Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 112
Filtrar
1.
World J Gastroenterol ; 26(30): 4442-4452, 2020 Aug 14.
Artigo em Inglês | MEDLINE | ID: mdl-32874056

RESUMO

BACKGROUND: Transarterial chemoembolization (TACE) is the first-line treatment for patients with unresectable liver cancer; however, TACE is associated with postembolization pain. AIM: To analyze the risk factors for acute abdominal pain after TACE and establish a predictive model for postembolization pain. METHODS: From January 2018 to September 2018, all patients with liver cancer who underwent TACE at our hospital were included. General characteristics; clinical, imaging, and procedural data; and postembolization pain were analyzed. Postembolization pain was defined as acute moderate-to-severe abdominal pain within 24 h after TACE. Logistic regression and a classification and regression tree were used to develop a predictive model. Receiver operating characteristic curve analysis was used to examine the efficacy of the predictive model. RESULTS: We analyzed 522 patients who underwent a total of 582 TACE procedures. Ninety-seven (16.70%) episodes of severe pain occurred. A predictive model built based on the dataset from classification and regression tree analysis identified known invasion of blood vessels as the strongest predictor of subsequent performance, followed by history of TACE, method of TACE, and history of abdominal pain after TACE. The area under the receiver operating characteristic curve was 0.736 [95% confidence interval (CI): 0.682-0.789], the sensitivity was 73.2%, the specificity was 65.6%, and the negative predictive value was 92.4%. Logistic regression produced similar results by identifying age [odds ratio (OR) = 0.971; 95%CI: 0.951-0.992; P = 0.007), history of TACE (OR = 0.378; 95%CI: 0.189-0.757; P = 0.007), history of abdominal pain after TACE (OR = 6.288; 95%CI: 2.963-13.342; P < 0.001), tumor size (OR = 1.978; 95%CI: 1.175-3.330; P = 0.01), multiple tumors (OR = 2.164; 95%CI: 1.243-3.769; P = 0.006), invasion of blood vessels (OR = 1.756; 95%CI: 1.045-2.950; P = 0.034), and TACE with drug-eluting beads (DEB-TACE) (OR = 2.05; 95%CI: 1.260-3.334; P = 0.004) as independent predictive factors for postembolization pain. CONCLUSION: Blood vessel invasion, TACE history, TACE with drug-eluting beads, and history of abdominal pain after TACE are predictors of acute moderate-to-severe pain. The predictive model may help medical staff to manage pain.

2.
Eur J Radiol ; 132: 109277, 2020 Sep 11.
Artigo em Inglês | MEDLINE | ID: mdl-32980726

RESUMO

PURPOSE: This work aimed to develop and validate a deep learning radiomics model for evaluating serosa invasion in gastric cancer. MATERIALS AND METHODS: A total of 572 gastric cancer patients were included in this study. Firstly, we retrospectively enrolled 428 consecutive patients (252 in the training set and 176 in the test set I) with pathological confirmed T3 or T4a. Subsequently, 144 patients who were clinically diagnosed cT3 or cT4a were prospectively allocated to the test set II. Histological verification was based on the surgical specimens. CT findings were determined by a panel of three radiologists. Conventional hand-crafted features and deep learning features were extracted from three phases CT images and were utilized to build radiomics signatures via machine learning methods. Incorporating the radiomics signatures and CT findings, a radiomics nomogram was developed via multivariable logistic regression. Its diagnostic ability was measured using receiver operating characteristiccurve analysis. RESULTS: The radiomics signatures, built with support vector machine or artificial neural network, showed good performance for discriminating T4a in the test I and II sets with area under curves (AUCs) of 0.76-0.78 and 0.79-0.84. The nomogram had powerful diagnostic ability in all training, test I and II sets with AUCs of 0.90 (95 % CI, 0.86-0.94), 0.87 (95 % CI, 0.82-0.92) and 0.90 (95 % CI, 0.85-0.96) respectively. The net reclassification index revealed that the radiomics nomogram had significantly better performance than the clinical model (p-values < 0.05). CONCLUSIONS: The deep learning radiomics model based on CT images is effective at discriminating serosa invasion in gastric cancer.

