scholarly journals An Externally Validated Dynamic Nomogram for Predicting Unfavorable Prognosis in Patients With Aneurysmal Subarachnoid Hemorrhage

2021 ◽  
Vol 12 ◽  
Author(s):  
Ping Hu ◽  
Yang Xu ◽  
Yangfan Liu ◽  
Yuntao Li ◽  
Liguo Ye ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Prognostic Factors ◽  
Subarachnoid Hemorrhage ◽  
Validation Cohort ◽  
Aneurysmal Subarachnoid Hemorrhage ◽  
External Validation ◽  
Brier Score ◽  
Web Based ◽  
Training Cohort ◽  
Multivariable Logistic Regression

Background: Aneurysmal subarachnoid hemorrhage (aSAH) leads to severe disability and functional dependence. However, no reliable method exists to predict the clinical prognosis after aSAH. Thus, this study aimed to develop a web-based dynamic nomogram to precisely evaluate the risk of poor outcomes in patients with aSAH.Methods: Clinical patient data were retrospectively analyzed at two medical centers. One center with 126 patients was used to develop the model. Least absolute shrinkage and selection operator (LASSO) analysis was used to select the optimal variables. Multivariable logistic regression was applied to identify independent prognostic factors and construct a nomogram based on the selected variables. The C-index and Hosmer–Lemeshow p-value and Brier score was used to reflect the discrimination and calibration capacities of the model. Receiver operating characteristic curve and calibration curve (1,000 bootstrap resamples) were generated for internal validation, while another center with 84 patients was used to validate the model externally. Decision curve analysis (DCA) and clinical impact curves (CICs) were used to evaluate the clinical usefulness of the nomogram.Results: Unfavorable prognosis was observed in 46 (37%) patients in the training cohort and 24 (29%) patients in the external validation cohort. The independent prognostic factors of the nomogram, including neutrophil-to-lymphocyte ratio (NLR) (p = 0.005), World Federation of Neurosurgical Societies (WFNS) grade (p = 0.002), and delayed cerebral ischemia (DCI) (p = 0.0003), were identified using LASSO and multivariable logistic regression. A dynamic nomogram (https://hu-ping.shinyapps.io/DynNomapp/) was developed. The nomogram model demonstrated excellent discrimination, with a bias-corrected C-index of 0.85, and calibration capacities (Hosmer–Lemeshow p-value, 0.412; Brier score, 0.12) in the training cohort. Application of the model to the external validation cohort yielded a C-index of 0.84 and a Brier score of 0.13. Both DCA and CIC showed a superior overall net benefit over the entire range of threshold probabilities.Conclusion: This study identified that NLR on admission, WFNS grade, and DCI independently predicted unfavorable prognosis in patients with aSAH. These factors were used to develop a web-based dynamic nomogram application to calculate the precise probability of a poor patient outcome. This tool will benefit personalized treatment and patient management and help neurosurgeons make better clinical decisions.

scholarly journals A Nomogram for Predicting the Need of Postoperative Tracheostomy in Patients With Aneurysmal Subarachnoid Hemorrhage

2021 ◽  
Vol 12 ◽  
Author(s):  
Xiao-Yong Chen ◽  
Yue Chen ◽  
Ni Lin ◽  
Jin-Yuan Chen ◽  
Chen-Yu Ding ◽  
...  
Keyword(s):  
Subarachnoid Hemorrhage ◽  
Predictive Power ◽  
Validation Cohort ◽  
Aneurysmal Subarachnoid Hemorrhage ◽  
External Validation ◽  
Area Under The Curve ◽  
World Federation ◽  
Training Cohort ◽  
Baseline Information ◽  
High World

