Ki-67 Status Research Articles

Nomograms for Predicting Disease-Free Survival Based on Core Needle Biopsy and Surgical Specimens in Female Breast Cancer Patients with Non-Pathological Complete Response to Neoadjuvant Chemotherapy

While a pathologic complete response (pCR) is regarded as a surrogate endpoint for pos-itive outcomes in breast cancer (BC) patients receiving neoadjuvant chemotherapy (NAC), fore-casting the prognosis of non-pCR patients is still an open issue. This study aimed to create and evaluate nomogram models for estimating the likelihood of disease-free survival (DFS) for non-pCR patients. A retrospective analysis of 607 non-pCR BC patients was conducted (2012-2018). After converting continuous variables to categorical variables, variables entering the model were progressively identified by univariate and multivariate Cox regression analyses, and then pre-NAC and post-NAC nomogram models were developed. Regarding their discrimination, ac-curacy, and clinical value, the performance of the models was evaluated by internal and external validation. Two risk assessments were performed for each patient based on two models; patients were separated into different risk groups based on the calculated cut-off values for each model, including low-risk (assessed by the pre-NAC model) to low-risk (assessed by the post-NAC model), high-risk to low-risk, low-risk to high-risk, and high-risk to high-risk groups. The DFS of different groups was assessed using the Kaplan-Meier method. Both pre-NAC and post-NAC nomogram models were built with clinical nodal (cN) status and estrogen receptor (ER), Ki67, and p53 status (all p < 0.05), showing good discrimination and calibration in both internal and external validation. We also assessed the performance of the two models in four subtypes, with the tri-ple-negative subtype showing the best prediction. Patients in the high-risk to high-risk subgroup have significantly poorer survival rates (p < 0.0001). Two robust and effective nomo-grams were developed to personalize the prediction of DFS in non-pCR BC patients treated with NAC.

Development and Assessment of a Novel Core Biopsy-Based Prediction Model for Pathological Complete Response to Neoadjuvant Chemotherapy in Women with Breast Cancer.

Purpose: Pathological complete response (pCR), the goal of NAC, is considered a surrogate for favorable outcomes in breast cancer (BC) patients administrated neoadjuvant chemotherapy (NAC). This study aimed to develop and assess a novel nomogram model for predicting the probability of pCR based on the core biopsy. Methods: This was a retrospective study involving 920 BC patients administered NAC between January 2012 and December 2018. The patients were divided into a primary cohort (769 patients from January 2012 to December 2017) and a validation cohort (151 patients from January 2017 to December 2018). After converting continuous variables to categorical variables, variables entering the model were sequentially identified via univariate analysis, a multicollinearity test, and binary logistic regression analysis, and then, a nomogram model was developed. The performance of the model was assessed concerning its discrimination, accuracy, and clinical utility. Results: The optimal predictive threshold for estrogen receptor (ER), Ki67, and p53 were 22.5%, 32.5%, and 37.5%, respectively (all p < 0.001). Five variables were selected to develop the model: clinical T staging (cT), clinical nodal (cN) status, ER status, Ki67 status, and p53 status (all p ≤ 0.001). The nomogram showed good discrimination with the area under the curve (AUC) of 0.804 and 0.774 for the primary and validation cohorts, respectively, and good calibration. Decision curve analysis (DCA) showed that the model had practical clinical value. Conclusions: This study constructed a novel nomogram model based on cT, cN, ER status, Ki67 status, and p53 status, which could be applied to personalize the prediction of pCR in BC patients treated with NAC.

Reproducibility of CT radiomic features in lung neuroendocrine tumours (NETs) patients: analysis in a heterogeneous population

BackgroundThe aim is to find a correlation between texture features extracted from neuroendocrine (NET) lung cancer subtypes, both Ki-67 index and the presence of lymph-nodal mediastinal metastases detected while using different computer tomography (CT) scanners.MethodsSixty patients with a confirmed pulmonary NET histological diagnosis, a known Ki-67 status and metastases, were included. After subdivision of primary lesions in baseline acquisition and venous phase, 107 radiomic features of first and higher orders were extracted. Spearman’s correlation matrix with Ward’s hierarchical clustering was applied to confirm the absence of bias due to the database heterogeneity. Nonparametric tests were conducted to identify statistically significant features in the distinction between patient groups (Ki-67 < 3—Group 1; 3 ≤ Ki-67 ≤ 20—Group 2; and Ki-67 > 20—Group 3, and presence of metastases).ResultsNo bias arising from sample heterogeneity was found. Regarding Ki-67 groups statistical tests, seven statistically significant features (p value < 0.05) were found in post-contrast enhanced CT; three in baseline acquisitions. In metastasis classes distinction, three features (first-order class) were statistically significant in post-contrast acquisitions and 15 features (second-order class) in baseline acquisitions, including the three features distinguishing between Ki-67 groups in baseline images (MCC, ClusterProminence and Strength).ConclusionsSome radiomic features can be used as a valid and reproducible tool for predicting Ki-67 class and hence the subtype of lung NET in baseline and post-contrast enhanced CT images. In particular, in baseline examination three features can establish both tumour class and aggressiveness.

