Temporal Lobe Injury Research Articles

Combined Petrosal Intertentorial Approach: A Cadaveric Study of Comparison With the Standard Combined Petrosectomy.

The combined petrosal intertentorial approach (CPIA) has been proposed as an alternative to standard combined petrosal approach (SCPA). CPIA has been designed to maintain integrity of the temporal dura with a view to reduce temporal lobe morbidity and venous complications. This study has been designed to perform a quantitative comparison between these approaches. Five human specimens were used for this study. CPIA was performed on one side and SCPA on the opposite side. The area of exposure (petroclival and brainstem), surgical freedom, and angles of attack to a predefined target were measured and compared. SCPA provided a significantly larger petroclival area of exposure (6.81 ± 0.60 cm2) over the CPIA (5.59 ± 0.59 cm2), P = .012. The area of brainstem exposed with SCPA was greater than with CPIA (7.17 ± 0.84 vs 5.63 ± 0.72, P = .014). The area of surgical freedom was greater in SCPA rather than in CPIA (8.59 ± 0.55 and 7.13 ± 0.96 cm2, respectively, P = .019). There was no significative difference between CPIA and SCPA in the vertical angles of attack for the Meckel cave, Dorello canal, and root entry zone of cranial nerve VII. Conversely, the horizontal angles of attack permitted by the CPIA were significantly smaller for the Meckel cave (52.36° ± 5.01° vs 64.4° ± 5.3°, P = .006) and root entry zone of cranial nerve VII (30.7° ± 4.4° vs 40.1° ± 6.2°, P = .025). CPIA is associated with a reduction in terms of the area of surgical freedom (22%), skull base (18%), brainstem exposure (17%), and horizontal angles of attack (18%-23%) when compared with SCPA. This loss in terms of exposure is counterbalanced by the advantage of keeping the temporal lobe covered by an extra layer of meningeal tissue, thus possibly reducing the risk of temporal lobe injury and venous infarction. These results need to be validated with adequate clinical experience.

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.

Causes of missed MRI diagnosis of radiotherapy-induced temporal lobe injury in nasopharyngeal carcinoma.

Radiotherapy is the primary treatment for nasopharyngeal carcinoma, but it frequently leads to radiotherapy-induced temporal lobe injury (RTLI). Magnetic resonance imaging (MRI) is the main diagnostic method for RTLI after radiotherapy for nasopharyngeal carcinoma, but it is prone to missed diagnoses. This study aims to investigate the causes of missed diagnoses of RTLI in nasopharyngeal carcinoma patients undergoing MRI after radiotherapy. Clinical and MRI data from nasopharyngeal carcinoma patients diagnosed and treated with radiotherapy at Xiangya Hospital of Central South University, from January 2010 to April 2021, were collected. Two radiologists reviewed all head and neck MRIs (including nasopharyngeal and brain MRIs) before and after radiotherapy of identify cases of late delayed response-type RTLI for the first time. If the original diagnosis of the initial RTLI in nasopharyngeal carcinoma patients did not report temporal lobe lesions, it was defined as a missed diagnosis. The first diagnosis of RTLI cases was divided into a missed diagnosis group and a non-missed diagnosis group. Clinical and imaging data were compared between the 2 groups, and multivariate logistic regression analysis was used to identify independent risk factors for MRI missed diagnoses of initial RTLI. A total of 187 nasopharyngeal carcinoma with post-radiotherapy RTLI were included. The original diagnostic reports missed 120 cases and accurately diagnosed 67 cases, with an initial RTLI diagnosis accuracy rate of 35.8% and a missed diagnosis rate of 64.2%. There were statistically significant differences between the missed diagnosis group and the non-missed diagnosis group in terms of lesion size, location, presence of contralateral temporal lobe lesions, white matter high signal, cystic degeneration, hemorrhage, fluid attenuated inversion recovery (FLAIR), and examination site (all P<0.05). Multivariate logistic regression analysis showed that lesions ≤25 mm, non-enhancing lesions, lesions without cystic degeneration or hemorrhage, lesions located only in the medial temporal lobe, and MRI examination only of the nasopharynx were independent risk factors for missed MRI diagnosis of initial RTLI (all P<0.05). The missed diagnosis of initial RTLI on MRI is mainly related to lesion size and location, imaging characteristics, and MRI examination site.

