Development and Validation of a Combined Model for Preoperative Prediction of Lymph Node Metastasis in Peripheral Lung Adenocarcinoma

Li, Qi; He, Xiaoqun; Fan, Xiaoxi; Zhu, Chaonan; Lv, Junwei; Luo, Tianyou

doi:10.3389/fonc.2021.675877

Cited by 10 publications

(17 citation statements)

References 41 publications

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“…Hattori et al 16 confirmed the significance of the presence of an air bronchogram in the lung adenocarcinoma as a predictor of LN-negative metastasis. However, Li et al 13 reported that tumors with an air bronchogram were more common in the LN-positive metastasis group than in the LN-negative metastasis group. Further validation with larger sample size is needed to confirm these results.…”

Section: Discussionmentioning

confidence: 98%

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Integrative Predictive Models of Computed Tomography Texture Parameters and Hematological Parameters for Lymph Node Metastasis in Lung Adenocarcinomas

Chen

Sun

et al. 2022

J Comput Assist Tomogr

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Objectives:The aims of the study were to integrate characteristics of computed tomography (CT), texture, and hematological parameters and to establish predictive models for lymph node (LN) metastasis in lung adenocarcinoma.Methods: A total of 207 lung adenocarcinoma cases with confirmed postoperative pathology and preoperative CT scans between February 2017 and April 2019 were included in this retrospective study. All patients were divided into training and 2 validation cohorts chronologically in the ratio of 3:1:1. The χ 2 test or Fisher exact test were used for categorical variables. The Shapiro-Wilk test and Mann-Whitney U test were used for continuous variables. Logistic regression and machine learning algorithm models based on CT characteristics, texture, and hematological parameters were used to predict LN metastasis. The performance of the multivariate models was evaluated using a receiver operating characteristic curve; prediction performance was evaluated in the validation cohorts. Decision curve analysis confirmed its clinical utility.Results: Logistic regression analysis demonstrated that pleural thickening ( P = 0.013), percentile 25th ( P = 0.033), entropy gray-level co-occurrence matrix 10 ( P = 0.019), red blood cell distribution width ( P = 0.012), and lymphocyte-to-monocyte ratio ( P = 0.049) were independent risk factors associated with LN metastasis. The area under the curve of the predictive model established using the previously mentioned 5 independent risk factors was 0.929 in the receiver operating characteristic analysis. The highest area under the curve was obtained in the training cohort (0.777 using Naive Bayes algorithm).Conclusions: Integrative predictive models of CT characteristics, texture, and hematological parameters could predict LN metastasis in lung adenocarcinomas. These findings may provide a reference for clinical decision making.

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Section: Discussionmentioning

confidence: 98%

“…Malignant lesions tend to cause pleural thickening and indentations close to the pleura. 13,14 Malignant tumors are prone to LN metastasis. The risk of LN metastasis is greater in lung adenocarcinomas, which are diagnosed as solid lesions.…”

Section: Discussionmentioning

confidence: 99%

Integrative Predictive Models of Computed Tomography Texture Parameters and Hematological Parameters for Lymph Node Metastasis in Lung Adenocarcinomas

Chen

Sun

et al. 2022

J Comput Assist Tomogr

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“…In recent years, radiomics has received increasing attention, and it is a technique for high-throughput extraction of quantitative features from medical images (10,11). Indeed, many studies have exhibited that quantitative radiomic image features of the primary tumor could be used as non-invasive biomarkers to predict LNM and were good predictive performance (12)(13)(14). For OLM of LUAD, Zhong et al (15) reported that the radiomics signature of the primary tumor based on CT scans had a significant predictive value.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Analysis Using 18F-FDG PET/CT to Predict Occult Lymph Node Metastasis in Patients With Clinical N0 Lung Adenocarcinoma