3.
J Magn Reson Imaging ; 2020 Aug 09.
Artigo em Inglês | MEDLINE | ID: mdl-32776391

RESUMO

BACKGROUND: Distant metastasis is the primary cause of treatment failure in locoregionally advanced nasopharyngeal carcinoma (LANPC). PURPOSE: To develop a model to evaluate distant metastasis-free survival (DMFS) in LANPC and to explore the value of additional chemotherapy to concurrent chemoradiotherapy (CCRT) for different risk groups. STUDY TYPE: Retrospective. POPULATION: In all, 233 patients with biopsy-confirmed nasopharyngeal carcinoma (NPC) from two hospitals. FIELD STRENGTH: 1.5T and 3T. SEQUENCE: Axial T2 -weighted (T2 -w) and contrast-enhanced T1 -weighted (CET1 -w) images. ASSESSMENT: Deep learning was used to build a model based on MRI images (including axial T2 -w and CET1 -w images) and clinical variables. Hospital 1 patients were randomly divided into training (n = 169) and validation (n = 19) cohorts; Hospital 2 patients were assigned to a testing cohort (n = 45). LANPC patients were divided into low- and high-risk groups according to their DMFS (P < 0.05). Kaplan-Meier survival analysis was performed to compare the DMFS of different risk groups and subgroup analysis was performed to compare patients treated with CCRT alone and treated with additional chemotherapy to CCRT in different risk groups, respectively. STATISTICAL TESTS: Univariate analysis was performed to identify significant clinical variables. The area under the receiver operating characteristic (ROC) curve (AUC) was used to assess the model performance. RESULTS: Our deep-learning model integrating the deep-learning signature, node (N) stage (from TNM staging), plasma Epstein-Barr virus (EBV)-DNA, and treatment regimens yielded an AUC of 0.796 (95% confidence interval [CI]: 0.729-0.863), 0.795 (95% CI: 0.540-1.000), and 0.808 (95% CI: 0.654-0.962) in the training, internal validation, and external testing cohorts, respectively. Low-risk patients treated with CCRT alone had longer DMFS than patients treated with additional chemotherapy to CCRT (P < 0.05). DATA CONCLUSION: The proposed deep-learning model, based on MRI features and clinical variates, facilitated the prediction of DMFS in LANPC patients. LEVEL OF EVIDENCE: 3. TECHNICAL EFFICACY STAGE: 4.

4.
Artigo em Inglês | MEDLINE | ID: mdl-32750940

RESUMO

OBJECTIVE: Radiomics, an emerging tool for medical image analysis, is potential towards precisely characterizing gastric cancer (GC). Whether using one-slice 2D annotation or whole-volume 3D annotation remains a long-time debate, especially for heterogeneous GC. We comprehensively compared 2D and 3D radiomic features' representation and discrimination capacity regarding GC, via three tasks (T LNM, lymph node metastasis's prediction; T LVI, lymphovascular invasion's prediction; T pT, pT4 or other pT stages' classification). METHODS: Four-center 539 GC patients were retrospectively enrolled and divided into the training and validation cohorts. From 2D or 3D regions of interest (ROIs) annotated by radiologists, radiomic features were extracted respectively. Feature selection and model construction procedures were customed for each combination of two modalities (2D or 3D) and three tasks. Subsequently, six machine learning models (Model 2D LNM, Model 3D LNM; Model 2D LVI, Model 3D LVI; Model 2D pT, Model 3D pT) were derived and evaluated to reflect modalities' performances in characterizing GC. Furthermore, we performed an auxiliary experiment to assess modalities' performances when resampling spacing different. RESULTS: Regarding three tasks, the yielded areas under the curve (AUCs) were: Model 2D LNM's 0.712 [95% confidence interval, 0.613-0.811], Model 3D LNM's 0.680 (0.584-0.775); Model 2D LVI's 0.677 (0.595-0.761), Model 3D LVI's 0.615 (0.528-0.703); Model 2D pT's 0.840 (0.793-0.875), Model 3D pT's 0.813 (0.779-0.901). Moreover, the auxiliary experiment indicated that Models 2D are statistically advantageous than Models 3D with different resampling spacings. CONCLUSION: Models constructed with 2D radiomic features revealed comparable performances with those constructed with 3D features in characterizing GC. SIGNIFICANCE: Our work indicated that time-saving 2D annotation would be the better choice in GC, and provided a related reference to further radiomics-based researches.

