Low Gray Level Research Articles

Prediction of different stages of rectal cancer: Texture analysis based on diffusion-weighted images and apparent diffusion coefficient maps.

BACKGROUNDIt is evident that an accurate evaluation of T and N stage rectal cancer is essential for treatment planning. It has not been extensively investigated whether texture features derived from diffusion-weighted imaging (DWI) images and apparent diffusion coefficient (ADC) maps are associated with the extent of local invasion (pathological stage T1-2 vs T3-4) and nodal involvement (pathological stage N0 vs N1-2) in rectal cancer.AIMTo predict different stages of rectal cancer using texture analysis based on DWI images and ADC maps.METHODSOne hundred and fifteen patients with pathologically proven rectal cancer, who underwent preoperative magnetic resonance imaging, including DWI, were enrolled, retrospectively. The ADC measurements (ADCmean, ADCmin, ADCmax) as well as texture features, including the gray level co-occurrence matrix parameters, the gray level run-length matrix parameters and wavelet parameters were calculated based on DWI (b = 0 and b = 1000) images and the ADC maps. Independent sample t-tests or Mann-Whitney U tests were used for statistical analysis. Multivariate logistic regression analysis was conducted to establish the models. The predictive performance was validated by receiver operating characteristic curve analysis. RESULTSDissimilarity, sum average, information correlation and run-length nonuniformity from DWIb=0 images, gray level nonuniformity, run percentage and run-length nonuniformity from DWIb=1000 images, and dissimilarity and run percentage from ADC maps were found to be independent predictors of local invasion (stage T3-4). The area under the operating characteristic curve of the model reached 0.793 with a sensitivity of 78.57% and a specificity of 74.19%. Sum average, gray level nonuniformity and the horizontal components of symlet transform (SymletH) from DWIb=0 images, sum average, information correlation, long run low gray level emphasis and SymletH from DWIb=1000 images, and ADCmax, ADCmean and information correlation from ADC maps were identified as independent predictors of nodal involvement. The area under the operating characteristic curve of the model reached 0.802 with a sensitivity of 80.77% and a specificity of 68.25%.CONCLUSIONTexture features extracted from DWI images and ADC maps are useful clues for predicting pathological T and N stages in rectal cancer.

Correlation Between Mammographic Radiomics Features and the Level of Tumor-Infiltrating Lymphocytes in Patients With Triple-Negative Breast Cancer.

Objectives: Tumor-infiltrating lymphocytes (TILs) have been identified as a significant prognostic indicator of response to neoadjuvant therapy and immunotherapy for triple-negative breast cancer (TNBC) patients. Herein, we aim to assess the association between TIL levels and mammographic features in TNBC patients.Methods: Forty-three patients with surgically proven TNBC who underwent preoperative mammography from January 2018 to December 2018 were recruited. Pyradiomics software was used to extract 204 quantitative radiomics features, including morphologic, grayscale, and textural features, from the segmented lesion areas. The correlation between radiological characteristics and TIL levels was evaluated by screening the most statistically significant radiological features using Mann–Whitney U-test and Pearson correlation coefficient. The patients were divided into two groups based on tumor TIL levels: patients with TIL levels <50% and those with TIL levels ≥50%. The correlation between TIL levels and clinicopathological characteristics was assessed using the chi-square test or Fisher's exact test. Mann–Whitney U-test and Pearson correlation coefficient were used to analyze the statistical significance and Pearson correlation coefficient of clinical pathological features, age, and radiological features.Results: Of 43 patients, 32 (74.4%) had low TIL levels and 11 (25.6%) had high TIL levels. The histological grade of the low TIL group was higher than that of the high TIL group (p = 0.043). The high TIL group had a more negative threshold Ki-67 level (<14%) than the low TIL group (p = 0.017). The six most important radiomics features [uniformity, variance, grayscale symbiosis matrix (GLCM) correlation, GLCM autocorrelation, gray level difference matrix (GLDM) low gray level emphasis, and neighborhood gray-tone difference matrix (NGTDM) contrast], representing qualitative mammographic image characteristics, were statistically different (p < 0.05) among the low and high TIL groups. Tumors in the high TIL group had a more non-uniform density and a smoother gradient of the tumor pattern than the low TIL group. The changes in Ki-67, age, epidermal growth factor receptor, radiomic characteristics, and Pearson correlation coefficient were statistically significant (p < 0.05).Conclusion: Mammography features not only distinguish high and low TIL levels in TNBC patients but also can act as imaging biomarkers to enhance diagnosis and the response of patients to neoadjuvant therapies and immunotherapies.