Objective: Early identification for the need of tracheostomy (TT) in aneurysmal subarachnoid hemorrhage (aSAH) patients remains one of the main challenges in clinical practice. Our study aimed to establish and validate a nomogram model for predicting postoperative TT in aSAH patients.Methods: Patients with aSAH receiving active treatment (interventional embolization or clipping) in our institution between June 2012 and December 2018 were retrospectively included. The effects of patients' baseline information, aneurysm features, and surgical factors on the occurrence of postoperative TT were investigated for establishing a nomogram in the training cohort with 393 patients. External validation for the nomogram was performed in the validation cohort with 242 patients.Results: After multivariate analysis, higher age, high neutrophil-to-lymphocyte ratio (NLR), high World Federation of Neurological Surgeons Scale (WFNS), and high Barrow Neurological Institute (BNI) grade were left in the final logistic regression model. The predictive power of the model was excellent in both training cohort and validation cohort [area under the curve (AUC): 0.924, 95% confidence interval [CI]: 0.893–0.948; AUC: 0.881, 95% CI: 0.833–0.919]. A nomogram consisting of these factors had a C-index of 0.924 (95% CI: 0.869–0.979) in the training cohort and was validated in the validation cohort (C-index: 0.881, 95% CI: 0.812–0.950). The calibration curves suggested good match between prediction and observation in both training and validation cohorts.Conclusion: Our study established and validated a nomogram model for predicting postoperative TT in aSAH patients.

Low-Dose Intravenous Heparin Infusion After Aneurysmal Subarachnoid Hemorrhage is Associated With Decreased Risk of Delayed Neurological Deficit and Cerebral Infarction

Neurosurgery ◽  
2020 ◽  
Author(s):  
Matthew J Kole ◽  
Aaron P Wessell ◽  
Beatrice Ugiliweneza ◽  
Gregory J Cannarsa ◽  
Enzo Fortuny ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Subarachnoid Hemorrhage ◽  
Cerebral Infarction ◽  
Low Dose ◽  
Aneurysmal Subarachnoid Hemorrhage ◽  
Neurological Deficits ◽  
Comparison Analysis ◽  
Cohort Comparison ◽  
Multivariable Logistic Regression ◽  
Intravenous Heparin

Abstract BACKGROUND Patients who survive aneurysmal subarachnoid hemorrhage (aSAH) are at risk for delayed neurological deficits (DND) and cerebral infarction. In this exploratory cohort comparison analysis, we compared in-hospital outcomes of aSAH patients administered a low-dose intravenous heparin (LDIVH) infusion (12 U/kg/h) vs those administered standard subcutaneous heparin (SQH) prophylaxis for deep vein thrombosis (DVT; 5000 U, 3 × daily). OBJECTIVE To assess the safety and efficacy of LDIVH in aSAH patients. METHODS We retrospectively analyzed 556 consecutive cases of aSAH patients whose aneurysm was secured by clipping or coiling at a single institution over a 10-yr period, including 233 administered the LDIVH protocol and 323 administered the SQH protocol. Radiological and outcome data were compared between the 2 cohorts using multivariable logistic regression and propensity score-based inverse probability of treatment weighting (IPTW). RESULTS The unadjusted rate of cerebral infarction in the LDIVH cohort was half that in SQH cohort (9 vs 18%; P = .004). Multivariable logistic regression showed that patients in the LDIVH cohort were significantly less likely than those in the SQH cohort to have DND (odds ratio (OR) 0.53 [95% CI: 0.33, 0.85]) or cerebral infarction (OR 0.40 [95% CI: 0.23, 0.71]). Analysis following IPTW showed similar results. Rates of hemorrhagic complications, heparin-induced thrombocytopenia and DVT were not different between cohorts. CONCLUSION This cohort comparison analysis suggests that LDIVH infusion may favorably influence the outcome of patients after aSAH. Prospective studies are required to further assess the benefit of LDIVH infusion in patients with aSAH.

scholarly journals A Web-Based Prediction Model for Cancer-Specific Survival of Elderly Patients With Early Hepatocellular Carcinoma: A Study Based on SEER Database

2022 ◽  
Vol 9 ◽  
Author(s):  
Taiyu He ◽  
Tianyao Chen ◽  
Xiaozhu Liu ◽  
Biqiong Zhang ◽  
Song Yue ◽  
...  
Keyword(s):  
Elderly Patients ◽  
Validation Cohort ◽  
External Validation ◽  
Risk Groups ◽  
Cancer Specific Survival ◽  
Web Based ◽  
Training Cohort ◽  
Kaplan Meier ◽  
Early Hcc ◽  
High Consistency