5.
Radiother Oncol ; 151: 1-9, 2020 Jul 04.
Artigo em Inglês | MEDLINE | ID: mdl-32634460

RESUMO

PURPOSE: To estimate the prognostic value of deep learning (DL) magnetic resonance (MR)-based radiomics for stage T3N1M0 nasopharyngeal carcinoma (NPC) patients receiving induction chemotherapy (ICT) prior to concurrent chemoradiotherapy (CCRT). METHODS: A total of 638 stage T3N1M0 NPC patients (training cohort: n = 447; test cohort: n = 191) were enrolled and underwent MRI scans before receiving ICT + CCRT. From the pretreatment MR images, DL-based radiomic signatures were developed to predict disease-free survival (DFS) in an end-to-end way. Incorporating independent clinical prognostic parameters and radiomic signatures, a radiomic nomogram was built through multivariable Cox proportional hazards method. The discriminative performance of the radiomic nomogram was assessed using the concordance index (C-index) and the Kaplan-Meier estimator. RESULTS: Three DL-based radiomic signatures were significantly correlated with DFS in the training (C-index: 0.695-0.731, all p < 0.001) and test (C-index: 0.706-0.755, all p < 0.001) cohorts. Integrating radiomic signatures with clinical factors significantly improved the predictive value compared to the clinical model in the training (C-index: 0.771 vs. 0.640, p < 0.001) and test (C-index: 0.788 vs. 0.625, p = 0.001) cohorts. Furthermore, risk stratification using the radiomic nomogram demonstrated that the high-risk group exhibited short-lived DFS compared to the low-risk group in the training cohort (hazard ratio [HR]: 6.12, p < 0.001), which was validated in the test cohort (HR: 6.90, p < 0.001). CONCLUSIONS: Our DL-based radiomic nomogram may serve as a noninvasive and useful tool for pretreatment prognostic prediction and risk stratification in stage T3N1M0 NPC.

6.
BMC Med Imaging ; 20(1): 77, 2020 07 08.
Artigo em Inglês | MEDLINE | ID: mdl-32641095

RESUMO

BACKGROUND: This study aimed to investigate integrating radiomics with clinical factors in cranial computed tomography (CT) to predict ischemic strokes in patients with silent lacunar infarction (SLI). METHODS: Radiomic features were extracted from baseline cranial CT images of patients with SLI. A least absolute shrinkage and selection operator (LASSO)-Cox regression analysis was used to select significant prognostic factors based on ModelC with clinical factors, ModelR with radiomic features, and ModelCR with both factors. The Kaplan-Meier method was used to compare stroke-free survival probabilities. A nomogram and a calibration curve were used for further evaluation. RESULTS: Radiomic signature (p < 0.01), age (p = 0.09), dyslipidemia (p = 0.03), and multiple infarctions (p = 0.02) were independently associated with future ischemic strokes. ModelCR had the best accuracy with 6-, 12-, and 18-month areas under the curve of 0.84, 0.81, and 0.79 for the training cohort and 0.79, 0.88, and 0.75 for the validation cohort, respectively. Patients with a ModelCR score < 0.17 had higher probabilities of stroke-free survival. The prognostic nomogram and calibration curves of the training and validation cohorts showed acceptable discrimination and calibration capabilities (concordance index [95% confidence interval]: 0.7864 [0.70-0.86]; 0.7140 [0.59-0.83], respectively). CONCLUSIONS: Radiomic analysis based on baseline CT images may provide a novel approach for predicting future ischemic strokes in patients with SLI. Older patients and those with dyslipidemia or multiple infarctions are at higher risk for ischemic stroke and require close monitoring and intensive intervention.

7.
J Immunother Cancer ; 8(2)2020 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-32636239

RESUMO

BACKGROUND: Tumor mutational burden (TMB) is a significant predictor of immune checkpoint inhibitors (ICIs) efficacy. This study investigated the correlation between deep learning radiomic biomarker and TMB, including its predictive value for ICIs treatment response in patients with advanced non-small-cell lung cancer (NSCLC). METHODS: CT images from 327 patients with TMB data (TMB median=6.067 mutations per megabase (range: 0 to 42.151)) were retrospectively collected and randomly divided into a training (n=236), validation (n=26), and test cohort (n=65). We used 3D-densenet to estimate the target tumor area, which used 1020 deep learning features to distinguish High-TMB from Low-TMB patients and establish the TMB radiomic biomarker (TMBRB). The TMBRB was developed in the training cohort combined with validation cohort and evaluated in the test cohort. The predictive value of TMBRB was assessed in a cohort of 123 NSCLC patients who had received ICIs (survival median=462 days (range: 16 to 1128)). RESULTS: TMBRB discriminated between High-TMB and Low-TMB patients in the training cohort (area under the curve (AUC): 0.85, 95% CI: 0.84 to 0.87))and test cohort (AUC: 0.81, 95% CI: 0.77 to 0.85). In this study, the predictive value of TMBRB was better than that of a histological subtype (AUC of training cohort: 0.75, 95% CI: 0.72 to 0.77; AUC of test cohort: 0.71, 95% CI: 0.66 to 0.76) or Radiomic model (AUC of training cohort: 0.75, 95% CI: 0.72 to 0.77; AUC of test cohort: 0.74, 95% CI: 0.69 to 0.79). When predicting immunotherapy efficacy, TMBRB divided patients into a high- and low-risk group with distinctly different overall survival (OS; HR: 0.54, 95% CI: 0.31 to 0.95; p=0.030) and progression-free survival (PFS; HR: 1.78, 95% CI: 1.07 to 2.95; p=0.023). Moreover, TMBRB had a better predictive ability when combined with the Eastern Cooperative Oncology Group performance status (OS: p=0.007; PFS: p=0.003). Visual analysis revealed that tumor microenvironment was important for predicting TMB. CONCLUSION: By combining deep learning technology and CT images, we developed an individual non-invasive biomarker that could distinguish High-TMB from Low-TMB, which might inform decisions on the use of ICIs in patients with advanced NSCLC.

