, contrast, small zone size emphasis, low gray level zone size emphasis, high gray level zone size emphasis, small zone high gray level emphasiswere found associated with CD3 activity and used to build a predictive model for CD3 infiltration in an independent data set of 69 GB patients (using a 50-50 split for training and testing). For the training set, the image-based prediction model for CD3 infiltration achieved accuracy of 97.1% and area under the curve (AUC) of 0.993. For the test set, the model achieved accuracy of 76.5% and AUC of 0.847. This suggests a relationship between image-derived textural features and CD3 T-cell infiltration enabling the noninvasive inference of intratumoral CD3 T-cell infiltration in GB patients, with potential value for the radiological assessment of response to immune therapeutics.

Background The prognostic relevance of extranodal extension (ENE) for salivary gland carcinoma (SGC) remains unclear. The present study is undertaken to investigate the predictive significance of pathological nodal parameters in surgically treated patients with nodal metastatic SGC. Methods This multicenter cohort included 114 patients with pathologically proven node‐positive SGC between 2000 and 2014. Possible correlations of clinicopathological parameters and outcomes were examined. Results The median follow‐up was 69 months (range, 11‐173 months). The multivariate analysis identified metastatic node number (1‐2 vs 3‐6; 1‐2 vs ≥7) as an independent predictor for regional control (P = 0.005; P = 0.02), locoregional control (P = 0.008; P = 0.04), distant metastasis‐free survival (P = 0.17; P = 0.006), disease‐free survival (P = 0.05; P = 0.002), and overall survival (P = 0.18; P = 0.009), whereas ENE was not associated with survival outcomes. Conclusions Metastatic node number, not ENE, is an independent node‐related prognosticator for SGC. Integration of ENE into the American Joint Committee on Cancer 8th edition staging criteria may not improve prognostic performance.

BackgroundImaging features derived from MRI scans can be used for not only breast cancer detection and measuring disease extent, but can also determine gene expression and patient outcomes. The relationships between imaging features, gene/protein expression, and response to therapy hold potential to guide personalized medicine. We aim to characterize the relationship between radiologist-annotated tumor phenotypic features (based on MRI) and the underlying biological processes (based on proteomic profiling) in the tumor.MethodsMultiple-response regression of the image-derived, radiologist-scored features with reverse-phase protein array expression levels generated association coefficients for each combination of image-feature and protein in the RPPA dataset. Significantly-associated proteins for features were analyzed with Ingenuity Pathway Analysis software. Hierarchical clustering of the results of the pathway analysis determined which features were most strongly correlated with pathway activity and cellular functions.ResultsEach of the twenty-nine imaging features was found to have a set of significantly correlated molecules, associated biological functions, and pathways.ConclusionsWe interrogated the pathway alterations represented by the protein expression associated with each imaging feature. Our study demonstrates the relationships between biological processes (via proteomic measurements) and MRI features within breast tumors.