Correlation of Before and After Invasive Breast Cancer Neoadjuvant Chemotherapy for NFkB, Cyclin D1, and Survivin Expression.

Patients undergoing neoadjuvant chemotherapy (NC) for invasive breast cancer (IBC) therapy need biomarkers to track their progress. Because of the relationship between NFkB, Survivin, and Cyclin D1 with NC resistance, the different expression levels of each of these biomarkers can be different between pre- and post-NC in IBC. However, no research has examined the correlation between these biomarkers before and after the NC expression. This study aimed to determine the correlation among them. Biomarkers expression (low and high) was used to classify 30 samples. ER, PR, HER2, Ki-67 status, tumor grade, age, and NC response were assessed. The amounts of Survivin, Cyclin D1, and NFkB were evaluated using immunohistochemistry, and the samples were classified based on the cut-off. Chi-square and linear regression were used to evaluate the data. No significant association was found with the changes in the expression of Survivin, Cyclin D1, and NFkB, both before and after the NC. Significant moderate correlations were shown between before and after the NC Survivin expression (r = 0.513) and Cyclin D1 expression (r = 0.543). The correlation between expression of NFkB before and after the NC was not significant. The high potential of these proteins as prognostic indicators was demonstrated by the strong positive association between the expression of Survivin and Cyclin D1 before and after the NC. This upregulation of biomarkers indicates chemoresistance in developing IBC in the presence of NC.

Efficacy evaluation of neoadjuvant chemotherapy in patients with HER2-low expression breast cancer: A real-world retrospective study.

To characterize the clinicopathological features and evaluate the neoadjuvant chemotherapy (NACT) efficacy of patients with human epidermal growth factor receptor 2 (HER2)-low breast cancer. A total of 905 breast cancer patients who received 4 cycles of thrice-weekly standard NACT in the First Affiliated Hospital of Chongqing Medical University were retrospectively enrolled, including 685 cases with HER2-low expression and 220 cases with HER2-negative expression. Clinicopathological features were compared between patients with HER2-negative and HER2-low expression. Univariate and multivariate logistic regression analyses were used to find the independent factors of achieving a pathological complete response (pCR) after NACT. There were significant differences in stage_N (P = 0.014), histological grade (P = 0.001), estrogen receptor (ER) status (P < 0.001), progesterone receptor (PgR) status (P < 0.001), NACT regimens (P = 0.032) and NACT efficacy (P = 0.037) between patients with HER2-negative and HER2-low expression breast cancer. In subgroup analysis, histological grade (P = 0.032), ER (P = 0.002), Ki-67 (P < 0.001) and HER2 status (P = 0.025) were independent predictors of achieving a pCR in ER-positive breast cancer. And the nomogram for pCR in ER-positive breast cancer showed great discriminatory ability with an AUC of 0.795. The calibration curve also showed that the predictive ability of the nomogram was a good fit to actual observations. Then, in the analysis of ER-negative breast cancer, only stage_N (P = 0.001) and Ki-67 (P = 0.018) were independent influencing factors of achieving a pCR in ER-negative breast cancer. HER2-low breast cancer was a different disease from HER2-negative breast cancer in clinicopathological features. Moreover, the NACT efficacy of HER2-low breast cancer patients was poorer.

Correlation of B-cell lymphoma 2 immunoexpression in invasive carcinoma of breast, no special type with hormone receptor status, proliferation index, and molecular subtypes.

