Radiation-Induced Temporal Lobe Injury Research Articles

A pretreatment multiparametric MRI-based radiomics-clinical machine learning model for predicting radiation-induced temporal lobe injury in patients with nasopharyngeal carcinoma.

To establish and validate a machine learning model using pretreatment multiparametric magnetic resonance imaging-based radiomics data with clinical data to predict radiation-induced temporal lobe injury (RTLI) in patients with nasopharyngeal carcinoma (NPC) after intensity-modulated radiotherapy (IMRT). Data from 230 patients with NPC who received IMRT (130 with RTLI and 130 without) were randomly divided into the training (n = 161) and validation cohort (n = 69) with a ratio of 7:3. Radiomics features were extracted from pretreatment apparent diffusion coefficient (ADC) map, T2-weighted imaging (T2WI), and CE-T1-weighted imaging (CE-T1WI). T-test, spearman rank correlation, and least absolute shrinkage and selection operator (LASSO) algorithm were employed to identify significant radiomics features. Clinical features were selected with univariate and multivariate analyses. Radiomics and clinical models were constructed using multiple machine learning classifiers, and a clinical-radiomics nomogram that combined clinical with radiomics features was developed. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were drawn to compare and verify the predictive performances of the clinical model, radiomics model, and clinical-radiomics nomogram. A total of 5064 radiomics features were extracted, from which 52 radiomics features were selected to construct the radiomics signature. The AUC of the radiomics signature based on multiparametric MRI was 0.980 in the training cohort and 0.969 in the validation cohort, outperforming the radiomics signature only based on T2WI and CE-T1WI (p < 0.05), which highlighted the significance of the DWI sequence in the prediction of temporal lobe injury. The area under the curve (AUC) of the clinical model was 0.895 in the training cohort and 0.905 in the validation cohort. The nomogram, which integrated radiomics and clinical features, demonstrated an impressive AUC value of 0.984 in the validation set; however, no statistically significant difference was observed compared to the radiomics model. The calibration curve and decision curve analysis of the nomogram demonstrated excellent predictive performance and clinical feasibility. The clinical-radiomics nomogram, integrating clinical features with radiomics features derived from pretreatment multiparametric MRI, exhibits compelling predictive performance for RTLI in patients diagnosed with NPC.

Evolution of radiation-induced temporal lobe injury after intensity-modulated radiation therapy in nasopharyngeal carcinoma: a large cohort retrospective study

BackgroundPrevious studies have demonstrated conflicting findings regarding the initial MRI patterns of radiotherapy-induced temporal lobe injury (RTLI) and the evolution of different RTLI patterns. The aim of this study was to evaluate the initial MRI pattern and evolution of RTLI in patients with nasopharyngeal carcinoma (NPC) by means of a large cohort study.MethodsData of patients with RTLI were retrospectively collected from two hospitals between January 2011 and December 2021. The injured lobes were categorized into three patterns based on initial MRI patterns: isolated white matter lesions (WMLs), isolated contrast-enhanced lesions (CELs), and combined WMLs and CELs. The latency period, MRI appearances, and temporal changes in WMLs and CELs were evaluated.ResultsA total of 913 RTLI patients with 1092 injured lobes were included in this study. The numbers of isolated WMLs, isolated CELs, and combined WMLs and CELs identified at the first MRI detection were 7 (0.6%), 172 (15.8%), and 913 (83.6%), respectively. The evolution of bilateral RTLI was different in the same patient, and that of unilateral RTLI combined with WMLs and CELs also may occur asynchronously. The time intervals from the initial MRI detection of isolated WMLs, isolated CELs, combined WMLs and CELs to the last negative MRI scan were 8.6, 8.9 and 11.0 months, respectively. A significant difference was observed in the time intervals between the three patterns (H = 14.287, P = 0.001). And the time interval was identified as an independent factor influencing the initial MRI pattern of RTLI after Poisson regression (P = 0.002).ConclusionBoth WMLs and CELs could be the initial and only MRI abnormalities in patients with RTLI. This study is of great significance in accurately diagnosing RTLI early and providing timely treatment options. Additionally, it provides clinical evidence for guidelines on NPC, emphasizing the importance of regular follow-up of NPC patients.

