Wound healing involves multiple populations of cells, the extracellular matrix, and soluble mediators\' actions like growth factors and cytokines. Wound care was the target of many research, utilizing new therapy techniques and the progression of acute and chronic wound treatments with techniques involving plants to improve healing and decrease the side effects of drugs. When fenugreek is applied to an ulcer, its anti-inflammatory components are released, reducing unnecessary inflammation and accelerating the healing process. Healing is controlled by growth factors that naturally activate and boost the proliferation of cells, such as Ki-67, which is associated with the growth fraction and represents the cell\'s ability to proliferate. The current study aims to assess the expression of Ki-67 in rat mucosal ulcers treated with fenugreek leave oil. Twenty-four male Wistar albino rats of 350-450 gm weight were used. The rats were grouped as follows; normal group (normal tissue without ulcer induction), control group (tissue with surgical ulcer induction on the right side), and study group (ulcer treated with fenugreek leave oil on the left side), and had been sacrificed at 3- and 7-day healing durations. Thereafter, the tissue specimens were used for immunohistochemical analysis of Ki-67. The obtained outcomes showed that expression of Ki-67 increased in groups where ulcers were induced, with significant differences between control and study groups on the 3rd day. It was concluded that the application of fenugreek oil had an accelerating effect on the healing process of mucosal ulcers, as indicated by the elevated expression level of Ki-67.

OBJECTIVE: To investigate the correlation between MRI findings and histological features for preoperative prediction of histological grading and Ki-67 expression level in alveolar soft part sarcoma (ASPS).
METHODS: A retrospective analysis was conducted on 63 ASPS patients (Jan 2017-May 2023). All patients underwent 3.0-T MRI examinations, including conventional sequences, dynamic contrast-enhanced scans with time-intensity curve analysis, and diffusion-weighted imaging with apparent diffusion coefficient (ADC) measurements. Patients were divided into low-grade (histological Grade I) and high-grade (histological Grade II/III) groups based on pathology. Immunohistochemistry was used to assess Ki-67 expression levels in ASPS. Statistical analysis included chi-square tests, Wilcoxon rank-sum test, binary logistic regression analysis, Spearman correlation analysis, and receiver operating characteristic curve analysis of various observational data.
RESULTS: There were 29 low-grade and 34 high-grade patients (26 males and 37 females) and a wide age range (5-68 years). Distant metastasis, tumor enhancement characteristics, and ADC values were independent predictors of high-grade ASPS. High-grade ASPS had lower ADC values (p = 0.002), with an area under the curve (AUC), sensitivity, and specificity of 0.723, 79.4%, and 58.6%, respectively, for high-grade prediction. There was a negative correlation between ADC values and Ki-67 expression (r = -0.526; p < 0.001). When the cut-off value of ADC was 0.997 × 10-3 mm²/s, the AUC, sensitivity, and specificity for predicting high Ki-67 expression were 0.805, 65.6%, and 83.9%, respectively.
CONCLUSIONS: Qualitative and quantitative MRI parameters are valuable for predicting histological grading and Ki-67 expression levels in ASPS.
UNASSIGNED: This study will help provide a more nuanced understanding of ASPS and guide personalized treatment strategies.
CONCLUSIONS: There is limited research on assessing ASPS prognosis through MRI. Metastasis, enhancement, and ADC correlated with histological grade; ADC related to Ki-67 expression. MRI provides clinicians with valuable information on ASPS grading and proliferation activity.