Using texture analysis as a predictive factor of subtype, grade and stage of renal cell carcinoma.

The aim of this study was to evaluate the correlation between the tissue texture analysis and the histological subtypes, grade and stage of the disease in patients with renal cell carcinoma (RCC). Seventy-seven patients who underwent computed tomography due to renal mass and diagnosed with RCC as a result of pathological examination were retrospectively analyzed. In these analyses, the demographic characteristics, pathological and radiological findings of the patients were evaluated. The masses were introduced to the Radiomics extension of the software and the first- and second-order texture analysis parameters were obtained. The correlation of these parameters with histological subtype, Fuhrman grade and TNM stage was investigated. In the comparison of the Radiomics values by stages, "minimum", "Long Run Low Gray-level Emphasis" values were higher in the stage 1-2 group, while "Energy", "Total energy", "Range", "Joint Average", "Sum Average", "Gray-Level Non-Uniformity", "Short-Run High Gray-level Emphasis ", "Run Length Non-Uniformity "and "High Gray-Level Run Emphasis "values were higher in the stage 3-4 group. Of these parameters, only "Gray-Level Non-Uniformity" and "Run Length Non-Uniformity'' values were significantly lower in tumors with low Fuhrman grade (1-2) and low TNM stage (1-2). There was no statistically significant correlation between the parameters found to be significant in histological subtype differentiation and Fuhrman grade and TNM stage. This study demonstrates that "Gray-Level Non-Uniformity" and "Run Length Non-Uniformity "parameters in the texture analysis method can be used to evaluate the prognosis in patients with RCC.

Baseline 18F-FDG PET radiomic features as predictors of 2-year event-free survival in diffuse large B cell lymphomas treated with immunochemotherapy.

To explore the prognostic value of positron emission tomography (PET) radiomic features in the field of diffuse large B cell lymphoma (DLBCL) treated with a first-line immunochemotherapy. One-hundred thirty-two patients newly diagnosed with DLBCL were retrospectively included. PET studies were reconstructed using an ordered subset expectation maximisation algorithm with point spread function modelling. The total metabolic tumour volume (MTV) was recorded for each patient, and the volume of interest structure of the largest target lesion was used to compute 18F-FDG textural parameters. Data was randomly split into training and validation datasets. Optimal cutoff values were determined by means of 2-year event-free survival (EFS) ROC analyses. Two-year EFS analyses were performed using Kaplan-Meier survival analyses and univariable and multivariable Cox regression models. The median follow-up was 27months, and the 2-year event-free survival (2y-EFS) was 77.3% in the entire population. ROC analyses for the 2y-EFS reached statistical significance for total MTV as well as four second-order metrics (homogeneity, contrast, correlation, dissimilarity) and five third-order metrics (LZE (Long-Zone Emphasis), LZLGE (Long-Zone Low-Grey Level Emphasis), LZHGE (Long-Zone High-Grey Level Emphasis), GLNU (Grey-Level Non-Uniformity) and ZP (Zone Percentage)). LZHGE displayed the highest ROC analysis accuracy (acc. = 0.76) and the best discriminant value on univariable Kaplan-Meier analysis (p < 0.0001, HR = 4.54). On multivariable analysis, including IPIaa, total MTV and LZHGE, LZHGE was the only independent predictor of 2y-EFS. These results were confirmed on the validation dataset. Baseline 18F-FDG PET heterogeneity of the largest lymphoma lesion is a promising predictor of 2y-EFS in newly diagnosed DLBCL treated with immunochemotherapy. •18F-FDG metabolic heterogeneity emerges as a new tool for survival prognostication of patients and has been explored in many solid tumours with promising results. • Baseline18F-FDG PET heterogeneity of the largest lymphoma lesion is an independent predictor of 2y-EFS in newly diagnosed DLBCL treated with immunochemotherapy. • DLBCL patients presenting with a heterogeneous tumour displayed a worse prognosis.

Extended Texture Analysis of Non-Enhanced Whole-Body MRI Image Data for Response Assessment in Multiple Myeloma Patients Undergoing Systemic Therapy.

