Advances in intelligent diagnosis methods for pulmonary ground-glass opacity nodules

Yang, Jing; Wang, Hailin; Chen, Geng; Dai, Yakang; Ji, Jiansong

doi:10.1186/s12938-018-0435-2

Cited by 12 publications

(8 citation statements)

References 48 publications

(58 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Also, nodule location was included in the model as an independent factor, suggesting that a GGN located peripherally or subpleurally is more likely to be malignant than one located in the central lung parenchyma, which is consistent with previous reports (24). Lobulation and pleural indentation were also common features of malignant GGNs (25,26) and were included in the model as CT morphological parameters, making the model more reliable. The PET metabolic parameter, SUV index, is also a helpful factor in the model, with higher SUV index values indicating a greater likelihood that the GGN is benign.…”

Section: Discussionsupporting

confidence: 83%

Establishment and verification of a prediction model based on clinical characteristics and positron emission tomography/computed tomography (PET/CT) parameters for distinguishing malignant from benign ground-glass nodules

Niu

Shao

et al. 2021

Quant Imaging Med Surg

View full text Add to dashboard Cite

Background: To develop and verify a prediction model for distinguishing malignant from benign groundglass nodules (GGNs) combined with clinical characteristics and 18 F-fluorodeoxyglucose (FDG) positron emission tomography-computed tomography (PET/CT) parameters.Methods: We retrospectively analyzed 170 patients (56 males and 114 females) with GGNs who underwent PET/CT and high-resolution CT examination in our hospital from November 2011 to December 2019.The clinical and imaging data of all patients were collected, and the nodules were randomly divided into a derivation set and a validation set. For the derivation set, we used multivariate logistic regression to develop a prediction model for distinguishing benign from malignant GGNs. A receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficacy of the model, and the data in the validation set were used to verify the prediction model.Results: Among the 170 patients, 197 GGNs were confirmed via postoperative pathological examination or clinical follow-up. There were 21 patients with 27 GGNs in the benign group and 149 patients with 170 GGNs in the adenocarcinoma group. A total of five parameters, including the patient's sex, nodule location, margin, pleural indentation, and standardized uptake value (SUV) index (the ratio of nodule SUVmax to liver SUVmean), were selected to develop a prediction model for distinguishing benign from malignant GGNs.The area under the curve (AUC) of the model was 0.875 in the derivation set, with a sensitivity of 0.702 and a specificity of 0.923. The positive likelihood ratio was 9.131, and the negative likelihood ratio was 0.322. In the validation set, the AUC of the model was 0.874, which was not significantly different from the derivation set (P=0.989).Conclusions: This study developed and validated a prediction model based on 18 F-FDG PET/CT imaging and clinical characteristics for distinguishing malignant from benign GGNs. The model showed good diagnostic efficacy and high specificity, which can improve the preoperative diagnosis of high-risk GGNs.

show abstract

Section: Discussionsupporting

confidence: 83%

Establishment and verification of a prediction model based on clinical characteristics and positron emission tomography/computed tomography (PET/CT) parameters for distinguishing malignant from benign ground-glass nodules

Niu

Shao

et al. 2021

Quant Imaging Med Surg

View full text Add to dashboard Cite

show abstract

“…Nodule character can be further judged focusing on the internal feature as margin blurry or clear, presence of spiculation, lobulation [28]. If any nodule on CT is round or oval with a clear margin and no interior feature is seen, in that case, it is assumed to be benign or malignant due to any cause, and repeated follow-up is done to ensure that nodule is reducing in diameter or volume [29]. If the volume keeps increasing in repeated follow-up, it will be considered a malignant cause for primary lung cancer.…”

Section: Discussionmentioning

confidence: 99%

The Influence of Quantitative Parameters Measured on CT with Different Slice Thicknesses on Evaluating the Growth of Pulmonary Nodules

