3T DCE-MRI Radiomics Improves Predictive Models of Complete Response to Neoadjuvant Chemotherapy in Breast Cancer

Montemezzi, Stefania; Benetti, Giulio; Bisighin, Maria Vittoria; Camera, Lucia; Caumo, Francesca; Fiorio, Elena; Zanelli, Sara; Zuffante, Michele; Cavedon, Carlo

doi:10.3389/fonc.2021.630780

Cited by 18 publications

(7 citation statements)

References 28 publications

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“…At present, however, the correlation between different molecular subtypes and ADC values remains unclear. However, according to several studies, the ADC values of different molecular subtypes are different [ 22 – 27 ], and there is no consensus on the best b value of breast DWI. This study further verified the predictive effect of enhanced MRI findings on the outcomes of neoadjuvant therapy.…”

Section: Discussionmentioning

confidence: 99%

Application of triple evaluation method in predicting the efficacy of neoadjuvant therapy for breast cancer

Han¹,

Li²,

Dong³

et al. 2023

World J Surg Onc

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Objective To analyze the factors related to the efficacy of neoadjuvant therapy for breast cancer and find appropriate evaluation methods for evaluating the efficacy of neoadjuvant therapy Methods A total of 143 patients with breast cancer treated by neoadjuvant chemotherapy at Baotou Cancer Hospital were retrospectively analyzed. The chemotherapy regimen was mainly paclitaxel combined with carboplatin for 1 week, docetaxel combined with carboplatin for 3 weeks, and was replaced with epirubicin combined with cyclophosphamide after evaluation of disease progression. All HER2-positive patients were treated with simultaneous targeted therapy, including trastuzumab single-target therapy and trastuzumab combined with pertuzumab double-target therapy. Combined with physical examination, color Doppler ultrasound, and magnetic resonance imaging (MRI), a systematic evaluation system was initially established—the “triple evaluation method.” A baseline evaluation was conducted before treatment. The efficacy was evaluated by physical examination and color Doppler every cycle, and the efficacy was evaluated by physical examination, color Doppler, and MRI every two cycles. Results The increase in ultrasonic blood flow after treatment could affect the efficacy of monitoring. The presence of two preoperative time–signal intensity curves is a therapeutically effective protective factor for inflow. The triple evaluation determined by physical examination, color Doppler ultrasound, and MRI in determining clinical efficacy is consistent with the effectiveness of the pathological gold standard. Conclusion The therapeutic effect of neoadjuvant therapy can be better evaluated by combining clinical physical examination, color ultrasound, and nuclear magnetic resonance evaluation. The three methods complement each other to avoid the insufficient evaluation of a single method, which is convenient for most prefecty-level hospitals. Additionally, this method is simple, feasible, and suitable for promotion.

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Section: Discussionmentioning

confidence: 99%

Application of triple evaluation method in predicting the efficacy of neoadjuvant therapy for breast cancer

Han¹,

Li²,

Dong³

et al. 2023

World J Surg Onc

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“…124 The current review extracted and organized the data in tabular form and summarized the application of MRI in breast cancer diagnosis (Table 3). 109,119,120,123,125–166…”

Section: Introductionmentioning

confidence: 99%

Machine learning and new insights for breast cancer diagnosis

Guo,

Zhang,

Yuan

et al. 2024

J Int Med Res

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Breast cancer (BC) is the most prominent form of cancer among females all over the world. The current methods of BC detection include X-ray mammography, ultrasound, computed tomography, magnetic resonance imaging, positron emission tomography and breast thermographic techniques. More recently, machine learning (ML) tools have been increasingly employed in diagnostic medicine for its high efficiency in detection and intervention. The subsequent imaging features and mathematical analyses can then be used to generate ML models, which stratify, differentiate and detect benign and malignant breast lesions. Given its marked advantages, radiomics is a frequently used tool in recent research and clinics. Artificial neural networks and deep learning (DL) are novel forms of ML that evaluate data using computer simulation of the human brain. DL directly processes unstructured information, such as images, sounds and language, and performs precise clinical image stratification, medical record analyses and tumour diagnosis. Herein, this review thoroughly summarizes prior investigations on the application of medical images for the detection and intervention of BC using radiomics, namely DL and ML. The aim was to provide guidance to scientists regarding the use of artificial intelligence and ML in research and the clinic.

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“…DCE-MRI can indirectly reflect tumor abnormal vascular proliferation through the hemodynamic characteristics of the lesion (13,14). However, DCE-MRI has limited predictive power for luminal and non-luminal molecular subtypes in patients with invasive breast cancer, and diagnostic accuracy based on DCE-MRI images is largely dependent on the radiologist's experience, which is highly subjective (15)(16)(17).…”

Section: Introductionmentioning

confidence: 99%

Application of DCE-MRI radiomics signature analysis in differentiating molecular subtypes of luminal and non-luminal breast cancer

et al. 2023

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BackgroundThe goal of this study was to develop and validate a radiomics signature based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) preoperatively differentiating luminal and non-luminal molecular subtypes in patients with invasive breast cancer.MethodsOne hundred and thirty-five invasive breast cancer patients with luminal (n = 78) and non-luminal (n = 57) molecular subtypes were divided into training set (n = 95) and testing set (n = 40) in a 7:3 ratio. Demographics and MRI radiological features were used to construct clinical risk factors. Radiomics signature was constructed by extracting radiomics features from the second phase of DCE-MRI images and radiomics score (rad-score) was calculated. Finally, the prediction performance was evaluated in terms of calibration, discrimination, and clinical usefulness.ResultsMultivariate logistic regression analysis showed that no clinical risk factors were independent predictors of luminal and non-luminal molecular subtypes in invasive breast cancer patients. Meanwhile, the radiomics signature showed good discrimination in the training set (AUC, 0.86; 95% CI, 0.78–0.93) and the testing set (AUC, 0.80; 95% CI, 0.65–0.95).ConclusionThe DCE-MRI radiomics signature is a promising tool to discrimination luminal and non-luminal molecular subtypes in invasive breast cancer patients preoperatively and noninvasively.

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3T DCE-MRI Radiomics Improves Predictive Models of Complete Response to Neoadjuvant Chemotherapy in Breast Cancer

Cited by 18 publications

References 28 publications

Application of triple evaluation method in predicting the efficacy of neoadjuvant therapy for breast cancer

Application of triple evaluation method in predicting the efficacy of neoadjuvant therapy for breast cancer

Machine learning and new insights for breast cancer diagnosis

Application of DCE-MRI radiomics signature analysis in differentiating molecular subtypes of luminal and non-luminal breast cancer

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