Identifying MRI-based radiomics features capable to assess response to systemic treatment in multiple myeloma (MM) patients. Retrospective analysis of whole-body MR-image data in 67 consecutive stage III MM patients (40 men; mean age, 60.4 years). Bone marrow involvement was evaluated using a standardized MR-imaging protocol consisting of T1w-, short-tau inversion recovery- (STIR-) and diffusion-weighted-imaging (DWI) sequences. Ninety-two radiomics features were evaluated, both in focally and diffusely involved bone marrow. Volumes of interest (VOI) were used. Response to treatment was classified according to International Myeloma Working Group (IMWG) criteria in complete response (CR), very-good and/or partial response (VGPR + PR), and non-response (stable disease (SD) and progressive disease (PD)). According to the IMWG-criteria, response categories were CR (n = 35), VGPR + PR (n = 19), and non-responders (n = 13). On apparent diffusion coefficient (ADC)-maps, gray-level small size matrix small area emphasis (Gray Level Size Zone (GLSZM) small area emphasis (SAE)) significantly correlated with CR (p < 0.001), whereas GLSZM non-uniformity normalized (NUN) significantly (p < 0.008) with VGPR/PR in focal medullary lesions (FL), whereas in diffuse involvement, 1st order root mean squared significantly (p < 0.001) correlated with CR, whereas for VGPR/PR Log (gray-level run-length matrix (GLRLM) Short Run High Gray Level Emphasis) proved significant (p < 0.003). On T1w, GLRLM NUN significantly (p < 0.002) correlated with CR in FL, whereas gray-level co-occurrence matric (GLCM) informational measure of correlation (Imc1) significantly (p < 0.04) correlated with VGPR/PR. For diffuse myeloma involvement, neighboring gray-tone difference matrix (NGTDM) contrast and 1st order skewness were significantly associated with CR and VGPR/PR (p < 0.001 for both). On STIR-images, CR correlated with gray-level co-occurrence matrix (GLCM) Informational Measure of Correlation (IMC) 1 (p < 0.001) in FL and 1st order mean absolute deviation in diffusely involved bone marrow (p < 0.001). VGPR/PR correlated at best in FL with GSZLM size zone NUN (p < 0.019) and in all other involved medullary areas with GLSZM large area low gray level emphasis (p < 0.001). GLSZM large area low gray level emphasis also significantly correlated with the degree of bone marrow infiltration assessed histologically (p = 0.006). GLCM IMC 1 proved significant throughout T1w/STIR sequences, whereas GLSZM NUN in STIR and ADC. MRI-based texture features proved significant to assess clinical and hematological response (CR, VPGR, and PR) in multiple myeloma patients undergoing systemic treatment.

Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms.

Considerable progress has been made in the assessment and management of non-small cell lung cancer (NSCLC) patients based on mutation status in the epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene (KRAS). At the same time, NSCLC management through KRAS and EGFR mutation profiling faces challenges. In the present work, we aimed to evaluate a comprehensive radiomics framework that enabled prediction of EGFR and KRAS mutation status in NSCLC patients based on radiomic features from low-dose computed tomography (CT), contrast-enhanced diagnostic quality CT (CTD), and positron emission tomography (PET) imaging modalities and use of machine learning algorithms. Our study involved NSCLC patients including 150 PET, low-dose CT, and CTD images. Radiomic features from original and preprocessed (including 64 bin discretizing, Laplacian-of-Gaussian (LOG), and Wavelet) images were extracted. Conventional clinically used standard uptake value (SUV) parameters and metabolic tumor volume (MTV) were also obtained from PET images. Highly correlated features were pre-eliminated, and false discovery rate (FDR) correction was performed with the resulting q-values reported for univariate analysis. Six feature selection methods and 12 classifiers were then used for multivariate prediction of gene mutation status (provided by polymerase chain reaction (PCR)) in patients. We performed 10-fold cross-validation for model tuning to improve robustness, and our developed models were assessed on an independent validation set with 68 patients (common in all three imaging modalities). The average area under the receiver operator characteristic curve (AUC) was utilized for performance evaluation. The best predictive power for conventional PET parameters was achieved by SUVpeak (AUC 0.69, p value = 0.0002) and MTV (AUC 0.55, p value = 0.0011) for EGFR and KRAS, respectively. Univariate analysis of extracted radiomics features improved AUC performance to 0.75 (q-value 0.003, Short-Run Emphasis feature of GLRLM from LOG preprocessed image of PET with sigma value 1.5) and 0.71 (q-value 0.00005, Large Dependence Low Gray-Level Emphasis feature of GLDM in LOG preprocessed image of CTD with sigma value 5) for EGFR and KRAS, respectively. Furthermore, multivariate machine learning-based AUC performances were significantly improved to 0.82 for EGFR (LOG preprocessed image of PET with sigma 3 with variance threshold (VT) feature selector and stochastic gradient descent (SGD) classifier (q-value = 4.86E-05) and 0.83 for KRAS (LOG preprocessed image of CT with sigma 3.5 with select model (SM) feature selector and SGD classifier (q-value = 2.81E-09). Our work demonstrated that non-invasive and reliable radiomics analysis can be successfully used to predict EGFR and KRAS mutation status in NSCLC patients. We demonstrated that radiomic features extracted from different image-feature sets could be used for EGFR and KRAS mutation status prediction in NSCLC patients and showed improved predictive power relative to conventional image-derived metrics.