B-cell lymphoma 2 is involved in various cancers including breast carcinoma. Its expression in breast cancer has been associated with good prognostic factors such as hormone receptor expression, low Ki-67, low grade, and earlier stage. It is also considered to be an independent prognostic factor for luminal and triple-negative tumors. We aimed to determine the expression of B-cell lymphoma 2 (BCL2) in different molecular subtypes of invasive ductal carcinoma of breast and its association with prognostic indicators. Fifty samples of invasive carcinoma of breast, no special type (NST), were categorized into molecular subtypes according to immunohistochemical expression of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor 2 (HER2), and Ki-67 and then evaluated for BCL2 expression. The expression of BCL2 was correlated with ER, PR, HER2, and Ki-67 and compared between luminal and nonluminal subtypes. The BCL2 expression was seen in 68% of the cases with a significant association with ER, PR, and luminal subtypes. No significant association of BCL2 expression was seen with grade, HER2 and Ki-67 status of the tumor, or age group of the patients. BCL2 expression is significantly associated with ER, PR, and luminal subtypes in breast cancer. BCL2 is a marker of good prognosis in invasive carcinoma of breast, NST.

Radiomics of dynamic contrast-enhanced magnetic resonance imaging parametric maps and apparent diffusion coefficient maps to predict Ki-67 status in breast cancer.

This study was aimed at evaluating whether a radiomics model based on the entire tumor region from breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parametric maps and apparent diffusion coefficient (ADC) maps could indicate the Ki-67 status of patients with breast cancer. This retrospective study enrolled 205 women with breast cancer who underwent clinicopathological examination. Among them, 93 (45%) had a low Ki-67 amplification index (Ki-67 positivity< 14%), and 112 (55%) had a high Ki-67 amplification index (Ki-67 positivity ≥ 14%). Radiomics features were extracted from three DCE-MRI parametric maps and ADC maps calculated from two different b values of diffusion-weighted imaging sequences. The patients were randomly divided into a training set (70% of patients) and a validation set (30% of patients). After feature selection, we trained six support vector machine classifiers by combining different parameter maps and used 10-fold cross-validation to predict the expression level of Ki-67. The performance of six classifiers was evaluated with receiver operating characteristic (ROC) analysis, sensitivity, and specificity in both cohorts. Among the six classifiers constructed, a radiomics feature set combining three DCE-MRI parametric maps and ADC maps yielded an area under the ROC curve (AUC) of 0.839 (95% confidence interval [CI], 0.768-0.895) within the training set and 0.795 (95% CI, 0.674-0.887) within the independent validation set. Additionally, the AUC value, compared with that for a single parameter map, was moderately increased by combining features from the three parametric maps. Radiomics features derived from the DCE-MRI parametric maps and ADC maps have the potential to serve as imaging biomarkers to determine Ki-67 status in patients with breast cancer.

A study on the role of proliferative index marker Ki67 in breast carcinoma

Background. Breast cancer is the most common malignancy and the leading cause of cancer death among women worldwide. This study was conducted to establish early diagnosis and therapy, thus improving prognosis and overall survival rate. Objectives. To assess the levels of proliferation index marker Ki67 and hormone receptor status in patients with breast carcinoma and to correlate Ki67 with ER, PR, Her2neu status, as well as to correlate Ki67 and hormone receptor status with onco-pathological parameters. Materials and Methods. 392 patients diagnosed with breast carcinoma over a period of four years were included in the study. Data regarding patient’s identification, history, clinical finding, investigations and histopathological report were recorded in a specific proforma. Result. Breast cancer was seen to affect people in the older age group, and invasive ductal carcinoma of nonspecific type was the most common type. Most patients had unifocal tumor with grade 2 disease. Stage II disease was most common stage among the patients studied and Luminal B subtype was the predominant type of molecular subtype. Most of the patients were Ki67 positive, i.e., Ki67 &gt;14%. The correlation of Ki67 with estrogen receptor, progesterone receptor, tumor size, mitotic count, lymph node ratio, lymphovascular invasion and extra-nodal extension was found to be statistically significant. Conclusions. There exists a statistically significant, positive correlation between breast cancers, its onco-pathological parameters, proliferation index marker Ki67 and hormone receptor status. Therefore, the judicious use of these tests (Ki67 is an old marker of proliferation but not routinely used) will help in the early appropriate treatment of patients and contribute to the assessment of treatment response and prognosis.

Preoperative Computed Tomography Radiomics Analysis for Predicting Receptors Status and Ki-67 Levels in Breast Cancer.