Background: Primary liver cancer is a common malignant tumor primarily represented by hepatocellular carcinoma (HCC). The number of elderly patients with early HCC is increasing, and older age is related to a worse prognosis. However, an accurate predictive model for the prognosis of these patients is still lacking.Methods: Data of eligible elderly patients with early HCC in Surveillance, Epidemiology, and End Results database from 2010 to 2016 were downloaded. Patients from 2010 to 2015 were randomly assigned to the training cohort (n = 1093) and validation cohort (n = 461). Patients' data in 2016 (n = 431) was used for external validation. Independent prognostic factors were obtained using univariate and multivariate analyses. Based on these factors, a cancer-specific survival (CSS) nomogram was constructed. The predictive performance and clinical practicability of our nomogram were validated. According to the risk scores of our nomogram, patients were divided into low-, intermediate-, and high-risk groups. A survival analysis was performed using Kaplan–Meier curves and log-rank tests.Results: Age, race, T stage, histological grade, surgery, radiotherapy, and chemotherapy were independent predictors for CSS and thus were included in our nomogram. In the training cohort and validation cohort, the concordance indices (C-indices) of our nomogram were 0.739 (95% CI: 0.714–0.764) and 0.756 (95% CI: 0.719–0.793), respectively. The 1-, 3-, and 5-year areas under receiver operating characteristic curves (AUCs) showed similar results. Calibration curves revealed high consistency between observations and predictions. In external validation cohort, C-index (0.802, 95%CI: 0.778–0.826) and calibration curves also revealed high consistency between observations and predictions. Compared with the TNM stage, nomogram-related decision curve analysis (DCA) curves indicated better clinical practicability. Kaplan–Meier curves revealed that CSS significantly differed among the three different risk groups. In addition, an online prediction tool for CSS was developed.Conclusions: A web-based prediction model for CSS of elderly patients with early HCC was constructed and validated, and it may be helpful for the prognostic evaluation, therapeutic strategy selection, and follow-up management of these patients.

Association between weekend admissions and mortality after aneurysmal subarachnoid hemorrhage: the “weekend effect” revisited

2020 ◽  
Vol 132 (4) ◽  
pp. 1167-1173 ◽  
Author(s):  
Mirriam Mikhail ◽  
Oliver G. S. Ayling ◽  
Matthew E. Eagles ◽  
George M. Ibrahim ◽  
R. Loch Macdonald
Keyword(s):  
Logistic Regression ◽  
Subarachnoid Hemorrhage ◽  
Neurological Outcome ◽  
Aneurysmal Subarachnoid Hemorrhage ◽  
Weekend Effect ◽  
Medical Specialties ◽  
Poor Grade ◽  
Multivariable Logistic Regression ◽  
Wide Range

OBJECTIVEHigher mortality has been reported with weekend or after-hours patient admission across a wide range of surgical and medical specialties, a phenomenon termed the “weekend effect.” The authors evaluated whether weekend admission contributed to death and long-term neurological outcome in patients following aneurysmal subarachnoid hemorrhage.METHODSA post hoc analysis of the Clazosentan to Overcome Neurological Ischemia and Infarction Occurring After Subarachnoid Hemorrhage (CONSCIOUS-1) study was conducted. Univariable and stepwise multivariable logistic regression analyses were performed to assess the associations between weekend admission and mortality and long-term neurological outcome.RESULTSOf 413 subjects included in the CONSCIOUS-1 study, 140 patients had been admitted during the weekend. A significant interaction was identified between weekend admission and neurological grade on presentation, suggesting that the outcomes of patients who had initially presented with a poor grade were disproportionately influenced by the weekend admission. On stepwise multivariable logistic regression in the subgroup of patients who had presented with a poor neurological grade (29 of 100 patients), admission on the weekend was found to be independently associated with death (OR 6.59, 95% CI 1.62–26.88, p = 0.009). Weekend admission was not associated with long-term neurological outcome.CONCLUSIONSWeekend admission was an independent risk factor for death within 12 weeks following aneurysmal subarachnoid hemorrhage in patients presenting with a poor neurological grade. Further work is required to identify and mitigate any mediating factors.