Exploring technical issues in personalized medicine: NSCLC survival prediction by quantitative image analysis-usefulness of density correction of volumetric CT data.

The aim of this study was to apply density correction method to the quantitative image analysis of non-small cell lung cancer (NSCLC) computed tomography (CT) images, determining its influence on overall survival (OS) prediction of surgically treated patients. Clinicopathological (CP) data and preoperative CT scans, pre- and post-contrast medium (CM) administration, of 57 surgically treated NSCLC patients, were retrospectively collected. After CT volumetric density measurement of primary gross tumour volume (GTV), aorta and tracheal air, density correction was conducted on GTV (reference values: aortic blood and tracheal air). For each resulting data set (combining CM administration and normalization), first-order statistical and textural features were extracted. CP and imaging data were correlated with patients 1-, 3- and 5-year OS, alone and combined (uni-/multivariate logistic regression and Akaike information criterion). Predictive performance was evaluated using the ROC curves and AUC values and compared among non-normalized/normalized data sets (DeLong test). The best predictive values were obtained when combining CP and imaging parameters (AUC values: 1year 0.72; 3years 0.82; 5years 0.78). After normalization resulted an improvement in predicting 1-year OS for some of the grey level size zonebased features (large zone low grey level emphasis) and for the combined CP-imaging model, a worse performance for grey level co-occurrence matrix (cluster prominence and shade) and first-order statistical (range) parameters for 1- and 5-year OS, respectively. The negative performance of cluster prominence in predicting 1-year OS was the only statistically significant result (p value 0.05). Density corrections of volumetric CT data showed an opposite influence on the performance of imaging quantitative features in predicting OS of surgically treated NSCLC patients, even if no statistically significant for almost all predictors.

Analysis of MRI radiomic features of hypoxic area in nasopharyngeal carcinoma patients

Objective To analyze the magnetic resonance imaging (MRI) radiomic performance of hypoxic area in nasopharyngeal carcinoma patients, aiming to provide a reference for identification and analysis of hypoxic area. Methods The MRI-T1, MRI-T2, MRI-T1+ and PET/CT images of 32 patients initially diagnosed with nasopharyngeal carcinoma were retrospectively analyzed. The gross tumor volume (GTV) of nasopharynx was countoured and the hypoxic areas (GTV-H) were identified by 18F-FMISO-PET images. The non-hypoxic areas (GTV-NH) were defined as the rest of areas removed GTV-H from GTV. The radiomic features of GTV-H and GTV-NH were extracted and compared. Results The average volume of GTV-H and GTV-NH was (10.92±11.02) cm3 and (7.21±5.70) cm3, respectively. The maximum rate of change was 46% for intensity direct-global min (ID-GM) on MRI-T1(P 0.7 and Youden index>0.5). The average rate of change was 136% for long run emphasis (LRE), long run high gray level emphasis (LRHGLE) and long run low gray level emphasis (LRLGLE) on MRI-T2(P 0.7 and Youden index>0.5). The high change rates was greater than 90% on MRI-T1+ (P 0.7 and Youden index>0.5) for ID-GM, LRE, LRHGLE and LRLGLE. Conclusions The hypoxic area of tumor target can be reflected by MRI radiomics on T1/T2/T1+ . Quantifying and tracking the variations of these features can bring benefit to recognize the hypoxic area of nasopharyngeal carcinoma tumor target. Key words: Nasopharyngeal neoplasm hypoxic area; Radiomics; Hypoxic area identification