To assess the prediction performance of preoperative chest computed tomography (CT) based radiomics features for estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor-2 (HER2+), and Ki-67 status of breast cancer. This study enrolled 108 breast cancer patients who received preoperative chest CT examinations in our institution from July 2018 to January 2020. Radiomics features were separately extracted from nonenhanced, arterial, and portal-venous phases CT images. The least absolute shrinkage and selection operator logistic regression was used for feature selection. Then the radiomics signatures for each phase and a combined model of 3 phases were built. Finally, the receiver operating characteristic curves and calibration curves were used to confirm the performance of the radiomics signatures and combined model. In addition, the decision curves were performed to estimate the clinical usefulness of the combined model. The 20 most predictive features were finally selected to build radiomics signatures for each phase. The combined model achieved the overall best performance than using either of the nonenhanced, arterial and portal-venous phases alone, achieving an area under the receiver operating characteristic curve of 0.870 for ER+ versus ER-, 0.797 for PR+ versus PR-, 0.881 for HER2+ versus HER2-, and 0.726 for Ki-67. The decision curve demonstrated that the CT-based radiomics features were clinically useful. This study indicated preopreative chest CT radiomics analysis might be able to assess ER, PR, HER2+, and Ki-67 status of breast cancer. The findings need further to be verified in future larger studies.

MRI Fat-Saturated T2-Weighted Radiomics Model for Identifying the Ki-67 Index of Soft Tissue Sarcomas.

Ki-67 expression has been shown to be an important risk factor associated with prognosis in patients with soft tissue sarcomas (STSs). Its assessment requires fine-needle biopsy and its accuracy can be influenced by tumor heterogeneity. To develop and test an MRI-based radiomics nomogram for identifying the Ki-67 status of STSs. Retrospective. A total of 149 patients at two independent institutions (training cohort [high Ki-67/low ki-67]: 102 [52/50], external validation cohort [high Ki-67/low ki-67]: 47 [28/19]) with STSs. Fat-saturated T2-weighted imaging (FS-T2WI) with a fat-suppressed fast spin/turbo spin echo sequence at 1.5T or 3T. After radiomics feature extraction, logistic regression (LR), random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN) were used to construct radiomics models to distinguish between high and low Ki-67 status. Clinical-MRI characteristics included age, gender, location, size, margin, and MRI morphological features (size, margin, signal intensity, and peritumoral hyperintensity) were assessed. Univariate and multivariate logistic regression analysis were applied for screening significant risk factors. Radiomics nomogram was constructed by radiomics signature and risk factors. Model performances (discrimination, calibration, and clinical usefulness) were validated in the validation cohort. The nomogram was assessed using the Harrell index of concordance (C-index), calibration curve analysis. The clinical utility of the model was assessed by decision curve analysis (DCA). LR, RF, SVM, and KNN models represented AUCs of 0.789, 0.755, 0.726, and 0.701 in the validation cohort (P> 0.05). The nomogram had a C-index of 0.895 (95% CI: 0.837-0.953) in the training cohort and 0.852 (95% CI: 0.796-0.957) in the validation cohort and it demonstrated good calibration and clinical utility (P=0.972 for the training cohort and P=0.727 for the validation cohort). This MRI-based radiomics nomogram developed showed good performance in identifying Ki-67 expression status in STSs. 2.

Identifying overall survival in 98 glioblastomas using VASARI features at 3T

PurposeThis study aims to evaluate qualitative and quantitative imaging metrics along with clinical features affecting overall survival in glioblastomas and to classify them into high survival and low survival groups based on 12, 19, and 24 months thresholds using machine learning. MethodsThe cohort consisted of 98 adult glioblastomas. A standard brain tumor magnetic resonance (MR) imaging protocol, was performed on a 3T MR scanner. Visually Accessible REMBRANDT Images (VASARI) features were assessed. A Kaplan-Meier survival analysis followed by a log-rank test and multivariate Cox regression analysis were used to investigate the effects of VASARI features along with the age, gender, the extent of resection, pre- and post-KPS, ki67 and P53 mutation status on overall survival. Supervised machine learning algorithms were employed to predict the survival of glioblastoma patients based on 12, 19, and 24 months thresholds. ResultsTumor location (p<0.001), the proportion of non-enhancing component (p=0.0482), and proportion of necrosis (p=0.02) were significantly associated with overall survival based on Kaplan-Meier analysis. Multivariate Cox regression analysis revealed that increases in proportion of non-enhancing component (p=0.040) and proportion of necrosis (p=0.039) were significantly associated with overall survival. Machine-learning models were successful in differentiating patients living longer than 12 months with 96.40% accuracy (sensitivity=97.22%, specificity=95.55%). The classification accuracies based on 19 and 24 months survival thresholds were 70.87% (sensitivity=83.02%, specificity=60.11%) and 74.66% (sensitivity=67.58%, specificity=82.08%), respectively. ConclusionEmploying clinical and VASARI features together resulted in a successful classification of glioblastomas that would have a longer overall survival.

