Applications of magnetic resonance spectroscopy in diagnosis of neurodegenerative diseases: A systematic review.

Background: Magnetic resonance spectroscopy (MRS) is an imaging technique used to measure metabolic changes in the tissue. Due to the lack of evidence, MRS is not a priority in diagnosing neurodegenerative diseases because it is a relatively specialized technique that requires specialized equipment and expertise to perform and interpret. This systematic review aimed to present a comprehensive collection of MRS results in the most common neurodegenerative diseases. Methods: A systematic search of four electronic databases (PubMed, Scopus, Web of Science, and ScienceDirect) was conducted for studies published from 2017 to 2022. Articles that provided specific biomarker levels were selected, and studies that assessed the diseases via treatment, featured MRS applying nuclei other than 1H, or compared different animal models were excluded. Results: A total of 25 articles, plus 3 articles for extra information in the introduction, were included in this review. Six of the most common neurodegenerative diseases, i.e., Alzheimer's and Parkinson's disease, Huntington chorea, ataxia, multiple sclerosis (MS), multiple system atrophy (MSA), and progressive supranuclear palsy (PSP) were examined via MRS. The changes and ratios of N-acetylaspartate (NAA) could be seen in all of these disorders, which could lead to early diagnosis. However, there are other biomarkers, such as Cr and Chon, which can give convincing results. Discussion: This observational study is the first synthesis of the latest evidence proving metabolic changes during neurodegenerative diseases using MRS as a diagnosis method. The findings indicate decreased N-acetylaspartate (NAA) and NAA/Cr ratios in Alzheimer's disease (AD), Parkinson's disease (PD), ataxias, and MS, reflecting neuronal loss or dysfunction. Increased choline and myo-inositol were noted in some studies, suggesting cell membrane turnover and neuroinflammation. Findings were less consistent for other metabolites like glutamate and gamma-aminobutyric acid. However, there were limitations due to the lack of studies on the same volumes of interest (VOIs) and the small number of participants.

Predicting severe radiation-induced oral mucositis in head and neck cancer patients using integrated baseline CT radiomic, dosimetry, and clinical features: A machine learning approach.

Purpose: To establish the early prediction models of radiation-induced oral mucositis (RIOM) based on baseline CT-based radiomic features (RFs), dosimetric data, and clinical features by machine learning models for head and neck cancer (HNC) patients. Methods: In this single-center prospective study, 49 HNCs treated with curative intensity modulated radiotherapy (IMRT) were enrolled. Baseline CT images (i.e., CT simulation), dosimetric, and clinical features were collected. RIOM was assessed using CTCAE v.5.0. RFs were extracted from manually-contoured oral mucosa structures. Minimum-redundancy-maximum-relevance (mRMR) method was applied to select the most informative radiomics, dosimetric, and clinical features. Then, binary prediction models were constructed for predicting acute RIOM based on the top mRMR-ranked radiomics, dosimetric, and clinical features alone or in combination, using random forest classifier algorithm. The predictive performance of models was assessed using the area under the receiver operating curve (AUC), accuracy, weighted-average based sensitivity, precision, and F1-measure. Results: Among extracted features, the top 10 RFs, the top 5 dose-volume features, and the top 5 clinical features were selected using mRMR method. The model exploiting the integrated features (10-radiomics + 5-dosimetric + 5-clinical) achieved the best prediction with AUC, accuracy, sensitivity, precision, and F1-measure values of 91.7 %, 90.0 %, 83.0 % 100.0 %, and 91.0 %, respectively. The model developed using baseline CT RFs alone provided the best performance compared to dose-volume features or clinical features alone, with an AUC of 87.0 %. Conclusion: Our results suggest that the integration of baseline CT radiomic features with dosimetric and clinical features showed promising potential to improve the performance of machine learning models in early prediction of RIOM. The ultimate goal is to personalize radiotherapy for HNC patients.

A machine learning approach for prediction of auditory brain stem response in patients after head-and-neck radiation therapy.

