Adherence to Healthy Dietary Patterns and Glioma: A Matched Case-Control Study

Recent studies have revealed a putative relationship between diet and glioma development and prognosis, but few studies have examined the association between overall diet and glioma risk. This study, conducted in China, employed a hospital-based case-control approach. The researchers utilized an a priori method based on dietary data to evaluate compliance scores for five healthy dietary patterns (the Mediterranean diet, the Dietary Approaches to Stop Hypertension (DASH) diet, the Mediterranean-DASH diet Intervention for Neurodegenerative Delay (MIND) diet, the Paleolithic diet, and the Planetary Health Diet) in 1012 participants. At the same time, data-driven methods were used to explore the association between dietary patterns and glioma via principal component analysis (PCA). In the multivariate model, adhering to the Mediterranean diet (odds ratio (OR) = 0.29; 95% confidence interval (95% CI): 0.17-0.52), the DASH diet (OR = 0.09; 95% CI: 0.04-0.18), the MIND diet (OR = 0.25; 95% CI: 0.14-0.44), and the Paleolithic diet (OR = 0.13; 95% CI: 0.06-0.25) was associated with a reduced glioma risk. The results of PCA suggested that increasing the intake of plant-based foods and fish and limiting foods rich in carbohydrates, fats, and salts were associated with a reduced glioma risk. There was a substantial nonlinear dose-response association between glioma and the Mediterranean diet score. However, the DASH diet score, the MIND diet score, and the Paleolithic diet score exhibited linear dose-response relationships. Therefore, this study finds that dietary patterns may be an influencing factor for glioma risk.

Automatic image segmentation and online survival prediction model of medulloblastoma based on machine learning

OBJECTIVES

To develop a dynamic nomogram containing radiomics signature and clinical features for estimating the overall survival (OS) of patients with medulloblastoma (MB) and design an automatic image segmentation model to reduce labor and time costs.

METHODS

Data from 217 medulloblastoma (MB) patients over the past 4 years were collected and separated into a training set and a test set. Intraclass correlation coefficient (ICC), random survival forest (RSF), and least absolute shrinkage and selection operator (LASSO) regression methods were employed to select variables in the training set. Univariate and multivariate Cox proportional hazard models, as well as Kaplan-Meier analysis, were utilized to determine the relationship among the radiomics signature, clinical features, and overall survival. A dynamic nomogram was developed. Additionally, a 3D-Unet deep learning model was used to train the automatic tumor delineation model.

RESULTS

Higher Rad-scores were significantly associated with worse OS in both the training and validation sets (p < 0.001 and p = 0.047, respectively). The Cox model combined clinical and radiomics signatures ([IBS = 0.079], [C-index = 0.747, SE = 0.045]) outperformed either radiomics signatures alone ([IBS = 0.081], [C-index = 0.738, SE = 0.041]) or clinical features alone ([IBS = 0.085], [C-index = 0.565, SE = 0.041]). The segmentation model had mean Dice coefficients of 0.80, 0.82, and 0.78 in the training, validation, and test sets respectively. A deep learning-based tumor segmentation model was built with Dice coefficients of 0.8372, 0.8017, and 0.7673 on the training set, validation set, and test set, respectively.

CONCLUSIONS

A combination of radiomics features and clinical characteristics enhances the accuracy of OS prediction in medulloblastoma patients. Additionally, building an MRI image automatic segmentation model reduces labor and time costs.

CLINICAL RELEVANCE STATEMENT

A survival prognosis model based on radiomics and clinical characteristics could improve the accuracy of prognosis estimation for medulloblastoma patients, and an MRI-based automatic tumor segmentation model could reduce the cost of time.

KEY POINTS

• A model that combines radiomics and clinical features can predict the survival prognosis of patients with medulloblastoma. • Online nomogram and image automatic segmentation model can help doctors better judge the prognosis of medulloblastoma and save working time. • The developed AI system can help doctors judge the prognosis of diseases and promote the development of precision medicine.