A MRI-based radiomics combined prediction model for radiation-induced temporal lobe injury in nasopharyngeal carcinoma patients

PurposeRadiation-induced temporal lobe injury (RTLI) is a common radiation-induced injury in patients with nasopharyngeal carcinoma (NPC). Our aim is to evaluate the potential factors associated with RTLI and to develop a predictive model to assess RTLI. MethodA total of 393 NPC patients who had received full course radiotherapy at two institutions were included. Patients from institution I were divided into a training cohort and a testing cohort according to the time of first-line treatment. Patients from institution II were used for external validation. The pre-treatment T1-weighted, T2-weighted and contrast-enhanced T1-weighted (T1c-weighted) magnetic resonance imaging (MRI) images before treatment were retrieved for radiomics feature extraction. The left and right temporal regions were separately defined and delineated as regions of interest (ROIs) by the MIM segmentation system. The relative contributions of different MRI sequences to the radiomics (Rad) score evaluation were compared. The least absolute shrinkage and selection operator (LASSO) regression algorithm was used to calculate Rad scores and develop comprehensive models. ResultsThe radiomics model consisting of all MRI features (combined T1-weighted, T2-weighted and T1c-weighted) was most associated with RTLI. The AUCs were 0.77 (95% CI: 0.69–0.85) in the testing cohort and 0.74 (95% CI: 0.70–0.79) in the external validation cohort. The AUC for the clinical-only model (including clinical stage and D0.5 cc) was 0.52 (95% CI: 0.31–0.74), whereas models including clinical factors (clinical stage), dosimetric factors (D0.5 cc) and Rad scores from all MRI sequences were more optimal. The AUC was 0.76 (95% CI: 0.67–0.84). ConclusionWe generated a feasible RTLI prediction model with radiomics, clinical and dosimetric factors.

916P Reducing radiation-induced temporal lobe injury by changing tumor prescription dose in nasopharyngeal carcinoma

916P Reducing radiation-induced temporal lobe injury by changing tumor prescription dose in nasopharyngeal carcinoma

Research trends of radiation induced temporal lobe injury in patients with nasopharyngeal carcinoma from 2000 to 2022: a bibliometric analysis

BackgroundIn patients with nasopharyngeal cancer (NPC), radiation-induced temporal lobe injury (TLI) is the most dreaded late-stage complication following radiation therapy (RT). We currently lack a definitive algorithmic administration for this entity. In the meantime, the pathogenesis of TLI and the mechanism-based interventions to prevent or treat this adverse effect remain unknown. To better answer the aforementioned questions, it is necessary to comprehend the intellectual foundations and prospective trends of this field through bibliometric analysis.MethodsArticles were gathered from the Web of Science Core Collection (WoSCC) database between 2000 and 2022. CiteSpace was utilized to create a country/institutional co-authorship network, perform dual-map analysis, and find keywords with citation bursts. VOSviewer was used to build networks based on author co-authorship, journal citation, co-citation analysis of authors, references, and journals, and keyword co-occurrence.ResultsA total of 140 articles and reviews were included in the final analysis. The number of publications has steadily increased with some fluctuations over the years. The country and institution contributing most to this field are the China and Sun Yat-Sen University. Han Fei was the most prolific author, while Lee Awm was the most frequently cited. The analysis of co-occurrence revealed three clusters, including: “radiation-induced injury or necrosis in NPC,” “clinical studies on chemotherapy/radiotherapy complications and survival in recurrent NPC,” and “IMRT/chemotherapy outcomes and toxicities in head and neck cancer”). Most recent keyword bursts were “volume,” “temporal lobe injury,” “toxicities,” “model,” “survival,” “intensity modulated radiotherapy,” “induced brain injury,” “head and neck cancer,” and “temporal lobe.”ConclusionThis study provides some insights of the major areas of interest in the field of radiation-induced TLI in patients with NPC by bibliometric analyses. This study assists scholars in locating collaborators and significant literature in this field, provides guidance for publishing journals, and identifies research hotspots. This analysis acknowledges significant contributions to the discipline and encourages the scientific community to conduct additional research.

