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1.
PLoS Med ; 16(11): e1002975, 2019 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-31743352

RESUMO

BACKGROUND: The Sustainable Development Goals (SDGs), adopted by all United Nations (UN) member states in 2015, established a set of bold and ambitious health-related targets to achieve by 2030. Understanding China's progress toward these targets is critical to improving population health for its 1.4 billion people. METHODS AND FINDINGS: We used estimates from the Global Burden of Disease (GBD) Study 2016, national surveys and surveillance data from China, and qualitative data. Twenty-eight of the 37 indicators included in the GBD Study 2016 were analyzed. We developed an attainment index of health-related SDGs, a scale of 0-100 based on the values of indicators. The projection model is adjusted based on the one developed by the GBD Study 2016 SDG collaborators. We found that China has achieved several health-related SDG targets, including decreasing neonatal and under-5 mortality rates and the maternal mortality ratios and reducing wasting and stunting for children. However, China may only achieve 12 out of the 28 health-related SDG targets by 2030. The number of target indicators achieved varies among provinces and municipalities. In 2016, among the seven measured health domains, China performed best in child nutrition and maternal and child health and reproductive health, with the attainment index scores of 93.0 and 91.8, respectively, followed by noncommunicable diseases (NCDs) (69.4), road injuries (63.6), infectious diseases (63.0), environmental health (62.9), and universal health coverage (UHC) (54.4). There are daunting challenges to achieve the targets for child overweight, infectious diseases, NCD risk factors, and environmental exposure factors. China will also have a formidable challenge in achieving UHC, particularly in ensuring access to essential healthcare for all and providing adequate financial protection. The attainment index of child nutrition is projected to drop to 80.5 by 2025 because of worsening child overweight. The index of NCD risk factors is projected to drop to 38.8 by 2025. Regional disparities are substantial, with eastern provinces generally performing better than central and western provinces. Sex disparities are clear, with men at higher risk of excess mortality than women. The primary limitations of this study are the limited data availability and quality for several indicators and the adoption of "business-as-usual" projection methods. CONCLUSION: The study found that China has made good progress in improving population health, but challenges lie ahead. China has substantially improved the health of children and women and will continue to make good progress, although geographic disparities remain a great challenge. Meanwhile, China faced challenges in NCDs, mental health, and some infectious diseases. Poor control of health risk factors and worsening environmental threats have posed difficulties in further health improvement. Meanwhile, an inefficient health system is a barrier to tackling these challenges among such a rapidly aging population. The eastern provinces are predicted to perform better than the central and western provinces, and women are predicted to be more likely than men to achieve these targets by 2030. In order to make good progress, China must take a series of concerted actions, including more investments in public goods and services for health and redressing the intracountry inequities.

2.
Eur Radiol ; 2019 Oct 30.
Artigo em Inglês | MEDLINE | ID: mdl-31673835

RESUMO

PURPOSE: To develop a radiomics-based model to stratify the risk of early progression (local/regional recurrence or metastasis) among patients with hypopharyngeal cancer undergoing chemoradiotherapy and modify their pretreatment plans. MATERIALS AND METHODS: We randomly assigned 113 patients into two cohorts: training (n = 80) and validation (n = 33). The radiomic significant features were selected in the training cohort using least absolute shrinkage and selection operator and Akaike information criterion methods, and they were used to build the radiomic model. The concordance index (C-index) was applied to evaluate the model's prognostic performance. A Kaplan-Meier analysis and the log-rank test were used to assess risk stratification ability of models in predicting progression. A nomogram was plotted to predict individual risk of progression. RESULTS: Composed of four significant features, the radiomic model showed good performance in stratifying patients into high- and low-risk groups of progression in both the training and validation cohorts (log-rank test, p = 0.00016, p = 0.0063, respectively). Peripheral invasion and metastasis were selected as significant clinical variables. The combined radiomic-clinical model showed good discriminative performance, with C-indices 0.804 (95% confidence interval (CI), 0.688-0.920) and 0.756 (95% CI, 0.605-0.907) in the training and validation cohorts, respectively. The median progression-free survival (PFS) in the high-risk group was significantly shorter than that in the low-risk group in the training (median PFS, 9.5 m and 19.0 m, respectively; p [log-rank] < 0.0001) and validation (median PFS, 11.3 m and 22.5 m, respectively; p [log-rank] = 0.0063) cohorts. CONCLUSIONS: A radiomics-based model was established to predict the risk of progression in hypopharyngeal cancer with chemoradiotherapy. KEY POINTS: • Clinical information showed limited performance in stratifying the risk of progression among patients with hypopharyngeal cancer. • Imaging features extracted from CECT and NCCT images were independent predictors of PFS. • We combined significant features and valuable clinical variables to establish a nomogram to predict individual risk of progression.