Healthy and Unhealthy Plant-Based Diets and Glioma in the Chinese Population

Plant-based diets have been suggested to help prevent various chronic diseases, including cancer. However, there are few reports on central nervous system tumors, and data on dose-response relationships are lacking. This individual-matched case-control study included 506 cases and 506 controls. The overall plant-based diet index (PDI), the healthy plant-based diet index (hPDI), and the unhealthy plant-based diet index (uPDI) were calculated using dietary information collected through a food frequency questionnaire, with higher scores indicating better adherence. We analyzed the relationship of plant-based diets with glioma. After adequate adjustment for confounders, PDI was associated with a reduced glioma risk (OR = 0.42, 95% CI: 0.24-0.72). Conversely, uPDI was associated with an elevated glioma risk (OR = 8.04, 95% CI: 4.15-15.60). However, hPDI was not significantly associated with glioma risk (OR = 0.83, 95% CI: 0.48-1.45). For subgroups, PDI was not significant in analyzing young age, BMI, or any pathological subtypes. The restricted cubic spline function showed a significant dose-response relationship between PDI (p-nonlinearity< 0.0001) and uPDI (p-nonlinearity= 0.0711) and glioma. Further analysis found that refined grains had the greatest effect on gliomas in the less healthy plant-based food group. Therefore, following a plant-based diet was linked to a lower risk of glioma, especially when consuming fewer unhealthy plant-based foods.

Chinese Dietary Indices and Glioma: New Insights of a Case-Control Study in the Chinese Population

Identifying modifiable factors in primary prevention strategies is a typical goal of glioma epidemiology. Among many glioma risk factors, diet was always considered as one. Most of the relevant studies thus far were concentrated on the West. It was crucial to investigate the connection between the Chinese diet and gliomas given the stark variations between western and eastern diets. A food frequency questionnaire including 114 items was used to investigate the food intake of the study subjects. The Chinese Dietary Quality Index (CDQI), the Chinese Dietary Balance Index (CDBI), the Dietary Antioxidant Index (DAI), the Dietary Inflammation Index (DII), and the Chinese Healthy Eating Index (CHEI) were calculated based on the data provided by the food frequency questionnaire to evaluate dietary quality, dietary balance, dietary antioxidants, dietary inflammation and adherence to the Chinese dietary guidelines in 506 glioma patients and 506 controls, respectively. After adjusting covariates, CHEI (OR = 0.90, 95% CI: 0.88-0.93) and DAI (OR = 0.61, 95% CI: 0.54-0.70) were correlated to a reduced glioma risk, and CDBI-based undernutrition (OR = 1.08, 95% CI: 1.06-1.12) and overnutrition (OR = 1.14, 95% CI: 1.09-1.20) and DII (OR = 2.20, 95% CI: 1.81-2.68) were correlated to an elevated glioma risk. Moreover, restrictive cubic spline analysis showed that there were significant nonlinear dose-response relationships between CHEI, CDBI, DAI, DII, and glioma. Therefore, adhering to the Chinese dietary guidelines was connected with a lower glioma risk, and undernutrition and overnutrition in the Chinese diet were associated with an increased risk of glioma.

Phytochemicals and Glioma: Results from Dietary Mixed Exposure

The information about phytochemicals' potential to prevent cancer is encouraging, including for glioma. However, most studies on phytochemicals and glioma mainly focused on preclinical studies. Their epidemiological studies were not sufficient, and the evidence on the dose-response relationship is usually limited. Therefore, this investigation examined the association between dietary phytochemical intake and glioma in Chinese adults. This case-control study was carried out in a hospital in China. Based on the dietary information obtained from the food frequency questionnaire, the researchers estimated the phytochemical intake of 506 patients with glioma and 506 controls. Compared with participants in the lowest tertile, the highest intakes of carotene, flavonoids, soy isoflavones, anthocyanin, and resveratrol were associated with a reduced risk of glioma. The WQS and BKMR models suggested that anthocyanin and carotene have a greater influence on glioma. The significant nonlinear dose-response associations between dietary phytochemicals and glioma were suggested using the restricted cubic spline function. According to this study on phytochemicals and glioma, higher intakes of carotene, flavonoids, soy isoflavones, anthocyanins, and resveratrol are linked to a lower risk of glioma. So, we might not be able to ignore how phytochemicals affect gliomas.