scholarly journals Immunohistochemical Signature Add Prognostic Value in Patients With Early and Intermediate Hepatocellular Carcinoma Underwent Curative Liver Resection

2021 ◽  
Vol 10 ◽  
Author(s):  
Yannan Bai ◽  
Yuane Lian ◽  
Xiaoping Chen ◽  
Jiayi Wu ◽  
Jianlin Lai ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Resection ◽  
Cox Regression ◽  
External Validation ◽  
Area Under The Curve ◽  
Intermediate Stage ◽  
Ki 67 ◽  
Discriminative Performance ◽  
Internal Validation ◽  
Training Cohort

Hepatocellular carcinoma (HCC) is the third most lethal cancer worldwide; however, accurate prognostic tools are still lacking. We aimed to identify immunohistochemistry (IHC)-based signature as a prognostic classifier to predict recurrence and survival in patients with HCC at Barcelona Clinic Liver Cancer (BCLC) early- and immediate-stage. In total, 567 patients who underwent curative liver resection at two independent centers were enrolled. The least absolute shrinkage and selection operator regression model was used to identify significant IHC features, and penalized Cox regression was used to further narrow down the features in the training cohort (n = 201). The candidate IHC features were validated in internal (n = 101) and external validation cohorts (n = 265). Three IHC features, hepatocyte paraffin antigen 1, CD34, and Ki-67, were identified as candidate predictors for recurrence-free survival (RFS), and were used to categorize patients into low- and high-risk recurrence groups in the training cohort (P < 0.001). The discriminative performance of the 3-IHC_based classifier was validated using internal and external cohorts (P < 0.001). Furthermore, we developed a 3-IHC_based nomogram integrating the BCLC stage, microvascular invasion, and 3-IHC_based classifier to predict 2- and 5-year RFS in the training cohort; this nomogram exhibited acceptable area under the curve values for the training, internal validation, and external validation cohorts (2-year: 0.817, 0.787, and 0.810; 5-year: 0.726, 0.662, and 0.715; respectively). The newly developed 3-IHC_based classifier can effectively predict recurrence and survival in patients with early- and intermediate-stage HCC after curative liver resection.

A Simple Approach to Adjust for Case-Mix when Comparing Institutional Cesarean Birth Rates

2019 ◽  
Author(s):  
James Jeffry Howbert ◽  
Ellen Kauffman ◽  
Kristin Sitcov ◽  
Vivienne Souter
Keyword(s):  
External Validation ◽  
Area Under The Curve ◽  
Case Mix ◽  
Maternal Weight ◽  
Internal Validation ◽  
Training Cohort ◽  
Cesarean Birth ◽  
Predictive Variables ◽  
Nulliparous Women ◽  
And Gender

Abstract Objective To develop a validated model to predict intrapartum cesarean in nulliparous women and to use it to adjust for case-mix when comparing institutional laboring cesarean birth (CB) rates. Study Design This multicenter retrospective study used chart-abstracted data on nulliparous, singleton, term births over a 7-year period. Prelabor cesareans were excluded. Logistic regression was used to predict the probability of CB for individual pregnancies. Thirty-five potential predictive variables were evaluated including maternal demographics, prepregnancy health, pregnancy characteristics, and newborn weight and gender. Models were trained on 21,017 births during 2011 to 2015 (training cohort), and accuracy assessed by prediction on 15,045 births during 2016 to 2017 (test cohort). Results Six variables delivered predictive success equivalent to the full set of 35 variables: maternal weight, height, and age, gestation at birth, medically-indicated induction, and birth weight. Internal validation within the training cohort gave a receiver operator curve with area under the curve (ROC-AUC) of 0.722. External validation using the test cohort gave ROC-AUC of 0.722 (0.713–0.731 confidence interval). When comparing observed and predicted CB rates at 16 institutions in the test cohort, five had significantly lower than predicted rates and three had significantly higher than predicted rates. Conclusion Six routine clinical variables used to adjust for case-mix can identify outliers when comparing institutional CB rates.

scholarly journals A Novel Nomogram to Predict Prolonged Survival After Hepatectomy in Repeat Recurrent Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Qiongxuan Fang ◽  
Ruifeng Yang ◽  
Dongbo Chen ◽  
Ran Fei ◽  
Pu Chen ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cox Regression ◽  
External Validation ◽  
Repeat Hepatectomy ◽  
Discriminative Ability ◽  
Training Cohort ◽  
Recurrent Hepatocellular Carcinoma ◽  
Kaplan Meier ◽  
Staging Systems ◽  
Better Than