Integration of ultrasound radiomics features and clinical factors: A nomogram model for identifying the Ki-67 status in patients with breast carcinoma.

ObjectiveThe aim of this study was to develop and validate an ultrasound-based radiomics nomogram model by integrating the clinical risk factors and radiomics score (Rad-Score) to predict the Ki-67 status in patients with breast carcinoma.MethodsUltrasound images of 284 patients (196 high Ki-67 expression and 88 low Ki-67 expression) were retrospectively analyzed, of which 198 patients belonged to the training set and 86 patients to the test set. The region of interest of tumor was delineated, and the radiomics features were extracted. Radiomics features underwent dimensionality reduction analysis by using the independent sample t test and least absolute shrinkage and selection operator (LASSO) algorithm. The support vector machine (SVM), logistic regression (LR), decision tree (DT), random forest (RF), naive Bayes (NB) and XGBoost (XGB) machine learning classifiers were trained to establish prediction model based on the selected features. The classifier with the highest AUC value was selected to convert the output of the results into the Rad-Score and was regarded as Rad-Score model. In addition, the logistic regression method was used to integrate Rad-Score and clinical risk factors to generate the nomogram model. The leave group out cross-validation (LGOCV) method was performed 200 times to verify the reliability and stability of the nomogram model.ResultsSix classifier models were established based on the 15 non-zero coefficient features. Among them, the LR classifier achieved the best performance in the test set, with the area under the receiver operating characteristic curve (AUC) value of 0.786, and was obtained as the Rad-Score model, while the XGB performed the worst (AUC, 0.615). In multivariate analysis, independent risk factor for high Ki-67 status was age (odds ratio [OR] = 0.97, p = 0.04). The nomogram model based on the age and Rad-Score had a slightly higher AUC than that of Rad-Score model (AUC, 0.808 vs. 0.798) in the test set, but no statistical difference (p = 0.144, DeLong test). The LGOCV yielded a median AUC of 0.793 in the test set.ConclusionsThis study proposed a convenient, clinically useful ultrasound radiomics nomogram model that can be used for the preoperative individualized prediction of the Ki-67 status in patients with BC.

Real-world data on breast pathologic complete response and disease-free survival after neoadjuvant chemotherapy for hormone receptor-positive, human epidermal growth factor receptor-2-negative breast cancer: a multicenter, retrospective study in China

BackgroundThe data in the real-world setting on breast pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) for hormone receptor–positive, human epidermal growth factor receptor-2-negative (HR+, HER2−) breast cancer (BC) is limited. The present study aims to screen for some predictors and investigate the prognostic significance of breast pCR after NAC in HR+, HER2− BC in China.MethodsThis was a multicenter, retrospective study. In this study, three hundred eighty-four HR+, HER2− BC patients who received NAC were enrolled between 2010 and 2016 from Shanghai Jiaotong University Breast Cancer Database (SJTU-BCDB). These patients were dichotomized according to the presence of breast pCR after NAC. Logistic analysis was used to screen for predictors associated with breast pCR. Kaplan-Meier (K-M) curve and a propensity score matching (PSM) analysis were performed to compare the disease-free survival (DFS) between the two groups. Cox regression was used to analyze the prognostic significance of breast pCR on DFS in HR+, HER2− BC. A nomogram model was established to predict the probability of DFS at 1, 3, and 5 years after NAC.ResultsFifty-seven patients (14.8%) achieved breast pCR. Univariate analysis showed that tumor size, estrogen receptor (ER), progesterone receptor (PR), and Ki67 were associated with breast pCR. Further, multivariate analysis showed that tumor size, PR, and Ki67 remained statistically significant. K-M curves showed a statistical difference between the breast pCR and non-pCR groups before PSM (p = 0.047), and a more significant difference was shown after PSM (p = 0.033). Cox regression after PSM suggested that breast pCR, adjuvant ET, clinical T stage, and Ki67 status were the significant predictive factors for DFS in HR+, HER2− BC patients. The adjusted hazards ratio (aHR) for breast pCR was 0.228 (95% CI, 0.070~0.739; p = 0.014), for adjuvant endocrine therapy was 0.217 (95% CI, 0.059~0.801; p = 0.022), for Ki67 was 1.027 (95% CI, 1.003~1.052; p = 0.027), for cT stages 2 and 3 compared with 1, the values were 1.331 (95% CI, 0.170~10.389), and 4.699 (95% CI, 0.537~41.142), respectively (p = 0.043). A nomogram was built based on these significant predictors, providing an integrated probability of DFS at 1, 3, and 5 years. The values of area under the receiver operating characteristic (ROC) curve (AUC) were 0.967, 0.991, and 0.787, at 1 year, 3 years, and 5 years, respectively, demonstrating the ability of the nomogram to predict the DFS.ConclusionsThis real-world study demonstrates that tumor size, PR, and Ki67 were independent predictive factors for breast pCR in HR+, HER2− BC. Breast pCR after NAC was an independent predictor for DFS in HR+, HER2− patients, regardless of a change in nodes. Furthermore, the nomogram built in our study could predict the probability of individualized DFS in HR+, HER2− BC patients.

