Tumor texture parameters of invasive ductal breast carcinoma in neoadjuvant chemotherapy: early identification of non-responders on breast MRI

Nadrljanski, Mirjan; Milošević, Zorica

doi:10.1016/j.clinimag.2020.04.016

Cited by 5 publications

(4 citation statements)

References 20 publications

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“…The changes in tumor size [ 8 , 9 , 37 ] and functional parameters [ 7 , 11 ] based on DCE-MRI after two NAC cycles and even after one cycle strongly predicted the final therapeutic response. Recent studies extended the longitudinal changes to texture [ 14 , 15 , 16 ] and radiomic features [ 25 , 26 ]. Delta-radiomics features after early treatment reflect therapy-induced changes in tumor morphology and heterogeneity, which may improve the performance of single pre-NAC images.…”

Section: Discussionmentioning

confidence: 99%

“…However, the middle stage of the NAC treatment is too late to make changes to the regimen in patients for whom NAC is ineffective. In another small-sample study [ 16 ], Nadrljanski et al found that DCE-MRI performed after two NAC cycles could reveal the differences in texture features between patients that respond and do not respond to NAC. Advancing scanning time point after the first cycle of NAC, we extracted comprehensive radiomic features from DCE-MRI before and after the first cycle, analyzed their differences, and built an optimal model for the earlier and more accurate prediction of patient outcomes.…”

Section: Discussionmentioning

confidence: 99%

“…Tumor morphology, vascularity, and cell density can be affected by NAC. Many feature-level MRI metrics have been explored to monitor treatment response, including tumor size [ 7 , 8 , 9 , 10 ], DCE kinetic parameters [ 7 , 11 , 12 ], diffusion measures [ 8 , 13 ], and MRI texture parameters [ 14 , 15 , 16 ]. Recent studies [ 17 , 18 , 19 , 20 ] have demonstrated the feasibility of radiomics for predicting NAC response.…”

Section: Introductionmentioning

confidence: 99%

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Delta-Radiomics Based on Dynamic Contrast-Enhanced MRI Predicts Pathologic Complete Response in Breast Cancer Patients Treated with Neoadjuvant Chemotherapy

Guo

Gao

et al. 2022

Cancers

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Objective: To investigate the value of delta-radiomics after the first cycle of neoadjuvant chemotherapy (NAC) using dynamic contrast-enhanced (DCE) MRI for early prediction of pathological complete response (pCR) in patients with breast cancer. Methods: From September 2018 to May 2021, a total of 140 consecutive patients (training, n = 98: validation, n = 42), newly diagnosed with breast cancer who received NAC before surgery, were prospectively enrolled. All patients underwent DCE-MRI at pre-NAC (pre-) and after the first cycle (1st-) of NAC. Radiomic features were extracted from the postcontrast early, peak, and delay phases. Delta-radiomics features were computed in each contrast phases. Least absolute shrinkage and selection operator (LASSO) and a logistic regression model were used to select features and build models. The model performance was assessed by receiver operating characteristic (ROC) analysis and compared by DeLong test. Results: The delta-radiomics model based on the early phases of DCE-MRI showed a highest AUC (0.917/0.842 for training/validation cohort) compared with that using the peak and delay phases images. The delta-radiomics model outperformed the pre-radiomics model (AUC = 0.759/0.617, p = 0.011/0.047 for training/validation cohort) in early phase. Based on the optimal model, longitudinal fusion radiomic models achieved an AUC of 0.871/0.869 in training/validation cohort. Clinical-radiomics model generated good calibration and discrimination capacity with AUC 0.934 (95%CI: 0.882, 0.986)/0.864 (95%CI: 0.746, 0.982) for training and validation cohort. Delta-radiomics based on early contrast phases of DCE-MRI combined clinicopathology information could predict pCR after one cycle of NAC in patients with breast cancer.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Delta-Radiomics Based on Dynamic Contrast-Enhanced MRI Predicts Pathologic Complete Response in Breast Cancer Patients Treated with Neoadjuvant Chemotherapy

Guo

Gao

et al. 2022

Cancers

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“…The changes of tumor heterogeneity in DCE-MRI between the preoperative and early NACT (e.g., two cycles of treatment) may provide information for early prediction of the eventual treatment outcome. Previous studies have demonstrated evidence of longitudinal changes in pharmacokinetic parameters (Dogan et al, 2019), tumor sizes (Tudorica et al, 2016), and tumor MRI texture parameters (Parikh et al, 2014;Henderson et al, 2017;Eun et al, 2020;Nadrljanski and Milosevic, 2020) being correlated with responses to NACT in breast cancer patients. These studies mainly analyzed the feature-level changes of the heterogeneity by evaluating longitudinal images within a tumor.…”

Section: Introductionmentioning

confidence: 99%

Radiomics of Tumor Heterogeneity in Longitudinal Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Predicting Response to Neoadjuvant Chemotherapy in Breast Cancer

Fan

Chen

You

et al. 2021

Front. Mol. Biosci.