Background: Repeat hepatectomy is an important treatment for patients with repeat recurrent hepatocellular carcinoma (HCC).Methods: This study was a multicenter retrospective analysis of 1,135 patients who underwent primary curative liver resection for HCC. One hundred recurrent patients with second hepatectomy were included to develop a nomogram to predict the risk of post-recurrence survival (PRS). Thirty-eight patients in another institution were used to externally validate the nomogram. Univariate and multivariate Cox regression analyses were used to identify independent risk factors of PRS. Discrimination, calibration, and the Kaplan–Meier curves were used to evaluate the model performance.Results: The nomogram was based on variables associated with PRS after HCC recurrence, including the tumor, node, and metastasis (TNM) stage; albumin and aspartate aminotransferase levels at recurrence; tumor size, site, differentiation of recurrences; and time to recurrence (TTR). The discriminative ability of the nomogram, as indicated by the C statistics (0.758 and 0.811 for training cohort and external validation cohorts, respectively), was shown, which was better than that of the TNM staging system (0.609 and 0.609, respectively). The calibration curves showed ideal agreement between the prediction and the real observations. The area under the curves (AUCs) of the training cohort and external validation cohorts were 0.843 and 0.890, respectively. The Kaplan–Meier curve of the established nomogram also performed better than those of both the TNM and the BCLC staging systems.Conclusions: We constructed a nomogram to predict PRS in patients with repeat hepatectomy (RH) after repeat recurrence of HCC.

scholarly journals Applications of Artificial Intelligence and Machine Learning in Diagnosis and Prognosis of COVID-19 infection: A systematic review

2021 ◽  
Vol 10 (1) ◽  
pp. 93
Author(s):  
Mahdieh Montazeri ◽  
Ali Afraz ◽  
Mitra Montazeri ◽  
Sadegh Nejatzadeh ◽  
Fatemeh Rahimi ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Prediction Models ◽  
External Validation ◽  
Area Under The Curve ◽  
Risk Of Bias ◽  
Prognostic Models ◽  
Discriminative Performance ◽  
Internal Validation ◽  
Diagnostic Models

Introduction: Our aim in this study was to summarize information on the use of intelligent models for predicting and diagnosing the Coronavirus disease 2019 (COVID-19) to help early and timely diagnosis of the disease.Material and Methods: A systematic literature search included articles published until 20 April 2020 in PubMed, Web of Science, IEEE, ProQuest, Scopus, bioRxiv, and medRxiv databases. The search strategy consisted of two groups of keywords: A) Novel coronavirus, B) Machine learning. Two reviewers independently assessed original papers to determine eligibility for inclusion in this review. Studies were critically reviewed for risk of bias using prediction model risk of bias assessment tool.Results: We gathered 1650 articles through database searches. After the full-text assessment 31 articles were included. Neural networks and deep neural network variants were the most popular machine learning type. Of the five models that authors claimed were externally validated, we considered external validation only for four of them. Area under the curve (AUC) in internal validation of prognostic models varied from .94 to .97. AUC in diagnostic models varied from 0.84 to 0.99, and AUC in external validation of diagnostic models varied from 0.73 to 0.94. Our analysis finds all but two studies have a high risk of bias due to various reasons like a low number of participants and lack of external validation.Conclusion: Diagnostic and prognostic models for COVID-19 show good to excellent discriminative performance. However, these models are at high risk of bias because of various reasons like a low number of participants and lack of external validation. Future studies should address these concerns. Sharing data and experiences for the development, validation, and updating of COVID-19 related prediction models is needed. 

Development and Validation of Prognostic Nomograms Predicting Recurrence and Survival in Patients with Solitary Hepatocellular Carcinoma Following Curative Liver Resection

2021 ◽  
Author(s):  
Xinxin Chen ◽  
Wenxia Qiu ◽  
Xuekun Xie ◽  
Zefeng Chen ◽  
Zhiwei Han ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Resection ◽  
Cox Regression ◽  
Disease Free Survival ◽  
Clinicopathological Characteristics ◽  
Tumor Diameter ◽  
Cox Regression Analysis ◽  
Tumor Capsule ◽  
Calibration Curves ◽  
Solitary Hepatocellular Carcinoma

