Comparative Evaluation of Median Versus Youden Index Dichotomization Methods: Exposure-Response Analysis of Mycophenolic Acid and Acyl-Glucuronide Metabolite

Assessing the predictive value of Ki-67 and progesterone receptor algorithms for recurrence and disease-free survival in meningiomas

Sophisticated refinements in histopathology are evolving to improve meningioma outcome prediction. The aim of this study is to evaluate the stand-alone performance of Ki-67 and progesterone receptor (PR) algorithm scores in meningiomas and their power in predicting recurrence and disease-free survival of the patients. Whole slide images of Ki-67 and PR-stained slides from 404 meningioma cases were analyzed by a digital image viewer and analysis software Virapath-2.1, which analyzes the tumor cells by size, color, and shape. Ki-67 scores were calculated in the hotspot region that contains at least 1000 tumor cells, while PR was calculated on the whole slide. The results were compared with WHO grade, tumor recurrence and disease-free survival (DFS). Mean Ki-67 scores were 4.2 ± 3.5, 12.1 ± 10.6 and 22. ± 8.5 for grade 1, 2 and 3 tumors (p < 0.05), while PR scores were 49 ± 35, 43 ± 34 and 16 ± 30, respectively (p > 0.05). Median survival of patients based on Ki-67 values ≤13.2 % and > 13.2 % was 122 versus 60 months (p = 0.004). Prediction of recurrence based on Ki-67 score was found to have acceptable discrimination (AUC = 0.74). PR expression was not found to correlate with DFS, but recurrent tumors had lower PR scores than non-recurrent tumors (31.3 ± 33.8 vs. 49.0 ± 33.0; p = 0.03). Elevated Ki-67 levels identified by the algorithm may classify meningioma patients at high recurrence risk and inform clinical management. Although PR scores did not correlate with DFS, lower expression in recurrent tumors suggests a role in recurrence risk assessment. Larger prospective studies are needed for routine clinical practice.

The Influence of the Home Food Environment on the Eating Behaviors, Family Meals, and Academic Achievement of Adolescents in Schools in the UAE

The eating behavior (EB) and habits developed during adolescence tend to persist into adulthood, with parents and caregivers playing a significant role in shaping their children's food choices. The home environment is a crucial setting for developing eating behavior during adolescence. This study aimed to explore the influence of the home food environment (HFE) and its correlates on EB, family meals (FMs), and academic achievement among adolescents in schools in the United Arab Emirates (UAE). A cross-sectional study was conducted with 304 school-aged adolescents from the UAE. The questionnaire included sociodemographic data, dietary habits, information related to the HFE (food availability and accessibility), physical activity, sleep patterns, and academic achievement. Several questionnaire items were combined to create an HFE score. These questions included the frequency of weekly family meals, meal preparation practices, and accessibility to healthy and unhealthy food products and snacks at home. The HFE score was dichotomized into favorable and unfavorable HFE scores. Similarly, EB and FM scores were generated by combining responses to various related questions. The participants' weights and heights were measured. The findings reported that more than half (55%) of the adolescents were either overweight or obese. The majority of the participants had favorable HFE (57.2%), EB (69.1%), and FM scores (58.2%). The significant correlates to the HFE were as follows: male participants whose parents attended college (OR: 0.31; 95% CI: 0.15-0.62; p < 0.001), high academic achievers (OR: 1.98; 95% CI: 1.02-3.82; p = 0.043), and those who were physically active (OR: 1.80; 95% CI: 1.14-2.85; p = 0.012), were more likely to have favorable HFE. Moreover, the HFE score showed a highly significant positive correlation with the EB score (r = 0.573, p < 0.001) and the FM score (r = 0.384, p < 0.001). These results underscore the critical role of a healthy HFE in shaping healthy positive eating behaviors and food choices among adolescents. They provide a foundation for developing effective, evidence-based policies that can impact the health and academic success of adolescents in the UAE.

A comparison of 18 F-FDG PET-based radiomics and deep learning in predicting regional lymph node metastasis in patients with resectable lung adenocarcinoma: a cross-scanner and temporal validation study

OBJECTIVE

The performance of 18 F-FDG PET-based radiomics and deep learning in detecting pathological regional nodal metastasis (pN+) in resectable lung adenocarcinoma varies, and their use across different generations of PET machines has not been thoroughly investigated. We compared handcrafted radiomics and deep learning using different PET scanners to predict pN+ in resectable lung adenocarcinoma.

METHODS

We retrospectively analyzed pretreatment 18 F-FDG PET from 148 lung adenocarcinoma patients who underwent curative surgery. Patients were separated into analog (n = 131) and digital (n = 17) PET cohorts. Handcrafted radiomics and a ResNet-50 deep-learning model of the primary tumor were used to predict pN+ status. Models were trained in the analog PET cohort, and the digital PET cohort was used for cross-scanner validation.

RESULTS

In the analog PET cohort, entropy, a handcrafted radiomics, independently predicted pN+. However, the areas under the receiver-operating-characteristic curves (AUCs) and accuracy for entropy were only 0.676 and 62.6%, respectively. The ResNet-50 model demonstrated a better AUC and accuracy of 0.929 and 94.7%, respectively. In the digital PET validation cohort, the ResNet-50 model also demonstrated better AUC (0.871 versus 0.697) and accuracy (88.2% versus 64.7%) than entropy. The ResNet-50 model achieved comparable specificity to visual interpretation but with superior sensitivity (83.3% versus 66.7%) in the digital PET cohort.

