Machine Learning Models for Risk Prediction of Lymph Nodes Metastasis in Non-Small Cell Lung Cancer: Development and Validation Study

2020 ◽  
Author(s):  
Miaochun Cai ◽  
Dong Shen ◽  
Zhihao Li ◽  
Jianmeng Zhou ◽  
Yingjun Chen ◽  
...  
Keyword(s):  
Random Forest ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Model Performance ◽  
Small Cell ◽  
Curve Analysis ◽  
Selection Model ◽  
Full Model ◽  
Small Cell Lung ◽  
Decision Curve Analysis

Abstract Background: To develop and validate machine learning models for risk prediction of lymph node metastasis (LNM) in non-small cell lung cancer (NSCLC) using clinicopathologic parameters and immunohistochemical features. Methods: From January 2010 to December 2019, 639 patients' data were continuously collected in Nanfang Hospital. We exacted immunohistochemical features and clinicopathological features from the electronic medical records of patients. We established two models (a full model and a selection model) and implemented three algorithms (random forest, support vector machine and penalized logistic regression). The model performance was evaluated in terms of discrimination (receiver operating characteristic curve (AUC)), calibration, and decision curve analysis. Results: AUROC (area under receiver operating characteristic curve) analysis (also calibration curves) showed that the selection model (AUC values for training and testing, 0.843 and 0.840 respectively) and the full model constructed using random forest (AUC values for training and testing, 0.855 and 0.863 respectively) performed best among all models. Decision curve analysis depicted that the full model and the selection model using random forest was clinically useful. The model performance of the full model and the selection model were comparable. Conclusion: The random forest model using clinicopathologic- immunohistochemical features can predict the LNM of NSCLC patients.

Machine-Learning and Stochastic Tumor Growth Models for Predicting Outcomes in Patients With Advanced Non–Small-Cell Lung Cancer

2019 ◽  
pp. 1-11 ◽  
Author(s):  
Kien Wei Siah ◽  
Sean Khozin ◽  
Chi Heem Wong ◽  
Andrew W. Lo
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Clinical Trials ◽  
Operating Characteristic ◽  
Advanced Nsclc ◽  
Characteristic Curve ◽  
Small Cell ◽  
Small Cell Lung ◽  
Out Of Sample ◽  
Operating Characteristic Curve

PURPOSE The prediction of clinical outcomes for patients with cancer is central to precision medicine and the design of clinical trials. We developed and validated machine-learning models for three important clinical end points in patients with advanced non–small-cell lung cancer (NSCLC)—objective response (OR), progression-free survival (PFS), and overall survival (OS)—using routinely collected patient and disease variables. METHODS We aggregated patient-level data from 17 randomized clinical trials recently submitted to the US Food and Drug Administration evaluating molecularly targeted therapy and immunotherapy in patients with advanced NSCLC. To our knowledge, this is one of the largest studies of NSCLC to consider biomarker and inhibitor therapy as candidate predictive variables. We developed a stochastic tumor growth model to predict tumor response and explored the performance of a range of machine-learning algorithms and survival models. Models were evaluated on out-of-sample data using the standard area under the receiver operating characteristic curve and concordance index (C-index) performance metrics. RESULTS Our models achieved promising out-of-sample predictive performances of 0.79 area under the receiver operating characteristic curve (95% CI, 0.77 to 0.81), 0.67 C-index (95% CI, 0.66 to 0.69), and 0.73 C-index (95% CI, 0.72 to 0.74) for OR, PFS, and OS, respectively. The calibration plots for PFS and OS suggested good agreement between actual and predicted survival probabilities. In addition, the Kaplan-Meier survival curves showed that the difference in survival between the low- and high-risk groups was significant (log-rank test P < .001) for both PFS and OS. CONCLUSION Biomarker status was the strongest predictor of OR, PFS, and OS in patients with advanced NSCLC treated with immune checkpoint inhibitors and targeted therapies. However, single biomarkers have limited predictive value, especially for programmed death-ligand 1 immunotherapy. To advance beyond the results achieved in this study, more comprehensive data on composite multiomic signatures is required.