Abstract Background: This work was designed to establish and verify our nomograms integrating clinicopathological characteristics with hematological biomarkers to predict both disease-free survival (DFS) and overall survival (OS) in solitary hepatocellular carcinoma (HCC) patients following hepatectomy.Methods: We scrutinized the data retrospectively from 414 patients with a clinicopathological diagnosis of solitary HCC from Guangxi Medical University Cancer Hospital (Nanning, China) between January 2004 and December 2012. Following the random separation of the samples in a 7:3 ratio into the training set and validation set, the former set was assessed by Cox regression analysis to develop two nomograms to predict the 1-year and 3-year DFS and OS (3-years and 5-years). This was followed by discrimination and calibration estimation employing Harrell’s C-index (C-index) and calibration curves, while the internal validation was also assessed.Results: In the training cohort, the tumor diameter, tumor capsule, macrovascular invasion, and alpha-fetoprotein (AFP) were included in the DFS nomogram. Age, tumor diameter, tumor capsule, macrovascular invasion, microvascular invasion, and aspartate aminotransferase (AST) were included in the OS nomogram. The C-index was 0.691 (95% CI: 0.644-0.738) for the DFS-nomogram and 0.713 (95% CI: 0.670-0.756) for the OS-nomogram. The survival probability calibration curves displayed a fine agreement between the predicted and observed ranges in both data sets. Conclusion: Our nomograms combined clinicopathological features with hematological biomarkers to emerge effective in predicting the DFS and OS in solitary HCC patients following curative liver resection. Therefore, the potential utility of our nomograms for guiding individualized treatment clinically and monitor the recurrence monitoring in these patients.

scholarly journals Establishment and Validation of an Integrated Model to Predict Postoperative Recurrence in Patients With Atypical Meningioma

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiao-Yong Chen ◽  
Jin-Yuan Chen ◽  
Yin-Xing Huang ◽  
Jia-Heng Xu ◽  
Wei-Wei Sun ◽  
...  
Keyword(s):  
Cox Regression ◽  
Validation Cohort ◽  
Clinical Laboratory ◽  
External Validation ◽  
Postoperative Recurrence ◽  
Integrated Model ◽  
Atypical Meningioma ◽  
Tumor Diameter ◽  
Roc Curve Analysis ◽  
Training Cohort

BackgroundThis study aims to establish an integrated model based on clinical, laboratory, radiological, and pathological factors to predict the postoperative recurrence of atypical meningioma (AM).Materials and MethodsA retrospective study of 183 patients with AM was conducted. Patients were randomly divided into a training cohort (n = 128) and an external validation cohort (n = 55). Univariable and multivariable Cox regression analyses, the least absolute shrinkage and selection operator (LASSO) regression analysis, time-dependent receiver operating characteristic (ROC) curve analysis, and evaluation of clinical usage were used to select variables for the final nomogram model.ResultsAfter multivariable Cox analysis, serum fibrinogen >2.95 g/L (hazard ratio (HR), 2.43; 95% confidence interval (CI), 1.05–5.63; p = 0.039), tumor located in skull base (HR, 6.59; 95% CI, 2.46-17.68; p < 0.001), Simpson grades III–IV (HR, 2.73; 95% CI, 1.01–7.34; p = 0.047), tumor diameter >4.91 cm (HR, 7.10; 95% CI, 2.52–19.95; p < 0.001), and mitotic level ≥4/high power field (HR, 2.80; 95% CI, 1.16–6.74; p = 0.021) were independently associated with AM recurrence. Mitotic level was excluded after LASSO analysis, and it did not improve the predictive performance and clinical usage of the model. Therefore, the other four factors were integrated into the nomogram model, which showed good discrimination abilities in training cohort (C-index, 0.822; 95% CI, 0.759–0.885) and validation cohort (C-index, 0.817; 95% CI, 0.716–0.918) and good match between the predicted and observed probability of recurrence-free survival.ConclusionOur study established an integrated model to predict the postoperative recurrence of AM.

scholarly journals Prediction of Post-hepatectomy Liver Failure in Patients With Hepatocellular Carcinoma Based on Radiomics Using Gd-EOB-DTPA-Enhanced MRI: The Liver Failure Model

2021 ◽  
Vol 11 ◽  
Author(s):  
Yuyan Chen ◽  
Zelong Liu ◽  
Yunxian Mo ◽  
Bin Li ◽  
Qian Zhou ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Failure ◽  
Treatment Strategies ◽  
Area Under The Curve ◽  
Training Cohort ◽  
Clinical Model ◽  
Preoperative Prediction ◽  
Medical Centers ◽  
Appropriate Treatment ◽  
Magnetic Resonance Imaging Mri

