Prediction efficacy of feature classification of solitary pulmonary nodules based on CT radiomics

Xu, Qingqing; Shan, Wenli; Zhu, Yinsu; Huang, Chencui; Bao, Si-yu; Guo, Lili

doi:10.1016/j.ejrad.2021.109667

Cited by 11 publications

(6 citation statements)

References 17 publications

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“…In recent years, many studies have been conducted to explore the use of conventional CT based on radiomic methods to distinguish benign and malignant pulmonary nodules [ 21 – 23 ], but few studies have focused on distinguishing pulmonary nodules using dual-energy CT based on radiomics. Xu et al [ 24 ] developed a radiomic model based on conventional CT to differentiate benign from malignant lesions, different types of malignant nodules, and benign from non-invasive malignant nodules. The AUCs ranged from 0.84 to 0.89 in the test sets.…”

Section: Discussionmentioning

confidence: 99%

The value of radiomics based on dual-energy CT for differentiating benign from malignant solitary pulmonary nodules

Gao

et al. 2022

BMC Med Imaging

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Objective To investigate the value of monochromatic dual-energy CT (DECT) images based on radiomics in differentiating benign from malignant solitary pulmonary nodules. Materials and methods This retrospective study was approved by the institutional review board, and informed consent was waived. Pathologically confirmed lung nodules smaller than 3 cm with integrated arterial phase and venous phase (AP and VP) gemstone spectral imaging were retrospectively identified. After extracting the radiomic features of each case, principal component analysis (PCA) was used for feature selection, and after training with the logistic regression method, three classification models (ModelAP, ModelVP and ModelCombination) were constructed. The performance was assessed by the area under the receiver operating curve (AUC), and the efficacy of the models was validated using an independent cohort. Results A total of 153 patients were included and divided into a training cohort (n = 107) and a validation cohort (n = 46). A total of 1130 radiomic features were extracted from each case. The PCA method selected 22, 25 and 35 principal components to construct the three models. The diagnostic accuracy of ModelAP, ModelVP and ModelCombination was 0.8043, 0.6739, and 0.7826 in the validation set, with AUCs of 0.8148 (95% CI 0.682–0.948), 0.7485 (95% CI 0.602–0.895), and 0.8772 (95% CI 0.780–0.974), respectively. The DeLong test showed that there were significant differences in the AUCs between ModelAP and ModelCombination (P = 0.0396) and between ModelVP and ModelCombination (P = 0.0465). However, the difference in AUCs between ModelAP and ModelVP was not significant (P = 0.5061). These results demonstrate that ModelCombination shows a better performance than the other models. Decision curve analysis proved the clinical utility of this model. Conclusions We developed a radiomics model based on monochromatic DECT images to identify solitary pulmonary nodules. This model could serve as an effective tool for discriminating benign from malignant pulmonary nodules in patients. The combination of arterial phase and venous phase imaging could significantly improve the model performance.

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Section: Discussionmentioning

confidence: 99%

The value of radiomics based on dual-energy CT for differentiating benign from malignant solitary pulmonary nodules

Gao

et al. 2022

BMC Med Imaging

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“…Medical workers are slow to process a large amount of CT data and easily lead to physical and mental fatigue of the workers. Therefore, the computer is used for effective classification, preprocessing, and diagnosis of CT images, showing very important research significance [ 15 ]. In this study, a segmentation algorithm based on the CNN was proposed to segment the lung CT images of patients with lung nodules, which improved and optimized the diagnosis of lung nodules in lung CT images, showing relatively high robustness.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning-Based Computed Tomography Imaging to Diagnose the Lung Nodule and Treatment Effect of Radiofrequency Ablation