Predictive accuracy of machine learning for radiation-induced temporal lobe injury in nasopharyngeal carcinoma patients: a systematic review and meta-analysis.

Radiotherapy is a common treatment for nasopharyngeal carcinoma (NPC) but can cause radiation-induced temporal lobe injury (RTLI), resulting in irreversible damage. Predicting RTLI at the early stage may help with that issue by personalized adjustment of radiation dose based on the predicted risk. Machine learning (ML) models have recently been used to predict RTLI but their predictive accuracy remains unclear because the reported concordance index (C-index) varied widely from around 0.31 to 0.97. Therefore, a meta-analysis was needed. The PubMed, Web of Science, Embase, and Cochrane Library databases were searched from inception to November 2022. Studies that fully develop one or more ML risk models of RTLI after radiotherapy for NPC were included. The Prediction model Risk Of Bias Assessment Tool (PROBAST) was used to assess the risk of bias in the included research. The primary outcome of this review was the C-index, specificity (Spe), and sensitivity (Sen). The meta-analysis included 14 studies with 15,573 NPC patients reporting a total of 72 prediction models. Overall, 94.44% of models were found to have a high risk of bias. Radiomics was included in 57 models, dosimetric predictors in 28, and clinical data in 27. The pooled C-index for ML models predicting RTLI was 0.77 [95% confidence interval (CI): 0.75-0.79] in the training set and 0.78 (95% CI: 0.75-0.81) in the validation set. The pooled Sen was 0.75 (95% CI: 0.69-0.80) in the training set and 0.70 (95% CI: 0.66-0.73) in the validation set and the pooled Spe was 0.78 (95% CI: 0.73-0.82) in the training set and 0.79 (95% CI: 0.75-0.82) in the validation set. Models with radiomics and clinical data achieved the most excellent discriminative performance, with a pooled C-index of 0.895. ML models can accurately predict RTLI at an early stage, allowing for timely interventions to prevent further damage. The kind of ML methods and the selection of predictors may influence the predictive accuracy.

Deep learning-based precise prediction and early detection of radiation-induced temporal lobe injury for nasopharyngeal carcinoma

Radiotherapy is the mainstay of treatment for nasopharyngeal carcinoma. Radiation-induced temporal lobe injury (TLI) can regress or resolve in the early phase, but it is irreversible at a later stage. However, no study has proposed a risk-based follow-up schedule for its early detection. Planning evaluation is difficult when dose-volume histogram (DVH) parameters are similar and optimization is terminated. This multicenter retrospective study included 6065 patients between 2014 and 2018. A 3D ResNet-based deep learning model was developed in training and validation cohorts and independently tested using concordance index in internal and external test cohorts. Accordingly, the patients were stratified into risk groups, and the model-predicted risks were used to develop risk-based follow-up schedules. The schedule was compared with the Radiation Therapy Oncology Group (RTOG) recommendation (every 3 months during the first 2 years and every 6 months in 3-5 years). Additionally, the model was used to evaluate plans with similar DVH parameters. Our model achieved concordance indexes of 0.831, 0.818, and 0.804, respectively, which outperformed conventional prediction models (all P<0.001). The temporal lobes in all the cohorts were stratified into three groups with discrepant TLI-free survival. Personalized follow-up schedules developed for each risk group could detect TLI 1.9 months earlier than the RTOG recommendation. According to a higher median predicted 3-year TLI-free survival (99.25% vs. 99.15%, P<0.001), the model identified a better plan than previous models. The deep learning model predicted TLI more precisely. The model-determined risk-based follow-up schedule detected the TLI earlier. The planning evaluation was refined because the model identified a better plan with a lower risk of TLI. The Sun Yat-sen University Clinical Research 5010 Program (2015020), Guangdong Basic and Applied Basic Research Foundation (2022A1515110356), Medical Scientific Research Foundation of Guangdong Province (A2022367), and Guangzhou Science and Technology Program (2023A04J1788).

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

This 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). This 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. Six 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. The radiomics-clinics combined model developed in this study showed good performance for predicting RTLI in patients with NPC.

