Malignancy risk stratification for solitary pulmonary nodule: A clinical practice guideline

Chen, Bojiang; Li, Qianrui; Hao, Qiukui; Tan, Jing; Liu, Yan; Zhu, Yuqi; Hu, Chengping; Qian, Guisheng; Zhang, Guozhen; Chen, Liangan; Zhou, Chengzhi; Zhang, Jian; Sun, Jiayuan; Jiang, Li; Zhang, Li; Wang, Qi; Jin, Yang; He, Yong; Song, Yong; Sun, Xin; Li, Wei Min

doi:10.1111/jebm.12476

Cited by 8 publications

(11 citation statements)

References 29 publications

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“…In our study, the analysis of the clinical characteristics of patients with T1 lung adenocarcinoma showed that there was no clear relationship between nodule inert-growth status and patient age and sex, and there was no significant difference between smoking and lung nodule inert-growth status. This is not consistent with previous studies showing that smoking is an independent risk factor for lung cancer ( 42 ). Research on artificial intelligence-aided image diagnosis systems has been reported in the literature.…”

Section: Discussioncontrasting

confidence: 99%

“…All patients were randomly allocated to the training (159) or testing cohort (42). Clinical characteristics and imaging features extracted during the first examination based on the AI pulmonary nodule assistant diagnosis system were selected as predictive variables, including age, sex, smoking status, nodule location, nodule type, minimum CT value, maximum CT value, average CT value, kurtosis (the kurtosis of nodule's CT values), skewness (the skewness of nodule's CT values), CT longest diameter, CT shortest diameter, CT average diameter, volume of nodule, proportion of solid components, nodule shape, lobulation, spiculation and pleural retraction.…”

Section: Inert Nodule Judgement Modelmentioning

confidence: 99%

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Neural network-based model for evaluating inert nodules and volume doubling time in T1 lung adenocarcinoma: a nested case−control study

et al. 2023

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ObjectiveThe purpose of this study is to establish model for assessing inert nodules predicting nodule volume-doubling.MethodsA total of 201 patients with T1 lung adenocarcinoma were analysed retrospectively pulmonary nodule information was predicted by an AI pulmonary nodule auxiliary diagnosis system. The nodules were classified into two groups: inert nodules (volume-doubling time (VDT)>600 days n=152) noninert nodules (VDT<600 days n=49). Then taking the clinical imaging features obtained at the first examination as predictive variables the inert nodule judgement model <sn</sn>>(INM) volume-doubling time estimation model (VDTM) were constructed based on a deep learning-based neural network. The performance of the INM was evaluated by the area under the curve (AUC) obtained from receiver operating characteristic (ROC) analysis the performance of the VDTM was evaluated by R2(determination coefficient).ResultsThe accuracy of the INM in the training and testing cohorts was 81.13% and 77.50%, respectively. The AUC of the INM in the training and testing cohorts was 0.7707 (95% CI 0.6779-0.8636) and 0.7700 (95% CI 0.5988-0.9412), respectively. The INM was effective in identifying inert pulmonary nodules; additionally, the R2 of the VDTM in the training cohort was 0.8008, and that in the testing cohort was 0.6268. The VDTM showed moderate performance in estimating the VDT, which can provide some reference during a patients’ first examination and consultationConclusionThe INM and the VDTM based on deep learning can help radiologists and clinicians distinguish among inert nodules and predict the nodule volume-doubling time to accurately treat patients with pulmonary nodules.

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Section: Discussioncontrasting

confidence: 99%

Section: Inert Nodule Judgement Modelmentioning

confidence: 99%

Neural network-based model for evaluating inert nodules and volume doubling time in T1 lung adenocarcinoma: a nested case−control study

et al. 2023

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“…As GGNs tend to develop inertly, regular CT monitoring is one of the main international treatment strategies for persistent GGNs ( 4 ). GGNs have been studied more and treated more aggressively in Southeast Asian countries ( 10 , 65 , 66 ). And the timing of surgical resection intervention depends mainly on the size of the GGN and the dynamic changes in imaging during follow-up ( 65 ).…”

Section: Treatment Of Ggnsmentioning

confidence: 99%

Multidimensional biological characteristics of ground glass nodules

Chen,

Li,

et al. 2024

Front. Oncol.

