Use of Pretreatment Multiparametric MRI to Predict Tumor Regression Pattern to Neoadjuvant Chemotherapy in Breast Cancer

Liu, Chen; Huang, Xiaomei; Chen, Xiaobo; Shi, Zhenwei; Liu, Chunling; Liang, Yingying; Huang, Xin; Chen, Minglei; Chen, Xin; Liang, Changhong; Liu, Zaiyi

doi:10.1016/j.acra.2023.02.024

Cited by 2 publications

(3 citation statements)

References 35 publications

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“…A more recent trio of studies in the context of multiparametric MRI analyses but using pretreatment MRI only are formed by the studies of Xiong et al, 72 Nemeth et al, 75 and Liu et al 78 In 2020, Xiong et al 72 developed a radiomic signature using multivariable logistic regression in breast cancer patients undergoing NAC (n = 125: 63 primary, 62 validation), using pretreatment multiparametric MRI. The combined prediction model yielded an AUC of 0.94 (95% CI, 0.85–1) for predicting the grade 1–2 group in the validation cohort.…”

Section: Ai-enhanced Mri In Response Assessment and Prediction Of Res...mentioning

confidence: 99%

“…The classifier with the best performance was SVM with a quadratic kernel, with a mean AUC of 0.83, sensitivity of 85%, and specificity of 75% in the test set. Lastly, in 2023, Liu et al 78 compared logistic regression, decision trees, and various other ML techniques for predicting the tumor regression pattern in 420 patients undergoing NAC, using pretreatment MRI morphologic and kinetic features along with clinical variables to obtain an AUC of 0.68 for the prediction of tumor regression patterns using the logistic regression model.…”

Section: Ai-enhanced Mri In Response Assessment and Prediction Of Res...mentioning

confidence: 99%

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Artificial Intelligence-Enhanced Breast MRI

et al. 2024

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Primary systemic therapy (PST) is the treatment of choice in patients with locally advanced breast cancer and is nowadays also often used in patients with early-stage breast cancer. Although imaging remains pivotal to assess response to PST accurately, the use of imaging to predict response to PST has the potential to not only better prognostication but also allow the de-escalation or omission of potentially toxic treatment with undesirable adverse effects, the accelerated implementation of new targeted therapies, and the mitigation of surgical delays in selected patients. In response to the limited ability of radiologists to predict response to PST via qualitative, subjective assessments of tumors on magnetic resonance imaging (MRI), artificial intelligence–enhanced MRI with classical machine learning, and in more recent times, deep learning, have been used with promising results to predict response, both before the start of PST and in the early stages of treatment. This review provides an overview of the current applications of artificial intelligence to MRI in assessing and predicting response to PST, and discusses the challenges and limitations of their clinical implementation.

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Section: Ai-enhanced Mri In Response Assessment and Prediction Of Res...mentioning

confidence: 99%

Section: Ai-enhanced Mri In Response Assessment and Prediction Of Res...mentioning

confidence: 99%

Artificial Intelligence-Enhanced Breast MRI

et al. 2024

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“…Notably, the volumetric parameters of 18 F-FDG PET/CT before NAC, such as the metabolic tumor volume and total lesion glycolysis, are significantly correlated with the disease-free survival of breast cancer patients ( 20 ). Recent studies have also shown that MRI features of tumors identified by pretreatment multiparametric MRI examination are related to breast cancer patients’ responses to NAC, and can predict the sensitivity of breast cancer patients to NAC ( 21 - 24 ). Several studies have shown that different MRI sequences of tumors can provide microstructural information, including information about blood supply, tissue density, and the shape and size of the lesion, and these MRI sequences are widely used in the clinical diagnosis of breast cancer ( 25 - 27 ).…”

Section: Introductionmentioning

confidence: 99%

Development of a multiparametric model for predicting the response to neoadjuvant chemotherapy in breast cancer

Qian,

Mao,

Dong

et al. 2024

Transl Cancer Res

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Background Choosing the appropriate treatment early and predicting the efficacy of neoadjuvant chemotherapy (NAC) for locally advanced breast cancer patients are of particular importance for clinicians. Developing and validating a multiparametric model for predicting NAC would be very meaningful for clinical practice. Methods This study included 91 patients with locally advanced breast cancer treated from 2016 to 2020. The correlation between multiparametric characteristics and the efficacy of NAC was examined. The data were randomly divided into training and validation sets. A least absolute shrinkage and selection operator (LASSO) regression analysis was used for the variable screening. A multivariable logistic regression analysis was used to construct the model. Calibration and decision curves were used to assess the performance of the established model. Results Lymph node metastasis, the first standard apparent diffusion coefficient (ADC) at the baseline, the change in the standard ADC at the first follow-up, the change in tumor volume at the first follow-up, and the clinical stage of the tumor at the baseline were selected for inclusion in the model. In the receiver operating characteristic (ROC) analysis, the areas under the curve (AUCs) were 0.984 [95% confidence interval (CI): 0.958–1] and 0.815 (95% CI: 0.509–1) for the primary and validation cohorts, respectively. The utility of the established model was confirmed by calibration and decision curves, and a nomogram was obtained. Conclusions A multiparametric model based on clinical-pathological-magnetic resonance imaging (MRI) features was established to predict the effect of NAC in patients with locally advanced breast cancer.

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Use of Pretreatment Multiparametric MRI to Predict Tumor Regression Pattern to Neoadjuvant Chemotherapy in Breast Cancer

Cited by 2 publications

References 35 publications

Artificial Intelligence-Enhanced Breast MRI

Artificial Intelligence-Enhanced Breast MRI

Development of a multiparametric model for predicting the response to neoadjuvant chemotherapy in breast cancer

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