The Natural Growth of Subsolid Nodules Predicted by Quantitative Initial CT Features: A Systematic Review

Gao, Chen; Wu, Linyu; Kong, Dexing; Xu, Maosheng; Zhou, Chunyuan

doi:10.3389/fonc.2020.00318

Cited by 11 publications

(16 citation statements)

References 44 publications

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“…Therefore, there may be errors in selecting the maximum or minimum CT value for identification. Whether the measurement of average CT value of lung GGNs can effectively identify the nature of GGNs, most scholars believe that the average CT value of invasive adenocarcinoma is higher than that of PGL, and in case the average CT value increases during follow-up of the nodule, clinical intervention should be carried out (Gao et al 2020 ; Tamura et al 2014 ). In this study, average CT value in the invasive adenocarcinoma cases with mGGNs was significantly higher than that of the PGL group, corroborating with most studies.…”

Section: Discussionmentioning

confidence: 99%

Dual-layer spectral detector CT (SDCT) can improve the detection of mixed ground-glass lung nodules

Zhang

Yin

Kang

et al. 2023

J Cancer Res Clin Oncol

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Background Mixed ground-glass lung nodules are a high-risk factor for lung adenocarcinoma. This study aimed to analyze the value of SDCT electron density imaging in the detection of mixed ground-glass lung nodules (GGNs). Method 150 patients with GGNs confirmed by chest SDCT and surgical pathology were retrospectively analyzed. GGNs were screened by two senior radiologists by the double-blind method based on conventional CT and SDCT electron density images. Average CT values and electron density (ED) values of GGNs were measured for all, solid and ground-glass. Result Thirty pGGN cases determined by conventional CT were found to be mGGN on electron density images, including 23 in the invasive adenocarcinoma group (detection rate of 35.38%), which was significantly higher than that of the PGL group (14.89%, P < 0.05). In electron density images, average CT values and ED values in the PGL and invasive adenocarcinoma groups with pGGNs were no difference. The average CT value and ED value were significantly higher in the mGGN invasive adenocarcinoma group compared with the PGL group ( P < 0.05). Meanwhile, ROC curve analysis of average CT value and ED value revealed AUC values for mGGN infiltration of 0.759 and 0.752. Conclusion SDCT can improve GGN visualization and increase the detection rate of mGGN compared with conventional CT. Attention should be paid to invasive adenocarcinoma for lung GGNs detected as mGGNs with high average CT value or ED value.

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Section: Discussionmentioning

confidence: 99%

Dual-layer spectral detector CT (SDCT) can improve the detection of mixed ground-glass lung nodules

Zhang

Yin

Kang

et al. 2023

J Cancer Res Clin Oncol

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“…Furthermore, the increased duration of follow up often increases the rate of no-shows. Therefore, several studies have sought to provide a greater diagnostic indicator for the growth of GGNs through the analysis of traditional imaging features, such as diameter and CT attenuation (10)(11)(12)(13). For example, Matsuguma et al showed there were significant differences in diameter between rapidly growing and non-growing pure GGNs (12).…”

Section: Introductionmentioning

confidence: 99%

The Growth Trend Predictions in Pulmonary Ground Glass Nodules Based on Radiomic CT Features

et al. 2020

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Background: The management of ground glass nodules (GGNs) remains a distinctive challenge. This study is aimed at comparing the predictive growth trends of radiomic features against current clinical features for the evaluation of GGNs.Methods: A total of 110 GGNs in 85 patients were included in this retrospective study, in which follow up occurred over a span ≥2 years. A total of 396 radiomic features were manually segmented by radiologists and quantitatively analyzed using an Analysis Kit software. After feature selection, three models were developed to predict the growth of GGNs. The performance of all three models was evaluated by a receiver operating characteristic (ROC) curve. The best performing model was also assessed by calibration and clinical utility. Results:After using a stepwise multivariate logistic regression analysis and dimensionality reduction, the diameter and five specific radiomic features were included in the clinical model and the radiomic model. The rad-score [odds ratio (OR) = 5.130; P < 0.01] and diameter (OR = 1.087; P < 0.05) were both considered as predictive indicators for the growth of GGNs. Meanwhile, the area under the ROC curve of the combined model reached 0.801. The high degree of fitting and favorable clinical utility was detected using the calibration curve with the Hosmer-Lemeshow test and the decision curve analysis was utilized for the nomogram.Conclusions: A combined model using the current clinical features alongside the radiomic features can serve as a powerful tool to assist clinicians in guiding the management of GGNs.