3.
BMC Med ; 17(1): 190, 2019 10 23.
Artigo em Inglês | MEDLINE | ID: mdl-31640711

RESUMO

BACKGROUND: In locoregionally advanced nasopharyngeal carcinoma (LANPC) patients, variance of tumor response to induction chemotherapy (ICT) was observed. We developed and validated a novel imaging biomarker to predict which patients will benefit most from additional ICT compared with chemoradiotherapy (CCRT) alone. METHODS: All patients, including retrospective training (n = 254) and prospective randomized controlled validation cohorts (a substudy of NCT01245959, n = 248), received ICT+CCRT or CCRT alone. Primary endpoint was failure-free survival (FFS). From the multi-parameter magnetic resonance images of the primary tumor at baseline, 819 quantitative 2D imaging features were extracted. Selected key features (according to their interaction effect between the two treatments) were combined into an Induction Chemotherapy Outcome Score (ICTOS) with a multivariable Cox proportional hazards model using modified covariate method. Kaplan-Meier curves and significance test for treatment interaction were used to evaluate ICTOS, in both cohorts. RESULTS: Three imaging features were selected and combined into ICTOS to predict treatment outcome for additional ICT. In the matched training cohort, patients with a high ICTOS had higher 3-year and 5-year FFS in ICT+CCRT than CCRT subgroup (69.3% vs. 45.6% for 3-year FFS, and 64.0% vs. 36.5% for 5-year FFS; HR = 0.43, 95% CI = 0.25-0.74, p = 0.002), whereas patients with a low ICTOS had no significant difference in FFS between the subgroups (p = 0.063), with a significant treatment interaction (pinteraction <  0.001). This trend was also found in the validation cohort with high (n = 73, ICT+CCRT 89.7% and 89.7% vs. CCRT 61.8% and 52.8% at 3-year and 5-year; HR = 0.17, 95% CI = 0.06-0.51, p <  0.001) and low ICTOS (n = 175, p = 0.31), with a significant treatment interaction (pinteraction = 0.019). Compared with 12.5% and 16.6% absolute benefit in the validation cohort (3-year FFS from 69.9 to 82.4% and 5-year FFS from 63.4 to 80.0% from additional ICT), high ICTOS group in this cohort had 27.9% and 36.9% absolute benefit. Furthermore, no significant survival improvement was found from additional ICT in both groups after stratifying low ICTOS patients into low-risk and high-risks groups, by clinical risk factors. CONCLUSION: An imaging biomarker, ICTOS, as proposed, identified patients who were more likely to gain additional survival benefit from ICT+CCRT (high ICTOS), which could influence clinical decisions, such as the indication for ICT treatment. TRIAL REGISTRATION: ClinicalTrials.gov , NCT01245959 . Registered 23 November 2010.

4.
Eur J Radiol ; 118: 231-238, 2019 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-31439247

RESUMO

PURPOSE: Cervical lymph node (LN) metastasis of papillary thyroid carcinoma (PTC) is critical for treatment and prognosis. We explored the feasibility of using radiomics to preoperatively predict cervical LN metastasis in PTC patients. METHOD: Total 221 PTC patients (training cohort: n = 154; validation cohort: n = 67; divided randomly at the ratio of 7:3) were enrolled and divided into 2 groups based on LN pathologic diagnosis (N0: n = 118; N1a and N1b: n = 88 and 15, respectively). We extracted 546 radiomic features from non-contrast and venous contrast-enhanced computed tomography (CT) images. We selected 8 groups of candidate feature sets by minimum redundancy maximum relevance (mRMR), and obtained 8 radiomic sub-signatures by support vector machine (SVM) to construct the radiomic signature. Incorporating the radiomic signature, CT-reported cervical LN status and clinical risk factors, a nomogram was constructed using multivariable logistic regression. The nomogram's calibration, discrimination, and clinical utility were assessed. RESULTS: The radiomic signature was associated significantly with cervical LN status (p < 0.01 for both training and validation cohorts). The radiomic signature showed better predictive performance than any radiomic sub-signatures devised by SVM. Addition of radiomic signature to the nomogram improved the predictive value (area under the curve (AUC), 0.807 to 0.867) in the training cohort; this was confirmed in an independent validation cohort (AUC, 0.795 to 0.822). Good agreement was observed using calibration curves in both cohorts. Decision curve analysis demonstrated the radiomic nomogram was worthy of clinical application. CONCLUSIONS: Our radiomic nomogram improved the preoperative prediction of cervical LN metastasis in PTC patients.