Systemic immune–inflammation index changes predict outcome in stage III non-small-cell lung cancer patients treated with concurrent chemoradiotherapy

2021 ◽  
Author(s):  
Taosheng Huang ◽  
Huanqian Zhang ◽  
Yunzheng Zhao ◽  
Yanping Li ◽  
Guofeng Wang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Operating Characteristic ◽  
Concurrent Chemoradiotherapy ◽  
Characteristic Curve ◽  
Small Cell ◽  
Stage Iii ◽  
Small Cell Lung ◽  
Operating Characteristic Curve ◽  
Stage Iii Nsclc ◽  
Nsclc Patients

Background: Although the systemic immune–inflammation index (SII) has been used to predict recurrence and survival in non-small-cell lung cancer (NSCLC) patients, the prognostic significance of change in SII (ΔSII) is unclear for stage III NSCLC patients treated with concurrent chemoradiotherapy (CCRT). In the present study we aimed to explore the association between ΔSII and the clinical outcomes of 142 patients with stage III NSCLC treated with CCRT. Methods: A total of 142 patients were included in this retrospective study. The SII values were calculated based on laboratory data regarding platelet, neutrophil and lymphocyte counts, and ΔSII was calculated using data acquired before and approximately 2 weeks after CCRT. The receiver operating characteristic curve was used to determine the optimal cut-off value for the peripheral blood inflammation index. Kaplan–Meier analysis and Cox proportional regression were used to analyze the prognostic value of ΔSII for overall survival (OS) and progression-free survival (PFS). Results: The area under the receiver operating characteristic curve for ΔSII (0.708) was larger than those for pre-CCRT SII (0.578) and post-CCRT SII (0.610). The optimal cut-off point for ΔSII was defined as 43. OS and PFS were better in patients with low ΔSII and in multivariate analysis, the ΔSII was an independent predictor of OS and PFS (p = 0.006 and p = 0.017, respectively). Conclusions: ΔSII is related to progression and death in patients with stage III NSCLC. The ΔSII can provide a detailed prognostic prediction for stage III NSCLC.

Serum miR-21 and miR-26a Levels Negatively Correlate with Severity of Cirrhosis in Patients with Chronic Hepatitis B

MicroRNA ◽  
2018 ◽  
Vol 8 (1) ◽  
pp. 86-92 ◽  
Author(s):  
Shili Jiang ◽  
Wei Jiang ◽  
Ying Xu ◽  
Xiaoning Wang ◽  
Yongping Mu ◽  
...  
Keyword(s):  
Chronic Hepatitis ◽  
Hepatitis B ◽  
Chronic Hepatitis B ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Curve Analysis ◽  
Pugh Class ◽  
Operating Characteristic Curve ◽  
Class C ◽  
Circulating Levels

Background and Objective: Accurately evaluating the severity of liver cirrhosis is essential for clinical decision making and disease management. This study aimed to evaluate the value of circulating levels of microRNA (miR)-26a and miR-21 as novel noninvasive biomarkers in detecting severity of cirrhosis in patients with chronic hepatitis B. </P><P> Methods: Thirty patients with clinically diagnosed chronic hepatitis B-related cirrhosis and 30 healthy individuals were selected. The serum levels of miR-26a and miR-21 were quantified by qRT-PCR. Receiver operating characteristic curve analysis was performed to evaluate the sensitivity and specificity of the miRNAs for detecting the severity of cirrhosis. Results: Serum miR-26a and miR-21 levels were found to be significantly downregulated in patients with severe cirrhosis scored at Child-Pugh class C in comparison to healthy controls (miR-26a p<0.01, and miR-21 p<0.001, respectively). The circulating miR-26a and miR-21 levels in patients were positively correlated with serum albumin concentration but negatively correlated with serum total bilirubin concentration and prothrombin time. Receiver operating characteristic curve analysis revealed that both serum miR-26a and miR-21 levels were associated with a high diagnostic accuracy for patients with cirrhosis scored at Child-Pugh class C (miR-26a Cut-off fold change at ≤0.4, Sensitivity: 84.62%, Specificity: 89.36%, P<0.0001; miR-21 Cut-off fold change at ≤0.6, Sensitivity: 84.62%, Specificity: 78.72%, P<0.0001). Our results indicate that the circulating levels of miR-26a and miR-21 are closely related to the extent of liver decompensation, and the decreased levels are capable of discriminating patients with cirrhosis at Child-Pugh class C from the whole cirrhosis cases.