Survival Benefit of Adjuvant Chemotherapy in Pulmonary Carcinoid Tumors.

Simple SummaryChemotherapy administration after surgical resection of pulmonary carcinoid is not well studied. While guidelines state adjuvant therapy may be appropriate in some scenarios, this is not observed clinically. We reviewed the literature to examine if adjuvant chemotherapy in patients with resected pulmonary carcinoid provided any survival benefit. The aim of this review is to aid clinicians when deciding on next steps for these patients after resection.Pulmonary carcinoid tumors are a rare subtype of neuroendocrine cell tumor found in approximately 1–2% of lung cancers. Management is primarily through surgical resection, with limited benefit of adjuvant therapy in the clinical setting. Genomic profiling is in the nascent stages to molecularly classify these tumors, but there are promising insights for future targeted therapy. A total of 80 abstracts were analyzed for further review with 11 included in our final analysis. Only 4 of the 11 reviewed in depth provided statistical analysis. We evaluated PFS, OS, 1- and 5-year survival as mentioned in the studies. Nodal and KI67 status were also analyzed. Based on the current literature, there is no definitive evidence that adjuvant chemotherapy after resection confers a survival benefit in typical or atypical carcinoids.

Measurement of Residual Breast Cancer Burden after Neoadjuvant Chemotherapy

Aim: To determine the frequency of residual cancer burden after neoadjuvant chemotherapy for breast cancer patients undergoing surgery. Study Design: Descriptive study. Place and Duration of Study: Department of Surgery, Dr. Ruth K.M. Pfau Civil Hospital Karachi from 8th May 2019 to 7th November 2019. Methodology: One hundred and twenty six females aged 25 to 75 years with stage IIA, IIB and IIIA were enrolled. Patients who had received standard protocol of neoadjuvant chemotherapy from outside i.e. six cycles of fluorouracil, anthracycline and cyclophosphomide (FAC) every 3 weeks and for HER2+ tumors, FAC was followed by 12 cycles of Trastuzumab every 3 weeks were included. Data regarding tumor characteristic, age, HER 2 status, PR status, KI 67status and nodal involvement was noted. Results: The mean age was 47.32±13 years and mean cancer cellularity was 14.88±10.4%. Nuclear grade III was present in 39.7% patients and 48.4% patients belonged to T1 stage. Majority of patients were menopausal. FAC regime was used in 58.7% patients. RCB I (minimal residual disease) was present in 12.7% patients, RCB II (moderate residual disease) was present in 51.6% patients and RCB III (extensive residual disease) was found in 35.7% patients. Cross tabulation for age, T stage, pathological AJCC classification, histological classification and nuclear grading was not significant (p&gt;0.05). Conclusion: Neoadjunctive therapy is associated with decrease rate of residual cancer burden. Keywords: Breast carcinoma. Neo adjunctive therapy, Decreases residual cancer burden

Prediction model of axillary lymph node status using automated breast ultrasound (ABUS) and ki-67 status in early-stage breast cancer