5.
Clin Transl Gastroenterol ; 10(8): e00070, 2019 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-31373932

RESUMO

OBJECTIVES: Models should be developed to assist choice between liver resection (LR) and transarterial chemoembolization (TACE) for hepatocellular carcinoma. METHODS: After separating 520 cases from 5 hospitals into training (n = 302) and validation (n = 218) data sets, we weighted the cases to control baseline difference and ensured the causal effect between treatments (LR and TACE) and estimated progression-free survival (PFS) difference. A noninvasive PFS model was constructed with clinical factors, radiological characteristics, and radiomic features. We compared our model with other 4 state-of-the-art models. Finally, patients were classified into subgroups with and without significant PFS difference between treatments. RESULTS: Our model included treatments, age, sex, modified Barcelona Clinic Liver Cancer stage, fusion lesions, hepatocellular carcinoma capsule, and 3 radiomic features, with good discrimination and calibrations (area under the curve for 3-year PFS was 0.80 in the training data set and 0.75 in the validation data set; similar results were achieved in 1- and 2-year PFS). The model had better accuracy than the other 4 models. A nomogram was built, with different scores assigned for LR and TACE. Separated by the threshold of score difference between treatments, for some patients, LR provided longer PFS and might be the better option (training: hazard ratio [HR] = 0.50, P = 0.014; validation: HR = 0.52, P = 0.026); in the others, LR provided similar PFS with TACE (training: HR = 0.84, P = 0.388; validation: HR = 1.14, P = 0.614). TACE may be better because it was less invasive. DISCUSSION: We propose an individualized model predicting PFS difference between LR and TACE to assist in the optimal treatment choice.

6.
BMC Med Imaging ; 19(1): 63, 2019 08 08.
Artigo em Inglês | MEDLINE | ID: mdl-31395012

RESUMO

BACKGROUND: To investigate the value of predictive nomogram in optimizing computed tomography (CT)-based differential diagnosis of primary progressive pulmonary tuberculosis (TB) from community-acquired pneumonia (CAP) in children. METHODS: This retrospective study included 53 patients with clinically confirmed pulmonary TB and 62 patients with CAP. Patients were grouped at random according to a 3:1 ratio (primary cohort n = 86, validation cohort n = 29). A total of 970 radiomic features were extracted from CT images and key features were screened out to build radiomic signatures using the least absolute shrinkage and selection operator algorithm. A predictive nomogram was developed based on the signatures and clinical factors, and its performance was assessed by the receiver operating characteristic curve, calibration curve, and decision curve analysis. RESULTS: Initially, 5 and 6 key features were selected to establish a radiomic signature from the pulmonary consolidation region (RS1) and a signature from lymph node region (RS2), respectively. A predictive nomogram was built combining RS1, RS2, and a clinical factor (duration of fever). Its classification performance (AUC = 0.971, 95% confidence interval [CI]: 0.912-1) was better than the senior radiologist's clinical judgment (AUC = 0.791, 95% CI: 0.636-0.946), the clinical factor (AUC = 0.832, 95% CI: 0.677-0.987), and the combination of RS1 and RS2 (AUC = 0.957, 95% CI: 0.889-1). The calibration curves indicated a good consistency of the nomogram. Decision curve analysis demonstrated that the nomogram was useful in clinical settings. CONCLUSIONS: A CT-based predictive nomogram was proposed and could be conveniently used to differentiate pulmonary TB from CAP in children.

7.
Mediators Inflamm ; 2019: 6519427, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31316301

RESUMO

This study is to investigate the role of regulatory B (Breg) cells in cervical cancer. In total, 70 cases of cervical cancer, 52 cases of cervical intraepithelial neoplasia (CIN), and 40 normal controls were enrolled. The percentage of Breg cells was detected by flow cytometry. Serum levels of IL-10 were measured by ELISA. The correlation between Breg cells and the clinical characterizations of cervical cancer was analyzed. The inhibition effect of Breg cells on CD8+ T cells was tested by blocking IL-10 in vitro. The percentage of CD19+CD5+CD1d+ Breg cells and the level of IL-10 of patients with cervical cancer or CIN were significantly higher than those in the control group (P < 0.05). And the postoperative levels of Breg cells and IL-10 were significantly lower than the preoperative levels (P < 0.05). Breg cells and the IL-10 level were positively correlated in cervical cancer patients (r = 0.516). In addition, the Breg cell percentage was closely related to the FIGO stages, lymph node metastasis, tumor differentiation, HPV infection, and the tumor metastasis of cervical cancer (P < 0.05). The Breg cell percentage was negatively correlated with CD8+ T cells of cervical cancer patients (r = -0.669). The level of IL-10 in the culture supernatant of Bregs treated with CpG was significantly higher than that of non-Bregs (P < 0.05). After coculture with Bregs, the quantity of CD8+ T cells to secrete perforin and Granzyme B was significantly decreased, and this effect was reversed after blocking IL-10 by a specific antibody. Breg cells are elevated in cervical cancer and associated with disease progression and metastasis. Moreover, they can inhibit the cytotoxicity of CD8+ T cells.

8.
Transl Oncol ; 12(9): 1229-1236, 2019 Jul 04.
Artigo em Inglês | MEDLINE | ID: mdl-31280094

RESUMO

PURPOSE: To build radiomic prediction models using contrast-enhanced computed tomography (CE-CT) to preoperatively predict malignant potential and mitotic count of gastrointestinal stromal tumors (GISTs). PATIENTS AND METHODS: A total of 333 GISTs patients were retrospectively included in our study. Radiomic features were extracted from the preoperative CE-CT images. According to postoperative pathology, patients were categorized by malignant potential and mitotic count, respectively. The most valuable radiomic features were chosen to build a logistic regression model to predict the malignant potential and a random forest classifier model to predict the mitotic count. The performance of radiomic models was assessed with the receiver operating characteristics curve. Our study further developed a radiomic nomogram to preoperatively predict malignant potential in a personalized way for patients with GISTs. RESULTS: The predictive model was built to discriminate high- from low-malignant potential GISTs with an area under the curve (AUC) of 0.882 (95% CI 0.823-0.942) in the training set and 0.920 (95% CI 0.870-0.971) in the validation set. Moreover, the other radiomic model was built to differentiate high- from low-mitotic count GISTs with an AUC of 0.820 (95% CI 0.753-0.887) in the training set and 0.769 (95% CI 0.654-0.883) in the validation set. CONCLUSION: The radiomic models using CE-CT showed a good predictive performance for preoperative risk stratification of GISTs and hold great potential for personalized clinical decision making.

