Identification of reproducible radiomic features from on-board volumetric images: A multi-institutional phantom study.

BACKGROUND: Radiomics analysis using on-board volumetric images has attracted research attention as a method for predicting prognosis during treatment; however, the lack of standardization is still one of the main concerns. PURPOSE: This study investigated the factors that influence the reproducibility of radiomic features extracted from on-board volumetric images using an anthropomorphic radiomics phantom. Furthermore, a phantom experiment was conducted with different treatment machines from multiple institutions as external validation to identify reproducible radiomic features. METHODS: The phantom was designed to be 35 × 20 × 20 cm with eight types of heterogeneous spheres (⌀ = 1, 2, and 3 cm). On-board volumetric images were acquired using 15 treatment machines from eight institutions. Of these, kilovoltage cone-beam computed tomography (kV-CBCT) image data acquired from four treatment machines at one institution were used as an internal evaluation dataset to explore the reproducibility of radiomic features. The remaining image data, including kV-CBCT, megavoltage-CBCT (MV-CBCT), and megavoltage computed tomography (MV-CT) provided by seven different institutions (11 treatment machines), were used as an external validation dataset. A total of 1,302 radiomic features, including 18 first-order, 75 texture, 465 (i.e., 93 × 5) Laplacian of Gaussian (LoG) filter-based, and 744 (i.e., 93 × 8) wavelet filter-based features, were extracted within the spheres. The intraclass correlation coefficient (ICC) was calculated to explore feature repeatability and reproducibility using an internal evaluation dataset. Subsequently, the coefficient of variation (COV) was calculated to validate the feature variability of external institutions. An absolute ICC exceeding 0.85 or COV under 5% was considered indicative of a highly reproducible feature. RESULTS: For internal evaluation, ICC analysis showed that the median percentage of radiomic features with high repeatability was 95.2%. The ICC analysis indicated that the median percentages of highly reproducible features for inter-tube current, reconstruction algorithm, and treatment machine were decreased by 20.8%, 29.2%, and 33.3%, respectively. For external validation, the COV analysis showed that the median percentage of reproducible features was 31.5%. A total of 16 features, including nine LoG filter-based and seven wavelet filter-based features, were indicated as highly reproducible features. The gray-level run-length matrix (GLRLM) was classified as containing the most frequent features (N = 8), followed by the gray-level dependence matrix (N = 7) and gray-level co-occurrence matrix (N = 1) features. CONCLUSIONS: We developed the standard phantom for radiomics analysis of kV-CBCT, MV-CBCT, and MV-CT images. With this phantom, we revealed that the differences in the treatment machine and image reconstruction algorithm reduce the reproducibility of radiomic features from on-board volumetric images. Specifically, the most reproducible features for external validation were LoG or wavelet filter-based GLRLM features. However, the acceptability of the identified features should be examined in advance at each institution before applying the findings to prognosis prediction.

Enantiopure O-ethyl phenylphosphonothioic acid: a solvating agent for the determination of enantiomeric excesses.

An improved method, which is highly reproducible, was developed for the enantioseparation of racemic O-ethyl phenylphosphonothioic acid (1a) with brucine by introducing seeding to a supersaturated solution of the diastereomeric salt mixture. The present method gave both diastereomeric salts in high yields with a diastereomeric ratio of >99.5:0.5 upon choosing the crystallization solvent (MeOH for the (R)-1a salt and MeOH/H2 O for the (S)-1a salt). The enantiopure acid showed a good chirality recognition ability for not only strong bases, such as amines and amino alcohols, but also weakly basic alcohols and was applicable as a solvating agent to the (1) H NMR determination of the enantiomeric excess of chiral amines, amino alcohols, and alcohols, including aliphatic substrates.

Feasibility of near-infrared spectroscopy for on-line grading of sawn lumber.

Near-infrared (NIR) spectroscopy, coupled with multivariate analysis, has been used to evaluate the wood properties of sawn lumber of Japanese larch (Larix kaempferi), whose diffuse reflection spectra were acquired under static and moving conditions. Prediction models of the dynamic modulus of elasticity (E(fr)), the modulus of elasticity in bending tests (E(b)), the bending strength (F(b)), the wood density (DEN), and the moisture content (MC) were developed using partial least squares (PLS) analysis. For all wood properties, models obtained from data collected under the moving condition as an analogue of on-line measurement were superior to those from the static condition data. The regression coefficients for the PLS models predicting the mechanical properties in both static and moving conditions showed clear peaks at the absorption bands due to the three major polymers of wood, i.e., cellulose, hemicellulose, and lignin. NIR spectroscopy has high potential for the on-line grading of sawn lumber.