Ki-67 response-guided preoperative chemotherapy for HER2-positive breast cancer: results of a randomised Phase 2 study

Development of an Intra- and Peritumoral Radiomics Nomogram Using Digital Breast Tomosynthesis for Preoperative Assessment of Ki-67 Expression in Invasive Breast Cancer

RATIONALE AND OBJECTIVES

This study aimed to develop a radiomics nomogram model using preoperative digital breast tomosynthesis (DBT) images to predict Ki-67 expression in patients with invasive breast cancer (IBC).

MATERIALS AND METHODS

This retrospective study involved a cohort of 289 patients with IBC, who were randomly divided into a training dataset (N= 202) and a validation dataset (N= 87). Ki-67 expression was categorized into low and high groups using a 14% threshold. Radiomics features from both the intra- and peritumoral regions of DBT images were used to develop the radiomics model, referred to as Radscore. Clinical and nomogram models were constructed using multivariate logistic regression. The performance of the established models was evaluated using receiver operating characteristic (ROC) curve analysis, calibration curve analysis, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discrimination improvement (IDI).

RESULTS

The clinical model was constructed using tumor size and DBT-reported lymph node metastasis (DBT_reported_LNM). By integrating Radscore_Combine-which incorporates both intra- and peritumoral radiomics features-along with tumor size and DBT_reported_LNM into the nomogram, the model achieved the highest area under the curve (AUC) values of 0.819 and 0.755 in the training and validation datasets, respectively. The notable improvement shown by the NRI and IDI suggests that Radscore_Combine could serve as a valuable biomarker for predicting Ki-67 expression effectively.

CONCLUSION

The nomogram offers a non-invasive method to predict Ki-67 expression in IBC patients, which could aid in creating personalized treatment plans.

Radiomics model building from multiparametric MRI to predict Ki-67 expression in patients with primary central nervous system lymphomas: a multicenter study

OBJECTIVES

To examine the correlation of apparent diffusion coefficient (ADC), diffusion weighted imaging (DWI), and T1 contrast enhanced (T1-CE) with Ki-67 in primary central nervous system lymphomas (PCNSL). And to assess the diagnostic performance of MRI radiomics-based machine-learning algorithms in differentiating the high proliferation and low proliferation groups of PCNSL.

METHODS

83 patients with PCNSL were included in this retrospective study. ADC, DWI and T1-CE sequences were collected and their correlation with Ki-67 was examined using Spearman's correlation analysis. The Kaplan-Meier method and log-rank test were used to compare the survival rates of the high proliferation and low proliferation groups. The radiomics features were extracted respectively, and the features were screened by machine learning algorithm and statistical method. Radiomics models of seven different sequence permutations were constructed. The area under the receiver operating characteristic curve (ROC AUC) was used to evaluate the predictive performance of all models. DeLong test was utilized to compare the differences of models.

RESULTS

Relative mean apparent diffusion coefficient (rADCmean) (ρ=-0.354, p = 0.019), relative mean diffusion weighted imaging (rDWImean) (b = 1000) (ρ = 0.273, p = 0.013) and relative mean T1 contrast enhancement (rT1-CEmean) (ρ = 0.385, p = 0.001) was significantly correlated with Ki-67. Interobserver agreements between the two radiologists were almost perfect for all parameters (rADCmean ICC = 0.978, 95%CI 0.966-0.986; rDWImean (b = 1000) ICC = 0.931, 95% CI 0.895-0.955; rT1-CEmean ICC = 0.969, 95% CI 0.953-0.980). The differences in PFS (p = 0.016) and OS (p = 0.014) between the low and high proliferation groups were statistically significant. The best prediction model in our study used a combination of ADC, DWI, and T1-CE achieving the highest AUC of 0.869, while the second ranked model used ADC and DWI, achieving an AUC of 0.828.

CONCLUSION

rDWImean, rADCmean and rT1-CEmean were correlated with Ki-67. The radiomics model based on MRI sequences combined is promising to distinguish low proliferation PCNSL from high proliferation PCNSL.

