Magnetic resonance imaging‐based radiomics model for predicting radiation‐induced temporal lobe injury in nasopharyngeal carcinoma after intensity‐modulated radiotherapy

Bao, Dan; Zhao, Yanfeng; Liu, Zhou; Xu, Haijun; Zhang, Ya; Yuan, Meng; Li, Lin; Meng, Lin; Zhao, Xinming; Luo, Dehong

doi:10.1002/hed.27200

Cited by 6 publications

(8 citation statements)

References 33 publications

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“…A recent genome-wide association study implicated the genetic susceptibility gene CEP128 in RTLI development ( 31 ). In the study by Bao et al ( 23 ), they found that T classification was the only independent clinical factor for the prediction of RTLI.…”

Section: Discussionmentioning

confidence: 99%

“…It was constructed with only texture features extracted from T2WI. In a recent study, Bao et al ( 23 ) found that a radiomics–clinics model combining clinical T staging and radiomics features extracted from T2-weighted fat-suppressed and T1WI-CE showed a better predictive capability in RTLI than T staging alone and a single radiomics model. In this study, the texture features constructed radiomics and radiomics-clinics combined models were extracted from T1WI, T2WI, and T1WI-CE sequences.…”

Section: Discussionmentioning

confidence: 99%

“…Thus, our early prediction models aimed at providing a strong basis for clinicians' implementation of effective measures to prevent or slow the worsening of RTLI. Bao et al ( 23 ) also developed a radiomics model based on MR images after the completion of radiotherapy for the prediction of RTLI and showed great performance in predicting RTLI. In addition, in the other study by Bao et al ( 34 ), they reported that the radiomics model derived from pretreatment MRI of the temporal lobe performed well in the prediction of RTLI in patients with NPC.…”

Section: Discussionmentioning

confidence: 99%

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MRI-based radiomics models for the early prediction of radiation-induced temporal lobe injury in nasopharyngeal carcinoma

et al. 2023

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ObjectiveThis study was conducted to develop and validate a radiomics-clinics combined model-based magnetic resonance imaging (MRI) radiomics and clinical features for the early prediction of radiation-induced temporal lobe injury (RTLI) in patients with nasopharyngeal carcinoma (NPC).MethodsThis retrospective study was conducted using data from 130 patients with NPC (80 patients with and 50 patients without RTLI) who received radiotherapy. Cases were assigned randomly to training (n = 91) and testing (n = 39) datasets. Data on 168 medial temporal lobe texture features were extracted from T1WI, T2WI, and T1WI-CE MRI sequences obtained at the end of radiotherapy courses. Clinics, radiomics, and radiomics–clinics combined models (based on selected radiomics signatures and clinical factors) were constructed using machine learning software. Univariate logistic regression analysis was performed to identify independent clinical factors. The area under the ROC curve (AUC) was performed to evaluate the performance of three models. A nomogram, decision curves, and calibration curves were used to assess the performance of the combined model.ResultsSix texture features and three independent clinical factors associated significantly with RTLI were used to build the combined model. The AUCs for the combined and radiomics models were 0.962 [95% confidence interval (CI), 0.9306–0.9939] and 0.904 (95% CI, 0.8431–0.9651), respectively, for the training cohort and 0.947 (95% CI, 0.8841–1.0000) and 0.891 (95% CI, 0.7903–0.9930), respectively, for the testing cohort. All of these values exceeded those for the clinics model (AUC = 0.809 and 0.713 for the training and testing cohorts, respectively). Decision curve analysis showed that the combined model had a good corrective effect.ConclusionThe radiomics–clinics combined model developed in this study showed good performance for predicting RTLI in patients with NPC.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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MRI-based radiomics models for the early prediction of radiation-induced temporal lobe injury in nasopharyngeal carcinoma

et al. 2023

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“…Previous studies have tried their best to analyze the relationships between radiomics features and clinical outcomes, such as overall survival (OS) [ 35 ], local recurrence [ 24 ], and side effects after radiotherapy [ 36 ]. We focused on the risk of early distant metastasis and a worse prognosis.…”

Section: Discussionmentioning

confidence: 99%

Pretreatment multiparametric MRI radiomics-integrated clinical hematological biomarkers can predict early rapid metastasis in patients with nasopharyngeal carcinoma