Objective: The present study aimed to assess machine learning (ML) models according to radiomic features to predict ototoxicity using auditory brain stem responses (ABRs) in patients with radiation therapy (RT) for head-and-neck cancers. Materials and Methods: The ABR test was performed on 50 patients having head-and-neck RT. Radiomic features were extracted from the brain stem in computed tomography images to generate a radiomic signature. Moreover, accuracy, sensitivity, specificity, the area under the curve, and mean cross-validation were used to evaluate six different ML models. Results: Out of 50 patients, 21 participants experienced ototoxicity. Furthermore, 140 radiomic features were extracted from the segmented area. Among the six ML models, the Random Forest method with 77% accuracy provided the best result. Conclusion: According to the ML approach, we showed the relatively high prediction power of the radiomic features in radiation-induced ototoxicity. To better predict the outcomes, future studies on a larger number of participants are recommended.

Prediction of chronic kidney disease in abdominal cancers radiation therapy using the functional assays of normal tissue complication probability models.

Aim: The purpose of this study is to predict chronic kidney disease (CKD) in the radiotherapy of abdominal cancers by evaluating clinical and functional assays of normal tissue complication probability (NTCP) models. Materials and Methods: Radiation renal damage was analyzed in 50 patients with abdominal cancers 12 months after radiotherapy through a clinical estimated glomerular filtration rate (eGFR). According to the common terminology criteria for the scoring system of adverse events, Grade 2 CKD (eGFR ≤30-59 ml/min/1.73 m2) was considered as the radiation therapy endpoint. Modeling and parameter estimation of NTCP models were performed for the Lyman-equivalent uniform dose (EUD), the logit-EUD critical volume (CV), the relative seriality, and the mean dose model. Results: The confidence interval of the fitted parameters was 95%. The parameter value of D50 was obtained 22-38 Gy, and the n and s parameters were equivalent to 0.006 -3 and 1, respectively. According to the Akaike's information criterion, the mean dose model predicts radiation-induced CKD more accurately than the other models. Conclusion: Although the renal medulla consists of many nephrons arranged in parallel, each nephron has a seriality architecture as renal functional subunits. Therefore, based on this principle and modeling results in this study, the whole kidney organs may have a serial-parallel combination or a secret architecture.

Radiation Protection Evaluation of Medical X-ray Imaging Centers in Qazvin, Iran.

ABSTRACT: Given the potential risks of x rays in imaging, it is necessary to evaluate the radiation safety of medical x-ray imaging rooms and radiation information of radiographers. A descriptive study has been carried out in Qazvin, Iran, in the form of a questionnaire answered by radiography technologists in private and public imaging centers to assess the level of knowledge, attitude, and performance about x-ray exposure and safety against it. Afterward, the radiation doses of public areas of the radiography centers were measured by a survey meter. A checklist was also completed, containing items about availability of ventilation facilities in x-ray rooms, radiation warning signs, radiation work schedules, documents of periodic blood test monitoring, exposure history of radiographers by film badge, and documents of the quality control of x-ray devices. The data analysis was performed by SPSS 16 using descriptive study, t-test, ANOVA, and Pearson's correlation coefficient test. The mean attitude and knowledge scores were 6.65 ± 1.63 out of 9 and 4.82 ± 2.06 out of 15, respectively. Sex, age, and workplace had no significant effect on the radiographers' knowledge and attitude, while occupational experience had a negative relationship, and level of education had a significant direct association with knowledge of the participants (p-value<0.05). The performance of the staff was somewhat good. Almost all x-ray imaging centers had good radiation protection conditions. The annual quality control of x-ray imaging devices was ensured. The radiation protection awareness among the medical radiographers in Qazvin city needs to be improved, especially among the experienced staff.

Radiomics analysis on CT images for prediction of radiation-induced kidney damage by machine learning models.