MRI radiomics-based interpretable model and nomogram for preoperative prediction of Ki-67 expression status in primary central nervous system lymphoma

Objectives

To investigate the value of interpretable machine learning model and nomogram based on clinical factors, MRI imaging features, and radiomic features to predict Ki-67 expression in primary central nervous system lymphomas (PCNSL).

Materials and methods

MRI images and clinical information of 92 PCNSL patients were retrospectively collected, which were divided into 53 cases in the training set and 39 cases in the external validation set according to different medical centers. A 3D brain tumor segmentation model was trained based on nnU-NetV2, and two prediction models, interpretable Random Forest (RF) incorporating the SHapley Additive exPlanations (SHAP) method and nomogram based on multivariate logistic regression, were proposed for the task of Ki-67 expression status prediction.

Results

The mean dice Similarity Coefficient (DSC) score of the 3D segmentation model on the validation set was 0.85. On the Ki-67 expression prediction task, the AUC of the interpretable RF model on the validation set was 0.84 (95% CI:0.81, 0.86; p < 0.001), which was a 3% improvement compared to the AUC of the nomogram. The Delong test showed that the z statistic for the difference between the two models was 1.901, corresponding to a p value of 0.057. In addition, SHAP analysis showed that the Rad-Score made a significant contribution to the model decision.

Conclusion

In this study, we developed a 3D brain tumor segmentation model and used an interpretable machine learning model and nomogram for preoperative prediction of Ki-67 expression status in PCNSL patients, which improved the prediction of this medical task.

Clinical relevance statement

Ki-67 represents the degree of active cell proliferation and is an important prognostic parameter associated with clinical outcomes. Non-invasive and accurate prediction of Ki-67 expression level preoperatively plays an important role in targeting treatment selection and patient stratification management for PCNSL thereby improving prognosis.

Cytomorphological Disparities in Invasive Breast Cancer Cells following Neoadjuvant Endocrine Therapy and Chemotherapy

INTRODUCTION

Neoadjuvant endocrine therapy (NAE) offers a breast-conserving surgery rate and clinical response rate similar to those of neoadjuvant chemotherapy (NAC), while presenting fewer adverse events and lower pathological complete response rates. The assessment of pathological response determines degenerative changes and predicts the prognosis of breast cancer treated with NAC. This study clarified the degenerative changes occurring in breast cancer following NAE.

METHODS

Our study encompassed two groups: NAE, consisting of 15 patients, and NAC, comprising 18 patients. Tissue samples were obtained from core needle biopsies and surgeries. Nuclear and cell areas were calculated using Autocell analysis. Furthermore, we assessed markers associated with microtubule depolymerization (KIF2A) and initiators of apoptosis (caspase-9).

RESULTS

In the NAC group, we observed significant increases in both cytoplasmic and cell areas. These changes in cytoplasm and cells were notably more pronounced in the NAC group compared to the NAE group. After treatment, KIF2A exhibited a decrease, with the magnitude of change being greater in the NET group than in the NAC group. However, no discernible differences were found in caspase-9 expression between the two groups.

CONCLUSION

Our findings indicate that NAE induces condensation in cancer cells via cell cycle arrest or apoptosis. Conversely, NAC leads to cell enlargement due to the absence of microtubule depolymerization. These discrepancies underscore the importance of accounting for these distinctions when establishing criteria for evaluating pathological responses.

An Automated Breast Volume Scanner-Based Intra- and Peritumoral Radiomics Nomogram for the Preoperative Prediction of Expression of Ki-67 in Breast Malignancy

RATIONALE AND OBJECTIVES

This study aimed to create and verify a nomogram for preoperative prediction of Ki-67 expression in breast malignancy to assist in the development of personalized treatment strategies.

