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Rationale and Objectives: Accurate assessment of lymphovascular invasion (LVI) in invasive breast cancer (IBC) plays a pivotal role in tailoring personalized treatment plans. This study aimed to investigate habitats-based spatial distributions to quantitatively measure tumor heterogeneity on multiparametric magnetic resonance imaging (MRI) scans and assess their predictive capability for LVI in patients with IBC., Materials and Methods: In this retrospective cohort study, we consecutively enrolled 241 women diagnosed with IBC between July 2020 and July 2023 and who had 1.5 T/T1-weighted images, fat-suppressed T2-weighted images, and dynamic contrast-enhanced MRI. Habitats-based spatial distributions were derived from the gross tumor volume (GTV) and gross tumor volume plus peritumoral volume (GPTV). GTV&#95;habitats and GPTV&#95;habitats were generated through sub-region segmentation, and their performances were compared. Subsequently, a combined nomogram was developed by integrating relevant spatial distributions with the identified MR morphological characteristics. Diagnostic performance was compared using receiver operating characteristic curve analysis and decision curve analysis. Statistical significance was set at p < 0.05., Results: GPTV&#95;habitats exhibited superior performance compared to GTV&#95;habitats. Consequently, the GPTV&#95;habitats, diffusion-weighted imaging rim signs, and peritumoral edema were integrated to formulate the combined nomogram. This combined nomogram outperformed individual MR morphological characteristics and the GPTV&#95;habitats index, achieving area under the curve values of 0.903 (0.847 -0.959), 0.770 (0.689 -0.852), and 0.843 (0.776 -0.910) in the training set and 0.931 (0.863 -0.999), 0.747 (0.613 -0.880), and 0.849 (0.759 -0.938) in the validation set., Conclusion: The combined nomogram incorporating the GPTV&#95;habitats and identified MR morphological characteristics can effectively predict LVI in patients with IBC., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Objectives: To explore the performance of multiparametric MRI-based radiomics in discriminating different human epidermal growth factor receptor 2 (HER2) expressing statuses (i.e., HER2-overexpressing, HER2-low-expressing, and HER2-zero-expressing) in breast cancer., Methods: A total of 771 breast cancer patients from two institutions were retrospectively studied. Five-hundred-eighty-one patients from Institution I were divided into a training dataset (n 1  = 407) and an independent validation dataset (n 1  = 174); 190 patients from Institution II formed the external validation dataset. All patients were categorized into HER2-overexpressing, HER2-low-expressing, and HER2-zero-expressing groups based on pathologic examination. Multiparametric (including T2-weighted imaging with fat suppression [T2WI-FS], diffusion-weighted imaging [DWI], apparent diffusion coefficient [ADC], and dynamic contrast-enhanced [DCE]) MRI-based radiomics features were extracted and then selected from the training dataset using the least absolute shrinkage and selection operator (LASSO) regression. Three predictive models to discriminate HER2-overexpressing vs. others, HER2-low expressing vs. others, and HER2-zero-expressing vs. others were developed based on the selected features. The model performance was evaluated using the area under the receiver operating characteristic curve (AUC)., Results: Eleven radiomics features from DWI, ADC, and DCE; one radiomics feature from DWI; and 17 radiomics features from DWI, ADC, and DCE were selected to build three predictive models, respectively. In training, independent validation, and external validation datasets, radiomics models achieved AUCs of 0.809, 0.737, and 0.725 in differentiating HER2-overexpressing from others; 0.779, 0.778, and 0.782 in differentiating HER2-low-expressing from others; and 0.889, 0.867, and 0.813 in differentiating HER2-zero-expressing from others, respectively., Conclusions: Multiparametric MRI-based radiomics model may preoperatively predict HER2 statuses in breast cancer patients., Clinical Relevance Statement: The MRI-based radiomics models could be used to noninvasively identify the new three-classification of HER2 expressing status in breast cancer, which is helpful to the decision-making for HER2-target therapies., Key Points: • Detecting HER2-overexpressing, HER2-low-expressing, and HER2-zero-expressing status in breast cancer patients is crucial for determining candidates for anti-HER2 therapy. • Radiomics features from multiparametric MRI significantly differed among HER2-overexpressing, HER2-low expressing, and HER2-zero-expressing breast cancers. • Multiparametric MRI-based radiomics could preoperatively evaluate three different HER2-expressing statuses and help to determine potential candidates for anti-HER2 therapy in breast cancer patients., (© 2024. The Author(s), under exclusive licence to European Society of Radiology.)