INTRODUCTION: We aimed to assess the power of radiomic features based on computed tomography to predict risk of chronic kidney disease in patients undergoing radiation therapy of abdominal cancers. METHODS: 50 patients were evaluated for chronic kidney disease 12 months after completion of abdominal radiation therapy. At the first step, the region of interest was automatically extracted using deep learning models in computed tomography images. Afterward, a combination of radiomic and clinical features was extracted from the region of interest to build a radiomic signature. Finally, six popular classifiers, including Bernoulli Naive Bayes, Decision Tree, Gradient Boosting Decision Trees, K-Nearest Neighbor, Random Forest, and Support Vector Machine, were used to predict chronic kidney disease. Evaluation criteria were as follows: accuracy, sensitivity, specificity, and area under the ROC curve. RESULTS: Most of the patients (58%) experienced chronic kidney disease. A total of 140 radiomic features were extracted from the segmented area. Among the six classifiers, Random Forest performed best with the accuracy and AUC of 94% and 0.99, respectively. CONCLUSION: Based on the quantitative results, we showed that a combination of radiomic and clinical features could predict chronic kidney radiation toxicities. The effect of factors such as renal radiation dose, irradiated renal volume, and urine volume 24-h on CKD was proved in this study.

Comparison of radiation and chemoradiation-induced sensorineural hearing loss in head and neck cancer patients.

AIM: The purpose of this study was to assess and compare the incidence and severity of sensorineural hearing loss (SNHL) in head-and-neck patients undergoing radiotherapy (RT) and concurrent cisplatin-based chemoradiotherapy (CRT). MATERIALS AND METHODS: Pure tone audiometry (PTA) was performed at 0.25-12 kHz on 35 RT and 25 CRT patients after 12-month followed up. The hearing loss was evaluated according to the Common Terminology Criteria for Adverse Events (CTCAE) criteria. RESULTS: SNHL increased to 84% in patients who had received CRT, compared with 26% increasing in patients who had treated with RT. There was an increased risk of SNHL at all frequencies for ears received a cochlear mean dose >50 Gy in RT group, compared to those receiving cochlear mean dose >30 Gy in CRT group. SNHL was more severe at higher frequencies in both patient groups. CONCLUSION: Characteristic of radiation-induced SNHL is different from CRT-induced SNHL, especially in threshold radiation dose and PTA frequency.

CT imaging markers to improve radiation toxicity prediction in prostate cancer radiotherapy by stacking regression algorithm.

PURPOSE: Radiomic features, clinical and dosimetric factors have the potential to predict radiation-induced toxicity. The aim of this study was to develop prediction models of radiotherapy-induced toxicities in prostate cancer patients based on computed tomography (CT) radiomics, clinical and dosimetric parameters. METHODS: In this prospective study, prostate cancer patients were included, and radiotherapy-induced urinary and gastrointestinal (GI) toxicities were assessed by Common Terminology Criteria for adverse events. For each patient, clinical and dose volume parameters were obtained. Imaging features were extracted from pre-treatment rectal and bladder wall CT scan of patients. Stacking algorithm and elastic net penalized logistic regression were used in order to feature selection and prediction, simultaneously. The models were fitted by imaging (radiomics model) and clinical/dosimetric (clinical model) features alone and in combinations (clinical-radiomics model). Goodness of fit of the models and performance of classifications were assessed using Hosmer and Lemeshow test, - 2log (likelihood) and area under curve (AUC) of the receiver operator characteristic. RESULTS: Sixty-four prostate cancer patients were studied, and 33 and 52 patients developed ≥ grade 1 GI and urinary toxicities, respectively. In GI modeling, the AUC for clinical, radiomics and clinical-radiomics models was 0.66, 0.71 and 0.65, respectively. To predict urinary toxicity, the AUC for radiomics, clinical and clinical-radiomics models was 0.71, 0.67 and 0.77, respectively. CONCLUSIONS: We have shown that CT imaging features could predict radiation toxicities and combination of imaging and clinical/dosimetric features may enhance the predictive performance of radiotoxicity modeling.

