Enhancing Ki-67 Prediction in Breast Cancer: Integrating Intratumoral and Peritumoral Radiomics From Automated Breast Ultrasound via Machine Learning.

RATIONALE AND OBJECTIVES: 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. MATERIALS AND 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. CONCLUSION: 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.

Radiomic Analysis Based on Gd-EOB-DTPA Enhanced MRI for the Preoperative Prediction of Ki-67 Expression in Hepatocellular Carcinoma.

RATIONALE AND OBJECTIVES: 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. MATERIALS AND METHODS: 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. RESULTS: 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. CONCLUSION: The RF models based on radiomic features of Gd-EOB-DTPA enhanced MRI achieved satisfactory performance in preoperatively predicting Ki-67 expression in HCC.

AI-based intra-tumor heterogeneity score of Ki67 expression as a prognostic marker for early-stage ER+/HER2- breast cancer.

Early-stage estrogen receptor positive and human epidermal growth factor receptor negative (ER+/HER2-) luminal breast cancer (BC) is quite heterogeneous and accounts for about 70% of all BCs. Ki67 is a proliferation marker that has a significant prognostic value in luminal BC despite the challenges in its assessment. There is increasing evidence that spatial colocalization, which measures the evenness of different types of cells, is clinically important in several types of cancer. However, reproducible quantification of intra-tumor spatial heterogeneity remains largely unexplored. We propose an automated pipeline for prognostication of luminal BC based on the analysis of spatial distribution of Ki67 expression in tumor cells using a large well-characterized cohort (n = 2,081). The proposed Ki67 colocalization (Ki67CL) score can stratify ER+/HER2- BC patients with high significance in terms of BC-specific survival (p < 0.00001) and distant metastasis-free survival (p = 0.0048). Ki67CL score is shown to be highly significant compared with the standard Ki67 index. In addition, we show that the proposed Ki67CL score can help identify luminal BC patients who can potentially benefit from adjuvant chemotherapy.

Value of Multimodal Diffusion-weighted Imaging in Preoperative Evaluation of Ki-67 Expression in Endometrial Carcinoma.

PURPOSE: To investigate the value of multimodal diffusion weighted imaging (DWI) in preoperative evaluation of Ki-67 expression of endometrial carcinoma (EC). MATERIALS AND 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.

Added value of CE-CT radiomics to predict high Ki-67 expression in hepatocellular carcinoma.

BACKGROUND: 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. METHODS: 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). RESULTS: 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). CONCLUSIONS: 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.

[Updated CAP guidelines for determining molecular biological subtypes of breast cancer]. / Obnovlennye rekomendatsii CAP po opredeleniyu molekulyarno-biologicheskikh podtipov raka molochnoi zhelezy.

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 Positive¼ and «HER2 Low¼ to characterize tumors with low expression of estrogen receptors and HER2.

Novel applications of histopathological markers to distinguish prognostic subgroups in colorectal adenocarcinoma.

OBJECTIVE: To explore the novel applications of histological factors by stratifying the prognostic markers of the overall CRC patients in subgroups. MATERIALS AND METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Quantitative parameters in novel spectral computed tomography for assessing gastric cancer and cell proliferation.

OBJECTIVES: 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. CONCLUSION: 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.

Immunohistochemical Expression of Ki-67, Dopamine D1 and Dopamine D2 Receptors in Meningiomas in a Tertiary Institution in Mexico.

Objectives Meningiomas (MNGs) are the most common intracranial tumors found in the adult population. While most intracranial MNGs may be surgically removed, a subset of patients remains ineligible for conventional treatment. This is either because of a lack of surgical access or due to atypical, anaplastic or invasive characteristics of the tumors. These patients may benefit from targeted therapies that focus on cell receptor expression. The aim of this study was to assess dopamine receptor (DR) and Ki-67 expression in the MGNs of patients treated with surgery in the Instituto Nacional de Neurología y Neurocirugía, Mexico. Materials and methods This study analyzed 23 patients with confirmed MNG diagnoses (10 female and 13 male (mean age: 44.5 years)) who had undergone surgical resection between 2010 and 2014 at our institution. In the collected samples, we performed analyses for Ki-67, Dopamine 1 and Dopamine 2 receptors' expression. Results For the markers Ki-67, DR-D1 and DR-D2, the mean percentual expressions were 18.9%, 23.02% and 8.33%. No significant correlation was found between the expressions of these receptors and the studied MNG characteristics. The expression index of Ki-67 showed a significant relation with mean age (p = 0.03) and prolactin levels (p = 0.02). Conclusions Samples showed varied expressions of the studied receptors. Despite the difference in expressions between the markers, more studies are needed to confirm the findings. In contrast to previous studies, we could not find any relationship between D2-R and tumor characteristics.

