scholarly journals Texture Analysis of Dynamic Contrast-Enhanced MRI in Evaluating Pathologic Complete Response (pCR) of Mass-Like Breast Cancer after Neoadjuvant Therapy

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Kun Cao ◽  
Bo Zhao ◽  
Xiao-Ting Li ◽  
Yan-Ling Li ◽  
Ying-Shi Sun
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Neoadjuvant Therapy ◽  
Texture Analysis ◽  
Pathologic Complete Response ◽  
Complete Response ◽  
Imaging Method ◽  
First Order ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced

Objectives. MRI is the standard imaging method in evaluating treatment response of breast cancer after neoadjuvant therapy (NAT), while identification of pathologic complete response (pCR) remains challenging. Texture analysis (TA) on post-NAT dynamic contrast-enhanced (DCE) MRI was explored to assess the existence of pCR in mass-like cancer. Materials and Methods. A primary cohort of 112 consecutive patients (40 pCR and 72 non-pCR) with mass-like breast cancers who received preoperative NAT were retrospectively enrolled. On post-NAT MRI, volumes of the residual-enhanced areas and TA first-order features (19 for each sequence) of the corresponding areas were achieved for both early- and late-phase DCE using an in-house radiomics software. Groups were divided according to the operational pathology. Receiver operating characteristic curves and binary logistic regression analysis were used to select features and achieve a predicting formula. Overall diagnostic abilities were compared between TA and radiologists’ subjective judgments. Validation was performed on a time-independent cohort of 39 consecutive patients. Results. TA features with high consistency (Cronbach’s alpha >0.9) between 2 observers showed significant differences between pCR and non-pCR groups. Logistic regression using features selected by ROC curves generated a synthesized formula containing 3 variables (volume of residual enhancement, entropy, and robust mean absolute deviation from early-phase) to yield AUC = 0.81, higher than that of using radiologists’ subjective judgment (AUC = 0.72), and entropy was an independent risk factor (P<0.001). Accuracy and sensitivity for identifying pCR were 83.93% and 70.00%. AUC of the validation cohort was 0.80. Conclusions. TA may help to improve the diagnostic ability of post-NAT MRI in identifying pCR in mass-like breast cancer. Entropy, as a first-order feature to depict residual tumor heterogeneity, is an important factor.

Laboratory indicators predict axillary nodal pathologic complete response after neoadjuvant therapy in breast cancer

2021 ◽  
Author(s):  
Peng Chen ◽  
Tong Zhao ◽  
Zhao Bi ◽  
Zhao-Peng Zhang ◽  
Li Xie ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Multivariate Analysis ◽  
Neoadjuvant Therapy ◽  
Predictive Factors ◽  
Molecular Subtype ◽  
Treatment Options ◽  
Pathologic Complete Response ◽  
Complete Response ◽  
Breast Cancer Patients ◽  
Logistic Analysis

 The purpose was to integrate clinicopathological and laboratory indicators to predict axillary nodal pathologic complete response (apCR) after neoadjuvant therapy (NAT). The pretreatment clinicopathological and laboratory indicators of 416 clinical nodal-positive breast cancer patients who underwent surgery after NAT were analyzed from April 2015 to 2020. Predictive factors of apCR were examined by logistic analysis. A nomogram was built according to logistic analysis. Among the 416 patients, 37.3% achieved apCR. Multivariate analysis showed that age, pathological grading, molecular subtype and neutrophil-to-lymphocyte ratio were independent predictors of apCR. A nomogram was established based on these four factors. The area under the curve (AUC) was 0.758 in the training set. The validation set showed good discrimination, with AUC of 0.732. In subtype analysis, apCR was 23.8, 47.1 and 50.8% in hormone receptor-positive/HER2-, HER2+ and triple-negative subgroups, respectively. According to the results of the multivariate analysis, pathological grade and fibrinogen level were independent predictors of apCR after NAT in HER2+ patients. Except for traditional clinicopathological factors, laboratory indicators could also be identified as predictive factors of apCR after NAT. The nomogram integrating pretreatment indicators demonstrated its distinguishing capability, with a high AUC, and could help to guide individualized treatment options.

scholarly journals Pharmacokinetic parameters and radiomics model based on dynamic contrast enhanced MRI for the preoperative prediction of sentinel lymph node metastasis in breast cancer

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Meijie Liu ◽  
Ning Mao ◽  
Heng Ma ◽  
Jianjun Dong ◽  
Kun Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Lymph Node ◽  
Regression Method ◽  
Pharmacokinetic Parameters ◽  
Combined Model ◽  
Dce Mri ◽  
Dynamic Contrast Enhanced ◽  
Preoperative Prediction ◽  
Contrast Enhanced