CONCLUSION

Applying deep learning across different generations of PET scanners may be feasible and better predict pN+ than handcrafted radiomics. Deep learning may complement visual interpretation and facilitate tailored therapeutic strategies for resectable lung adenocarcinoma.

DNA Repair Protein HELQ and XAB2 as Chemoresponse and Prognosis Biomarkers in Ascites Tumor Cells of High-Grade Serous Ovarian Cancer

Nucleotide excision repair (NER) is an important mediator for responsiveness of platinum-based chemotherapy. Our study is aimed at investigating the NER-related genes expression in ascites tumor cells and its application in the prediction of chemoresponse in high-grade serous ovarian cancer (HGSC) patients. The relationship between 16 NER-related genes and the prognosis of ovarian cancer was analyzed in the TCGA database. NER-related genes including HELQ and XAB2 expressions were determined via immunocytochemistry in ascites cell samples from 92 ovarian cancer patients prior to primary cytoreduction surgery. Kaplan-Meier analysis and Cox model were used to investigate the association between NER-related gene expression and prognosis/chemotherapeutic response. Predicting models were constructed using a training cohort of 60 patients and validated in a validation cohort of 32 patients. We found that high expression of HELQ and XAB2 in the training cohort was associated with poor prognosis (for HELQ, P = 0.001, HR = 2.83, 95% CI: 1.46-5.49; for XAB2, P = 0.008, HR = 2.38, 95% CI: 1.23-4.63) and platinum resistance (for HELQ, P < 0.001; for XAB2, P = 0.006). In the validation cohort, the combination of HELQ and XAB2 (AUC = 0.863) showed the highest AUC. The expression levels of HELQ (RR 5.7, 95% CI 1.7-19.2) and XAB2 (RR 3.2, 95% CI 0.9-10.8) in ascites tumor cells were positively correlated to the risk of platinum resistance. In summary, we revealed that the expression levels of HELQ and XAB2 are candidate predictors for primary chemotherapy responsiveness and prognosis in HGSC. Ascites cytology is applicable as a promising method for chemosensitivity prediction in HGSC.

Tumor glycolytic heterogeneity improves detection of regional nodal metastasis in patients with lung adenocarcinoma

OBJECTIVE

The diagnostic performance of ¹⁸F-FDG PET for detecting regional lymph node metastasis in resectable lung cancer is variable, and its sensitivity for adenocarcinoma is even lower. We aimed to evaluate the value of ¹⁸F-FDG PET-derived features in predicting pathological lymph node metastasis in patients with lung adenocarcinoma.

METHODS

We retrospectively analyzed pretreatment ¹⁸F-FDG PET-derived features of 126 lung adenocarcinoma patients who underwent curative surgery. A logistic regression model was used to analyze the association between study variables and pathological regional lymph node status obtained from the curative surgery. Furthermore, Cox regression analysis was used to test the effect of the study variables on survival outcomes, including disease-free survival (DFS) and overall survival (OS).

RESULTS

The primary tumor entropy (OR = 1.7, p = 0.014) and visual interpretation of regional nodes via ¹⁸F-FDG PET (OR = 2.5, p = 0.026) independently predicted pathological regional lymph node metastasis. The areas under the receiver-operating-characteristic curves were 0.631, 0.671, and 0.711 for visual interpretation, primary tumor entropy, and their combination, respectively. Based on visual interpretation, a primary tumor entropy ≥ 3.0 improved the positive predictive value of positive visual interpretation from 51.2% to 63.0%, whereas an entropy < 3.0 improved the negative predictive value of negative visual interpretation from 75.3% to 82.6%. In cases with positive visual interpretation and low entropy, or negative visual interpretation and high entropy, the nodal metastasis rates were approximately 30%. In the survival analyses, the primary tumor entropy was also independently associated with DFS (HR = 2.7, p = 0.001) and OS (HR = 4.8, p = 0.001).

CONCLUSIONS

Our preliminary results show that the primary tumor entropy may improve ¹⁸F-FDG PET visual interpretation in predicting pathological nodal metastasis in lung adenocarcinoma, and may also show a survival prognostic value. This versatile biomarker may facilitate tailored therapeutic strategies for patients with resectable lung adenocarcinoma.

Prognostic Value of Combing Primary Tumor and Nodal Glycolytic-Volumetric Parameters of 18F-FDG PET in Patients with Non-Small Cell Lung Cancer and Regional Lymph Node Metastasis

We investigated whether the combination of primary tumor and nodal ¹⁸F-FDG PET parameters predict survival outcomes in patients with nodal metastatic non-small cell lung cancer (NSCLC) without distant metastasis. We retrospectively extracted pre-treatment ¹⁸F-FDG PET parameters from 89 nodal-positive NSCLC patients (stage IIB–IIIC). The Cox proportional hazard model was used to identify independent prognosticators of overall survival (OS) and progression-free survival (PFS). We devised survival stratification models based on the independent prognosticators and compared the model to the American Joint Committee on Cancer (AJCC) staging system using Harrell’s concordance index (c-index). Our results demonstrated that total TLG (the combination of primary tumor and nodal total lesion glycolysis) and age were independent risk factors for unfavorable OS (p < 0.001 and p = 0.001) and PFS (both p < 0.001), while the Eastern Cooperative Oncology Group scale independently predicted poor OS (p = 0.022). Our models based on the independent prognosticators outperformed the AJCC staging system (c-index = 0.732 versus 0.544 for OS and c-index = 0.672 versus 0.521 for PFS, both p < 0.001). Our results indicate that incorporating total TLG with clinical factors may refine risk stratification in nodal metastatic NSCLC patients and may facilitate tailored therapeutic strategies in this patient group.