The relationship between Ki-67 expression and imaging signs and pathological features in GISTs.

Introduction: To investigate the correlations between the Ki-67 index and plain-scan computerized tomography (CT) signs and pathological features of gastrointestinal stromal tumor (GIST) tissue. Materials and methods: Data from 186 patients with GIST diagnosed by pathology and immunohistochemistry (IHC) in Peking University First Hospital from May 2016 to May 2022 were analyzed. The patients were divided into two groups: Ki-67 ≤5% and >5%. Correlation analysis, univariate and multivariate Logistic regression analysis were used to explore the correlations between CT signs, pathological features, and Ki-67 expression. Results: Univariate indicators correlated with the Ki-67 index were mitotic count, pathological grade, tumor hemorrhage, tumor necrosis, tumor size, and tumor density. Multivariate Logistic regression indicated that the mitotic count [odds ratio (OR) 10.222, 95% confidence interval (CI) 4.312-31.039], pathological grade (OR 2.139, 95% CI 1.397-3.350), and tumor size (OR 1.096, 95% CI 1.020-1.190) were independently associated with the Ki-67 expression level. The concordance indexes (C-index) for the pathological features and CT signs models were 0.876 (95% CI 0.822-0.929) and 0.697 (95% CI 0.620-0.774), respectively, with positive predictive values of 93.62% and 58.11% and negative predictive values of 81.29% and 75.89%, respectively. After internal verification by the Bootstrap method, the fitting degree of the pathological features model was found to be better than that of the CT signs model. Conclusion: Mitotic count, pathological risk grading, and tumor size are independent risk factors correlating with high Ki-67 index. These results indicate that the Ki-67 index reflects tumor malignancy and can predict recurrence and metastasis of GIST.

Quantitative parameters in novel spectral computed tomography: Assessment of Ki-67 expression in patients with gastric adenocarcinoma.

BACKGROUND: The level of Ki-67 expression has served as a prognostic factor in gastric cancer. The quantitative parameters based on the novel dual-layer spectral detector computed tomography (DLSDCT) in discriminating the Ki-67 expression status are unclear. AIM: To investigate the diagnostic ability of DLSDCT-derived parameters for Ki-67 expression status in gastric carcinoma (GC). METHODS: Dual-phase enhanced abdominal DLSDCT was performed preoperatively in 108 patients with gastric adenocarcinoma. Primary tumor monoenergetic CT attenuation value at 40-100 kilo electron volt (kev), the slope of the spectral curve (λHU), iodine concentration (IC), normalized IC (nIC), effective atomic number (Zeff) and normalized Zeff (nZeff) in the arterial phase (AP) and venous phase (VP) were retrospectively compared between patients with low and high Ki-67 expression in gastric adenocarcinoma. Spearman's correlation coefficient was used to analyze the association between the above parameters and Ki-67 expression status. Receiver operating characteristic (ROC) curve analysis was performed to compare the diagnostic efficacy of the statistically significant parameters between two groups. RESULTS: Thirty-seven and 71 patients were classified as having low and high Ki-67 expression, respectively. CT40 kev-VP, CT70 kev-VP, CT100 kev-VP, and Zeff-related parameters were significantly higher, but IC-related parameters were lower in the group with low Ki-67 expression status than the group with high Ki-67 expression status, and other analyzed parameters showed no statistical difference between the two groups. Spearman's correlation analysis showed that CT40 kev-VP, CT70 kev-VP, CT100 kev-VP, Zeff, and nZeff exhibited a negative correlation with Ki-67 status, whereas IC and nIC had positive correlation with Ki-67 status. The ROC analysis demonstrated that the multi-variable model of spectral parameters performed well in identifying the Ki-67 status [area under the curve (AUC) = 0.967; sensitivity 95.77%; specificity 91.89%)]. Nevertheless, the differentiating capabilities of single-variable model were moderate (AUC value 0.630 - 0.835). In addition, the nZeff VP and nICVP (AUC 0.835 and 0.805) showed better performance than CT40 kev-VP, CT70 kev-VP and CT100 kev-VP (AUC 0.630, 0.631 and 0.662) in discriminating the Ki-67 status. CONCLUSION: Quantitative spectral parameters are feasible to distinguish low and high Ki-67 expression in gastric adenocarcinoma. Zeff and IC may be useful parameters for evaluating the Ki-67 expression.

