MRI and PET/CT for evaluation of the pathological response to neoadjuvant chemotherapy in breast cancer: A systematic review and meta-analysis

Li, Huimin; Yao, Liang; Jin, Penghui; Hu, Lin; Li, Xiaofei; Guo, Tiankang; Yang, Kehu

doi:10.1016/j.breast.2018.04.018

Cited by 91 publications

(73 citation statements)

References 38 publications

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“…Various prediction methods have been proposed to predict the responses to NAC in patients with breast cancer, including physical examination and medical imaging tests like mammography, ultrasonography, diffuse optical spectroscopic (8), breast MRI, and PET/CT (9,10). Although MRI-based methods cannot detect pCR with adequate accuracy (11), it is currently the most accurate method for determining response to NAC (9,10,12). Specifically, contrast-enhanced MRI was considered as the most reliable technique for evaluating the responses to NAC (13,14) at present, as its measurements of perfusion and permeability of tissue microvessels are sensitive to angiogenic changes.…”

Section: Introductionmentioning

confidence: 99%

Radiomics of Multiparametric MRI for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer: A Multicenter Study

Liu

et al. 2019

Clinical Cancer Research

342

298

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Purpose: We evaluated the performance of the newly proposed radiomics of multiparametric MRI (RMM), developed and validated based on a multicenter dataset adopting a radiomic strategy, for pretreatment prediction of pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer. Experimental Design: A total of 586 potentially eligible patients were retrospectively enrolled from four hospitals (primary cohort and external validation cohort 1-3). Quantitative imaging features were extracted from T2-weighted imaging, diffusion-weighted imaging, and contrast-enhanced T1-weighted imaging before NAC for each patient. With features selected using a coarse to fine feature selection strategy, four radiomic signatures were constructed based on each of the three MRI sequences and their combination. RMM was developed based on the best radiomic signature incorporating with independent clinicopathologic risk factors. The performance of RMM was assessed with respect to its discrimination and clinical usefulness, and compared with that of clinical information-based prediction model. Results: Radiomic signature combining multiparametric MRI achieved an AUC of 0.79 (the highest among the four radiomic signatures). The signature further achieved good performances in hormone receptor-positive and HER2negative group and triple-negative group. RMM yielded an AUC of 0.86, which was significantly higher than that of clinical model in two of the three external validation cohorts. Conclusions: The study suggested a possibility that RMM provided a potential tool to develop a model for predicting pCR to NAC in breast cancer.

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Section: Introductionmentioning

confidence: 99%

Radiomics of Multiparametric MRI for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer: A Multicenter Study

Liu

et al. 2019

Clinical Cancer Research

342

298

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“…A recent meta-analysis reviewed the findings of 13 studies comparing MRI and PET/CT in predicting response after neoadjuvant treatment (15). The pooled sensitivity for PET/CT and MRI was 77% and 88%, respectively.…”

Section: Histological Examinationmentioning

confidence: 99%

“…On the other hand, FDG-PET has been shown to have low sensitivity for small lesions (13). To date, several studies examined the role of PET/CT in predicting response to neoadjuvant chemotherapy when performed after the completion of neoadjuvant chemotherapy and before surgery, with inconsistent findings, most of them comparing the findings with that of MRI (6,(14)(15)(16)(17)(18)(19).…”

Section: Introductionmentioning

confidence: 99%

The Value of F-18-fluorodeoxyglucose Positron Emission Tomography/Computed Tomography for the Detection of Residual Breast Tumor or Axillary Metastasis after Neoadjuvant Chemotherapy in Invasive Ductal Carcinoma of the Breast