UNASSIGNED: To evaluate the value of the malignant subregion-based texture analysis in predicting Ki-67 status in breast cancer.
UNASSIGNED: The dynamic contrast-enhanced magnetic resonance imaging data of 119 histopathologically confirmed breast cancer patients (81 patients with high Ki-67 expression status) from January 2018 to February 2023 in our hospital were retrospectively collected. According to the enhancement curve of each voxel within the tumor, three subregions were divided: washout subregion, plateau subregion, and persistent subregion. The washout subregion and the plateau subregion were merged as the malignant subregion. The texture features of the malignant subregion were extracted using Pyradiomics software for texture analysis. The differences in texture features were compared between the low and high Ki-67 expression cohorts and then the receiver operating characteristic (ROC) curve analysis to evaluate the predictive performance of texture features on Ki-67 expression. Finally, a support vector machine (SVM) classifier was constructed based on differential features to predict the expression level of Ki-67, the performance of the classifier was evaluated using ROC analysis and confirmed using 10-fold cross-validation.
UNASSIGNED: Through comparative analysis, 51 features exhibited significant differences between the low and high Ki-67 expression cohorts. Following feature reduction, 5 features were selected to build the SVM classifier, which achieved an area under the ROC curve (AUC) of 0.77 (0.68-0.87) for predicting the Ki-67 expression status. The accuracy, sensitivity, and specificity were 0.76, 0.80, and 0.68, respectively. The average AUC from the 10-fold cross-validation was 0.72 ± 0.14.
UNASSIGNED: The texture features of the malignant subregion in breast cancer were potential biomarkers for predicting Ki-67 expression level in breast cancer, which might be used to precisely diagnose and guide the treatment of breast cancer.

OBJECTIVE: Traditional Ki-67 evaluation in breast cancer (BC) via core needle biopsy is limited by repeatability and heterogeneity. The automated breast ultrasound system (ABUS) offers reproducibility but is constrained to morphological and echoic assessments. Radiomics and machine learning (ML) offer solutions, but their integration for improving Ki-67 predictive accuracy in BC remains unexplored. This study aims to enhance ABUS by integrating ML-assisted radiomics for Ki-67 prediction in BC, with a focus on both intratumoral and peritumoral regions.
METHODS: A retrospective analysis was conducted on 936 BC patients, split into training (n = 655) and testing (n = 281) cohorts. Radiomics features were extracted from intra- and peritumoral regions via ABUS. Feature selection involved Z-score normalization, intraclass correlation, Wilcoxon rank sum tests, minimum redundancy maximum relevance, and least absolute shrinkage and selection operator logistic regression. ML classifiers were trained and optimized for enhanced predictive accuracy. The interpretability of the optimized model was further augmented by employing Shapley additive explanations (SHAP).
RESULTS: Of the 2632 radiomics features in each patient, 15 were significantly associated with Ki-67 levels. The support vector machine (SVM) was identified as the optimal classifier, with area under the receiver operating characteristic curve values of 0.868 (training) and 0.822 (testing). SHAP analysis indicated that five peritumoral and two intratumoral features, along with age and lymph node status, were key determinants in the predictive model.
CONCLUSIONS: Integrating ML with ABUS-based radiomics effectively enhances Ki-67 prediction in BC, demonstrating the SVM model\'s strong performance with both radiomics and clinical factors.

OBJECTIVE: To investigate the value of multimodal diffusion weighted imaging (DWI) in preoperative evaluation of Ki-67 expression of endometrial carcinoma (EC).
METHODS: Patients who had undergone pelvic DWI, intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI) sequence MRI scan before surgery were retrospectively enrolled. Single index model, double index model, and DKI were used for post-processing of the DWI data, and the apparent diffusion coefficient (ADC), real diffusion coefficient (D), pseudo diffusion coefficient (D*), perfusion fraction (f), non-Gaussian mean diffusion kurtosis (MK), mean diffusion coefficient (MD) and anisotropy fraction (FA) were calculated and compared between the Ki-67 high (≥50%) and low (<50%) expression groups.
RESULTS: Forty-two patients with a median age of 56 (range 37 - 75) years were enrolled, including 15 patients with a high Ki-67 (≥50%) expression and 27 with a low Ki-67 (<50%) expression. The MK (0.91 ± 0.12 vs. 0.76 ± 0.12) was significantly (P<0.05) higher while MD (0.99 ± 0.17 vs. 1.16 ± 0.22), D (0.55 ± 0.06 vs. 0.62 ± 0.08), and f (0.21 vs. 0.28) were significantly (P<0.05) lower in the high than in the low expression group. The combined model of MK, MD, D, and f-values had the largest area under the curve (AUC) value of 0.869 (95% CI: 0.764-0.974), sensitivity 0.733 and specificity 0.852, followed by the MK value with an AUC value 0.827 (95% CI: 0.700-0.954), sensitivity 0.733 and specificity 0.815.
CONCLUSIONS: IVIM and DKI have certain diagnostic values for preoperative evaluation of the EC Ki-67 expression, and the combined model has the highest diagnostic efficiency.