9.
Eur J Radiol ; 116: 128-134, 2019 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-31153553

RESUMO

OBJECTIVES: To noninvasively differentiate meningioma grades by deep learning radiomics (DLR) model based on routine post-contrast MRI. METHODS: We enrolled 181 patients with histopathologic diagnosis of meningioma who received post-contrast MRI preoperative examinations from 2 hospitals (99 in the primary cohort and 82 in the validation cohort). All the tumors were segmented based on post-contrast axial T1 weighted images (T1WI), from which 2048 deep learning features were extracted by the convolutional neural network. The random forest algorithm was used to select features with importance values over 0.001, upon which a deep learning signature was built by a linear discriminant analysis classifier. The performance of our DLR model was assessed by discrimination and calibration in the independent validation cohort. For comparison, a radiomic model based on hand-crafted features and a fusion model were built. RESULTS: The DLR signature comprised 39 deep learning features and showed good discrimination performance in both the primary and validation cohorts. The area under curve (AUC), sensitivity, and specificity for predicting meningioma grades were 0.811(95% CI, 0.635-0.986), 0.769, and 0.898 respectively in the validation cohort. DLR performance was superior over the hand-crafted features. Calibration curves of DLR model showed good agreements between the prediction probability and the observed outcome of high-grade meningioma. CONCLUSIONS: Using routine MRI data, we developed a DLR model with good performance for noninvasively individualized prediction of meningioma grades, which achieved a quantization capability superior over the hand-crafted features. This model has potential to guide and facilitate the clinical decision-making of whether to observe or to treat patients by providing prognostic information.


Assuntos
Aprendizado Profundo , Imagem por Ressonância Magnética/métodos , Neoplasias Meníngeas/diagnóstico por imagem , Neoplasias Meníngeas/patologia , Meningioma/diagnóstico por imagem , Meningioma/patologia , Cuidados Pré-Operatórios/métodos , Adolescente , Adulto , Idoso , Algoritmos , Área Sob a Curva , Estudos de Coortes , Feminino , Humanos , Masculino , Meninges/diagnóstico por imagem , Meninges/patologia , Pessoa de Meia-Idade , Gradação de Tumores , Prognóstico , Reprodutibilidade dos Testes , Estudos Retrospectivos , Sensibilidade e Especificidade , Adulto Jovem
10.
Infect Dis Poverty ; 8(1): 44, 2019 Jun 11.
Artigo em Inglês | MEDLINE | ID: mdl-31182164

RESUMO

BACKGROUND: Tuberculosis (TB) prevalence is closely associated with poverty in China, and poor patients face more barriers to treatment. Using an insurance-based approach, the China-Gates TB program Phase II was implemented between 2012 and 2014 in three cities in China to improve access to TB care and reduce the financial burden on patients, particularly among the poor. This study aims to assess the program effects on service use, and its equity impact across different income groups. METHODS: Data from 788 and 775 patients at baseline and final evaluation were available for analysis respectively. Inpatient and outpatient service utilization, treatment adherence, and patient satisfaction were assessed before and after the program, across different income groups (extreme poverty, moderate poverty and non-poverty), and in various program cities, using descriptive statistics and multi-variate regression models. Key stakeholder interviews were conducted to qualitatively evaluate program implementation and impacts. RESULTS: After program implementation, the hospital admission rate increased more for the extreme poverty group (48.5 to 70.7%) and moderate poverty group (45.0 to 68.1%), compared to the non-poverty group (52.9 to 64.3%). The largest increase in the number of outpatient visits was also for the extreme poverty group (4.6 to 5.7). The proportion of patients with good medication adherence increased by 15 percentage points in the extreme poverty group and by ten percentage points in the other groups. Satisfaction rates were high in all groups. Qualitative feedback from stakeholders also suggested that increased reimbursement rates, easier reimbursement procedures, and allowance improved patients' service utilization. Implementation of case-based payment made service provision more compliant to clinical pathways. CONCLUSION: Patients in extreme or moderate poverty benefited more from the program compared to a non-poverty group, indicating improved equity in TB service access. The pro-poor design of the program provides important lessons to other TB programs in China and other countries to better address TB care for the poor.