Tip Estimation Method in Phantoms for Curved Needle Using 2D Transverse Ultrasound Images

Flexible needles have been widely used in minimally invasive surgeries, especially in percutaneous interventions. Among the interventions, tip position of the curved needle is very important, since it directly affects the success of the surgeries. In this paper, we present a method to estimate the tip position of a long-curved needle by using 2D transverse ultrasound images from a robotic ultrasound system. Ultrasound is first used to detect the cross section of long-flexible needle. A new imaging approach is proposed based on the selection of numbers of pixels with a higher gray level, which can directly remove the lower gray level to highlight the needle. After that, the needle shape tracking method is proposed by combining the image processing with the Kalman filter by using 3D needle positions, which develop a robust needle tracking procedure from 1 mm to 8 mm scan intervals. Shape reconstruction is then achieved using the curve fitting method. Finally, the needle tip position is estimated based on the curve fitting result. Experimental results showed that the estimation error of tip position is less than 1 mm within 4 mm scan intervals. The advantage of the proposed method is that the shape and tip position can be estimated through scanning the needle’s cross sections at intervals along the direction of needle insertion without detecting the tip.

Radiogenomic analysis of vascular endothelial growth factor in patients with diffuse gliomas

ObjectiveTo predict vascular endothelial growth factor (VEGF) expression in patients with diffuse gliomas using radiomic analysis.Materials and methodsPreoperative magnetic resonance images were retrospectively obtained from 239 patients with diffuse gliomas (World Health Organization grades II–IV). The patients were randomly assigned to a training group (n = 160) or a validation group (n = 79) at a 2:1 ratio. For each patient, a total of 431 radiomic features were extracted. The minimum redundancy maximum relevance (mRMR) algorithm was used for feature selection. A machine-learning model for predicting VEGF status was then developed using the selected features and a support vector machine classifier. The predictive performance of the model was evaluated in both groups using receiver operating characteristic curve analysis, and correlations between selected features were assessed.ResultsNine radiomic features were selected to generate a VEGF-associated radiomic signature of diffuse gliomas based on the mRMR algorithm. This radiomic signature consisted of two first-order statistics or related wavelet features (Entropy and Minimum) and seven textural features or related wavelet features (including Cluster Tendency and Long Run Low Gray Level Emphasis). The predictive efficiencies measured by the area under the curve were 74.1% in the training group and 70.2% in the validation group. The overall correlations between the 9 radiomic features were low in both groups.ConclusionsRadiomic analysis facilitated efficient prediction of VEGF status in diffuse gliomas, suggesting that using tumor-derived radiomic features for predicting genomic information is feasible.

Contrast-enhanced CT and texture analysis of mass-forming pancreatitis and cancer in the pancreatic head

Objective: To explore the value of contrast-enhanced CT combined with texture analysis in differentiating pancreatic cancer from mass-forming pancreatitis in pancreatic head. Methods: A retrospective study collected 21 patients with pancreatic head mass-forming pancreatitis confirmed by surgery or biopsy and 47 patients with pancreatic ductal adenocarcinoma confirmed by surgery. The patients visited the Affiliated Hospital of Nanjing University of Chinese Medicine and the First Affiliated Hospital of Wannan Medical College between January 2014 and December 2017. Gender, age and CT findings were collected. The parenchymal phase was selected for texture analysis. The minimum absolute shrinkage and selection operator (LASSO) method was applied for dimensionality reduction.Two independent sample t-tests or Mann-Whitney U test were used for continuous variables based on the Shapiro-Wilks normality test results. Categorical variables were tested by Chi-square or Fisher test. By multivariable regression analysis, CT findings, CT texture analysis, CT findings combined with texture analysis prediction models were established. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance of individual indicators and each prediction model. The Delong test was used to compare the area under the curve (AUC) of each model. Results: The CT findings prediction model consisted of CT value of lesion on pancreatic parenchymal phase and pancreatic duct penetrating sign. The texture analysis prediction model consists of root mean square and low grey level run emphasis_angle135. The AUC of them were not statistically different (Z=0.150,P>0.05). The combined predictive model had the better diagnostic performance (AUC 0.944, sensitivity 83.0%, specificity 95.2%, +LR 17.43, -LR 0.18) than CT sign prediction model (Z=2.008, P<0.05) and texture analysis prediction model(Z=2.236, P<0.05) were significantly different. Conclusions: The CT findings model and the texture analysis model have equivalent diagnostic performance in the differentiation of mass-forming pancreatitis and pancreatic cancer. The enhanced CT combined with texture analysis model has the best diagnostic efficiency and can further improve the diagnostic ability.