Anti-Hu-Associated Encephalomyelitis as a Presentation of Primary Extrapulmonary Small Cell Carcinoma of the Small Bowel: A Case Report.

Small cell carcinoma (SCC) is a neuroendocrine tumor (NET) commonly found in the lung, known for rapid proliferation and early metastasis. Extrapulmonary small cell carcinomas (ESCC) are rare, with GI tract carcinomas exceedingly so. Due to the lack of clinical data on the treatment of ESCC, the standard regimen is the same as the SCC of the lung. Documented accounts of paraneoplastic encephalomyelitis associated with NETs are also uncommon. We present a patient who suffered from neurologic deficits before being diagnosed with paraneoplastic encephalomyelitis from a duodenal ESCC. The patient presented with ear pain and hematemesis. New symptoms arose after the resolution of initial symptoms, including shortness of breath and numbness. Autoimmune workup was positive for anti-Hu antibodies. A position emission tomography (PET) scan showed increased uptake in the duodenal region. Biopsy results from a duodenal ulcer revealed poorly differentiated neuroendocrine carcinoma with positive synaptophysin and strong positivity of Ki-67, consistent with ESCC. Numerous treatments, including platinum-based chemotherapy, yielded no neurologic improvement for the patient. This case details an atypical presentation of ESCC, which should be considered in patients suspected of paraneoplastic encephalomyelitis.

Nomogram based on MRI for preoperative prediction of Ki-67 expression in patients with intrahepatic mass cholangiocarcinoma.

OBJECTIVES: To validate a new nomogram based on magnetic resonance imaging (MRI) for pre-operative prediction of Ki-67 expression in patients with intrahepatic mass cholangiocarcinoma (IMCC). METHODS: A total of 78 patients with clinicopathologically confirmed IMCC who underwent pre-operative gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid enhanced MRI between 2016 and 2022 were enrolled in the training and validation group (53 patients and 25 patients, respectively). Images including qualitative, quantitative MRI features and clinical data were evaluated. Univariate analysis and multivariate logistic regression were used to select the independent predictors and establish different predictive models. The predictive performance was validated by operating characteristic curve (ROC) analysis, calibration curve, and decision curve analysis (DCA). The validation cohort was used to test the predictive performance of the optimal model. The nomogram was constructed with the optimal model. RESULTS: In the training cohort, independent predictors obtained from the combined model were DWI (OR 1822.741; 95% CI 6.189, 536,781.805; P = 0.01) and HBP enhancement pattern (OR 14.270; 95% CI 1.044, 195.039; P = 0.046). The combined model showed the good performance (AUC 0.981; 95% CI 0.952, 1.000) for predicting Ki-67 expression. In the validation cohort, The combined model (AUC 0.909; 95% CI 0.787, 1.000)showed the best performance compared to the clinical model (AUC 0.448; 95% CI 0.196, 0.700) and MRI model (AUC 0.770; 95% CI 0.570, 0.970). CONCLUSION: This new nomogram has a good performance in predicting Ki-67 expression in patients with IMCC, which could help the decision-making of the patients' therapy strategies.

Development of an ultrasound-based radiomics nomogram to preoperatively predict Ki-67 expression level in patients with breast cancer.