Added value of histogram analysis of ADC in predicting radiation-induced temporal lobe injury of patients with nasopharyngeal carcinoma treated by intensity-modulated radiotherapy

BackgroundThis study evaluated the predictive potential of histogram analysis derived from apparent diffusion coefficient (ADC) maps in radiation-induced temporal lobe injury (RTLI) of nasopharyngeal carcinoma (NPC) after intensity-modulated radiotherapy (IMRT).ResultsPretreatment diffusion-weighted imaging (DWI) of the temporal lobes of 214 patients with NPC was retrospectively analyzed to obtain ADC histogram parameters. Of the 18 histogram parameters derived from ADC maps, 7 statistically significant variables in the univariate analysis were included in the multivariate logistic regression analysis. The final best prediction model selected by backward stepwise elimination with Akaike information criteria as the stopping rule included kurtosis, maximum energy, range, and total energy. A Rad-score was established by combining the four variables, and it provided areas under the curve (AUCs) of 0.95 (95% confidence interval [CI] 0.91–0.98) and 0.89 (95% CI 0.81–0.97) in the training and validation cohorts, respectively. The combined model, integrating the Rad-score with the T stage (p = 0.02), showed a favorable prediction performance in the training and validation cohorts (AUC = 0.96 and 0.87, respectively). The calibration curves showed a good agreement between the predicted and actual RTLI occurrences.ConclusionsPretreatment histogram analysis of ADC maps and their combination with the T stage showed a satisfactory ability to predict RTLI in NPC after IMRT.

Dosiomics Risk Model for Predicting Radiation Induced Temporal Lobe Injury and Guiding Individual Intensity-Modulated Radiation Therapy

We aimed to assess the value of dose distribution-based dosiomics and planning computed tomography-based radiomics to predict radiation-induced temporal lobe injury (TLI) and guide individualized intensity modulated radiation therapy. A total of 5599 nasopharyngeal carcinoma patients were enrolled, including 2503, 1072, 988, and 1036 patients in the training, validation, prospective test, and external test cohorts, respectively. The concordance index (C-index) was used to compare the performance of the radiomics and dosiomics models with that of the quantitative analyses of normal tissue effects in the clinic and Wen's models. The predicted TLI-free survival rates of redesigned simulated plans with the same dose-volume histogram but different dose distributions for same patient in a cohort of 30 randomly selected patients were compared by the Wilcoxon matched-pairs signed-rank test. The radiomics and dosiomics signatures were constructed based on 30 selected computed tomography features and 10 selected dose distribution features, respectively, which were important predictors of TLI-free survival (all P <.001). However, the radiomics signature had a low C-index. The dosiomics risk model combining the dosiomics signature, D1cc, and age had favorable performance, with C-index values of 0.776, 0.811, 0.805, and 0.794 in the training, validation, prospective test, and external test cohorts, respectively, which were better than those of the quantitative analyses of normal tissue effects in the clinic model and Wen's model (all P <.001). The dosiomics risk model can further distinguish patients in a same risk category divided by other models (all P <.05). Conversely, the other models were unable to separate populations classified by the dosiomics risk model (all P > .05). Two simulated plans with the same dose-volume histogram but different dose distributions had different TLI-free survival rates predicted by dosiomics risk model (all P ≤ .002). The dosiomics risk model was superior to traditional models in predicting the risk of TLI. This is a promising approach to precisely predict radiation-induced toxicities and guide individualized intensity modulated radiation therapy.

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

To develop a model based on magnetic resonance imaging (MRI) radiomics and clinical features for predicting radiation-induced temporal lobe injury (RTLI) in patients with nasopharyngeal carcinoma (NPC) after intensity-modulated radiotherapy (IMRT). Two hundred and sixteen patients with NPC were retrospectively included. Radiomics features were extracted and selected. The logistic regression analysis was performed for prediction models construction. The area under the receiver operating characteristic curve (AUC) was calculated for performance evaluation. Three radiomics features were selected to construct the radiomics signature (AUC of 0.94 and 0.92). The clinical-radiomics model, integrating radiomics signature with T classification, achieved higher predictive performance in the training and validation cohorts (AUC of 0.95 and 0.93), as well as improved accuracy of the classification of RTLI outcomes (net reclassification improvement: 0.711; 95% CI: 0.57-0.86; p < 0.001). The clinical-radiomics model and radiomics signature both showed great performance in predicting RTLI in patients with NPC.

