Radiomics analysis of R2* maps to predict early recurrence of single hepatocellular carcinoma after hepatectomy.

Objectives: This study aimed to evaluate the effectiveness of radiomics analysis with R2* maps in predicting early recurrence (ER) in single hepatocellular carcinoma (HCC) following partial hepatectomy. Methods: We conducted a retrospective analysis involving 202 patients with surgically confirmed single HCC having undergone preoperative magnetic resonance imaging between 2018 and 2021 at two different institutions. 126 patients from Institution 1 were assigned to the training set, and 76 patients from Institution 2 were assigned to the validation set. A least absolute shrinkage and selection operator (LASSO) regularization was conducted to operate a logistic regression, then features were identified to construct a radiomic score (Rad-score). Uni- and multi-variable tests were used to assess the correlations of clinicopathological features and Rad-score with ER. We then established a combined model encompassing the optimal Rad-score and clinical-pathological risk factors. Additionally, we formulated and validated a predictive nomogram for predicting ER in HCC. The nomogram's discrimination, calibration, and clinical utility were thoroughly evaluated. Results: Multivariable logistic regression revealed the Rad-score, microvascular invasion (MVI), and α fetoprotein (AFP) level > 400 ng/mL as significant independent predictors of ER in HCC. We constructed a nomogram based on these significant factors. The areas under the receiver operator characteristic curve of the nomogram and precision-recall curve were 0.901 and 0.753, respectively, with an F1 score of 0.831 in the training set. These values in the validation set were 0.827, 0.659, and 0.808. Conclusion: The nomogram that integrates the radiomic score, MVI, and AFP demonstrates high predictive efficacy for estimating the risk of ER in HCC. It facilitates personalized risk classification and therapeutic decision-making for HCC patients.

Electroacupuncture Activated Impaired Brain Areas and Improved Mental Status and Sleep Quality in Primary Insomnia Patients.

Objective: This study aimed to investigate the specific neurological mechanisms underlying the effects of electroacupuncture at Shenmen (Heart 7) with Neiguan (Pericardium 6) acupoints in patients with primary insomnia (PI). We sought to understand these mechanisms by comparing changes in areaal homogeneity (ReHo) before and after treatment in PI patients and healthy controls (HC). Methods: Between November 2019 and November 2021, we recruited 17 primary insomnia patients (PI group) and 20 matched healthy controls (HC group) as study subjects from Zhaoqing First People's Hospital. Before electroacupuncture treatment, all participants completed the Pittsburgh Sleep Quality Index (PSQI), Hamilton Depression Rating Scale (HAMD), and Hamilton Anxiety Rating Scale (HAMA) assessments. Resting-state magnetic resonance imaging (MRI) scans were conducted before and after two sessions of electroacupuncture at Shenmen and Neiguan acupoints. Results: Before treatment, primary insomnia patients showed higher PSQI (χ2=1.964; P = .017), HAMA (χ2=2.016; P = .027), and HAMD scores (χ2=2.367; P = .013) compared to healthy controls, and increased ReHo values were observed in the left amygdala, bilateral middle temporal gyrus, and left posterior cingulate gyrus in PI patients, while decreased ReHo values were found in the left posterior cingulate gyrus, right middle frontal gyrus, and right precuneus. After treatment, ReHo values increased in the left superior frontal gyrus, right parahippocampal gyrus, and right cingulate gyrus, while they decreased in the left amygdala and right angular gyrus. Primary insomnia disrupts brain areas in the default network, salience network, and parts of the affective cognitive network. Conclusion: Electroacupuncture at Shenmen and Neiguan acupoints partially activated impaired brain areas in patients with primary insomnia, leading to improvements in mental status and sleep quality. This offers a novel perspective for the clinical treatment of primary insomnia.

Multiparametric MRI combined with clinical factors to predict glypican-3 expression of hepatocellular carcinoma.