8.
BMC Health Serv Res ; 20(1): 630, 2020 Jul 09.
Artigo em Inglês | MEDLINE | ID: mdl-32646423

RESUMO

BACKGROUND: China's rapidly aging population has led to many challenges related to the health care delivery and financing. Since 2007, the Urban Residents Basic Medical Insurance (URBMI) program has provided financial protection for older adults living in urban areas not already covered by other health insurance schemes. We conducted a national level assessment on this population's health needs and health service utilization. METHODS: Records for 9646 individuals over the age of 60 were extracted for analysis from two National Health Service Surveys conducted in 2008 and 2013. Multiple regression models were used to examine associations between socioeconomic factors, health needs and health service utilization while controlling for demographic characteristics and survey year. RESULTS: Self-reported illness, especially non-communicable diseases (NCDs) increased significantly between 2008 and 2013 regardless of insurance enrollment, age group or income level. In 2013, over 75% of individuals reported at least one NCD. Outpatient services decreased for the uninsured but increased for those with insurance. Middle- and high-income groups with insurance experienced a higher increase in outpatient visits and hospital admissions than the low-income group. Forgone hospital admissions (defined as an admission indicated by a doctor but which was declined or not followed through by the patient) decreased. However, over 20% of individuals had to forgo necessary hospital admissions, and 40% of these cases were due to financial barriers. Outpatient visits and hospital admissions increased between 2008 and 2013, and insured individuals utilized more services than those without insurance. CONCLUSION: After the implementation of URBMI, health service utilization increased and forgone hospital admissions decreased, indicating the program helped to improve access to health services. However, there was still a marked difference in utilization among different income groups, with the high-income group experiencing the greatest increase. This factor calls for further attention to be given to issues related to equity. Prevalence of self-reported NCDs greatly increased among the study population between 2008 and 2013, suggesting that health insurance programs need to ensure they cover sufficient support for the treatment and prevention of NCDs.

9.
Radiother Oncol ; 150: 73-80, 2020 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-32540334

RESUMO

BACKGROUND AND PURPOSE: Risk prediction of overall survival (OS) is crucial for gastric cancer (GC) patients to assess the treatment programs and may guide personalized medicine. A novel deep learning (DL) model was proposed to predict the risk for OS based on computed tomography (CT) images. MATERIALS AND METHODS: We retrospectively collected 640 patients from three independent centers, which were divided into a training cohort (center 1 and center 2, n = 518) and an external validation cohort (center 3, n = 122). We developed a DL model based on the architecture of residual convolutional neural network. We augmented the size of training dataset by image transformations to avoid overfitting. We also developed radiomics and clinical models for comparison. The performance of the three models were comprehensively assessed. RESULTS: Totally 518 patients were prepared by data augmentation and fed into DL model. The trained DL model significantly classified patients into high-risk and low-risk groups in training cohort (P-value <0.001, concordance index (C-index): 0.82, hazard ratio (HR): 9.79) and external validation cohort (P-value <0.001, C-index: 0.78, HR: 11.76). Radiomics model was developed with selected 24 features and clinical model was developed with three significant clinical variables (P-value <0.05). The comparison illustrated DL model had the best performance for risk prediction of OS according to the C-index (training: DL vs Clinical vs Radiomics = 0.82 vs 0.73 vs 0.66; external validation: 0.78 vs 0.71 vs 0.72). CONCLUSION: The DL model is a powerful model for risk assessment, and potentially serves as an individualized recommender for decision-making in GC patients.

