Development of a novel nomogram-based model incorporating 3D radiomic signatures and lung CT radiological features for differentiating invasive adenocarcinoma from adenocarcinoma in situ and minimally invasive adenocarcinoma

Ren, He; Xiao, Zhao; Chen, Ling; Wang, Jiayi; Wu, Shiyu; Zeng, Yanan; Li, Ping

doi:10.21037/qims-22-491

Cited by 4 publications

(2 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It uses complex image analysis technology to rapidly analyze and validate medical imaging data, and automatically or semi-automatically extract a large amount of quantifiable information or image features from the ROI of images and apply them to clinical practice, so as to improve the accuracy of diagnosis, prognosis, and prediction of tumor lesions ( 14 , 15 ). The basis of radiomics feature extraction is that ROI is accurately segmented, and correct ROI ensures the reliability of radiomics research ( 16 , 17 ). In this study, small pulmonary nodules in the lung window of patients with preoperative CT enhancement were the ROI of this study.…”

Section: Discussionmentioning

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

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

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

View full text Add to dashboard Cite

show abstract

“…In radiomics, traditional image features, such as shape, grayscale, and texture, are extracted, after which pattern recognition models are applied for classification and prediction ( 17 ). Currently, several standardized software packages for extracting radiomics features have been developed, with the Python-implemented PyRadiomics ( 18 ) being one of the most well-known and widely used in nodule feature extraction ( 19 ).…”

Section: Introductionmentioning

confidence: 99%

A graph neural network model for the diagnosis of lung adenocarcinoma based on multimodal features and an edge-generation network

Li¹,

Zhou²,

Wang³

et al. 2023

Quant Imaging Med Surg

View full text Add to dashboard Cite

Background Lung cancer is a global disease with high lethality, with early screening being considerably helpful for improving the 5-year survival rate. Multimodality features in early screening imaging are an important part of the prediction for lung adenocarcinoma, and establishing a model for adenocarcinoma diagnosis based on multimodal features is an obvious clinical need. Through our practice and investigation, we found that graph neural networks (GNNs) are excellent platforms for multimodal feature fusion, and the data can be completed using the edge-generation network. Therefore, we propose a new lung adenocarcinoma multiclassification model based on multimodal features and an edge-generation network. Methods According to a ratio of 80% to 20%, respectively, the dataset of 338 cases was divided into the training set and the test set through 5-fold cross-validation, and the distribution of the 2 sets was the same. First, the regions of interest (ROIs) cropped from computed tomography (CT) images were separately fed into convolutional neural networks (CNNs) and radiomics processing platforms. The results of the 2 parts were then input into a graph embedding representation network to obtain the fused feature vectors. Subsequently, a graph database based on the clinical and semantic features was established, and the data were supplemented by an edge-generation network, with the fused feature vectors being used as the input of the nodes. This enabled us to clearly understand where the information transmission of the GNN takes place and improves the interpretability of the model. Finally, the nodes were classified using GNNs. Results On our dataset, the proposed method presented in this paper achieved superior results compared to traditional methods and showed some comparability with state-of-the-art methods for lung nodule classification. The results of our method are as follows: accuracy (ACC) =66.26% (±4.46%), area under the curve (AUC) =75.86% (±1.79%), F1-score =64.00% (±3.65%), and Matthews correlation coefficient (MCC) =48.40% (±5.07%). The model with the edge-generating network consistently outperformed the model without it in all aspects. Conclusions The experiments demonstrate that with appropriate data=construction methods GNNs can outperform traditional image processing methods in the field of CT-based medical image classification. Additionally, our model has higher interpretability, as it employs subjective clinical and semantic features as the data construction approach. This will help doctors better leverage human–computer interactions.

show abstract

Preoperative predicting invasiveness of lung adenocarcinoma manifesting as ground-glass nodules based on multimodal images of dual-layer spectral detector CT radiomics models

Chang,

Xing,

Shang

et al. 2023

J Cancer Res Clin Oncol

View full text Add to dashboard Cite

Development of a novel nomogram-based model incorporating 3D radiomic signatures and lung CT radiological features for differentiating invasive adenocarcinoma from adenocarcinoma in situ and minimally invasive adenocarcinoma

Cited by 4 publications

References 33 publications

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

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

A graph neural network model for the diagnosis of lung adenocarcinoma based on multimodal features and an edge-generation network

Preoperative predicting invasiveness of lung adenocarcinoma manifesting as ground-glass nodules based on multimodal images of dual-layer spectral detector CT radiomics models

Contact Info

Product

Resources

About