A validated novel preoperative index to predict the extent of intraperitoneal contamination in patients with acute abdominal pathology: A cohort study

2019 ◽  
Vol 30 (7-8) ◽  
pp. 221-228
Author(s):  
Shahab Hajibandeh ◽  
Shahin Hajibandeh ◽  
Nicholas Hobbs ◽  
Jigar Shah ◽  
Matthew Harris ◽  
...  
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Curve Analysis ◽  
Emergency Laparotomy ◽  
Contamination Index ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Aims To investigate whether an intraperitoneal contamination index (ICI) derived from combined preoperative levels of C-reactive protein, lactate, neutrophils, lymphocytes and albumin could predict the extent of intraperitoneal contamination in patients with acute abdominal pathology. Methods Patients aged over 18 who underwent emergency laparotomy for acute abdominal pathology between January 2014 and October 2018 were randomly divided into primary and validation cohorts. The proposed intraperitoneal contamination index was calculated for each patient in each cohort. Receiver operating characteristic curve analysis was performed to determine discrimination of the index and cut-off values of preoperative intraperitoneal contamination index that could predict the extent of intraperitoneal contamination. Results Overall, 468 patients were included in this study; 234 in the primary cohort and 234 in the validation cohort. The analyses identified intraperitoneal contamination index of 24.77 and 24.32 as cut-off values for purulent contamination in the primary cohort (area under the curve (AUC): 0.73, P < 0.0001; sensitivity: 84%, specificity: 60%) and validation cohort (AUC: 0.83, P < 0.0001; sensitivity: 91%, specificity: 69%), respectively. Receiver operating characteristic curve analysis also identified intraperitoneal contamination index of 33.70 and 33.41 as cut-off values for feculent contamination in the primary cohort (AUC: 0.78, P < 0.0001; sensitivity: 87%, specificity: 64%) and validation cohort (AUC: 0.79, P < 0.0001; sensitivity: 86%, specificity: 73%), respectively. Conclusions As a predictive measure which is derived purely from biomarkers, intraperitoneal contamination index may be accurate enough to predict the extent of intraperitoneal contamination in patients with acute abdominal pathology and to facilitate decision-making together with clinical and radiological findings.

scholarly journals AB0644 EVALUATION OF MONOCYTE TO HIGH-DENSITY LIPOPROTEIN RATIO AND CAROTID INTIMA MEDIA THICKNESS IN GOUT PATIENTS

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 1355.1-1355
Author(s):  
C. Kadiyoran ◽  
A. Kucuk ◽  
H. Aydemir ◽  
A. U. Uslu
Keyword(s):  
High Density Lipoprotein ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Density Lipoprotein ◽  
Intima Media Thickness ◽  
Carotid Intima Media Thickness ◽  
Curve Analysis ◽  
High Density ◽  
Media Thickness ◽  
Operating Characteristic Curve