Objectives: Preoperative prediction of post-hepatectomy liver failure (PHLF) in patients with hepatocellular carcinoma (HCC) is significant for developing appropriate treatment strategies. We aimed to establish a radiomics-based clinical model for preoperative prediction of PHLF in HCC patients using gadolinium-ethoxybenzyl-diethylenetriamine (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI).Methods: A total of 144 HCC patients from two medical centers were included, with 111 patients as the training cohort and 33 patients as the test cohort, respectively. Radiomics features and clinical variables were selected to construct a radiomics model and a clinical model, respectively. A combined logistic regression model, the liver failure (LF) model that incorporated the developed radiomics signature and clinical risk factors was then constructed. The performance of these models was evaluated and compared by plotting the receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC) with 95% confidence interval (CI).Results: The radiomics model showed a higher AUC than the clinical model in the training cohort and the test cohort for predicting PHLF in HCC patients. Moreover, the LF model had the highest AUCs in both cohorts [0.956 (95% CI: 0.955–0.962) and 0.844 (95% CI: 0.833–0.886), respectively], compared with the radiomics model and the clinical model.Conclusions: We evaluated quantitative radiomics features from MRI images and presented an externally validated radiomics-based clinical model, the LF model for the prediction of PHLF in HCC patients, which could assist clinicians in making treatment strategies before surgery.

A new staging system for radiotherapy-based treatment of hepatocellular carcinoma: A multicenter cohort study.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e16155-e16155
Author(s):  
Ting-Shi Su ◽  
Shi-Xiong Liang ◽  
Li-Qing Li ◽  
Qiu-Hua Liu ◽  
Xiao-Fei Zhu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
External Validation ◽  
Staging System ◽  
Time Dependent ◽  
Multicenter Cohort Study ◽  
Discriminatory Ability ◽  
Training Cohort ◽  
Multicenter Cohort ◽  
Staging Systems

e16155 Background: External beam radiation therapy has been used as a palliative to radical treatment of hepatocellular carcinoma (HCC) depending on different tumor status, liver function and patient's general state of health. The existing models of HCC staging cannot perfectly predict the prognosis of radiotherapy. In this study, we aimed to set up a new staging system for radiotherapy-based treatment by incorporating bilirubin-albumin (ALBI) grade and tumor status for the prognostic classifications of HCC. Methods: This multicenter cohort study included 878 HCC patients who received radiotherapy-based treatment. A new staging system was established: stage I, solitary nodule without macrovascular invasion or 2-3 nodules with no more than 3.0 cm each other and PS 0-2 (Ia: ALBI-1 grade; Ib: ALBI-2 or 3 grade); stage II: 2-3 nodules with anyone more than 3.0 cm or ≥4 nodules and PS 0-2 (IIa: ALBI-1 grade; IIb: ALBI-2 grade); stage III: macrovascular invasion or regional lymph node metastasis or distant metastasis and PS 0-2 (IIIa: ALBI-1 grade; IIIb:ALBI-2 grade); stage IV: ALBI-3 grade without stage I patient or/and PS score 3-4. The new modified staging system and the existing staging systems, such as the BCLC, TNM, CNLC staging systems were used for prognostic analysis. All patients were separated into different stages and substages. The long-term overall survival outcomes and time-dependent receiver operating characteristic (ROC) were analyzed. Results: A training cohort of 595 patients underwent stereotactic body radiotherapy (SBRT) from 2011 to 2017 and an external validation cohort of 283 patients underwent intensity-modulated radiotherapy (IMRT) from 2000 to 2013 were included into establishing and validating the new staging system. In the training cohort, the median follow-up time was 55 months (range, 6–100 months), and the new staging system had a good discriminatory ability to separate patients into different stages with 4 notably different curves and substages with 7 notably different curves. BCLC staging could not differentiate stage 0 to A, and stage C to D in these selected patients. TNM staging could not completely distinguish stage IIIb to IV, but also stage Ia to Ib. CNLC staging could not differentiate among stage IIIa, IIIb, and IV. In the external validation, the median follow-up time was 95 months (range, 9–120 months), and the new staging system also had a good discriminatory ability to separate patients into different stages with 4 notably different curves and substages with 7 notably different curves. The new staging system had a better area under curve of time-dependent ROC than BCLC, TNM and CNLC staging in both SBRT and IMRT cohorts. Conclusions: The new modified (Su’s) staging system could provide a good discriminatory ability to separate patients into different stages and substages after radiotherapy treatment. It may be used to supplement the other HCC staging systems.

scholarly journals Tumor Marker-Based Definition of the Transarterial Chemoembolization-Refractoriness in Intermediate-Stage Hepatocellular Carcinoma: A Multi-Cohort Study

Cancers ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 1721 ◽  
Author(s):  
Jun Sik Yoon ◽  
Dong Hyun Sinn ◽  
Jeong-Hoon Lee ◽  
Hwi Young Kim ◽  
Cheol-Hyung Lee ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cohort Study ◽  
Tumor Marker ◽  
Systemic Therapy ◽  
Transarterial Chemoembolization ◽  
External Validation ◽  
Intermediate Stage ◽  
Prognostic Impact ◽  
High Baseline ◽  
Definition Of

Background: For patients with hepatocellular carcinoma (HCC), the definition of refractoriness to transarterial chemoembolization (TACE), which might make them a candidate for systemic therapy, is still controversial. We aimed to derive and validate a tumor marker-based algorithm to define the refractoriness to TACE in patients with intermediate-stage HCC. Methods: This multi-cohort study was comprised of patients who underwent TACE for treatment-naïve intermediate-stage HCC. We derived a prediction model for overall survival (OS) using the pre- and post-TACE model to predict tumor recurrence after living donor liver transplantation (MoRAL) (i.e., MoRAL score = 11×√protein induced by vitamin K absence-II + 2×√alpha-fetoprotein), which was proven to reflect both tumor burden and biologic aggressiveness of HCC in the explant liver, from a training cohort (n = 193). These results were externally validated in both an independent hospital cohort (from two large-volume centers, n = 140) and a Korean National Cancer Registry sample cohort (n = 149). Results: The changes in MoRAL score (ΔMoRAL) after initial TACE was an independent predictor of OS (MoRAL-increase vs. MoRAL-non-increase: adjusted hazard ratio (HR) = 2.18, 95% confidence interval (CI) = 1.37–3.46, p = 0.001; median OS = 18.8 vs. 37.8 months). In a subgroup of patients with a high baseline MoRAL score (≥89.5, 25th percentile and higher), the prognostic impact of ΔMoRAL was more pronounced (MoRAL-increase vs. MoRAL-non-increase: HR = 3.68, 95% CI = 1.54–8.76, p < 0.001; median OS = 9.9 vs. 37.4 months). These results were reproduced in the external validation cohorts. Conclusion: The ΔMoRAL after the first TACE, a simple and objective index, provides refined prognostication for patients with intermediate-stage HCC. Proceeding to a second TACE may not provide additional survival benefits in cases of a MoRAL-increase after the first TACE in patients with a high baseline MoRAL score (≥89.5), who might be candidates for systemic therapy.

A cardiovascular risk score for hypertensive patients previously admitted to hospital

2019 ◽  
Vol 18 (6) ◽  
pp. 492-500 ◽  
Author(s):  
Ana M Martínez-Díaz ◽  
Antonio Palazón-Bru ◽  
David M Folgado-de la Rosa ◽  
Dolores Ramírez-Prado ◽  
Patricia Llópez-Espinós ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Cardiovascular Disease ◽  
Cardiovascular Risk ◽  
Cox Regression ◽  
External Validation ◽  
Arterial Disease ◽  
Internal Validation ◽  
Hypertensive Patients ◽  
One Year

Background: Cardiovascular risk scales in hypertensive populations have limitations for clinical practice. Aims: To develop and internally validate a predictive model to estimate one-year cardiovascular risk for hypertensive patients admitted to hospital. Methods: Cohort study of 303 hypertensive patients admitted through the Emergency Department in a Spanish region in 2015–2017. The main variable was the onset of cardiovascular disease during follow-up. The secondary variables were: gender, age, educational level, family history of cardiovascular disease, Charlson score and its individual conditions, living alone, quality of life, smoking, blood pressure, physical activity and adherence to the Mediterranean diet. A Cox regression model was constructed to predict cardiovascular disease one year after admission. This was then adapted to a points system, externally validated by bootstrapping (discrimination and calibration) and implemented in a mobile application for Android. Results: A total of 93 patients developed cardiovascular disease (30.7%) over a mean period of 1.68 years. The predictors in the points system were: gender, age, myocardial infarction, heart failure, peripheral arterial disease and daily activity (quality of life). The internal validation by bootstrapping was satisfactory. Conclusion: A novel points system was developed to predict short-term cardiovascular disease in hypertensive patients after hospital admission. External validation studies are needed to corroborate the results obtained.

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