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Objectives@#. The necessity to develop a method for prognostication and to identify novel biomarkers for personalized medicine in patients with head and neck squamous cell carcinoma (HNSCC) cannot be overstated. Recently, pathomics, which relies on quantitative analysis of medical imaging, has come to the forefront. CXCL8, an essential inflammatory cytokine, has been shown to correlate with overall survival (OS). This study examined the relationship between CXCL8 mRNA expression and pathomics features and aimed to explore the biological underpinnings of CXCL8. @*Methods@#. Clinical information and transcripts per million mRNA sequencing data were obtained from The Cancer Genome Atlas (TCGA)-HNSCC dataset. We identified correlations between CXCL8 mRNA expression and patient survival rates using a Kaplan-Meier survival curve. A retrospective analysis of 313 samples diagnosed with HNSCC in the TCGA database was conducted. Pathomics features were extracted from hematoxylin and eosin–stained images, and then the minimum redundancy maximum relevance, with recursive feature elimination (mRMR-RFE) method was applied, followed by screening with the logistic regression algorithm. @*Results@#. Kaplan-Meier curves indicated that high expression of CXCL8 was significantly associated with decreased OS. The logistic regression pathomics model incorporated 16 radiomics features identified by the mRMR-RFE method in the training set and demonstrated strong performance in the testing set. Calibration plots showed that the probability of high gene expression predicted by the pathomics model was in good agreement with actual observations, suggesting the model’s high clinical applicability. @*Conclusion@#. The pathomics model of CXCL8 mRNA expression serves as an effective tool for predicting prognosis in patients with HNSCC and can aid in clinical decision-making. Elevated levels of CXCL8 expression may lead to reduced DNA damage and are associated with a pro-inflammatory tumor microenvironment, offering a potential therapeutic target.
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Objective:To explore the structural brain network changes in healthy first-degree relatives of depressed patients and their relationship with depressive episodes.Methods:Prospectively, 200 healthy first-degree relatives of depressed patients admitted to Jiangsu University Hospital from May 2017 to June 2018 were collected. Meanwhile, 50 matched healthy controls without family history of depression (HC/FH-) were collected by questionnaire in the nearby community as study subjects. All study subjects underwent systemic magnetic resonance imaging scans and assessment of relevant scales after enrollment, followed by longitudinal follow-up (every 3 months) for up to 3 years. The diagnostic and statistical manual of mental disorders, 4th edition, structured interview was used to assess whether the subjects became depressed during the follow-up period. First-degree relatives who experienced depression during follow-up were included in the group of first-degree relatives who experienced depression (DD/FH+), whereas first-degree relatives who did not experience depression were included in the group of first-degree relatives who did not experience depression (HC/FH+). Subjects′ depression severity and whether they experienced major stressful life events were assessed by the 24-item Hamilton Depression Rating Scale (HDRS) and the Holmes and Rahe Social Readjustment Rating Scale, respectively. Correlations between subjects′ brain structural networks and HDRS scores were explored based on Pearson correlation analysis. Logistic regression models were constructed to investigate the predictive efficacy of brain structural network attributes on depression.Results:Significant group differences existed in the HC/FH- group (50 cases), HC/FH+ group (115 cases), and DD/FH+ group (21 cases) in feeder connectivity (17.62±1.34, 17.03±1.39, 15.82±1.12, F=13.63, P<0.001), global efficiency (0.24±0.03, 0.23±0.03, 0.22±0.03, F=4.73, P=0.010), right insula node efficiency (0.20±0.02, 0.21±0.01, 0.20±0.01, F=4.62, P=0.011), left hippocampal node efficiency (0.27±0.01, 0.27±0.01, 0.24±0.02, F=18.56, P<0.001), and left amygdala node efficiency (0.24±0.02, 0.24±0.02, 0.23±0.01, F=3.40, P=0.036). Logistic regression models showed feeder connectivity ( OR=0.55, 95% CI 0.38-0.78, P=0.001) and left hippocampal nodal efficiency ( OR=0.58, 95% CI 0.40-0.81, P<0.001) predicted the occurrence of final depression and had good predictive efficacy with an area under the curve of 0.75, 0.78, respectively. Correlation analysis showed that feeder connectivity ( r=-0.58, P=0.006) and left hippocampal node efficiency ( r=-0.60, P=0.004) at baseline in the DD/FH+ group correlated with their HDRS scores at the first follow-up. Conclusion:Among healthy first-degree relatives of depressed patients, those who exhibit decreased feeder connectivity and left hippocampal nodal efficiency are susceptible to developing this disease.
