Potential Antihuman Epidermal Growth Factor Receptor 2 Target Therapy Beneficiaries: The Role of MRI‐Based Radiomics in Distinguishing Human Epidermal Growth Factor Receptor 2‐Low Status of Breast Cancer

BACKGROUND Multiparametric MRI radiomics could distinguish human epidermal growth factor receptor 2 (HER2)-positive from HER2-negative breast cancers. However, its value for further distinguishing HER2-low from HER2-negative breast cancers has not been investigated. PURPOSE To investigate whether multiparametric MRI-based radiomics can distinguish HER2-positive from HER2-negative breast cancers (task 1) and HER2-low from HER2-negative breast cancers (task 2). STUDY TYPE Retrospective. POPULATION Task 1: 310 operable breast cancer patients from center 1 (97 HER2-positive and 213 HER2-negative); task 2: 213 HER2-negative patients (108 HER2-low and 105 HER2-zero); 59 patients from center 2 (16 HER2-positive, 27 HER2-low and 16 HER2-zero) for external validation. FIELD STRENGTH/SEQUENCE A 3.0 T/T1-weighted contrast-enhanced imaging (T1CE), diffusion-weighted imaging (DWI)-derived apparent diffusion coefficient (ADC). ASSESSMENT Patients in center 1 were assigned to a training and internal validation cohort at a 2:1 ratio. Intratumoral and peritumoral features were extracted from T1CE and ADC. After dimensionality reduction, the radiomics signatures (RS) of two tasks were developed using features from T1CE (RS-T1CE), ADC (RS-ADC) alone and T1CE + ADC combination (RS-Com). STATISTICAL TESTS Mann-Whitney U tests, the least absolute shrinkage and selection operator, receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). RESULTS For task 1, RS-ADC yielded higher area under the ROC curve (AUC) in the training, internal, and external validation of 0.767/0.725/0.746 than RS-T1CE (AUC = 0.733/0.674/0.641). For task 2, RS-T1CE yielded higher AUC of 0.765/0.755/0.678 than RS-ADC (AUC = 0.706/0.608/0.630). For both of task 1 and task 2, RS-Com achieved the best performance with AUC of 0.793/0.778/0.760 and 0.820/0.776/0.711, respectively, and obtained higher clinical benefit in DCA compared with RS-T1CE and RS-ADC. The calibration curves of all RS demonstrated a good fitness. DATA CONCLUSION Multiparametric MRI radiomics could noninvasively and robustly distinguish HER2-positive from HER2-negative breast cancers and further distinguish HER2-low from HER2-negative breast cancers. EVIDENCE LEVEL 3. TECHNICAL EFFICACY Stage 2.

[1]  Huina Zhang,et al.  Current Biological, Pathological and Clinical Landscape of HER2-Low Breast Cancer , 2022, Cancers.

[2]  Xin Sun,et al.  Diagnostic value of core needle biopsy for determining HER2 status in breast cancer, especially in the HER2-low population , 2022, Breast Cancer Research and Treatment.

[3]  Shasha Lv,et al.  Development and validation of a clinicoradiomic nomogram to assess the HER2 status of patients with invasive ductal carcinoma , 2022, BMC Cancer.

[4]  J. Rahnenführer,et al.  Long-term prognostic significance of HER2-low and HER2-zero in node-negative breast cancer. , 2022, European journal of cancer.

[5]  Y. Teng,et al.  Delta-Radiomics Based on Dynamic Contrast-Enhanced MRI Predicts Pathologic Complete Response in Breast Cancer Patients Treated with Neoadjuvant Chemotherapy , 2022, Cancers.

[6]  Y. Li,et al.  Distinct clinical and somatic mutational features of breast tumors with high-, low-, or non-expressing human epidermal growth factor receptor 2 status , 2022, BMC Medicine.

[7]  V. Kristensen,et al.  Quantification of Tumor Hypoxia through Unsupervised Modelling of Consumption and Supply Hypoxia MR Imaging in Breast Cancer , 2022, Cancers.

[8]  A. Jemal,et al.  Cancer statistics, 2022 , 2022, CA: a cancer journal for clinicians.

[9]  Jiandong Yin,et al.  Radiomics Nomogram Based on Radiomics Score from Multiregional Diffusion-Weighted MRI and Clinical Factors for Evaluating HER-2 2+ Status of Breast Cancer , 2021, Diagnostics.