Assuntos
Assistência à Saúde/economia , Acesso aos Serviços de Saúde/economia , Aceitação pelo Paciente de Cuidados de Saúde , Satisfação do Paciente , Tuberculose/economia , Tuberculose/psicologia , Adulto , Idoso , Antituberculosos/economia , Antituberculosos/uso terapêutico , China , Estudos Transversais , Feminino , Hospitalização/economia , Hospitalização/estatística & dados numéricos , Humanos , Reembolso de Seguro de Saúde , Modelos Logísticos , Masculino , Adesão à Medicação , Pessoa de Meia-Idade , Satisfação do Paciente/economia , Pobreza/estatística & dados numéricos , Avaliação de Programas e Projetos de Saúde , Inquéritos e Questionários , Tuberculose/tratamento farmacológico
11.
Talanta ; 200: 408-414, 2019 Aug 01.
Artigo em Inglês | MEDLINE | ID: mdl-31036202

RESUMO

Extracellular vesicles (EVs) are cell-excreted membrane particles existing in a variety of biological fluids. As potential noninvasive biomarkers, EVs have received wide attention in recent years. However, usual EVs assays are complex, time-consuming and costly, thus limiting their clinical utility. Simple and rapid EVs quantification within biological fluids remains challenging. Here, we developed a simple, rapid strategy for EVs quantification, which combined with lateral flow assay and membrane biotinylation strategy. By utilizing biotin-functionalized phosphatidylethanolamine (DSPE-PEG-Biotin), the membrane of EVs could be successfully modified with biotin under strong hydrophobic interactions. Subsequently, based on the high affinity between streptavidin and biotin, quantification assay was achieved by lateral flow assay with fluorescent nanospheres (FNs) as a reporter. Biotinylation of biogenic EVs could be reached to 85%. This proposed method enables sensitive detection of 2.0 × 103 particles/µL. The whole procedure time was within 1 h. Furthermore, this approach was used to detect EVs in biological samples, demonstrating potential clinical applications.


Assuntos
Biotina/química , Biotinilação , Vesículas Extracelulares/química , Corantes Fluorescentes/química , Nanosferas/química , Fosfatidiletanolaminas/química , Humanos , Interações Hidrofóbicas e Hidrofílicas , Tamanho da Partícula , Espectrometria de Fluorescência , Propriedades de Superfície , Células Tumorais Cultivadas
12.
Eur J Radiol ; 115: 16-21, 2019 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-31084754

RESUMO

PURPOSE: To evaluate the performance of a multi-parametric MRI (mp-MRI)-based radiomics signature for discriminating between clinically significant prostate cancer (csPCa) and insignificant PCa (ciPCa). MATERIALS AND METHODS: Two hundred and eighty patients with pathology-proven PCa were enrolled and were randomly divided into training and test cohorts. Eight hundred and nineteen radiomics features were extracted from mp-MRI for each patient. The minority group in the training cohort was balanced via the synthetic minority over-sampling technique (SMOTE) method. We used minimum-redundancy maximum-relevance (mRMR) selection and the LASSO algorithm for feature selection and radiomics signature building. The classification performance of the radiomics signature for csPCa and ciPCa was evaluated by receiver operating characteristic curve analysis in the training and test cohorts. RESULTS: Nine features were selected for the radiomics signature building. Significant differences in the radiomics signature existed between the csPCa and ciPCa groups in both the training and test cohorts (p < 0.01 for both). The AUC, sensitivity and specificity of the radiomics signature were 0.872 (95% CI: 0.823-0.921), 0.883, and 0.753, respectively, in the training cohort, and 0.823 (95% CI: 0.669-0.976), 0.841, and 0.727, respectively, in the test cohort. CONCLUSION: Mp-MRI-based radiomics signature have the potential to noninvasively discriminate between csPCa and ciPCa.


Assuntos
Aprendizado de Máquina , Neoplasias da Próstata/patologia , Idoso , Algoritmos , Humanos , Imagem por Ressonância Magnética/métodos , Masculino , Gradação de Tumores , Curva ROC , Estudos Retrospectivos , Sensibilidade e Especificidade
13.
Eur Radiol ; 29(7): 3325-3337, 2019 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-30972543

RESUMO

OBJECTIVES: To develop and validate a radiomics nomogram to preoperative prediction of isocitrate dehydrogenase (IDH) genotype for astrocytomas, which might contribute to the pretreatment decision-making and prognosis evaluating. METHODS: One hundred five astrocytomas (Grades II-IV) with contrast-enhanced T1-weighted imaging (CE-T1WI), T2 fluid-attenuated inversion recovery (T2FLAIR), and apparent diffusion coefficient (ADC) map were enrolled in this study (training cohort: n = 74; validation cohort: n = 31). IDH1/2 genotypes were determined using Sanger sequencing. A total of 3882 radiomics features were extracted. Support vector machine algorithm was used to build the radiomics signature on the training cohort. Incorporating radiomics signature and clinico-radiological risk factors, the radiomics nomogram was developed. Receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to assess these models. Kaplan-Meier survival analysis and log rank test were performed to assess the prognostic value of the radiomics nomogram. RESULTS: The radiomics signature was built by six selected radiomics features and yielded AUC values of 0.901 and 0.888 in the training and validation cohorts. The radiomics nomogram based on the radiomics signature and age performed better than the clinico-radiological model (training cohort, AUC = 0.913 and 0.817; validation cohort, AUC = 0.900 and 0.804). Additionally, the survival analysis showed that prognostic values of the radiomics nomogram and IDH genotype were similar (log rank test, p < 0.001; C-index = 0.762 and 0.687; z-score test, p = 0.062). CONCLUSIONS: The radiomics nomogram might be a useful supporting tool for the preoperative prediction of IDH genotype for astrocytoma, which could aid pretreatment decision-making. KEY POINTS: • The radiomics signature based on multiparametric and multiregional MRI images could predict IDH genotype of Grades II-IV astrocytomas. • The radiomics nomogram performed better than the clinico-radiological model, and it might be an easy-to-use supporting tool for IDH genotype prediction. • The prognostic value of the radiomics nomogram was similar with that of the IDH genotype, which might contribute to prognosis evaluating.


