scholarly journals Development and Validation of a Clinical Score for Predicting the Risk of Tympanosclerosis Before Surgery

2020 ◽  
pp. 014556132095167
Author(s):  
Zhihuai Dong ◽  
Mingguang Zhou ◽  
Gaofei Ye ◽  
Jing Ye ◽  
Mang Xiao
Keyword(s):  
Otitis Media ◽  
Calibration Curve ◽  
Validation Cohort ◽  
Predictive Accuracy ◽  
Strong Predictor ◽  
Multivariate Logistic Regression Analysis ◽  
Clinical Score ◽  
Type I ◽  
Discriminative Ability ◽  
Training Cohort

Objective: To develop and validate a clinical score to predict the risk of tympanosclerosis before surgery. Methods: A sample of 404 patients who underwent middle ear microsurgery for otitis media was enrolled. These patients were randomly divided into 2 cohorts: the training cohort (n = 243, 60%) and the validation cohort (n = 161, 40%). The preoperative predictors of tympanosclerosis were determined by multivariate logistic regression analysis and implemented using a clinical score tool. The predictive accuracy and discriminative ability of the clinical score were determined by the area under the curve (AUC) and the calibration curve. Results: The multivariate analysis in the training cohort (n = 243, 60%) identified independent factors for tympanosclerosis as the female sex (odds ratio [OR]: 3.83; 95% CI: 1.66-9.37), the frequency-specific air-bone gap at 250 Hz ≥ 45 dB HL (OR: 3.68; 95% CI: 1.68-8.57), aditus ad antrum blockage (OR: 3.29; 95% CI: 1.38-8.43), type I eardrum calcification (OR: 25.37; 95% CI: 8.41-88.91) or type II eardrum calcification (OR: 18.86; 95% CI: 6.89-58.77), and a history of otitis media ≥ 10 years (OR: 4.10; 95% CI: 1.58-11.83), which were all included in the clinical score tool. The AUC of the clinical score for predicting tympanosclerosis was 0.89 (95% CI: 0.85-0.93) in the training cohort and 0.89 (95% CI: 0.84-0.95) in the validation cohort. The calibration curve also showed good agreement between the predicted and observed probability. Conclusions: The clinical score achieved an optimal prediction of tympanosclerosis before surgery. The presence of calcification pearls on the promontorium tympani is a strong predictor of tympanosclerosis with stapes fixation.

scholarly journals Prognostic Nomogram for Postoperative Patients With Gastroesophageal Junction Cancer of No Distant Metastasis

2021 ◽  
Vol 11 ◽  
Author(s):  
Qiang Guo ◽  
YuanYuan Peng ◽  
Heng Yang ◽  
JiaLong Guo
Keyword(s):  
Regression Analysis ◽  
Surgical Resection ◽  
Distant Metastasis ◽  
Calibration Curve ◽  
Cox Regression ◽  
Validation Cohort ◽  
Predictive Accuracy ◽  
Gastroesophageal Junction ◽  
Training Cohort ◽  
Cox Regression Analysis

BackgroundGastroesophageal junction (GEJ) was one of the most common malignant tumors. However, the value of clinicopathological features in predicting the prognosis of postoperative patients with GEJ cancer and without distant metastasis was still unclear.MethodsThe 3425 GEJ patients diagnosed and underwent surgical resection without distant metastasis in the Surveillance, Epidemiology and End Results (SEER) database from 2010 to 2015 were enrolled,and they were randomly divided into training and validation cohorts with 7:3 ratio. Univariate and multivariate Cox regression analysis were used to determine the predictive factors that constituted the nomogram. The predictive accuracy and discriminability of Nomogram were determined by the area under the curve (AUC), C index, and calibration curve, and the influence of various factors on prognosis was explored.Results2,400 patients were designed as training cohort and 1025 patients were designed as validation cohort. The percentages of the distribution of demographic and clinicopathological characteristics in the training and validation cohorts tended to be the same. In the training cohort, multivariate Cox regression analysis revealed that the age, tumor grade, T stage and N stage were independent prognostic risk factors for patients with GEJ cancer without distant metastasis. The C index of nomogram model was 0.667. The AUC of the receiver operating characteristic (ROC) analysis for 3- and 5-year overall survival (OS) were 0.704 and 0.71, respectively. The calibration curve of 3- and 5-year OS after operation showed that there was the best consistency between nomogram prediction and actual observation. In the validation cohort, the C index of nomogram model, the AUC of 3- and 5-year OS, and the calibration curve were similar to the training cohort.ConclusionsNomogram could evaluate the prognosis of patients with GEJ cancer who underwent surgical resection without distant metastasis.

