scholarly journals Radiomics nomogram for the preoperative prediction of lymph node metastasis in pancreatic ductal adenocarcinoma

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Yun Bian ◽  
Shiwei Guo ◽  
Hui Jiang ◽  
Suizhi Gao ◽  
Chengwei Shao ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Lymph Node ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Validation Cohort ◽  
Ductal Adenocarcinoma ◽  
Arterial Phase ◽  
Multivariable Logistic Regression Analysis ◽  
Preoperative Prediction ◽  
Multivariable Logistic Regression ◽  
Radiomics Signature

Abstract Purpose To develop and validate a radiomics nomogram for the preoperative prediction of lymph node (LN) metastasis in pancreatic ductal adenocarcinoma (PDAC). Materials and methods In this retrospective study, 225 patients with surgically resected, pathologically confirmed PDAC underwent multislice computed tomography (MSCT) between January 2014 and January 2017. Radiomics features were extracted from arterial CT scans. The least absolute shrinkage and selection operator method was used to select the features. Multivariable logistic regression analysis was used to develop the predictive model, and a radiomics nomogram was built and internally validated in 45 consecutive patients with PDAC between February 2017 and December 2017. The performance of the nomogram was assessed in the training and validation cohort. Finally, the clinical usefulness of the nomogram was estimated using decision curve analysis (DCA). Results The radiomics signature, which consisted of 13 selected features of the arterial phase, was significantly associated with LN status (p < 0.05) in both the training and validation cohorts. The multivariable logistic regression model included the radiomics signature and CT-reported LN status. The individualized prediction nomogram showed good discrimination in the training cohort [area under the curve (AUC), 0.75; 95% confidence interval (CI), 0.68–0.82] and in the validation cohort (AUC, 0.81; 95% CI, 0.69–0.94) and good calibration. DCA demonstrated that the radiomics nomogram was clinically useful. Conclusions The presented radiomics nomogram that incorporates the radiomics signature and CT-reported LN status is a noninvasive, preoperative prediction tool with favorable predictive accuracy for LN metastasis in patients with PDAC.

scholarly journals Development of a Nomogram Combining Clinical Risk Factors and Dual-Energy Spectral CT Parameters for the Preoperative Prediction of Lymph Node Metastasis in Patients With Colorectal Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Yuntai Cao ◽  
Jing Zhang ◽  
Haihua Bao ◽  
Guojin Zhang ◽  
Xiaohong Yan ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Logistic Regression ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Dual Energy ◽  
Arterial Phase ◽  
Clinical Factors ◽  
Primary Tumors ◽  
Node Metastasis ◽  
Preoperative Prediction

ObjectiveThis study aimed to develop a dual-energy spectral computed tomography (DESCT) nomogram that incorporated both clinical factors and DESCT parameters for individual preoperative prediction of lymph node metastasis (LNM) in patients with colorectal cancer (CRC).Material and MethodsWe retrospectively reviewed 167 pathologically confirmed patients with CRC who underwent enhanced DESCT preoperatively, and these patients were categorized into training (n = 117) and validation cohorts (n = 50). The monochromatic CT value, iodine concentration value (IC), and effective atomic number (Eff-Z) of the primary tumors were measured independently in the arterial phase (AP) and venous phase (VP) by two radiologists. DESCT parameters together with clinical factors were input into the prediction model for predicting LNM in patients with CRC. Logistic regression analyses were performed to screen for significant predictors of LNM, and these predictors were presented as an easy-to-use nomogram. The receiver operating characteristic curve and decision curve analysis (DCA) were used to evaluate the clinical usefulness of the nomogram.ResultsThe logistic regression analysis showed that carcinoembryonic antigen, carbohydrate antigen 199, pericolorectal fat invasion, ICAP, ICVP, and Eff-ZVP were independent predictors in the predictive model. Based on these predictors, a quantitative nomogram was developed to predict individual LNM probability. The area under the curve (AUC) values of the nomogram were 0.876 in the training cohort and 0.852 in the validation cohort, respectively. DCA showed that our nomogram has outstanding clinical utility.ConclusionsThis study presents a clinical nomogram that incorporates clinical factors and DESCT parameters and can potentially be used as a clinical tool for individual preoperative prediction of LNM in patients with CRC.

scholarly journals Development and validation of nomograms to intraoperatively predict metastatic patterns in regional lymph nodes in patients diagnosed with esophageal cancer