Assuntos
Astrocitoma/genética , Isocitrato Desidrogenase/genética , Nomogramas , Adulto , Algoritmos , Área Sob a Curva , Astrocitoma/diagnóstico por imagem , Astrocitoma/patologia , Astrocitoma/cirurgia , Sistemas de Apoio a Decisões Clínicas , Imagem de Difusão por Ressonância Magnética/métodos , Feminino , Genótipo , Humanos , Interpretação de Imagem Assistida por Computador/métodos , Estimativa de Kaplan-Meier , Imagem por Ressonância Magnética/métodos , Masculino , Pessoa de Meia-Idade , Gradação de Tumores , Cuidados Pré-Operatórios/métodos , Prognóstico , Curva ROC , Reprodutibilidade dos Testes , Estudos Retrospectivos , Fatores de Risco , Máquina de Vetores de Suporte , Adulto Jovem
14.
Cancer Imaging ; 19(1): 21, 2019 Apr 26.
Artigo em Inglês | MEDLINE | ID: mdl-31027510

RESUMO

BACKGROUND: Predicting early recurrence (ER) after radical therapy for HCC patients is critical for the decision of subsequent follow-up and treatment. Radiomic features derived from the medical imaging show great potential to predict prognosis. Here we aim to develop and validate a radiomics nomogram that could predict ER after curative ablation. METHODS: Total 184 HCC patients treated from August 2007 to August 2014 were included in the study and were divided into the training (n = 129) and validation(n = 55) cohorts randomly. The endpoint was recurrence free survival (RFS). A set of 647 radiomics features were extracted from the 3 phases contrast enhanced computed tomography (CECT) images. The minimum redundancy maximum relevance algorithm (MRMRA) was used for feature selection. The least absolute shrinkage and selection operator (LASSO) Cox regression model was used to build a radiomics signature. Recurrence prediction models were built using clinicopathological factors and radiomics signature, and a prognostic nomogram was developed and validated by calibration. RESULTS: Among the four radiomics models, the portal venous phase model obtained the best performance in the validation subgroup (C-index = 0.736 (95%CI:0.726-0.856)). When adding the clinicopathological factors to the models, the portal venous phase combined model also yielded the best predictive performance for training (C-index = 0.792(95%CI:0.727-0.857) and validation (C-index = 0.755(95%CI:0.651-0.860) subgroup. The combined model indicated a more distinct improvement of predictive power than the simple clinical model (ANOVA, P < 0.0001). CONCLUSIONS: This study successfully built a radiomics nomogram that integrated clinicopathological and radiomics features, which can be potentially used to predict ER after curative ablation for HCC patients.


Assuntos
Carcinoma Hepatocelular/diagnóstico por imagem , Neoplasias Hepáticas/diagnóstico por imagem , Nomogramas , Tomografia Computadorizada por Raios X , Idoso , Carcinoma Hepatocelular/epidemiologia , Carcinoma Hepatocelular/patologia , Feminino , Humanos , Neoplasias Hepáticas/epidemiologia , Neoplasias Hepáticas/patologia , Masculino , Pessoa de Meia-Idade , Prognóstico
15.
Environ Sci Pollut Res Int ; 26(17): 17591-17607, 2019 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-31025285

RESUMO

The indirect carbon emission embodied in the intermediate input is also an important indicator of assessing a producer's carbon emissions. Structural analysis of indirect carbon emissions is helpful to understand the responsibilities between producers and pay efforts to key areas. The aim of this study is to analyze indirect carbon emissions embodied in intermediate input between sectors and explore the distribution structure of indirect carbon emissions flow network (namely, ICEFN). Based on the modified input-output model and complex network theory, this study constructed four directed and weighted ICEFNs with 28 sectors from 1997 to 2012. The results show that indirect carbon emissions between sectors are significantly higher than direct carbon emissions, accounting for nearly 70% of the total carbon emissions of China. Second, we analyzed the embodied carbon emission intensity (namely, ECI) of each sector. Although the ECI has been decreasing over time, the decrease has increasingly diminished, which indicates that the additional carbon emission reductions are more difficult. Third, we identified the key sectors which play different roles in the ICEFNs. Meanwhile, we studied the key paths which show more closed relationships between some sectors in ICEFNs. Finally, based on the above analysis, we made policy recommendations.