Background:The aim of this study is to investigate, evaluation of monocyte to high density liporotein ratio and carotid intima media thickness in gout patients.Objectives:Gout disease is an autoinflammatory disease caused by the accumulation of monosodium urate crystals (MSU) in tissues and organs due to hyperuricemia (1). It is a common cause of arthritis due to the changes in lifestyle and eating habits. The effects of the inflammatory process and hyperuricemia in gout are not limited to the joints, but are associated with increased atherosclerosis and cardiovascular disease (1,2) Monocyte to high-density lipoprotein cholesterol ratio (MHR) is a systemic inflammatory marker and has recently been used quite widely for the evaluation of inflammation in cardiovascular disorders (3,4).Methods:Fourty eight patients who were evaluated in the rheumatology clinic with an arthritis attack and diagnosed with Gout, and 48 healthy individuals whose age, gender and body mass index were matched were included in our study. Basic laboratory and biochemical parameters of the period when gout patients were asymptomatic were examined. Carotid intima-media thickness (CIMT), which is a non-invasive procedure due to its widespread use, was used as a marker.Results:MHR and CIMT values were 18.22 ± 9.01 and 0.76 ± 0.11 mm in patients with gout. In the control group, it was 13.62 ± 4.48 and 0.65 ± 0.13 (p = 0.002, p <0.0001, respectively). When evaluated within the study group, it was found that there was a positive correlation between MHR and CIMT (r = 0.253, p = 0.013), and according to linear regression analysis, there was an independent relationship between MHR and CIMT (beta [β] = 0.293, p = 0.049). When assessing Gout patients in the study population, a cutoff value of 13.85 with sensitivity of 66 %, specificity of 53 %, and p = 0.011 (area under curve: 0.650, 95% confidence interval 0.540-0.760), was observed according to receiver-operating characteristic curve analysis (Figure 1).Figure 1.Receiver-operating characteristic curve analysis.Conclusion:This study showed us that MHR can be an inexpensive and easily accessible marker that can be used in the evaluation of atherosclerotic lesions. We think that studies with larger number of patients are needed on this subject.References:[1]Çukurova S, Pamuk ON, Unlu Ercument, Pamuk GE, Cakir NE. Subclinical atherosclerosis in gouty arthritis patients: a comparative study. Rheumatol Int. 2012 Jun; 3 2(6): 1769-73.[2]Choi HK, Curhan G. Independent impact of gout on mortality and risk for coronary heart disease. Circulation 2007 Aug 21; 116 (8): 894-900.[3]McAdams-DeMarco MA, Maynard JW, Coresh J, Baer AN.Anemia and the onset of gout in a population-based cohort of adults: Atherosclerosis Risk in Communities study. Arthritis Res Ther. 2012 Aug 20; 14(4): R193.[4]Enhos A, Cosansu K, Huyut MA, Turna F, Karacop E, Bakshaliyev N, Nadir A, Ozdemir R, Uluganyan M. Assessment of the Relationship between Monocyte to High-Density Lipoprotein Ratio and Myocardial Bridge. Arq Bras Cardiol. 2019 Jan;112(1):12-17.Disclosure of Interests:None declared.

Diagnostic Value of CD4/CD8 in Scrub Typhus

2021 ◽  
Author(s):  
Kangkang Hong ◽  
Ziping Shu ◽  
Laodong Li ◽  
Yu Zhong ◽  
Weiqian Chen ◽  
...  
Keyword(s):  
Hiv Infection ◽  
Bloodstream Infection ◽  
Operating Characteristic ◽  
Scrub Typhus ◽  
Characteristic Curve ◽  
Community Acquired Pneumonia ◽  
Curve Analysis ◽  
Control Groups ◽  
Infection Group ◽  
Operating Characteristic Curve

Scrub typhus is often misdiagnosed in febrile patients, leading to antibiotic abuse and multiple complications. We conducted a retrospective record review at the Fourth Affiliated Hospital of Guangxi Medical University in China. Data were collected on 52 patients with a confirmed diagnosis of scrub typhus and complete clinical data. In addition, data were collected on 52 patients with bloodstream infection, 25 patients with HIV infection, 112 patients with common community-acquired pneumonia (CCAP), and 36 patients with severe community-acquired pneumonia (SCAP) to serve as control groups. The peripheral blood CD4 and CD8 counts, CD4/CD8 ratio, C-reactive protein, procalcitonin, alanine aminotransferase, aspartate aminotransferase, creatinine, and β2 microglobulin levels; and the white blood cell count and neutrophil percentage were compared between the scrub typhus and the control groups. The value of these biomarkers in the diagnosis of scrub typhus was assessed using receiver–operating characteristic curve analysis. The scrub typhus group had a significantly lower CD4 count and CD4/CD8 ratio than the bloodstream infection, CCAP, and SCAP groups, and a significantly greater CD4 count and CD4/CD8 ratio than the HIV infection group. In contrast, the scrub typhus group had a significantly greater CD8 count than the bloodstream infection and CCAP and SCAP groups, and it had a lower level of CD8 than the HIV infection group. The areas under the curve of CD4/CD8 were more than 0.93 in the receiver–operating characteristic curve analysis. These findings suggest that the CD4/CD8 ratio is a useful ancillary test for diagnosing scrub typhus.

