Intravoxel incoherent motion radiomics nomogram for predicting tumor treatment responses in nasopharyngeal carcinoma

BACKGROUND

Intravoxel incoherent motion (IVIM) plays an important role in predicting treatment responses in patient with nasopharyngeal carcinoma (NPC). The goal of this study was to develop and validate a radiomics nomogram based on IVIM parametric maps and clinical data for the prediction of treatment responses in NPC patients.

METHODS

Eighty patients with biopsy-proven NPC were enrolled in this study. Sixty-two patients had complete responses and 18 patients had incomplete responses to treatment. Each patient received a multiple b-value diffusion-weighted imaging (DWI) examination before treatment. Radiomics features were extracted from IVIM parametric maps derived from DWI image. Feature selection was performed by the least absolute shrinkage and selection operator method. Radiomics signature was generated by support vector machine based on the selected features. Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) values were used to evaluate the diagnostic performance of radiomics signature. A radiomics nomogram was established by integrating the radiomics signature and clinical data.

RESULTS

The radiomics signature showed good prognostic performance to predict treatment response in both training (AUC = 0.906, P<0.001) and testing (AUC = 0.850, P<0.001) cohorts. The radiomic nomogram established by integrating the radiomic signature with clinical data significantly outperformed clinical data alone (C-index, 0.929 vs 0.724; P<0.0001).

CONCLUSIONS

The IVIM-based radiomics nomogram provided high prognostic ability to treatment responses in patients with NPC. The IVIM-based radiomics signature has the potential to be a new biomarker in prediction of the treatment responses and may affect treatment strategies in patients with NPC.

Survival rate prediction of nasopharyngeal carcinoma patients based on MRI and gene expression using a deep neural network

To achieve a better treatment regimen and follow-up assessment design for intensity-modulated radiotherapy (IMRT)-treated nasopharyngeal carcinoma (NPC) patients, an accurate progression-free survival (PFS) time prediction algorithm is needed. We propose developing a PFS prediction model of NPC patients after IMRT treatment using a deep learning method and comparing that with the traditional texture analysis method. One hundred and fifty-one NPC patients were included in this retrospective study. T1-weighted, proton density and dynamic contrast-enhanced magnetic resonance (MR) images were acquired. The expression level of five genes (HIF-1α, EGFR, PTEN, Ki-67, and VEGF) and infection of Epstein-Barr (EB) virus were tested. A residual network was trained to predict PFS from MR images. The output as well as patient characteristics were combined using a linear regression model to provide a final PFS prediction. The prediction accuracy was compared with that of the traditional texture analysis method. A regression model combining the deep learning output with HIF-1α expression and Epstein-Barr infection provides the best PFS prediction accuracy (Spearman correlation R²  = 0.53; Harrell's C-index = 0.82; receiver operative curve [ROC] analysis area under the curve [AUC] = 0.88; log-rank test hazard ratio [HR] = 8.45), higher than a regression model combining texture analysis with HIF-1α expression (Spearman correlation R²  = 0.14; Harrell's C-index =0.68; ROC analysis AUC = 0.76; log-rank test HR = 2.85). The deep learning method does not require a manually drawn tumor region of interest. MR image processing using deep learning combined with patient characteristics can provide accurate PFS prediction for nasopharyngeal carcinoma patients and does not rely on specific kernels or tumor regions of interest, which is needed for the texture analysis method.

Dynamic contrast-enhanced magnetic resonance imaging-based radiomics for the prediction of progression-free survival in advanced nasopharyngeal carcinoma

To establish a multidimensional nomogram model for predicting progression-free survival (PFS) and risk stratification in patients with advanced nasopharyngeal carcinoma (NPC). This retrospective cross-sectional study included 156 patients with advanced NPC who underwent dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Radiomic features were extracted from the efflux rate constant (Ktrans ) and extracellular extravascular volume (Ve ) mapping derived from DCE-MRI. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was applied for feature selection. The Radscore was constructed using the selected features with their respective weights in the LASSO Cox regression analysis. A nomogram model combining the Radscore and clinical factors was built using multivariate Cox regression analysis. The C-index was used to assess the discrimination power of the Radscore and nomogram. The Kaplan-Meier method was used for survival analysis. Of the 360 radiomic features, 28 were selected (7, 6, and 15 features extracted from Ktrans , Ve, and Ktrans +Ve images, respectively). The combined Radscore k trans +Ve (C-index, 0.703, 95% confidence interval [CI]: 0.571-0.836) showed higher efficacy in predicting the prognosis of advanced NPC than Radscore k trans (C-index, 0.693; 95% CI, 0.560-0.826) and Radscore Ve (C-index, 0.614; 95% CI, 0.481-0.746) did. Multivariable Cox regression analysis revealed clinical stage, T stage, and treatment with nimotuzumab as risk factors for PFS. The nomogram established by Radscore k trans +Ve and risk factors (C-index, 0.732; 95% CI: 0.599-0.864) was better than Radscore k trans +Ve in predicting PFS in patients with advanced NPC. A lower Radscore k trans +Ve (HR 3.5584, 95% CI 2.1341-5.933), lower clinical stage (hazard ratio [HR] 1.5982, 95% CI 0.5262-4.854), lower T stage (HR 1.4365, 95% CI 0.6745-3.060), and nimotuzumab (NTZ) treatment (HR 0.7879, 95% CI 0.4899-1.267) were associated with longer PFS. Kaplan-Meier analysis showed a lower PFS in the high-risk group than in the low-risk group (p<0.0001). The nomogram based on combined pretreatment DCE-MRI radiomics features, NTZ, and clinicopathological risk factors may be considered as a noninvasive imaging marker for predicting individual PFS in patients with advanced NPC.

