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

Dosimetric parameters predict radiation-induced temporal lobe necrosis in nasopharyngeal carcinoma patients: A systematic review and meta-analysis

This systematic review examines the role of dosimetric parameters in predicting temporal lobe necrosis (TLN) risk in nasopharyngeal carcinoma (NPC) patients treated with three-dimensional conformal RT (3D-CRT), intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT). TLN is a serious late complication that can adversely affect the quality of life of NPC patients. Understanding the relationship between dosimetric parameters and TLN can guide treatment planning and minimize radiation-related complications. A comprehensive search identified relevant studies published up to July 2023. Studies reporting on dosimetric parameters and TLN in NPC patients undergoing 3D-CRT, IMRT, and VMAT were included. TLN incidence, follow-up duration, and correlation with dosimetric parameters of the temporal lobe were analyzed. The review included 30 studies with median follow-up durations ranging from 28 to 110 months. The crude incidence of TLN varied from 2.3 % to 47.3 % and the average crude incidence of TLN is approximately 14 %. Dmax and D1cc emerged as potential predictors of TLN in 3D-CRT and IMRT-treated NPC patients. Threshold values of >72 Gy for Dmax and >62 Gy for D1cc were associated with increased TLN risk. However, other factors should also be considered, including host characteristics, tumor-specific features and therapeutic factors. In conclusion, this systematic review highlights the significance of dosimetric parameters, particularly Dmax and D1cc, in predicting TLN risk in NPC patients undergoing 3D-CRT, IMRT, and VMAT. The findings provide valuable insights that can help in developing optimal treatment planning strategies and contribute to the development of clinical guidelines in this field.

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

Background

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.

Methods

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).

Results

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.

Conclusions

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.

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

Objective

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).

Methods

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.

Results

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.

Conclusion

The radiomics–clinics combined model developed in this study showed good performance for predicting RTLI in patients with NPC.

Radiotherapy-Related Neurologic Complications in Patients with Nasopharyngeal Carcinoma: A Multicenter Epidemiologic Study in Southern China

BACKGROUND

We aim at describing the incidence, potential predisposing factors, and progression of major radiotherapy-related neurologic complications (RRNC) in nasopharyngeal carcinoma (NPC)-endemic regions, especially southern China.

METHODS

We performed a multicenter longitudinal retrospective study with clinical follow-ups in 22,302 patients with post-radiotherapy NPC between January 2003 and June 2017 covering three major residential areas. Epidemiology, potential predisposing/protective factors, clinicopathologic progression, and survival conditions of each RRNC were separately recorded and analyzed on the basis of their related clinical, radiologic, and laboratory parameters.

RESULTS

949 new cases of RRNCs occurred among the 22,302 patients with post-radiotherapy NPC during 101,714 person years' follow-up, which is equal to an incidence density rate of 9.3 new cases per 1000 person year. Radiation-induced cranial nerve palsy showed the highest incidence (2.68%, 597/22,302) with the earliest onset (median latency, 4.45 years) as well. Patients benefited from intensity-modulated radiotherapy (IMRT) over conventional radiotherapy (CRT) in both overall survival (median survival 13.2 years for IMRT vs. 8.3 years for CRT) and RRNC-free survival (except for epilepsy and cranial nerve palsy). Causes of death varied substantially between patients with or without RRNCs.

CONCLUSIONS

Our study indicates a non-negligible incidence of RRNC spectrum in southern China in the past ten years. IMRT is one of the most significant protectors against development and progression of RRNCs.

IMPACT

Our findings support the hypothesis that patients with NPC with preexisting predispositions would receive long-term benefits from IMRT and other dose-related modulations (like hyperfractionation and dose conformation).

Potential Defects and Improvements of Equivalent Uniform Dose Prediction Model Based on the Analysis of Radiation-Induced Brain Injury

PURPOSE

To study the impact of dose distribution on volume-effect parameter and predictive ability of equivalent uniform dose (EUD) model, and to explore the improvements.

METHODS AND MATERIALS

The brains of 103 nasopharyngeal carcinoma patients treated with IMRT were segmented according to dose distribution (brain and left/right half-brain for similar distributions but different sizes; V _D with different D for different distributions). Predictive ability of EUD_{V D} (EUD of V _D ) for radiation-induced brain injury was assessed by receiver operating characteristics curve (ROC) and area under the curve (AUC). The optimal volume-effect parameter a of EUD was selected when AUC was maximal (mAUC). Correlations between mAUC, a and D were analyzed by Pearson correlation analysis. Both mAUC and a in brain and half-brain were compared by using paired samples t-tests. The optimal D _V and V _D points were selected for a simple comparison.

RESULTS

The mAUC of brain/half-brain EUD was 0.819/0.821 and the optimal a value was 21.5/22. When D increased, mAUC of EUD_{V D} increased, while a decreased. The mAUC reached the maximum value when D was 50-55 Gy, and a was always 1 when D ≥55 Gy. The difference of mAUC/a between brain and half-brain was not significant. If a was in range of 1 to 22, AUC of brain/half-brain EUD_{V55 Gy} (0.857-0.830/0.845-0.830) was always larger than that of brain/half-brain EUD (0.681-0.819/0.691-0.821). The AUCs of optimal dose/volume points were 0.801 (brain D_{2.5 cc}), 0.823 (brain V_{70 Gy}), 0.818 (half-brain D_{1 cc}), and 0.827 (half-brain V_{69 Gy}), respectively. Mean dose (equal to EUD_{V D} with a = 1) of high-dose volume (V_{50 Gy}-V_{60 Gy}) was superior to traditional EUD and dose/volume points.

CONCLUSION

Volume-effect parameter of EUD is variable and related to dose distribution. EUD with large low-dose volume may not be better than simple dose/volume points. Critical-dose-volume EUD could improve the predictive ability and has an invariant volume-effect parameter. Mean dose may be the case in which critical-dose-volume EUD has the best predictive ability.

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.