Dataset Information

Comprehensive Computed Tomography Radiomics Analysis of Lung Adenocarcinoma for Prognostication.

ABSTRACT:

Background

In this era of personalized medicine, there is an expanded demand for advanced imaging biomarkers that reflect the biology of the whole tumor. Therefore, we investigated a large number of computed tomography-derived radiomics features along with demographics and pathology-related variables in patients with lung adenocarcinoma, correlating them with overall survival.

Materials and methods

Three hundred thirty-nine patients who underwent operation for lung adenocarcinoma were included. Analysis was performed using 161 radiomics features, demographic, and pathologic variables and correlated each with patient survival. Prognostic performance for survival was compared among three models: (a) using only clinicopathological data; (b) using only selected radiomics features; and (c) using both clinicopathological data and selected radiomics features.

Results

At multivariate analysis, age, pN, tumor size, type of operation, histologic grade, maximum value of the outer 1/3 of the tumor, and size zone variance were statistically significant variables. In particular, maximum value of outer 1/3 of the tumor reflected tumor microenvironment, and size zone variance represented intratumor heterogeneity. Integration of 31 selected radiomics features with clinicopathological variables led to better discrimination performance.

Conclusion

Radiomics approach in lung adenocarcinoma enables utilization of the full potential of medical imaging and has potential to improve prognosis assessment in clinical oncology.

Implications for practice

Two radiomics features were prognostic for lung cancer survival at multivariate analysis: (a) maximum value of the outer one third of the tumor reflects the tumor microenvironment and (b) size zone variance represents the intratumor heterogeneity. Therefore, a radiomics approach in lung adenocarcinoma enables utilization of the full potential of medical imaging and could play a larger role in clinical oncology.

SUBMITTER: Lee G

PROVIDER: S-EPMC6058328 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Publications

Comprehensive Computed Tomography Radiomics Analysis of Lung Adenocarcinoma for Prognostication.

Lee Geewon G Park Hyunjin H Sohn Insuk I Lee Seung-Hak SH Song So Hee SH Kim Hyeseung H Lee Kyung Soo KS Shim Young Mog YM Lee Ho Yun HY

The oncologist 20180405 7

<h4>Background</h4>In this era of personalized medicine, there is an expanded demand for advanced imaging biomarkers that reflect the biology of the whole tumor. Therefore, we investigated a large number of computed tomography-derived radiomics features along with demographics and pathology-related variables in patients with lung adenocarcinoma, correlating them with overall survival.<h4>Materials and methods</h4>Three hundred thirty-nine patients who underwent operation for lung adenocarcinoma ...[more]

PMID: 29622699

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Project description:BackgroundAs an emerging technology, radiomics is being widely used in the diagnosis of early lung cancer due to its excellent diagnostic performance. However, there is a lack of studies that apply radiomics to the diagnosis of malignancy of lung adenocarcinoma. Thus, we used computed tomography (CT)-based radiomics to construct a model for the diagnosis of high-risk lung adenocarcinoma.MethodsData of 170 patients who underwent surgical treatment at the First Affiliated Hospital of Soochow University and had a maximum nodule diameter ≤2 cm on preoperative CT images between January 2020 and December 2021 were retrospectively analyzed. All enrolled patients were randomly divided into experimental and validation groups according to the ratio of 7:3. The diagnosis of lung adenocarcinoma was based on postoperative pathological results. The region of interest was delineated on preoperative CT images, and the radiomics features were extracted. The least absolute shrinkage and selection operator (LASSO) was used to screen the radiomics features thus obtaining the radiomics score (Radscore), which was the basis of the radiomics model. Based on the multivariate regression analysis, independent predictors were screened from the clinical baseline data and imaging features thus constructing clinical model. Multivariate logistic regression was used to combine independent predictors and the Radscore to form a comprehensive nomogram. The diagnostic performance of constructed models was evaluated based on receiver operating characteristic (ROC) curves and decision curve analysis (DCA).ResultsThe sensitivity and specificity of the clinical model based on consolidation-to-tumor ratio (CTR), lobulated signs and vascular anomaly signs was 70.0% and 76.7% in the validation group. The radiomics model [area under the curve (AUC) 0.926; 95% confidence interval (CI): 0.857-0.995] and the comprehensive model (AUC 0.922; 95% CI: 0.851-0.992) performed better than clinical model (AUC 0.839; 95% CI: 0.720-0.958) in the validation group. The sensitivity and specificity of the comprehensive model was 85.0% and 80.0% in the validation group. DCA of radiomics model and comprehensive model suggested they have better net survival benefit than clinical model.ConclusionsCompared with clinical model, radiomics model and comprehensive model had better diagnostic performance in distinguishing malignant degree of lung adenocarcinoma.

Project description:BackgroundThe high recurrence rate after radical resection of pancreatic ductal adenocarcinoma (PDAC) leads to its poor prognosis. We aimed to develop a model to preoperatively predict the risk of recurrence based on computed tomography (CT) radiomics and multiple clinical parameters.MethodsDatasets were retrospectively collected and analysed of 220 PDAC patients who underwent contrast-enhanced computed tomography (CE-CT) and received radical resection at 3 institutions in China between 2013 and 2017, with 153 from one institution as a training set, the remaining 67 as a validation set. For each patient, CT radiomics features were extracted from intratumoral and peritumoral regions to establish intratumoral, peritumoral and combined radiomics models using artificial neural network (ANN) algorithm. By incorporating clinical factors, radiomics-clinical nomograms were finally built by multivariable logistic regression analysis to predict 1- and 2-year recurrence risk.FindingsThe developed radiomics model integrating intratumoral and peritumoral radiomics features was superior to the conventionally constructed model merely using intratumoral radiomics features. Further, radiomics-clinical nomograms outperformed other models in predicting 1-year recurrence with an area under the receiver operating characteristic curve (AUROC) of 0.916 (95%CI, 0.860-0.955) in the training set and 0.764 (95%CI, 0.644-0.859) in the validation set, and 2-year recurrence with an AUROC of 0.872 (95%CI: 0.809-0.921) in the training set and 0.773 (95%CI, 0.654-0.866) in the validation set.InterpretationThis study has developed and externally validated a radiomics-clinical nomogram integrating intra- and peritumoral CT radiomics signature as well as clinical factors to predict the recurrence risk of PDAC after radical resection, which will facilitate optimized and individualized treatment strategies.FundingThis work was supported by the National Key R&D Program of China [grant number: 2018YFE0114800], the General Program of National Natural Science Foundation of China [grant number: 81772562, 2017; 81871351, 2018], the Fundamental Research Funds for the Central Universities [grant number: 2021FZZX005-08], and Zhejiang Provincial Key Projects of Technology Research [grant number: WKJ-ZJ-2033].

Dataset Information

Comprehensive Computed Tomography Radiomics Analysis of Lung Adenocarcinoma for Prognostication.

Background

Materials and methods

Results

Conclusion

Implications for practice

Publications

Comprehensive Computed Tomography Radiomics Analysis of Lung Adenocarcinoma for Prognostication.

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