Predictive Clinicopathologic and Dynamic Contrast-Enhanced MRI Findings for Tumor Response to Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer

Eom, Hye-Joung; Hee, Joo; Choi, Woo Jung; Chae, Eun Young; Shin, Hee Jung; Kim, Hak Hee

doi:10.2214/ajr.16.17125

Cited by 36 publications

(26 citation statements)

References 30 publications

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“…A previous study showed that TNBC was more uneven on dynamic contrastenhanced MRI (29). Although the imaging modes used were The clinicopathology of tumors can reflect tumor biology and affect the outcome of chemotherapy in TNBC patients (30)(31)(32)(33). Our study found that the TNBC histological grade of the high TIL group was lower than that of the low TIL group (84%).…”

Section: Discussionmentioning

confidence: 46%

Correlation Between Mammographic Radiomics Features and the Level of Tumor-Infiltrating Lymphocytes in Patients With Triple-Negative Breast Cancer

Meng

Huang

et al. 2020

Front. Oncol.

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Objectives: Tumor-infiltrating lymphocytes (TILs) have been identified as a significant prognostic indicator of response to neoadjuvant therapy and immunotherapy for triple-negative breast cancer (TNBC) patients. Herein, we aim to assess the association between TIL levels and mammographic features in TNBC patients. Methods: Forty-three patients with surgically proven TNBC who underwent preoperative mammography from January 2018 to December 2018 were recruited. Pyradiomics software was used to extract 204 quantitative radiomics features, including morphologic, grayscale, and textural features, from the segmented lesion areas. The correlation between radiological characteristics and TIL levels was evaluated by screening the most statistically significant radiological features using Mann-Whitney U-test and Pearson correlation coefficient. The patients were divided into two groups based on tumor TIL levels: patients with TIL levels <50% and those with TIL levels ≥50%. The correlation between TIL levels and clinicopathological characteristics was assessed using the chi-square test or Fisher's exact test. Mann-Whitney U-test and Pearson correlation coefficient were used to analyze the statistical significance and Pearson correlation coefficient of clinical pathological features, age, and radiological features. Conclusion: Mammography features not only distinguish high and low TIL levels in TNBC patients but also can act as imaging biomarkers to enhance diagnosis and the response of patients to neoadjuvant therapies and immunotherapies.

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Section: Discussionmentioning

confidence: 46%

Correlation Between Mammographic Radiomics Features and the Level of Tumor-Infiltrating Lymphocytes in Patients With Triple-Negative Breast Cancer

Meng

Huang

et al. 2020

Front. Oncol.

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“…Published studies with this goal are limited by small cohort sizes 18,20–22,24,25 . Furthermore, most of these studies used RECIST to measure response 18,21,24,25 , even though pCR is highly correlated with event-free survival and overall survival 1 .…”

Section: Discussionmentioning

confidence: 99%

“…Studies using pathologic assessment to determine response have varying definitions of pCR 20,22 . In some of these studies patients with positive lymph nodes 20,22 , residual DCIS 20,22 , or even residual invasive carcinoma in the breast 22 are categorized as having pCR.…”

Section: Discussionmentioning

confidence: 99%

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Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set

Cain

Saha

Harowicz

et al. 2018

Breast Cancer Res Treat

143

133

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Purpose To determine whether a multivariate machine learning-based model using computer-extracted features of pre-treatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can predict pathologic complete response (pCR) to neoadjuvant therapy (NAT) in breast cancer patients. Methods Institutional review board approval was obtained for this retrospective study of 288 breast cancer patients at our institution who received NAT and had a pre-treatment breast MRI. A comprehensive set of 529 radiomic features was extracted from each patient’s pretreatment MRI. The patients were divided into equal groups to form a training set and an independent test set. Two multivariate machine learning models (logistic regression and a support vector machine) based on imaging features were trained to predict pCR in (a) all patients with NAT, (b) patients with neoadjuvant chemotherapy (NACT), and (c) triple negative or human epidermal growth factor receptor 2-positive (TN/HER2+) patients who had NAT. The multivariate models were tested using the independent test set, and the area under the receiver operating characteristics (ROC) curve (AUC) was calculated. Results Out of the 288 patients, 64 achieved pCR. The AUC values for predicting pCR in TN/HER+ patients who received NAT were significant (.707, 95%CI: 0.582–0.833, p < 0.002). Conclusions The multivariate models based on pre-treatment MRI features were able to predict pCR in TN/HER2+ patients.

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“…Intratumoral heterogeneity is associated with prognosis [9,10]. A study showed a positive relationship of tumor heterogeneity with recurrence-free survival in breast cancer patients [11].…”

Section: Introductionmentioning

confidence: 99%

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

Cao

Zhao

et al. 2019

Journal of Oncology

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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.

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Predictive Clinicopathologic and Dynamic Contrast-Enhanced MRI Findings for Tumor Response to Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer

Cited by 36 publications

References 30 publications

Correlation Between Mammographic Radiomics Features and the Level of Tumor-Infiltrating Lymphocytes in Patients With Triple-Negative Breast Cancer

Correlation Between Mammographic Radiomics Features and the Level of Tumor-Infiltrating Lymphocytes in Patients With Triple-Negative Breast Cancer

Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set

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

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