scholarly journals CT-Based Radiomics Helps to Predict Residual Lung Lesions in COVID-19 Patients at Three Months after Discharge

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1814
Author(s):  
Jia Huang ◽  
Feihong Wu ◽  
Leqing Chen ◽  
Jie Yu ◽  
Wengang Sun ◽  
...  
Keyword(s):  
Operating Characteristic ◽  
Characteristic Curve ◽  
Lung Region ◽  
Training Set ◽  
Test Set ◽  
Psychological Burden ◽  
Lung Lesions ◽  
Model Based ◽  
Entire Lung

Background: In this study, our focus was on pulmonary sequelae of coronavirus disease 2019 (COVID-19). We aimed to develop and validate CT-based radiomic models for predicting the presence of residual lung lesions in COVID-19 survivors at three months after discharge. Methods: We retrospectively enrolled 162 COVID-19 confirmed patients in our hospital (84 patients with residual lung lesions and 78 patients without residual lung lesions, at three months after discharge). The patients were all randomly allocated to a training set (n = 114) or a test set (n = 48). Radiomic features were extracted from chest CT images in different regions (entire lung or lesion) and at different time points (at hospital admission or at discharge) to build different models, sequentially, or in combination, as follows: (1) Lesion_A model (based on the lesion region at admission CT); (2) Lesion_D model (based on the lesion region at discharge CT); (3) Δlesion model (based on the lesion region at admission CT and discharge CT); (4) Lung_A model (based on the lung region at admission CT); (5) Lung_D model (based on the lung region at discharge CT); (6) Δlung model (based on the lung region at admission CT and discharge CT). The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were used to evaluate the predictive performances of the radiomic models. Results: Among the six models, the Lesion_D and the Δlesion models achieved better predictive efficacy, with AUCs of 0.907 and 0.927, sensitivity of 0.898 and 0.763, and specificity of 0.855 and 0.964 in the training set, and AUCs of 0.875 and 0.837, sensitivity of 0.920 and 0.680, and specificity of 0.826 and 0.913 in the test set, respectively. Conclusions: The CT-based radiomic models showed good predictive effects on the presence of residual lung lesions in COVID-19 survivors at three months after discharge, which may help doctors to plan follow-up work and to reduce the psychological burden of COVID-19 survivors.

scholarly journals A clinical scoring system for pediatric hand-foot-mouth disease

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hui Huang ◽  
Li Deng ◽  
Liping Jia ◽  
Runan Zhu
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Scoring System ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Training Set ◽  
Test Set ◽  
Hand Foot Mouth Disease ◽  
Clinical Scoring ◽  
Exposure History ◽  
Operating Characteristic Curve

Abstract Background The aim of the present study was to develop a clinical scoring system for the diagnosis of hand-foot-mouth disease (HFMD) with improved accuracy. Methods A retrospective analysis was performed on standardized patient history and clinical examination data obtained from 1435 pediatric patients under the age of three years who presented with acute rash illness and underwent enterovirus nucleic acid detection. Patients were then divided into the HFMD (1094 patients) group or non-HFMD (341 patients) group based on a positive or a negative result from the assay, respectively. We then divided the data into a training set (1004 cases, 70%) and a test set (431 cases, 30%) using a random number method. Multivariate logistic regression was performed on 15 clinical variables (e.g. age, exposure history, number of rash spots in a single body region) to identify variables highly predictive of a positive diagnosis in the training set. Using the variables with high impact on the diagnostic accuracy, we generated a scoring system for predicting HFMD and subsequently evaluated this system in the test set by receiver operating characteristic curve (ROC curve). Results Using the logistic model, we identified seven clinical variables (age, exposure history, and rash density at specific regions of the body) to be included into the scoring system. The final scores ranged from − 5 to 24 (higher scores positively predicted HFMD diagnosis). Through our training set, a cutoff score of 7 resulted in a sensitivity of 0.76 and specificity of 0.68. The area under the receiver operating characteristic curve (AUC) was 0.804 (95% confidence interval [CI]: 0.773–0.835) (P < 0.001). Using the test set, we obtained an AUC of 0.76 (95% CI: 0.710–0.810) with a sensitivity of 0.76 and a specificity of 0.62. These results from the test set were consistent with those from the training set. Conclusions This study establishes an objective scoring system for the diagnosis of typical and atypical HFMD using measures accessible through routine clinical encounters. Due to the accuracy and sensitivity achieved by this scoring system, it can be employed as a rapid, low-cost method for establishing diagnoses in children with acute rash illness.

