scholarly journals Development and Validation of a Nomogram for Predicting Radiation-Induced Temporal Lobe Injury in Nasopharyngeal Carcinoma

2020 ◽  
Vol 10 ◽  
Author(s):  
Wenqiang Guan ◽  
Kang Xie ◽  
Yixin Fan ◽  
Stefan Lin ◽  
Rui Huang ◽  
...  
Keyword(s):  
Nasopharyngeal Carcinoma ◽  
Temporal Lobe ◽  
Validation Cohort ◽  
External Validation ◽  
Multivariable Logistic Regression Analysis ◽  
Maximum Point ◽  
Good Discrimination ◽  
Internal Validation ◽  
Dose Volume Histograms ◽  
Radiation Induced

BackgroundThe purpose was to develop and validate a nomogram for prediction on radiation-induced temporal lobe injury (TLI) in patients with nasopharyngeal carcinoma (NPC).MethodsThe prediction model was developed based on a primary cohort that consisted of 194 patients. The data was gathered from January 2008 to December 2010. Clinical factors associated with TLI and dose–volume histograms for 388 evaluable temporal lobes were analyzed. Multivariable logistic regression analysis was used to develop the predicting model, which was conducted by R software. The performance of the nomogram was assessed with calibration and discrimination. An external validation cohort contained 197 patients from January 2011 to December 2013.ResultsAmong the 391 patients, 77 patients had TLI. Prognostic factors contained in the nomogram were Dmax (the maximum point dose) of temporal lobe, D1cc (the maximum dose delivered to a volume of 1 ml), T stage, and neutrophil-to-lymphocyte ratios (NLRs). The Internal validation showed good discrimination, with a C-index of 0.847 [95%CI 0.800 to 0.893], and good calibration. Application of the nomogram in the external validation cohort still obtained good discrimination (C-index, 0.811 [95% CI, 0.751 to 0.870]) and acceptable calibration.ConclusionsThis study developed and validated a nomogram, which may be conveniently applied for the individualized prediction of TLI.

scholarly journals Development and Validation a Nomogram Incorporating CT Radiomics Signatures and Radiological Features for Differentiating Invasive Adenocarcinoma From Adenocarcinoma In Situ and Minimally Invasive Adenocarcinoma Presenting as Ground-Glass Nodules Measuring 5-10mm in Diameter

2021 ◽  
Vol 11 ◽  
Author(s):  
Lili Shi ◽  
Weiya Shi ◽  
Xueqing Peng ◽  
Yi Zhan ◽  
Linxiao Zhou ◽  
...  
Keyword(s):  
Validation Cohort ◽  
External Validation ◽  
Invasive Adenocarcinoma ◽  
Good Discrimination ◽  
Internal Validation ◽  
Radiological Features ◽  
Minimally Invasive Adenocarcinoma ◽  
Ground Glass Nodules ◽  
Good Calibration

PurposeTo develop and validate a nomogram for differentiating invasive adenocarcinoma (IAC) from adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) presenting as ground-glass nodules (GGNs) measuring 5-10mm in diameter.Materials and MethodsThis retrospective study included 446 patients with 478 GGNs histopathologically confirmed AIS, MIA or IAC. These patients were assigned to a primary cohort, an internal validation cohort and an external validation cohort. The segmentation of these GGNs on thin-slice computed tomography (CT) were performed semi-automatically with in-house software. Radiomics features were then extracted from unenhanced CT images with PyRadiomics. Radiological features of these GGNs were also collected. Radiomics features were investigated for usefulness in building radiomics signatures by spearman correlation analysis, minimum redundancy maximum relevance (mRMR) feature ranking method and least absolute shrinkage and selection operator (LASSO) classifier. Multivariable logistic regression analysis was used to develop a nomogram incorporating the radiomics signature and radiological features. The performance of the nomogram was assessed with discrimination, calibration, clinical usefulness and evaluated on the validation cohorts.ResultsFive radiomics features remained after features selection. The model incorporating radiomics signatures and four radiological features (bubble-like appearance, tumor-lung interface, mean CT value, average diameter) showed good calibration and good discrimination with AUC of 0.831(95%CI, 0.772~0.890). Application of the nomogram in the internal validation cohort with AUC of 0.792 (95%CI, 0.712~0.871) and in the external validation cohort with AUC of 0.833 (95%CI, 0.729-0.938) also indicated good calibration and good discrimination. The decision curve analysis demonstrated that the nomogram was clinically useful.ConclusionThis study presents a nomogram incorporating the radiomics signatures and radiological features, which can be used to predict the risk of IAC in patients with GGNs measuring 5-10mm in diameter individually.