MATERIALS AND METHODS

This retrospective study received approval from the institutional review board and included a cohort of 197 patients with breast malignancy who were admitted to our hospital. Ki-67 expression was divided into two groups based on a 14% threshold: low and high. A radiomics signature was built utilizing 1702 radiomics features based on an intra- and peritumoral (10 mm) regions of interest. Using multivariate logistic regression, radiomics signature, and ultrasound (US) characteristics, the nomogram was developed. To evaluate the model's calibration, clinical application, and predictive ability, decision curve analysis (DCA), the calibration curve, and the receiver operating characteristic curve were used, respectively.

RESULTS

The final nomogram included three independent predictors: tumor size (P = .037), radiomics signature (P < .001), and US-reported lymph node status (P = .018). The nomogram exhibited satisfactory performance in the training cohort, demonstrating a specificity of 0.944, a sensitivity of 0.745, and an area under the curve (AUC) of 0.905. The validation cohort recorded a specificity of 0.909, a sensitivity of 0.727, and an AUC of 0.882. The DCA showed the nomogram's clinical utility, and the calibration curve revealed a high consistency among the expected and detected values.

CONCLUSION

The nomogram used in this investigation can accurately predict Ki-67 expression in people with malignant breast tumors, helping to develop personalized treatment approaches.

Nomogram for Early Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Combining Both Clinicopathological and Imaging Indicators

To construct a nomogram for early prediction of pathological complete response (pCR) in patients with breast cancer (BC) after neoadjuvant chemotherapy (NAC). A total of 257 patients with BC from the fourth hospital of Hebei Medical University were included in the study. The patients were divided into training (n = 128) and validation groups (n = 129). Variables were screened using univariate and multivariate logistic regression analyses, and the nomogram model was set up based on the training group. The training and validation groups were validated using the receiver operating characteristic (ROC) curves and calibration plots. The diagnostic value of the nomogram was evaluated using decision curve analysis (DCA). Indicators such as hormone receptor status, clinical TNM stage, and change rate in apparent diffusion coefficient of breast magnetic resonance imaging after two NAC cycles were used for nomogram construction. The calibration plots showed high consistency between nomogram-predicted and actual pCR probabilities in the training and validation groups. The areas under the curve of the ROC curve with discrimination ability were 0.942 and 0.921 in the training and validation groups, respectively. This showed an excellent discrimination ability of our nomogram for pCR prediction. Further, DCA showed favorable diagnostic value in our model. The nomogram may be instructive to clinicians for early prediction of pCR and helpful to adjust the treatment program on time in neoadjuvant management.

Ki-67 as a Prognostic Biomarker in Invasive Breast Cancer

The advent of molecular medicine has transformed breast cancer management. Breast cancer is now recognised as a heterogenous disease with varied morphology, molecular features, tumour behaviour, and response to therapeutic strategies. These parameters are underpinned by a combination of genomic and immunohistochemical tumour factors, with estrogen receptor (ER) status, progesterone receptor (PgR) status, human epidermal growth factor receptor-2 (HER2) status, Ki-67 proliferation indices, and multigene panels all playing a contributive role in the substratification, prognostication and personalization of treatment modalities for each case. The expression of Ki-67 is strongly linked to tumour cell proliferation and growth and is routinely evaluated as a proliferation marker. This review will discuss the clinical utility, current pitfalls, and promising strategies to augment Ki-67 proliferation indices in future breast oncology.

Prognostic value of Ki-67 in nasopharyngeal carcinoma: a meta-analysis

The prognostic value of Ki-67 in nasopharyngeal carcinoma (NPC) was controversial according to previous studies. We aimed to clarify the association between K-67 expression and survival in NPC through meta-analysis. We conducted a meta-analysis to explore the potential prognostic effect of Ki-67 on overall survival (OS), disease-free survival (DFS), distant metastasis-free survival (DMFS), and local recurrence-free survival (LRFS) in NPC. A total of 13 studies comprising 1314 NPC patients were included. High Ki-67 expression was associated with poor OS (hazard ratio [HR]= 2.70, 95% confidence interval [CI]= 1.97-3.71, P<0.001), DFS (HR = 1.93, 95% CI = 1.49-2.50, P<0.001), and LRFS (HR = 1.86, 95% CI = 1.11-3.12, P=0.019). However, there was no significant association between Ki-67 and DMFS (HR = 1.37, 95% CI = 0.78-2.38, P=0.270). Furthermore, the prognostic role of Ki-67 was maintained throughout different sample sizes, analyses of HR, and study designs for OS and DFS in various subgroups. Elevated Ki-67 expression is a reliable prognostic factor for poorer survival outcomes in NPC.