10.
Med Phys ; 2020 Jun 26.
Artigo em Inglês | MEDLINE | ID: mdl-32592224

RESUMO

PURPOSE: Preoperative and noninvasive prognosis evaluation remains challenging for gastric cancer. Novel preoperative prognostic biomarkers should be investigated. This study aimed to develop multidetector-row computed tomography (MDCT)-guided prognostic models to direct follow-up strategy and improve prognosis. METHODS: A retrospective dataset of 353 gastric cancer patients were enrolled from two centers and allocated to three cohorts: training cohort (n = 166), internal validation cohort (n = 83), and external validation cohort (n = 104). Quantitative radiomic features were extracted from MDCT images. The least absolute shrinkage and selection operator penalized Cox regression was adopted to construct a radiomic signature. A radiomic nomogram was established by integrating the radiomic signature and significant clinical risk factors. We also built a preoperative tumor-node-metastasis staging model for comparison. All models were evaluated considering the abilities of risk stratification, discrimination, calibration, and clinical use. RESULTS: In the two validation cohorts, the established four-feature radiomic signature showed robust risk stratification power (P = 0.0260 and 0.0003, log-rank test). The radiomic nomogram incorporated radiomic signature, extramural vessel invasion, clinical T stage, and clinical N stage, outperforming all the other models (concordance index = 0.720 and 0.727) with good calibration and decision benefits. Also, the 2-yr disease-free survival (DFS) prediction was most effective (time-dependent area under curve = 0.771 and 0.765). Moreover, subgroup analysis indicated that the radiomic signature was more sensitive in risk stratifying patients with advanced clinical T/N stage. CONCLUSIONS: The proposed MDCT-guided radiomic signature was verified as a prognostic factor for gastric cancer. The radiomic nomogram was a noninvasive auxiliary model for preoperative individualized DFS prediction, holding potential in promoting treatment strategy and clinical prognosis.

11.
Int J Cardiovasc Imaging ; 36(10): 2039-2050, 2020 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-32488454

RESUMO

To explore the superiority of radiomics analysis in the diagnostic performance of coronary computed tomography angiography (CCTA) for identifying myocardial ischaemia and predicting major adverse cardiovascular events (MACE). A total of 105 lesions from 88 patients who underwent CCTA and invasive fractional flow reserve measurement were collected as the training set, and another 31 patients with CCTA and clinical outcome information were used as the validation set. Conventional CCTA features included the stenosis diameter, length, Agatston score and high-risk plaque characteristics. After extracting and selecting radiomics features, the robustness of the radiomics features was examined, and then conventional and radiomics models were established using logistic regressions. The area under the receiver operating characteristic (ROC) curve (AUC) and Net Reclassification Index (NRI) were analysed to compare the discrimination and classification abilities between the two models in both the training and validation sets. A total of 1409 radiomics features were extracted, and three wavelet features were finally screened out. The robustness test showed good stability for the refined radiomics features. Compared with the conventional model, the radiomics model displayed a significantly improved diagnostic performance in the training set (AUC 0.762 vs. 0.631, 95% confidence interval [CI] 0.671-0.853 vs. 0.519-0.742, P = 0.058) but a slightly improved diagnostic performance in the validation set (AUC 0.671 vs. 0.592, 95% CI 0.466-0.875 vs. 0.519-0.742, P = 0.448). The NRI of the radiomics model was increased in both the training and validation sets (NRI 0.198 and 0.238, respectively). Quantitative radiomics analysis was feasible and might help to improve the diagnostic performance of CCTA but is still controversial for predicting MACE.


Assuntos
Angiografia por Tomografia Computadorizada , Angiografia Coronária , Doença da Artéria Coronariana/diagnóstico por imagem , Estenose Coronária/diagnóstico por imagem , Vasos Coronários/diagnóstico por imagem , Reserva Fracionada de Fluxo Miocárdico , Interpretação de Imagem Radiográfica Assistida por Computador , Idoso , Doença da Artéria Coronariana/fisiopatologia , Estenose Coronária/fisiopatologia , Vasos Coronários/fisiopatologia , Estudos de Viabilidade , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Valor Preditivo dos Testes , Prognóstico , Reprodutibilidade dos Testes , Estudos Retrospectivos , Índice de Gravidade de Doença , Análise de Ondaletas
12.
IEEE Rev Biomed Eng ; 2020 Apr 27.
Artigo em Inglês | MEDLINE | ID: covidwho-162739

RESUMO

Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading rapidly around the world, resulting in a massive death toll. Lung infection or pneumonia is the common complication of COVID-19, and imaging techniques, especially computed tomography (CT), have played an important role in diagnosis and treatment assessment of the disease. Herein, we review the imaging characteristics and computing models that have been applied for the management of COVID-19. CT, positron emission tomography - CT (PET/CT), lung ultrasound, and magnetic resonance imaging (MRI) have been used for detection, treatment, and follow-up. The quantitative analysis of imaging data using artificial intelligence (AI) is also explored. Our findings indicate that typical imaging characteristics and their changes can play crucial roles in the detection and management of COVID-19. In addition, AI or other quantitative image analysis methods are urgently needed to maximize the value of imaging in the management of COVID-19.

