Shape and texture-based radiomics signature on CT effectively discriminates benign from malignant renal masses.

OBJECTIVES: Using a radiomics framework to quantitatively analyze tumor shape and texture features in three dimensions, we tested its ability to objectively and robustly distinguish between benign and malignant renal masses. We assessed the relative contributions of shape and texture metrics separately and together in the prediction model. MATERIALS AND METHODS: Computed tomography (CT) images of 735 patients with 539 malignant and 196 benign masses were segmented in this retrospective study. Thirty-three shape and 760 texture metrics were calculated per tumor. Tumor classification models using shape, texture, and both metrics were built using random forest and AdaBoost with tenfold cross-validation. Sensitivity analyses on five sub-cohorts with respect to the acquisition phase were conducted. Additional sensitivity analyses after multiple imputation were also conducted. Model performance was assessed using AUC. RESULTS: Random forest classifier showed shape metrics featuring within the top 10% performing metrics regardless of phase, attaining the highest variable importance in the corticomedullary phase. Convex hull perimeter ratio is a consistently high-performing shape feature. Shape metrics alone achieved an AUC ranging 0.64-0.68 across multiple classifiers, compared with 0.67-0.75 and 0.68-0.75 achieved by texture-only and combined models, respectively. CONCLUSION: Shape metrics alone attain high prediction performance and high variable importance in the combined model, while being independent of the acquisition phase (unlike texture). Shape analysis therefore should not be overlooked in its potential to distinguish benign from malignant tumors, and future radiomics platforms powered by machine learning should harness both shape and texture metrics. KEY POINTS: • Current radiomics research is heavily weighted towards texture analysis, but quantitative shape metrics should not be ignored in their potential to distinguish benign from malignant renal tumors. • Shape metrics alone can attain high prediction performance and demonstrate high variable importance in the combined shape and texture radiomics model. • Any future radiomics platform powered by machine learning should harness both shape and texture metrics, especially since tumor shape (unlike texture) is independent of the acquisition phase and more robust from the imaging variations.

Petunia hybrida CAROTENOID CLEAVAGE DIOXYGENASE7 is involved in the production of negative and positive branching signals in petunia.

One of the key factors that defines plant form is the regulation of when and where branches develop. The diversity of form observed in nature results, in part, from variation in the regulation of branching between species. Two CAROTENOID CLEAVAGE DIOXYGENASE (CCD) genes, CCD7 and CCD8, are required for the production of a branch-suppressing plant hormone. Here, we report that the decreased apical dominance3 (dad3) mutant of petunia (Petunia hybrida) results from the mutation of the PhCCD7 gene and has a less severe branching phenotype than mutation of PhCCD8 (dad1). An analysis of the expression of this gene in wild-type, mutant, and grafted petunia suggests that in petunia, CCD7 and CCD8 are coordinately regulated. In contrast to observations in Arabidopsis (Arabidopsis thaliana), ccd7ccd8 double mutants in petunia show an additive phenotype. An analysis using dad3 or dad1 mutant scions grafted to wild-type rootstocks showed that when these plants produce adventitious mutant roots, branching is increased above that seen in plants where the mutant roots are removed. The results presented here indicate that mutation of either CCD7 or CCD8 in petunia results in both the loss of an inhibitor of branching and an increase in a promoter of branching.

Quantitative Contour Analysis as an Image-based Discriminator Between Benign and Malignant Renal Tumors.

OBJECTIVE: To investigate whether morphologic analysis can differentiate between benign and malignant renal tumors on clinically acquired imaging. MATERIALS AND METHODS: Between 2009 and 2014, 3-dimensional tumor volumes were manually segmented from contrast-enhanced computerized tomography (CT) images from 150 patients with predominantly solid, nonmacroscopic fat-containing renal tumors: 100 renal cell carcinomas and 50 benign lesions (eg, oncocytoma and lipid-poor angiomyolipoma). Tessellated 3-dimensional tumor models were created from segmented voxels using MATLAB code. Eleven shape descriptors were calculated: sphericity, compactness, mean radial distance, standard deviation of the radial distance, radial distance area ratio, zero crossing, entropy, Feret ratio, convex hull area and convex hull perimeter ratios, and elliptic compactness. Morphometric parameters were compared using the Wilcoxon rank-sum test to investigate whether malignant renal masses demonstrate more morphologic irregularity than benign ones. RESULTS: Only CHP in sagittal orientation (median 0.96 vs 0.97) and EC in coronal orientation (median 0.92 vs 0.93) differed significantly between malignant and benign masses (P = .04). When comparing these 2 metrics between coronal and sagittal orientations, similar but nonsignificant trends emerged (P = .07). Other metrics tested were not significantly different in any imaging plane. CONCLUSION: Computerized image analysis is feasible using shape descriptors that otherwise cannot be visually assessed and used without quantification. Shape analysis via the transverse orientation may be reasonable, but encompassing all 3 planar dimensions to characterize tumor contour can achieve a more comprehensive evaluation. Two shape metrics (CHP and EC) may help distinguish benign from malignant renal tumors, an often challenging goal to achieve on imaging and biopsy.

Gain-of-function phenotypes of many CLAVATA3/ESR genes, including four new family members, correlate with tandem variations in the conserved CLAVATA3/ESR domain.

Secreted peptide ligands are known to play key roles in the regulation of plant growth, development, and environmental responses. However, phenotypes for surprisingly few such genes have been identified via loss-of-function mutant screens. To begin to understand the processes regulated by the CLAVATA3 (CLV3)/ESR (CLE) ligand gene family, we took a systems approach to gene identification and gain-of-function phenotype screens in transgenic plants. We identified four new CLE family members in the Arabidopsis (Arabidopsis thaliana) genome sequence and determined their relative transcript levels in various organs. Overexpression of CLV3 and the 17 CLE genes we tested resulted in premature mortality and/or developmental timing delays in transgenic Arabidopsis plants. Overexpression of 10 CLE genes and the CLV3 positive control resulted in arrest of growth from the shoot apical meristem (SAM). Overexpression of nearly all the CLE genes and CLV3 resulted in either inhibition or stimulation of root growth. CLE4 expression reversed the SAM proliferation phenotype of a clv3 mutant to one of SAM arrest. Dwarf plants resulted from overexpression of five CLE genes. Overexpression of new family members CLE42 and CLE44 resulted in distinctive shrub-like dwarf plants lacking apical dominance. Our results indicate the capacity for functional redundancy of many of the CLE ligands. Additionally, overexpression phenotypes of various CLE family members suggest roles in organ size regulation, apical dominance, and root growth. Similarities among overexpression phenotypes of many CLE genes correlate with similarities in their CLE domain sequences, suggesting that the CLE domain is responsible for interaction with cognate receptors.