Objectives: The present study aims at establishing a noninvasive and reliable model for the preoperative prediction of glypican 3 (GPC3)-positive hepatocellular carcinoma (HCC) based on multiparametric magnetic resonance imaging (MRI) and clinical indicators. Methods: As a retrospective study, the subjects included 158 patients from two institutions with surgically-confirmed single HCC who underwent preoperative MRI between 2020 and 2022. The patients, 102 from institution I and 56 from institution II, were assigned to the training and the validation sets, respectively. The association of the clinic-radiological variables with the GPC3 expression was investigated through performing univariable and multivariable logistic regression (LR) analyses. The synthetic minority over-sampling technique (SMOTE) was used to balance the minority group (GPC3-negative HCCs) in the training set, and diagnostic performance was assessed by the area under the curve (AUC) and accuracy. Next, a prediction nomogram was developed and validated for patients with GPC3-positive HCC. The performance of the nomogram was evaluated through examining its calibration and clinical utility. Results: Based on the results obtained from multivariable analyses, alpha-fetoprotein levels > 20 ng/mL, 75th percentile ADC value < 1.48 ×103 mm2/s and R2* value ≥ 38.6 sec-1 were found to be the significant independent predictors of GPC3-positive HCC. The SMOTE-LR model based on three features achieved the best predictive performance in the training (AUC, 0.909; accuracy, 83.7%) and validation sets (AUC, 0.829; accuracy, 82.1%) with a good calibration performance and clinical usefulness. Conclusions: The nomogram combining multiparametric MRI and clinical indicators is found to have satisfactory predictive efficacy for preoperative prediction of GPC3-positive HCC. Accordingly, the proposed method can promote individualized risk stratification and further treatment decisions of HCC patients.

Gadoxetic acid-enhanced MRI combined with T1 mapping and clinical factors to predict Ki-67 expression of hepatocellular carcinoma.

Objectives: To explore the predictive value of gadoxetic acid-enhanced magnetic resonance imaging (MRI) combined with T1 mapping and clinical factors for Ki-67 expression in hepatocellular carcinoma (HCC). Methods: A retrospective study was conducted on 185 patients with pathologically confirmed solitary HCC from two institutions. All patients underwent preoperative T1 mapping on gadoxetic acid-enhanced MRI. Patients from institution I (n = 124) and institution II (n = 61) were respectively assigned to the training and validation sets. Univariable and multivariable analyses were performed to assess the correlation of clinico-radiological factors with Ki-67 labeling index (LI). Based on the significant factors, a predictive nomogram was developed and validated for Ki-67 LI. The performance of the nomogram was evaluated on the basis of its calibration, discrimination, and clinical utility. Results: Multivariable analysis showed that alpha-fetoprotein (AFP) levels > 20ng/mL, neutrophils to lymphocyte ratio > 2.25, non-smooth margin, tumor-to-liver signal intensity ratio in the hepatobiliary phase ≤ 0.6, and post-contrast T1 relaxation time > 705 msec were the independent predictors of Ki-67 LI. The nomogram based on these variables showed the best predictive performance with area under the receiver operator characteristic curve (AUROC) 0.899, area under the precision-recall curve (AUPRC) 0.946 and F1 score of 0.912; the respective values were 0.823, 0.879 and 0.857 in the validation set. The Kaplan-Meier curves illustrated that the cumulative recurrence probability at 2 years was significantly higher in patients with high Ki-67 LI than in those with low Ki-67 LI (39.6% [53/134] vs. 19.6% [10/51], p = 0.011). Conclusions: Gadoxetic acid-enhanced MRI combined with T1 mapping and several clinical factors can preoperatively predict Ki-67 LI with high accuracy, and thus enable risk stratification and personalized treatment of HCC patients.

Clinical Effect of Driver Mutations of KRAS, CDKN2A/P16, TP53, and SMAD4 in Pancreatic Cancer: A Meta-Analysis.

Objective: To evaluate the prognostic value of driver mutations in the KRAS, CDKN2A/P16, TP53, and SMAD4 genes in pancreatic cancer to aid in the design of therapeutic strategies. Search Strategy: A systematic search was conducted using PubMed, MEDLINE, Springer, and Cochrane library to identify eligible studies published between January 1990 and June 2018 that reported an association between driver mutations in these genes and survival data. Inclusion Criteria: Articles which passed the primary screen were further scrutinized for the presence of all the following items: (1) cohort studies or case-control studies, evaluating the relationship between driver mutations and cancer; (2) cancer diagnoses clearly proved; and (3) hazard ratios (HR) and 95% confidence intervals (CIs) were characterized by sufficient information. Data Extraction and Analysis: Selection of included articles, data extraction, and methodological quality assessments were, respectively, conducted by two authors. Results: The meta-analysis was composed of 17 studies on the P53, 8 on SMAD4, 7 on CDKN2A/P16, and 2 on KRAS, containing 3373 samples. Our pooled results demonstrated that the patients with overexpression of the P53 (HR = 1.249, 95% CI = 1.003-1.554, p = 0.047), SMAD4 (HR = 1.397, 95% CI = 1.015-1.922, p = 0.040), CDKN2A/P16 (HR = 0.916, 95% CI = 0.583-1.439, p = 0.704), and KRAS (HR = 1.68, 95% CI = 1.27-2.22, p < 0.001) mutations all had poorer overall survival. Conclusion: This systematic review and meta-analysis supports the use of driver mutations in the P53, SMAD4, and KRAS genes as prognostic markers for pancreatic cancer.