Imaging and Clinicopathologic Features Associated With Pathologic Complete Response in HER2-positive Breast Cancer Receiving Neoadjuvant Chemotherapy With Dual HER2 Blockade

Yoon, Ga Young; Chae, Eun Young; Shin, Hee Jung; Choi, Woo Jung; Kim, Hak Hee; Kim, Jeong Eun; Kim, Sung‐Bae

doi:10.1016/j.clbc.2019.06.015

Cited by 10 publications

(5 citation statements)

References 24 publications

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“…Several studies have demonstrated the effectiveness of US 13 , 14 , 15 , 16 and MRI 1 , 2 , 3 , 4 , 5 , 10 , 11 , 14 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 in predicting TILs in BC. However, only one study has evaluated TILs in BC in multimodality (US and MRI).…”

Section: Discussionmentioning

confidence: 99%

“… 13 , 14 , 15 , 16 MRI characteristics, such as tumor size, margin, apparent diffusion coefficient (ADC), and enhancement pattern, have been associated with TIL levels of BC. 1 , 2 , 3 , 4 , 5 , 10 , 11 , 14 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 However, no conclusive results were obtained.…”

Section: Introductionmentioning

confidence: 96%

“… 2 , 3 , 4 TIL levels are positively correlated with clinical benefit in HER2-positive BC. 5 , 6 , 7 , 8 , 9 , 10 According to the fifth edition of the World Health Organization’s classification of tumors, TIL quantification in HER2-positive subtypes was supported. Recent research suggests investigating the role of TIL subpopulations in HER2-positive BC.…”

Section: Introductionmentioning

confidence: 99%

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Radiological Features for Predicting the Status of CD8-Positive Lymphocytes in HER2 Positive Breast Cancer

Fan,

Li,

Zhong

et al. 2024

Balkan Med J

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Background: The level of tumor-infiltrating lymphocytes (TILs) in human epidermal growth factor receptor type 2 (HER2)-positive breast cancer (BC) is positively correlated with pathological complete response. Aims: To investigate the relationship between ultrasound (US) and magnetic resonance imaging (MRI) features and the level of CD8-positive TILs (CD8 + -TILs) in patients with HER2-positive BC. Study Design: Retrospective cohort study. Methods: This retrospective study included 155 consecutive women with HER2-positive BC. Patients were divided into two groups: CD8 + -TIL low (< 35%) and CD8 + -TIL high (≥ 35%) groups. US and MRI features were evaluated using the BI-RADS lexicon, and the apparent diffusion coefficient (ADC) value was calculated using RadiAnt software. Univariate and multivariate analyses revealed the optimal US and MRI features for predicting CD8 + -TIL levels. Receiver operating characteristic analysis and the Delong test were used to compare the diagnostic performance of US and MRI features. Furthermore, implementing a nomogram will increase clinical utility. Results: Univariate analysis of US features showed significant differences in shape, orientation, and posterior echo between the two groups; however, there were no significant differences in margins, internal echo, and microcalcification. Multifactorial analysis revealed that shape, orientation, and posterior echo were independent risk factors, with odds ratios of 11.62, 2.70, and 0.16, respectively. In terms of MRI features, ADC was an independent predictor of CD8 + -TIL levels. These three US features and the ADC performed well, with area under the curve (AUC) values of 0.802 and 0.705, respectively. The combination of US and ADC values had higher predictive efficacy (AUC = 0.888) than either US or ADC alone ( p = 0.009, US_ADC vs. US; p < 0.001, US_ADC vs. ADC). Conclusion: US features (shape, orientation, and posterior echo) and ADC value may be a valuable tool for estimating CD8 + -TIL levels in HER2-positive BC. The nomogram may help clinicians in making decisions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

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Radiological Features for Predicting the Status of CD8-Positive Lymphocytes in HER2 Positive Breast Cancer

Fan,

Li,

Zhong

et al. 2024

Balkan Med J

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“…Fine pleomorphic/fine linear or linear branching calcification morphology on mammography (OR, 7.23), PR negativity (OR, 6.76), and a high TILs (tumor infiltrating lymphocytes) level (OR, 5.92) were independent factors associated with pCR in patients receiving neoadjuvant chemotherapy with dual HER2 blockade (Fig. 3) [37]. Low tumor peak enhancement at MRI indicating less aggressiveness was significantly associated with high TILs (OR, 1.01; p = .020) [38].…”

Section: Luminal Subtypementioning

confidence: 98%

Imaging features of breast cancer molecular subtypes: state of the art

Cho

2021

J Pathol Transl Med

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Characterization of breast cancer molecular subtypes has been the standard of care for breast cancer management. We aimed to provide a review of imaging features of breast cancer molecular subtypes for the field of precision medicine. We also provide an update on the recent progress in precision medicine for breast cancer, implications for imaging, and recent observations in longitudinal functional imaging with radiomics.

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“…Several potential imaging biomarkers for predicting pCR before NST have been assessed. Although a few mammographic or ultrasound features have been associated with pCR [ 6 , 7 ], pCR prediction using conventional images is limited. Antunovic et al [ 8 ] and Li et al [ 9 ] reported that pretreatment positron emission tomography/computed tomography (PET/CT) radiomics features were potential predictors of pCR, and the area under the receiver operating characteristic curve (AUC) ranged from 0.70 to 0.73.…”

Section: Introductionmentioning

confidence: 99%

Integration of Clinical and CT-Based Radiomic Features for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Systemic Therapy in Breast Cancer

Tsai

et al. 2022

Cancers

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The purpose of the present study was to examine the potential of a machine learning model with integrated clinical and CT-based radiomics features in predicting pathologic complete response (pCR) to neoadjuvant systemic therapy (NST) in breast cancer. Contrast-enhanced CT was performed in 329 patients with breast tumors (n = 331) before NST. Pyradiomics was used for feature extraction, and 107 features of seven classes were extracted. Feature selection was performed on the basis of the intraclass correlation coefficient (ICC), and six ICC thresholds (0.7–0.95) were examined to identify the feature set resulting in optimal model performance. Clinical factors, such as age, clinical stage, cancer cell type, and cell surface receptors, were used for prediction. We tried six machine learning algorithms, and clinical, radiomics, and clinical–radiomics models were trained for each algorithm. Radiomics and clinical–radiomics models with gray level co-occurrence matrix (GLCM) features only were also built for comparison. The linear support vector machine (SVM) regression model trained with radiomics features of ICC ≥0.85 in combination with clinical factors performed the best (AUC = 0.87). The performance of the clinical and radiomics linear SVM models showed statistically significant difference after correction for multiple comparisons (AUC = 0.69 vs. 0.78; p < 0.001). The AUC of the radiomics model trained with GLCM features was significantly lower than that of the radiomics model trained with all seven classes of radiomics features (AUC = 0.85 vs. 0.87; p = 0.011). Integration of clinical and CT-based radiomics features was helpful in the pretreatment prediction of pCR to NST in breast cancer.

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Imaging and Clinicopathologic Features Associated With Pathologic Complete Response in HER2-positive Breast Cancer Receiving Neoadjuvant Chemotherapy With Dual HER2 Blockade

Cited by 10 publications

References 24 publications

Radiological Features for Predicting the Status of CD8-Positive Lymphocytes in HER2 Positive Breast Cancer

Radiological Features for Predicting the Status of CD8-Positive Lymphocytes in HER2 Positive Breast Cancer

Imaging features of breast cancer molecular subtypes: state of the art

Integration of Clinical and CT-Based Radiomic Features for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Systemic Therapy in Breast Cancer

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