scholarly journals IER-SICH Nomogram to Predict Symptomatic Intracerebral Hemorrhage After Thrombectomy for Stroke

Stroke ◽  
2019 ◽  
Vol 50 (4) ◽  
pp. 909-916 ◽  
Author(s):  
Manuel Cappellari ◽  
Salvatore Mangiafico ◽  
Valentina Saia ◽  
Giovanni Pracucci ◽  
Sergio Nappini ◽  
...  
Keyword(s):  
Intracerebral Hemorrhage ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Training Set ◽  
Test Set ◽  
Symptomatic Intracerebral Hemorrhage ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Background and Purpose— As a reliable scoring system to detect the risk of symptomatic intracerebral hemorrhage after thrombectomy for ischemic stroke is not yet available, we developed a nomogram for predicting symptomatic intracerebral hemorrhage in patients with large vessel occlusion in the anterior circulation who received bridging of thrombectomy with intravenous thrombolysis (training set), and to validate the model by using a cohort of patients treated with direct thrombectomy (test set). Methods— We conducted a cohort study on prospectively collected data from 3714 patients enrolled in the IER (Italian Registry of Endovascular Stroke Treatment in Acute Stroke). Symptomatic intracerebral hemorrhage was defined as any type of intracerebral hemorrhage with increase of ≥4 National Institutes of Health Stroke Scale score points from baseline ≤24 hours or death. Based on multivariate logistic models, the nomogram was generated. We assessed the discriminative performance by using the area under the receiver operating characteristic curve. Results— National Institutes of Health Stroke Scale score, onset-to-end procedure time, age, unsuccessful recanalization, and Careggi collateral score composed the IER-SICH nomogram. After removing Careggi collateral score from the first model, a second model including Alberta Stroke Program Early CT Score was developed. The area under the receiver operating characteristic curve of the IER-SICH nomogram was 0.778 in the training set (n=492) and 0.709 in the test set (n=399). The area under the receiver operating characteristic curve of the second model was 0.733 in the training set (n=988) and 0.685 in the test set (n=779). Conclusions— The IER-SICH nomogram is the first model developed and validated for predicting symptomatic intracerebral hemorrhage after thrombectomy. It may provide indications on early identification of patients for more or less postprocedural intensive management.

scholarly journals Evaluation of factors that predict the success rate of trial of labor after the cesarean section

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yang Mi ◽  
Pengfei Qu ◽  
Na Guo ◽  
Ruimiao Bai ◽  
Jiayi Gao ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Cesarean Section ◽  
Receiver Operating Characteristic Curve ◽  
Success Rate ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Predictive Ability ◽  
Training Set ◽  
History Of ◽  
Operating Characteristic Curve

Abstract Background For most women who have had a previous cesarean section, vaginal birth after cesarean section (VBAC) is a reasonable and safe choice, but which will increase the risk of adverse outcomes such as uterine rupture. In order to reduce the risk, we evaluated the factors that may affect VBAC and and established a model for predicting the success rate of trial of the labor after cesarean section (TOLAC). Methods All patients who gave birth at Northwest Women’s and Children’s Hospital from January 2016 to December 2018, had a history of cesarean section and voluntarily chose the TOLAC were recruited. Among them, 80% of the population was randomly assigned to the training set, while the remaining 20% were assigned to the external validation set. In the training set, univariate and multivariate logistic regression models were used to identify indicators related to successful TOLAC. A nomogram was constructed based on the results of multiple logistic regression analysis, and the selected variables included in the nomogram were used to predict the probability of successfully obtaining TOLAC. The area under the receiver operating characteristic curve was used to judge the predictive ability of the model. Results A total of 778 pregnant women were included in this study. Among them, 595 (76.48%) successfully underwent TOLAC, whereas 183 (23.52%) failed and switched to cesarean section. In multi-factor logistic regression, parity = 1, pre-pregnancy BMI < 24 kg/m2, cervical score ≥ 5, a history of previous vaginal delivery and neonatal birthweight < 3300 g were associated with the success of TOLAC. The area under the receiver operating characteristic curve in the prediction and validation models was 0.815 (95% CI: 0.762–0.854) and 0.730 (95% CI: 0.652–0.808), respectively, indicating that the nomogram prediction model had medium discriminative power. Conclusion The TOLAC was useful to reducing the cesarean section rate. Being primiparous, not overweight or obese, having a cervical score ≥ 5, a history of previous vaginal delivery or neonatal birthweight < 3300 g were protective indicators. In this study, the validated model had an approving predictive ability.