Background: Previous studies have used different imaging sequences and different enhanced phases for breast lesion calsification in radiomics. The optimal sequence and contrast enhanced phase is unclear., Purpose: To identify the optimal magnetic resonance imaging (MRI) radiomics model for lesion clarification, and to simulate its incremental value for multiparametric MRI (mpMRI)-guided biopsy., Study Type: Retrospective., Population: 329 female patients (138 malignant, 191 benign), divided into a training set (first site, n = 192) and an independent test set (second site, n = 137)., Field Strength/sequence: 3.0-T, fast spoiled gradient-echo and fast spin-echo T1-weighted imaging (T1WI), fast spin-echo T2-weighted imaging (T2WI), echo-planar diffusion-weighted imaging (DWI), and fast spoiled gradient-echo contrast-enhanced MRI (CE-MRI)., Assessment: Two breast radiologists with 3 and 10 years' experience developed radiomics model on CE-MRI, CE-MRI + DWI, CE-MRI + DWI + T2WI, CE-MRI + DWI + T2WI + T1WI at each individual phase (P) and for multiple combinations of phases. The optimal radiomics model (Rad-score) was identified as having the highest area under the receiver operating characteristic curve (AUC) in the test set. Specificity was compared between a traditional mpMRI model and an integrated model (mpMRI + Rad-score) at sensitivity >98%., Statistical Tests: Wilcoxon paired-samples signed rank test, Delong test, McNemar test. Significance level was 0.05 and Bonferroni method was used for multiple comparisons (P = 0.007, 0.05/7)., Results: For radiomics models, CE-MRI/P3 + DWI + T2WI achieved the highest performance in the test set (AUC = 0.888, 95% confidence interval: 0.833-0.944). The integrated model had significantly higher specificity (55.3%) than the mpMRI model (31.6%) in the test set with a sensitivity of 98.4%., Data Conclusion: The CE-MRI/P3 + DWI + T2WI model is the optimized choice for breast lesion classification in radiomics, and has potential to reduce benign biopsies (100%-specificity) from 68.4% to 44.7% while retaining sensitivity >98%., Level of Evidence: 3 TECHNICAL EFFICACY: Stage 2., (© 2023 International Society for Magnetic Resonance in Medicine.)

Purpose: To investigate whether longitudinal changes in multiparametric MRI can predict early response to neoadjuvant chemotherapy (NAC) for HER2-positive breast cancer (BC) and to further establish quantitative models based on these features., Methods: A total of 164 HER2-positive BC patients from three centers were included. MRI was performed at baseline and after two cycles of NAC (early post-NAC). Clinicopathological characteristics were enrolled. MRI features were evaluated at baseline and early post-NAC, as well as longitudinal changes in multiparametric MRI, including changes in the largest diameter (LD) of the tumor (ΔLD), apparent diffusion coefficient (ADC) values (ΔADC), and time-signal intensity curve (TIC) (ΔTIC). The patients were divided into a training set (n = 95), an internal validation set (n = 31), and an independent external validation set (n = 38). Univariate and multivariate logistic regression analyses were used to identify the independent indicators of pCR, which were then used to establish the clinicopathologic model and combined model. The AUC was used to evaluate the predictive power of the different models and calibration curves were used to evaluate the consistency of the prediction of pCR in different models. Additionally, decision curve analysis (DCA) was employed to determine the clinical usefulness of the different models., Results: Two models were enrolled in this study, including the clinicopathologic model and the combined model. The LD at early post-NAC (OR=0.913, 95 % CI=0.953-0.994 p = 0.026), ΔADC (OR=1.005, 95 % CI=1.005-1.008, p = 0.007), and ΔTIC (OR=3.974, 95 % CI=1.276-12.358, p = 0.017) were identified as the best predictors of NAC response. The combined model constructed by the combination of LD at early post-NAC, ΔADC, and ΔTIC showed good predictive performance in the training set (AUC=0.87), internal validation set (AUC=0.78), and external validation set (AUC=0.79), which performed better than the clinicopathologic model in all sets., Conclusions: The changes in multiparametric MRI can predict early treatment response for HER2-positive BC and may be helpful for individualized treatment planning., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

This study aims to develop an MRI-based radiomics model to assess the likelihood of recurrence in luminal B breast cancer. The study analyzed medical images and clinical data from 244 patients with luminal B breast cancer. Of 244 patients, 35 had experienced recurrence and 209 had not. The patients were randomly divided into the training set (51.5 ± 12.5 years old; n = 171) and the test set (51.7 ± 11.3 years old; n = 73) in a ratio of 7:3. The study employed univariate and multivariate Cox regression along with the least absolute shrinkage and selection operator (LASSO) regression methods to select radiomics features and calculate a risk score. A combined model was constructed by integrating the risk score with the clinical and pathological characteristics. The study identified two radiomics features (GLSZM and GLRLM) from DCE-MRI that were used to calculate a risk score. The AUCs were 0.860 and 0.868 in the training set and 0.816 and 0.714 in the testing set for 3- and 5-year recurrence risk, respectively. The combined model incorporating the risk score, pN, and endocrine therapy showed improved predictive power, with AUCs of 0.857 and 0.912 in the training set and 0.943 and 0.945 in the testing set for 3- and 5-year recurrence risk, respectively. The calibration curve of the combined model showed good consistency between predicted and measured values. Our study developed an MRI-based radiomics model that integrates clinical and radiomics features to assess the likelihood of recurrence in luminal B breast cancer. The model shows promise for improving clinical risk stratification and treatment decision-making., (© 2024. The Author(s).)