Development and Validation of Nomogram to Predict Very Early Recurrence of Combined Hepatocellular-Cholangiocarcinoma After Hepatic Resection: A Multi-Institutional Study

2021 ◽  
Author(s):  
Yijun Wu ◽  
Hongzhi Liu ◽  
Jianxing Zeng ◽  
Yifan Chen ◽  
Guoxu Fang ◽  
...  
Keyword(s):  
Validation Cohort ◽  
Risk Groups ◽  
Early Recurrence ◽  
Curve Analysis ◽  
Multivariable Logistic Regression Analysis ◽  
Good Discrimination ◽  
Decision Curve Analysis ◽  
High Incidence ◽  
Combined Hepatocellular Cholangiocarcinoma ◽  
Development And Validation

Abstract Background and Objectives Combined hepatocellular cholangiocarcinoma (cHCC) has a high incidence of early recurrence. The objective of this study is to construct a model predicting very early recurrence (VER)(ie, recurrence within 6 months after surgery) of cHCC. Methods 131 consecutive patients from Eastern Hepatobiliary Surgery Hospital served as a development cohort to construct a nomogram predicting VER by using multivariable logistic regression analysis. The model was internally and externally validated in an validation cohort of 90 patients from Mengchao Hepatobiliary Hospital using the C concordance statistic, calibration analysis and decision curve analysis (DCA). Results The VER nomogram contains microvascular invasion(MiVI), macrovascular invasion(MaVI) and CA19-9>25mAU/mL. The model shows good discrimination with C-indexes of 0.77 (95%CI: 0.69 - 0.85 ) and 0.76 (95%CI:0.66 - 0.86) in the development cohort and validation cohort respectively. Decision curve analysis demonstrated that the model are clinically useful and the calibration of our model was favorable. Our model stratified patients into two different risk groups, which exhibited significantly different VER. Conclusions Our model demonstrated favorable performance in predicting VER in cHCC patients.

Age-adapted percentiles of measured glomerular filtration in healthy individuals: extrapolation to living kidney donors over 65 years

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Pierre Delanaye ◽  
François Gaillard ◽  
Jessica van der Weijden ◽  
Geir Mjøen ◽  
Ingela Ferhman-Ekholm ◽  
...  
Keyword(s):  
Decision Making ◽  
Glomerular Filtration ◽  
Validation Cohort ◽  
External Validation ◽  
Healthy Individuals ◽  
Kidney Donors ◽  
Internal Validation ◽  
Living Kidney Donors ◽  
Multi Center Study ◽  
Elderly Living

Abstract Objectives Most data on glomerular filtration rate (GFR) originate from subjects <65 years old, complicating decision-making in elderly living kidney donors. In this retrospective multi-center study, we calculated percentiles of measured GFR (mGFR) in donors <65 years old and extrapolated these to donors ≥65 years old. Methods mGFR percentiles were calculated from a development cohort of French/Belgian living kidney donors <65 years (n=1,983), using quantiles modeled as cubic splines (two linear parts joining at 40 years). Percentiles were extrapolated and validated in an internal cohort of donors ≥65 years (n=147, France) and external cohort of donors and healthy subjects ≥65 years (n=329, Germany, Sweden, Norway, France, The Netherlands) by calculating percentages within the extrapolated 5th–95th percentile (P5–P95). Results Individuals in the development cohort had a higher mGFR (99.9 ± 16.4 vs. 86.4 ± 14 and 82.7 ± 15.5 mL/min/1.73 m2) compared to the individuals in the validation cohorts. In the internal validation cohort, none (0%) had mGFR below the extrapolated P5, 12 (8.2%) above P95 and 135 (91.8%) between P5–P95. In the external validation cohort, five subjects had mGFR below the extrapolated P5 (1.5%), 25 above P95 (7.6%) and 299 (90.9%) between P5–P95. Conclusions We demonstrate that extrapolation of mGFR from younger donors is possible and might aid with decision-making in elderly donors.

scholarly journals A Modified TNM Classification for Primary Operable Colorectal Cancer

2020 ◽  
Author(s):  
Chundong Zhang ◽  
Zubing Mei ◽  
Junpeng Pei ◽  
Masanobu Abe ◽  
Xiantao Zeng ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Validation Cohort ◽  
Tnm Classification ◽  
Characteristic Curve ◽  
External Validation ◽  
Model Fitting ◽  
Information Criteria ◽  
Stage I ◽  
Internal Validation ◽  
Clinical Benefits

