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 CT-Based Radiomics Analysis to Predict Malignancy in Patients with Intraductal Papillary Mucinous Neoplasm (IPMN) of the Pancreas

Cancers ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3089
Author(s):  
David Tobaly ◽  
Joao Santinha ◽  
Riccardo Sartoris ◽  
Marco Dioguardi Burgio ◽  
Celso Matos ◽  
...  
Keyword(s):  
Malignant Tumors ◽  
External Validation ◽  
Multivariate Model ◽  
Tumor Segmentation ◽  
Low Grade ◽  
Intraductal Papillary Mucinous Neoplasms ◽  
Training Cohort ◽  
Mucinous Neoplasms ◽  
Regression Approach ◽  
Preoperative Ct

To assess the performance of CT-based radiomics analysis in differentiating benign from malignant intraductal papillary mucinous neoplasms of the pancreas (IPMN), preoperative scans of 408 resected patients with IPMN were retrospectively analyzed. IPMNs were classified as benign (low-grade dysplasia, n = 181), or malignant (high grade, n = 128, and invasive, n = 99). Clinicobiological data were reported. Patients were divided into a training cohort (TC) of 296 patients and an external validation cohort (EVC) of 112 patients. After semi-automatic tumor segmentation, PyRadiomics was used to extract radiomics features. A multivariate model was developed using a logistic regression approach. In the training cohort, 85/107 radiomics features were significantly different between patients with benign and malignant IPMNs. Unsupervised clustering analysis revealed four distinct clusters of patients with similar radiomics features patterns with malignancy as the most significant association. The multivariate model differentiated benign from malignant tumors in TC with an area under the ROC curve (AUC) of 0.84, sensitivity (Se) of 0.82, specificity (Spe) of 0.74, and in EVC with an AUC of 0.71, Se of 0.69, Spe of 0.57. This large study confirms the high diagnostic performance of preoperative CT-based radiomics analysis to differentiate between benign from malignant IPMNs.

scholarly journals Assessment of a Revised Management Strategy for Patients With Intraductal Papillary Mucinous Neoplasms Involving the Main Pancreatic Duct

JAMA Surgery ◽  
2017 ◽  
Vol 152 (1) ◽  
pp. e163349 ◽  
Author(s):  
Motokazu Sugimoto ◽  
Irmina A. Elliott ◽  
Andrew H. Nguyen ◽  
Stephen Kim ◽  
V. Raman Muthusamy ◽  
...  
Keyword(s):  
Pancreatic Duct ◽  
Main Pancreatic Duct ◽  
Management Strategy ◽  
Intraductal Papillary Mucinous Neoplasms ◽  
Mucinous Neoplasms

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 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 Development and validation of the nomogram based on INR and eGFR for estimation of mortality in patients with acute-on-chronic hepatitis B liver failure

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Shengnan Li ◽  
Xiehua Zhang ◽  
Qian Li ◽  
Binyue Lv ◽  
Yefan Zhang ◽  
...  
Keyword(s):  
Chronic Hepatitis ◽  
Liver Disease ◽  
Hepatitis B ◽  
Liver Failure ◽  
Chronic Hepatitis B ◽  
Calibration Curve ◽  
Validation Cohort ◽  
External Validation ◽  
Short Term ◽  
Short Term Mortality

Abstract Aims and objectives Acute-on-chronic hepatitis B liver failure (ACHBLF) is a critical clinical syndrome with a high short-term mortality evolved from chronic hepatitis B (CHB)-related liver disease. Prediction of mortality risk and early intervention can improve the prognosis of patients. This study aimed to develop and validate the nomogram for short-time mortality estimation in ACHBLF patients defined according to Asian Pacific Association for the Study of the Liver (APASL). Methods A study of 105 ACHBLF patients with 90-day follow up was performed to develop the nomogram. Patients were randomly assigned to derivation cohort (n = 75) and validation cohort (n = 35) according to 7:3. Concordance index (C-index), calibration curve and decision curve analysis (DCA) were used to evaluate the nomogram. We also compared the nomogram with APASL ACLF research consortium (AARC) score, model for end-stage liver disease (MELD) score, MELD with serum sodium (MELD-Na) score and albumin-bilirubin (ALBI) score. The nomogram was validated using an external cohort including 40 patients. Results The 28-day and 90-day mortality of 105 patients were respectively 49.52% and 55.24%. Albumin (ALB), international normalized ratio (INR) and estimated glomerular filtration rate (eGFR) were independent predictors for 28-day mortality; INR and eGFR were independent predictors for 90-day mortality. C-index of Nomogram-1 for 28-day mortality and Nomogram-2 for 90-day mortality were respectively 0.82 and 0.81. Calibration curve and Hosmer–Lemeshow test (Nomogram-1, 0.323; Nomogram-2, 0.231) showed optimal agreement between observed and predicted death. Areas under receiver operator characteristic curve(AUROC) of Nomogram-1(0.772) and Nomogram-2(0.771) were larger compared with AARC, MELD, MELD-Na and ALBI score. The results were well estimated in the external validation cohort. Conclusions This study highlighted the predictive value of eGFR, and the nomogram based on INR and eGFR could effectively estimate individualized risk for short-term mortality of ACHBLF patients defined according to APASL.

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.

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