13.
IEEE Rev Biomed Eng ; PP2020 Apr 27.
Artigo em Inglês | MEDLINE | ID: mdl-32356760

RESUMO

Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading rapidly around the world, resulting in a massive death toll. Lung infection or pneumonia is the common complication of COVID-19, and imaging techniques, especially computed tomography (CT), have played an important role in diagnosis and treatment assessment of the disease. Herein, we review the imaging characteristics and computing models that have been applied for the management of COVID-19. CT, positron emission tomography - CT (PET/CT), lung ultrasound, and magnetic resonance imaging (MRI) have been used for detection, treatment, and follow-up. The quantitative analysis of imaging data using artificial intelligence (AI) is also explored. Our findings indicate that typical imaging characteristics and their changes can play crucial roles in the detection and management of COVID-19. In addition, AI or other quantitative image analysis methods are urgently needed to maximize the value of imaging in the management of COVID-19.

14.
Ann Surg Oncol ; 27(10): 4057-4065, 2020 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-32424585

RESUMO

BACKGROUND AND PURPOSE: Nuclear grades of clear cell renal cell carcinoma (ccRCC) are usually confirmed by invasive methods. Radiomics is a quantitative tool that uses non-invasive medical imaging for tumor diagnosis and prognosis. In this study, a radiomics approach was proposed to analyze the association between preoperative computed tomography (CT) images and nuclear grades of ccRCC. METHODS: Our dataset included 320 ccRCC patients from two centers and was divided into a training set (n = 124), an internal test set (n = 123), and an external test set (n = 73). A radiomic feature set was extracted from unenhanced, corticomedullary phase, and nephrographic phase CT images. The maximizing independent classification information criteria function and recursive feature elimination with cross-validation were used to select effective features. Random forests were used to build a final model for predicting nuclear grades, and area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of radiomic features and models. RESULTS: The radiomic features from the three CT phases could effectively distinguished the four nuclear grades. A combined model, merging radiomic features and clinical characteristics, obtained good predictive performances in the internal test set (AUC 0.77, 0.75, 0.79, and 0.85 for the four grades, respectively), and performance was further confirmed in the external test set, with AUCs of 0.75, 0.68, and 0.73 (no fourth-level data). CONCLUSION: The combination of CT radiomic features and clinical characteristics could discriminate the nuclear grades in ccRCC, which may help in assisting treatment decision making.

15.
J Magn Reson Imaging ; 2020 May 28.
Artigo em Inglês | MEDLINE | ID: mdl-32462799

RESUMO

BACKGROUND: Nuclear grade is of importance for treatment selection and prognosis in patients with clear cell renal cell carcinoma (ccRCC). PURPOSE: To develop and validate an MRI-based radiomic model for preoperative predicting WHO/ISUP nuclear grade in ccRCC. STUDY TYPE: Retrospective. POPULATION: In all, 379 patients with histologically confirmed ccRCC. Training cohort (n = 252) and validation cohort (n = 127) were randomly assigned. FIELD STRENGTH/SEQUENCE: Pretreatment 3.0T renal MRI. Imaging sequences were fat-suppressed T2 WI, contrast-enhanced T1 WI, and diffusion weighted imaging. ASSESSMENT: Three prediction models were developed using selected radiomic features, radiomic and clinicoradiologic characteristics, and a model containing only clinicoradiologic characteristics. Receiver operating characteristic (ROC) curves and area under the curve (AUC) were used to assess the predictive performance of these models in predicting high-grade ccRCC. STATISTICAL TESTS: The least absolute shrinkage and selection operator (LASSO) and minimum redundancy maximum relevance (mRMR) method were used for the selection of radiomic features and clinicoradiologic characteristics, respectively. Multivariable logistic regression analysis was used to develop the radiomic signature of radiomic features and clinicoradiologic model of clinicoradiologic characteristics. RESULTS: The radiomic signature showed good performance in discriminating high-grade (grades 3 and 4) from low-grade (grades 1 and 2) ccRCC, with sensitivity, specificity, and AUC of 77.3%, 80.0%, and 0.842, respectively, in the validation cohort. The radiomic model, combining radiomic signature and clinicoradiologic characteristics, displayed good predictive ability for high-grade with sensitivity, specificity, and accuracy of 63.6%, 93.3%, and 88.2%, respectively, in the validation cohort. The radiomic model showed a significantly better performance than the clinicoradiologic model (P < 0.05). DATA CONCLUSION: Multiparametric MRI-based radiomic model can predict WHO/ISUP grade in patients with ccRCC with satisfying performance, and thus could help the physician to improve treatment decisions. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.