A deep learning-based method for the prediction of temporal lobe injury in patients with nasopharyngeal carcinoma

PurposeTo establish a deep learning-based model to predict radiotherapy-induced temporal lobe injury (TLI). Materials and methodsSpatial features of dose distribution within the temporal lobe were extracted using both the three-dimensional convolution (C3D) network and the dosiomics method. The Minimal Redundancy-Maximal-Relevance (mRMR) method was employed to rank the extracted features and select the most relevant ones. Four machine learning (ML) classifiers, including logistic regression (LR), k-nearest neighbors (kNN), support vector machines (SVM) and random forest (RF), were used to establish prediction models. Nested sampling and hyperparameter tuning methods were applied to train and validate the prediction models. For comparison, a prediction model base on the conventional D0.5cc of the temporal lobe obtained from dose volume (DV) histogram was established. The area under the receiver operating characteristic (ROC) curve (AUC) was utilized to compare the predictive performance of the different models. ResultsA total of 127 nasopharyngeal carcinoma (NPC) patients were included in the study. In the model based on C3D deep learning features, the highest AUC value of 0.843 was achieved with 5 features. For the dosiomics features model, the highest AUC value of 0.715 was attained with 1 feature. Both of these models demonstrated superior performance compared to the prediction model based on DV parameters, which yielded an AUC of 0.695. ConclusionThe prediction model utilizing C3D deep learning features outperformed models based on dosiomics features or traditional parameters in predicting the onset of TLI. This approach holds promise for predicting radiation-induced toxicities and guide individualized radiotherapy.

Deletion of concurrent chemotherapy on the basis of sequential chemoradiotherapy for non-metastatic stage T4 nasopharyngeal carcinoma in IMRT era.

Intensity-modulated radiotherapy (IMRT) combined with concurrent chemotherapy is deemed as the mainstay treatment in locoregionally advanced nasopharyngeal carcinoma (NPC). Nevertheless, the tolerance of severe acute toxicity of concurrent chemotherapy was unsatisfied. In addition, T4 is the predicting factor of poor prognosis for NPC patients. In this retrospective analysis, the long-term outcomes IMRT combined by induction chemotherapy deleting concurrent chemotherapy with or without adjuvant chemotherapy for T4 non-metastatic NPC were analyzed. From January 2005 to November 2016, a total of 145 biopsy-proven non-metastatic T4 NPC was treated with IMRT combined by induction chemotherapy with or without adjuvant chemotherapy. The survival and side effects of the patients were analyzed. Median follow-up time was 74 months (ranges, 8-186 months). 10.0%, 61.3%, 27.3%, and 1.3% developed grade 1, 2, 3, and 4 mucositis during IMRT, respectively. 5.5% and 2.0% patients experienced grade 1 and 2 nausea and vomiting; no patients developed grade 3 or 4 nausea and vomiting. Of 145 patients enrolled, 5-year and 10-year overall survival(OS) rates were 73.7% and 53.9%, local progression-free survival(LPFS) rates were 86.1% and 71.6%, regional progression-free survival(RPFS) rates were 96.7% and 92.8%, distant metastasis-free survival (DMFS) rates were 86.7%, 78.2%, respectively. At the last follow-up, five patients developed cranial nerve injury, one patient developed mandibular bone necrosis, four patients developed temporal lobe injury, four patients developed nasopharyngeal massive hemorrhage (three cases after recurrence and one case without recurrence), and five patients developed second primary tumor. The survival outcomes of treating T4 NPC IMRT combined by induction chemotherapy deleting concurrent chemotherapy with or without adjuvant chemotherapy are encouraging. Moreover, mucosal reaction, nausea, and vomiting reaction were reduced during IMRT.

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.

Normal tissue complication probability model of temporal lobe injury following re-irradiation of IMRT for local recurrent nasopharyngeal carcinoma.

