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.

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 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 Nomogram for Predicting Lymph Node Metastasis in Submucosal Colorectal Cancer

2017 ◽  
Vol 102 (3-4) ◽  
pp. 102-108
Author(s):  
Shiki Fujino ◽  
Norikatsu Miyoshi ◽  
Masayuki Ohue ◽  
Masayoshi Yasui ◽  
Keijiro Sugimura ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Logistic Regression ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Prediction Model ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Medical Center ◽  
Univariate Analysis ◽  
Node Metastasis

In colorectal cancer (CRC), the possibility of lymph node (LN) metastasis is an important consideration when deciding on treatment. We developed a nomogram for predicting lymph node metastasis of submucosal (SM) CRC. The medical records of 509 patients with SM CRC from 1984 to 2012 were retrospectively investigated. All the patients underwent curative surgical resection at the Osaka Medical Center for Cancer and Cardiovascular Diseases. A total 113 patients with inadequate data were excluded. Using a group of 293 patients who underwent surgery from 1984 to 2008, a logistic regression model was used to develop a prediction model for LN metastasis. The prediction model was validated in an additional group of 103 patients who underwent surgery from 2009 to 2012. Univariate analysis of pathologic factors showed the influence of low histologic grade (muc, por, sig; P &lt; 0.001), positive lymphatic invasion (P &lt; 0.001), positive vascular invasion (P = 0.036), and tumor SM invasion depth (P = 0.098) in LN metastasis. Using these variables, a nomogram predicting LN metastasis was constructed using a logistic regression model with an area under the curve (AUC) of 0.717. The prediction model was validated by an external dataset in an independent patient group with an AUC of 0.920. We developed a novel and reliable nomogram predicting LN metastasis through the integration of 4 pathologic factors. This prediction model may help clinicians to decide on personalized treatment following endoscopic resection.

scholarly journals Protocol for the development and validation of a risk prediction model for stillbirths from 35 weeks gestation in Australia

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Jessica K. Sexton ◽  
Michael Coory ◽  
Sailesh Kumar ◽  
Gordon Smith ◽  
Adrienne Gordon ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Prediction Model ◽  
Risk Prediction ◽  
Late Pregnancy ◽  
Risk Prediction Model ◽  
Past Century ◽  
Reporting Standards ◽  
Robust Method ◽  
Predictive Values ◽  
Multivariable Logistic Regression

Abstract Background Despite advances in the care of women and their babies in the past century, an estimated 1.7 million babies are born still each year throughout the world. A robust method to estimate a pregnant woman’s individualized risk of late-pregnancy stillbirth is needed to inform decision-making around the timing of birth to reduce the risk of stillbirth from 35 weeks of gestation in Australia, a high-resource setting. Methods This is a protocol for a cross-sectional study of all late-pregnancy births in Australia (2005–2015) from 35 weeks of gestation including 5188 stillbirths among 3.1 million births at an estimated rate of 1.7 stillbirths per 1000 births. A multivariable logistic regression model will be developed in line with current TransparentReporting of a multivariable prediction model forIndividualPrognosis orDiagnosis (TRIPOD) guidelines to estimate the gestation-specific probability of stillbirth with prediction intervals. Candidate predictors were identified from systematic reviews and clinical consultation and will be described through univariable regression analysis. To generate a final model, elimination by backward stepwise multivariable logistic regression will be performed. The model will be internally validated using bootstrapping with 1000 repetitions and externally validated using a temporally unique dataset. Overall model performance will be assessed with R2, calibration, and discrimination. Calibration will be reported using a calibration plot with 95% confidence intervals (α = 0.05). Discrimination will be measured by the C-statistic and area underneath the receiver-operator curves. Clinical usefulness will be reported as positive and negative predictive values, and a decision curve analysis will be considered. Discussion A robust method to predict a pregnant woman’s individualized risk of late-pregnancy stillbirth is needed to inform timely, appropriate care to reduce stillbirth. Among existing prediction models designed for obstetric use, few have been subject to internal and external validation and many fail to meet recommended reporting standards. In developing a risk prediction model for late-gestation stillbirth with both providers and pregnant women in mind, we endeavor to develop a validated model for clinical use in Australia that meets current reporting standards.

