Dataset Information

A Novel Model Incorporating Tumor Stiffness, Blood Flow Characteristics, and Ki-67 Expression to Predict Responses After Neoadjuvant Chemotherapy in Breast Cancer.

ABSTRACT:

Objective

To investigate the ability of tumor stiffness, tumor blood flow, and Ki-67 expression alone or in combination in predicting the pathological response to neoadjuvant chemotherapy (NACT) in breast cancer.

Patients and methods

This prospective cohort study included 145 breast cancer patients treated with NACT. Tumor stiffness (maximum stiffness (Emax), mean stiffness (Emean)), blood score (BS), and their relative changes, were evaluated before (t0), during (t1-t5), and at the end of NACT (t6) by shear-wave elastography and optical imaging. Ki-67 expression was quantitatively evaluated by immunohistochemistry using core biopsy specimens obtained before NACT. Pathological responses were evaluated by residual cancer burden. The ability of tumor stiffness, BS, Ki-67, and predRCB-which combined ?Emean (t2) (the relative changes in Emean after the second NACT cycle), BS2 (BS after the second NACT cycle), and Ki-67-in predicting tumor responses was compared using receiver operating characteristic curves and the Z-test.

Results

Tumor stiffness and BS decreased during NACT. ?Emean (t2), BS2, and Ki-67 had better predictive performance than other indexes in identifying a favorable response (AUC = 0.82, 0.81, and 0.80) and resistance responses (AUC = 0.85, 0.79, and 0.84), with no significant differences between the three (p > 0.05). PredRCB had better predictive performance than any parameter alone for a favorable response (AUC = 0.90) and resistance (AUC = 0.93).

Conclusion

Tumor stiffness, BS, and Ki-67 expression showed good and similar abilities for predicting the pathological response to NACT, and predRCB was a significantly better predictor than each index alone. These results may help design therapeutic strategies for breast cancer patients undergoing NACT.

SUBMITTER: Zhang J

PROVIDER: S-EPMC7753215 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

A Novel Model Incorporating Tumor Stiffness, Blood Flow Characteristics, and Ki-67 Expression to Predict Responses After Neoadjuvant Chemotherapy in Breast Cancer.

Zhang Jing J Gao Song S Zheng Qiaojin Q Kang Ye Y Li Jianyi J Zhang Shuo S Shang Cong C Tan Xueying X Ren Weidong W Ma Yan Y

Frontiers in oncology 20201208

<h4>Objective</h4>To investigate the ability of tumor stiffness, tumor blood flow, and Ki-67 expression alone or in combination in predicting the pathological response to neoadjuvant chemotherapy (NACT) in breast cancer.<h4>Patients and methods</h4>This prospective cohort study included 145 breast cancer patients treated with NACT. Tumor stiffness (maximum stiffness (Emax), mean stiffness (Emean)), blood score (BS), and their relative changes, were evaluated before (t0), during (t1-t5), and at t ...[more]

PMID: 33364197

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Project description:BackgroundThe discordance of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and Ki-67 cell nuclear proliferation antigen status in patients with locally advanced breast cancer pre- and post-neoadjuvant chemotherapy (NAC) is quite common. This study aimed to assess the frequency of changes in receptor status after NAC in patients with invasive ductal breast cancer and the prognostic impact of such changes.MethodsThe study comprised 670 patients who were diagnosed with invasive ductal breast carcinoma and treated with both NAC and surgery from 2012-2017. Hormone receptor (HR; including ER and PR), HER2, and Ki-67 status was assessed before NAC and in residual invasive tumor cells of the surgical specimens. The prognostic impact of receptor conversions in breast cancer patients treated with NAC was evaluated in this retrospective study.ResultsThe conversion of ER was related to overall survival (OS; P=0.008) and disease-free survival (DFS; P=0.004). Patients whose ER status was always positive had the best prognosis, and those who were always negative had the worst prognosis. Similar results were also found for PR status, as the conversion of PR status was also related to OS (P<0.001) and DFS (P<0.001). At the same time, the conversion of Ki-67 status was related to OS (P=0.042) and DFS (P=0.037), and patients whose Ki-67 status was ≤20% persistently after NAC had the best survival among the 4 groups, while those whose Ki-67 status changed from ≤20% to >20% after NAC had the worst survival. Nevertheless, there was no statistical significance in the conversion of HER2 status. In multivariate Cox regression analyses, PR conversion and post-neoadjuvant pathological lymph node stage (ypN) were independent prognostic factors for DFS (P=0.008, <0.001) and OS (P=0.002, <0.001).ConclusionsChanges in receptor status between pre-treatment and residual disease after NAC are common. Moreover, these alterations have an impact on the survival outcome of invasive ductal breast cancer patients. Thus, receptor status should be re-evaluated routinely before and after NAC to guide individualized treatment.

Project description:BackgroundThis 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.MethodsA 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).ResultsHCCs 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).ConclusionsPreoperative 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.

Dataset Information

A Novel Model Incorporating Tumor Stiffness, Blood Flow Characteristics, and Ki-67 Expression to Predict Responses After Neoadjuvant Chemotherapy in Breast Cancer.

Objective

Patients and methods

Results

Conclusion

Publications

A Novel Model Incorporating Tumor Stiffness, Blood Flow Characteristics, and Ki-67 Expression to Predict Responses After Neoadjuvant Chemotherapy in Breast Cancer.

Similar Datasets

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