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Breast tumor morphological and vascular characteristics can be changed during neoadjuvant chemotherapy (NACT). The early changes in tumor heterogeneity can be quantitatively modeled by longitudinal dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), which is useful in predicting responses to NACT in breast cancer. In this retrospective analysis, 114 female patients with unilateral unifocal primary breast cancer who received NACT were included in a development (n = 61) dataset and a testing dataset (n = 53). DCE-MRI was performed for each patient before and after treatment (two cycles of NACT) to generate baseline and early follow-up images, respectively. Feature-level changes (delta) of the entire tumor were evaluated by calculating the relative net feature change (deltaRAD) between baseline and follow-up images. The voxel-level change inside the tumor was evaluated, which yielded a Jacobian map by registering the follow-up image to the baseline image. Clinical information and the radiomic features were fused to enhance the predictive performance. The area under the curve (AUC) values were assessed to evaluate the prediction performance. Predictive models using radiomics based on pre- and post-treatment images, Jacobian maps and deltaRAD showed AUC values of 0.568, 0.767, 0.630 and 0.726, respectively. When features from these images were fused, the predictive model generated an AUC value of 0.771. After adding the molecular subtype information in the fused model, the performance was increased to an AUC of 0.809 (sensitivity of 0.826 and specificity of 0.800), which is significantly higher than that of the baseline imaging- and Jacobian map-based predictive models (p = 0.028 and 0.019, respectively). The level of tumor heterogeneity reduction (evaluated by texture feature) is higher in the NACT responders than in the nonresponders. The results suggested that changes in DCE-MRI features that reflect a reduction in tumor heterogeneity following NACT could provide early prediction of breast tumor response. The prediction was improved when the molecular subtype information was combined into the model.

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Longitudinal ultrasound-based AI model predicts axillary lymph node response to neoadjuvant chemotherapy in breast cancer: a multicenter study

Fu,

Lei,

Huang

et al. 2024

Eur Radiol

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Objectives Developing a deep learning radiomics model from longitudinal breast ultrasound and sonographer’s axillary ultrasound diagnosis for predicting axillary lymph node (ALN) response to neoadjuvant chemotherapy (NAC) in breast cancer. Methods Breast cancer patients undergoing NAC followed by surgery were recruited from three centers between November 2016 and December 2022. We collected ultrasound images for extracting tumor-derived radiomics and deep learning features, selecting quantitative features through various methods. Two machine learning models based on random forest were developed using pre-NAC and post-NAC features. A support vector machine integrated these data into a fusion model, evaluated via the area under the curve (AUC), decision curve analysis, and calibration curves. We compared the fusion model’s performance against sonographer’s diagnosis from pre-NAC and post-NAC axillary ultrasonography, referencing histological outcomes from sentinel lymph node biopsy or axillary lymph node dissection. Results In the validation cohort, the fusion model outperformed both pre-NAC (AUC: 0.899 vs. 0.786, p < 0.001) and post-NAC models (AUC: 0.899 vs. 0.853, p = 0.014), as well as the sonographer’s diagnosis of ALN status on pre-NAC and post-NAC axillary ultrasonography (AUC: 0.899 vs. 0.719, p < 0.001). Decision curve analysis revealed patient benefits from the fusion model across threshold probabilities from 0.02 to 0.98. The model also enhanced sonographer’s diagnostic ability, increasing accuracy from 71.9% to 79.2%. Conclusion The deep learning radiomics model accurately predicted the ALN response to NAC in breast cancer. Furthermore, the model will assist sonographers to improve their diagnostic ability on ALN status before surgery. Clinical relevance statement Our AI model based on pre- and post-neoadjuvant chemotherapy ultrasound can accurately predict axillary lymph node metastasis and assist sonographer’s axillary diagnosis. Key Points Axillary lymph node metastasis status affects the choice of surgical treatment, and currently relies on subjective ultrasound. Our AI model outperformed sonographer’s visual diagnosis on axillary ultrasound. Our deep learning radiomics model can improve sonographers’ diagnosis and might assist in surgical decision-making.

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Tumor texture parameters of invasive ductal breast carcinoma in neoadjuvant chemotherapy: early identification of non-responders on breast MRI

Cited by 5 publications

References 20 publications

Delta-Radiomics Based on Dynamic Contrast-Enhanced MRI Predicts Pathologic Complete Response in Breast Cancer Patients Treated with Neoadjuvant Chemotherapy

Delta-Radiomics Based on Dynamic Contrast-Enhanced MRI Predicts Pathologic Complete Response in Breast Cancer Patients Treated with Neoadjuvant Chemotherapy

Radiomics of Tumor Heterogeneity in Longitudinal Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Predicting Response to Neoadjuvant Chemotherapy in Breast Cancer

Longitudinal ultrasound-based AI model predicts axillary lymph node response to neoadjuvant chemotherapy in breast cancer: a multicenter study

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