scholarly journals Identification of Multi-omics Biomarkers and Construction of the Novel Prognostic Model for Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Xiaokai Yan ◽  
Chiying Xiao ◽  
Kunyan Yue ◽  
Min Chen ◽  
Hang Zhou
Keyword(s):  
Hepatocellular Carcinoma ◽  
Survival Analysis ◽  
Prognostic Model ◽  
Prognostic Models ◽  
Prognostic Indicators ◽  
Omics Data ◽  
Training Set ◽  
Test Set ◽  
Model Based ◽  
Cox Analysis

Abstract Background: Change in the genome plays a crucial role in cancerogenesis and many biomarkers can be used as effective prognostic indicators in diverse tumors. Currently, although many studies have constructed some predictive models for hepatocellular carcinoma (HCC) based on molecular signatures, the performance of which is unsatisfactory. To fill this shortcoming, we hope to construct a novel and accurate prognostic model with multi-omics data to guide prognostic assessments of HCC. Methods: The TCGA training set was used to identify crucial biomarkers and construct single-omic prognostic models through difference analysis, univariate Cox, and LASSO/stepwise Cox analysis. Then the performances of single-omic models were evaluated and validated through survival analysis, Harrell’s concordance index (C-index), and receiver operating characteristic (ROC) curve, in the TCGA test set and external cohorts. Besides, a comprehensive model based on multi-omics data was constructed via multiple Cox analysis, and the performance of which was evaluated in the TCGA training set and TCGA test set. Results: We identified 16 key mRNAs, 20 key lncRNAs, 5 key miRNAs, 5 key CNV genes, and 7 key SNPs which were significantly associated with the prognosis of HCC, and constructed 5 single-omic models which showed relatively good performance in prognostic prediction with c-index ranged from 0.63 to 0.75 in the TCGA training set and test set. Besides, we validated the mRNA model and the SNP model in two independent external datasets respectively, and good discriminating abilities were observed through survival analysis (P < 0.05). Moreover, the multi-omics model based on mRNA, lncRNA, miRNA, CNV, and SNP information presented a quite strong predictive ability with c-index over 0.80 and all AUC values at 1,3,5-years more than 0.84.Conclusion: In this study, we identified many biomarkers that may help study underlying carcinogenesis mechanisms in HCC, and constructed five single-omic models and an integrated multi-omics model that may provide effective and reliable guides for prognosis assessment and treatment decision-making.

scholarly journals A New Measure of Pulse Rate Variability and Detection of Atrial Fibrillation Based on Improved Time Synchronous Averaging

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xiaodong Ding ◽  
Yiqin Wang ◽  
Yiming Hao ◽  
Yi Lv ◽  
Rui Chen ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Pulse Rate ◽  
Euclidean Distance ◽  
Operating Characteristic ◽  
Pulse Wave ◽  
Characteristic Curve ◽  
Signal Quality ◽  
Training Set ◽  
Pulse Rate Variability ◽  
Operating Characteristic Curve

Background. Pulse rate variability monitoring and atrial fibrillation detection algorithms have been widely used in wearable devices, but the accuracies of these algorithms are restricted by the signal quality of pulse wave. Time synchronous averaging is a powerful noise reduction method for periodic and approximately periodic signals. It is usually used to extract single-period pulse waveforms, but has nothing to do with pulse rate variability monitoring and atrial fibrillation detection traditionally. If this method is improved properly, it may provide a new way to measure pulse rate variability and to detect atrial fibrillation, which may have some potential advantages under the condition of poor signal quality. Objective. The objective of this paper was to develop a new measure of pulse rate variability by improving existing time synchronous averaging and to detect atrial fibrillation by the new measure of pulse rate variability. Methods. During time synchronous averaging, two adjacent periods were regarded as the basic unit to calculate the average signal, and the difference between waveforms of the two adjacent periods was the new measure of pulse rate variability. 3 types of distance measures (Euclidean distance, Manhattan distance, and cosine distance) were tested to measure this difference on a simulated training set with a capacity of 1000. The distance measure, which can accurately distinguish regular pulse rate and irregular pulse rate, was used to detect atrial fibrillation on the testing set with a capacity of 62 (11 with atrial fibrillation, 8 with premature contraction, and 43 with sinus rhythm). The receiver operating characteristic curve was used to evaluate the performance of the indexes. Results. The Euclidean distance between waveforms of the two adjacent periods performs best on the training set. On the testing set, the Euclidean distance in atrial fibrillation group is significantly higher than that of the other two groups. The area under receiver operating characteristic curve to identify atrial fibrillation was 0.998. With the threshold of 2.1, the accuracy, sensitivity, and specificity were 98.39%, 100%, and 98.04%, respectively. This new index can detect atrial fibrillation from pulse wave signal. Conclusion. This algorithm not only provides a new perspective to detect AF but also accomplishes the monitoring of PRV and the extraction of single-period pulse wave through the same technical route, which may promote the popularization and application of pulse wave.