scholarly journals CT-Based Radiomics Nomogram for Differentiation of Anterior Mediastinal Thymic Cyst From Thymic Epithelial Tumor

2021 ◽  
Vol 11 ◽  
Author(s):  
Chengzhou Zhang ◽  
Qinglin Yang ◽  
Fan Lin ◽  
Heng Ma ◽  
Haicheng Zhang ◽  
...  
Keyword(s):  
Calibration Curve ◽  
Clinical Decision Making ◽  
Validation Cohort ◽  
Multivariate Logistic Regression Analysis ◽  
Thymic Epithelial Tumors ◽  
Training Cohort ◽  
Epithelial Tumors ◽  
Ct Findings ◽  
Thymic Cysts ◽  
Conventional Ct

ObjectivesThis study aimed to distinguish preoperatively anterior mediastinal thymic cysts from thymic epithelial tumors via a computed tomography (CT)-based radiomics nomogram.MethodsThis study analyzed 74 samples of thymic cysts and 116 samples of thymic epithelial tumors as confirmed by pathology examination that were collected from January 2014 to December 2020. Among the patients, 151 cases (scanned at CT 1) were selected as the training cohort, and 39 cases (scanned at CT 2 and 3) served as the validation cohort. Radiomics features were extracted from pre-contrast CT images. Key features were selected by SelectKBest and least absolute shrinkage and selection operator and then used to build a radiomics signature (Rad-score). The radiomics nomogram developed herein via multivariate logistic regression analysis incorporated clinical factors, conventional CT findings, and Rad-score. Its performance in distinguishing the samples of thymic cysts from those of thymic epithelial tumors was assessed via discrimination, calibration curve, and decision curve analysis (DCA).ResultsThe radiomics nomogram, which incorporated 16 radiomics features and 3 conventional CT findings, including lesion edge, lobulation, and CT value, performed better than Rad-score, conventional CT model, and the clinical judgment by radiologists in distinguishing thymic cysts from thymic epithelial tumors. The area under the receiver operating characteristic (ROC) curve of the nomogram was 0.980 [95% confidence interval (CI), 0.963–0.993] in the training cohort and 0.992 (95% CI, 0.969–1.000) in the validation cohort. The calibration curve and the results of DCA indicated that the nomogram has good consistency and valuable clinical utility.ConclusionThe CT-based radiomics nomogram presented herein may serve as an effective and convenient tool for differentiating thymic cysts from thymic epithelial tumors. Thus, it may aid in clinical decision-making.

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.

scholarly journals 1358. Using natural language processing to optimize case ascertainment of acute otitis media in a large, state-wide pediatric practice network

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S690-S691
Author(s):  
Joshua C Herigon ◽  
Amir Kimia ◽  
Marvin Harper
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Otitis Media ◽  
Language Processing ◽  
Validation Cohort ◽  
Recursive Partitioning ◽  
Acute Otitis Media ◽  
Clinical Documentation ◽  
Training Cohort ◽  
Icd Codes

Abstract Background Antibiotics are the most commonly prescribed drugs for children and frequently inappropriately prescribed. Outpatient antimicrobial stewardship interventions aim to reduce inappropriate antibiotic use. Previous work has relied on diagnosis coding for case identification which may be inaccurate. In this study, we sought to develop automated methods for analyzing note text to identify cases of acute otitis media (AOM) based on clinical documentation. Methods We conducted a cross-sectional retrospective chart review and sampled encounters from 7/1/2018 – 6/30/2019 for patients < 5 years old presenting for a problem-focused visit. Complete note text and limited structured data were extracted for 12 randomly selected weekdays (one from each month during the study period). An additional weekday was randomly selected for validation. The primary outcome was correctly identifying encounters where AOM was present. Human review was considered the “gold standard” and was compared to ICD codes, a natural language processing (NLP) model, and a recursive partitioning (RP) model. Results A total of 2,724 encounters were included in the training cohort and 793 in the validation cohort. ICD codes and NLP had good performance overall with sensitivity 91.2% and 93.1% respectively in the training cohort. However, NLP had a significant drop-off in performance in the validation cohort (sensitivity: 83.4%). The RP model had the highest sensitivity (97.2% training cohort; 94.1% validation cohort) out of the 3 methods. Figure 1. Details of encounters included in the training and validation cohorts. Table 1. Performance of ICD coding, a natural language processing (NLP) model, and a recursive partitioning (RP) model for identifying cases of acute otitis media (AOM) Conclusion Natural language processing of outpatient pediatric visit documentation can be used successfully to create models accurately identifying cases of AOM based on clinical documentation. Combining NLP and structured data can improve automated case detection, leading to more accurate assessment of antibiotic prescribing practices. These techniques may be valuable in optimizing outpatient antimicrobial stewardship efforts. Disclosures All Authors: No reported disclosures

scholarly journals An Immune Risk Score Predicts Survival of Patients with Myelodysplastic Syndrome

