Development of a radiomics nomogram based on the 2D and 3D CT features to predict the survival of non-small cell lung cancer patients

Yang, Lifeng; Yang, Jingbo; Zhou, Xiaobo; Huang, Luqi; Zhao, Weiling; Wang, Tao; Zhuang, Jian; Tian, Jie

doi:10.1007/s00330-018-5770-y

Cited by 114 publications

(86 citation statements)

References 31 publications

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“…In addition, the decision curve analysis demonstrated that the radiomics nomogram was superior to 4 other clinical models (tumor volume, TNM stage, TNM stage and tumor volume, and clinical model) across the majority of the range of reasonable threshold probabilities, which indicated that the radiomics nomogram added incremental value to the traditional staging system and other clinicalpathologic factors for individualized estimations. We believe that with the combination of the rad-score and clinicopathological factors to construct an OS nomogram, the predictive performance was largely improved, suggesting that the rad-score played an important role in the predictive accuracy of the OS of patients with NSCLC, a result that was consistent with the results of previous studies (46,47). It is worth nothing that because of variations in technical parameters or inconsistent imaging parameters, a limited sample size, and heterogeneous patient characteristics, radiomic features may be insignificant in predicting prognosis in certain situations.…”

Section: Discussionsupporting

confidence: 89%

Development and Validation of a Radiomics Nomogram Based on 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography and Clinicopathological Factors to Predict the Survival Outcomes of Patients With Non-Small Cell Lung Cancer

Yang

Zhong

et al. 2020

Front. Oncol.

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In this study, we developed and validated a radiomics nomogram by combining the radiomic features extracted from 18 F-fluorodeoxyglucose positron emission tomography/computed tomography (18 F-FDG PET/CT) images and clinicopathological factors to evaluate the overall survival (OS) of patients with non-small cell lung cancer (NSCLC). Patients and Methods: A total of 315 consecutive patients with NSCLC (221 in the training cohort and 94 in the validation cohort) were enrolled in this study. A total of 840 radiomic features were extracted from the CT and PET images. Three radiomic scores (rad-scores) were calculated using the least absolute shrinkage and selection operator (LASSO) Cox regression based on subsets of CT, PET, and PET/CT radiomic features. A multivariate Cox regression analysis was performed for each rad-score combined with clinicopathological factors to determine the independent risk factors. The OS nomogram was constructed based on the PET/CT rad-score and independent clinicopathological factors. Validation and calibration were conducted to evaluate the performance of the model in the training and validation cohorts, respectively. Results: A total of 144 (45.71%) women and 171 (54.29%) men with NSCLC were enrolled in this study. The PET/CT rad-score combined with the clinical model had the best C-index (0.776 and 0.789 for the training and validation cohorts, respectively). Distant metastasis, stage, carcinoembryonic antigen (CEA), and targeted therapy were independent risk factors for patients with NSCLC. The validation curve Yang et al. PET/CT Radiomics to Predict the Survival of NSCLC showed that the OS nomogram had a strong predictive power in patients' survival. The calibration curve showed that the predicted survival time was significantly close to the observed one. Conclusion: A radiomic nomogram based on 18 F-FDG PET/CT rad-score and clinicopathological factors had good predictive performance for the survival outcome, offering feasible, and practical guidance for individualized management of patients with NSCLC.

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Section: Discussionsupporting

confidence: 89%

Development and Validation of a Radiomics Nomogram Based on 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography and Clinicopathological Factors to Predict the Survival Outcomes of Patients With Non-Small Cell Lung Cancer

Yang

Zhong

et al. 2020

Front. Oncol.