We tried to establish the normal tissue complication probability (NTCP) model of temporal lobe injury of recurrent nasopharyngeal carcinoma (NPC) patients after two courses of intensity modulated radiotherapy (IMRT) to provide more reliable dose-volume data reference to set the temporal lobe tolerance dose for recurrent NPC patients in the future. Recurrent NPC patients were randomly divided into training data set and validation data set in a ratio of 2:1, All the temporal lobes (TLs) were re-contoured as R/L structures and named separately in the MIM system. The dose distribution of the initial IMRT plan was deformed into the second course planning CT via MIM software to get the deformed dose. Equivalent dose of TLs in 2Gy fractions was calculated via linear quadratic model, using an α/β=3 for temporal lobes. NTCP model that correlated the irradiated volume of the temporal lobe and? the clinical variables were evaluated in a multivariate prediction model using AUC analysis. From Jan. 2010 to Dec. 2020, 78 patients were enrolled into our study. Among which 26 (33.3%) developed TLI. The most important factors affecting TLI was the sum-dose d1.5cc of TL, while the possible clinical factors did not reach statistically significant differences in multivariate analysis. According to NTCP model, the TD5 and TD50 EQD2 dose of sum-dose d1.5cc were 65.26Gy (46.72-80.69Gy) and 125.25Gy (89.51-152.18Gy), respectively. For the accumulated EQD2 dose, the area under ROC shadow was 0.8702 (0.7577-0.9828) in model validation, p<0.001. In this study, a NTCP model of temporal lobe injury after a second course of IMRT for recurrent nasopharyngeal carcinoma was established. TD5 and TD50 doses of temporal lobe injury after re-RT were obtained according to the model, and the model was verified by validation set data.

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

To construct radiomics models based on MRI at different time points for the early prediction of cystic brain radionecrosis (CBRN) for nasopharyngeal carcinoma (NPC). A 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). The 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. The 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.

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.

Demystifying the Role of Radiation Dose Bath in Brain Injury Using Machine Learning.

It is well known that high-dose radiation results in brain injury, the long-term effects of low-dose radiation, however, is uncertain. The purpose of this study was to investigate the role of low-dose bath on MRI changes, on both clinical and micro-architectural levels, using machine learning and computational methods. Six hundreds and fifty-six temporal lobe and 45 whole brain DVHs from patients treated for nasopharyngeal cancer (NPC) with IMRT or proton were included in this study. Temporal lobe injury (TLI) was defined as development of new T1 enhancement on MRI with or without surrounding T2 edema. Patients were divided randomly into train (50%) and test (50%) sets. Accuracy and AUC were used to evaluate the model performance. Minimum redundancy maximum relevance (MRMR) or SHAP algorithms was employed for feature selection. Support vector machine or random forest was used for classification. Automated cortical region segmentation using FreeSurfer v6 was performed in 33 patients with a minimum follow-up of 4 years. Architectural and biological MRI changes were determined in 34 different brain regions for each individual patient. The top-ranked temporal lobe features predicting TLI were V66/V38 for IMRT patients and V10 for proton patients with an AUC of 0.95 and 0.74, respectively. For whole brain, the top features were V16 and V13 with an AUC of 0.70. The rates of TLI at 5 years for V10-20(whole brain) ≥ 180cc and V10-20(whole brain) < 180 were 39.5% and 6.2%, respectively (HR = 5.5, 95% CI 1.4-22.0, p = 0.02). There were global changes in gray matter thickness, with most pronounced changes occurred in parietal lobe (-4.79%, p = 0.007) and occipital lobe (-5.68%, p = 0.03). Similarly, there were diffuse changes in white matter and subcortical volume. After radiation, the frontal lobe increased by 17.5% (p = 0.04), lateral ventricle 41% (p = 0.03), and choroid plexus 34.3% (p = 0.03). Low-dose radiation bath is associated with increased risk of temporal lobe injury and global disruption in brain architecture in NPC survivors.

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

The Lyman Normal Tissue Complication Probability Model and Risk Prediction for Temporal Lobe Injury after Re-Irradiation in Patients with Recurrent Nasopharyngeal Carcinoma

The risk of temporal lobe injury (TLI) in recurrent nasopharyngeal carcinoma (rNPC) patients with intensity-modulated radiation therapy (IMRT) is high. We aimed to construct the normal tissue complication probability (NTCP) model for TLI of rNPC and establish a risk predictive model. We retrospectively analyzed 103 patients with rNPC who had received two courses of IMRT in our institution. The 206 temporal lobes (TLs) of these patients were randomly divided into a training (n = 144) and validation group (n = 62). We determined the mean value of the following parameters to construct the Lyman NTCP model: TD50(1) (the dose with a 50% probability of complications to an organ when all volumes are irradiated), m [steepness of the dose-response at TD50(1)], and n (the parameter related to volume effect). The most predictive dosimetric parameter and clinical variables were integrated in Cox proportional hazards models. A nomogram was developed for predicting risk of TLs. The parameters of the fitted NTCP model were TD50(1) = 107.84 Gy (95% confidence interval (CI), [97.15, 118.54]), m = 0.16 (95% CI, [0.14, 0.19]), and n = 0.04 (95% CI, [0.01, 0.06]). The cumulative dose delivered to 0.1 cm3 of temporal lobe volume (D0.1cc-c) was the most predictive dosimetric parameter for TLI. The Kaplan-Meier curves showed a significant difference in 2-year TLI-free survival among different risk groups according to the total score of nomograms. The TD50(1) of TLI in patients with rNPC is 107.84 Gy in Lyman NTCP model. The nomogram model can accurately predict the risk of TLI for individual.