et al. 2022

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IntroductionThe aim of this work was to determine the feasibility of using a deep learning approach to predict occult lymph node metastasis (OLM) based on preoperative FDG-PET/CT images in patients with clinical node-negative (cN0) lung adenocarcinoma.Materials and MethodsDataset 1 (for training and internal validation) included 376 consecutive patients with cN0 lung adenocarcinoma from our hospital between May 2012 and May 2021. Dataset 2 (for prospective test) used 58 consecutive patients with cN0 lung adenocarcinoma from June 2021 to February 2022 at the same center. Three deep learning models: PET alone, CT alone, and combined model, were developed for the prediction of OLM. The performance of the models was evaluated on internal validation and prospective test in terms of accuracy, sensitivity, specificity, and areas under the receiver operating characteristic curve (AUCs).ResultsThe combined model incorporating PET and CT showed the best performance, achieved an AUC of 0.81 [95% confidence interval (CI): 0.61, 1.00] in the prediction of OLM in internal validation set (n = 60) and an AUC of 0.87 (95% CI: 0.75, 0.99) in the prospective test set (n = 58). The model achieved 87.50% sensitivity, 80.00% specificity, and 81.00% accuracy in the internal validation set and achieved 75.00% sensitivity, 88.46% specificity, and 86.60% accuracy in the prospective test set.ConclusionThis study presented a deep learning approach to enable the prediction of occult nodal involvement based on the PET/CT images before surgery in cN0 lung adenocarcinoma, which would help clinicians select patients who would be suitable for sublobar resection.

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“…Radiomics, as an emerging discipline, can non-invasively transform medical images into multidimensional, potential information through quantizing features that are imperceptible to the naked eyes, and explore features’ associations with pathophysiological changes [ 16 , 17 , 18 ]. Recently, radiomics analysis in the brain, lung, pancreas and prostate is widely applied with the progress of state-of-the-art informatic technology, aiming to achieving computer-aided-diagnosis and providing clinical support in the decision-making process [ 19 , 20 , 21 , 22 ]. Past studies denoted the success of radiomics depend on interpretability, repeatability and reproductibility of constructed models.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Multiple Radiomics Models for Identifying Histological Grade of Pancreatic Ductal Adenocarcinoma Preoperatively Based on Multiphasic Contrast-Enhanced Computed Tomography: A Two-Center Study in Southwest China

Liao

Yang

et al. 2022

Diagnostics

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Background: We designed and validated the value of multiple radiomics models for diagnosing histological grade of pancreatic ductal adenocarcinoma (PDAC), holding a promise of assisting in precision medicine and providing clinical therapeutic strategies. Methods: 198 PDAC patients receiving surgical resection and pathological confirmation were enrolled and classified as 117 low-grade PDAC and 81 high-grade PDAC group. An external validation group was used to assess models’ performance. Available radiomics features were selected using GBDT algorithm on the basis of the arterial and venous phases, respectively. Five different machine learning models were built including k-nearest neighbour, logistic regression, naive bayes model, support vector machine, and random forest using ten times tenfold cross-validation. Multivariable logistic regression analysis was applied to establish clinical model and combined model. The models’ performance was assessed according to its predictive performance, calibration curves, and decision curves. A nomogram was established for visualization. Survival analysis was conducted for stratifying the overall survival prior to treatment. Results: In the training group, the RF model demonstrated the optimal predictive ability and robustness with an AUC of 0.943; the SVM model achieved the secondary performance, followed by Bayes model. In the external validation group, these three models (Bayes, RF, SVM) also achieved the top three predictive ability. A clinical model was built by selected clinical features with an AUC of 0.728, and combined model was established by an RF model and a clinical model with an AUC of 0.961. The log-rank test revealed that the low-grade group survived longer than the high-grade group. Conclusions: The multiphasic CECT radiomics models offered an accurate and noninvasive perspective to differentiate histological grade in PDAC and advantages of machine learning models including RF, SVM and Bayes were more remarkable.

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Development and Validation of a Combined Model for Preoperative Prediction of Lymph Node Metastasis in Peripheral Lung Adenocarcinoma

Cited by 10 publications

References 41 publications

Integrative Predictive Models of Computed Tomography Texture Parameters and Hematological Parameters for Lymph Node Metastasis in Lung Adenocarcinomas

Integrative Predictive Models of Computed Tomography Texture Parameters and Hematological Parameters for Lymph Node Metastasis in Lung Adenocarcinomas

Deep Learning Analysis Using 18F-FDG PET/CT to Predict Occult Lymph Node Metastasis in Patients With Clinical N0 Lung Adenocarcinoma

Comparison of Multiple Radiomics Models for Identifying Histological Grade of Pancreatic Ductal Adenocarcinoma Preoperatively Based on Multiphasic Contrast-Enhanced Computed Tomography: A Two-Center Study in Southwest China

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