Propensity scores: a tool to help quantify treatment effects in observational studies

Patino, Cecilia María; Ferreira, Juliana Carvalho

doi:10.1590/s1806-37562017000000099

Cited by 4 publications

(4 citation statements)

References 3 publications

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“…The propensity score was estimated using a non-parsimonious multivariate logistic regression model, with sex as a grouping variable and all baseline features listed in Table 1 as covariates. The greedy-matching algorithm was used to generate one-to-one matched pairings without replacement, and the difference of the propensity score between the 2 groups was allowed to be within 0.1 [ 22 ].…”

Section: Methodsmentioning

confidence: 99%

Association Between Thyroid Function and Body Composition in Type 2 Diabetes Mellitus (T2DM) Patients: Does Sex Have a Role?

et al. 2021

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Section: Methodsmentioning

confidence: 99%

Association Between Thyroid Function and Body Composition in Type 2 Diabetes Mellitus (T2DM) Patients: Does Sex Have a Role?

et al. 2021

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“…The greedy-matching algorithm was used to generate 1:4 matching pairs with replacement. Since the sample size was small, the difference in propensity score between the two groups was set within 0.6 [34]. The speci c parameters were listed in the supplementary material.…”

Section: Discussionmentioning

confidence: 99%

Application of dual-stream 3D convolutional neural network based on 18F-FDG PET/CT in distinguishing benign and invasive adenocarcinoma in ground-glass lung nodules

Shao¹,

Niu²,

Shao³

et al. 2021

Preprint

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Purpose: This work aims to train, validate and test a dual-stream three-dimensional convolutional neural network (3D-CNN) based on Fluorine 18 (18F)-fluorodeoxyglucose (FDG) PET/CT to distinguish benign lesions and invasive adenocarcinoma (IAC) in ground-glass nodules (GGNs).Methods: We retrospectively analyzed patients with suspicious GGNs who underwent 18F-FDG PET/CT in our hospital from November 2011 to November 2020. The patients with benign lesions or IAC were selected for this study. According to the ratio of 7:3, the data was randomly divided into training data and testing data. Partial image feature extraction software was used to segment PET and CT images, and the training data after using the data augmentation was used for the training and validation (5-fold cross-validation) of the three CNNs (PET, CT, and PET/CT networks).Results: A total of 23 benign nodules and 92 IAC nodules from 106 patients were included in this study. In the validation set, the performance of PET network (accuracy of 0.92 ± 0.02; sensitivity and specificity of 0.97 ± 0.03 and 0.94 ± 0.04) were better than CT network (accuracy of 0.84 ± 0.03; sensitivity and specificity of 0.90 ± 0.07 and 0.90 ± 0.04); in the testing set, the performance of both networks declined. However, when the specificity was close, the accuracy and sensitivity of PET network (0.79 ± 0.05 and 0.90 ± 0.05) were still higher than CT network (0.72 ± 0.05 and 0.79 ± 0.08). For the dual-stream PET/CT network, its performance was almost the same as PET network in the validation set; while in testing set, although its performance decreased (accuracy was 0.81 ± 0.03; sensitivity and specificity were 0.92 ± 0.03 and 0.86 ± 0.00), it was still higher than CT or PET networks. Moreover, the accuracy of PET/CT network was higher than two nuclear medicine physicians [Physician 1 (3-year experience): 0.70 and Physician 2 (10-year experience): 0.73]. Conclusion: The 3D-CNN based on 18F-FDG PET/CT can be used to distinguish benign lesions and IAC in GGNs, and the performance is better when both CT and PET images are used together.

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“…The greedy-matching algorithm was used to generate 1:4 matching pairs with replacement. Since the sample size was small, the difference in propensity score between the two groups was set within 0.6 [ 36 ]. The specific parameters are listed in Additional file 1 .…”

Section: Methodsmentioning

confidence: 99%

Application of dual-stream 3D convolutional neural network based on 18F-FDG PET/CT in distinguishing benign and invasive adenocarcinoma in ground-glass lung nodules

et al. 2021

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Purpose This work aims to train, validate, and test a dual-stream three-dimensional convolutional neural network (3D-CNN) based on fluorine 18 (18F)-fluorodeoxyglucose (FDG) PET/CT to distinguish benign lesions and invasive adenocarcinoma (IAC) in ground-glass nodules (GGNs). Methods We retrospectively analyzed patients with suspicious GGNs who underwent 18F-FDG PET/CT in our hospital from November 2011 to November 2020. The patients with benign lesions or IAC were selected for this study. According to the ratio of 7:3, the data were randomly divided into training data and testing data. Partial image feature extraction software was used to segment PET and CT images, and the training data after using the data augmentation were used for the training and validation (fivefold cross-validation) of the three CNNs (PET, CT, and PET/CT networks). Results A total of 23 benign nodules and 92 IAC nodules from 106 patients were included in this study. In the training set, the performance of PET network (accuracy, sensitivity, and specificity of 0.92 ± 0.02, 0.97 ± 0.03, and 0.76 ± 0.15) was better than the CT network (accuracy, sensitivity, and specificity of 0.84 ± 0.03, 0.90 ± 0.07, and 0.62 ± 0.16) (especially accuracy was significant, P-value was 0.001); in the testing set, the performance of both networks declined. However, the accuracy and sensitivity of PET network were still higher than that of CT network (0.76 vs. 0.67; 0.85 vs. 0.70). For dual-stream PET/CT network, its performance was almost the same as PET network in the training set (P-value was 0.372–1.000), while in the testing set, although its performance decreased, the accuracy and sensitivity (0.85 and 0.96) were still higher than both CT and PET networks. Moreover, the accuracy of PET/CT network was higher than two nuclear medicine physicians [physician 1 (3-year experience): 0.70 and physician 2 (10-year experience): 0.73]. Conclusion The 3D-CNN based on 18F-FDG PET/CT can be used to distinguish benign lesions and IAC in GGNs, and the performance is better when both CT and PET images are used together.

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Propensity scores: a tool to help quantify treatment effects in observational studies

Cited by 4 publications

References 3 publications

Association Between Thyroid Function and Body Composition in Type 2 Diabetes Mellitus (T2DM) Patients: Does Sex Have a Role?

Association Between Thyroid Function and Body Composition in Type 2 Diabetes Mellitus (T2DM) Patients: Does Sex Have a Role?

Application of dual-stream 3D convolutional neural network based on 18F-FDG PET/CT in distinguishing benign and invasive adenocarcinoma in ground-glass lung nodules

Application of dual-stream 3D convolutional neural network based on 18F-FDG PET/CT in distinguishing benign and invasive adenocarcinoma in ground-glass lung nodules

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