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 33-34
Author(s):  
Yang Liang ◽  
Fang Hu ◽  
Yu-Jun Dai ◽  
Yun Wang ◽  
Huan Li
Keyword(s):  
Regression Analysis ◽  
Myelodysplastic Syndrome ◽  
Risk Score ◽  
Prognostic Model ◽  
Validation Cohort ◽  
Gene Set Enrichment Analysis ◽  
Type I ◽  
Immune Gene ◽  
Training Cohort ◽  
Immune Score

Background: Myelodysplastic syndrome (MDS) was characterized as ineffective hematopoiesis, increased transformation to acute myeloid leukemia (AML), and accompanied by immune system dysfunction. However, the immune signature of MDS remains elusive. Methods: The clinical data (age, sex, international prognostic score system (IPSS), hemoglobin, blast, RBC transfusion dependence, and corresponding subject-level survival) as well as expression profiles of MDS (CD34+ cells) were obtained from Gene Expression Omnibus (GEO: GSE 58831; GSE 114922). A robust prognosis model of immune genes was constructed by the least absolute shrinkage and selection operator (LASSO) regression analysis. Survival analysis for prognostic model was carried out through the Kaplan-Meier curve and Log-rank test. The receiver operating characteristic (ROC) curves and area under the curve (AUC) were used to assess the accuracy of prognostic models. Immune score for different subtype were calculated further by single sample gene set enrichment analysis (ssGSEA). Result: A novel robust immune gene prognostic model indicate that subtype with lower risk score were longer overall survival (OS) than subtype with higher risk score in training cohort (Figure1 A, C). The model was further verified by the validation cohort (Figure1 B, D). The multivariate Cox regression analysis demonstrated the model was an independent prognostic factor for OS prediction with hazard ratios of 56.694 (95% CIs: 9.038−355.648), 3.009 (95% CIs: 1.042−8.692) both in train cohort and external validation cohort respectively (Figure1 G, H). The AUC of 5- year were 0.92 (95% CIs: 0.86 - 0.97) and 0.7 (95% CIs: 0.51 - 0.89) for OS respectively in training cohort and validation cohort (Figure1 E, F). Furthermore, ssGSEA showed higher risk score subtype was significantly associated with higher immune score of check point, human leukocyte antigen (HLA), T cell co-inhibition and type I interferon (IFN) response (Figure1 K-N), which indicating that the poor outcome might be caused by tumor-associated immune response dysfunction partly. Conclusion: We constructed a robust immune gene prognostic model, which have a potential prognostic value for MDS patients and may provide evidence for personalized immunotherapy. Figure Disclosures No relevant conflicts of interest to declare.

scholarly journals Development and Validation of Nomogram to Preoperatively Predict Intraoperative Cerebrospinal Fluid Leakage in Endoscopic Pituitary Surgery: A Retrospective Cohort Study

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiangming Cai ◽  
Junhao Zhu ◽  
Jin Yang ◽  
Chao Tang ◽  
Feng Yuan ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cerebrospinal Fluid ◽  
Prediction Model ◽  
Calibration Curve ◽  
Validation Cohort ◽  
Laboratory Data ◽  
Pituitary Surgery ◽  
Cerebrospinal Fluid Leakage ◽  
Training Cohort ◽  
Preoperative Prediction

BackgroundPituitary adenomas (PAs) are the most common tumor of the sellar region. PA resection is the preferred treatment for patients with clear indications for surgery. Intraoperative cerebrospinal fluid (iCSF) leakage is a major complication of PA resection surgery. Risk factors for iCSF leakage have been studied previously, but a predictive nomogram has not yet been developed. We constructed a nomogram for preoperative prediction of iCSF leakage in endoscopic pituitary surgery.MethodsA total of 232 patients who underwent endoscopic PA resection at the Department of Neurosurgery in Jinling Hospital between January of 2018 and October of 2020 were enrolled in this retrospective study. Patients treated by a board-certified neurosurgeon were randomly classified into a training cohort or a validation cohort 1. Patients treated by other qualified neurosurgeons were included in validation cohort 2. A range of demographic, clinical, radiological, and laboratory data were acquired from the medical records. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and uni- and multivariate logistic regression were utilized to analyze these features and develop a nomogram model. We used a receiver operating characteristic (ROC) curve and calibration curve to evaluate the predictive performance of the nomogram model.ResultsVariables were comparable between the training cohort and validation cohort 1. Tumor height and albumin were included in the final prediction model. The area under the curve (AUC) of the nomogram model was 0.733, 0.643, and 0.644 in training, validation 1, and validation 2 cohorts, respectively. The calibration curve showed satisfactory homogeneity between the predicted probability and actual observations. Nomogram performance was stable in the subgroup analysis.ConclusionsTumor height and albumin were the independent risk factors for iCSF leakage. The prediction model developed in this study is the first nomogram developed as a practical and effective tool to facilitate the preoperative prediction of iCSF leakage in endoscopic pituitary surgery, thus optimizing treatment decisions.