Assuntos
Poluentes Atmosféricos/análise , Monitoramento Ambiental/métodos , Poluição do Ar/estatística & dados numéricos , Carbono/análise , Dióxido de Carbono/análise , China
16.
Eur J Radiol ; 114: 38-44, 2019 May.
Artigo em Inglês | MEDLINE | ID: mdl-31005174

RESUMO

PURPOSE: To investigate the efficiency of radiomics signature in discriminating between benign and malignant prostate lesions with similar biparametric magnetic resonance imaging (bp-MRI) findings. EXPERIMENTAL DESIGN: Our study consisted of 331 patients underwent bp-MRI before pathological examination from January 2013 to November 2016. Radiomics features were extracted from peripheral zone (PZ), transition zone (TZ), and lesion areas segmented on images obtained by T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and its derivative apparent-diffusion coefficient (ADC) imaging. The individual prediction model, built using the clinical data and biparametric MRI features (Bp signature), was prepared using data of 232 patients and validated using data of 99 patients. The predictive performance was calculated and demonstrated using receiver operating characteristic (ROC) curves, calibration curves, and decision curves. RESULTS: The Bp signature, based on the six selected radiomics features of bp-MRI, showed better discrimination in the validation cohort (area under the curve [AUC], 0.92) than on each subcategory images (AUC, 0.81 on T2WI; AUC, 0.77 on DWI; AUC, 0.89 on ADC). The differential diagnostic efficiency was poorer with the clinical model (AUC, 0.73), built using the selected independent clinical risk factors with statistical significance (P < 0.05), than with the Bp signature. Discrimination efficiency improved when including the Bp signature and clinical factors [i.e., the individual prediction model (AUC, 0.93)]. CONCLUSION: The Bp signature, based on bp-MRI, performed better than each single imaging modality. The individual prediction model including the radiomics signatures and clinical factors showed better preoperative diagnostic performance, which could contribute to clinical individualized treatment.


Assuntos
Neoplasias da Próstata/diagnóstico , Adulto , Idoso , Área Sob a Curva , Estudos de Coortes , Diagnóstico Diferencial , Imagem de Difusão por Ressonância Magnética/métodos , Humanos , Imagem por Ressonância Magnética/métodos , Masculino , Pessoa de Meia-Idade , Curva ROC , Estudos Retrospectivos
17.
Clin Cancer Res ; 25(14): 4271-4279, 2019 Jul 15.
Artigo em Inglês | MEDLINE | ID: mdl-30975664

RESUMO

PURPOSE: We aimed to evaluate the value of deep learning on positron emission tomography with computed tomography (PET/CT)-based radiomics for individual induction chemotherapy (IC) in advanced nasopharyngeal carcinoma (NPC). EXPERIMENTAL DESIGN: We constructed radiomics signatures and nomogram for predicting disease-free survival (DFS) based on the extracted features from PET and CT images in a training set (n = 470), and then validated it on a test set (n = 237). Harrell's concordance indices (C-index) and time-independent receiver operating characteristic (ROC) analysis were applied to evaluate the discriminatory ability of radiomics nomogram, and compare radiomics signatures with plasma Epstein-Barr virus (EBV) DNA. RESULTS: A total of 18 features were selected to construct CT-based and PET-based signatures, which were significantly associated with DFS (P < 0.001). Using these signatures, we proposed a radiomics nomogram with a C-index of 0.754 [95% confidence interval (95% CI), 0.709-0.800] in the training set and 0.722 (95% CI, 0.652-0.792) in the test set. Consequently, 206 (29.1%) patients were stratified as high-risk group and the other 501 (70.9%) as low-risk group by the radiomics nomogram, and the corresponding 5-year DFS rates were 50.1% and 87.6%, respectively (P < 0.0001). High-risk patients could benefit from IC while the low-risk could not. Moreover, radiomics nomogram performed significantly better than the EBV DNA-based model (C-index: 0.754 vs. 0.675 in the training set and 0.722 vs. 0.671 in the test set) in risk stratification and guiding IC. CONCLUSIONS: Deep learning PET/CT-based radiomics could serve as a reliable and powerful tool for prognosis prediction and may act as a potential indicator for individual IC in advanced NPC.

18.
Theranostics ; 9(5): 1303-1322, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-30867832

RESUMO

Medical imaging can assess the tumor and its environment in their entirety, which makes it suitable for monitoring the temporal and spatial characteristics of the tumor. Progress in computational methods, especially in artificial intelligence for medical image process and analysis, has converted these images into quantitative and minable data associated with clinical events in oncology management. This concept was first described as radiomics in 2012. Since then, computer scientists, radiologists, and oncologists have gravitated towards this new tool and exploited advanced methodologies to mine the information behind medical images. On the basis of a great quantity of radiographic images and novel computational technologies, researchers developed and validated radiomic models that may improve the accuracy of diagnoses and therapy response assessments. Here, we review the recent methodological developments in radiomics, including data acquisition, tumor segmentation, feature extraction, and modelling, as well as the rapidly developing deep learning technology. Moreover, we outline the main applications of radiomics in diagnosis, treatment planning and evaluations in the field of oncology with the aim of developing quantitative and personalized medicine. Finally, we discuss the challenges in the field of radiomics and the scope and clinical applicability of these methods.