16.
J Magn Reson Imaging ; 52(4): 1102-1109, 2020 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-32212356

RESUMO

BACKGROUND: Gleason score (GS) is a histologic prognostic factor and the basis of treatment decision-making for prostate cancer (PCa). Treatment regimens between lower-grade (GS ≤7) and high-grade (GS >7) PCa differ largely and have great effects on cancer progression. PURPOSE: To investigate the use of different sequences in biparametric MRI (bpMRI) of the prostate gland for noninvasively distinguishing high-grade PCa. STUDY TYPE: Retrospective. POPULATION: In all, 489 patients (training cohort: N = 326; test cohort: N = 163) with PCa between June 2008 and January 2018. FIELD STRENGTH/SEQUENCE: 3.0T, pelvic phased-array coils, bpMRI including T2 -weighted imaging (T2 WI) and diffusion-weighted imaging (DWI); apparent diffusion coefficient map extracted from DWI. ASSESSMENT: The whole prostate gland was delineated. Radiomic features were extracted and selected using the Kruskal-Wallis test, the minimum redundancy-maximum relevance, and the sequential backward elimination algorithm. Two single-sequence radiomic (T2 WI, DWI) and two combined (T2 WI-DWI, T2 WI-DWI-Clinic) models were respectively constructed and validated via logistic regression. STATISTICAL TESTS: The Kruskal-Wallis test and chi-squared test were utilized to evaluate the differences among variable groups. P < 0.05 determined statistical significance. The area under the receiver operating characteristic curve (AUC), specificity, sensitivity, and accuracy were used to evaluate model performance. The Delong test was conducted to compare the differences between the AUCs of all models. RESULT: All radiomic models showed significant (P < 0.001) predictive performances. Between the single-sequence radiomic models, the DWI model achieved the most encouraging results, with AUCs of 0.801 and 0.787 in the training and test cohorts, respectively. For the combined models, the T2 WI-DWI models acquired an AUC of 0.788, which was almost the same with DWI in the test cohort, and no significant difference was found between them (training cohort: P = 0.199; test cohort: P = 0.924). DATA CONCLUSION: Radiomics based on bpMRI can noninvasively identify high-grade PCa before the operation, which is helpful for individualized diagnosis of PCa. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:1102-1109.

17.
Infect Genet Evol ; 81: 104266, 2020 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-32114254

RESUMO

The incidence and mortality of cervical cancer, which mainly results from the infection of human papillomavirus (HPV) is significantly increasing in Xinjiang. According to the previous research, the incidence of HPV-68 in cervical cancer patients in Xinjiang is significantly higher than in other parts of China. HPV E6 and E7 oncoproteins play a crucial role in cervical cancer, and can be used as ideal targets for therapeutic vaccines. Therefore, we analyzed and identified the possible T-cell and B-cell dominant epitopes and various aspects of HPV-68 E6 and E7 oncoproteins, including the physicochemical properties, secondary and tertiary structures using a bioinformatic approach, which provided a basis for designing an effective HPV infection therapeutic vaccine. The results showed that E6 oncoproteins was an unstable and hydrophilic protein, while E7 oncoproteins was unstable and hydrophilic protein. The secondary structure of the E6 oncoproteins consisted of 45.57% alpha helixes, 14.56% extended strands, 4.43% beta turns and 35.44% random coils. The secondary structure of E7 oncoproteins consisted of 35.45% alpha helixes, 17.27% extended strands, 0.91% beta turns and 46.36% random coils. Moreover, our results identified 5 dominant T-cell epitopes and 6 dominant B-cell epitopes in the E6 oncoproteins structure and 5 dominant T-cell epitopes and 3 dominant B-cell epitopes in E7 oncoproteins. In conclusion, this study provides comprehensive biological information about the HPV-68 E6 and E7 oncoproteins, which will lay a theoretical foundation for multi-epitope vaccines against HPV infection.