Clinical value of PET/CT with carbon-11 4DST in the evaluation of malignant and benign lung tumors

OBJECTIVES

The aim of this study was to assess the clinical value of [¹¹C]4DST uptake in patients with lung nodules, including benign and malignant tumors, and to assess the correlation between [¹¹C]4DST uptake and proliferative activity of tumors in comparison with [¹⁸F]FDG uptake.

METHODS

Twenty-six patients (22 males and 4 females, mean age of 65.5-year-old) were analyzed in this prospective study. Patients underwent [¹¹C]4DST and [¹⁸F]FDG PET/CT imaging on the same day. Diagnosis of each lung nodule was confirmed by histopathological examination of tissue specimens at surgery, or during clinical follow-up after the PET/CT studies. To assess the utility of the semi-quantitative evaluation method, the SUV_max was calculated of [¹¹C]4DST and [¹⁸F]FDG uptake by the lesion. Proliferative activities of each tumor as indicated by the immunohistochemical Ki-67 index was also estimated using surgical specimens of patients. Then the relationship between the SUV_max of both PET/CT and the Ki-67 index was examined. Furthermore, the relationship between the uptake of [¹¹C]4DST or [¹⁸F]FDG and the histopathological findings, the clinical stage, and the clinical outcome of patients were also assessed.

RESULTS

There was a positive linear relationship between the SUV_max of [¹¹C]4DST images and the Ki-67 index (Correlation coefficients = 0.68). The SUV_max of [¹¹C]4DST in the 26 lung nodules were 1.65 ± 0.40 for benign lesions, 3.09 ± 0.83 for adenocarcinomas (P < 0.001 between benign and adenocarcinoma), and 2.92 ± 0.58 for SqCCs (P < 0.001 between benign and SqCC). Whereas, the SUVmax of [¹⁸F]FDG were 2.38 ± 2.27 for benign lesions, 6.63 ± 4.24 for adenocarcinomas (n.s.), and 7.52 ± 2.84 for SqCCs (n.s.). The relationship between TNM tumor stage and the SUV_max of [¹¹C]4DST were 2.54 ± 0.37 for T1, 3.48 ± 0.57 for T2, and 4.17 ± 0.72 for T3 (P < 0.005 between T1 and T2, and P < 0.001 between T1 and T3). In comparison with the TNM pathological stage, SUVmax of [¹¹C]4DST were 2.63 ± 0.49 for stage I, 3.36 ± 0.23 for stage II, 3.40 ± 1.12 for stage III, and 4.65 for stage IV (P < 0.05 between stages I and II). In comparison of the clinical outcome, the SUV_max of [¹¹C]4DST were 2.72 ± 0.56 for the no recurrence (No Rec.) group, 3.10 ± 0.33 for the recurrence-free with adjuvant chemotherapy after the surgery (the No Rec. Adjv. CTx. group) and 4.66 ± 0.02 for the recurrence group (Rec. group) (P < 0.001 between the No Rec and Rec. groups, and P < 0.005 between the No Rec. Adjv. CTx. and Rec. groups).

CONCLUSIONS

PET/CT with [¹¹C]4DST is as feasible for imaging of lung tumors as [¹⁸F]FDG PET/CT. For diagnosing lung tumors, [¹¹C]4DST PET is useful in distinguishing benign nodules from malignancies. [¹¹C]4DST uptake in lung carcinomas is correlated with the proliferative activity of tumors, indicating a promising noninvasive PET imaging of DNA synthesis in malignant lung tumors.