Abstract Background The American Joint Committee on Cancer (AJCC) 8th tumor/node/metastasis (TNM) classification for colorectal cancer (CRC) has limited ability to predict prognosis. Methods We included 45,379 eligible stage I-III CRC patients from the Surveillance, Epidemiology, and End Results Program. Patients were randomly assigned individually to a training (N =31,772) or an internal validation cohort (N =13,607). External validation was performed in 10,902 additional patients. Patients were divided according to T and N stage permutations. Survival analyses were conducted by a Cox proportional hazard model and Kaplan-Meier analysis, with T1N0 as the reference. Area under receiver operating characteristic curve (AUC) and Akaike information criteria (AIC) were applied for prognostic discrimination and model-fitting, respectively. Clinical benefits were further assessed by decision curve analyses. Results We created a modified TNM (mTNM) classification: stages I (T1-2N0-1a), IIA (T1N1b, T2N1b, T3N0), IIB (T1-2N2a-2b, T3N1a-1b, T4aN0), IIC (T3N2a, T4aN1a-2a, T4bN0), IIIA (T3N2b, T4bN1a), IIIB (T4aN2b, T4bN1b), and IIIC (T4bN2a-2b). In the internal validation cohort, compared to the AJCC 8th TNM classification, the mTNM classification showed superior prognostic discrimination (AUC = 0.675 vs. 0.667, respectively; two-sided P &lt;0.001) and better model-fitting (AIC = 70,937 vs. 71,238, respectively). Similar findings were obtained in the external validation cohort. Decision curve analyses revealed that the mTNM had superior net benefits over the AJCC 8th TNM classification in the internal and external validation cohorts. Conclusions The mTNM classification provides better prognostic discrimination than AJCC 8th TNM classification, with good applicability in various populations and settings, to help better stratify stage I-III CRC patients into prognostic groups.

Surgical management of radiation-induced temporal lobe necrosis in patients with nasopharyngeal carcinoma: Report of 14 cases

Head & Neck ◽  
2010 ◽  
Vol 33 (10) ◽  
pp. 1493-1500 ◽  
Author(s):  
Yong-gao Mou ◽  
Ke Sai ◽  
Zhen-ning Wang ◽  
Xiang-heng Zhang ◽  
Yan-chun Lu ◽  
...  
Keyword(s):  
Nasopharyngeal Carcinoma ◽  
Surgical Management ◽  
Temporal Lobe ◽  
Temporal Lobe Necrosis ◽  
Radiation Induced

scholarly journals Effect of Aging-Related Genes on the Prognosis of Colon Cancer

2021 ◽  
Author(s):  
Yushu Liu ◽  
Jiantao Gong ◽  
Yanyi Huang ◽  
Qunguang Jiang
Keyword(s):  
Colon Cancer ◽  
Cancer Patients ◽  
Clinical Characteristics ◽  
Cox Regression ◽  
Validation Cohort ◽  
External Validation ◽  
Predictive Ability ◽  
Lasso Regression ◽  
Internal Validation ◽  
Cox Regression Analysis

Abstract Background:Colon cancer is a common malignant cancer with high incidence and poor prognosis. Cell senescence and apoptosis are important mechanisms of tumor occurrence and development, in which aging-related genes(ARGs) play an important role. This study aimed to establish a prognostic risk model based on ARGs for diagnosis and prognosis prediction of colon cancer .Methods: We downloaded transcriptome data and clinical information of colon cancer patients from the Cancer Genome Atlas(TCGA) database and the microarray dataset(GSE39582) from the Gene Expression Omnibus(GEO) database. Univariate COX, least absolute shrinkage and selection operator(LASSO) regression algorithm and multivariate COX regression analysis were used to construct a 6-ARG prognosis model and calculated the riskScore. The prognostic signatures is validated by internal validation cohort and external validation cohort(GSE39582).In addition, functional enrichment pathways and immune microenvironment of aging-related genes(ARGs) were also analyzed. We also analyzed the correlation between rsikScore and clinical features and constructed a nomogram based on riskScore. We are the first to construct prognostic nomogram based on ARGs.Results: Through univariate COX,LASSO regression algorithm and multivariate COX regression analysis,6 prognostic ARGs (PDPK1,RAD52,GSR,IL7,BDNF and SERPINE1) were screened out and riskScore was constructed. We have verified that riskScore has good prognostic value in both internal validation cohort and external validation cohort. Pathway enrichment and immunoanalysis of ARGs provide a direction for the treatment of colon cancer patients. We also found that riskScore was closely related to the clinical characteristics of patients. Based on riskScore and related clinical features, we constructed a nomogram, which has good predictive performance.Conclusion: The 6-ARG prognostic signature we constructed has a certain clinical predictive ability. Its riskScore is also closely related to clinical characteristics, and nomogram based on this has stronger predictive ability than a single indicator. ARGs and the nomogram we constructed may provide a promising treatment for colon cancer patients.

scholarly journals Nomogram for Prediction of the International Study Group of Liver Surgery (ISGLS) Grade B/C Posthepatectomy Liver Failure in HBV-related Hepatocellular carcinoma Patients: an external validation and prospective application study