scholarly journals Computed-Tomography-Based Radiomics Model for Predicting the Malignant Potential of Gastrointestinal Stromal Tumors Preoperatively: A Multi-Classifier and Multicenter Study

2021 ◽  
Vol 11 ◽  
Author(s):  
Minhong Wang ◽  
Zhan Feng ◽  
Lixiang Zhou ◽  
Liang Zhang ◽  
Xiaojun Hao ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Random Forest ◽  
Risk Stratification ◽  
Gastrointestinal Stromal Tumors ◽  
Validation Cohort ◽  
External Validation ◽  
Random Forest Model ◽  
Training Cohort ◽  
Stromal Tumors ◽  
Forest Model

Background: Our goal was to establish and verify a radiomics risk grading model for gastrointestinal stromal tumors (GISTs) and to identify the optimal algorithm for risk stratification.Methods: We conducted a retrospective analysis of 324 patients with GISTs, the presence of which was confirmed by surgical pathology. Patients were treated at three different hospitals. A training cohort of 180 patients was collected from the largest center, while an external validation cohort of 144 patients was collected from the other two centers. To extract radiomics features, regions of interest (ROIs) were outlined layer by layer along the edge of the tumor contour on CT images of the arterial and portal venous phases. The dimensionality of radiomic features was reduced, and the top 10 features with importance value above 5 were selected before modeling. The training cohort used three classifiers [logistic regression, support vector machine (SVM), and random forest] to establish three GIST risk stratification prediction models. The receiver operating characteristic curve (ROC) was used to compare model performance, which was validated by external data.Results: In the training cohort, the average area under the curve (AUC) was 0.84 ± 0.07 of the logistic regression, 0.88 ± 0.06 of the random forest, and 0.81 ± 0.08 of the SVM. In the external validation cohort, the AUC was 0.85 of the logistic regression, 0.90 of the random forest, and 0.80 of the SVM. The random forest model performed the best in both the training and the external validation cohorts and could be generalized.Conclusion: Based on CT radiomics, there are multiple machine-learning models that can predict the risk of GISTs. Among them, the random forest algorithm had the highest prediction efficiency and could be readily generalizable. Through external validation data, we assume that the random forest model may be used as an effective tool to guide preoperative clinical decision-making.

scholarly journals A Novel Clinical Nomogram for Predicting Lymph Node Metastasis in Ovarian Cancer: A SEER Analysis and External Validation in a Tertiary Center

2021 ◽  
Author(s):  
Guiping Zhang ◽  
Wei Ren
Keyword(s):  
Logistic Regression ◽  
Lymph Node ◽  
Validation Cohort ◽  
External Validation ◽  
Internal Validation ◽  
Training Cohort ◽  
Preoperative Serum ◽  
Histology Grade ◽  
Serum Ca125 ◽  
Serum Ca125 Level

Abstract Introduction The aim of the study is to investigate the risk factors for developing lymph node metastases (LNM) in cases diagnosed as a presumed early-stage ovarian carcinoma (OC). Methodology Information of patients who had been diagnosed as OC in 2018 was obtained from the SEER database. We enrolled 104 OC patients in General Hospital of Northern Theatre Command for external validation. A logistic regression was conducted to determine the independent predictors for LNM, which were used for establishing a nomogram. In order to evaluate the reliability of nomogram, we applied a receiver operating characteristic curve (ROC) analysis, calibration curves and plotted decision curves. Results We found that age(≥70, OR=0.544, p=0.022), histology type (Mucinous carcinoma, OR=0.390, p=0.001; Endometrioid carcinoma, OR=7.946, p=0.053; Others, OR=2.400, p=0.040), histology grade (Grade II, OR=2.423, p=0.028; Grade III, OR=1.982, p=0.152; Grade IV, OR=1.594, p=0.063) and preoperative serum CA125 level (positive, OR=2.236, p=0.001) were all significant predictors of LNM. The AUC of the model training cohort, internal validation cohort, and external validation cohort were 0.78, 0.79 and 0.76 respectively. The calibration curves showed that the predicted outcome fitted well to the observed outcome in the training cohort (p=0.825) internal validation cohort (p=0.503), and external validation cohort (p=0.108). The decision curves showed the nomogram had more benefits than the All or None scheme if the threshold probability is >50% and <100% in training cohort and internal validation cohort, >30% and <90% in the external validation cohort. Conclusion The multivariate logistic regression showed that age, histology type, histology grade and preoperative serum CA125 level were all significant predictors of LNM. The nomogram established using the above variables had great performance for clinical applying.

