Development and validation a radiomics nomogram for diagnosing occult brain metastases in patients with stage IV lung adenocarcinoma

Cong, Ping; Qiu, Qingtao; Li, Xingchao; Sun, Qian; Yu, Xiaoming; Yin, Yong

doi:10.21037/tcr-21-702

Cited by 6 publications

(4 citation statements)

References 35 publications

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“…It indicates that intratumoral homogeneity may be the reason for differences in survival. In addition, most of the features in the Rad score-MS were wavelets, similar to the findings of others ( 22 , 23 ), as well as our previous studies ( 24 , 25 ). This is likely because the multifrequency decomposition of the original MRI image captured more extensive information about the tumor heterogeneity and phenotype to decode long-term survival rules, which clinicians cannot assess with the naked eye.…”

Section: Discussionsupporting

confidence: 90%

Radiomics Nomogram Based on Multiple-Sequence Magnetic Resonance Imaging Predicts Long-Term Survival in Patients Diagnosed With Nasopharyngeal Carcinoma

Qiu

Qin

et al. 2022

Front. Oncol.

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PurposeAlthough the tumor–node–metastasis staging system is widely used for survival analysis of nasopharyngeal carcinoma (NPC), tumor heterogeneity limits its utility. In this study, we aimed to develop and validate a radiomics model, based on multiple-sequence magnetic resonance imaging (MRI), to estimate the probability of overall survival in patients diagnosed with NPC.MethodsMultiple-sequence MRIs, including T1-weighted, T1 contrast, and T2-weighted imaging, were collected from patients diagnosed with NPC. Radiomics features were extracted from the contoured gross tumor volume of three sequences from each patient using the least absolute shrinkage and selection operator with the Cox regression model. The optimal Rad score was determined using 12 of the 851 radiomics features derived from the multiple-sequence MRI and its discrimination power was compared in the training and validation cohorts. For better prediction performance, an optimal nomogram (radiomics nomogram-MS) that incorporated the optimal Rad score and clinical risk factors was developed, and a calibration curve and a decision curve were used to further evaluate the optimized discrimination power.ResultsA total of 504 patients diagnosed with NPC were included in this study. The optimal Rad score was significantly correlated with overall survival in both the training [C-index: 0.731, 95% confidence interval (CI): 0.709–0.753] and validation cohorts (C-index: 0.807, 95% CI: 0.782–0.832). Compared with the nomogram developed with only single-sequence MRI, the radiomics nomogram-MS had a higher discrimination power in both the training (C-index: 0.827, 95% CI: 0.809–0.845) and validation cohorts (C-index: 0.836, 95% CI: 0.815–0.857). Analysis of the calibration and decision curves confirmed the effectiveness and utility of the optimal radiomics nomogram-MS.ConclusionsThe radiomics nomogram model that incorporates multiple-sequence MRI and clinical factors may be a useful tool for the early assessment of the long-term prognosis of patients diagnosed with NPC.

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Section: Discussionsupporting

confidence: 90%

Radiomics Nomogram Based on Multiple-Sequence Magnetic Resonance Imaging Predicts Long-Term Survival in Patients Diagnosed With Nasopharyngeal Carcinoma

Qiu

Qin

et al. 2022

Front. Oncol.

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“…The pleural depression sign is caused by the scar tissue in the tumor pulling the adjacent visceral pleura. In the scar tissue of the tumor, there are changes in arachidonic acid metabolism, the overexpression of eicosanoid signaling genes, and the remodeling of alveolar capillaries, which are conducive to the shedding of tumor cells, indicating that the tumor foci with CT signs of pleural depression have great potential for distant metastasis [ 24 ]. Glszm_SmallAreaHighGrayLevelEmphasis is a small area of high gray emphasis in the gray area size matrix and is a texture feature.…”

Section: Discussionmentioning

confidence: 99%

Value of CT Radiomics and Clinical Features in Predicting Bone Metastases in Patients with NSCLC