P‐4.4: A design to improve the uniformity of AMOLED panel

In this paper, we have proposed a method to improve the uniformity of our panel display. Through the small angle of gate signal, we have successfully improved the feedthrough status of different areas on the panel, and increased the nine‐point uniformity from 65.9 percent to 90.4 percent in low gray level. Finally, using this timing, we successfully obtained a 31‐inch display with uniform brightness.

P‐6.11: Research on the Influence of LCD dummy design on around mura

The around Mura is a common defect of TFT_LCD. In this paper, the analysis of around Mura on the black and low gray level shows that the around display unevenness caused by the difference between the Transmittance and color coordinates of the Cell gap anomaly. Due to the poor design and process of the Dummy area, the AA edge glass Bending caused the unevenness of the around Cellgap. Through optimization of design and process, this paper effectively improves the issue; and gives the Dummy area related design optimization suggestions and process choices, which fundamentally solves around mura

P‐1.15: A new optical compensation scheme for AMOLED displays with a‐IGZO TFT and a‐Si:H PIN diode

This paper proposed a new optical compensation scheme for AMOLED display based on a sensor unit in pixel. The sensor unit includes an a‐IGZO TFT, which is adopted as a switch due to its low off current, a‐Si:H PIN diode as optical sensor due to its high photoconductive gain, and a capacitor to store charges. Owing to the large off current difference under different brightness, the luminance of every pixel can be acquired by PIN sensor, and then data voltage is adjusted accordingly. The scheme is adopted in a 5 inch AMOLED display, and the results show this method can capture the pixel brightness accurately, and effectively compensate low gray level light uniformity.

Radiomic Features of the Nigrosome-1 Region of the Substantia Nigra: Using Quantitative Susceptibility Mapping to Assist the Diagnosis of Idiopathic Parkinson's Disease.

Introduction: The loss of nigrosome-1, which is also referred to as the swallow tail sign (STS) in T2*-weighted iron-sensitive magnetic resonance imaging (MRI), has recently emerged as a new biomarker for idiopathic Parkinson's disease (IPD). However, consistent recognition of the STS is difficult due to individual variations and different imaging parameters. Radiomics might have the potential to overcome these shortcomings. Therefore, we chose to explore whether radiomic features of nigrosome-1 of substantia nigra (SN) based on quantitative susceptibility mapping (QSM) could help to differentiate IPD patients from healthy controls (HCs).Methods: Three-dimensional multi-echo gradient-recalled echo images (0.86 × 0.86 × 1.00 mm3) were obtained at 3.0-T MRI for QSM in 87 IPD patients and 77 HCs. Regions of interest (ROIs) of the SN below the red nucleus were manually drawn on both sides, and subsequently, volumes of interest (VOIs) were segmented (these ROIs included four 1-mm slices). Then, 105 radiomic features (including 18 first-order features, 13 shape features, and 74 texture features) of bilateral VOIs in the two groups were extracted. Forty features were selected according to the ensemble feature selection method, which combined analysis of variance, random forest, and recursive feature elimination. The selected features were further utilized to distinguish IPD patients from HC using the SVM classifier with 10 rounds of 3-fold cross-validation. Finally, the representative features were analyzed using an unpaired t-test with Bonferroni correction and correlated with the UPDRS-III scores.Results: The classification results from SVM were as follows: area under curve (AUC): 0.96 ± 0.02; accuracy: 0.88 ± 0.03; sensitivity: 0.89 ± 0.06; and specificity: 0.87 ± 0.07. Five representative features were selected to show their detailed difference between IPD patients and HCs: 10th percentile and median in IPD patients were higher than those in HCs (all p < 0.00125), while Gray Level Run Length Matrix (GLRLM)-Long Run Low Gray Level Emphasis, Gray Level Size Zone Matrix (GLSZM)–Gray Level Non-Uniformity, and volume (all p < 0.00125) in IPD patients were lower than those in HCs. The 10th percentile was positively correlated with UPDRS-III score (r = 0.35, p = 0.001).Conclusion: Radiomic features of the nigrosome-1 region of SN based on QSM could be useful in the diagnosis of IPD and could serve as a surrogate marker for the STS.