Purpose: Accurate identification of primary breast cancer and axillary positive-node response to neoadjuvant chemotherapy (NAC) is important for determining appropriate surgery strategies. We aimed to develop combining models based on breast multi-parametric magnetic resonance imaging and clinicopathologic characteristics for predicting therapeutic response of primary tumor and axillary positive-node prior to treatment., Materials and Methods: A total of 268 breast cancer patients who completed NAC and underwent surgery were enrolled. Radiomics features and clinicopathologic characteristics were analyzed through the analysis of variance and the least absolute shrinkage and selection operator algorithm. Finally, 24 and 28 optimal features were selected to construct machine learning models based on 6 algorithms for predicting each clinical outcome, respectively. The diagnostic performances of models were evaluated in the testing set by the area under the curve (AUC), sensitivity, specificity, and accuracy., Results: Of the 268 patients, 94 (35.1 %) achieved breast cancer pathological complete response (bpCR) and of the 240 patients with clinical positive-node, 120 (50.0 %) achieved axillary lymph node pathological complete response (apCR). The multi-layer perception (MLP) algorithm yielded the best diagnostic performances in predicting apCR with an AUC of 0.825 (95 % CI, 0.764-0.886) and an accuracy of 77.1 %. And MLP also outperformed other models in predicting bpCR with an AUC of 0.852 (95 % CI, 0.798-0.906) and an accuracy of 81.3 %., Conclusions: Our study established non-invasive combining models to predict the therapeutic response of primary breast cancer and axillary positive-node prior to NAC, which may help to modify preoperative treatment and determine post-NAC surgery strategy., Competing Interests: Declaration of competing interest The authors have no relevant financial or non-financial interests to disclose., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Purpose: To evaluate the effectiveness of both synthetic magnetic resonance imaging (SyMRI) and conventional diffusion-weighted imaging (DWI) for identifying the human epidermal growth factor receptor 2 (HER2) status in breast cancer (BC) patients., Method: In this retrospective study, 114 women with DWI and SyMRI were pathologically classified into three groups: HER2-overexpressing (n = 40), HER2-low-expressing (n = 53), and HER2-zero-expressing (n = 21). T1 and T2 relaxation times and proton density (PD) were assessed before and after enhancement, and the resulting quantitative parameters produced by SyMRI were recorded as T1, T2, and PD and T1e, T2e, and PDe. Logistic regression was used to identify the best indicators for classifying patients based on HER2 expression. The discriminative performance of the models was evaluated using receiver operating characteristic (ROC) curves., Results: Our preliminary study revealed significant differences in progesterone receptor (PR) status, Ki-67 index, and axillary lymph node (ALN) count among the HER2-zero, -low, and -overexpressing groups (p < 0.001 to p = 0.03). SyMRI quantitative indices showed significant differences among BCs in the three HER2 subgroups, except for ΔT2 (p < 0.05). our results indicate that PDe achieved an area under the curve（AUC）of 0.849 (95 % CI: 0.760-0.915) for distinguishing HER2-low and -overexpressing BCs. Further investigation revealed that both the PDe and ADC were indicators for predicting differences among patients with HER2-zero and HER2-low-expressing BC, with AUCs of 0.765(95 % CI: 0.652-0.855) and 0.684(95 % CI: 0.565-0.787), respectively. The addition of the PDe to the ADC improved the AUC to 0.825(95 % CI: 0.719-0.903)., Conclusions: SyMRI could noninvasively and robustly predict the HER2 expression status of patients with BC., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: Ductal carcinoma in situ (DCIS) is often reclassified as invasive cancer in the final pathology report of the surgical specimen. It is of significant clinical relevance to acknowledge the possibility of underestimating invasive disease when utilizing preoperative biopsies for a DCIS diagnosis. In cases where such histologic upgrades occur, it is imperative to consider them in the preoperative planning process, including the potential inclusion of sentinel lymph node biopsy due to the risk of axillary lymph node metastasis., Purpose: To assess the capability of breast multiparametric magnetic resonance imaging (MP-MRI) in differentiating between pure DCIS and microinvasive carcinoma (MIC)., Material and Methods: Between January 2018 and November 2022, this retrospective study enrolled patients with biopsy-proven DCIS who had undergone