Prognostic Value of Pre-Treatment CT Radiomics and Clinical Factors for the Overall Survival of Advanced (IIIB–IV) Lung Adenocarcinoma Patients

Abstract

PurposeThe purpose of this study was to investigate the prognostic value of pre-treatment CT radiomics and clinical factors for the overall survival (OS) of advanced (IIIB–IV) lung adenocarcinoma patients.MethodsThis study involved 165 patients with advanced lung adenocarcinoma. The Lasso–Cox regression model was used for feature selection and radiomics signature building. Then a clinical model was built based on clinical factors; a combined model in the form of nomogram was constructed with both clinical factors and the radiomics signature. Harrell’s concordance index (C-Index) and Receiver operating characteristic (ROC) curves at cut-off time points of 1-, 2-, and 3- year were used to estimate and compare the predictive ability of all three models. Finally, the discriminatory ability and calibration of the nomogram were analyzed.ResultsThirteen significant features were selected to build the radiomics signature whose C-indexes were 0.746 (95% CI, 0.699 to 0.792) in the training cohort and 0.677 (95% CI, 0.597 to 0.766) in the validation cohort. The C-indexes of combined model achieved 0.799 (95% CI, 0.757 to 0.84) in the training cohort and 0.733 (95% CI, 0.656 to 0.81) in the validation cohort, which outperformed the clinical model and radiomics signature. Moreover, the areas under the curve (AUCs) of the radiomic signature for 2-year prediction was superior to that of the clinical model. The combined model had the best AUCs for 2- and 3-year predictions.ConclusionsRadiomic signatures and clinical factors have prognostic value for OS in advanced (IIIB–IV) lung adenocarcinoma patients. The optimal model should be selected according to different cut-off time points in clinical application.

Prognostic Value of a CT Radiomics-Based Nomogram for the Overall Survival of Patients with Nonmetastatic BCLC Stage C Hepatocellular Carcinoma after Stereotactic Body Radiotherapy

“…The study participants were randomly assigned to the training set (70%) and the validation set (30%). Following the methods of Hong et al [ 19 ], the LASSO Cox regression model was performed to get the prognostic radiomics features from the training datasets, which was a commonly used method for selecting features from high-dimensional radiomics data. The Rad-score of each participant was calculated by weighting the selected features based on their respective coefficients [ 19 ].…”

“…The association between the Rad-score and post-SBRT survival was evaluated via the Kaplan–Meier survival analysis. Patients were assigned into high- and low-risk groups based on the Rad-score cutoff point [ 19 ]. The best cutoff point was calculated on the basis of training datasets applying the “survminer” package and tested on validation datasets.…”

“…Te calculation of the C-index was made to assess discrimination power. [19]. In the training set, sex, age, ECOG, the N stage, BED, MVI, the CTP class, hepatitis, pre-SBRT AFP, the largest tumor size, and the number of tumors were evaluated based on univariate Cox regression analysis.…”

A review of radiomics and genomics applications in cancers: the way towards precision medicine

“…Traditional statistical methods, specifically univariate and multivariate analyses, are used to identify the statistically significant variables and radiomics signatures associated with the outcome [59]. Afterwards, logistic regression and Cox proportional hazards regression are often used to build a radiomics-clinical predictive signature that incorporates clinical risk factors and radiomics features, providing a basis for diagnosis, clinical treatment options, and prognostic prediction [60,61].…”

Identification of pulmonary adenocarcinoma and benign lesions in isolated solid lung nodules based on a nomogram of intranodal and perinodal CT radiomic features

“…As a noninvasive new technique, radiomics can extract features with high throughput for analysis. It has been widely used in many aspects, such as differentiation of benign and malignant pulmonary nodules ( 15 – 19 ), invasion and metastasis ( 20 , 21 ), histological classification ( 22 ), gene expression ( 23 ), and treatment prognosis ( 24 ). The classification of benign and malignant pulmonary nodules, in particular, have achieved excellence in radiomics, from purely benign-malignant differentiation to differentiation with inflammatory granulomas ( 15 ), tuberculous granulomas ( 19 ), and cryptococcal infections ( 17 ).…”