Multiple parameters from ultrafast dynamic contrast-enhanced magnetic resonance imaging to discriminate between benign and malignant breast lesions: Comparison with apparent diffusion coefficient

Cao, Ying; Wang, Xiaoxia; Shi, Jinfang; Zeng, Xin; Du, Lihong; Li, Qing; Nickel, Dominik; Zhou, Xiaoshu; Zhang, Jiuquan

doi:10.1016/j.diii.2023.01.006

Cited by 9 publications

(7 citation statements)

References 36 publications

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“…We found that patients with a longer TTP had a higher probability of positive LVI, which might be related to the reduced focal blood flow caused by tumor emboli. Numerous studies have shown that TTP is a diagnostic indicator of malignant breast lesions 31 and an independent predictive factor of pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) 32 – 34 . One recent study analyzed the relationship between TTP and LVI, but found no significant differences between patients with and without LVI 35 .…”

Section: Discussionmentioning

confidence: 99%

Predictive value of MRI-based deep learning model for lymphovascular invasion status in node-negative invasive breast cancer

Liang,

Li,

Yao

et al. 2024

Sci Rep

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To retrospectively assess the effectiveness of deep learning (DL) model, based on breast magnetic resonance imaging (MRI), in predicting preoperative lymphovascular invasion (LVI) status in patients diagnosed with invasive breast cancer who have negative axillary lymph nodes (LNs). Data was gathered from 280 patients, including 148 with LVI-positive and 141 with LVI-negative lesions. These patients had undergone preoperative breast MRI and were histopathologically confirmed to have invasive breast cancer without axillary LN metastasis. The cohort was randomly split into training and validation groups in a 7:3 ratio. Radiomics features for each lesion were extracted from the first post-contrast dynamic contrast-enhanced (DCE)-MRI. The Least Absolute Shrinkage and Selection Operator (LASSO) regression method and logistic regression analyses were employed to identify significant radiomic features and clinicoradiological variables. These models were established using four machine learning (ML) algorithms and one DL algorithm. The predictive performance of the models (radiomics, clinicoradiological, and combination) was assessed through discrimination and compared using the DeLong test. Four clinicoradiological parameters and 10 radiomic features were selected by LASSO for model development. The Multilayer Perceptron (MLP) model, constructed using both radiomic and clinicoradiological features, demonstrated excellent performance in predicting LVI, achieving a high area under the curve (AUC) of 0.835 for validation. The DL model (MLP-radiomic) achieved the highest accuracy (AUC = 0.896), followed by DL model (MLP-combination) with an AUC of 0.835. Both DL models were significantly superior to the ML model (RF-clinical) with an AUC of 0.720. The DL model (MLP), which integrates radiomic features with clinicoradiological information, effectively aids in the preoperative determination of LVI status in patients with invasive breast cancer and negative axillary LNs. This is beneficial for making informed clinical decisions.

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Section: Discussionmentioning

confidence: 99%

Predictive value of MRI-based deep learning model for lymphovascular invasion status in node-negative invasive breast cancer

Liang,

Li,

Yao

et al. 2024

Sci Rep

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“…Other studies provided strong evidence that cut-off ADC values can be obtained to predict axillary lymph node metastasis [21]. However, as stated in the literature [2], the ADC map is easily affected by acquisition parameters (e.g., repetition time and echo time). Thus, some studies revealed unsuccessful and inaccurate ADC cut-off values for breast lesion classi cation.…”

Section: Adc As a Predictor Of Breast Cancer Molecular Subtypes And P...mentioning

confidence: 95%

“…Previous studies that have addressed these parameters aimed to differentiate between benign and malignant breast lesions. Results were promising where there was a signi cant enhancement in diagnostic performance when adding ADC to ultrafast parameters [2]. Previous studies also aimed to evaluate the prognostic role of the ultrafast MRI protocol in different breast cancer histopathologies such as DCIS [5,14].…”

Section: Adc As a Predictor Of Breast Cancer Molecular Subtypes And P...mentioning

confidence: 99%

“…Recently introduced ultrafast MRI was used in breast imaging because of its ability to generate high temporal resolution and high-speed images providing a large amount of data about breast lesions that can e ciently differentiate between benign and malignant lesions [2]. This opened a new horizon for research to study its ability to identify the histopathological and molecular criteria of breast cancer.…”

Section: Introductionmentioning

confidence: 99%

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Assing the role of combined ultrafast breast MRI and diffusion-weighted image in predicting breast cancer prognosis: A prospective comparative study.