Correlation between contrast-enhanced cone-beam breast computed tomography features and prognostic staging in breast cancer

2021 ◽  
Author(s):  
Wei-mei Ma ◽  
Jiao Li ◽  
Shuang-gang Chen ◽  
Pei-qiang Cai ◽  
Shen Chen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Goodness Of Fit ◽  
Characteristic Curve ◽  
Cone Beam ◽  
Imaging Biomarkers ◽  
Training Set ◽  
Test Set ◽  
Ps Ii ◽  
Contrast Enhanced ◽  
Significant Difference

Objective: To evaluate whether contrast-enhanced cone-beam breast CT (CE-CBBCT) features can risk-stratify prognostic stage in breast cancer. Methods: Overall, 168 biopsy-proven breast cancer patients were analysed: 115 patients in the training set underwent scanning using v. 1.5 CE-CBBCT between August 2019 and December 2019, whereas 53 patients in the test set underwent scanning using v. 1.0 CE-CBBCT between May 2012 and August 2014. All patients were restaged according to the American Joint Committee on Cancer eighth edition prognostic staging system. Following the combination of CE-CBBCT imaging parameters and clinicopathological factors, predictors that were correlated with stratification of prognostic stage via logistic regression were analysed. Predictive performance was assessed according to the area under the receiver operating characteristic curve (AUC). Goodness-of-fit of the models was assessed using the Hosmer-Lemeshow test. Results: As regards differentiation between prognostic stage (PS) I and II/III, increased tumour-to-breast volume ratio (TBR), rim enhancement pattern, and the presence of penetrating vessels were significant predictors for PS II/III disease (p < 0.05). The AUCs in the training and test sets were 0.967 [95% confidence interval (CI) 0.938–0.996; p < 0.001] and 0.896 (95% CI, 0.809–0.983; p = 0.001), respectively. Two features were selected in the training set of PS II vs III, including tumour volume [odds ratio (OR)=1.817, p = 0.019] and calcification (OR = 4.600, p = 0.040), achieving an AUC of 0.790 (95% CI, 0.636–0.944, p = 0.001). However, there was no significant difference in the test set of PS II vs III (P＞0.05). Conclusion: CE-CBBCT imaging biomarkers may provide a large amount of anatomical and radiobiological information for the pre-operative distinction of prognostic stage. Advances in knowledge: CE-CBBCT features have distinctive promise for stratification of prognostic stage in breast cancer.

Core Genome Allelic Profiles of Clinical Klebsiella pneumoniae Strains Using a Random Forest Algorithm Based on Multilocus Sequence Typing Scheme for Hypervirulence Analysis

2020 ◽  
Vol 221 (Supplement_2) ◽  
pp. S263-S271 ◽  
Author(s):  
Peng Lan ◽  
Qiucheng Shi ◽  
Ping Zhang ◽  
Yan Chen ◽  
Rushuang Yan ◽  
...  
Keyword(s):  
Random Forest ◽  
Klebsiella Pneumoniae ◽  
Multilocus Sequence Typing ◽  
Operating Characteristic ◽  
Core Genome ◽  
Characteristic Curve ◽  
Random Forest Algorithm ◽  
The Core ◽  
Model Based ◽  
Operating Characteristic Curve