scholarly journals Factors associated with pericolic fat stranding of colon cancer on computed tomography colonography

2018 ◽  
Vol 7 (2) ◽  
pp. 205846011875757
Author(s):  
Tsuyoshi Morimoto ◽  
Takayuki Yamada ◽  
Kunihisa Miyakawa ◽  
Yasuo Nakajima
Keyword(s):  
Computed Tomography ◽  
Colon Cancer ◽  
Logistic Regression ◽  
Lymph Node ◽  
Univariate Analysis ◽  
Depth Of Invasion ◽  
Factors Associated ◽  
Longitudinal Length ◽  
Fat Stranding ◽  
Group Comparisons

Background Pericolic fat stranding on computed tomography (CT) scans has been an important feature for staging colon cancer. However, the factors associated with pericolic fat stranding have not been elucidated to date. Purpose To determine factors associated with pericolic fat stranding of colon cancer on CT colonography (CTC). Material and Methods Overall, 150 patients with 155 colon cancer lesions were retrospectively assessed by two radiologists for pericolic fat stranding on CTC. Circumferential proportion of the tumor (CPtumor; <50%, 50–75%, and ≥75%), longitudinal length, depth of invasion (≤T2, T3, T4), lymph node and distant metastasis, and lymphovascular invasion were recorded. Univariate and multivariate logistic regression analyses were performed between pericolic fat stranding and each factor. Multi-group comparisons were performed for the CPtumor and depth of invasion. Results Pericolic fat stranding was identified in 57 lesions (36.8%). Univariate analysis revealed significant associations of pericolic fat stranding with all factors ( P < 0.027), except for lymph node metastasis ( P = 0.087). Multi-group comparisons revealed that pericolic fat stranding was more frequent with increasing CPtumor ( P < 0.001); however, no significant differences were observed beyond subserosal infiltration ( P = 0.225). Logistic regression analysis revealed the CPtumor (<75% vs. ≥75%; P = 0.008, <50% vs. 50–75%; P = 0.047) and longitudinal length ( P = 0.001) as explainable variables. Conclusion Pericolic fat stranding identified on CT images of colon cancer is demonstrated more frequently with increasing circumferential proportion of the tumor and longitudinal length.

scholarly journals A CASE OF NODAL MALIGNANT LYMPHOMA IN THE REGIONAL LYMPH NODE AT A RADICAL OPERATION FOR ESOPHAGEAL CANCER

2000 ◽  
Vol 61 (9) ◽  
pp. 2317-2320 ◽  
Author(s):  
Yoshiichi MAEURA ◽  
Mahumi SAITO ◽  
Nobuhisa UEDA ◽  
Seiichi MATSUNAGA ◽  
Shigeru OKAMOTO
Keyword(s):  
Esophageal Cancer ◽  
Lymph Node ◽  
Malignant Lymphoma ◽  
Regional Lymph Node ◽  
Radical Operation

Abstract TP50: A Critical Assessment of the Golden Hour and the Impact of Procedural Timing in Stroke Thrombectomy: A Single-Center Study

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Aaron P Wessell ◽  
Helio De Paula Carvahlo ◽  
Elizabeth Le ◽  
Gregory Cannarsa ◽  
Matthew J Kole ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Mechanical Thrombectomy ◽  
Univariate Analysis ◽  
Multivariable Logistic Regression Analysis ◽  
Procedure Time ◽  
Golden Hour ◽  
Poor Outcome ◽  
Multivariable Logistic Regression ◽  
Scale Scores ◽  
The Impact