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The detection rate of ground glass nodules (GGNs) has increased in recent years because of their malignant potential but relatively indolent biological behavior; thus, correct GGN recognition and management has become a research focus. Many scholars have explored the underlying mechanism of the indolent progression of GGNs from several perspectives, such as pathological type, genomic mutational characteristics, and immune microenvironment. GGNs have different major mutated genes at different stages of development; EGFR mutation is the most common mutation in GGNs, and p53 mutation is the most abundant mutation in the invasive stage of GGNs. Pure GGNs have fewer genomic alterations and a simpler genomic profile and exhibit a gradually evolving genomic mutation profile as the pathology progresses. Compared to advanced lung adenocarcinoma, GGN lung adenocarcinoma has a higher immune cell percentage, is under immune surveillance, and has less immune escape. However, as the pathological progression and solid component increase, negative immune regulation and immune escape increase gradually, and a suppressive immune environment is established gradually. Currently, regular computer tomography monitoring and surgery are the main treatment strategies for persistent GGNs. Stereotactic body radiotherapy and radiofrequency ablation are two local therapeutic alternatives, and systemic therapy has been progressively studied for lung cancer with GGNs. In the present review, we discuss the characterization of the multidimensional molecular evolution of GGNs that could facilitate more precise differentiation of such highly heterogeneous lesions, laying a foundation for the development of more effective individualized treatment plans.

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“…The size and density of PN, which feature early manifestations of LC, are key evaluation parameters in chest CT imaging [4] . Building predictive models based on size and density could be of great clinical signi cance in identifying malignant PN early and bringing bene ts to patients and society.…”

Section: Introductionmentioning

confidence: 99%

Predictive Diagnosis of Malignant Pulmonary Nodules Using Machine Learning Techniques

Yue

Song

et al. 2023

Preprint

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Introduction Pulmonary nodules (PN) are a common finding in computed tomography (CT) images of the chest, and are identified in millions of patients each year in the United States. Accurate diagnosis of PN is crucial for early detection of cancer and proper treatment. This study aimed to investigate the applicability of machine learning (ML) algorithms in predicting malignant PN. Methods A total of 130 patients who underwent tumor resection and were pathologically diagnosed with PN were included in this study. Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Tree (CART), and eXtreme Gradient Boosting (XGBoost) algorithms were employed to predict malignant PN outcomes. The most important features for malignant PN were identified using the RF, CART, and XGBoost algorithms. Results 80 patients were included in the final analysis, with 62.5% of nodules being malignant and 37.5% being benign. The four algorithms RF, SVM, CART and XGBoost all show great performance, with the AUC reaching 0.97, 0.92, 0.91, and 0.98, respectively. Additionally, the RF algorithm performed the best, with an accuracy of 0.9583, specificity of 0.8889, sensitivity of 1.0000, Kappa of 0.9091, positive predictive value (PPV) of 0.9375, and negative predictive value (NPV) of 1.0000. Besides, age, size, and density were identified as the most important features for predicting malignant PN. Conclusion ML algorithms can provide accurate prediction of malignant PN, which could help establish an early auxiliary diagnosis model. This model could facilitate early detection, diagnosis, and treatment of PN, potentially improving the quality of life and reducing mortality rates. However, further studies with larger sample sizes are needed to confirm the findings of this study.

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Malignancy risk stratification for solitary pulmonary nodule: A clinical practice guideline

Cited by 8 publications

References 29 publications

Neural network-based model for evaluating inert nodules and volume doubling time in T1 lung adenocarcinoma: a nested case−control study

Neural network-based model for evaluating inert nodules and volume doubling time in T1 lung adenocarcinoma: a nested case−control study

Multidimensional biological characteristics of ground glass nodules

Predictive Diagnosis of Malignant Pulmonary Nodules Using Machine Learning Techniques

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