Predictive model for the probability of malignancy in solitary pulmonary nodules: a meta-analysis

Chen, Gang; Bai, Tian; Wen, Liang; Liu, Yu

doi:10.1186/s13019-022-01859-x

Cited by 6 publications

(8 citation statements)

References 27 publications

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“…The diagnostic utility of individual CT features is relatively limited when evaluating PNs. In prior meta-analyses assessing the diagnostic performance of lobulation sign, calcification, and spiculation as approaches to differential diagnosis of PNs, the AUC values were between 0.65 and 0.76 [ 1 – 3 ]. The AUC for the diagnostic utility of PET alone in the present study was 0.82, but the pooled specificity was just 51%.…”

Section: Discussionmentioning

confidence: 99%

“…A given predictive model was considered to exhibit high diagnostic performance if it exhibited an NLR < 0.2 or a PLR > 5. An area under the SROC curve (AUC) value greater than 0.8 was also considered to indicate a high degree of diagnostic utility [ 3 ]. RevMan v 5.3 was used to compare pooled SUV max values between benign and malignant PNs.…”

Section: Methodsmentioning

confidence: 99%

“…Pulmonary nodules (PNs) are small (≤ 3 cm) lesions surrounded by lung parenchymal tissue that are not transparent and not the results of atelectasis, mediastinal lymphadenopathy, or pleural effusion [ 1 – 3 ]. In cases where these nodules are > 6 mm in size, computed tomography (CT)-based routine follow-up is warranted [ 4 ], with a 1.1-fold increase in the risk of PN malignancy with each 1 mm increase in diameter [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

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18F-FDG PET/CT based model for predicting malignancy in pulmonary nodules: a meta-analysis

Li,

Shi,

2024

J Cardiothorac Surg

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Background Several studies to date have reported on the development of positron emission tomography (PET)/computed tomography (CT)-based models intended to effectively distinguish between benign and malignant pulmonary nodules (PNs). This meta-analysis was designed with the goal of clarifying the utility of these PET/CT-based conventional parameter models as diagnostic tools in the context of the differential diagnosis of PNs. Methods Relevant studies published through September 2023 were identified by searching the Web of Science, PubMed, and Wanfang databases, after which Stata v 12.0 was used to conduct pooled analyses of the resultant data. Results This meta-analysis included a total of 13 retrospective studies that analyzed 1,731 and 693 malignant and benign PNs, respectively. The respective pooled sensitivity, specificity, PLR, and NLR values for the PET/CT-based studies developed in these models were 88% (95%CI: 0.86–0.91), 78% (95%CI: 0.71–0.85), 4.10 (95%CI: 2.98–5.64), and 0.15 (95%CI: 0.12–0.19). Of these endpoints, the pooled analyses of model sensitivity (I2 = 69.25%), specificity (I2 = 78.44%), PLR (I2 = 71.42%), and NLR (I2 = 67.18%) were all subject to significant heterogeneity. The overall area under the curve value (AUC) value for these models was 0.91 (95%CI: 0.88–0.93). When differential diagnosis was instead performed based on PET results only, the corresponding pooled sensitivity, specificity, PLR, and NLR values were 92% (95%CI: 0.85–0.96), 51% (95%CI: 0.37–0.66), 1.89 (95%CI: 1.36–2.62), and 0.16 (95%CI: 0.07–0.35), with all four being subject to significant heterogeneity (I2 = 88.08%, 82.63%, 80.19%, and 86.38%). The AUC for these pooled analyses was 0.82 (95%CI: 0.79–0.85). Conclusions These results suggest that PET/CT-based models may offer diagnostic performance superior to that of PET results alone when distinguishing between benign and malignant PNs.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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18F-FDG PET/CT based model for predicting malignancy in pulmonary nodules: a meta-analysis

Li,

Shi,

2024

J Cardiothorac Surg

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“…Efforts to distinguish between SPNs that are likely to be benign and those likely to be malignant are generally centered on the clinical data, computed tomography (CT) ndings, and tumor marker levels for each patient [8][9][10]. There have been many efforts to establish predictive models capable of assessing SPN malignancy risk by combining several predictors associated with malignant nodules [9], yielding models with sensitivities from 84%-91% and speci cities from 74%-80%, together with area under the curve (AUC) values from 0.83-0.89 [9]. There is thus a clear opportunity to develop even more accurate predictive models for SPN assessment.…”

Section: Introductionmentioning

confidence: 99%

CT radiomics based model for differentiating malignant and benign small (≤20mm) solid pulmonary nodules