P‐158: Research on the Influence of LCD Dummy Design on around Mura

The around Mura is a common defect of TFT_LCD. In this paper, the analysis of around Mura on the black and low gray level shows that the around display unevenness caused by the difference between the Transmittance and color coordinates of the Cell gap anomaly. Due to the poor design and process of the Dummy area, the AA edge glass Bending caused the unevenness of the around Cellgap. Through optimization of design and process, this paper effectively improves the issue; and gives the Dummy area related design optimization suggestions and process choices, which fundamentally solves around mura

P‐160: Analysis on the Edge Mura Under Low Gray Level and Improvement in LCDs

The edge mura under low gray level in LCD panel edge can be reduced by decreasing the height difference of main photo spacer (PS) in active area and dummy PS in dummy area. By simulation and experiment, a factor H is proposed to evaluate and predict the edge mura.

P‐5: A new optical compensation scheme for AMOLED displays with a‐IGZO TFT and a‐Si:H PIN diode

This paper proposed a new optical compensation scheme for AMOLED display based on a sensor unit in pixel The sensor unit includes an a‐IGZO TFT, which is adopted as a switch due to its low off current, a‐Si:H PIN diode as optical sensor due to its high photoconductive gain, and a capacitor to store charges. Owing to the large off current difference under different brightness, the luminance of every pixel can be acquired by PIN sensor, and then data voltage is adjusted accordingly. The scheme is adopted in a 5 inch AMOLED display, and the results show this method can capture the pixel brightness accurately, and effectively compensate low gray level light uniformity.

Improvement of display performance of electrowetting displays by optimized waveforms and error diffusion

AbstractThe electrowetting display (EWD) has a low gray level, and there are some phenomena that affect the display quality, such as oil backflow and charge trapping. In this paper, an error diffusion algorithm based on pixel neighborhood gray information and direct current (DC)‐balanced driving waveform are proposed for display quality of EWD. The proposed algorithm and driving waveform are implemented in the proposed driving system based on Digital Visual Interface (DVI) video codec system and field‐programmable gate array (FPGA) control system. The experiment results show that the driving waveform suppresses the oil backflow, charge trapping and keeps DC balance, and the proposed error diffusion preserves the edges and richness of image. The whole driving system successfully plays video in real time with computer and improves the display quality of EWD.

Dosiomics: Extracting 3D Spatial Features From Dose Distribution to Predict Incidence of Radiation Pneumonitis.

Radiation pneumonitis (RP) is one of the major toxicities of thoracic radiation therapy. RP incidence has been proven to be closely associated with the dosimetric factors and normal tissue control possibility (NTCP) factors. However, because these factors only utilize limited information of the dose distribution, the prediction abilities of these factors are modest. We adopted the dosiomics method for RP prediction. The dosiomics method first extracts spatial features of the dose distribution within ipsilateral, contralateral, and total lungs, and then uses these extracted features to construct prediction model via univariate and multivariate logistic regression (LR). The dosiomics method is validated using 70 non-small cell lung cancer (NSCLC) patients treated with volumetric modulated arc therapy (VMAT) radiotherapy. Dosimetric and NTCP factors based prediction models are also constructed to compare with the dosiomics features based prediction model. For the dosimetric, NTCP and dosiomics factors/features, the most significant single factors/features are the mean dose, parallel/serial (PS) NTCP and gray level co-occurrence matrix (GLCM) contrast of ipsilateral lung, respectively. And the area under curve (AUC) of univariate LR is 0.665, 0.710 and 0.709, respectively. The second significant factors are V5 of contralateral lung, equivalent uniform dose (EUD) derived from PS NTCP of contralateral lung and the low gray level run emphasis of gray level run length matrix (GLRLM) of total lungs. The AUC of multivariate LR is improved to 0.676, 0.744, and 0.782, respectively. The results demonstrate that the univariate LR of dosiomics features has approximate predictive ability with NTCP factors, and the multivariate LR outperforms both the dosimetric and NTCP factors. In conclusion, the spatial features of dose distribution extracted by the dosiomics method effectively improves the prediction ability.