Chen

et al. 2022

Contrast Media & Molecular Imaging

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Objective. To explore the CT radiomic features and clinical imaging features of the primary tumor in patients with nonsmall cell lung cancer (NSCLC) before treatment and their predictive value for the occurrence of bone metastases. Methods. From June 2017 to June 2021, 195 patients with NSCLC who were pathologically diagnosed without any treatment in the Cancer Hospital Affiliated to Hainan Medical College were retrospectively analyzed, and they were divided into a bone metastasis group and a nonbone metastasis group. The relationship between clinical imaging features and bone metastasis in patients was analyzed by the t-test, rank sum test, and χ2 test. At the same time, ITK software was used to extract the radiomic characteristics of the primary tumor of the patients, and the patients were randomly divided into a training group and a validation group in a ratio of 7 : 3. The training model was validated in the validation group, and the performance of the model for predicting bone metastases in NSCLC patients was verified by the ROC curve, and a multivariate logistic regression prediction model was established based on the omics parameters extracted from the best prediction model combined with clinical image features. Results. Seven features were screened from the primary tumor by LASSO to establish a model for predicting metastasis. The area under the curve was 0.82 and 0.73 in the training and validation sets. The best omics signature and univariate analysis suggested clinical imaging factors P < 0.05 associated with bone metastases were included in multivariate binary logistic analysis to obtain clinical characteristics of the primary tumor such as gender (OR = 0.141, 95% CI: 0.022–0.919, P = 0.04), increased Cyfra21-1 (OR = 0.12, 95% CI: 0.018–0.782, P = 0.027), Fe content in blood (OR = 0.774, 95% CI: 0.626–0.958, P = 0.018), CT signs such as lesion homogeneity (OR = 0.052, 95% CI: 0.006–0.419, P = 0.006), pleural indentation sign (OR = 0.007, 95% CI: 0.001–0.696, P = 0.034), and omics characteristics glszm_Small Area High Gray Level Emphasis (OR = 0.016, 95% CI: 0.001–0.286, P = 0.005) were independent risk factors for bone metastasis in patients. Conclusion. The prediction model established based on radiomics and clinical imaging features has high predictive performance for the occurrence of bone metastasis in NSCLC patients.

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“…At present, most of the research on radiomics in the lungs lies in the identification of benign and malignant nodules, gene mutations and molecular phenotypes, tumor case classification and prediction of lung cancer prognosis, and there are few studies in predicting brain metastases. Cong [35] constructed CT omics features and R-scores using eight wavelet-based radiomics features, which were significantly correlated with brain metastases. The optimal AUC of the line graph constructed by combining the R-score and the location of the primary tumor was 0.873 (95% CI: 0.866-0.80) in the validation set, with an average accuracy of 0.827 (95% CI: 0.820-0.834).…”

Section: Discussionmentioning

confidence: 99%

The application of different machine learning models based on PET/CT images and EGFR in predicting brain metastasis of adenocarcinoma of the lung

Kong,

Yin,

Zou

et al. 2024

BMC Cancer

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Objective To explore the value of six machine learning models based on PET/CT radiomics combined with EGFR in predicting brain metastases of lung adenocarcinoma. Methods Retrospectively collected 204 patients with lung adenocarcinoma who underwent PET/CT examination and EGFR gene detection before treatment from Cancer Hospital Affiliated to Shandong First Medical University in 2020. Using univariate analysis and multivariate logistic regression analysis to find the independent risk factors for brain metastasis. Based on PET/CT imaging combined with EGFR and PET metabolic indexes, established six machine learning models to predict brain metastases of lung adenocarcinoma. Finally, using ten-fold cross-validation to evaluate the predictive effectiveness. Results In univariate analysis, patients with N2-3, EGFR mutation-positive, LYM%≤20, and elevated tumor markers(P<0.05) were more likely to develop brain metastases. In multivariate Logistic regression analysis, PET metabolic indices revealed that SUVmax, SUVpeak, Volume, and TLG were risk factors for lung adenocarcinoma brain metastasis(P<0.05). The SVM model was the most efficient predictor of brain metastasis with an AUC of 0.82 (PET/CT group),0.70 (CT group),0.76 (PET group). Conclusions Radiomics combined with EGFR machine learning model as a new method have higher accuracy than EGFR mutation alone. SVM model is the most effective method for predicting brain metastases of lung adenocarcinoma, and the prediction efficiency of PET/CT group is better than PET group and CT group.

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Development and validation a radiomics nomogram for diagnosing occult brain metastases in patients with stage IV lung adenocarcinoma

Cited by 6 publications

References 35 publications

Radiomics Nomogram Based on Multiple-Sequence Magnetic Resonance Imaging Predicts Long-Term Survival in Patients Diagnosed With Nasopharyngeal Carcinoma

Radiomics Nomogram Based on Multiple-Sequence Magnetic Resonance Imaging Predicts Long-Term Survival in Patients Diagnosed With Nasopharyngeal Carcinoma

Value of CT Radiomics and Clinical Features in Predicting Bone Metastases in Patients with NSCLC

The application of different machine learning models based on PET/CT images and EGFR in predicting brain metastasis of adenocarcinoma of the lung

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