Background: Previous studies have demonstrated the importance keeping thrombectomy procedure times ≤60 min., termed the ‘golden hour’. In the current study, we further investigate the significance of the ‘golden hour’ and the impact of procedural timing on clinical outcomes after mechanical thrombectomy. Methods: We performed an analysis of 319 consecutive mechanical thrombectomy patients at a single Comprehensive Stroke Center from April 2012 through February 2019. Bivariate analyses compared patients grouped according to procedure time ≤60 min. or >60 min. and time of stroke onset-to-endovascular therapy (OTE) ≤6 hours or >6 hours. Logistic regression was used to determine independent predictors of poor outcome at 90-days defined by modified Rankin Scale (mRS) scores of 3-6. Results: A procedure time ≤60 min. was associated with increased revascularization rates (88% vs. 67%; p<0.001) and a greater percentage of good outcomes at 90-days (47% vs. 31%; p=0.003). Multivariable logistic regression revealed that greater age (OR 1.03, 95% CI 1.004-1.051; p=0.023), higher admission NIHSS score (OR 1.10, 95% CI 1.038-1.159; p=0.001), and history of diabetes mellitus (OR 1.94, 95% CI 1.049-3.580; p=0.035) were independently associated with a greater odds of poor outcome. Modified TICI scale scores of 2C (OR 0.12, 95% CI 0.047-0.313; p<0.001) and 3 (OR 0.19, 95% CI 0.079-0.445; p<0.001) were associated with a reduced odds of poor outcome. Although not statistically significant on univariate analysis, OTE ≤6 hrs. was independently associated with a reduced odds of poor outcome (OR 0.41, 95% CI 0.212-0.809; p=0.010) in the final multivariate model (AUC 0.800). Procedure time ≤60 min. did not have a significant independent association with clinical outcome on multivariate analysis (p=0.095). Conclusions: Thrombectomy procedure times beyond 60 min. are associated with lower overall revascularization rates and worse 90 day functional outcomes when compared to faster thrombectomy procedures. However, thrombectomy procedure time was not predictive of outcome on multivariable logistic regression analysis. Our study emphasizes the significance of achieving revascularization despite the requisite procedure time.

A Risk-Prediction Model For Identifying Venous Thromboembolism In Hospitalized Pediatric Patients: A Single Institution Retrospective Case-Control Analysis

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 2957-2957
Author(s):  
Ruchika Goel ◽  
Jessy Dhillon ◽  
Craig Malli ◽  
Kishen Sahota ◽  
Prabhjot Seehra ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Venous Thromboembolism ◽  
Risk Prediction ◽  
Research Funding ◽  
Pediatric Patients ◽  
Univariate Analysis ◽  
Case Control ◽  
Control Analysis ◽  
Multivariable Logistic Regression

Abstract Introduction Venous thromboembolism (VTE) is increasing in children, especially in the tertiary care setting. Hospital-associated VTE (HA-VTE) is a potentially preventable cause of major morbidity and mortality. However, the incidence of HA-VTE VTE is low in children Risk stratification tools may aid in identification of hospitalized high risk pediatric patients who may benefit from VTE prophylaxis. Methods We conducted a case-control study of pediatric patients with HA-VTE (21 years or younger at the time of diagnosis) admitted to the Johns Hopkins Hospital from 2008-2010. Cases were identified using ICD-9 codes for DVT and PE and verified by reviewing hospital records and radiologic imaging reports. HA-VTE was defined as: 1) VTE was diagnosed ≥48 hours after hospital admission without signs/symptoms of VTE on admission, or 2) VTE was diagnosed within 90 days of hospital discharge. Two contemporaneous controls matched for age, sex and admission unit were selected for each case. Records of cases and controls were reviewed for presence of a priori identified putative VTE risk factors at admission. Univariate and conditional multivariable logistic regression analyses with backward elimination were used to develop risk-prediction models. Based on results of univariate analysis, we sought to evaluate two multivariable models, one without length of stay (LOS) with relevance to assessment at admission, and one in which LOS was included with relevance to re-assessment after several days of hospitalization. All variables selected for the multivariable model were tested for interaction with a significance threshold level of p<0.2. Except for this, all hypothesis testing was two tailed and a p value of <0.05 was considered significant. Receiver operator curves (ROC) were constructed using risk factors on multivariate analysis. Results Table 1 lists the results putative risk factors by univariate analysis with a) significantly higher odds of VTE and b) higher odds of VTE but not statistically significant. In multivariable logistic regression analysis, central venous catheter (CVC), VTE predisposition and immobility or LOS >5 days were independently associated with HA-VTE. The combination of CVC and VTE predisposition with either immobility or LOS was predictive of HA-VTE (area under the curve for ROC of 76.6% and 80.6%, Table 2). Conclusion We found independently associated risk factors with that may potentially be used in a predictive model of HA-VTE in children. Further prospective validation studies of these and other risk factors may serve as the basis of future risk-stratified randomized control trials of primary prevention of pediatric HA-VTE. Disclosures: Streiff: Bristol Myers Squibb: Research Funding; Sanofi: Consultancy, Honoraria; Eisai, Daiichi-Sankyo, Boehringer-Ingelheim, Janssen HealthCare: Consultancy. Strouse:NIH: Research Funding; Doris Duke Charitable Foundation: Research Funding; Masimo Corporation: Membership on an entity's Board of Directors or advisory committees, Research Funding. Takemoto:Novonordisk: Research Funding.