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“…All patients had advanced tumors with a mean diameter of 63 ± 23 mm, so each slice already contained a representative view of the tumor that might be sufficient to estimate the tumor type (see Figure 4). Two previous studies compared 2D and 3D radiomic feature performance for lung cancer in CT (49,50). The first one did not find any significant difference between 2D and 3D results (49), while the second study reported better performance using the 3D analysis (50).…”

Section: Discussionmentioning

confidence: 95%

“…Two previous studies compared 2D and 3D radiomic feature performance for lung cancer in CT (49,50). The first one did not find any significant difference between 2D and 3D results (49), while the second study reported better performance using the 3D analysis (50). Yet, for these two studies, the 2D analysis was limited to the slice that included the largest cross-section of the lesion, while in our so-called 2D approach, we still accounted for all slices encompassing the tumor.…”

Section: Discussionmentioning

confidence: 99%

Correction for Magnetic Field Inhomogeneities and Normalization of Voxel Values Are Needed to Better Reveal the Potential of MR Radiomic Features in Lung Cancer

et al. 2020

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“…LASSO‐logistic regression and nomogram have been applied to the prediction of lymph node metastasis in colorectal cancer and bladder cancer and in the survival prediction of non‐small cell lung cancer [25‐27]. The positive results of these articles reveal the effectiveness of radiomics in similar studies.…”

Section: Discussionmentioning

confidence: 99%

Preoperative diagnosis of malignant pulmonary nodules in lung cancer screening with a radiomics nomogram

Liu

Wang

Yang

et al. 2020

Cancer Communications

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Background Lung cancer is the most commonly diagnosed cancer worldwide. Its survival rate can be significantly improved by early screening. Biomarkers based on radiomics features have been found to provide important physiological information on tumors and considered as having the potential to be used in the early screening of lung cancer. In this study, we aim to establish a radiomics model and develop a tool to improve the discrimination between benign and malignant pulmonary nodules. Methods A retrospective study was conducted on 875 patients with benign or malignant pulmonary nodules who underwent computed tomography (CT) examinations between June 2013 and June 2018. We assigned 612 patients to a training cohort and 263 patients to a validation cohort. Radiomics features were extracted from the CT images of each patient. Least absolute shrinkage and selection operator (LASSO) was used for radiomics feature selection and radiomics score calculation. Multivariate logistic regression analysis was used to develop a classification model and radiomics nomogram. Radiomics score and clinical variables were used to distinguish benign and malignant pulmonary nodules in logistic model. The performance of the radiomics nomogram was evaluated by the area under the curve (AUC), calibration curve and Hosmer‐Lemeshow test in both the training and validation cohorts. Results A radiomics score was built and consisted of 20 features selected by LASSO from 1288 radiomics features in the training cohort. The multivariate logistic model and radiomics nomogram were constructed using the radiomics score and patients’ age. Good discrimination of benign and malignant pulmonary nodules was obtained from the training cohort (AUC, 0.836; 95% confidence interval [CI]: 0.793‐0.879) and validation cohort (AUC, 0.809; 95% CI: 0.745‐0.872). The Hosmer‐Lemeshow test also showed good performance for the logistic regression model in the training cohort (P = 0.765) and validation cohort (P = 0.064). Good alignment with the calibration curve indicated the good performance of the nomogram. Conclusions The established radiomics nomogram is a noninvasive preoperative prediction tool for malignant pulmonary nodule diagnosis. Validation revealed that this nomogram exhibited excellent discrimination and calibration capacities, suggesting its clinical utility in the early screening of lung cancer.

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Development of a radiomics nomogram based on the 2D and 3D CT features to predict the survival of non-small cell lung cancer patients

Cited by 114 publications

References 31 publications

Development and Validation of a Radiomics Nomogram Based on 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography and Clinicopathological Factors to Predict the Survival Outcomes of Patients With Non-Small Cell Lung Cancer

Development and Validation of a Radiomics Nomogram Based on 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography and Clinicopathological Factors to Predict the Survival Outcomes of Patients With Non-Small Cell Lung Cancer

Correction for Magnetic Field Inhomogeneities and Normalization of Voxel Values Are Needed to Better Reveal the Potential of MR Radiomic Features in Lung Cancer

Preoperative diagnosis of malignant pulmonary nodules in lung cancer screening with a radiomics nomogram

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