BackgroundAutomated breast ultrasound (ABUS) is a useful choice in breast disease diagnosis. The axillary lymph node (ALN) status is crucial for predicting the clinical classification and deciding on the treatment of early-stage breast cancer (EBC) and could be the primary indicator of locoregional recurrence. We aimed to establish a prediction model using ABUS features of primary breast cancer to predict ALN status. MethodsA total of 469 lesions were divided into the axillary lymph node metastasis (ALNM) group and the no ALNM (NALNM) group. Univariate analysis and multivariate analysis were used to analyze the difference of clinical factors and ABUS features between the two groups, and a predictive model of ALNM was established. Pathological results were as the gold standard.ResultsKi-67, maximum diameter (MD), posterior feature shadowing or enhancement and hyperechoic halo were significant risk factors for ALNM in multivariate logistic regression analysis (P < 0.05). The four risk factors were used to build the predictive model, and it achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.791 (95% CI: 0.751, 0.831). The accuracy, sensitivity and specificity of the prediction model were 72.5%, 69.1% and 75.26%. The positive predictive value (PPV) and negative predictive value (NPV) were 66.08% and 79.93%, respectively. Distance to skin, MD, margin, shape, internal echo pattern, orientation, posterior features, and hyperechoic halo showed significant differences between stage I and stage II (P < 0.001).ConclusionABUS features and Ki-67 can meaningfully predict ALNM in EBC and the prediction model may facilitate a more effective therapeutic schedule.

Habitat radiomics analysis of pet/ct imaging in high-grade serous ovarian cancer: Application to Ki-67 status and progression-free survival.

Purpose: We aim to develop and validate PET/ CT image-based radiomics to determine the Ki-67 status of high-grade serous ovarian cancer (HGSOC), in which we use the metabolic subregion evolution to improve the prediction ability of the model. At the same time, the stratified effect of the radiomics model on the progression-free survival rate of ovarian cancer patients was illustrated. Materials and methods: We retrospectively reviewed 161 patients with HGSOC from April 2013 to January 2019. 18F-FDG PET/ CT images before treatment, pathological reports, and follow-up data were analyzed. A randomized grouping method was used to divide ovarian cancer patients into a training group and validation group. PET/ CT images were fused to extract radiomics features of the whole tumor region and radiomics features based on the Habitat method. The feature is dimensionality reduced, and meaningful features are screened to form a signature for predicting the Ki-67 status of ovarian cancer. Meanwhile, survival analysis was conducted to explore the hierarchical guidance significance of radiomics in the prognosis of patients with ovarian cancer. Results: Compared with texture features extracted from the whole tumor, the texture features generated by the Habitat method can better predict the Ki-67 state (p < 0.001). Radiomics based on Habitat can predict the Ki-67 expression accurately and has the potential to become a new marker instead of Ki-67. At the same time, the Habitat model can better stratify the prognosis (p < 0.05). Conclusion: We found a noninvasive imaging predictor that could guide the stratification of prognosis in ovarian cancer patients, which is related to the expression of Ki-67 in tumor tissues. This method is of great significance for the diagnosis and treatment of ovarian cancer.

How Reliable Are Gene Expression-Based and Immunohistochemical Biomarkers Assessed on a Core-Needle Biopsy? A Study of Paired Core-Needle Biopsies and Surgical Specimens in Early Breast Cancer.

Simple SummaryThis study aims to assess the reliability of using a core-needle biopsy (CNB) for preoperative tumor characterization using gene expression analysis and conventional immunohistochemistry (IHC) analysis for clinical biomarkers in early breast cancer. Obtaining a preoperative CNB is a standard procedure in the evaluation of a primary breast cancer, with previous studies suggesting that it can be considered trustworthy to perform immunohistochemical analysis on CNB samples. However, little research has been carried out to evaluate whether gene expression-based biomarker assessment can be carried out reliably on the preoperative CNB. This information is important as genomic profiling is gaining ever-increasing importance in the treatment of early breast cancer.In early breast cancer, a preoperative core-needle biopsy (CNB) is vital to confirm the malignancy of suspected lesions and for assessing the expression of treatment predictive and prognostic biomarkers in the tumor to choose the optimal treatments, emphasizing the importance of obtaining reliable results when biomarker status is assessed on a CNB specimen. This study aims to determine the concordance between biomarker status assessed as part of clinical workup on a CNB compared to a medically untreated surgical specimen. Paired CNB and surgical specimens from 259 patients that were part of the SCAN-B cohort were studied. The concordance between immunohistochemical (IHC) and gene expression (GEX) based biomarker status was investigated. Biomarkers of interest included estrogen receptor (ER; specifically, the alpha variant), progesterone receptor (PgR), Ki67, HER2, and tumor molecular subtype. In general, moderate to very good correlation in biomarker status between the paired CNB and surgical specimens was observed for both IHC assessment (83–99% agreement, kappa range 0.474–0.917) and GEX assessment (70–97% agreement, kappa range 0.552–0.800), respectively. However, using IHC, 52% of cases with low Ki67 status in the CNB shifted to high Ki67 status in the surgical specimen (McNemar’s p = 0.011). Similarly, when using GEX, a significant shift from negative to positive ER (47%) and from low to high Ki67 (16%) was observed between the CNB and surgical specimen (McNemar’s p = 0.027 and p = 0.002 respectively). When comparing biomarker status between different techniques (IHC vs. GEX) performed on either CNBs or surgical specimens, the agreement in ER, PgR, and HER2 status was generally over 80% in both CNBs and surgical specimens (kappa range 0.395–0.708), but Ki67 and tumor molecular subtype showed lower concordance levels between IHC and GEX (48–62% agreement, kappa range 0.152–0.398). These results suggest that both the techniques used for collecting tissue samples and analyzing biomarker status have the potential to affect the results of biomarker assessment, potentially also impacting treatment decisions and patient survival outcomes.