Bashanti¹,

Fu²,

Li³

et al. 2021

IJMI

View full text Add to dashboard Cite

We did this study to investigate the effect of thick (5mm) and thin (1 or 0.625 mm) slice thickness of CT images on evaluating pulmonary nodules' growth to improve their diagnostic accuracy. The clinical and CT data of 251 patients with lung nodules and two follow-up CTs from October 2016 to October 2019 were analyzed retrospectively. Malignant nodules were confirmed by pathology, and benign nodules were confirmed by pathology or follow-up. Two radiologists double-blindly assessed the CT features (density, shape, lobes, border), maximum diameter, and volume of nodules on the thick (5MM) and thin (≤1MM) images of two follow-up CTs. We use One-way analysis of variance for quantitative data; the X2 test or FISHER exact probability method was used for qualitative data; and the ROC curve was used to analyze the diagnostic power of nodule size, volume, and change in differentiating benign and malignant lesions. Among 251 pulmonary nodules, 117 (46.6%) benign nodules and 134 (53.3%) malignant nodules. During the CT follow-up, the volume measured on the thick-section image, the diameter, and the volume measured on the thin-section image were statistically different in benign and malignant lung nodules (P<0.001). In contrast, the diameter measured on the thick-section image was similar between these two groups (P=0.328). For benign and malignant pulmonary nodules, the diameter, volume, and change measured on the thin-section image were significantly larger than the thick-section image's data (P<0.001). The ROC curve showed that the diagnostic efficiency of volume was higher compared to the diameter. There were significant differences in nodule type, density change, shape, lobulation, and pleural retraction between benign and malignant nodules for CT features. Accurately assessing the volume changes combined with CT characteristics will help improve lung nodules' diagnosis accuracy. Volume measured on thin-section (1mm) CT images is the best quantitative parameter for assessing the change of pulmonary nodules. Combining Volume change with CT characteristics would help to improve the diagnostic accuracy.

show abstract

“…Vascular convergence is verified when the vessels converge to a nodule without adjoining or contacting the edge of the nodule ( Figure 1 e) [ 57 , 61 , 62 ]. This phenomenon reflects angiogenesis [ 57 ].…”

Section: Pathophysiologic Featuresmentioning

confidence: 99%

Comprehensive Perspective for Lung Cancer Characterisation Based on AI Solutions Using CT Images

Pereira

Freitas

Costa

et al. 2020

JCM

View full text Add to dashboard Cite

Lung cancer is still the leading cause of cancer death in the world. For this reason, novel approaches for early and more accurate diagnosis are needed. Computer-aided decision (CAD) can be an interesting option for a noninvasive tumour characterisation based on thoracic computed tomography (CT) image analysis. Until now, radiomics have been focused on tumour features analysis, and have not considered the information on other lung structures that can have relevant features for tumour genotype classification, especially for epidermal growth factor receptor (EGFR), which is the mutation with the most successful targeted therapies. With this perspective paper, we aim to explore a comprehensive analysis of the need to combine the information from tumours with other lung structures for the next generation of CADs, which could create a high impact on targeted therapies and personalised medicine. The forthcoming artificial intelligence (AI)-based approaches for lung cancer assessment should be able to make a holistic analysis, capturing information from pathological processes involved in cancer development. The powerful and interpretable AI models allow us to identify novel biomarkers of cancer development, contributing to new insights about the pathological processes, and making a more accurate diagnosis to help in the treatment plan selection.

show abstract

Advances in intelligent diagnosis methods for pulmonary ground-glass opacity nodules

Cited by 12 publications

References 48 publications

Establishment and verification of a prediction model based on clinical characteristics and positron emission tomography/computed tomography (PET/CT) parameters for distinguishing malignant from benign ground-glass nodules

Establishment and verification of a prediction model based on clinical characteristics and positron emission tomography/computed tomography (PET/CT) parameters for distinguishing malignant from benign ground-glass nodules

The Influence of Quantitative Parameters Measured on CT with Different Slice Thicknesses on Evaluating the Growth of Pulmonary Nodules

Comprehensive Perspective for Lung Cancer Characterisation Based on AI Solutions Using CT Images

Contact Info

Product

Resources

About