Objective: To develop and validate a radiomics nomogram that could incorporate clinicopathological characteristics and ultrasound (US)-based radiomics signature to non-invasively predict Ki-67 expression level in patients with breast cancer (BC) preoperatively. Methods: A total of 328 breast lesions from 324 patients with BC who were pathologically confirmed in our hospital from June 2019 to October 2020 were included, and they were divided into high Ki-67 expression level group and low Ki-67 expression level group. Routine US and shear wave elastography (SWE) were performed for each lesion, and the ipsilateral axillary lymph nodes (ALNs) were scanned for abnormal changes. The datasets were randomly divided into training and validation cohorts with a ratio of 7:3. Correlation analysis and the least absolute shrinkage and selection operator (LASSO) were used to select the radiomics features obtained from gray-scale US images of BC patients, and each radiomics score (Rad-score) was calculated. Afterwards, multivariate logistic regression analysis was used to establish a radiomics nomogram based on the radiomics signature and clinicopathological characteristics. The prediction performance of the nomogram was assessed by the area under the receiver operating characteristic curve (AUC), the calibration curve, and decision curve analysis (DCA) using the results of immunohistochemistry as the gold standard. Results: The radiomics signature, consisted of eight selected radiomics features, achieved a nearly moderate prediction efficacy with AUC of 0.821 (95% CI:0.764-0.880) and 0.713 (95% CI:0.612-0.814) in the training and validation cohorts, respectively. The radiomics nomogram, incorporating maximum diameter of lesions, stiff rim sign, US-reported ALN status, and radiomics signature showed a promising performance for prediction of Ki-67 expression level, with AUC of 0.904 (95% CI:0.860-0.948) and 0.890 (95% CI:0.817-0.964) in the training and validation cohorts, respectively. The calibration curve and DCA indicated promising consistency and clinical applicability. Conclusion: The proposed US-based radiomics nomogram could be used to non-invasively predict Ki-67 expression level in BC patients preoperatively, and to assist clinicians in making reliable clinical decisions.

Prediction of Histological Grades and Ki-67 Expression of Hepatocellular Carcinoma Based on Sonazoid Contrast Enhanced Ultrasound Radiomics Signatures.

Objectives: Histopathological tumor grade and Ki-67 expression level are key aspects concerning the prognosis of patients with hepatocellular carcinoma (HCC) lesions. The aim of this study was to investigate whether the radiomics model derived from Sonazoid contrast-enhanced (S-CEUS) images could predict histological grades and Ki-67 expression of HCC lesions. Methods: This prospective study included 101 (training cohort: n = 71; validation cohort: n = 30) patients with surgical resection and histopathologically confirmed HCC lesions. Radiomics features were extracted from the B mode and Kupffer phase of S-CEUS images. Maximum relevance minimum redundancy (MRMR) and least absolute shrinkage and selection operator (LASSO) were used for feature selection, and a stepwise multivariate logit regression model was trained for prediction. Model accuracy, sensitivity, and specificity in both training and testing datasets were used to evaluate performance. Results: The prediction model derived from Kupffer phase images (CE-model) displayed a significantly better performance in the prediction of stage III HCC patients, with an area under the receiver operating characteristic curve (AUROC) of 0.908 in the training dataset and 0.792 in the testing set. The CE-model demonstrated generalizability in identifying HCC patients with elevated Ki-67 expression (>10%) with a training AUROC of 0.873 and testing AUROC of 0.768, with noticeably higher specificity of 92.3% and 80.0% in training and testing datasets, respectively. Conclusions: The radiomics model constructed from the Kupffer phase of S-CEUS images has the potential for predicting Ki-67 expression and histological stages in patients with HCC.

Characterization of the Tumor Microenvironment and the Biological Processes with a Role in Prostatic Tumorigenesis.