Nomogram Based on Clinical and Radiomics Data for Predicting Radiation-induced Temporal Lobe Injury in Patients with Non-metastatic Stage T4 Nasopharyngeal Carcinoma

AimsTo use pre-treatment magnetic resonance imaging-based radiomics data with clinical data to predict radiation-induced temporal lobe injury (RTLI) in nasopharyngeal carcinoma (NPC) patients with stage T4/N0–3/M0 within 5 years after radiotherapy. Materials and methodsThis study retrospectively examined 98 patients (198 temporal lobes) with stage T4/N0–3/M0 NPC. Participants were enrolled into a training cohort or a validation cohort in a ratio of 7:3. Radiomics features were extracted from pre-treatment magnetic resonance imaging that were T1-and T2-weighted. Spearman rank correlation, the t-test and the least absolute shrinkage and selection operator (LASSO) algorithm were used to select significant radiomics features; machine-learning models were used to generate radiomics signatures (Rad-Scores). Rad-Scores and clinical factors were integrated into a nomogram for prediction of RTLI. Nomogram discrimination was evaluated using receiver operating characteristic analysis and clinical benefits were evaluated using decision curve analysis. ResultsParticipants were enrolled into a training cohort (n = 139) or a validation cohort (n = 59). In total, 3568 radiomics features were initially extracted from T1-and T2-weighted images. Age, Dmax, D1cc and 16 stable radiomics features (six from T1-weighted and 10 from T2-weighted images) were identified as independent predictive factors. A greater Rad-Score was associated with a greater risk of RTLI. The nomogram showed good discrimination, with a C-index of 0.85 (95% confidence interval 0.79–0.92) in the training cohort and 0.82 (95% confidence interval 0.71–0.92) in the validation cohort. ConclusionWe developed models for the prediction of RTLI in patients with stage T4/N0–3/M0 NPC using pre-treatment radiomics data and clinical data. Nomograms from these pre-treatment data improved the prediction of RTLI. These results may allow the selection of patients for earlier clinical interventions.

A MRI-based radiomics model predicting radiation-induced temporal lobe injury in nasopharyngeal carcinoma.

To develop and validate a radiomics-based model for predicting radiation-induced temporal lobe injury (RTLI) in nasopharyngeal carcinoma (NPC) by pretreatment MRI of the temporal lobe. A total of 216 patients with diagnosed NPC were retrospectively reviewed. Patients were randomly allocated to the training (n = 136) and the validation cohort (n = 80). Radiomics features were extracted from pretreatment contrast-enhanced T1- or fat-suppressed T2 weighted MRI. A radiomics signature was generated by the least absolute shrinkage and selection operator (LASSO) regression algorithm, Pearson correlation analysis, and univariable logistic analysis. Clinical features were selected with logistic regression analysis. Multivariable logistic regression analysis was conducted to develop three models for RTLI prediction in the training cohort: namely radiomics signature, clinical variables, and clinical-radiomics parameters. A radiomics nomogram was used and assessed with respect to calibration, discrimination, reclassification, and clinical application. The radiomics signature, composed of two radiomics features, was significantly associated with RTLI. The proposed radiomics model demonstrated favorable discrimination in both the training (AUC, 0.89) and the validation cohort (AUC, 0.92), outperforming the clinical prediction model (p < 0.05). Combining radiomics and clinical features, higher AUCs were achieved (AUC, 0.93 and 0.95), as well as a better calibration and improved accuracy of the prediction of RTLI. The clinical-radiomics model showed also excellent performance in predicting RTLI in different clinical-pathologic subgroups. A radiomics model derived from pretreatment MRI of the temporal lobe showed persuasive performance for predicting radiation-induced temporal lobe injury in nasopharyngeal carcinoma. • Radiomics features from pretreatment MRI are associated with radiation-induced temporal lobe injury in nasopharyngeal carcinoma. • The radiomics model shows better predictive performance than a clinical model and was similar to a clinical-radiomics model. • A clinical-radiomics model shows excellent performance in the prediction of radiation-induced temporal lobe injury in different clinical-pathologic subgroups.