Sun,

Zhou,

et al. 2024

Preprint

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Background At present, computed tomography (CT) radiomics-based models capable of evaluating small (≤ 20 mm) solid pulmonary nodules (SPNs) are lacking. Accordingly, the present study sought to develop a CT radiomics-based model capable of differentiating between benign and malignant small SPNs. Methods Between January 2019 and November 2021, this study enrolled consecutive patients presenting with small SPNs, randomly assigning these individuals to training and testing cohorts at an 8:2 ratio. CT images were processed to extract radiomics features, with a radiomics scoring model being developed based on the features selected in the training group through univariate and multivariate logistic regression analyses. The testing cohort was then used to validate the developed predictive model. Results In total, this study included 210 patients in the training (n = 168) and testing (n = 42) cohorts. Radiomics scores were ultimately calculated based on 9 selected CT radiomics features. Traditional CT and clinical risk factors associated with malignancy in SPNs included lobulation (P < 0.001), spiculation (P < 0.001), and a larger diameter (P < 0.001). The developed CT radiomics scoring model consisted of the following formula: X = -6.773 + 12.0705×radiomics score + 2.5313×lobulation + 3.1761×spiculation + 0.3253×diameter. The CT radiomics-based model, CT radiomics score, and clinicoradiological score were associated with area under the curve (AUC) values of 0.957, 0.945, and 0.853, respectively, in the training cohort, while the testing cohort exhibited corresponding AUC values of 0.943, 0.916, and 0.816. Conclusions The CT radiomics-based model designed in the present study offers valuable diagnostic accuracy when employed to distinguish between benign and malignant SPNs.

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“…With the wide application of low-dose multislice spiral computed tomography (CT) and the increase of the number of physical examinations, the detection rate of pulmonary nodules is increasing ( 3 ). About 1% of pulmonary nodules are malignant tumors, most of which are lung adenocarcinoma (LUAD) ( 4 ). The management of LUAD with different invasiveness degrees is different: for pre-invasive lung nodules with slow growth rate, only regular follow-up is required, whereas invasive lung nodules require elective or immediate surgical treatment ( 5 , 6 ).…”

Section: Introductionmentioning

confidence: 99%

The predictive accuracy of CT radiomics combined with machine learning in predicting the invasiveness of small nodular lung adenocarcinoma

Liu¹,

Jia²,

Yu³

et al. 2023

Transl Lung Cancer Res

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Background: Conventionally, the judgment of whether small pulmonary nodules are invasive is mainly made by thoracic surgeons according to the chest computed tomography (CT) features of patients.However, there are limits to how much useful information can be obtained from this approach. A large number of feature information was extracted from CT images by CT radiomics. The machine learning algorithm was used to construct models based on radiomic characteristics to predict the invasiveness of lung adenocarcinoma (LUAD) with a good prediction accuracy.Methods: A total of 416 patients with pathologically confirmed preinvasive lesions and LUAD after videoassisted thoracoscopic surgery (VATS) in the Department of Thoracic Surgery of the First People's Hospital of Yunnan Province from February 2020 to February 2022 were retrospectively analyzed. According to random classification, patients were divided into 2 groups. The RadCloud platform was used to extract radiomics features, and the most relevant radiomics features were selected by continuous dimension reduction method. Then, 6 machine learning algorithms were used to establish and verify the prediction model of small lung nodular adenocarcinoma invasiveness. Receiver operating characteristic (ROC) curve and area under curve (AUC) were used to evaluate the predictive performance.Results: There were 78 cases of pre-invasive lesions and 226 cases of invasive lesions in the training group, and 34 cases of pre-invasive lesions and 78 cases of invasive lesions in the validation group. In the training group, the AUC values of the 6 models were all more than 0.914, the 95% confidence interval (CI) was 0.857-1.00, the sensitivity was equal or more than 0.87, and the specificity was equal or more than 0.85. In the validation group, the AUC values of the 6 models were all equal or more than 0.732, the 95% CI was 0.651-1.00, the sensitivity was equal or more than 0.7, and the specificity was more than 0.77. Conclusions:Machine learning algorithms were used to construct models to predict the invasiveness of small nodular LUAD based on radiomics features, which it could provide more evidence for doctors to make ^ ORCID: 0000-0002-0596-2685.

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Predictive model for the probability of malignancy in solitary pulmonary nodules: a meta-analysis

Cited by 6 publications

References 27 publications

18F-FDG PET/CT based model for predicting malignancy in pulmonary nodules: a meta-analysis

18F-FDG PET/CT based model for predicting malignancy in pulmonary nodules: a meta-analysis

CT radiomics based model for differentiating malignant and benign small (≤20mm) solid pulmonary nodules

The predictive accuracy of CT radiomics combined with machine learning in predicting the invasiveness of small nodular lung adenocarcinoma

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