[Altered dynamics of brain spontaneous activity in betel quid dependence chewers]

Objective: To investigate the dynamic characteristics of brain spontaneous activity in betel quid dependence (BQD) chewers and its relationship with clinical indexes. Method: This study was conducted in Hainan General Hospital from April to December 2019 and the data of 53 BQD chewers (37 males and 16 females, aged 20 to 58(38±11) years) and 37 healthy controls (HC) (24 males and 13 females, aged 23-57(42±12) years) were collected. All these subjects underwent resting-state functional magnetic resonance imaging (rs-fMRI) scan. The dynamic characteristics of resting fMRI indexes, including dynamic amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo) and degree centrality (DC) of these subjects were calculated using the sliding time window method, parameters such as age and dynamic local consistency were analyzed and compared between the two groups. Pearson correlation was used to analyze the relationship between dynamic indexes, betel quid dependence score (BQDS) and disease duration in BQD group. Results: BQD chewers showed decreased dynamic ALFF in the left orbital prefrontal cortex (0.138±0.041 vs 0.171±0.070), the right temporal pole superior temporal gyrus (0.277±0.070 vs 0.319±0.086) and the right inferior parietal lobule (0.223±0.052 vs 0.259±0.088) than HC (all P<0.05). For regional homogeneity, BQD chewers showed a decrease dynamic ReHo in the right inferior temporal gyrus (0.055±0.008 vs 0.061±0.009), the orbital prefrontal cortex (0.058±0.005 vs 0.063±0.008), the right inferior frontal gyrus (0.081±0.006 vs 0.087±0.011), the right superior occipital gyrus (0.056±0.007 vs 0.062±0.008), the left precentral gyrus (0.068±0.008 vs 0.074±0.008), and the left superior frontal gyrus (0.058±0.008 vs 0.064±0.009) than HC (all P<0.05). BQD chewers showed an increase dynamic ReHo in the right precuneus (0.095±0.009 vs 0.089±0.008) (P<0.05). There was no significant difference in DC between the two groups (all P>0.05). The relationships between three dynamic ALFF and BQDS (r=-0.104, -0.015, -0.119), seven dynamic ReHo and BQDS (r=-0.099, -0.141, -0.055, -0.078, -0.027, -0.111, -0.090), three dynamic ALFF and disease duration (r=-0.122, -0.095, -0.171), and seven dynamic ReHo and disease duration (r=0.242, -0.252, 0.035, 0.056, 0.047, 0.081, 0.169) were not statistically significant (all P>0.05). Conclusions: BQD chewers showed a decrease dynamic ReHo and/or ALFF in multiple brain regions dominated by prefrontal cortex, and an increase dynamic ReHo in the right precuneus.

Hippocampal subfield morphology in regular hemodialysis patients

BACKGROUND

Hippocampal alterations have been implicated in the pathophysiology of cognitive impairment in hemodialysis patients. The hippocampus consists of several distinct subfields, and the molecular mechanisms underlying cognition might be associated with specific hippocampal subfield volume changes. However, this has not yet been investigated in hemodialysis patients. This study aimed to explore volumetric abnormalities in hippocampal subfields in regular hemodialysis patients.

METHODS

High-resolution T1-weighted structural images were collected in 61 subjects including 36 hemodialysis patients and 25 healthy controls. A state-of-the-art hippocampal segmentation approach was adopted to segment the hippocampal subfields. Group differences in hippocampal subfield volumes were assessed in Python with a statsmodels module using an ordinary least squares regression with age and sex as nuisance effects.

RESULTS

Hemodialysis patients had significantly smaller volumes in the bilateral hippocampus (p < 0.05/2, Bonferroni corrected), CA (cornu ammonis) 1, CA4, granule cell and molecular layer of the dentate gyrus, hippocampus-amygdala-transition-area and molecular layer of the hippocampus than healthy controls (p < 0.05/24, Bonferroni corrected). Hemodialysis patients also had lower volumes in the left hippocampal tail and right fimbria than healthy controls (p < 0.05/24, Bonferroni corrected). Hippocampal subfield volumes were associated with neuropsychological test scores, the duration of disease and hemoglobin levels.

CONCLUSIONS

We found smaller hippocampal subfield volumes in hemodialysis patients, which were associated with impaired cognition, supporting their role in memory disturbance in the hemodialysis population. However, multiple clinical factors may have confounded the results, and therefore, the interpretation of these results needs to be cautious.