Abstract Background Hypervirulent Klebsiella pneumoniae (hvKP) infections can have high morbidity and mortality rates owing to their invasiveness and virulence. However, there are no effective tools or biomarkers to discriminate between hvKP and nonhypervirulent K. pneumoniae (nhvKP) strains. We aimed to use a random forest algorithm to predict hvKP based on core-genome data. Methods In total, 272 K. pneumoniae strains were collected from 20 tertiary hospitals in China and divided into hvKP and nhvKP groups according to clinical criteria. Clinical data comparisons, whole-genome sequencing, virulence profile analysis, and core genome multilocus sequence typing (cgMLST) were performed. We then established a random forest predictive model based on the cgMLST scheme to prospectively identify hvKP. The random forest is an ensemble learning method that generates multiple decision trees during the training process and each decision tree will output its own prediction results corresponding to the input. The predictive ability of the model was assessed by means of area under the receiver operating characteristic curve. Results Patients in the hvKP group were younger than those in the nhvKP group (median age, 58.0 and 68.0 years, respectively; P &lt; .001). More patients in the hvKP group had underlying diabetes mellitus (43.1% vs 20.1%; P &lt; .001). Clinically, carbapenem-resistant K. pneumoniae was less common in the hvKP group (4.1% vs 63.8%; P &lt; .001), whereas the K1/K2 serotype, sequence type (ST) 23, and positive string tests were significantly higher in the hvKP group. A cgMLST-based minimal spanning tree revealed that hvKP strains were scattered sporadically within nhvKP clusters. ST23 showed greater genome diversification than did ST11, according to cgMLST-based allelic differences. Primary virulence factors (rmpA, iucA, positive string test result, and the presence of virulence plasmid pLVPK) were poor predictors of the hypervirulence phenotype. The random forest model based on the core genome allelic profile presented excellent predictive power, both in the training and validating sets (area under receiver operating characteristic curve, 0.987 and 0.999 in the training and validating sets, respectively). Conclusions A random forest algorithm predictive model based on the core genome allelic profiles of K. pneumoniae was accurate to identify the hypervirulent isolates.

scholarly journals Variables Affecting Peri-Implant Radiographic Bone Loss-8-23 Years Follow-Up

2020 ◽  
Vol 10 (23) ◽  
pp. 8591
Author(s):  
Michael Saminsky ◽  
Anat Ben Dor ◽  
Jacob Horwitz
Keyword(s):  
Bone Loss ◽  
Goodness Of Fit ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Factors Associated ◽  
Implant Placement ◽  
Dental Records

The aim of this study is to evaluate factors associated with long-term peri-implant bone-loss and to create a statistical model explaining bone-loss. The dental records in a private periodontal practice were screened for implant-patients with a minimal follow-up period of 8 years with periapical radiographs at implant-placement (T0) and last follow-up (Tf). Collected data included demographics, general health, medications, periodontal parameters, implant parameters, bone augmentation procedures, restoration and antagonist data, number of supportive periodontal appointments (SPT), and radiographic bone-loss between T0 and Tf. Bivariate and Mixed Logistic Regression analyses were performed. “Goodness-of-fit” of the model was elaborated with Receiver Operating Characteristic Curve (ROC) analyses. Thirty-seven patients receiving 142 implants were included. Mean clinical follow-up period was 11.7 ± 3.7 years (range 8–23). Most implants 64.4% were SPT-maintained more than twice a year. Patients with osteoporosis and smokers were prone to increased radiographic peri-implant bone-loss. External-hex implants placed without guided bone regeneration (GBR) and implants 10–12 mm long and diameter of 3.7–4 mm showed less peri-implant bone-loss. The model’s Area Under the Curve (AUC) was 76.9% (Standard Error 4.6%, CI 67.8%–86%).

scholarly journals Prediction model for the onset risk of impaired fasting glucose: a 10-year longitudinal retrospective cohort health check-up study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yuqi Wang ◽  
Liangxu Wang ◽  
Yanli Su ◽  
Li Zhong ◽  
Bin Peng
Keyword(s):  
Blood Pressure ◽  
Prediction Model ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Fasting Glucose ◽  
Density Lipoprotein ◽  
Health Check ◽  
Predictor Variables ◽  
Training Set ◽  
Test Set