Predictors of thermal increase in magnetic resonance–guided focused ultrasound treatment for essential tremor: histogram analysis of skull density ratio values for 1024 elements

2021 ◽  
pp. 1-6
Author(s):  
Ken Iijima ◽  
Hajime Yokota ◽  
Toshio Yamaguchi ◽  
Masayuki Nakano ◽  
Takahiro Ouchi ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Operating Characteristic ◽  
Focused Ultrasound ◽  
Characteristic Curve ◽  
Density Ratio ◽  
Predictive Ability ◽  
Curve Analysis ◽  
Regression Analyses ◽  
Multivariate Logistic Regression ◽  
Operating Characteristic Curve

OBJECTIVE Sufficient thermal increase capable of generating thermocoagulation is indispensable for an effective clinical outcome in patients undergoing magnetic resonance–guided focused ultrasound (MRgFUS). The skull density ratio (SDR) is one of the most dominant predictors of thermal increase prior to treatment. However, users currently rely only on the average SDR value (SDRmean) as a screening criterion, although some patients with low SDRmean values can achieve sufficient thermal increase. The present study aimed to examine the numerical distribution of SDR values across 1024 elements to identify more precise predictors of thermal increase during MRgFUS. METHODS The authors retrospectively analyzed the correlations between the skull parameters and the maximum temperature achieved during unilateral ventral intermediate nucleus thalamotomy with MRgFUS in a cohort of 55 patients. In addition, the numerical distribution of SDR values was quantified across 1024 elements by using the skewness, kurtosis, entropy, and uniformity of the SDR histogram. Next, the authors evaluated the correlation between the aforementioned indices and a peak temperature > 55°C by using univariate and multivariate logistic regression analyses. Receiver operating characteristic curve analysis was performed to compare the predictive ability of the indices. The diagnostic performance of significant factors was also assessed. RESULTS The SDR skewness (SDRskewness) was identified as a significant predictor of thermal increase in the univariate and multivariate logistic regression analyses (p < 0.001, p = 0.013). Moreover, the receiver operating characteristic curve analysis indicated that the SDRskewness exhibited a better predictive ability than the SDRmean, with area under the curve values of 0.847 and 0.784, respectively. CONCLUSIONS The SDRskewness is a more accurate predictor of thermal increase than the conventional SDRmean. The authors suggest setting the SDRskewness cutoff value to 0.68. SDRskewness may allow for the inclusion of treatable patients with essential tremor who would have been screened out based on the SDRmean exclusion criterion.

scholarly journals Machine learning for identification of surgeries with high risks of cancellation

2018 ◽  
Vol 26 (1) ◽  
pp. 141-155 ◽  
Author(s):  
Li Luo ◽  
Fengyi Zhang ◽  
Yao Yao ◽  
RenRong Gong ◽  
Martina Fu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Predictive Value ◽  
Operating Characteristic ◽  
Sampling Methods ◽  
Characteristic Curve ◽  
Support Vector ◽  
Chi Square ◽  
Stable Performance ◽  
Operating Characteristic Curve

Surgery cancellations waste scarce operative resources and hinder patients’ access to operative services. In this study, the Wilcoxon and chi-square tests were used for predictor selection, and three machine learning models – random forest, support vector machine, and XGBoost – were used for the identification of surgeries with high risks of cancellation. The optimal performances of the identification models were as follows: sensitivity − 0.615; specificity − 0.957; positive predictive value − 0.454; negative predictive value − 0.904; accuracy − 0.647; and area under the receiver operating characteristic curve − 0.682. Of the three models, the random forest model achieved the best performance. Thus, the effective identification of surgeries with high risks of cancellation is feasible with stable performance. Models and sampling methods significantly affect the performance of identification. This study is a new application of machine learning for the identification of surgeries with high risks of cancellation and facilitation of surgery resource management.

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