Bayoumi,

Karam,

Abdallah

et al. 2024

Preprint

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Background Ultrafast breast MRI derived kinetic parameters demonstrated almost equivalent efficacy to conventional DCE-MRI as a screening tool in detecting breast cancer. However, its role as prognostic imaging markers is still questionable. The aim of this study is to use ultrafast MRI-derived kinetic parameters in combination with ADC (Apparent Diffusion Coefficient) as a prognostic imaging predictor. Methods This prospective study was conducted on 82 female patients with 94 pathologically proven breast cancers. Ultrafast breast MRI was obtained using the TWIST (time-resolved angiography with stochastic trajectories) sequence. From the ultrafast sequence, MS (Maximum slope) and TTE (Time to enhancement) parameters were obtained. ADC values were derived from the DWI (diffusion-weighted image) sequence (b value = 0/ 500/1000 s/mm2). Results MS was significantly higher in grade 3 breast cancers compared to grades 1 and 2 (p-value = 0.016). On ROC curve analysis, an MS cut-off value of > 22.75%/s showed the best accuracy among the three parameters (66.3%) in predicting Ki positivity. However, ADC cut-off value < 0.84 x10-3mm2/s was the only significant predictor of HER 2 positivity among the three parameters (sensitivity = 100%, specificity = 66.7% and AUC = 0.817). On adding the two ultrafast parameters (MS and TTE) to the ADC the specificity raised to 82.2% and AUC raised to 0.850 in predicting Her2 positivity. Conclusions Adding ADC derived from DWI to the ultrafast-derived parameters can improve the overall performance of breast MRI in predicting aggressive types of breast cancers, hence predicting the patient prognosis.

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“…Recently, quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has emerged as a functional imaging technique through an ultrafast scanning method with high temporal resolution that can capture hemodynamic information of tissue blood flow, microvascular perfusion, and vascular permeability [18,19]. DCE-MRI could be used for characterizing histopathologic features of various tumors [20][21][22]. However, DCE-MRI has been applied to HCC in only a few studies due to the nature of the dual blood supply of the liver [22,23].…”

Section: Introductionmentioning

confidence: 99%

Bi-regional dynamic contrast-enhanced MRI for prediction of microvascular invasion in solitary BCLC stage A hepatocellular carcinoma

Zhu,

Feng,

Wang

et al. 2024

Insights Imaging

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Objectives To construct a combined model based on bi-regional quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), as well as clinical-radiological (CR) features for predicting microvascular invasion (MVI) in solitary Barcelona Clinic Liver Cancer (BCLC) stage A hepatocellular carcinoma (HCC), and to assess its ability for stratifying the risk of recurrence after hepatectomy. Methods Patients with solitary BCLC stage A HCC were prospective collected and randomly divided into training and validation sets. DCE perfusion parameters were obtained both in intra-tumoral region (ITR) and peritumoral region (PTR). Combined DCE perfusion parameters (CDCE) were constructed to predict MVI. The combined model incorporating CDCE and CR features was developed and evaluated. Kaplan–Meier method was used to investigate the prognostic significance of the model and the survival benefits of different hepatectomy approaches. Results A total of 133 patients were included. Total blood flow in ITR and arterial fraction in PTR exhibited the best predictive performance for MVI with areas under the curve (AUCs) of 0.790 and 0.792, respectively. CDCE achieved AUCs of 0.868 (training set) and 0.857 (validation set). A combined model integrated with the α-fetoprotein, corona enhancement, two-trait predictor of venous invasion, and CDCE could improve the discrimination ability to AUCs of 0.966 (training set) and 0.937 (validation set). The combined model could stratify the prognosis of HCC patients. Anatomical resection was associated with a better prognosis in the high-risk group (p < 0.05). Conclusion The combined model integrating DCE perfusion parameters and CR features could be used for MVI prediction in HCC patients and assist clinical decision-making. Critical relevance statement The combined model incorporating bi-regional DCE-MRI perfusion parameters and CR features predicted MVI preoperatively, which could stratify the risk of recurrence and aid in optimizing treatment strategies. Key Points Microvascular invasion (MVI) is a significant predictor of prognosis for hepatocellular carcinoma (HCC). Quantitative DCE-MRI could predict MVI in solitary BCLC stage A HCC; the combined model improved performance. The combined model could help stratify the risk of recurrence and aid treatment planning. Graphical Abstract

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Multiple parameters from ultrafast dynamic contrast-enhanced magnetic resonance imaging to discriminate between benign and malignant breast lesions: Comparison with apparent diffusion coefficient

Cited by 9 publications

References 36 publications

Predictive value of MRI-based deep learning model for lymphovascular invasion status in node-negative invasive breast cancer

Predictive value of MRI-based deep learning model for lymphovascular invasion status in node-negative invasive breast cancer

Assing the role of combined ultrafast breast MRI and diffusion-weighted image in predicting breast cancer prognosis: A prospective comparative study.

Bi-regional dynamic contrast-enhanced MRI for prediction of microvascular invasion in solitary BCLC stage A hepatocellular carcinoma

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