scholarly journals NCC-BCBM, A Nomogram Prognostic Model In Breast Cancer Patients With Bone Metastases

2021 ◽  
Author(s):  
Jie Zhai ◽  
Qiang Liu ◽  
Ping Bai ◽  
Zhongzhao Wang ◽  
Yi Fang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Bone Metastasis ◽  
Cancer Patients ◽  
Calibration Curve ◽  
Prognostic Model ◽  
Predictive Accuracy ◽  
Molecular Subtype ◽  
Breast Cancer Patients ◽  
Discriminative Ability ◽  
Independent Cohort

Abstract Accurate prediction tools to facilitate risk stratification and therapeutic strategies for breast cancer patients with bone metastasis (BCBM) are lacking. We constructed and validated a new nomogram prognostic model, named NCC-BCBM, for breast cancer patients with bone metastasis using a large BCBM cohort from the SEER database. Clinical information for 8655 patients diagnosed from 2011 to 2013 was collected to develop the model. The predictive accuracy and discriminative ability of the nomogram were evaluated by concordance index (C-index) and calibration curve. The model was further validated in an independent cohort of 4634 BCBM patient. The following clinical variables were enrolled in the final prognostic model: age, race, surgery, radiation therapy, chemotherapy, laterality, grade, molecular subtype, American Joint Committee on Cancer (AJCC T) stage, AJCC N stage and extra metastatic sites except bone. The C-index for the developed model in training cohort was 0.702 (95% CI, 0.696 to 0.709). The calibration curve for probability of 1-year, 3-year and 5-year survival showed good agreement between prediction by nomogram and direct observation. C-index that validated in an independent cohort was 0.748 (95% CI, 0.737 to 0.759). We developed and validated a nomogram prognostic model for BCBM patients and it resulted in good performance.

scholarly journals Development and validation of a nomogram risk prediction model for malignancy in dermatomyositis patients: a retrospective study

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12626
Author(s):  
Jiaojiao Zhong ◽  
Yunan He ◽  
Jianchi Ma ◽  
Siyao Lu ◽  
Yushi Wu ◽  
...  
Keyword(s):  
Protective Factor ◽  
Validation Cohort ◽  
Malignant Tumors ◽  
Univariate Analysis ◽  
Multivariate Logistic Regression Analysis ◽  
Area Under The Curve ◽  
Poor Prognostic Factor ◽  
Training Cohort ◽  
Discrimination Ability ◽  
Logistic Analysis

Background Dermatomyositis accompanied with malignancy is a common poor prognostic factor of dermatomyositis. Thus, the early prediction of the risk of malignancy in patients with dermatomyositis can significantly improve the prognosis of patients. However, the identification of antibodies related to malignancy in dermatomyositis patients has not been widely implemented in clinical practice. Herein, we established a predictive nomogram model for the diagnosis of dermatomyositis associated with malignancy. Methods We retrospectively analyzed 240 cases of dermatomyositis patients admitted to Sun Yat-sen Memorial Hospital, Sun Yat-sen University from January 2002 to December 2019. According to the year of admission, the first 70% of the patients were used to establish a training cohort, and the remaining 30% were assigned to the validation cohort. Univariate analysis was performed on all variables, and statistically relevant variables were further included in a multivariate logistic regression analysis to screen for independent predictors. Finally, a nomogram was constructed based on these independent predictors. Bootstrap repeated sampling calculation C-index was used to evaluate the model’s calibration, and area under the curve (AUC) was used to evaluate the model discrimination ability. Results Multivariate logistic analysis showed that patients older than 50-year-old, dysphagia, refractory itching, and elevated creatine kinase were independent risk factors for dermatomyositis associated with malignancy, while interstitial lung disease was a protective factor. Based on this, we constructed a nomogram using the above-mentioned five factors. The C-index was 0.780 (95% CI [0.690–0.870]) in the training cohort and 0.756 (95% CI [0.618–0.893]) in the validation cohort, while the AUC value was 0.756 (95% CI [0.600–0.833]). Taken together, our nomogram showed good calibration and was effective in predicting which dermatomyositis patients were at a higher risk of developing malignant tumors.