[10]  Lirong Song,et al.  Intratumoral and Peritumoral Radiomics Based on Functional Parametric Maps from Breast DCE‐MRI for Prediction of HER‐2 and Ki‐67 Status , 2021, Journal of magnetic resonance imaging : JMRI.

[11]  P. Prasanna,et al.  Optimizing the Peritumoral Region Size in Radiomics Analysis for Sentinel Lymph Node Status Prediction in Breast Cancer. , 2020, Academic radiology.

[12]  I. Witzel,et al.  Targeting the Human Epidermal Growth Factor Receptor Family in Breast Cancer beyond HER2 , 2020, Breast Care.

[13]  Yan Bai,et al.  Radiomics Signatures Based on Multiparametric MRI for the Preoperative Prediction of the HER2 Status of Patients with Breast Cancer. , 2020, Academic radiology.

[14]  Thomas E Yankeelov,et al.  Multiparametric Analysis of Longitudinal Quantitative MRI Data to Identify Distinct Tumor Habitats in Preclinical Models of Breast Cancer , 2020, Cancers.

[15]  Shao-Feng Duan,et al.  Preoperative MRI-Based Radiomic Machine-Learning Nomogram May Accurately Distinguish Between Benign and Malignant Soft-Tissue Lesions: A Two-Center Study. , 2020, Journal of magnetic resonance imaging : JMRI.

[16]  A. Sapino,et al.  Evolving concepts in HER2 evaluation in breast cancer: heterogeneity, HER2-low carcinomas and beyond. , 2020, Seminars in cancer biology.

[17]  Hugh Chen,et al.  From local explanations to global understanding with explainable AI for trees , 2020, Nature Machine Intelligence.

[18]  W. Long,et al.  Preoperative prediction of lymphovascular invasion in invasive breast cancer with dynamic contrast‐enhanced‐MRI‐based radiomics , 2019, Journal of magnetic resonance imaging : JMRI.

[19]  Ying Sun,et al.  Pretreatment MRI radiomics analysis allows for reliable prediction of local recurrence in non-metastatic T4 nasopharyngeal carcinoma , 2019, EBioMedicine.

[20]  R. Greil,et al.  HER2 Directed Antibody-Drug-Conjugates beyond T-DM1 in Breast Cancer , 2019, International journal of molecular sciences.

[21]  M. Hatt,et al.  External validation of a combined PET and MRI radiomics model for prediction of recurrence in cervical cancer patients treated with chemoradiotherapy , 2018, European Journal of Nuclear Medicine and Molecular Imaging.

[22]  E. Weiland,et al.  Diagnostic performance of initial enhancement analysis using ultra-fast dynamic contrast-enhanced MRI for breast lesions , 2018, European Radiology.

[23]  A. Larionov.  Current Therapies for Human Epidermal Growth Factor Receptor 2-Positive Metastatic Breast Cancer Patients , 2018, Front. Oncol..

[24]  M. Papotti,et al.  Optimal Ki67 cut-off for luminal breast cancer prognostic evaluation: a large case series study with a long-term follow-up , 2016, Breast Cancer Research and Treatment.

[25]  Taiwo A. Togun,et al.  In Situ Quantitative Measurement of HER2mRNA Predicts Benefit from Trastuzumab-Containing Chemotherapy in a Cohort of Metastatic Breast Cancer Patients , 2014, PloS one.

[26]  P. Lambin,et al.  Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach , 2014, Nature Communications.

[27]  Manojkumar Saranathan,et al.  DIfferential subsampling with cartesian ordering (DISCO): A high spatio‐temporal resolution dixon imaging sequence for multiphasic contrast enhanced abdominal imaging , 2012, Journal of magnetic resonance imaging : JMRI.

[28]  Matthew W. Conklin,et al.  Why the stroma matters in breast cancer , 2012, Cell adhesion & migration.

[29]  Michael Detmar,et al.  Lymphangiogenesis and cancer. , 2011, Genes & cancer.

[30]  T. Uematsu.  Focal breast edema associated with malignancy on T2-weighted images of breast MRI: peritumoral edema, prepectoral edema, and subcutaneous edema , 2014, Breast Cancer.

[31]  Cheng Li,et al.  Adjusting batch effects in microarray expression data using empirical Bayes methods. , 2007, Biostatistics.

[32]  W. McGuire,et al.  Human breast cancer: correlation of relapse and survival with amplification of the HER-2/neu oncogene. , 1987, Science.