Abstract Background Impaired fasting glucose (IFG) is a prediabetic condition. Considering that the clinical symptoms of IFG are inconspicuous, these tend to be easily ignored by individuals, leading to conversion to diabetes mellitus (DM). In this study, we established a prediction model for the onset risk of IFG in the Chongqing health check-up population to provide a reference for prevention in a health check-up cohort. Methods We conducted a retrospective longitudinal cohort study in Chongqing, China from January 2009 to December 2019. The qualified subjects were more than 20 years old and had more than two health check-ups. After following the inclusion and exclusion criteria, the cohort population was randomly divided into a training set and a test set at a ratio of 7:3. We first selected the predictor variables through the univariate generalized estimation equation (GEE), and then the training set was used to establish the IFG risk model based on multivariate GEE. Finally, the sensitivity, specificity, and receiver operating characteristic curves were used to verify the performance of the model. Results A total of 4,926 subjects were included in this study, with an average of 3.87 check-up records, including 2,634 males and 2,292 females. There were 442 IFG cases during the follow-up period, including 286 men and 156 women. The incidence density was 26.88/1000 person-years for men and 18.53/1000 person-years for women (P<0.001). The predictor variables of our prediction model include male (relative risk (RR) =1.422, 95 % confidence interval (CI): 0.923-2.193, P=0.3849), age (RR=1.030, 95 %CI: 1.016-1.044, P<0.0001), waist circumference (RR=1.005, 95 %CI: 0.999-1.012, P=0.0975), systolic blood pressure (RR=1.004, 95 %CI: 0.993-1.016, P=0.4712), diastolic blood pressure (RR=1.023, 95 %CI: 1.005-1.041, P=0.0106), obesity (RR=1.797, 95 %CI: 1.126-2.867, P=0.0140), triglycerides (RR=1.107, 95 %CI: 0.943-1.299, P=0.2127), high-density lipoprotein cholesterol (RR=0.992, 95 %CI: 0.476-2.063, P=0.9818), low-density lipoprotein cholesterol (RR=1.793, 95 %CI: 1.085-2.963, P=0.0228), blood urea (RR=1.142, 95 %CI: 1.022-1.276, P=0.0192), serum uric acid (RR=1.004, 95 %CI: 1.002-1.005, P=0.0003), total cholesterol (RR=0.674, 95 %CI: 0.403-1.128, P=0.1331), and serum creatinine levels (RR=0.960, 95 %CI: 0.945-0.976, P<0.0001). The area under the receiver operating characteristic curve (AUC) in the training set was 0.740 (95 %CI: 0.712-0.768), and the AUC in the test set was 0.751 (95 %CI: 0.714-0.817). Conclusions The prediction model for the onset risk of IFG had good predictive ability in the health check-up cohort.

scholarly journals Use of Radiomics to Improve Diagnostic Performance of PI-RADS v2.1 in Prostate Cancer

2021 ◽  
Vol 10 ◽  
Author(s):  
Mou Li ◽  
Ling Yang ◽  
Yufeng Yue ◽  
Jingxu Xu ◽  
Chencui Huang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Diagnostic Performance ◽  
Roc Analysis ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Training Set ◽  
Combined Model ◽  
Contrast Enhanced ◽  
Apparent Diffusion ◽  
Validation Set

ObjectiveTo investigate whether a radiomics model can help to improve the performance of PI-RADS v2.1 in prostate cancer (PCa).MethodsThis was a retrospective analysis of 203 patients with pathologically confirmed PCa or non-PCa between March 2015 and December 2016. Patients were divided into a training set (n = 141) and a validation set (n = 62). The radiomics model (Rad-score) was developed based on multi-parametric MRI including T2 weighted imaging (T2WI), diffusion weighted imaging (DWI), apparent diffusion coefficient (ADC) imaging, and dynamic contrast enhanced (DCE) imaging. The combined model involving Rad-score and PI-RADS was compared with PI-RADS for the diagnosis of PCa by using the receiver operating characteristic curve (ROC) analysis.ResultsA total of 112 (55.2%) patients had PCa, and 91 (44.8%) patients had benign lesions. For PCa versus non-PCa, the Rad-score had a significantly higher area under the ROC curve (AUC) [0.979 (95% CI, 0.940–0.996)] than PI-RADS [0.905 (0.844–0.948), P = 0.002] in the training set. However, the AUC between them was insignificant in the validation set [0.861 (0.749–0.936) vs. 0.845 (0.731–0.924), P = 0.825]. When Rad-score was added to PI-RADS, the performance of the PI-RADS was significantly improved for the PCa diagnosis (AUC = 0.989, P &lt; 0.001 for the training set and AUC = 0.931, P = 0.038 for the validation set).ConclusionsThe radiomics based on multi-parametric MRI can help to improve the diagnostic performance of PI-RADS v2.1 in PCa.

Sign in / Sign up

Export Citation Format

Share Document