Overview of Ten DIR Algorithms.

<p>Overview of Ten DIR Algorithms.</p

Boxplots of comparisons of performance on different ROIs for representative DIR algorithms, including the HSLK (optical flow-based), OD/DISC (demons-based), LS (level-set-based), BSpline (spline-based) and rigid registration (RR).

<p>For each subgraph, no significant difference in pairwise comparisons is marked with “~”, otherwise a statistically significant difference exists. The meanings of each box in this figure are the same as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0175906#pone.0175906.g002" target="_blank">Fig 2</a>.</p

Comprehensive evaluation of ten deformable image registration algorithms for contour propagation between CT and cone-beam CT images in adaptive head & neck radiotherapy - Fig 2

<p>Boxplots of DSC (a), PE (b) and HD (c) by different DIR algorithms for all structures in all treatment stages. The boxes run from the 25th to 75th percentile; the two ends of the whiskers represent the 5% and 95% percentiles of the data, the horizontal line and the square in the box represent the median and mean values, respectively. The diamonds represent outliers. The letters above each box indicate whether a statistically significant difference exists between any two DIRs. No common letter between any two algorithms indicates that the two DIRs are significantly different. RR is an abbreviation for rigid registration.</p

The mean, standard deviation (SD), 95% confidence interval (95% CI) and interquartile range (IQR) of the slopes of relative volume changes for each structure.

<p>The mean, standard deviation (SD), 95% confidence interval (95% CI) and interquartile range (IQR) of the slopes of relative volume changes for each structure.</p

Patients’ characteristics.

<p>Patients’ characteristics.</p

Comparisons across ROIs and stages for different registration algorithms.

<p>In each subgraph, the horizontal and vertical axes represent stages ranging from 1 to 6 and the metric values, respectively. Eleven algorithms are marked by different symbols and colors.</p

Additional file 1: Appendix A. of Comprehensive target geometric errors and margin assessment in stereotactic partial breast irradiation

2D fiducial coordinates to 3D fiducial position conversion. Appendix B. Margin calculations. Appendix C. Multivariate linear regression model. (DOCX 29 kb

Additional file 1: of Investigating rectal toxicity associated dosimetric features with deformable accumulated rectal surface dose maps for cervical cancer radiotherapy

Appendix A. TOP-DIR algorithm. Appendix B. 3D-2D rectum surface dose mapping. Appendix C. Sequential forward feature selection (SFS) algorithm. (DOCX 155 kb

Additional file 1: Table S1. of Low-dose lymphocyte immunotherapy rebalances the peripheral blood Th1/Th2/Treg paradigm in patients with unexplained recurrent miscarriage

Antibody list. (DOC 34 kb

DataSheet_1_Evaluation of molecular receptors status in breast cancer using an mpMRI-based feature fusion radiomics model: mimicking radiologists’ diagnosis.docx

ObjectiveTo investigate the performance of a novel feature fusion radiomics (RFF) model that incorporates features from multiparametric MRIs (mpMRI) in distinguishing different statuses of molecular receptors in breast cancer (BC) preoperatively.Methods460 patients with 466 pathology-confirmed BCs who underwent breast mpMRI at 1.5T in our center were retrospectively included hormone receptor (HR) positive (HR+) (n=336) and HR negative (HR-) (n=130). The HR- patients were further categorized into human epidermal growth factor receptor 2 (HER-2) enriched BC (HEBC) (n=76) and triple negative BC (TNBC) (n=54). All lesions were divided into a training/validation cohort (n=337) and a test cohort (n=129). Volumes of interest (VOIs) delineation, followed by radiomics feature extraction, was performed on T2WI, DWI600 (b=600 s/mm2), DWI800 (b=800 s/mm2), ADC map, and DCE1-6 (six continuous DCE-MRI) images of each lesion. Simulating a radiologist’s work pattern, 150 classification base models were constructed and analyzed to determine the top four optimum sequences for classifying HR+ vs. HR-, TNBC vs. HEBC, TNBC vs. non-TNBC in a random selected training cohort (n=337). Building upon these findings, the optimal single sequence models (Rss) and combined sequences models (RFF) were developed. The AUC, sensitivity, accuracy and specificity of each model for subtype differentiation were evaluated. The paired samples Wilcoxon signed rank test was used for performance comparison.ResultsDuring the three classification tasks, the optimal single sequence for classifying HR+ vs. HR- was DWI600, while the ADC map, derived from DWI800 performed the best in distinguishing TNBC vs. HEBC, as well as identifying TNBC vs. non-TNBC, with corresponding training AUC values of 0.787, 0.788, and 0.809, respectively. Furthermore, the integration of the top four sequences in RFF models yielded improved performance, achieving AUC values of 0.809, 0.805 and 0.847, respectively. Consistent results was observed in both the training/validation and testing cohorts, with AUC values of 0.778, 0.787, 0.818 and 0.726, 0.773, 0.773, respectively (all p ConclusionThe RFF model, integrating mpMRI radiomics features, demonstrated promising ability to mimic radiologists’ diagnosis for preoperative identification of molecular receptors of BC.</p