Evaluation of the stereochemical quality of predicted RNA 3D models in the RNA-Puzzles submissions.

In silico prediction is a well-established approach to derive a general shape of an RNA molecule based on its sequence or secondary structure. This paper reports an analysis of the stereochemical quality of the RNA three-dimensional models predicted using dedicated computer programs. The stereochemistry of 1052 RNA 3D structures, including 1030 models predicted by fully automated and human-guided approaches within 22 RNA-Puzzles challenges and reference structures, is analyzed. The evaluation is based on standards of RNA stereochemistry that the Protein Data Bank requires from deposited experimental structures. Deviations from standard bond lengths and angles, planarity, or chirality are quantified. A reduction in the number of such deviations should help in the improvement of RNA 3D structure modeling approaches.

Establishment of Reference Reagents for Single-Radial-Immunodiffusion Assay on the 2022/23 Seasonal Influenza Vaccine in Japan and Their Quality Validation.

Potency tests for influenza vaccines are currently performed using a single-radial immunodiffusion (SRID) assay, which requires a reference antigen and anti-hemagglutinin (HA) serum as reference reagents. Reagents must be newly prepared each time a strain used for vaccine production is modified. Therefore, establishing reference reagents of consistent quality is crucial for conducting vaccine potency tests accurately and precisely. Here, we established reference reagents for the SRID assay to conduct lot release tests of quadrivalent influenza vaccines in Japan during the 2022/23 influenza season. The potency of reference antigens during storage was confirmed. Furthermore, we evaluated the cross-reactivity of each antiserum raised against the HA protein of the 2 lineages of influenza B virus toward different lineages of influenza B virus antigens to select a suitable procedure for the SRID assay for accurate measurement. Finally, the intralaboratory reproducibility of the SRID assay using the established reference reagents was validated, and the SRID reagents had sufficient consistent quality, comparable to that of the reagents used for testing vaccines during previous influenza seasons. Our study contributes to the quality control of influenza vaccines.

Scanner-specific validation of a CT simulator using a COPD-emulated anthropomorphic phantom.

Traditional methods of quantitative analysis of CT images typically involve working with patient data, which is often expensive and limited in terms of ground truth. To counter these restrictions, quantitative assessments can instead be made through Virtual Imaging Trials (VITs) which simulate the CT imaging process. This study sought to validate DukeSim (a scanner-specific CT simulator) utilizing clinically relevant biomarkers for a customized anthropomorphic chest phantom. The physical phantom was imaged utilizing two commercial CT scanners (Siemens Somatom Force and Definition Flash) with varying imaging parameters. A computational version of the phantom was simulated utilizing DukeSim for each corresponding real acquisition. Biomarkers were computed and compared between the real and virtually acquired CT images to assess the validity of DukeSim. The simulated images closely matched the real images both qualitatively and quantitatively, with the average biomarker percent difference of 3.84% (range 0.19% to 18.27%). Results showed that DukeSim is reasonably well validated across various patient imaging conditions and scanners, which indicates the utility of DukeSim for further VIT studies where real patient data may not be feasible.

Task-based validation and application of a scanner-specific CT simulator using an anthropomorphic phantom.

BACKGROUND: Quantitative analysis of computed tomography (CT) images traditionally utilizes real patient data that can pose challenges with replicability, efficiency, and radiation exposure. Instead, virtual imaging trials (VITs) can overcome these hurdles through computer simulations of models of patients and imaging systems. DukeSim is a scanner-specific CT imaging simulator that has previously been validated with simple cylindrical phantoms, but not with anthropomorphic conditions and clinically relevant measurements. PURPOSE: To validate a scanner-specific CT simulator (DukeSim) for the assessment of lung imaging biomarkers under clinically relevant conditions across multiple scanners using an anthropomorphic chest phantom, and to demonstrate the utility of virtual trials by studying the effects or radiation dose and reconstruction kernels on the lung imaging quantifications. METHODS: An anthropomorphic chest phantom with customized tube inserts was imaged with two commercial scanners (Siemens Force and Siemens Flash) at 28 dose and reconstruction conditions. A computational version of the chest phantom was used with a scanner-specific CT simulator (DukeSim) to simulate virtual images corresponding to the settings of the real acquisitions. Lung imaging biomarkers were computed from both real and simulated CT images and quantitatively compared across all imaging conditions. The VIT framework was further utilized to investigate the effects of radiation dose (20-300 mAs) and reconstruction settings (Qr32f, Qr40f, and Qr69f reconstruction kernels using ADMIRE strength 3) on the accuracy of lung imaging biomarkers, compared against the ground-truth values modeled in the computational chest phantom. RESULTS: The simulated CT images matched closely the real images for both scanners and all imaging conditions qualitatively and quantitatively, with the average biomarker percent error of 3.51% (range 0.002%-18.91%). The VIT study showed that sharper reconstruction kernels had lower accuracy with errors in mean lung HU of 84-94 HU, lung volume of 797-3785 cm3 , and lung mass of -800 to 1751 g. Lower tube currents had the lower accuracy with errors in mean lung HU of 6-84 HU, lung volume of 66-3785 cm3 , and lung mass of 170-1751 g. Other imaging biomarkers were consistent under the studied reconstruction settings and tube currents. CONCLUSION: We comprehensively evaluated the realism of DukeSim in an anthropomorphic setup across a diverse range of imaging conditions. This study paves the way toward utilizing VITs more reliably for conducting medical imaging experiments that are not practical using actual patient images.