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.

High-Dose Levetiracetam: A Novel Post-Arrest Anti-Arrhythmic Medication? (P8-7.005)

<h3>Objective:</h3> To describe a case of post-cardiac arrest refractory myoclonus and ventricular tachycardia responsive to high-dose levetiracetam. <h3>Background:</h3> Levetiracetam is a first-line agent for anoxic myoclonus. However, levetiracetam is not known to have anti-arrhythmia properties, particularly in persistent arrhythmias following cardiac arrest. <h3>Design/Methods: Case Description:</h3> A 17-month-old female presented in ventricular fibrillation arrest thought to be secondary to methamphetamine toxicity and a mitochondrial genetic mutation (discovered and confirmed later in her hospitalization). Return of spontaneous circulation was achieved after prolonged resuscitation. Seven days later, she demonstrated frequent (&gt;40/minute), large-amplitude, non-stereotyped, brief movements involing the neck and all extremities consistent with myoclonus. Elecetroencephalogram (EEG) determined movements to be non-epileptic. Magnetic resonance imaging (MRI) on day 8 post-arrest was without evidence of diffusion restriction or T2-flair abnormalities and was thought to represent pseudonormalization in setting of hypoxic injury. Her movements were refractory to: ketamine, morphine, midazolam, dexmedetomidine, and cisatracurium. She developed frequent ectopy and intermittent ventricular tachycardia refractory to esmolol, sotalol, and lidocaine. After receiving 60 milligrams/kilogram (mg/kg) of intravenous levetiracetam, both the myoclonus and arrhythmia temporarily normalized, but returned on maintenance dosing of 30 mg/kg twice a day (BID). After increasing the maintenance dosing to 50 mg/kg BID, her myoclonus and arrhythmia both improved again with sustained benefit with higher dosing. <h3>Results:</h3> NA <h3>Conclusions:</h3> A sustained improvement in post-cardiac arrest refractory myoclonus and tachyarrhythmia was achieved with high-dose levetiracetam. Peripheral overactive N-type calcium currents from sympathetic hyperactivation have been implicated in ventricular arrhythmogenesis. Hypoxic injury to the brainstem could cause a brainstem reticular reflex myoclonus as well as damage to the autonomic relay nuclei. Temporal lobe injury could cause subcortical myoclonus as well as increased sympathetic outflow. Levetiracetam’s blockade of calcium currents at any of these sites could be an explanation for its beneficial effect on both myoclonus and tachyarrhythmia in this patient. <b>Disclosure:</b> Dr. Stulberg has nothing to disclose. Dr. O’Connor has nothing to disclose. Dr. Swartwood has nothing to disclose.

Efficacy of concurrent chemoradiotherapy alone for loco-regionally advanced nasopharyngeal carcinoma: long-term follow-up analysis