scholarly journals Individualized predictions of early isolated distal deep vein thrombosis in patients with acute ischemic stroke: a retrospective study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hao-Ran Cheng ◽  
Gui-Qian Huang ◽  
Zi-Qian Wu ◽  
Yue-Min Wu ◽  
Gang-Qiang Lin ◽  
...  
Keyword(s):  
Deep Vein Thrombosis ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Multivariate Logistic Regression Analysis ◽  
Training Cohort ◽  
Vein Thrombosis ◽  
Distal Deep Vein Thrombosis ◽  
Deep Vein

Abstract Background Although isolated distal deep vein thrombosis (IDDVT) is a clinical complication for acute ischemic stroke (AIS) patients, very few clinicians value it and few methods can predict early IDDVT. This study aimed to establish and validate an individualized predictive nomogram for the risk of early IDDVT in AIS patients. Methods This study enrolled 647 consecutive AIS patients who were randomly divided into a training cohort (n = 431) and a validation cohort (n = 216). Based on logistic analyses in training cohort, a nomogram was constructed to predict early IDDVT. The nomogram was then validated using area under the receiver operating characteristic curve (AUROC) and calibration plots. Results The multivariate logistic regression analysis revealed that age, gender, lower limb paralysis, current pneumonia, atrial fibrillation and malignant tumor were independent risk factors of early IDDVT; these variables were integrated to construct the nomogram. Calibration plots revealed acceptable agreement between the predicted and actual IDDVT probabilities in both the training and validation cohorts. The nomogram had AUROC values of 0.767 (95% CI: 0.742–0.806) and 0.820 (95% CI: 0.762–0.869) in the training and validation cohorts, respectively. Additionally, in the validation cohort, the AUROC of the nomogram was higher than those of the other scores for predicting IDDVT. Conclusions The present nomogram provides clinicians with a novel and easy-to-use tool for the prediction of the individualized risk of IDDVT in the early stages of AIS, which would be helpful to initiate imaging examination and interventions timely.

scholarly journals A Nomogram for Preoperatively Predicting the Ki-67 Index of a Pituitary Tumor: A Retrospective Cohort Study

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiangming Cai ◽  
Junhao Zhu ◽  
Jin Yang ◽  
Chao Tang ◽  
Feng Yuan ◽  
...  
Keyword(s):  
Risk Factors ◽  
Calibration Curve ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Treatment Strategies ◽  
Predictive Performance ◽  
Maximum Dimension ◽  
Ki 67 ◽  
Training Cohort ◽  
Index Level

BackgroundThe Ki-67 index is an indicator of proliferation and aggressive behavior in pituitary adenomas (PAs). This study aims to develop and validate a predictive nomogram for forecasting Ki-67 index levels preoperatively in PAs.MethodsA total of 439 patients with PAs underwent PA resection at the Department of Neurosurgery in Jinling Hospital between January 2018 and October 2020; they were enrolled in this retrospective study and were classified randomly into a training cohort (n = 300) and a validation cohort (n = 139). A range of clinical, radiological, and laboratory characteristics were collected. The Ki-67 index was classified into the low Ki-67 index (<3%) and the high Ki-67 index (≥3%). Least absolute shrinkage and selection operator algorithm and uni- and multivariate logistic regression analyses were applied to identify independent risk factors associated with Ki-67. A nomogram was constructed to visualize these risk factors. The receiver operation characteristic curve and calibration curve were computed to evaluate the predictive performance of the nomogram model.ResultsAge, primary-recurrence subtype, maximum dimension, and prolactin were included in the nomogram model. The areas under the curve (AUCs) of the nomogram model were 0.694 in the training cohort and 0.658 in the validation cohort. A well-fitted calibration curve was also generated for the nomogram model. A subgroup analysis revealed stable predictive performance for the nomogram model. A correlation analysis revealed that age (R = −0.23; p < 0.01), maximum dimension (R = 0.17; p < 0.01), and prolactin (R = 0.16; p < 0.01) were all significantly correlated with the Ki-67 index level.ConclusionsAge, primary-recurrence subtype, maximum dimension, and prolactin are independent predictors for the Ki-67 index level. The current study provides a novel and feasible nomogram, which can further assist neurosurgeons to develop better, more individualized treatment strategies for patients with PAs by predicting the Ki-67 index level preoperatively.

Sign in / Sign up

Export Citation Format

Share Document