The complexity of tumor shape, spiculatedness, correlates with tumor radiomic shape features

Abstract

Radiomics extracts high-throughput quantitative data from medical images to contribute to precision medicine. Radiomic shape features have been shown to correlate with patient outcomes. However, how radiomic shape features vary in function of tumor complexity and tumor volume, as well as with method used for meshing and voxel resampling, remains unknown. The aims of this study are to create tumor models with varying degrees of complexity, or spiculatedness, and evaluate their relationship with quantitatively extracted shape features. Twenty-eight tumor models were mathematically created using spherical harmonics with the spiculatedness degree d being increased by increments of 3 (d = 11 to d = 92). Models were 3D printed with identical bases of 5 cm, imaged with a CT scanner with two different slice thicknesses, and semi-automatically delineated. Resampling of the resulting masks on a 1 × 1 × 1 mm3 grid was performed, and the voxel size of each model was then calculated to eliminate volume differences. Four MATLAB-based algorithms (isosurface (M1), isosurface filter (M2), isosurface remeshing (M3), and boundary (M4)) were used to extract nine 3D features (Volume, Surface area, Surface-to-volume, Compactness1, Compactness2, Compactness3, Spherical Disproportion, Sphericity and Fractional Concavity). To quantify the impact of 3D printing, acquisition, segmentation and meshing, features were computed directly from the stereolithography (STL) file format that was used for 3D printing, and compared to those computed. Changes in feature values between 0.6 and 2 mm slice acquisitions were also compared. Spearman’s rank-order correlation coefficients were computed to determine the relationship of each shape feature with spiculatedness for each of the four meshing algorithms. Percent changes were calculated between shape features extracted from the original and resampled contoured images to evaluate the influence of spatial resampling. Finally, the percent change in shape features when the volume was changed from 25% to 150% of their original volume was quantified for three distinct tumor models and compared to the percent change observed when modifying the spiculatedness of the model from d = 11 to d = 92. Values extracted using isosurface remeshing method are the closest to the STL reference ones, with mean differences less than 10.8% (Compactness2) for all features. Seven of the eight features had strong significant correlations with tumor model complexity irrespective of the meshing algorithm (r > 0.98, p < 10-4), with fractional concavity having the lowest correlation coefficient (r = 0.83, p < 10-4, M2). Comparisons of features extracted from the 0.6 and 2 mm slice thicknesses showed that mean differences were from 2.1% (Compactness3) to 12.7% (Compactness2) for the isosurface remeshing method. Resampling on a 1 × 1 × 1 mm3 grid resulted in between 1.3% (Compactness3) to 9.5% (Fractional Concavity) mean changes in feature values. Compactness2, Compactness3, Spherical Disproportion, Sphericity and Fractional Concavity...

PET/CT Radiomics in Lung Cancer: An Overview

“…In most cases, their objective is to differentiate between round, smooth, and regular lesions from spiculated, elongated, and irregular ones. Apart from volume, common shape features are compactness, elongation, rectangular fit, spherical disproportion, sphericity, surface area, and surface-to-volume ratio [24,28,29]. Clearly, shape features are more easily assessed at CT than PET due to the higher image resolution of the former.…”

Application of radiomics for the prediction of HPV status for patients with head and neck cancers

“…22,[46][47][48][49] Identification of optimal classification methods and the best radiomic features for prognostic analyses in head and neck cancer patients may indeed broaden the scope of radiomics in precision oncology. [48][49][50][51][52] In this study we aim to investigate the potential value of radiomic features extracted from gross tumor volumes (GTVs) delineated on diagnostic CE-CT images of oropharyngeal cancer patients as potential biomarkers of HPV status.…”

Tumor volume and sphericity as predictors of local control after stereotactic radiosurgery for limited number (1‐4) brain metastases from nonsmall cell lung cancer

“…However, some recent publications show that the volume could be an important predictor of treatment response . Similarly, recent studies to radiological features have reported that the shape of tumor, such as compactness, convexity, complexity, and nodularity, can be used as a prognostic factor for the treatment, but the researches on them are still lacking . Because there are limited published data examining tumor volume and shape, we have conducted a review of our institution's experiences analyzing patients who were initially treated with SRS alone.…”