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Objective:To clarify the evidences of hippocampal injury after radiotherapy avoiding hippocampus and explore its relationships with cognition.Methods:A prospective design was adopted in this study.A total of 183 patients with nasopharyngeal carcinoma treated by intensity modulated radiation therapy (IMRT group) and 30 matched healthy control (HC group)were collected in the Affiliated Hospital of Jiangsu University and Southeast University Affiliated Zhongda Hospital from January 2017 to December 2019. All subjects were assessed by Montreal Cognitive Assessment (MoCA-B) at baseline and 6 months after radiotherapy, then the patients with nasopharyngeal carcinoma were divided into cognitive impairment group and non-cognitive impairment group. Subjects were scanned with Siemens 3.0 T MR, and T 1WI was used as analysis sequence.The individual standardized hippocampus ROIs were extracted based on Montreal Neurological Institute(MNI) brain template.All texture features were calculated using the Radiomics developed by C++and Delphi, and the intra group correlation coefficients (ICC), average direction, machine learning (random forest) and autocorrelation matrix were used for reducing the features dimension. One-way ANOVA and generalized linear models were used to compare the differences among different groups. Pearson correlations analyses were used to evaluate the relationships between important texture features and clinical data. Logistic regressions were used to calculate the abilities of texture features to predict cognitive impairment. Results:After 9 patients who lost follow-up were excluded, a total of 164 patients with nasopharyngeal carcinoma were included as IMRT group.Texture features of ROIs were extracted and dimensionally reduced successfully. Five differences features (Variance, Entropy, GlevNonU, RLNonUni and Contrast)were found among HC group, cognitive impairment group and non-cognitive impairment group, and the last three further showed significant differences within IMRT group (GlevNonU, P=0.011;RLNonUni, P<0.001;Contrast, P<0.001). Hippocampal doses were positively correlated with Variance ( r=0.448, P<0.05), and negatively correlated with Entropy ( r=-0.461, P<0.05). There was a positive correlation between MoCA-B scores with GlevNonU, RLNonUniand Contrast ( r=0.503, P<0.05; r=0.587, P<0.05; r=0.531, P<0.05). GlevNonU and Contrast were independent predictors of cognitive impairment in hippocampal avoidance of radiotherapy (OR=0.731, 95%CI 0.610-0.857; OR=0.651, 95%CI 0.496-0.853). Conclusion:Results of texture analysis could be used as micro imaging evidences of hippocampal injury in radiotherapy avoiding hippocampus, and could also effectively predict the occurrences of cognitive impairment.
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Objective:To explore the diagnostic value of radiomics features based on chest CT plain scan in differentiating thymoma from other anterior mediastinal lesions.Methods:The data of 128 patients with anterior mediastinal lesions from January 2018 to January 2021 in the Affiliated Hospital of Jiangsu University were retrospectively analyzed. According to the pathological criteria, all patients were divided into thymoma group (67 cases) and non-thymoma group (61 cases). The radiomics analysis module based on MATLAB platform was used to analyze images of CT plain scan, and then radiomics features of the whole lesions were extracted. The radiomics features were screened by using group difference analysis, Boruta algorithm and collinearity detection stepwise. The receiver operating characteristic (ROC) curves for the single diagnosis and the combined diagnosis of thymoma were plotted with the selected features, and the area under the curve (AUC) was calculated to analyze the diagnostic performance of the selected features.Results:A total of 851 radiomics features were extracted, and 4 radiomics features with statistically significant differences were finally selected after multi-step dimensionality reduction, including robust mean absolute deviation, gray level non-uniformity, wavelet-LLH run variance and wavelet-HLL dependence non-uniformity. ROC curves analysis showed that the AUC of 4 radiomics features was 0.712, 0.634, 0.660 and 0.699, respectively in the single diagnosis; the specificity was 70.2%, 61.2%, and 61.2%, respectively; and the sensitivity was 60.7%, 60.6%, 68.8% and 70.5%, respectively. AUC value of the four combined detection was 0.881, the sensitivity and specificity was 75.4% and 89.6%, respectively; and the diagnostic efficiency was significantly improved.Conclusion:The radiomics features based on CT plain sans have a certain value and application potential in the differential diagnosis of thymoma and other anterior mediastinal lesions.