Prostate intratumoral heterogeneity, driven by epithelial−mesenchymal plasticity, contributes to the limited treatment response, and it is therefore necessary to use the biomarkers to improve patient prognostic survival. We aimed to characterize the tumor microenvironment (T lymphocyte infiltration, intratumoral CD34, and KI-67 expressions) by immunohistochemistry methods and to study the biological mechanisms (cell cycle, cell proliferation by adhesion glycoproteins, cell apoptosis) involved in the evolution of the prostate tumor process by flow-cytometry techniques. Our results showed that proliferative activity (S-phase) revealed statistically significant lower values of prostate adenocarcinoma (PCa) and benign prostatic hyperplasia (BPH) reported at non-malignant adjacent cell samples (PCa 4.32 ± 4.91; BPH 2.35 ± 1.37 vs. C 10.23 ± 0.43, p < 0.01). Furthermore, 68% of BPH cases and 88% of patients with PCa had aneuploidy. Statistically increased values of cell proliferation (CD34+ CD61+) were observed in prostate adenocarcinoma and hyperplasia cases reported to non-malignant adjacent cell samples (PCa 28.79 ± 10.14; BPH 40.65 ± 11.88 vs. C 16.15 ± 2.58, p < 0.05). The CD42b+ cell population with a role in cell adhesion, and metastasis had a significantly increased value in PCa cases (38.39 ± 11.23) reported to controls (C 26.24 ± 0.62, p < 0.01). The intratumoral expression of CD34 showed a significantly increased pattern of PCa tissue samples reported to controls (PCa 26.12 ± 6.84 vs. C 1.50 ± 0.70, p < 0.01). Flow cytometric analysis of the cell cycle, apoptosis, and adhesion glycoproteins with a critical role in tumoral cell proliferation, T cell infiltrations, Ki-67, and CD 34 expressions by IHC methods are recommended as techniques for the efficient means of measurement for adenocarcinoma and hyperplasia prostate tissue samples and should be explored in the future.

Radical resection and reconstruction in patients with adenoid cystic carcinoma in the minor salivary glands of the palate.

BACKGROUND: This study evaluated the clinical outcomes of the patients with adenoid cystic carcinoma (ACC) of the minor salivary glands of the palate. METHODS: Forty-four patients with stage I-II disease and 14 patients with stage III-IV disease underwent radical excision and reconstruction with a facial-submental artery island flap (FSAIF) and titanium mesh plus a free anterolateral thigh flap (ALTF) and radiotherapy respectively. Patients with stage III-IV disease subsequently received cobalt Co 60 adjuvant radiotherapy. Ki-67 expression was determined semiquantitatively in 52 patients with ACC by based on the cytoplasm staining intensity and percentage of positively stained tumor cells. RESULTS: The median (range) follow-up was 32.9 (14-58) months. Forty-one (71.7%) patients survived without disease recurrence. Nine patients (15.5%) survived with recurrent tumors (four with local recurrence, three with regional recurrence requiring salvage surgery, and two with distant metastasis); among these patients, five had overlapping recurrence. Eight patients (13.8%) died of regional, distant, or multiorgan metastasis (range: 22-42 months). The overall median (95% CI) survival time was 32.5 (25.0-39.5) months, and the median (95% CI) progression-free survival time was 32.9 (28.5-36.9) months. Rates of survival and recurrence differed significantly between patients with low- and high-grade tumors, patients with clinical stage I-II disease and those with stage III-IV disease, patients with and without lymph node metastasis, patients who underwent radical excision with versus without radiotherapy, and patients with low and high Ki-67 expression. CONCLUSION: Radical resection and reconstruction with FSAIF is suitable methods for the the treatment of stage I-II ACC of the minor salivary glands of the palate. Stage III-IV tumors require radical resection, reconstruction with titanium mesh and free ALTF, and radiotherapy.

Gastrointestinal Stromal Tumor and Ki-67 as a Prognostic Indicator.