Berberoğlu¹

2020

Istanbul Med J

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Introduction: Accurate evaluation of pathological response after neoadjuvant chemotherapy would aid in treatment and surgical planning as well as prediction of outcomes. This study examined the value of F-18-fluorodeoxyglucose positron emission tomography/computed tomography (F-18-FDG PET/ CT) in predicting pathologically confirmed residual tumor in breast or presence of axillary metastasis when performed after completion of neoadjuvant therapy in patients with invasive ductal carcinoma (IDC) of the breast cancer. Methods: This retrospective study included 52 IDC of the breast who received neoadjuvant chemotherapy and underwent F-18-FDG PET/CT between 2015 and 2019 after completion of neoadjuvant chemotherapy. Diagnostic performance parameters of F-18-FDG PET/CT for predicting residual tumor or presence of axillary metastasis were estimated based on histopathological findings. Results: All patients had IDC. F-18-FDG PET/CT exhibited high specificity for both locations (89.5% and 93.8% and for breast and axilla, respectively). The sensitivity of the method, on the other hand, was low for both locations (66.7% and 30.0% for breast and axilla, respectively), particularly for axilla. Falsenegative rate (i.e., missing rate) for breast and axilla was 9.1% and 0% for the tumors >8 mm in diameter. Conclusion: F-18-FDG PET/CT does not seem to provide reliable information on the presence of a residual tumor or node metastasis when performed after the completion of neoadjuvant treatment in IDC of the breast. New diagnostic modalities utilized at different time points or including a combination of different imaging methods are warranted.

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“…In a recent meta-analysis of 969 patients from 18 studies, MRI had a summary ROC (sROC) AUC of 0.89(13). When comparing PET-CT to MRI in a meta-analysis of 13 studies with 575 patients, MRI was more accurate, summary ROC (sROC) AUC: 0.88 vs. 0.84(34). In contrast, in a meta-analysis of 10 studies, PET-CT outperformed MRI during NAC treatment(15).…”

mentioning

confidence: 94%

Early Assessment Window for Predicting Breast Cancer Neoadjuvant Therapy Using Biomarkers, Ultrasound, and Diffuse Optical Tomography

Zhu

Ademuyiwa

Young

et al. 2020

Preprint

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Background: Neoadjuvant Therapy (NAT) permits less aggressive breast and axillary surgery and better assessment of systemic response. Establishing accurate and early predictors of NAT response would help limit morbidity of ineffective regimens through modification of treatment regimens and thereby optimize clinical outcomes. The purpose of the study was to assess the utility of tumor biomarkers, ultrasound (US) and US-guided diffuse optical tomography (DOT) in early prediction of breast cancer response to NAT.MethodsThis prospective HIPAA compliant study was approved by the institutional review board. Forty one patients were imaged with US and US-guided DOT prior to NAT, at completion of the first three treatment cycles, and prior to definitive surgery from February 2017 to January 2020. Miller-Payne grading was used to assess pathologic response. Receiver operating characteristic curves (ROCs) were derived from logistic regression using independent variables, including: tumor biomarkers, US maximum diameter, percentage reduction of the diameter (%US), pretreatment maximum total hemoglobin concentration (HbT) and percentage reduction in HbT (%HbT) at different treatment time points. Resulting ROCs were compared using area under the curve (AUC). Statistical significance was tested using two-sided two-sample student t-test with P<0.05 considered statistically significant. ResultsThirty-eight patients (mean age =47, range 24-71 years) successfully completed the study, including 15 HER2+ of which 11 were ER+; 12 ER+ or PR+/HER2-, and 11 triple negative. The combination of HER2 and ER biomarkers, %HbT at the end of cycle 1 (EOC1) and %US (EOC1) provided the best early prediction, AUC = 0.941 (95% CI: 0.869–1.0). Similarly an AUC of 0.910 (95% CI: 0.810–1.0) with %US (EOC1) and %HbT (EOC1) can be achieved independent of HER2 and ER status. The most accurate prediction, AUC = 0.974 (95% CI: 0.933–1.0), was achieved with %US at EOC1 and %HbT (EOC3) independent of biomarker status. ConclusionThe combined use of tumor HER2 and ER status, US, and US-guided DOT may provide accurate prediction of NAT response as early as the completion of the first treatment cycle. Clinical Trial Registration number: NCT02891681Registration time: September 7, 2016https://clinicaltrials.gov/ct2/show/NCT02891681

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MRI and PET/CT for evaluation of the pathological response to neoadjuvant chemotherapy in breast cancer: A systematic review and meta-analysis

Cited by 91 publications

References 38 publications

Radiomics of Multiparametric MRI for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer: A Multicenter Study

Radiomics of Multiparametric MRI for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer: A Multicenter Study

The Value of F-18-fluorodeoxyglucose Positron Emission Tomography/Computed Tomography for the Detection of Residual Breast Tumor or Axillary Metastasis after Neoadjuvant Chemotherapy in Invasive Ductal Carcinoma of the Breast

Early Assessment Window for Predicting Breast Cancer Neoadjuvant Therapy Using Biomarkers, Ultrasound, and Diffuse Optical Tomography

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