19.
Infect Dis Poverty ; 8(1): 21, 2019 Mar 24.
Artigo em Inglês | MEDLINE | ID: mdl-30904025

RESUMO

BACKGROUND: In response to the high financial burden of health services facing tuberculosis (TB) patients in China, the China-Gates TB project, Phase II, has implemented a new financing and payment model as an important component of the overall project in three cities in eastern, central and western China. The model focuses on increasing the reimbursement rate for TB patients and reforming provider payment methods by replacing fee-for-service with a case-based payment approach. This study investigated changes in out-of-pocket (OOP) health expenditure and the financial burden on TB patients before and after the interventions, with a focus on potential differential impacts on patients from different income groups. METHODS: Three sample counties in each of the three prefectures: Zhenjiang, Yichang and Hanzhong were chosen as study sites. TB patients who started and completed treatment before, and during the intervention period, were randomly sampled and surveyed at the baseline in 2013 and final evaluation in 2015 respectively. OOP health expenditure and percentage of patients incurring catastrophic health expenditure (CHE) were calculated for different income groups. OLS regression and logit regression were conducted to explore the intervention's impacts on patient OOP health expenditure and financial burden after adjusting for other covariates. Key-informant interviews and focus group discussions were conducted to understand the reasons for any observed changes. RESULTS: Data from 738 (baseline) and 735 (evaluation) patients were available for analysis. Patient mean OOP health expenditure increased from RMB 3576 to RMB 5791, and the percentage of patients incurring CHE also increased after intervention. The percentage increase in OOP health expenditure and the likelihood of incurring CHE were significantly lower for patients from the highest income group as compared to the lowest. Qualitative findings indicated that increased use of health services not covered by the standard package of the model was likely to have caused the increase in financial burden. CONCLUSIONS: The implementation of the new financing and payment model did not protect patients, especially those from the lowest income group, from financial difficulty, due partly to their increased use of health service. More financial resources should be mobilized to increase financial protection, particularly for poor patients, while cost containment strategies need to be developed and effectively implemented to improve the effective coverage of essential healthcare in China.


Assuntos
Custos de Cuidados de Saúde/estatística & dados numéricos , Gastos em Saúde/estatística & dados numéricos , Pobreza/economia , Pobreza/estatística & dados numéricos , Tuberculose/economia , Adulto , Idoso , China , Comorbidade , Custos e Análise de Custo , Feminino , Humanos , Seguro Saúde , Entrevistas como Assunto , Masculino , Pessoa de Meia-Idade , Análise de Regressão , Fatores Socioeconômicos
20.
Eur Radiol ; 29(7): 3820-3829, 2019 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-30701328

RESUMO

OBJECTIVE: To develop a radiomic nomogram for preoperative prediction of axillary lymph node (LN) metastasis in breast cancer patients. METHODS: Preoperative magnetic resonance imaging data from 411 breast cancer patients was studied. Patients were assigned to either a training cohort (n = 279) or a validation cohort (n = 132). Eight hundred eight radiomic features were extracted from the first phase of T1-DCE images. A support vector machine was used to develop a radiomic signature, and logistic regression was used to develop a nomogram. RESULTS: The radiomic signature based on 12 LN status-related features was constructed to predict LN metastasis, its prediction ability was moderate, with an area under the curve (AUC) of 0.76 and 0.78 in training and validation cohorts, respectively. Based on a radiomic signature and clinical features, a nomogram was developed and showed excellent predictive ability for LN metastasis (AUC 0.84 and 0.87 in training and validation sets, respectively). Another radiomic signature was constructed to distinguish the number of metastatic LNs (less than 2 positive nodes/more than 2 positive nodes), which also showed moderate performance (AUC 0.79). CONCLUSIONS: We developed a nomogram and a radiomic signature that can be used to identify LN metastasis and distinguish the number of metastatic LNs (less than 2 positive nodes/more than 2 positive nodes). Both nomogram and radiomic signature can be used as tools to assist clinicians in assessing LN metastasis in breast cancer patients. KEY POINTS: • ALNM is an important factor affecting breast cancer patients' treatment and prognosis. • Traditional imaging examinations have limited value for evaluating axillary LNs status. • We developed a radiomic nomogram based on MR imagings to predict LN metastasis.


Assuntos
Axila/patologia , Neoplasias da Mama/patologia , Linfonodos/patologia , Metástase Linfática/diagnóstico por imagem , Imagem por Ressonância Magnética/métodos , Nomogramas , Adulto , Idoso , Feminino , Humanos , Metástase Linfática/patologia , Linfoma/patologia , Pessoa de Meia-Idade , Prognóstico , Estudos Retrospectivos
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