Gastrointestinal stromal tumors (GISTs) albeit rare, are the most common mesenchymal neoplasms of our gastrointestinal (GI) tract. GISTs present with nonspecific symptoms and are found incidentally on endoscopy or imaging. A significant portion of GIST diagnoses expresses KIT/CD117 and DOG-1 tissue markers which are pathognomonic for GIST. More recently, Ki-67 was found to be a significant prognostic marker for determining the risk of recurrence. We present a patient with a mesenchymal mass in the small intestine with pathognomonic features of GIST and expression of Ki-67, an important immunocytochemical marker of proliferation. The patient was a 71-year-old male with a history of hyperlipidemia and hypertension. He presented to the emergency department complaining of bloody diarrhea for two days, with associated nausea, vomiting, and abdominal cramping. Initial blood pressure on presentation was 77/52 mm Hg. Computed tomography (CT) of the abdomen and pelvis revealed a large solid mass with cystic components. The mass was not visualized with esophagogastroduodenoscopy or colonoscopy, and surgical intervention was warranted. A 14 cm x 11.5 cm x 10 cm tumor was found in the ileum. The tumor was excised with small bowel segmental resection and the specimen was sent for pathological evaluation. Immunohistochemical analysis confirmed the diagnosis of GIST with diffuse CD117/c-Kit protein expression. The tumor was high grade with a high mitotic rate at 30 mitoses/50 high-power fields (HPF) and had spindle cell morphology. Of note, 10% of the tumor cells were positive for Ki-67. GISTs have a high risk of recurrence and a more favorable prognosis with advancements in management. Prior to imatinib therapy in the early 2000s, GISTs prognosis was very poor, as they are resistant to most conventional chemotherapeutic agents and radiation. While the prognosis is fair, surgical resection and imatinib therapy have improved outcomes and risk of recurrence. Prognosis and risk of recurrence can be determined by assessing the mitotic rate, tumor size, and recently, expression of Ki-67. Ki-67 provides a reliable and reproducible approach to assess the prognosis of GIST.

Radioproteomics in Breast Cancer: Prediction of Ki-67 Expression With MRI-based Radiomic Models.

RATIONALE AND OBJECTIVES: We aimed to investigate the value of magnetic resonance image (MRI)-based radiomics in predicting Ki-67 expression of breast cancer. METHODS: In this retrospective study, 159 lesions from 154 patients were included. Radiomic features were extracted from contrast-enhanced T1-weighted MRI (C+MRI) and apparent diffusion coefficient (ADC) maps, with open-source software. Dimension reduction was done with reliability analysis, collinearity analysis, and feature selection. Two different Ki-67 expression cut-off values (14% vs 20%) were studied as reference standard for the classifications. Input for the models were radiomic features from individual MRI sequences or their combination. Classifications were performed using a generalized linear model. RESULTS: Considering Ki-67 cut-off value of 14%, training and testing AUC values were 0.785 (standard deviation [SD], 0.193) and 0.849 for ADC; 0.696 (SD, 0.150) and 0.695 for C+MRI; 0.755 (SD, 0.171) and 0.635 for the combination of both sequences, respectively. Regarding Ki-67 cut-off value of 20%, training and testing AUC values were 0.744 (SD, 0.197) and 0.617 for ADC; 0.629 (SD, 0.251) and 0.741 for C+MRI; 0.761 (SD, 0.207) and 0.618 for the combination of both sequences, respectively. CONCLUSION: ADC map-based selected radiomic features coupled with generalized linear modeling might be a promising non-invasive method to determine the Ki-67 expression level of breast cancer.

Prognostic Value of Ki-67 Expression in Advanced Lung Squamous Cell Carcinoma Patients Treated with Chemotherapy.

BACKGROUND: The relationship between the Ki-67 expression level and chemotherapy response and survival prognosis in advanced lung squamous cell carcinoma (SCC) remains unclear. METHODS: A total of 101 patients were included in the study. All patients received systemic first-line platinum-based chemotherapy. The Ki-67 expression level was determined by immunohistochemistry analysis. RESULTS: The Ki-67 expression level was positively correlated with an increase in tumor T stage (P = 0.0140), N stage (P < 0.0001), and M stage (P < 0.0001) in advanced lung SCC. High Ki-67 expression could predict chemotherapy response (area under the curve = 0.7524, P < 0.0001). Patients with tumors that expressed high levels of Ki-67 had shorter overall survival (OS) (18.8 months vs 25.5 months, P = 0.0002) and progression-free survival (PFS) (4.8 months vs 6.7 months, P < 0.0001). Cox analysis found Ki-67 expression to be an independent prognostic biomarker of shortened OS (P = 0.009) and PFS (P = 0.008). CONCLUSION: Ki-67 expression may affect chemotherapy response and thus has prognostic value. Ki-67 expression may be a promising prognostic biomarker for advanced lung SCC.