Individualized clinical target volume delineation and efficacy analysis in unilateral nasopharyngeal carcinoma treated with intensity-modulated radiotherapy (IMRT): 10-year summary.

To evaluate the long-term local control, failure patterns, and toxicities after individualized clinical target volume (CTV) delineation in unilateral nasopharyngeal carcinoma (NPC) treated with intensity-modulated radiotherapy (IMRT). Unilateral NPC was defined as a nasopharyngeal mass confined to one side of the nasopharynx and did not exceed the midline. From November 2003 to December 2017, 95 patients were retrospectively included. All patients received IMRT. The CTVs were determined based on the distance from the gross tumor. The contralateral para-pharyngeal space and skull base orifices were spared from irradiation. There were three local recurrences and eight regional recurrences in 10 patients during an 84-month follow-up. All local recurrences were within PGTVnx, and all in-field recurrences. No recurrences were found in traditional high-risk areas including contralateral the para-pharyngeal space and skull base orifices. The 10-year local-recurrence-free survival, regional-recurrence-free survival and overall survival were 96.2%, 90.5% and 84.7%, respectively. The dosimetry parameters of the tumor-contralateral organs were all lower than the values of the tumor-ipsilateral side (P < 0.05). The late toxicities occurred mainly in the tumor-ipsilateral organs, including radiation-induced temporal lobe injury, impaired visuality, hearing loss and subcutaneous fibrosis. Individualized CTV delineation in unilateral NPC could yield excellent long-term local control with limited out-of-field recurrences, reduced dose to tumor- contralateral organs and mild late toxicities, which is worthy of further exploration.

Research progress on mechanism and imaging of temporal lobe injury induced by radiotherapy for head and neck cancer.

Radiotherapy (RT) is an effective treatment for head and neck cancer (HNC). Radiation-induced temporal lobe injury (TLI) is a serious complication of RT. Late symptoms of radiation-induced TLI are irreversible and manifest as memory loss, cognitive impairment, and even temporal lobe necrosis (TLN). It is currently believed that the mechanism of radiation-induced TLI involves microvascular injury, neuron and neural stem cell injury, glial cell damage, inflammation, and the production of free radicals. Significant RT-related structural changes and dose-dependent changes in gray matter (GM) and white matter (WM) volume and morphology were observed through computed tomography (CT) and magnetic resonance imaging (MRI) which were common imaging assessment tools. Diffusion tensor imaging (DTI), dispersion kurtosis imaging (DKI), susceptibility-weighted imaging (SWI), resting-state functional magnetic resonance (rs-fMRI), magnetic resonance spectroscopy (MRS), and positron emission tomography (PET) can be used for early diagnosis and prognosis evaluation according to functional, molecular, and cellular processes of TLI. Early diagnosis of TLI is helpful to reduce the incidence of TLN and its related complications. This review summarizes the clinical features, mechanisms, and imaging of radiation-induced TLI in HNC patients. KEY POINTS: • Radiation-induced temporal lobe injury (TLI) is a clinical complication and its symptoms mainly include memory impairment, headache, and cognitive impairment. • The mechanisms of TLI include microvascular injury, cell injury, and inflammatory and free radical injury. Significant RT-related structural changes and dose-dependent changes in TL volume and morphology were observed through CT and MRI. • SWI, MRS, DTI, and DKI and other imaging examinations can detect anatomical and functional, molecular, and cellular changes of TLI.

Half-Brain Delineation for Prediction of Radiation-Induced Temporal Lobe Injury in Nasopharyngeal Carcinoma Receiving Intensity-Modulated Radiotherapy