RNA sequencing-based single sample predictors of molecular subtype and risk of recurrence for clinical assessment of early-stage breast cancer

Multigene assays for molecular subtypes and biomarkers can aid management of early invasive breast cancer. Using RNA-sequencing we aimed to develop single-sample predictor (SSP) models for clinical markers, subtypes, and risk of recurrence (ROR). A cohort of 7743 patients was divided into training and test set. We trained SSPs for subtypes and ROR assigned by nearest-centroid (NC) methods and SSPs for biomarkers from histopathology. Classifications were compared with Prosigna in two external cohorts (ABiM, n = 100 and OSLO2-EMIT0, n = 103). Prognostic value was assessed using distant recurrence-free interval. Agreement between SSP and NC for PAM50 (five subtypes) was high (85%, Kappa = 0.78) for Subtype (four subtypes) very high (90%, Kappa = 0.84) and for ROR risk category high (84%, Kappa = 0.75, weighted Kappa = 0.90). Prognostic value was assessed as equivalent and clinically relevant. Agreement with histopathology was very high or high for receptor status, while moderate for Ki67 status and poor for Nottingham histological grade. SSP and Prosigna concordance was high for subtype (OSLO-EMIT0 83%, Kappa = 0.73 and ABiM 80%, Kappa = 0.72) and moderate and high for ROR risk category (68 and 84%, Kappa = 0.50 and 0.70, weighted Kappa = 0.70 and 0.78). Pooled concordance for emulated treatment recommendation dichotomized for chemotherapy was high (85%, Kappa = 0.66). Retrospective evaluation suggested that SSP application could change chemotherapy recommendations for up to 17% of postmenopausal ER+/HER2-/N0 patients with balanced escalation and de-escalation. Results suggest that NC and SSP models are interchangeable on a group-level and nearly so on a patient level and that SSP models can be derived to closely match clinical tests.

Identification of HMGB2 associated with proliferation, invasion and prognosis in lung adenocarcinoma via weighted gene co-expression network analysis

BackgroundHigh mobility group protein B2 (HMGB2) is a multifunctional protein that plays various roles in different cellular compartments. Moreover, HMGB2 serves as a potential prognostic biomarker and therapeutic target for lung adenocarcinoma (LUAD).MethodsIn this study, the expression pattern, prognostic implication, and potential role of HMGB2 in LUAD were evaluated using the integrated bioinformatics analyses based on public available mRNA expression profiles from The Cancer Genome Atlas and Gene Expression Omnibus databases, both at the single-cell level and the tissue level. Further study in the patient-derived samples was conducted to explore the correlation between HMGB2 protein expression levels with tissue specificity, (tumor size-lymph node-metastasis) TNM stage, pathological grade, Ki-67 status, and overall survival. In vitro experiments, such as CCK-8, colony-formation and Transwell assay, were performed with human LUAD cell line A549 to investigate the role of HMGB2 in LUAD progression. Furthermore, xenograft tumor model was generated with A549 in nude mice.ResultsThe results showed that the HMGB2 expression was higher in the LUAD samples than in the adjacent normal tissues and was correlated with high degree of malignancy in different public data in this study. Besides, over-expression of HMGB2 promoted A549 cells proliferation and migration while knocking down of HMGB2 suppressed the tumor promoting effect.ConclusionsOur study indicated that HMGB2 was remarkably highly expressed in LUAD tissues, suggesting that it is a promising diagnostic and therapeutic marker for LUAD in the future.