Abstract Background To establish pharmacokinetic parameters and a radiomics model based on dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) for predicting sentinel lymph node (SLN) metastasis in patients with breast cancer. Methods A total of 164 breast cancer patients confirmed by pathology were prospectively enrolled from December 2017 to May 2018, and underwent DCE-MRI before surgery. Pharmacokinetic parameters and radiomics features were derived from DCE-MRI data. Least absolute shrinkage and selection operator (LASSO) regression method was used to select features, which were then utilized to construct three classification models, namely, the pharmacokinetic parameters model, the radiomics model, and the combined model. These models were built through the logistic regression method by using 10-fold cross validation strategy and were evaluated on the basis of the receiver operating characteristics (ROC) curve. An independent validation dataset was used to confirm the discriminatory power of the models. Results Seven radiomics features were selected by LASSO logistic regression. The radiomics model, the pharmacokinetic parameters model, and the combined model yielded area under the curve (AUC) values of 0.81 (95% confidence interval [CI]: 0.72 to 0.89), 0.77 (95% CI: 0.68 to 0.86), and 0.80 (95% CI: 0.72 to 0.89), respectively, for the training cohort and 0.74 (95% CI: 0.59 to 0.89), 0.74 (95% CI: 0.59 to 0.90), and 0.76 (95% CI: 0.61 to 0.91), respectively, for the validation cohort. The combined model showed the best performance for the preoperative evaluation of SLN metastasis in breast cancer. Conclusions The model incorporating radiomics features and pharmacokinetic parameters can be conveniently used for the individualized preoperative prediction of SLN metastasis in patients with breast cancer.

NSABP B-41, a randomized neoadjuvant trial: Genes and signatures associated with pathologic complete response (pCR).

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 511-511
Author(s):  
Sandra M. Swain ◽  
Gong Tang ◽  
Heather Ann Brauer ◽  
David Goerlitz ◽  
Peter C. Lucas ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Epithelial Mesenchymal Transition ◽  
Pathologic Complete Response ◽  
Housekeeping Genes ◽  
Complete Response ◽  
Her2 Positive ◽  
Treatment Benefit ◽  
Mesenchymal Transition ◽  
B 41

511 Background: NSABP B-41 randomly assigned 529 patients (pts) with HER2 positive breast cancer to neoadjuvant trastuzumab (T), lapatinib (L), or combination (T+L), with weekly paclitaxel following doxorubicin and cyclophosphamide. No significant differences in pCR were found, but overall survival was significantly increased for pCR. Methods: RNA was extracted from FFPE tumor specimens, run on the NanoString Breast Cancer 360 Plus panel. Gene counts were normalized to include housekeeping genes, 33 biological signatures from 776 genes across 23 pathways and transformed into logarithm scale with base two. Univariate logistic regression was used to screen candidate genes and signatures that are prognostic of pCR, with false discovery rate controlled at 0.1. Multivariable logistic regression with lasso regularization was used for model selection. Results: 194 core biopsy samples were available; 69 treated with T, 64 with L and 61 with T+L. 20 prognostic genes are selected for trastuzumab-based regimens (TBR), including the epithelial-mesenchymal transition (HEMK1, GRB7, ERBB2, TMPRSS4), adhesion and migration (ITGB6, COL27A1, NRCAM), JAK-STAT (SOCS2), Hedgehog (LRP2), ER signaling (ELOVL2, MAPT), DNA damage and repair (NPEPPS, PRKDC), MAPK (DUSP6, PRKCB), Apoptosis (BCL2), proliferation (TFDP1). ERBB2 expression are associated with pCR in patients on TBR (OR = 1.73), but not for patients on L (interaction p = 0.01). HER2-Enriched correlation (p < 0.001), ESR1 (OR = 0.78, 95% CI = 0.69-0.88, p < 0.001), PD1 (OR = 1.68, 95% CI = 1.12-2.52, p = 0.01) and Tumor Inflammation Score (OR = 1.58, 95% CI = 1.18-2.11, p = 0.002) are associated with pCR in TBR. No genes or signatures were found to be predictive of treatment benefit from L added to T. Conclusions: BC360 highlighted tumor progression and signaling genes prognostic for TBR. HER2-Enriched correlation, ERBB2 and PD1 expression, and immune activation signatures were associated with pCR in TBR and may provide personalized treatment guidance.

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