PurposeTo investigate the role of half-brain delineation in the prediction of radiation-induced temporal lobe injury (TLI) in nasopharyngeal carcinoma (NPC) receiving intensity-modulated radiotherapy (IMRT).Methods and MaterialsA total of 220 NPC cases treated with IMRT and concurrent platinum-based chemotherapy were retrospectively analyzed. Dosimetric parameters of temporal lobes, half-brains, and brains included maximum dose (Dmax), doses covering certain volume (DV) from 0.03 to 20 cc and absolute volumes receiving specific dose (VD) from 40 to 80 Gy. Inter-structure variability was assessed by coefficients of variation (CV) and paired samples t-tests. Receiver operating characteristic curve (ROC) and Youden index were used for screening dosimetric parameters to predict TLI. Dose/volume response curve was calculated using the logistic dose/volume response model.ResultsCVs of brains, left/right half-brains, and left/right temporal lobes were 9.72%, 9.96%, 9.77%, 27.85%, and 28.34%, respectively. Each DV in temporal lobe was significantly smaller than that in half-brain (P < 0.001), and the reduction ranged from 3.10% to 45.98%. The area under the curve (AUC) of DV and VD showed an “increase-maximum-decline” behavior with a peak as the volume or dose increased. The maximal AUCs of DVs in brain, half-brain and temporal lobe were 0.808 (D2cc), 0.828 (D1.2cc) and 0.806 (D0.6cc), respectively, and the maximal AUCs of VDs were 0.818 (D75Gy), 0.834 (V72Gy) and 0.814 (V70Gy), respectively. The cutoffs of V70Gy (0.86 cc), V71Gy (0.72 cc), V72Gy (0.60 cc), and V73Gy (0.45 cc) in half-brain had better Youden index. TD5/5 and TD50/5 of D1.2cc were 58.7 and 80.0 Gy, respectively. The probability of TLI was higher than >13% when V72Gy>0 cc, and equal to 50% when V72Gy = 7.66 cc.ConclusionHalf-brain delineation is a convenient and stable method which could reduce contouring variation and could be used in NPC patients. D1.2cc and V72Gy of half-brain are feasible for TLI prediction model. The dose below 70 Gy may be relatively safe for half-brain. The cutoff points of V70–73Gy could be considered when the high dose is inevitable.

Development and Validation of a Nomogram for Predicting Radiation-Induced Temporal Lobe Injury in Nasopharyngeal Carcinoma.

BackgroundThe purpose was to develop and validate a nomogram for prediction on radiation-induced temporal lobe injury (TLI) in patients with nasopharyngeal carcinoma (NPC).MethodsThe prediction model was developed based on a primary cohort that consisted of 194 patients. The data was gathered from January 2008 to December 2010. Clinical factors associated with TLI and dose–volume histograms for 388 evaluable temporal lobes were analyzed. Multivariable logistic regression analysis was used to develop the predicting model, which was conducted by R software. The performance of the nomogram was assessed with calibration and discrimination. An external validation cohort contained 197 patients from January 2011 to December 2013.ResultsAmong the 391 patients, 77 patients had TLI. Prognostic factors contained in the nomogram were Dmax (the maximum point dose) of temporal lobe, D1cc (the maximum dose delivered to a volume of 1 ml), T stage, and neutrophil-to-lymphocyte ratios (NLRs). The Internal validation showed good discrimination, with a C-index of 0.847 [95%CI 0.800 to 0.893], and good calibration. Application of the nomogram in the external validation cohort still obtained good discrimination (C-index, 0.811 [95% CI, 0.751 to 0.870]) and acceptable calibration.ConclusionsThis study developed and validated a nomogram, which may be conveniently applied for the individualized prediction of TLI.

Machine-learning based MRI radiomics models for early detection of radiation-induced brain injury in nasopharyngeal carcinoma

BackgroundEarly radiation-induced temporal lobe injury (RTLI) diagnosis in nasopharyngeal carcinoma (NPC) is clinically challenging, and prediction models of RTLI are lacking. Hence, we aimed to develop radiomic models for early detection of RTLI.MethodsWe retrospectively included a total of 242 NPC patients who underwent regular follow-up magnetic resonance imaging (MRI) examinations, including contrast-enhanced T1-weighted and T2-weighted imaging. For each MRI sequence, four non-texture and 10,320 texture features were extracted from medial temporal lobe, gray matter, and white matter, respectively. The relief and 0.632 + bootstrap algorithms were applied for initial and subsequent feature selection, respectively. Random forest method was used to construct the prediction model. Three models, 1, 2 and 3, were developed for predicting the results of the last three follow-up MRI scans at different times before RTLI onset, respectively. The area under the curve (AUC) was used to evaluate the performance of models.ResultsOf the 242 patients, 171 (70.7%) were men, and the mean age of all the patients was 48.5 ± 10.4 years. The median follow-up and latency from radiotherapy until RTLI were 46 and 41 months, respectively. In the testing cohort, models 1, 2, and 3, with 20 texture features derived from the medial temporal lobe, yielded mean AUCs of 0.830 (95% CI: 0.823–0.837), 0.773 (95% CI: 0.763–0.782), and 0.716 (95% CI: 0.699–0.733), respectively.ConclusionThe three developed radiomic models can dynamically predict RTLI in advance, enabling early detection and allowing clinicians to take preventive measures to stop or slow down the deterioration of RTLI.