This study aimed to develop a computed tomography (CT) model to predict Ki-67 expression in hepatocellular carcinoma (HCC) and to examine the added value of radiomics to clinico-radiological features.
A total of 208 patients (training set, n = 120; internal test set, n = 51; external validation set, n = 37) with pathologically confirmed HCC who underwent contrast-enhanced CT (CE-CT) within 1 month before surgery were retrospectively included from January 2014 to September 2021. Radiomics features were extracted and selected from three phases of CE-CT images, least absolute shrinkage and selection operator regression (LASSO) was used to select features, and the rad-score was calculated. CE-CT imaging and clinical features were selected using univariate and multivariate analyses, respectively. Three prediction models, including clinic-radiologic (CR) model, rad-score (R) model, and clinic-radiologic-radiomic (CRR) model, were developed and validated using logistic regression analysis. The performance of different models for predicting Ki-67 expression was evaluated using the area under the receiver operating characteristic curve (AUROC) and decision curve analysis (DCA).
HCCs with high Ki-67 expression were more likely to have high serum α-fetoprotein levels (P = 0.041, odds ratio [OR] 2.54, 95% confidence interval [CI]: 1.04-6.21), non-rim arterial phase hyperenhancement (P = 0.001, OR 15.13, 95% CI 2.87-79.76), portal vein tumor thrombus (P = 0.035, OR 3.19, 95% CI: 1.08-9.37), and two-trait predictor of venous invasion (P = 0.026, OR 14.04, 95% CI: 1.39-144.32). The CR model achieved relatively good and stable performance compared with the R model (AUC, 0.805 [95% CI: 0.683-0.926] vs. 0.678 [95% CI: 0.536-0.839], P = 0.211; and 0.805 [95% CI: 0.657-0.953] vs. 0.667 [95% CI: 0.495-0.839], P = 0.135) in the internal and external validation sets. After combining the CR model with the R model, the AUC of the CRR model increased to 0.903 (95% CI: 0.849-0.956) in the training set, which was significantly higher than that of the CR model (P = 0.0148). However, no significant differences were found between the CRR and CR models in the internal and external validation sets (P = 0.264 and P = 0.084, respectively).
Preoperative models based on clinical and CE-CT imaging features can be used to predict HCC with high Ki-67 expression accurately. However, radiomics cannot provide added value.

To develop and validate a random forest model based on radiomic features in Gd-EOB-DTPA enhanced MRI for predicting the Ki-67 expression in solitary HCC.
This retrospective study analyzed 258 patients with solitary HCC. Significant clinicoradiological factors were identified through univariate and multivariate analyses for distinguishing HCC with high (>20%) and low (≤20%) Ki-67 expression. Radiomic features were extracted at Gd-EOB-DTPA enhanced MRI. The recursive feature elimination (RFE) strategy was employed to screen robust radiomic features, and the Random Forest (RF) algorithm was utilized to rank radiomic features and construct prediction models. The AUC, accuracy, precision, recall, and f1-score were used to evaluate the performance of RF models.
Multivariate analysis identified serum AFP level, tumor size, growth type, and peritumoral enhancement as independent predictors for HCC with high Ki-67 expression. The clinicoradiological-radiomic model that incorporated the clinicoradiological predictors and the top ten radiomic features outperformed the clinicoradiological model in the training set (AUCs 0.876 vs. 0.780; p < 0.001), though the test set did not have a statistical significance (AUCs 0.809 vs. 0.723; p = 0.123). The addition of clinicoradiological predictors did not yield a significant improvement in the performance of radiomic features in both sets (training, p = 0.692; test, p = 0.229). Decision curve analysis further confirmed the clinical utility of the RF models.
The RF models based on radiomic features of Gd-EOB-DTPA enhanced MRI achieved satisfactory performance in preoperatively predicting Ki-67 expression in HCC.