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Fei Zhao ◽  
Rong-Xin Lu ◽  
Jin-Yuan Liu ◽  
Jun Fan ◽  
Hao-Ran Lin ◽  
...  
Keyword(s):  
Esophageal Cancer ◽  
Logistic Regression ◽  
Lymph Node ◽  
Prediction Model ◽  
Validation Cohort ◽  
Regional Lymph Node ◽  
Univariate Analysis ◽  
Clinical Usefulness ◽  
Multivariable Logistic Regression ◽  
Good Calibration

Abstract Background An accurate intraoperative prediction of lymph node metastatic risk can help surgeons in choosing precise surgical procedures. We aimed to develop and validate nomograms to intraoperatively predict patterns of regional lymph node (LN) metastasis in patients with esophageal cancer. Methods The prediction model was developed in a training cohort consisting of 487 patients diagnosed with esophageal cancer who underwent esophagectomy with complete LN dissection from January 2016 to December 2016. Univariate and multivariable logistic regression were used to identify independent risk factors that were incorporated into a prediction model and used to construct a nomogram. Contrast-enhanced computed tomography reported LN status and was an important comparative factor of clinical usefulness in a validation cohort. Nomogram performance was assessed in terms of calibration, discrimination, and clinical usefulness. An independent validation cohort comprised 206 consecutive patients from January 2017 to December 2017. Results Univariate analysis and multivariable logistic regression revealed three independent predictors of metastatic regional LNs, three independent predictors of continuous regional LNs, and two independent predictors of skipping regional LNs. Independent predictors were used to build three individualized prediction nomograms. The models showed good calibration and discrimination, with area under the curve (AUC) values of 0.737, 0.738, and 0.707. Application of the nomogram in the validation cohort yielded good calibration and discrimination, with AUC values of 0.728, 0.668, and 0.657. Decision curve analysis demonstrated that the three nomograms were clinically useful in the validation cohort. Conclusion This study presents three nomograms that incorporate clinicopathologic factors, which can be used to facilitate the intraoperative prediction of metastatic regional LN patterns in patients with esophageal cancer.

Development and Validation of a Prognostic Model to Predict Hearing Recovery for Patients With Chronic Otitis Media

2021 ◽  
pp. 014556132110655
Author(s):  
Fengyang Xie ◽  
Xiaoyue Zhen ◽  
Haiyuan Zhu ◽  
Yan Kou ◽  
Changle Li ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Otitis Media ◽  
Validation Cohort ◽  
Area Under The Curve ◽  
Chronic Otitis Media ◽  
Ossicular Chain ◽  
Multivariable Logistic Regression Analysis ◽  
Canal Wall ◽  
Multivariable Logistic Regression ◽  
Hearing Recovery

Objective To explore the factors affecting postoperative hearing recovery in chronic otitis media (COM) patients, establish a clinical prediction model for hearing recovery, and verify the accuracy of the model. Methods Data of patients with COM who were admitted to our hospital between January 1, 2012 and September 30, 2020 were retrospectively analyzed. We collected data on relevant clinicopathological characteristics of patients. The patients were randomly divided into the development cohort and validation cohorts. A postoperative air-bone gap (ABG) ≤20 dB was defined as successful hearing recovery. Univariate and multivariable logistic regression analyses were used to investigate the association of several prognostic factors with hearing recovery. These factors were then used to establish a nomogram. The model was subjected to bootstrap internal validation and performance evaluation in terms of discrimination, calibration, and clinical validity. Results This study included 2146 patients with COM: the development cohort comprised 1610 patients (mean [standard deviation; SD] age, 44.1 [14.7] years; 733 men [45.5%]) and the validation cohort included 536 patients (mean [SD] age, 42.9 [14.4] years; 234 men [43.7%]). Multivariable logistic regression analysis showed that age, duration of onset, styles of surgery (tympanoplasty, canal wall up-CWU, or canal wall down-CWD), ossicular prosthesis, granulation or calcified blocks around the ossicular chain, ossicular chain integrity, duration of drilling, eustachian tube dysfunction, mixed hearing loss, semicircular canal fistula, and second surgery were associated with hearing recovery. A nomogram based on these variables was constructed. The area under the curve was 0.797 (95% confidence interval [CI], 0.778–0.812) in the development cohort and 0.798 (95% CI, 0.7605–0.8355) in the validation cohort. Conclusions This study demonstrated the various clinical factors correlated with hearing recovery in patients with COM. The nomogram developed with these data could provide personalized risk estimates of hearing recovery to enhance preoperative counseling and help to set realistic expectations in patients.