18.
Pharmacogenomics ; 21(4): 279-291, 2020 03.
Artigo em Inglês | MEDLINE | ID: mdl-32180492

RESUMO

Aim: Concerns for fatal severe cutaneous adverse reactions (SCARs) hamper allopurinol use. Methods and material: We adopted a health system perspective to evaluate the cost-effectiveness of HLA-B*58:01 genotyping before allopurinol initiation. A decision tree compared three treatment strategies in gout patients with chronic kidney disease who have higher risk for SCAR. They were standard allopurinol treatment followed by febuxostat in nonresponders, test-positive patients receive febuxostat while test-negative receive allopurinol and universal use of febuxostat. Results: The first strategy was the most cost effective. Genotyping dominated universal febuxostat use. Time horizon and SCAR incidence were the most influential factors on the incremental cost-effectiveness ratio. Conclusion: HLA-B*58:01 genotyping compared with standard allopurinol-febuxostat sequential treatment does not provide good value for money in gout with chronic kidney disease.

19.
Artigo em Inglês | MEDLINE | ID: mdl-32098125

RESUMO

Background: With support from the Gates Foundation, the Chinese Center for Disease Control and Prevention (China CDC) introduced a new financing model for tuberculosis (TB) care. This paper reviews the development of the associated financing policies and payment methods in three project sites and analyzes the factors impacting on policy implementation and outcomes. Methods: We reviewed policy papers and other relevant documents issued in the project sites. Semi-structured qualitative interviews were conducted with key stakeholders at provincial, city and county levels. Thematic analysis was applied to identify themes and develop interpretations. Results: The China CDC guideline proposed the introduction of a case-based payment based on TB treatment clinical pathways, increased reimbursement rates and financial assistance for the poorest TB patients. Contrary to expectations, TB patients with complications and/or comorbidities were often excluded from the program by hospitals that were concerned the cost of care would exceed the case-based payment ceiling. In addition, doctors frequently prescribed services and/or drugs beyond the coverage of the benefit package for those in the program. Consequently, actual reimbursement rates were low and poor patients still faced a heavy financial burden, though the utilization of services increased, especially by poorer patients. Qualitative interviews revealed three main factors affecting payment policy implementation. They were: hospital managers' concern on the potential for reduced revenue generation; their fear that patients would regard the service provided as sub-standard if they were not prescribed the full range of available treatments; and a lack of mechanisms to effectively monitor and support the implementation process. Conclusions: While the intervention had some success in improving access to TB care, the challenges of implementing the policy in what proved to be an unreceptive and often antagonistic context resulted in divergences from the original design that frustrated its aim of reducing the financial burden on patients.


Assuntos
Assistência à Saúde/economia , Formulação de Políticas , Tuberculose/economia , Tuberculose/terapia , China , Comorbidade , Implementação de Plano de Saúde , Humanos
20.
Eur J Radiol ; 125: 108825, 2020 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-32035324

RESUMO

PURPOSE: To determine if texture features of diffusion weighted imaging (DWI) on MRI of metastatic gastrointestinal stromal tumor (mGIST) have correlation with overall survival (OS). METHOD: Fifty-one GIST patients with metastatic lesions who received imatinib targeted therapy were included. Texture features of the largest metastatic lesion were analyzed using inhouse software. Three types of texture features were assessed: fractal features, gray-level co-occurrence matrix (GLCM) features, and gray-level run-length matrix (GLRLM) features. The features were extracted from the regions of interest (ROIs) on T2-weighted imaging (T2WI), DWI and apparent diffusion coefficient (ADC) maps. Histogram analysis was performed on ADC maps. Patients were followed up until death. Kaplan-Meier analysis was performed to determine the correlation of texture features with OS. The curves of the high- and low-risk groups were compared using log-rank test. The prognostic efficacy of the predictors was assessed by calculating the concordance probability. RESULTS: The median survival time was 43.5 months (range, 3.97-120.90 m). Four DWI and three ADC texture features showed significant correlation with OS on univariate analysis (p < 0.05). DWI_L_GLCM_maximum_probability [hazard ratio (HR): 2.062 (1.357-3.131)], ADC_H_GLRLM_mean [HR: 2.174 (1.457-3.244)], and ADC_O_GLCM_cluster_shade [HR: 1.882 (1.324-2.674)] were identified as representative prognostic indicators. The optimum threshold levels for these three features were 1.19×100, 1.71×10 and 2.19×0.1, respectively. Neither histogram analysis values nor fractal features revealed significant correlation with survival status (p > 0.05). CONCLUSIONS: Texture features of the mGIST on DWI exhibited correlation with overall survival. High-grade heterogeneity was associated with poor prognosis.

SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA