Prediction Model For Recurrence Research Articles

ASO Author Reflections: Validated Prediction Model of Early Recurrence after Resection for Gallbladder Cancer: Identifying a Subset of Patients Who May be Better Served with Neoadjuvant Therapy

ASO Author Reflections: Validated Prediction Model of Early Recurrence after Resection for Gallbladder Cancer: Identifying a Subset of Patients Who May be Better Served with Neoadjuvant Therapy

Novel Models Predict Postsurgical Recurrence and Overall Survival for Patients with Hepatitis B Virus-Related Solitary Hepatocellular Carcinoma ≤10 cm and Without Portal Venous Tumor Thrombus.

The predictive model of postsurgical recurrence for solitary early hepatocellular carcinoma (SE-HCC) is not well established. The aim of this study was to develop a novel model for prediction of postsurgical recurrence and survival for patients with hepatitis B virus (HBV)-related SE-HCC ≤10 cm. Data from 1,081 patients with HBV-related SE-HCC ≤10 cm who underwent curative liver resection from 2003 to 2016 in our center were collected retrospectively and randomly divided into the derivation cohort (n = 811) and the internal validation cohort (n = 270). Eight hundred twenty-three patients selected from another four tertiary hospitals served as the external validation cohort. Postsurgical recurrence-free survival (RFS) and overall survival (OS) predictive nomograms were generated. The discriminatory accuracies of the nomograms were compared with six conventional hepatocellular carcinoma (HCC) staging systems. Tumor size, differentiation, microscopic vascular invasion, preoperative α-fetoprotein, neutrophil-to-lymphocyte ratio, albumin-to-bilirubin ratio, and blood transfusion were identified as the risk factors associated with RFS and OS. RFS and OS predictive nomograms based on these seven variables were generated. The C-index was 0.83 (95% confidence interval [CI], 0.79-0.87) for the RFS-nomogram and 0.87 (95% CI, 0.83-0.91) for the OS-nomogram. Calibration curves showed good agreement between actual observation and nomogram prediction. Both C-indices of the two nomograms were substantially higher than those of the six conventional HCC staging systems (0.54-0.74 for RFS; 0.58-0.76 for OS) and those of HCC nomograms reported in literature. The novel nomograms were shown to be accurate at predicting postoperative recurrence and OS for patients with HBV-related SE-HCC ≤10 cm after curative liver resection. This multicenter study proposed recurrence or mortality predictive nomograms for patients with hepatitis B virus-related solitary early hepatocellular carcinoma ≤10 cm after curative liver resection. A close postsurgical surveillance protocol and adjuvant therapy should be considered for patients at high risk of recurrence.

D-TSVR Recurrence Prediction Driven by Medical Big Data in Cancer

Secondary use of medical big data is becoming increasingly popular in healthcare services and clinical research in the medical industry. Cancer recurrence is a common phenomenon of cancer patients after treatment (recovery period). Studying the time and influencing factors of cancer recurrence can provide effective clinical intervention means, which is the gospel of cancer patients. In this article, a sample of 50 000 cases of seven cancer patients, including liver cancer, lung cancer, kidney cancer, breast cancer, uterine cancer, stomach cancer, and bowel cancer, was collected. A twin support regression vector machine (TSVR) algorithm based on dependent nearest neighbor (DNN) weighting is proposed, the eplion-TSVR model is improved by DNN-weighted algorithm with local information mining function, and the solution of the improved model is derived. It is proposed to use the cuckoo algorithm to determine the optimal parameters of DNN to determine the optimal dependency region domain. In this article, the improved TSVR algorithm is used to establish a cancer recurrence prediction model. The prediction accuracy of the model for various cancers can reach more than 91%, which is significantly higher than that of convolutional neural network and e-TSVR models.

Prediction Models for Recurrence of Chronic Subdural Hematoma in Patients Underwent Twist-Drill Craniostomy Combined With Urokinase Instillation.

The recurrence of chronic subdural hematoma (CSDH) is high post-treatment. In this study, we aimed to construct individualized models for prediction of the postoperative recurrence of CSDH in patients underwent twist-drill craniostomy combined with urokinase (UK) instillation. In total, 183 patients with CSDH were retrospectively enrolled. In summary, 21 candidate factors were retrieved from past medical records. The least absolute shrinkage and selection operator regression was adopted to reduce the high dimensionality of data. Four predictors: preoperative hematoma volume, encephalatrophy, brain re-expansion, and UK instillation frequency were filtered from the 21 candidate factors using the least absolute shrinkage and selection operator method. Binary logistic regression model was employed to establish preoperative and postoperative prediction models. The preoperative model included preoperative hematoma volume and encephalatrophy whereas the postoperative model included brain re-expansion and UK instillation frequency. The predictive performance of the nomograms was evaluated by the receiver operating characteristic curve and calibration chart. Area under curve of the preoperative and postoperative models were 0.755 (95% confidence interval: 0.690-0.889) and 0.782 (95% confidence interval: 0.720-0.936), respectively, indicating good discrimination ability. The calibration results showed good fitting between the predicted probability and the actual probability. Finally, a decision curve analysis revealed excellent clinical performance of the proposed nomograms. Functionally, the preoperative model was used to identify high-risk patients with CSDH and application of UK, while the postoperative model was applied to guide physician-patients communication during follow-up. These 2 prediction models provide a basis for further clinical and experimental studies.

Risk factors and risk prediction models for colorectal cancer metastasis and recurrence: an umbrella review of systematic reviews and meta-analyses of observational studies

BackgroundThere is a clear need for systematic appraisal of models/factors predicting colorectal cancer (CRC) metastasis and recurrence because clinical decisions about adjuvant treatment are taken on the basis of such variables.MethodsWe conducted an umbrella review of all systematic reviews of observational studies (with/without meta-analysis) that evaluated risk factors of CRC metastasis and recurrence. We also generated an updated synthesis of risk prediction models for CRC metastasis and recurrence. We cross-assessed individual risk factors and risk prediction models.ResultsThirty-four risk factors for CRC metastasis and 17 for recurrence were investigated. Twelve of 34 and 4/17 risk factors with p < 0.05 were estimated to change the odds of the outcome at least 3-fold. Only one risk factor (vascular invasion for lymph node metastasis [LNM] in pT1 CRC) presented convincing evidence. We identified 24 CRC risk prediction models. Across 12 metastasis models, six out of 27 unique predictors were assessed in the umbrella review and four of them changed the odds of the outcome at least 3-fold. Across 12 recurrence models, five out of 25 unique predictors were assessed in the umbrella review and only one changed the odds of the outcome at least 3-fold.ConclusionsThis study provides an in-depth evaluation and cross-assessment of 51 risk factors and 24 prediction models. Our findings suggest that a minority of influential risk factors are employed in prediction models, which indicates the need for a more rigorous and systematic model construction process following evidence-based methods.

The EORTC-DeCOG nomogram adequately predicts outcomes of patients with sentinel node–positive melanoma without the need for completion lymph node dissection

PurposeBased on recent advances in the management of patients with sentinel node (SN)–positive melanoma, we aimed to develop prediction models for recurrence, distant metastasis (DM) and overall mortality (OM). MethodsThe derivation cohort consisted of 1080 patients with SN-positive melanoma from nine European Organization for Research and Treatment of Cancer (EORTC) centres. Prognostic factors for recurrence, DM and OM were studied with Cox regression analysis. Significant factors were incorporated in the models. Performance was assessed by discrimination (c-index) and calibration in cross-validation across centres. The models were externally validated using a prospective cohort consisting of 705 German patients with SN-positive: 473 trial participants of the German Dermatologic Cooperative Oncology Group study (DeCOG-SLT) and 232 screened patients. A nomogram was developed for graphical presentation. ResultsThe final model for recurrence and the calibrated models for DM and OM included ulceration, age, SN tumour burden and Breslow thickness. The models showed reasonable calibration. The c-index for the recurrence, DM and OM model was 0.68, 0.70 and 0.70, respectively, and 0.70, 0.72 and 0.74, respectively, in external validation. The EORTC-DeCOG model identified a robust low-risk group, with all identified low-risk patients (approximately 4% of the entire population) having a 5-year recurrence probability of <25% and an overall 5-year recurrence rate of 13%. A model including information on completion lymph node dissection (CLND) showed only marginal improvement in model performance. ConclusionsThe EORTC-DeCOG nomogram provides an adequate prognostic tool for patients with SN-positive melanoma, without the need for CLND. It showed consistent results across validation. The nomogram could be used for patient counselling and might aid in adjuvant therapy decision-making.

Kinetics of SARS-CoV-2 Positivity of Infected and Recovered Patients: A Single Center COVID-19 Experience and Potential Implications

BACKGROUND: Recurrence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positive detection in infected but recovered individuals has been reported. Patients who have recovered from coronavirus disease 2019 (COVID-19) could profoundly impact the health care system if a subset were to be polymerase chain reaction (PCR)-positive again with reactivated SARS-CoV-2. We sought to define the kinetics and relevance of PCR-positive recurrence during recovery from acute COVID-19 to better understand risks for prolonged infectivity and reinfection. METHODS: A series of COVID-19 414 patients, at The Second Affiliated Hospital of Southern University of Science and Technology in Shenzhen, China from January 11 to April 23, 2020. Univariable and multivariable statistical analysis of inpatient data were performed to develop an algorithm to predict patients at risk of recurrence of PCR positivity. FINDINGS: 16·7% recovered patients with PCR positive recurring one to three times, despite being in strict quarantine. Younger patients with mild pulmonary respiratory syndrome had higher risk of PCR positivity recurrence. The recurrence prediction model had an area under the ROC curve of 0·786. INTERPRETATION: This case series provides clinical characteristics of recovered COVID-19 patients with recurrent SARS-CoV-2 positivity, despite strict quarantine, at a 16·7% rate. Use of a recurrence prediction algorithm may identify patients at high risk of recurrent SARS-CoV-2 positivity and help understand reactivation and reinfection possibilities to establish protocols for health policy. FUNDING STATEMENT: This work was supported by grants from Sanming Project of Medicine in Shenzhen (Jia Huang, No. SZSM201812065); Bill & Melinda Gates Foundations (Lei Liu); and from National Natural Science Foundation of China (Jia Huang, No. 81501651) DECLARATION OF INTERESTS: The authors declare no competing interests. ETHICS APPROVAL STATEMENT: This study was approved by the Ethics Committee of the Second Affiliated Hospital of Southern University of Science and Technology. Written informed consent was obtained from all patients.

Development of Sex-Stratified Prediction Models for Recurrent Venous Thromboembolism: A Danish Nationwide Cohort Study

To optimize decision making for anticoagulant treatment duration after incident venous thromboembolism, we derived and internally validated two clinically applicable sex-specific prediction models for venous thromboembolism recurrence, discarding the traditional categorization of provoked and unprovoked venous thromboembolism. This study was based on data from Danish nationwide registries. We identified all routine care in- and outpatients with completed anticoagulant treatment for incident venous thromboembolism from 2012 through 2017. The outcome was recurrent venous thromboembolism within 2 years. Risk scores were derived using Cox regression analysis and a backward selection process on a set of 24 potential predictors. Performance was assessed through calibration and discrimination using bootstrap techniques to internally validate the scores. The study included 11,519 patients. Risk scores under the joint acronym AIM-SHA-RP were developed. Age, Incident pulmonary embolism, and recent Major surgery were predictors for both sexes; Statin treatment, Heart disease and Antiplatelet treatment were predictors specifically for men, while chronic Renal disease and recent Pneumonia or sepsis were predictors specifically for women. The risk scores were well calibrated and identified a low- (< 5%), intermediate- (5-10%), and high-risk (> 10%) group for both sexes. Generally, discriminative capacities, as measured by the c-statistic, were limited. We developed two clinically applicable risk scores to estimate the risk of recurrent venous thromboembolism after completed anticoagulant treatment. The risk scores can potentially guide treatment duration of anticoagulation after incident venous thromboembolism but require further external validation before implemented in clinical practice.

Radiomics-Based Outcome Prediction for Pancreatic Cancer Following Stereotactic Body Radiotherapy.

(1) Background: Radiomics use high-throughput mining of medical imaging data to extract unique information and predict tumor behavior. Currently available clinical prediction models poorly predict treatment outcomes in pancreatic adenocarcinoma. Therefore, we used radiomic features of primary pancreatic tumors to develop outcome prediction models and compared them to traditional clinical models. (2) Methods: We extracted and analyzed radiomic data from pre-radiation contrast-enhanced CTs of 74 pancreatic cancer patients undergoing stereotactic body radiotherapy. A panel of over 800 radiomic features was screened to create overall survival and local-regional recurrence prediction models, which were compared to clinical prediction models and models combining radiomic and clinical information. (3) Results: A 6-feature radiomic signature was identified that achieved better overall survival prediction performance than the clinical model (mean concordance index: 0.66 vs. 0.54 on resampled cross-validation test sets), and the combined model improved the performance slightly further to 0.68. Similarly, a 7-feature radiomic signature better predicted recurrence than the clinical model (mean AUC of 0.78 vs. 0.66). (4) Conclusion: Overall survival and recurrence can be better predicted with models based on radiomic features than with those based on clinical features for pancreatic cancer.

A preoperative risk model for early recurrence after radical resection may facilitate initial treatment decisions concerning the use of neoadjuvant therapy for patients with pancreatic ductal adenocarcinoma

BackgroundNeoadjuvant chemotherapy may benefit patients with pancreatic ductal adenocarcinoma with resectable and borderline disease. Inappropriate use of neoadjuvant therapy, however, may lead to the loss of therapeutic opportunities. Until an effective prediction model of individual drug sensitivity is established, no accurate model exists to help surgeons decide on the appropriate use of neoadjuvant chemotherapy. We hypothesized that early recurrence in patients undergoing upfront, early resection may be an indication for neoadjuvant chemotherapy. Therefore, we aimed to use preoperative clinical parameters to establish a model of early recurrence to select patients at high risk for neoadjuvant chemotherapy. MethodsPatients who underwent resection for pancreatic ductal adenocarcinoma between January 2014 and November 2017 were analyzed retrospectively. After the minimum P-value approach, the patients were divided into three groups: early recurrence, middle recurrence, and late/non-recurrence. Preoperative clinicopathologic factors that could predict early recurrence were included in a Cox proportional hazards regression model for univariate and multivariate analyses. The factors related to early recurrence were included to establish nomogram and decision tree models, which were then validated in 68 patients. ResultsWe found that 235 (72.5%) of 324 patients had recurrence with a median recurrence-free survival of 210 days. The early recurrence, middle recurrence, and late/non-recurrence groups differed in preoperative carbohydrate antigen 19-9 and carcinoembryonic antigen levels, “resectability” on cross-sectional imaging, resection requiring a vascular resection, T stage, tumor size, and adjuvant chemotherapy. The best cutoff value of early recurrence was the first 162 days postoperatively. Univariate and multivariate analyses showed that selected preoperative chief complaints, lymph node enlargement and resectability on cross-sectional imaging, preoperative carbohydrate antigen 19-9 levels >210 kU/L, and a neutrophil/lymphocyte ratio >4.2 were independent predictors for early recurrence. ConclusionWe have successfully built a prediction model of early recurrence of patients with pancreatic ductal adenocarcinoma with the optimal cutoff early-recurrence value of 162 days. Our nomogram and decision tree models may be used to select those at high risk for early recurrence to guide preoperative decision-making concerning the use of neoadjuvant therapy in those patients who have “resectable” disease and not only the more classic criteria of borderline resectability.

Identification of an exosomal long non-coding RNAs panel for predicting recurrence risk in patients with colorectal cancer.

Recurrence is a major cause of cancer-related deaths in colorectal cancer (CRC) patients, but the current strategies are limited to predict this clinical behavior. Our aim is to develop a recurrence prediction model based on long non-coding RNAs (lncRNAs) in exosomes of serum to improve the prediction accuracy. In discovery phase, 11 lncRNAs were found to be associated with CRC recurrence in tissues using high-throughput lncRNAs microarray and reverse transcription quantitative real-time PCR. And, 9 of them were correlated with their expression levels of serum exosomes. In training phase, a model based on 5-exosomal lncRNAs (exolncRNAs) panel was constructed, and showed high distinguish capability for recurrent CRC patients. ROC showed the panel was superior to serum CEA and CA19-9 in prediction of CRC recurrence. In both training and test sets, high-risk patients defined by the 5-exolncRNAs panel had poor recurrence free and overall survival. And, COX model showed it was an independent factor for CRC prognosis. Moreover, there was a significant relationship in detection of 5-exolncRNAs between plasma samples and paired serum samples. In summary, the 5-exolncRNAs panel robustly stratifies CRC patients’ risk of recurrence, enabling more accurate prediction of prognosis.

Exome sequencing identified six copy number variations as a prediction model for recurrence of primary prostate cancers with distinctive prognosis.

BackgroundProstate cancer (PCa) is a common type of malignancy, which represents one of the leading causes of death among men worldwide. Copy number variations (CNVs) and gene fusions play important roles in PCa and may serve as markers for the prognosis of this condition.MethodsWe have presently conducted an analysis of CNVs and gene fusions in PCa, using whole exome sequencing (WES) data of primary tumors. For this, a cohort of 74 PCa patients, including 30 recurrent and 44 non-recurrent cases, were assessed during 5 years of follow-up.ResultsWe have identified 66 CNVs that were specific to the primary tumor tissues from the recurrent PCa group. Most of duplicated genomic regions were located in 8q2, suggesting that this chromosomal region could be important for the prognosis of PCa. Meanwhile, we have developed a random forest model, using six selected CNVs, with an accuracy near 90% for predicting PCa recurrence according to a 10-fold cross validation. In addition, we have detected 16 recurrent oncogenic gene fusions in PCa. Among these, ALK (ALK receptor tyrosine kinase)-involved fusions were the most common type of gene fusion (n=7). Four of these fusions (i.e., EML4-ALK, STRN-ALK, CLTC-ALK, ETV6-ALK) were previously identified in other cancer types, while the remaining three gene fusions (FRYL-ALK, ABL1-ALK, and BCR-ALK) were here identified.ConclusionsOur findings expand the current understanding in regard to prostate carcinogenesis. Current data might be further used for assay development as well as to predict PCa recurrence, using primary tissues.

Challenges and countermeasures in the treatment of luminal breast cancer

Luminal breast cancer is the most common subtype of breast cancer, representing more than 60% of all breast cancers. Endocrine resistance and late recurrence are two challenges in the treatment of luminal breast cancer. To overcome endocrine resistance in multiple levels, high-dose-fulvestrant can inhibit estrogen-receptor (ER)-dependent pathways, while targeted drugs can block ER-independent pathways.To reduce the risk of late recurrence in luminal breast cancer, recurrence prediction model should be formed. For patients with high risk of late recurrence, extended endocrine therapy, combination of ovarian function suppression (OFS) or vascular endothelial growth factor (VEGF) inhibitor could be utilized. Based on the challenges of the treatment, scientific research achievements can be used in clinical practice, and finally optimize the clinical treatment strategy.

Evaluation of a serum tumour marker-based recurrence prediction model after radiofrequency ablation for hepatocellular carcinoma.

A recent study showed that serum tumour marker-based MoRAL score (11×√protein induced by vitamin K absence-II [PIVKA] +2×√alpha-foetoprotein [AFP]) can reflect both tumour burden and aggressiveness of hepatocellular carcinoma (HCC). This study aimed to evaluate whether baseline MoRAL score could predict tumour recurrence after radiofrequency ablation (RFA) for very-early/early-stage HCC. A total of 576 HCC patients who underwent RFA as initial treatment were enrolled from two tertiary referral hospitals (256 in development cohort and 320 in validation cohort). The primary endpoint was recurrence-free survival (RFS) and the secondary endpoints included cumulative risks of intrahepatic distant recurrence (IDR) and extrahepatic metastasis (EM). In the development cohort, MoRAL score was an independent prognostic factor of RFS (P=.02). The optimal cutoff MoRAL score for predicting RFS was 68. Patients with high MoRAL score (>68) showed significantly shorter RFS than did those with low MoRAL score (hazard ratio [HR]=2.04, P<.001). The 5-year RFS rates were 32.3% and 53.2% in high- and low-MoRAL groups respectively. Risks of both IDR (HR=1.76, P=.003) and EM (HR=8.25, P=.006) were also significantly higher in high MoRAL group. These results were reproduced in the validation cohort: RFS (HR=1.81, P<.001; 5-year RFS rates=27.7% vs 53.6%) was significantly shorter and risks of IDR (HR=1.59, P=.003) and EM (HR=6.19, P=.004) were significantly higher in high MoRAL group. A high MoRAL score of >68 was significant a predictive factor of tumour recurrence after RFA for very-early/early-stage HCC. Moreover, it might be warranted to evaluate EM in patients with high baseline MoRAL scores.

Identification of a novel gene signature for the prediction of recurrence in HCC patients by machine learning of genome-wide databases

Hepatocellular carcinoma (HCC) is a common malignant tumor in China. In the present study, we aimed to construct and verify a prediction model of recurrence in HCC patients using databases (TCGA, AMC and Inserm) and machine learning methods and obtain the gene signature that could predict early relapse of HCC. Statistical methods, such as feature selection, survival analysis and Chi-Square test in R software, were used to analyze and select mutant genes related to disease free survival (DFS), race and vascular invasion. In addition, whole-exome sequencing was performed on 10 HCC patients recruited from our center, and the sequencing results were compared with the databases. Using the databases and machine learning methods, the prediction model of recurrence was constructed and optimized, and the selected mutant genes were verified in the test group. The accuracy of prediction was 74.19%. Moreover, these 10 patients from our center were used to verify these mutant genes and the prediction model, and a success rate of 80% was achieved. Collectively, we discovered recurrence-related genes and established recurrence prediction model of recurrence for HCC patients, which could provide significant guidance for clinical prediction of recurrence.

Development of a multivariable prediction model for plantar foot ulcer recurrence in high-risk people with diabetes

IntroductionForty per cent of people with diabetes who heal from a foot ulcer recur within 1 year. The aim was to develop a prediction model for plantar foot ulcer recurrence...

Development of screening tools to predict the risk of recurrence and related complications following anal fistula surgery: protocol for a prospective cohort study

IntroductionPostoperative recurrence and related complications are common and related to poor outcomes in patients with anal fistula (AF). Due to being associated with short-term and long-term cure rates, perioperative complications...

Systematic review: risk prediction models for recurrence of hepatocellular carcinoma after liver transplantation.

Recurrence of hepatocellular carcinoma (HCC) after liver transplantation (LT) is a significant clinical problem associated with poor surgical outcomes. This study aims to summarize the current evidence on risk prediction models of HCC recurrence after LT. PubMed and EMBASE were searched to May 25, 2019, for relevant articles. Studies originally designed to develop or validate a risk prediction model for HCC recurrence after LT were included. Two independent authors summarized the study characteristics and evaluated the risk of bias and applicability concerns in the included studies. From 26 included studies, 18 original risk prediction models were determined, but only five models were externally validated. The average number of predictors involved in the construction of risk models was three. The most frequently employed predictors were alpha-fetoprotein, tumor size, vascular invasion, tumor number, tumor differentiation, and neutrophil-lymphocyte ratio. Most studies showed good discriminatory performance (AUC >0.75). The overall quality of the included studies was generally low. Most of the original models lacked the highly recommended external and prospective validation in diverse populations. The AFP model was the well-validated preoperative risk model that can stratify patients into high- and low-risk groups.

Outcomes of trastuzumab therapy in HER2-positive early breast cancer patients: extended follow-up of JBCRG-cohort study 01

BackgroundPrevious large trials of trastuzumab (TZM) demonstrated improved outcomes in patients with HER2-positive early breast cancer. However, its effectiveness and safety in Japanese patients is not yet clear. Recently, new anti-HER2 agents were developed to improve treatment outcomes, but the patient selection criteria remain controversial.PurposeThe aim of this study was to evaluate the long-term effectiveness of TZM therapy as perioperative therapy for HER2-positive operable breast cancer in daily clinical practice and to create a recurrence prediction model for therapeutic selection.MethodsAn observational study was conducted in Japan (UMIN000002737) to observe the prognosis of women (n = 2024) with HER2-positive invasive breast cancer who received TZM for stage I–III C disease between July 2009 and June 2011. Moreover, a recurrence-predicting model was designed to evaluate the risk factors for recurrence.ResultsThe 5- and 10-year disease-free survival (DFS) rates were 88.9 (95% CI 87.5–90.3%) and 82.4% (95% CI 79.2–85.6%), respectively. The 5- and 10-year overall survival (OS) rates were 96% (95% CI 95.1–96.9%) and 92.7% (95% CI 91.1–94.3%), respectively. Multivariate analysis revealed that the risk factors for recurrence were an age of ≥ 70 years, T2 or larger tumors, clinically detected lymph node metastasis, histological tumor diameter of > 1 cm, histologically detected lymph node metastasis (≥ n2), and the implementation of preoperative treatment. The 5-year recurrence rate under the standard treatment was estimated to be > 10% in patients with a score of 3 or greater on the recurrence-predicting model.ConclusionThe recurrence-predicting model designed in this study may improve treatment selection of patients with stage I–III C disease. However, further studies are needed to validate the scores generated by this model.

Comparison of Prediction Models based on Risk Factors and Retinal Characteristics Associated with Recurrence One Year after Ischemic Stroke

Objectives: To develop risk estimation models for 1-year ischemic stroke recurrence using clinical risk factors and retinal characteristics. Methods: From June 2017 to January 2019, 332 patients with first-ever ischemic stroke were enrolled and followed up in the Shenzhen Traditional Chinese Medicine hospital in China. The primary endpoint was defined as fatal or recurrent stroke after 1 year of the index stroke. Clinical risk factors and retinal characteristics were identified by multivariate logistic models. Results: The multivariate logistic model with only clinical risk factors showed that Cerebral Atherosclerosis (OR 1.68, 95%CI: 1.000-2.81), white matter lesions (OR 3.61, 95%CI: 2.18-5.98), and Cardiac disease (OR 1.88, 95%CI: 1.02-3.46) were statistically significantly associated with higher stroke recurrence risk. The sensitivity and specificity of this model were 69.1% and 68.4% respectively. The multivariate logistic model with only retinal characteristics showed that central retinal venule equivalent (OR .34, 95%CI: .14-.83), hemorrhage (OR .6, 95%CI: .41-.88), exudate (OR 1.64, 95%CI: 1.16-2.32), central retinal artery equivalent (OR 2.95, 95%CI: 1.23-7.08), and Aangle (OR 0.8, 95%CI: .61-1.004) were statistically significantly associated with stroke recurrence. The sensitivity and specificity of the model were 62.0% and 64.4% respectively. The multivariate logistic model with both clinical risk factors and retinal characteristics showed that cerebral atherosclerosis (OR 1.74, 95%CI: 1.020-2.981), white matter lesions (OR 3.65, 95%CI: 2.17-6.13), cardiac disease (OR 1.99, 95%CI: 1.06-3.74), hemorrhage (OR .68, 95%CI: .49-.96), exudate (OR 1.65, 95%CI: 1.16-2.36) were independent risk factors of stroke recurrence. The sensitivity and specificity of the model were 72.5% and 70.7% respectively. Conclusions: Combining the traditional risk factors of stroke with the retinal vessels characteristics to establish the recurrent cerebral infarction prediction model may improve the accuracy of the prediction.