2020 ◽  
Author(s):  
Jiazhou Ye ◽  
Rong-yun Mai ◽  
Wei-xing Guo ◽  
Yan-yan Wang ◽  
Liang Ma ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Validation Cohort ◽  
External Validation ◽  
International Study ◽  
Study Group ◽  
Internal Validation ◽  
Predictive Values ◽  
Posthepatectomy Liver Failure ◽  
International Study Group ◽  
Prospective Application

Abstract Background & Aims: To develop a nomogram for predicting the International Study Group of Liver Surgery (ISGLS) grade B/C posthepatectomy liver failure (PHLF) in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) patients. Methods: Patients initially treated with hepatectomy were included. Univariate regression analysis and stochastic forest algorithm were applied to extract the core indicators and reduce redundancy bias. The nomogram was then constructed by using multivariate logistic regression, and validated in internal and external cohorts, and a prospective clinical application. Results: There were 900, 300 and 387 participants in training, internal and external validation cohorts, with the morbidity of grade B/C PHLF were 13.5%, 11.6% and 20.2%, respectively. The nomogram was generated by integrating preoperative total bilirubin, platelet count, prealbumin, aspartate aminotransferase, prothrombin time and standard future liver remnant volume, then achieved good prediction performance in training (AUC=0.868, 95%CI=0.808–0.880), internal validation (AUC=0.868, 95%CI=0.794–0.916) and external validation cohorts (AUC=0.820, 95%CI=0.756–0.861), with well-fitted calibration curves. Negative predictive values were significantly higher than positive predictive values in training cohort (97.6% vs. 33.0%), internal validation cohort (97.4% vs. 25.9%) and external validation cohort (94.3% vs. 41.1%), respectively. Patients who had a nomogram score <169 or ≧169 were considered to have low or high risk of grade B/C PHLF. Prospective application of the nomogram accurately predicted grade B/C PHLF in clinical practise. Conclusions: The nomogram has a good performance in predicting ISGLS grade B/C PHLF in HBV-related HCC patients and determining appropriate candidates for hepatectomy.

Abstract 16172: Predicting the Presence of a Mutation Resulting in Familial Hypercholesterolemia - Development of a Prediction Model in a Cohort of 64,000 Subjects

Circulation ◽  
2014 ◽  
Vol 130 (suppl_2) ◽  
Author(s):  
Joost Besseling ◽  
Johannes B Reitsma ◽  
Gerard K Hovingh ◽  
Barbara A Hutten
Keyword(s):  
Prediction Model ◽  
Familial Hypercholesterolemia ◽  
General Practitioners ◽  
Dna Analysis ◽  
External Validation ◽  
Lipid Lowering ◽  
Multivariable Logistic Regression Analysis ◽  
Lipid Lowering Therapy ◽  
Internal Validation ◽  
Premature Cardiovascular Disease

Introduction: Familial hypercholesterolemia (FH) is a hereditary disease that warrants early diagnosis to prevent premature cardiovascular disease (CVD) by initiating therapy. A definitive diagnosis is made by demonstrating a causal mutation. This might not only increase therapy adherence, but is also an important requirement for cascade screening. However, DNA analysis is costly and careful selection of subjects is important. Unfortunately, the accuracy of current selection criteria is poor. Hypothesis: We set out to develop a model to predict the presence of an FH causing mutation in persons referred by general practitioners. Methods: All participants in the Dutch FH screening program from January 1994 till January 2014 were included. Cross-sectional data was available on medical history, lipid profile and DNA analysis. The primary outcome was the presence of a deleterious FH mutation. We developed a prediction model using multivariable logistic regression analysis. Results: Our study population consisted of 25,809 FH patients and 38,297 unaffected relatives. Our final model included age, gender, levels of LDL-cholesterol, HDL-cholesterol and triglycerides, history of CVD, use of statins and other lipid lowering therapy, smoking and alcohol. The performance was good: the area under the receiver operating characteristic curve (AUC) was 85.2% (95% CI: 84.9 - 85.5). The model was well calibrated with a slope of 1.02 (1 is optimal). Internal validation was excellent: the AUC was unaltered in 100 bootstrap samples. The performance of the model can be illustrated by selecting subjects with a predicted probability of 30% or lower. This would identify 87.0% of all unaffected subjects, and avoid testing in 46.1% of the population. Conclusions: We developed a model to predict the presence of a deleterious FH mutation in subjects referred by general practitioners. Our model showed good discrimination and calibration, with no signs of overfitting during internal validation. After external validation, we will develop an interactive web-based calculator or smart phone application that can be used to calculate the probability of the presence of an FH mutation in individual subjects and will facilitate the use of our prediction model in daily clinical practice.

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