scholarly journals Disparities in the Use of Neoadjuvant Therapy for Resectable Pancreatic Ductal Adenocarcinoma

2020 ◽  
Vol 18 (5) ◽  
pp. 556-563 ◽  
Author(s):  
Jordan M. Cloyd ◽  
Chengli Shen ◽  
Heena Santry ◽  
John Bridges ◽  
Mary Dillhoff ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Neoadjuvant Therapy ◽  
Education Level ◽  
Ductal Adenocarcinoma ◽  
Multivariable Logistic Regression Analysis ◽  
Median Income ◽  
Related Factors ◽  
T Stage ◽  
Immediate Surgery

Background: Current guidelines support either immediate surgical resection or neoadjuvant therapy (NT) for patients with resectable pancreatic ductal adenocarcinoma (PDAC). However, which patients are selected for NT and whether disparities exist in the use of NT for PDAC are not well understood. Methods: Using the National Cancer Database from 2004 through 2016, the clinical, demographic, socioeconomic, and hospital-related characteristics of patients with stage I/II PDAC who underwent immediate surgery versus NT followed by surgery were compared. Results: Among 58,124 patients who underwent pancreatectomy, 8,124 (14.0%) received NT whereas 50,000 (86.0%) did not. Use of NT increased significantly throughout the study period (from 3.5% in 2004 to 26.4% in 2016). Multivariable logistic regression analysis showed that travel distance, education level, hospital facility type, clinical T stage, tumor size, and year of diagnosis were associated with increased use of NT, whereas comorbidities, uninsured/Medicaid status, South/West geography, left-sided tumor location, and increasing age were associated with immediate surgery (all P<.001). Based on logistic regression–derived interaction factors, the association between NT use and median income, education level, Midwest location, clinical T stage, and clinical N stage significantly increased over time (all P<.01). Conclusions: In addition to traditional clinicopathologic factors, several demographic, socioeconomic, and hospital-related factors are associated with use of NT for PDAC. Because NT is used increasingly for PDAC, efforts to reduce disparities will be critical in improving outcomes for all patients with pancreatic cancer.

scholarly journals Pancreatic Ductal Adenocarcinoma at CT: A Combined Nomogram Model to Preoperatively Predict Cancer Stage and Survival Outcome

2021 ◽  
Vol 11 ◽  
Author(s):  
Chunyuan Cen ◽  
Liying Liu ◽  
Xin Li ◽  
Ailan Wu ◽  
Huan Liu ◽  
...  
Keyword(s):  
Overall Survival ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Clinical Characteristics ◽  
Validation Cohort ◽  
Stage I ◽  
Ductal Adenocarcinoma ◽  
Training Cohort ◽  
Calibration Curves ◽  
Kaplan Meier ◽  
Radiomics Signature

ObjectivesTo construct a nomogram model that combines clinical characteristics and radiomics signatures to preoperatively discriminate pancreatic ductal adenocarcinoma (PDAC) in stage I-II and III-IV and predict overall survival.MethodsA total of 135 patients with histopathologically confirmed PDAC who underwent contrast-enhanced CT were included. A total of 384 radiomics features were extracted from arterial phase (AP) or portal venous phase (PVP) images. Four steps were used for feature selection, and multivariable logistic regression analysis were used to build radiomics signatures and combined nomogram model. Performance of the proposed model was assessed by using receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA). Kaplan-Meier analysis was applied to analyze overall survival in the stage I-II and III-IV PDAC groups.ResultsThe AP+PVP radiomics signature showed the best performance among the three radiomics signatures [training cohort: area under the curve (AUC) = 0.919; validation cohort: AUC = 0.831]. The combined nomogram model integrating AP+PVP radiomics signature with clinical characteristics (tumor location, carcinoembryonic antigen level, and tumor maximum diameter) demonstrated the best discrimination performance (training cohort: AUC = 0.940; validation cohort: AUC = 0.912). Calibration curves and DCA verified the clinical usefulness of the combined nomogram model. Kaplan-Meier analysis showed that overall survival of patients in the predicted stage I-II PDAC group was longer than patients in stage III-IV PDAC group (p&lt;0.0001).ConclusionsWe propose a combined model with excellent performance for the preoperative, individualized, noninvasive discrimination of stage I-II and III-IV PDAC and prediction of overall survival.

Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer

2016 ◽  
Vol 34 (18) ◽  
pp. 2157-2164 ◽  
Author(s):  
Yan-qi Huang ◽  
Chang-hong Liang ◽  
Lan He ◽  
Jie Tian ◽  
Cui-shan Liang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Risk Factors ◽  
Lymph Node ◽  
Validation Cohort ◽  
Good Discrimination ◽  
Internal Validation ◽  
Preoperative Prediction ◽  
Individualized Prediction ◽  
Radiomics Signature ◽  
Good Calibration

Purpose To develop and validate a radiomics nomogram for preoperative prediction of lymph node (LN) metastasis in patients with colorectal cancer (CRC). Patients and Methods The prediction model was developed in a primary cohort that consisted of 326 patients with clinicopathologically confirmed CRC, and data was gathered from January 2007 to April 2010. Radiomic features were extracted from portal venous–phase computed tomography (CT) of CRC. Lasso regression model was used for data dimension reduction, feature selection, and radiomics signature building. Multivariable logistic regression analysis was used to develop the predicting model, we incorporated the radiomics signature, CT-reported LN status, and independent clinicopathologic risk factors, and this was presented with a radiomics nomogram. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. Internal validation was assessed. An independent validation cohort contained 200 consecutive patients from May 2010 to December 2011. Results The radiomics signature, which consisted of 24 selected features, was significantly associated with LN status (P < .001 for both primary and validation cohorts). Predictors contained in the individualized prediction nomogram included the radiomics signature, CT-reported LN status, and carcinoembryonic antigen level. Addition of histologic grade to the nomogram failed to show incremental prognostic value. The model showed good discrimination, with a C-index of 0.736 (C-index, 0.759 and 0.766 through internal validation), and good calibration. Application of the nomogram in the validation cohort still gave good discrimination (C-index, 0.778 [95% CI, 0.769 to 0.787]) and good calibration. Decision curve analysis demonstrated that the radiomics nomogram was clinically useful. Conclusion This study presents a radiomics nomogram that incorporates the radiomics signature, CT-reported LN status, and clinical risk factors, which can be conveniently used to facilitate the preoperative individualized prediction of LN metastasis in patients with CRC.

scholarly journals Distinguish Coronavirus Disease 2019 Patients in General Surgery Emergency by CIAAD Scale: Development and Validation of a Prediction Model Based on 822 Cases in China

2020 ◽  
Author(s):  
Bangbo Zhao ◽  
Yingxin Wei ◽  
Wenwu Sun ◽  
Cheng Qin ◽  
Xingtong Zhou ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Acute Abdomen ◽  
Logistic Regression Analysis ◽  
Validation Cohort ◽  
Curve Analysis ◽  
Multivariable Logistic Regression Analysis ◽  
Decision Curve Analysis ◽  
Multivariable Logistic Regression

ABATRACTIMPORTANCEIn the epidemic, surgeons cannot distinguish infectious acute abdomen patients suspected COVID-19 quickly and effectively.OBJECTIVETo develop and validate a predication model, presented as nomogram and scale, to distinguish infectious acute abdomen patients suspected coronavirus disease 2019 (COVID-19).DESIGNDiagnostic model based on retrospective case series.SETTINGTwo hospitals in Wuhan and Beijing, China.PTRTICIPANTS584 patients admitted to hospital with laboratory confirmed SARS-CoV-2 from 2 Jan 2020 to15 Feb 2020 and 238 infectious acute abdomen patients receiving emergency operation from 28 Feb 2019 to 3 Apr 2020.METHODSLASSO regression and multivariable logistic regression analysis were conducted to develop the prediction model in training cohort. The performance of the nomogram was evaluated by calibration curves, receiver operating characteristic (ROC) curves, decision curve analysis (DCA) and clinical impact curves in training and validation cohort. A simplified screening scale and managing algorithm was generated according to the nomogram.RESULTSSix potential COVID-19 prediction variables were selected and the variable abdominal pain was excluded for overmuch weight. The five potential predictors, including fever, chest computed tomography (CT), leukocytes (white blood cells, WBC), C-reactive protein (CRP) and procalcitonin (PCT), were all independent predictors in multivariable logistic regression analysis (p ≤0.001) and the nomogram, named COVID-19 Infectious Acute Abdomen Distinguishment (CIAAD) nomogram, was generated. The CIAAD nomogram showed good discrimination and calibration (C-index of 0.981 (95% CI, 0.963 to 0.999) and AUC of 0.970 (95% CI, 0.961 to 0.982)), which was validated in the validation cohort (C-index of 0.966 (95% CI, 0.960 to 0.972) and AUC of 0.966 (95% CI, 0.957 to 0.975)). Decision curve analysis revealed that the CIAAD nomogram was clinically useful. The nomogram was further simplified into the CIAAD scale.CONCLUSIONSWe established an easy and effective screening model and scale for surgeons in emergency department to distinguish COVID-19 patients from infectious acute abdomen patients. The algorithm based on CIAAD scale will help surgeons manage infectious acute abdomen patients suspected COVID-19 more efficiently.