Knowledge-Based Planning for Robustly Optimized Intensity-Modulated Proton Therapy of Head and Neck Cancer Patients.

PURPOSE: To assess the performance of a proton-specific knowledge-based planning (KBP) model in the creation of robustly optimized intensity-modulated proton therapy (IMPT) plans for treatment of advanced head and neck (HN) cancer patients. METHODS: Seventy-three patients diagnosed with advanced HN cancer previously treated with volumetric modulated arc therapy (VMAT) were selected and replanned with robustly optimized IMPT. A proton-specific KBP model, RapidPlanPT (RPP), was generated using 53 patients (20 unilateral cases and 33 bilateral cases). The remaining 20 patients (10 unilateral and 10 bilateral cases) were used for model validation. The model was validated by comparing the target coverage and organ at risk (OAR) sparing in the RPP-generated IMPT plans with those in the expert plans. To account for the robustness of the plan, all uncertainty scenarios were included in the analysis. RESULTS: All the RPP plans generated were clinically acceptable. For unilateral cases, RPP plans had higher CTV_primary V100 (1.59% ± 1.24%) but higher homogeneity index (HI) (0.7 ± 0.73) than had the expert plans. In addition, the RPP plans had better ipsilateral cochlea Dmean (-5.76 ± 6.11 Gy), with marginal to no significant difference between RPP plans and expert plans for all other OAR dosimetric indices. For the bilateral cases, the V100 for all clinical target volumes (CTVs) was higher for the RPP plans than for the expert plans, especially the CTV_primary V100 (5.08% ± 3.02%), with no significant difference in the HI. With respect to OAR sparing, RPP plans had a lower spinal cord Dmax (-5.74 ± 5.72 Gy), lower cochlea Dmean (left, -6.05 ± 4.33 Gy; right, -4.84 ± 4.66 Gy), lower left and right parotid V20Gy (left, -6.45% ± 5.32%; right, -6.92% ± 3.45%), and a lower integral dose (-0.19 ± 0.19 Gy). However, RPP plans increased the Dmax in the body outside of CTV (body-CTV) (1.2 ± 1.43 Gy), indicating a slightly higher hotspot produced by the RPP plans. CONCLUSION: IMPT plans generated by a broad-scope RPP model have a quality that is, at minimum, comparable with, and at times superior to, that of the expert plans. The RPP plans demonstrated a greater robustness for CTV coverage and better sparing for several OARs.

Texture-based, automatic contour validation for online adaptive replanning: A feasibility study on abdominal organs.

PURPOSE: Evaluation of contour accuracy in radiation therapy planning requires manual interaction and is one of the most limiting bottlenecks for online replanning. This study aims to develop an automatic approach to rapidly evaluate contour quality based on image texture features to facilitate the routine practice of online adaptive replanning (OLAR). METHOD: Fifty-five pancreas cancer patients were selected from a clinical database of patients treated at our institution from 2011 to 2018. For each patient, the pancreas head and duodenum were contoured in five images (one fraction per week) resulting in a total of 275 CT image sets with corresponding ground-truth contours. A second set of inaccurate contours was generated using deformable-image-registration-based contour propagation. Three subregions, core, inner shell and outer shell, were generated from the contour of each organ. Texture features were extracted from each subregion and descriptive features of each subregion were identified using the image set with corresponding ground-truth contours. A three-level decision tree model was constructed based on texture constraints empirically determined for the three subregions. The two datasets containing ground truth and inaccurate contours were merged. Randomized threefold cross-validation was performed and repeated three times. RESULTS: The first level of the decision tree utilizes textures derived from principal component analysis of a subset of extracted features from the core subregion (five PCs for pancreas head, seven PCs for duodenum). The second and third levels of the decision tree use gray-level co-occurrence matrix (GLCM)-based cluster prominence to reject inaccurate contours. The trained model identifies accurate and inaccurate contours with an average sensitivity/specificity of 85%/91% for the pancreas head and 92%/92% for the duodenum contours. The false-positive rate is 9% and 8% for pancreas head and duodenum, respectively. The execution time is less than 15 s using a standard desktop computer. CONCLUSION: Quantitative image features can be used to develop a model to rapidly validate the quality of an organ contour. Our model accurately classifies unseen contours as accurate or inaccurate with high sensitivity and specificity. As auto-segmentation continues to improve in quality and accuracy, this method may be integrated into a fully automatic pipeline for auto-segmentation, contour-quality evaluation and contour correction, which would replace the time-consuming manual review process, thereby facilitating the more routine practice of OLAR.

Reliability of Quality Assessments in Research Synthesis: Securing the Highest Quality Bioinformation for HIT.

Current trends in bio-medicine include research synthesis and dissemination of bioinformation by means of health (bio) information technology (H[b] IT). Research must secure the validity and reliability of assessment tools to quantify research quality in the pursuit of the best available evidence. Our concerted work in this domain led to the revision of three instruments for that purpose, including the stringent characterization of inter-rater reliability and coefficient of agreement. It is timely and critical to advance the methodological development of the science of research synthesis by strengthening the reliability of existing measure of research quality in order to ensure H[b] IT efficacy and effectiveness.