Phase II trial of combination therapy using S-1, cisplatin, and radiation for distant metastatic esophageal cancer (stage IVb).

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. e14596-e14596
Author(s):  
Hiroaki Iwase ◽  
Masaaki Shimada ◽  
Tomoyuki Tsuzuki ◽  
Hidemi Goto
Keyword(s):  
Esophageal Cancer ◽  
Lymph Node ◽  
Combination Therapy ◽  
Phase Ii ◽  
Phase Ii Trial ◽  
Regional Lymph Node ◽  
Locally Advanced ◽  
Primary Lesion ◽  
Dysphagia Score ◽  
Stage Ivb

e14596 Background: S-1 is an orally active fluoropyrimidine that enhances the efficacy of radiotherapy (RT) and has low gastrointestinal toxicity. Our previous phase II study demonstrated that definitive chemoradiotherapy (CRT) with S-1 and cisplatin was well-tolerated and had favorable activity for locally advanced esophageal cancer (Iwase H et al, Proc. ASCO 2011, Abst 4034). The present study was a phase II trial of combination therapy using S-1, cisplatin, and RT for distant metastatic esophageal cancer (DMEC). Methods: S-1 (80 mg/m2/day) was given orally for 14 consecutive days from Day 1 and cisplatin (70 mg/m2) was administered on Day 14, both with 3 weeks of RT (2.0 Gy per traction) 5 times per week for the primary lesion and metastases in the neck, which initiated on Day 1. One Cycle equals 5 weeks, 2 weeks of chemotherapy concurrent with 3 weeks of RT followed by 2 weeks of complete rest. After 2 cycles, only chemotherapy with S-1 and cisplatin were administered. Results: Forty-one patients with DMEC (Stage IVb) were enrolled between March 2002 and February 2011. 37/male7/female, median age 67.5 years (48-82). The median survival follow-up time was 16.6 months and 37 patients (90.2%) completed the combination treatment. The most common adverse event was neutropenia. Grades 3 and 4 neutropenia were observed in 29.2% and 12.2%, respectively. In general, non-hematological adverse events were mild and the most common were Grade 2 nausea (34.1%), esophageal pain and oral mucositis (17.1% each), and renal dysfunction (9.8%). The overall response rate was 65.9% comprising 93.2% in the primary lesion, 60% in the liver metastasis, 64.3.% in the lung, 54.5% in the distant lymph node, 83.3% in the regional lymph node metastasis, and 50% in the other distant metastases. Thirty-nine patients (88.6%) showed improvement in their dysphagia score. The median progression-free and overall survival durations were 5.3 [95% confidence interval (CI), 4.1 to 6.0] and 13.1 months (95% CI, 9.8 to 15.6), respectively. Conclusions: Combination therapy using S-1, cisplatin, and RT has a promising safety and efficacy profile. Potentially, this regimen could become the baseline treatment for patients with DMEC.

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