OBJECTIVE: To investigate the diagnostic ability of novel spectral CT-derived parameters for gastric cancer histological types and Ki-67 expression.
METHODS: A total of 72 patients with histologically proven gastric cancer (GC) were retrospectively included in this study. All patients underwent dual-phase enhanced abdominal spectral CT. The arterial (AP) and venous phase (VP) slope of the spectral curve (λHU), iodine concentration (IC), normalized IC (NIC), effective atomic number (Zeff) and iodine-no-water concentration were retrospectively compared between patients with low and high Ki-67 expression levels and with different histological types in GC patients. The ROI was outlined independently by two senior physicians, and the average of three measurements at the largest level was taken. In addition, interobserver reproducibility was assessed by Bland-Altman analysis. Correlations between quantitative parameters and Ki-67 expression levels were assessed by Spearman\'s correlation coefficients.
RESULTS: The values between the mucinous group and nonmucinous carcinoma group were significantly different in both phases. The IC, NIC, and iodine-no-water concentration in the VP were significantly different among the Ki-67_L, Ki-67_M, and Ki-67_H groups. Spearman rank correlation analysis demonstrated a positive correlation between Ki-67 expression levels and IC, NIC, and iodine-no-water concentration in the VP, with correlation coefficients of 0.304, 0.424, and 0.322, respectively.
CONCLUSIONS: Quantitative spectral parameters can discriminate between low and high Ki-67 expression and different histological types in GC. The NIC, IC and iodine-no-water concentration can be useful parameters for evaluating of Ki-67 expression levels.

To explore the novel applications of histological factors by stratifying the prognostic markers of the overall CRC patients in subgroups.
A total of 17 histopathological and molecular factors were retrospectively collected and systematically analyzed for the prediction of CRC prognosis in the overall and stratified subgroups by using the Kaplan-Meier curve analysis as well as the Cox regression test. The χ2 test was used to analyze the correlation of the prognostic markers with other factors.
The histopathological markers including the lymph node metastasis (LNM), perineural/venous invasion (PVI), TNM stage, the local recurrence or distant metastasis after surgery (R/M) and the molecular markers Ki-67 expression as well as KRAS mutation were identified to be the independent prognostic biomarkers in the overall CRC. The differential prognosis of LNM was found to be significant in age, tumor site, histological classification (histo_classification), cell differentiation, and KRAS/NRAS/BRAF (KNB) mutation stratified subgroups. The PVI was discovered to differently predict survival for patients in age, histo_classification, differentiation, and R/M stratified subgroups. Same as LNM and PVI, TNM was also found to demonstrate differential prognosis in age, tumor site, histo_classification, differentiation, R/M status and KRAS/KNB mutation stratified subgroups. More importantly, R/M was firstly identified not to be terrible for patients in age, histo_classification, LNM, TNM, Ki-67, and KRAS/KNB stratified subgroups. Besides, KRAS mutation was innovatively found to show differential prognosis in age, differentiation, and LNM stratified subgroups.
The stratification analyses of prognostic markers in CRC patients indicate novel applications of the above histopathological and molecular markers in clinic and the findings provide new insights into future investigations of precision pathology.
The pathological markers LNM, PVI, TNM stage, R/M, the histological marker Ki-67 expression and the molecular marker KRAS mutation are all the early biomarkers capable of independently predicting the 2-year survival rate for CRC.Differential prognosis of the histopathological and molecular markers is commonly found in age, tumor site, differentiation, histological type, LNM, TNM, and R/M stratified CRC subgroups.

Updated 2023 guidelines from the College of American Pathologists (CAP) on immunohistochemical detection of human epidermal growth factor receptor type 2 (HER2), receptors of estrogen (ER) and progesterone (PgR), and the cell proliferation marker Ki-67 in breast cancer are presented. Attention is drawn to the emergence of two new terms «ER Low Positive» and «HER2 Low» to characterize tumors with low expression of estrogen receptors and HER2.
Представлены обновленные рекомендации 2023 г. Колледжа американских патологов (CAP), посвященные иммуногистохимическому определению рецептора эпидермального фактора роста человека 2-го типа (HER2), рецепторов эстрогена (ER) и прогестерона (PgR), а также белка-маркера клеточной пролиферации Ki-67 при раке молочной железы. Обращено внимание на появление двух новых терминов: ER Low Positive и HER2 Low для характеристики опухолей с низкой экспрессией рецепторов эстрогенов и HER2.