scholarly journals A CT-Based Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Periampullary Carcinomas

2021 ◽  
Vol 11 ◽  
Author(s):  
Lei Bi ◽  
Yubo Liu ◽  
Jingxu Xu ◽  
Ximing Wang ◽  
Tong Zhang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Lymph Node ◽  
Clinical Utility ◽  
Clinical Decision Making ◽  
Multivariate Logistic Regression Model ◽  
Training Set ◽  
Preoperative Prediction ◽  
Validation Set ◽  
Periampullary Carcinomas ◽  
Radiomics Signature

PurposeTo establish and validate a radiomics nomogram for preoperatively predicting lymph node (LN) metastasis in periampullary carcinomas.Materials and MethodsA total of 122 patients with periampullary carcinoma were assigned into a training set (n = 85) and a validation set (n = 37). The preoperative CT radiomics of all patients were retrospectively assessed and the radiomic features were extracted from portal venous-phase images. The one-way analysis of variance test and the least absolute shrinkage and selection operator regression were used for feature selection. A radiomics signature was constructed with logistic regression algorithm, and the radiomics score was calculated. Multivariate logistic regression model integrating independent risk factors was adopted to develop a radiomics nomogram. The performance of the radiomics nomogram was assessed by its calibration, discrimination, and clinical utility with independent validation.ResultsThe radiomics signature, constructed by seven selected features, was closely related to LN metastasis in the training set (p &lt; 0.001) and validation set (p = 0.017). The radiomics nomogram that incorporated radiomics signature and CT-reported LN status demonstrated favorable calibration and discrimination in the training set [area under the curve (AUC), 0.853] and validation set (AUC, 0.853). The decision curve indicated the clinical utility of our nomogram.ConclusionOur CT-based radiomics nomogram, incorporating radiomics signature and CT-reported LN status, could be an individualized and non-invasive tool for preoperative prediction of LN metastasis in periampullary carcinomas, which might assist clinical decision making.

scholarly journals The Construction and Development of a Clinical Prediction Model to Assess Lymph Node Metastases in Osteosarcoma

2022 ◽  
Vol 9 ◽  
Author(s):  
Wenle Li ◽  
Shengtao Dong ◽  
Bing Wang ◽  
Haosheng Wang ◽  
Chan Xu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Validation Cohort ◽  
Predictive Ability ◽  
Multivariable Logistic Regression Analysis ◽  
Clinical Prediction ◽  
Clinical Prediction Model ◽  
Multivariable Logistic Regression

Background: This study aimed to construct a clinical prediction model for osteosarcoma patients to evaluate the influence factors for the occurrence of lymph node metastasis (LNM).Methods: In our retrospective study, a total of 1,256 patients diagnosed with chondrosarcoma were enrolled from the SEER (Surveillance, Epidemiology, and End Results) database (training cohort, n = 1,144) and multicenter dataset (validation cohort, n = 112). Both the univariate and multivariable logistic regression analysis were performed to identify the potential risk factors of LNM in osteosarcoma patients. According to the results of multivariable logistic regression analysis, A nomogram were established and the predictive ability was assessed by calibration plots, receiver operating characteristics (ROCs) curve, and decision curve analysis (DCA). Moreover, Kaplan-Meier plot of overall survival (OS) was plot and a web calculator visualized the nomogram.Results: Five independent risk factors [chemotherapy, surgery, lung metastases, lymphatic metastases (M-stage) and tumor size (T-stage)] were identified by multivariable logistic regression analysis. What's more, calibration plots displayed great power both in training and validation group. DCA presented great clinical utility. ROCs curve provided the predictive ability in the training cohort (AUC = 0.805) and the validation cohort (AUC = 0.808). Moreover, patients in LNN group had significantly better survival than that in LNP group both in training and validation group.Conclusion: In this study, we constructed and developed a nomogram with risk factors, which performed well in predicting risk factors of LNM in osteosarcoma patients. It may give a guide for surgeons and oncologists to optimize individual treatment and make a better clinical decision.

Sign in / Sign up

Export Citation Format

Share Document