scholarly journals Development and Validation of a Nomogram for Preoperative Prediction of Lymph Node Metastasis in Lung Adenocarcinoma Based on Radiomics Signature and Deep Learning Signature

2021 ◽  
Vol 11 ◽  
Author(s):  
Jia Ran ◽  
Ran Cao ◽  
Jiumei Cai ◽  
Tao Yu ◽  
Dan Zhao ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Deep Learning ◽  
Prediction Model ◽  
Validation Cohort ◽  
External Validation ◽  
Training Cohort ◽  
Preoperative Prediction ◽  
Lemeshow Test ◽  
Radiomics Signature

Background and PurposeThe preoperative LN (lymph node) status of patients with LUAD (lung adenocarcinoma) is a key factor for determining if systemic nodal dissection is required, which is usually confirmed after surgery. This study aimed to develop and validate a nomogram for preoperative prediction of LN metastasis in LUAD based on a radiomics signature and deep learning signature.Materials and MethodsThis retrospective study included a training cohort of 200 patients, an internal validation cohort of 40 patients, and an external validation cohort of 60 patients. Radiomics features were extracted from conventional CT (computed tomography) images. T-test and Extra-trees were performed for feature selection, and the selected features were combined using logistic regression to build the radiomics signature. The features and weights of the last fully connected layer of a CNN (convolutional neural network) were combined to obtain a deep learning signature. By incorporating clinical risk factors, the prediction model was developed using a multivariable logistic regression analysis, based on which the nomogram was developed. The calibration, discrimination and clinical values of the nomogram were evaluated.ResultsMultivariate logistic regression analysis showed that the radiomics signature, deep learning signature, and CT-reported LN status were independent predictors. The prediction model developed by all the independent predictors showed good discrimination (C-index, 0.820; 95% CI, 0.762 to 0.879) and calibration (Hosmer-Lemeshow test, P=0.193) capabilities for the training cohort. Additionally, the model achieved satisfactory discrimination (C-index, 0.861; 95% CI, 0.769 to 0.954) and calibration (Hosmer-Lemeshow test, P=0.775) when applied to the external validation cohort. An analysis of the decision curve showed that the nomogram had potential for clinical application.ConclusionsThis study presents a prediction model based on radiomics signature, deep learning signature, and CT-reported LN status that can be used to predict preoperative LN metastasis in patients with LUAD.

Evidence for a conditioning effect of inhalational anesthetics on angiographic vasospasm after aneurysmal subarachnoid hemorrhage

2020 ◽  
Vol 133 (1) ◽  
pp. 152-158 ◽  
Author(s):  
Umeshkumar Athiraman ◽  
Diane Aum ◽  
Ananth K. Vellimana ◽  
Joshua W. Osbun ◽  
Rajat Dhar ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Subarachnoid Hemorrhage ◽  
Brain Injury ◽  
Aneurysmal Subarachnoid Hemorrhage ◽  
Multivariate Logistic Regression Analysis ◽  
Secondary Brain Injury ◽  
Large Artery ◽  
Inhalational Anesthetics ◽  
Angiographic Vasospasm ◽  
Inhalational Anesthetic

OBJECTIVEDelayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage (SAH) is characterized by large-artery vasospasm, distal autoregulatory dysfunction, cortical spreading depression, and microvessel thrombi. Large-artery vasospasm has been identified as an independent predictor of poor outcome in numerous studies. Recently, several animal studies have identified a strong protective role for inhalational anesthetics against secondary brain injury after SAH including DCI—a phenomenon referred to as anesthetic conditioning. The aim of the present study was to assess the potential role of inhalational anesthetics against cerebral vasospasm and DCI in patients suffering from an SAH.METHODSAfter IRB approval, data were collected retrospectively for all SAH patients admitted to the authors’ hospital between January 1, 2010, and December 31, 2013, who received general anesthesia with either inhalational anesthetics only (sevoflurane or desflurane) or combined inhalational (sevoflurane or desflurane) and intravenous (propofol) anesthetics during aneurysm treatment. The primary outcomes were development of angiographic vasospasm and development of DCI during hospitalization. Univariate and logistic regression analyses were performed to identify independent predictors of these endpoints.RESULTSThe cohort included 157 SAH patients whose mean age was 56 ± 14 (± SD). An inhalational anesthetic–only technique was employed in 119 patients (76%), while a combination of inhalational and intravenous anesthetics was employed in 34 patients (22%). As expected, patients in the inhalational anesthetic–only group were exposed to significantly more inhalational agent than patients in the combination anesthetic group (p < 0.05). Multivariate logistic regression analysis identified inhalational anesthetic–only technique (OR 0.35, 95% CI 0.14–0.89), Hunt and Hess grade (OR 1.51, 95% CI 1.03–2.22), and diabetes (OR 0.19, 95% CI 0.06–0.55) as significant predictors of angiographic vasospasm. In contradistinction, the inhalational anesthetic–only technique had no significant impact on the incidence of DCI or functional outcome at discharge, though greater exposure to desflurane (as measured by end-tidal concentration) was associated with a lower incidence of DCI.CONCLUSIONSThese data represent the first evidence in humans that inhalational anesthetics may exert a conditioning protective effect against angiographic vasospasm in SAH patients. Future studies will be needed to determine whether optimized inhalational anesthetic paradigms produce definitive protection against angiographic vasospasm; whether they protect against other events leading to secondary brain injury after SAH, including microvascular thrombi, autoregulatory dysfunction, blood-brain barrier breakdown, neuroinflammation, and neuronal cell death; and, if so, whether this protection ultimately improves patient outcome.

scholarly journals Radiomic nomogram based on MRI to predict grade of branching type intraductal papillary mucinous neoplasms of the pancreas: a multicenter study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sijia Cui ◽  
Tianyu Tang ◽  
Qiuming Su ◽  
Yajie Wang ◽  
Zhenyu Shu ◽  
...  
Keyword(s):  
Calibration Curve ◽  
Main Pancreatic Duct ◽  
Validation Cohort ◽  
External Validation ◽  
High Grade ◽  
Intraductal Papillary Mucinous Neoplasms ◽  
Training Cohort ◽  
Independent Validation ◽  
Medical Centers ◽  
Mucinous Neoplasms

