Letter to the Editor Regarding “Long-Term Follow-Up of Ground-Glass Nodules After 5 Years of Stability.” by Lee et al., J Thorac Oncol 2019;14:1370–7

Huang, Chung-Yao; Huang, Chun-Chao; Huang, Wei‐Ming; Liang, Ching-Ping; Wu, Fu-Zong

doi:10.1016/j.hlc.2020.02.010

Cited by 6 publications

(5 citation statements)

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“…Radiomics is based on computerized algorithms that process different imaging modalities (ultrasound, CT, PET, MRI, and conventional radiology) by analyzing the selected region of interest (ROI) in medical imaging tasks [18]. Radiomic processes consist of four steps: (1) nodule segmentation; (2) feature extraction; (3) feature selection and reduction; and (4) model development and validation for discrimination, as shown in Figure 1 [19].…”

Section: Radiomic Feature Analysis and Workflowmentioning

confidence: 99%

Radiomics in Early Lung Cancer Diagnosis: From Diagnosis to Clinical Decision Support and Education

Yang

et al. 2022

Diagnostics

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Lung cancer is the most frequent cause of cancer-related death around the world. With the recent introduction of low-dose lung computed tomography for lung cancer screening, there has been an increasing number of smoking- and non-smoking-related lung cancer cases worldwide that are manifesting with subsolid nodules, especially in Asian populations. However, the pros and cons of lung cancer screening also follow the implementation of lung cancer screening programs. Here, we review the literature related to radiomics for early lung cancer diagnosis. There are four main radiomics applications: the classification of lung nodules as being malignant/benign; determining the degree of invasiveness of the lung adenocarcinoma; histopathologic subtyping; and prognostication in lung cancer prediction models. In conclusion, radiomics offers great potential to improve diagnosis and personalized risk stratification in early lung cancer diagnosis through patient–doctor cooperation and shared decision making.

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Section: Radiomic Feature Analysis and Workflowmentioning

confidence: 99%

Radiomics in Early Lung Cancer Diagnosis: From Diagnosis to Clinical Decision Support and Education

Yang

et al. 2022

Diagnostics

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“…The overall survival rate after surgery for noninvasive GGN patients can be close to 100%, and there is a promising 5-year survival rate of 80%-95% in the case of invasive GGNs. However, they are characterized by very slow growth, so regular follow-up management and a wait-and-see policy are advocated by many experts (2). The Fleischner Society Guidelines for management of incidental subsolid nodules was published in 2017 and recommended follow-up intervals ranging from 3 months to several years (3).…”

Section: Introductionmentioning

confidence: 99%

Consecutive Serial Non-Contrast CT Scan-Based Deep Learning Model Facilitates the Prediction of Tumor Invasiveness of Ground-Glass Nodules

Yin

et al. 2021

Front. Oncol.

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IntroductionTumors are continuously evolving biological systems which can be monitored by medical imaging. Previous studies only focus on single timepoint images, whether the performance could be further improved by using serial noncontrast CT imaging obtained during nodule follow-up management remains unclear. In this study, we evaluated DL model for predicting tumor invasiveness of GGNs through analyzing time series CT imagesMethodsA total of 168 pathologically confirmed GGN cases (48 noninvasive lesions and 120 invasive lesions) were retrospectively collected and randomly assigned to the development dataset (n = 123) and independent testing dataset (n = 45). All patients underwent consecutive noncontrast CT examinations, and the baseline CT and 3-month follow-up CT images were collected. The gross region of interest (ROI) patches containing only tumor region and the full ROI patches including both tumor and peritumor regions were cropped from CT images. A baseline model was built on the image features and demographic features. Four DL models were proposed: two single-DL model using gross ROI (model 1) or full ROI patches (model 3) from baseline CT images, and two serial-DL models using gross ROI (model 2) or full ROI patches (model 4) from consecutive CT images (baseline scan and 3-month follow-up scan). In addition, a combined model integrating serial full ROI patches and clinical information was also constructed. The performance of these predictive models was assessed with respect to discrimination and clinical usefulness.ResultsThe area under the curve (AUC) of the baseline model, models 1, 2, 3, and 4 were 0.562 [(95% confidence interval (C)], 0.406~0.710), 0.693 (95% CI, 0.538–0.822), 0.787 (95% CI, 0.639–0.895), 0.727 (95% CI, 0.573–0.849), and 0.811 (95% CI, 0.667–0.912) in the independent testing dataset, respectively. The results indicated that the peritumor region had potential to contribute to tumor invasiveness prediction, and the model performance was further improved by integrating imaging scans at multiple timepoints. Furthermore, the combined model showed best discrimination ability, with AUC, sensitivity, specificity, and accuracy achieving 0.831 (95% CI, 0.690–0.926), 86.7%, 73.3%, and 82.2%, respectively.ConclusionThe DL model integrating full ROIs from serial CT images shows improved predictive performance in differentiating noninvasive from invasive GGNs than the model using only baseline CT images, which could benefit the clinical management of GGNs.

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“…As low-grade malignancies, GGOs progress slowly and are managed mainly through routine CT examinations with excellent prognoses. 27 , 63 Even with delayed surgeries, GGOs generally do not result in poorer survival for patients with LUAD. 64 , 65 Recent studies on pulmonary premalignancy have enhanced our understanding, yet the underlying biological mechanisms sustaining decades-long static GGO pathology remain elusive, 66 , 67 , 68 likely associated with both tumor-extrinsic and -intrinsic regulations.…”

Section: Discussionmentioning

confidence: 99%

Multicellular ecotypes shape progression of lung adenocarcinoma from ground-glass opacity toward advanced stages

Deng,

Xia,

Zhang

et al. 2024

Cell Reports Medicine

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Letter to the Editor Regarding “Long-Term Follow-Up of Ground-Glass Nodules After 5 Years of Stability.” by Lee et al., J Thorac Oncol 2019;14:1370–7

Cited by 6 publications

References 5 publications

Radiomics in Early Lung Cancer Diagnosis: From Diagnosis to Clinical Decision Support and Education

Radiomics in Early Lung Cancer Diagnosis: From Diagnosis to Clinical Decision Support and Education

Consecutive Serial Non-Contrast CT Scan-Based Deep Learning Model Facilitates the Prediction of Tumor Invasiveness of Ground-Glass Nodules

Multicellular ecotypes shape progression of lung adenocarcinoma from ground-glass opacity toward advanced stages

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