Development and Validation of a Nomogram for Predicting Radiation-Induced Temporal Lobe Injury in Nasopharyngeal Carcinoma

Radiation-induced temporal lobe injury (TLI) is one of the most serious late complications after definitive radiotherapy of nasopharyngeal carcinoma patients. We aimed to develop and validate a nomogram for prediction of radiation-induced temporal lobe injury in patients with nasopharyngeal carcinoma. The prediction model was developed in a cohort that consisted of 194 patients with clinicopathologically confirmed nasopharyngeal carcinoma, and data was gathered from January 2007 to December 2009. TLI was diagnosed by follow-up enhanced magnetic resonance imaging. Clinical factors associated with TLI and Dose-volume histograms for 388 evaluable temporal lobes were analyzed. Multivariable logistic regression analysis was used to develop the predicting model. The performance of the nomogram was assessed with respect to its calibration and discrimination. Internal validation was assessed by 1000 bootstrap resamples. Of the 194 patients, 59 patients developed TLI. Predictors contained in the prediction nomogram included Dmax (the maximum point dose) of temporal lobe (p=.000, 1.563 [95%, 1.398-1.747]), D1cc (the maximum dose delivered to a volume of 1 mL) (p=.000, 0.865 [95%, 0.825-0.907]), concurrent chemotherapy (p=.060, 7.671 [95%, 0.918-64.086]), T stage (p=.070, 11.115 [95%, 0.825-79.739]). The nomogram prediction model was developed and Internal validation showed good discrimination and calibration. The ROC curve analysis showed that the C index was 0.8036 [95% CI, 0.7558-0.8515]. This study presents a nomogram that incorporates Dmax, D1cc, T stage, and concurrent chemotherapy, which may be conveniently used to facilitate the individualized prediction of TLI in patients with nasopharyngeal carcinoma.

Comparing Different Normal Tissue Complication Probability (NTCP) Models of Radiation-Induced Temporal Lobe Injury after Intensity-Modulated Radiation Therapy for Nasopharyngeal Carcinoma

To compare different normal tissue complication probability (NTCP) models to predict the incidence of radiation-induced temporal lobe injury (TLI) for nasopharyngeal carcinoma (NPC) patients who received intensity-modulated radiotherapy (IMRT). Data in 351 patients with nasopharyngeal carcinoma treated with IMRT were reviewed retrospectively. A large number of dose-volume effect models has been fitted to data on the occurrence of TLI after IMRT for NPC. The following models were considered: (1) Lyman model and (2) logit-formula with dose-volume histogram (DVH) reduced to generalized equivalent uniform dose (EUD), (3) serial reconstruction unit (RU) model, (4) Poisson-EUD model, and (5) mean dose model. The fitting was done with the maximum likelihood method. Twenty-nine of 351 patients (8.3%) developed TLI; 21 patients had unilateral TLI, and eight had bilateral TLI. As assessed qualitatively and quantitatively, the Lyman-EUD model fitted the data very well [a=∞, m=0.07 (0.011, 5.0), TD50=81.0 Gy (68.5,119.5Gy)], and was superior to the remaining models. Therefore, it demonstrates that temporal lobe is a serial organ. Temporal lobe mean dose did not correlate to TLI. Our study clearly confirms a dose effect for TLI after IMRT for NPC. The Lyman model was the most accurate predictor for the incidence of TLI. It suggests that delivery of a high dose of radiation to a small volume of the temporal lobe is unsafe.