Abstract Background Accurate diagnosis of high-grade branching type intraductal papillary mucinous neoplasms (BD-IPMNs) is challenging in clinical setting. We aimed to construct and validate a nomogram combining clinical characteristics and radiomic features for the preoperative prediction of low and high-grade in BD-IPMNs. Methods Two hundred and two patients from three medical centers were enrolled. The high-grade BD-IPMN group comprised patients with high-grade dysplasia and invasive carcinoma in BD-IPMN (n = 50). The training cohort comprised patients from the first medical center (n = 103), and the external independent validation cohorts comprised patients from the second and third medical centers (n = 48 and 51). Within 3 months prior to surgery, all patients were subjected to magnetic resonance examination. The volume of interest was delineated on T1-weighted (T1-w) imaging, T2-weighted (T2-w) imaging, and contrast-enhanced T1-weighted (CET1-w) imaging, respectively, on each tumor slice. Quantitative image features were extracted using MITK software (G.E.). The Mann-Whitney U test or independent-sample t-test, and LASSO regression, were applied for data dimension reduction, after which a radiomic signature was constructed for grade assessment. Based on the training cohort, we developed a combined nomogram model incorporating clinical variables and the radiomic signature. Decision curve analysis (DCA), a receiver operating characteristic curve (ROC), a calibration curve, and the area under the ROC curve (AUC) were used to evaluate the utility of the constructed model based on the external independent validation cohorts. Results To predict tumor grade, we developed a nine-feature-combined radiomic signature. For the radiomic signature, the AUC values of high-grade disease were 0.836 in the training cohort, 0.811 in external validation cohort 1, and 0.822 in external validation cohort 2. The CA19–9 level and main pancreatic duct size were identified as independent parameters of high-grade of BD-IPMNs using multivariate logistic regression analysis. The CA19–9 level and main pancreatic duct size were then used to construct the radiomic nomogram. Using the radiomic nomogram, the high-grade disease-associated AUC values were 0.903 (training cohort), 0.884 (external validation cohort 1), and 0.876 (external validation cohort 2). The clinical utility of the developed nomogram was verified using the calibration curve and DCA. Conclusions The developed radiomic nomogram model could effectively distinguish high-grade patients with BD-IPMNs preoperatively. This preoperative identification might improve treatment methods and promote personalized therapy in patients with BD-IPMNs.

Refining TNM-8 M1 categories with anatomic subgroups for previously untreated de novo metastatic nasopharyngeal carcinoma.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 6046-6046
Author(s):  
Sik-Kwan Chan ◽  
Cheng Lin ◽  
Shao Hui Huang ◽  
Tin Ching Chau ◽  
Qiaojuan Guo ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Nasopharyngeal Carcinoma ◽  
Bone Metastases ◽  
Validation Cohort ◽  
De Novo ◽  
Multivariable Analysis ◽  
Term Survival ◽  
Training Cohort ◽  
Study Results ◽  
Multivariable Analyses

6046 Background: The eighth edition TNM (TNM-8) classified de novo metastatic (metastatic disease at presentation) nasopharyngeal carcinoma (NPC) as M1 without further subdivision. However, survival heterogeneity exists and long-term survival has been observed in a subset of this population. We hypothesize that certain metastatic characteristics could further segregate survival for de novo M1 NPC. Methods: Patients with previously untreated de novo M1 NPC prospectively treated in two academic institutions (The University of Hong Kong [n = 69] and Provincial Clinical College of Fujian Medical University [n = 114] between 2007 and 2016 were recruited and re-staged based on TNM-8 in this study. They were randomized in 2:1 ratio to generate a training cohort (n = 120) and validation cohort (n = 63) respectively. Univariable and multivariable analyses (MVA) were performed for the training cohort to identify the anatomic prognostic factors of overall survival (OS). We then performed recursive partitioning analysis (RPA) which incorporated the anatomic prognostic factors identified in multivariable analyses and derived a new set of RPA stage groups (Anatomic-RPA groups) which predicted OS in the training cohort. The significance of Anatomic-RPA groups in the training cohort was then validated in the validation cohort. UVA and MVA were performed again on the validation cohorts to identify significant OS prognosticators. Results: The training and the validation cohorts had a median follow-up of 27.2 months and 30.2 months, respectively, with the 3-year OS of 51.6% and 51.1%, respectively. Univariable analysis (UVA) and multivariable analysis (MVA) revealed that co-existing liver and bone metastases was the only factor prognostic of OS. Anatomic-RPA groups based on the anatomic prognostic factors identified in UVA and MVA yielded good segregation (M1a: no co-existing liver and bone metastases and M1b: co-existing both liver and bone metastases; median OS 39.5 and 23.7 months respectively; P =.004). RPA for the validation set also confirmed good segregation with co-existing liver and bone metastases (M1a: no co-existing liver and bone metastases and M1b: co-existing liver and bone metastases), with median OS 47.7 and 16.0 months, respectively; P =.008). It was also the only prognostic factor in UVA and MVA in the validation cohort. Conclusions: Our Anatomic-RPA M1 stage groups with anatomical factors provided better subgroup segregation for de novo M1 NPC. The study results provide a robust justification to refine M1 categories in future editions of TNM staging classification.

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