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“…Initial size ( 31 ), CT attenuation ( 3 ), and history of lung cancer ( 30 ) were associated with GGN growth according to the first-time CT results and clinical factors. For those pulmonary nodules which require follow-up, previous studies focus on classifying and few rely on prediction factors for the GGO growth.…”

Section: Discussionmentioning

confidence: 99%

“…However, low-dose computed tomography (LDCT) is an effective screening tool for reducing lung cancer mortality in high-risk individuals ( 2 ). With the popularization of the LDCT for lung cancer screening, the detection rate for pulmonary nodules, particularly sub-solid nodules (SSNs), has been significantly improved ( 3 ).…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based growth prediction for sub-solid pulmonary nodules on CT images

Liao

Yan

et al. 2022

Front. Oncol.

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BackgroundEstimating the growth of pulmonary sub-solid nodules (SSNs) is crucial to the successful management of them during follow-up periods. The purpose of this study is to (1) investigate the measurement sensitivity of diameter, volume, and mass of SSNs for identifying growth and (2) seek to establish a deep learning-based model to predict the growth of SSNs.MethodsA total of 2,523 patients underwent at least 2-year examination records retrospectively collected with sub-solid nodules. A total of 2,358 patients with 3,120 SSNs from the NLST dataset were randomly divided into training and validation sets. Patients from the Yibicom Health Management Center and Guangdong Provincial People’s Hospital were collected as an external test set (165 patients with 213 SSN). Trained models based on LUNA16 and Lndb19 datasets were employed to automatically obtain the diameter, volume, and mass of SSNs. Then, the increase rate in measurements between cancer and non-cancer groups was studied to evaluate the most appropriate way to identify growth-associated lung cancer. Further, according to the selected measurement, all SSNs were classified into two groups: growth and non-growth. Based on the data, the deep learning-based model (SiamModel) and radiomics model were developed and verified.ResultsThe double time of diameter, volume, and mass were 711 vs. 963 days (P = 0.20), 552 vs. 621 days (P = 0.04) and 488 vs. 623 days (P< 0.001) in the cancer and non-cancer groups, respectively. Our proposed SiamModel performed better than the radiomics model in both the NLST validation set and external test set, with an AUC of 0.858 (95% CI 0.786–0.921) and 0.760 (95% CI 0.646–0.857) in the validation set and 0.862 (95% CI 0.789–0.927) and 0.681 (95% CI 0.506–0.841) in the external test set, respectively. Furthermore, our SiamModel could use the data from first-time CT to predict the growth of SSNs, with an AUC of 0.855 (95% CI 0.793–0.908) in the NLST validation set and 0.821 (95% CI 0.725–0.904) in the external test set.ConclusionMass increase rate can reflect more sensitively the growth of SSNs associated with lung cancer than diameter and volume increase rates. A deep learning-based model has a great potential to predict the growth of SSNs.

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The Natural Growth of Subsolid Nodules Predicted by Quantitative Initial CT Features: A Systematic Review

Cited by 11 publications

References 44 publications

Dual-layer spectral detector CT (SDCT) can improve the detection of mixed ground-glass lung nodules

Dual-layer spectral detector CT (SDCT) can improve the detection of mixed ground-glass lung nodules

The Growth Trend Predictions in Pulmonary Ground Glass Nodules Based on Radiomic CT Features

Deep learning-based growth prediction for sub-solid pulmonary nodules on CT images

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