Cochlea CT radiomics predicts chemoradiotherapy induced sensorineural hearing loss in head and neck cancer patients: A machine learning and multi-variable modelling study.

OBJECTIVES: Immediately or after head-and-neck (H&N) cancer chemoradiotherapy (CRT), patients may undergone significant sensorineural hearing loss (SNHL) which could affect their quality of life. Radiomic feature analysis is proposed to predict SNHL induced by CRT. MATERIAL AND METHODS: 490 image features of 94 cochlea from 47 patients treated with three dimensional conformal RT (3DCRT) for different H&N cancers were extracted from CT images. Different machine learning (ML) algorithms and also least absolute shrinkage and selection operator (LASSO) penalized logistic regression were implemented on radiomic features for feature selection, classification and prediction. Also, LASSO penalized logistic model was used for outcome modelling. RESULTS: The predictive power of ten ML methods was more than 70% (in accuracy, precision and area under the curve of receiver operating characteristic curve (AUC)). According to the LASSO penalized logistic modelling, 10 of the 490 radiomic features selected as the associated features with SNHL status. All of the 10 features were statistically associated with SNHL (all of adjusted P-values < .001). CONCLUSION: CT radiomic analysis proposed in this study, could help in the prediction of hearing loss induced by chemoradiation. Our study also, demonstrates that combination of radiomic features with clinical and dosimetric variables can model radiotherapy outcome such as SNHL.

Normal tissue complication probability modeling of radiation-induced sensorineural hearing loss after head-and-neck radiation therapy.

PURPOSE: The aim of this study was to generate the dose-response curves by six normal tissue complication probability (NTCP) models and ranking the models for prediction of radiation induced sensorineural hearing loss (SNHL) caused by head and neck radiation therapy (RT). MATERIALS AND METHODS: Pure tone audiometry (PTA) was performed on 70 ears of patients for 12 months after the completion of RT. The SNHL was defined as a threshold shift ≤15 dB at two contiguous frequencies according to the common toxicity criteria for adverse events scoring system. The models evaluated were: Lyman and Logit; Mean Dose; relative seriality (RS); Individual critical volume (CV); and population CV models. Maximum likelihood analysis was used to fit the models to experimental data. The appropriateness of the fit was determined by the two-sample Kolmogorov-Smirnov test. Ranking of the models was made according to Akaike's information criterion. RESULTS: The dose of 50% complication rate (D50) was 51-60 Gy. Three of the examined models fitted well with clinical data in a 95% confidence interval. The RS model was ranked as the best model of prediction for radiation induced SNHL. CONCLUSIONS: Cochlea shows a different behavior against different NTCP models; it may be due to its small size.

Short-term cohort study on sensorineural hearing changes in head and neck radiotherapy.

The aim of this study was to investigate the risk of sensorineural hearing loss (SNHL) and the relationship between SNHL and radiation dose to the cochlea and frequency range of hearing loss in patients with head and neck cancer. Pure tone audiometry at 250-12,000 Hz was performed on 29 patients diagnosed with head and neck tumours who were treated with 3-dimensional conformal radiation therapy and followed up for 6 months. Paired t test indicated that the mean air conduction threshold before and after radiotherapy was significantly different (paired t test, p < 0.001). SNHL was observed in 15 patients (51 %) according to CTCAE. SNHL increased to 77 % in patients who had received at least five concurrent cisplatin cycles. There was an increased risk of SNHL for ears receiving a mean dose of 5000 cGy compared to those receiving <5000 cGy. SNHL was more severe at higher frequencies of pure tone audiometry in patients with cisplatin-based chemoradiation. The ototoxicity effect of radiation and cisplatin must be considered in the treatment of head and neck tumours. Increasing the dose of cisplatin, radiation dose of cochlea and follow-up interval time may result in increasing severity and frequency of hearing loss incidences. However, characteristic of radiation-induced SNHL seems to be different from chemoradiation-induced SNHL.