Guo

Li²,

Yang³

et al. 2021

Journal of Healthcare Engineering

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This study aimed to detect and diagnose the lung nodules as early as possible to effectively treat them, thereby reducing the burden on the medical system and patients. A lung computed tomography (CT) image segmentation algorithm was constructed based on the deep learning convolutional neural network (CNN). The clinical data of 69 patients with lung nodules diagnosed by needle biopsy and pathological comprehensive diagnosis at hospital were collected for specific analysis. The CT image segmentation algorithm was used to distinguish the nature and volume of lung nodules and compared with other computer aided design (CAD) software (Philips ISP). 69 patients with lung nodules were treated by radiofrequency ablation (RFA). The results showed that the diagnostic sensitivity of the CT image segmentation algorithm based on the CNN was obviously higher than that of the Philips ISP for solid nodules <5 mm (63 cases vs. 33 cases) ( P < 0.05 ); it was the same result for the subsolid nodule <5 mm (33 case vs. 5 cases) ( P < 0.05 ) that was slightly higher for solid and subsolid nodules with a diameter of 5–10 mm (37 cases vs. 28 cases) ( P < 0.05 ). In addition, the CNN algorithm can reach all detection for calcified nodules and pleural nodules (7 cases; 5 cases), and the diagnostic sensitivities were much better than those of Philips ISP (2 cases; 3 cases) ( P < 0.05 ). Patients with pulmonary nodules treated by RFA were in good postoperative condition, with a half-year survival rate of 100% and a one-year survival rate of 72.4%. Therefore, it could be concluded that the CT image segmentation algorithm based on the CNN could effectively detect and diagnose the lung nodules early, and the RFA could effectively treat the lung nodules.

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“…Considering the wide application of preoperative enhanced CT in clinical practice, this study incorporated traditional imaging features into clinical data. Peritumoral enhancement is significantly correlated with MVI, which may be caused by changes in peritumoral blood flow and abnormal perfusion [ 24 , 25 ]. The presence of intratumoral arteries often indicates rapid growth and strong invasiveness of tumors [ 26 , 27 ].…”

Section: Discussionmentioning

confidence: 99%

Predicting microvascular invasion in small (≤ 5 cm) hepatocellular carcinomas using radiomics-based peritumoral analysis

Wang,

Cheng,

et al. 2024

Insights Imaging

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Objective We assessed the predictive capacity of computed tomography (CT)-enhanced radiomics models in determining microvascular invasion (MVI) for isolated hepatocellular carcinoma (HCC) ≤ 5 cm within peritumoral margins of 5 and 10 mm. Methods Radiomics software was used for feature extraction. We used the least absolute shrinkage and selection operator (LASSO) algorithm to establish an effective model to predict patients’ preoperative MVI status. Results The area under the curve (AUC) values in the validation sets for the 5- and 10-mm radiomics models concerning arterial tumors were 0.759 and 0.637, respectively. In the portal vein phase, they were 0.626 and 0.693, respectively. Additionally, the combined radiomics model for arterial tumors and the peritumoral 5-mm margin had an AUC value of 0.820. The decision curve showed that the combined tumor and peritumoral radiomics model exhibited a somewhat superior benefit compared to the traditional model, while the fusion model demonstrated an even greater advantage, indicating its significant potential in clinical application. Conclusion The 5-mm peritumoral arterial model had superior accuracy and sensitivity in predicting MVI. Moreover, the combined tumor and peritumoral radiomics model outperformed both the individual tumor and peritumoral radiomics models. The most effective combination was the arterial phase tumor and peritumor 5-mm margin combination. Using a fusion model that integrates tumor and peritumoral radiomics and clinical data can aid in the preoperative diagnosis of the MVI of isolated HCC ≤ 5 cm, indicating considerable practical value. Critical relevance statement The radiomics model including a 5-mm peritumoral expansion is a promising noninvasive biomarker for preoperatively predicting microvascular invasion in patients diagnosed with a solitary HCC ≤ 5 cm. Key points • Radiomics features extracted at a 5-mm distance from the tumor could better predict hepatocellular carcinoma microvascular invasion. • Peritumoral radiomics can be used to capture tumor heterogeneity and predict microvascular invasion. • This radiomics model stands as a promising noninvasive biomarker for preoperatively predicting MVI in individuals. Graphical Abstract

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Prediction efficacy of feature classification of solitary pulmonary nodules based on CT radiomics

Cited by 11 publications

References 17 publications

The value of radiomics based on dual-energy CT for differentiating benign from malignant solitary pulmonary nodules

The value of radiomics based on dual-energy CT for differentiating benign from malignant solitary pulmonary nodules

Deep Learning-Based Computed Tomography Imaging to Diagnose the Lung Nodule and Treatment Effect of Radiofrequency Ablation

Predicting microvascular invasion in small (≤ 5 cm) hepatocellular carcinomas using radiomics-based peritumoral analysis

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