BackgroundTo analysis the clinical outcomes of concurrent chemoradiotherapy (CCRT) alone based on 10-year results for loco-regionally advanced nasopharyngeal carcinoma (LANPC), so as to provide evidence for individualized treatment strategy and designing appropriate clinical trial for different risk LANPC patients.MethodsConsecutive patients with stage III-IVa (AJCC/UICC 8th) were enrolled in this study. All patients received radical intensity-modulated radiotherapy (IMRT) and concurrent cisplatin chemotherapy (CDDP). The hazard ratios (HRs) of death risk in patients with T3N0 was used as baseline, relative HRs were calculated by a Cox proportional hazard model to classify different death risk patients. Survival curves for the time-to-event endpoints were analyzed by the Kaplan–Meier method and compared using the log-rank test. All statistical tests were conducted at a two-sided level of significance of 0.05.ResultsA total of 456 eligible patients were included. With 12-year median follow-up, 10-year overall survival (OS) was 76%. 10-year loco-regionally failure-free survival (LR-FFS), distant failure-free survival (D-FFS) and failure-free survival (FFS) were 72%, 73% and 70%, respectively. Based on the relative hazard ratios (HRs) of death risk, LANPC patients were classified into 3 subgroups, low-risk group (T1-2N2 and T3N0-1) contained 244 patients with HR < 2; medium-risk group (T3N2 and T4N0-1) contained 140 patients with HR of 2 – 5; high-risk group (T4N2 and T1-4N3) contained 72 patients with HR > 5. The 10-year OS for patients in low-, medium-, and high-risk group were 86%, 71% and 52%, respectively. Significantly differences of OS rates were found between each of the two groups (low-risk group vs. medium-risk group, P < 0.001; low-risk group vs. high-risk group, P < 0.001; and medium-risk group vs. high-risk group, P = 0.002, respectively). Grade 3–4 late toxicities included deafness/otitis (9%), xerostomia (4%), temporal lobe injury (5%), cranial neuropathy (4%), peripheral neuropathy (2%), soft tissue damage (2%) and trismus (1%).ConclusionsOur classification criteria demonstrated that significant heterogeneity in death risk among TN substages for LANPC patients. IMRT plus CDDP alone maybe suitable for low-risk LANPC (T1-2N2 or T3N0-1), but not for medium- and high-risk patients. These prognostic groupings provide a practicable anatomic foundation to guide individualized treatment and select optimal targeting in the future clinical trials.

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.

Irreversible Sensorineural Hearing Loss in Post Covid-19 Patients-An Institutional Prospective Study.

SARS-CoV-2 virus could damage the hearing by several mechanisms, it could damage the auditory centre in the temporal lobe and direct peripheral injury to the sensory cells of the cochlear due to neurotropism of the virus. A Prospective hospital-based observational study was carried out at a tertiary care centre for a period of 22months from January 2021 to October 2022. A total of 100 patients were selected according to inclusion criteria and exclusion criteria. Pure-tone audiometry (PTA) was done as a screening test; patients were followed up till 6months. Among those patients who had hearing loss, repeat audiological profile (PTA) was done after 1month, 13(65%) patients were found to have normal hearing. After 3months, repeat PTA was done, 18 patients (90%) found to have normal hearing and the remaining 2 patients followed up till 6months and again PTA was done where only 1 patient continued to have sensorineural hearing loss.

Surgical morbidity of the extradural anterior petrosal approach: the Lariboisière experience.

Concerns about the approach-related morbidity of the extradural anterior petrosal approach (EAPA) have been raised, especially regarding temporal lobe and venous injuries, hearing impairment, facial nerve palsy, cerebrospinal fluid fistula, and seizures. There is lack in the literature of studies with detailed analysis of surgical complications. The authors have presented a large series of patients who were treated with EAPA, focusing on complications and their avoidance. The authors carried out a retrospective review of patients who underwent EAPA at their institution between 2012 and 2021. They collected preoperative clinical characteristics, operative reports, operative videos, findings on neuroimaging, histological diagnosis, postoperative course, and clinical status at last follow-up. For pathologies without petrous bone invasion, the amount of petrous apex drilling was calculated and classified as low (< 70% of the volume) or high (≥ 70%). Complications were dichotomized as approach related and resection related. This study included 49 patients: 26 with meningiomas, 10 brainstem cavernomas, 4 chondrosarcomas, 4 chordomas, 2 schwannomas, 1 epidermoid cyst, 1 cholesterol granuloma, and 1 osteoblastoma. The most common approach-related complications were temporal lobe injury (6.1% of patients), seizures (6.1%), pseudomeningocele (6.1%), hearing impairment (4.1%), and dry eye (4.1%). Approach-related complications occurred most commonly in patients with a meningioma (p = 0.02) and Meckel's cave invasion (p = 0.02). Gross-total or near-total resection was correlated with a higher rate of tumor resection-related complications (p = 0.02) but not approach-related complications (p = 0.76). Inferior, lateral, and superior tumoral extension were not correlated with a higher rate of tumor resection-related complications. No correlation was found between high amount of petrous bone drilling and approach- or resection-related complications. EAPA is a challenging approach that deals with critical neurovascular structures and demands specific skills to be safely performed. Contrary to general belief, its approach-related morbidity seems to be acceptable at dedicated skull base centers. Morbidity can be lowered with careful examination of the preoperative neuroradiological workup, appropriate patient selection, and attention to technical details.