Cao,

Wang,

Song

et al. 2024

BMC Cancer

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Background To establish and validate a predictive model combining pretreatment multiparametric MRI-based radiomic signatures and clinical characteristics for the risk evaluation of early rapid metastasis in nasopharyngeal carcinoma (NPC) patients. Methods The cutoff time was used to randomly assign 219 consecutive patients who underwent chemoradiation treatment to the training group (n = 154) or the validation group (n = 65). Pretreatment multiparametric magnetic resonance (MR) images of individuals with NPC were employed to extract 428 radiomic features. LASSO regression analysis was used to select radiomic features related to early rapid metastasis and develop the Rad-score. Blood indicators were collected within 1 week of pretreatment. To identify independent risk variables for early rapid metastasis, univariate and multivariate logistic regression analyses were employed. Finally, multivariate logistic regression analysis was applied to construct a radiomics and clinical prediction nomogram that integrated radiomic features and clinical and blood inflammatory predictors. Results The NLR, T classification and N classification were found to be independent risk indicators for early rapid metastasis by multivariate logistic regression analysis. Twelve features associated with early rapid metastasis were selected by LASSO regression analysis, and the Rad-score was calculated. The AUC of the Rad-score was 0.773. Finally, we constructed and validated a prediction model in combination with the NLR, T classification, N classification and Rad-score. The area under the curve (AUC) was 0.936 (95% confidence interval (95% CI): 0.901–0.971), and in the validation cohort, the AUC was 0.796 (95% CI: 0.686–0.905). Conclusions A predictive model that integrates the NLR, T classification, N classification and MR-based radiomics for distinguishing early rapid metastasis may serve as a clinical risk stratification tool for effectively guiding individual management.

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“…Radiomics turns the deep-seated feature information hidden in conventional medical images into quantitative data invisible to naked eyes (8,9). At present, there have been several studies that use MRI at different time points to construct radiomics models for predicting RTLI in NPC (10)(11)(12)(13)(14)(15)(16). Some studies have developed radiomics nomogram models based on MRI at the end of intensity modulated radiotherapy (IMRT) to predict the RTLI in NPC patients, and these models have shown outstanding predictive performance (11,13,15).…”

Section: Introductionmentioning

confidence: 99%

MRI-based radiomics models predict cystic brain radionecrosis of nasopharyngeal carcinoma after intensity modulated radiotherapy

Hou,

He,

et al. 2024

Front. Neurol.

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ObjectiveTo construct radiomics models based on MRI at different time points for the early prediction of cystic brain radionecrosis (CBRN) for nasopharyngeal carcinoma (NPC).MethodsA total of 202 injured temporal lobes from 155 NPC patients with radiotherapy-induced temporal lobe injury (RTLI) after intensity modulated radiotherapy (IMRT) were included in the study. All the injured lobes were randomly divided into the training (n = 143) and validation (n = 59) sets. Radiomics models were constructed by using features extracted from T2WI at two different time points: at the end of IMRT (post-IMRT) and the first-detected RTLI (first-RTLI). A delta-radiomics feature was defined as the percentage change in a radiomics feature from post-IMRT to first-RTLI. The radiomics nomogram was constructed by combining clinical risk factors and radiomics signatures using multivariate logistic regression analysis. Predictive performance was evaluated using area under the curve (AUC) from receiver operating characteristic analysis and decision curve analysis (DCA).ResultsThe post-IMRT, first-RTLI, and delta-radiomics models yielded AUC values of 0.84 (95% CI: 0.76–0.92), 0.86 (95% CI: 0.78–0.94), and 0.77 (95% CI: 0.67–0.87), respectively. The nomogram exhibited the highest AUC of 0.91 (95% CI: 0.85–0.97) and sensitivity of 0.82 compared to any single radiomics model. From the DCA, the nomogram model provided more clinical benefit than the radiomics models or clinical model.ConclusionThe radiomics nomogram model combining clinical factors and radiomics signatures based on MRI at different time points after radiotherapy showed excellent prediction potential for CBRN in patients with NPC.

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Magnetic resonance imaging‐based radiomics model for predicting radiation‐induced temporal lobe injury in nasopharyngeal carcinoma after intensity‐modulated radiotherapy

Cited by 6 publications

References 33 publications

MRI-based radiomics models for the early prediction of radiation-induced temporal lobe injury in nasopharyngeal carcinoma

MRI-based radiomics models for the early prediction of radiation-induced temporal lobe injury in nasopharyngeal carcinoma

Pretreatment multiparametric MRI radiomics-integrated clinical hematological biomarkers can predict early rapid metastasis in patients with nasopharyngeal carcinoma

MRI-based radiomics models predict cystic brain radionecrosis of nasopharyngeal carcinoma after intensity modulated radiotherapy

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