preoperative breast MP-MRI. We assessed various MP-MRI features, including size, morphology, margins, internal enhancement pattern, extent of disease, presence of peritumoral edema, time-intensity curve value, diffusion restriction, and ADC value. Subsequently, a logistic regression analysis was conducted to explore the association of these features with the pathological outcome., Results: Of 129 patients with biopsy-proven DCIS, 36 had foci of micro-infiltration on surgical specimens and eight were diagnosed with invasive ductal carcinoma (IDC). The presence of micro-infiltration foci was significantly associated with several MP-MRI features, including tumor size ( P  <0.001), clustered ring enhancement ( P  <0.001), segmental distribution ( P  <0.001), diffusion restriction ( P  = 0.005), and ADC values <1.3 × 10 -3 mm 2 /s ( P  = 0.004)., Conclusion: Breast MP-MRI has the potential to predict the presence of micro-infiltration foci in biopsy-proven DCIS and may serve as a valuable tool for guiding therapeutic planning., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Purpose: To investigate the treatment efficacy of intra-arterial (IA) trastuzumab treatment using multiparametric magnetic resonance imaging (MRI) in a human breast cancer xenograft model., Materials and Methods: Human breast cancer cells (BT474) were stereotaxically injected into the brains of nude mice to obtain a xenograft model. The mice were divided into four groups and subjected to different treatments (IA treatment [IA-T], intravenous treatment [IV-T], IA saline injection [IA-S], and the sham control group). MRI was performed before and at 7 and 14 d after treatment to assess the efficacy of the treatment. The tumor volume, apparent diffusion coefficient (ADC), and dynamic contrast-enhanced (DCE) MRI parameters (Ktrans, Kep, Ve, and Vp) were measured., Results: Tumor volumes in the IA-T group at 14 d after treatment were significantly lower than those in the IV-T group (13.1 mm3 [interquartile range 8.48-16.05] vs. 25.69 mm3 [IQR 20.39-30.29], p = 0.005), control group (IA-S, 33.83 mm3 [IQR 32.00-36.30], p<0.01), and sham control (39.71 mm3 [IQR 26.60-48.26], p <0.001). The ADC value in the IA-T group was higher than that in the control groups (IA-T, 7.62 [IQR 7.23-8.20] vs. IA-S, 6.77 [IQR 6.48-6.87], p = 0.044 and vs. sham control, 6.89 [IQR 4.93-7.48], p = 0.004). Ktrans was significantly decreased following the treatment compared to that in the control groups (p = 0.002 and p<0.001 for vs. IA-S and sham control, respectively). Tumor growth was decreased in the IV-T group compared to that in the sham control group (25.69 mm3 [IQR 20.39-30.29] vs. 39.71 mm3 [IQR 26.60-48.26], p = 0.27); there was no significant change in the MRI parameters., Conclusion: IA treatment with trastuzumab potentially affects the early response to treatment, including decreased tumor growth and decrease of Ktrans, in a preclinical brain tumor model., Competing Interests: The authors have no conflicts of interest to declare., (Copyright: © 2024 Kim et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Purpose: The purpose of this study was to assess the predictive performance of multiparametric magnetic resonance imaging (MRI) for molecular subtypes and interpret features using SHapley Additive exPlanations (SHAP) analysis., Material and Methods: Patients with breast cancer who underwent pre-treatment MRI (including ultrafast dynamic contrast-enhanced MRI, magnetic resonance spectroscopy, diffusion kurtosis imaging and intravoxel incoherent motion) were recruited between February 2019 and January 2022. Thirteen semantic and thirteen multiparametric features were collected and the key features were selected to develop machine-learning models for predicting molecular subtypes of breast cancers (luminal A, luminal B, triple-negative and HER2-enriched) by using stepwise logistic regression. Semantic model and multiparametric model were built and compared based on five machine-learning classifiers. Model decision-making was interpreted using SHAP analysis., Results: A total of 188 women (mean age, 53 ± 11 [standard deviation] years; age range: 25-75 years) were enrolled and further divided into training cohort (131 women) and validation cohort (57 women). XGBoost demonstrated good predictive performance among five machine-learning classifiers. Within the validation cohort, the areas under the receiver operating characteristic curves (AUCs) for the semantic models ranged from 0.693 (95% confidence interval [CI]: 0.478-0.839) for HER2-enriched subtype to 0.764 (95% CI: 0.681-0.908) for luminal A subtype, inferior to multiparametric models that yielded AUCs ranging from 0.771 (95% CI: 0.630-0.888) for HER2-enriched subtype to 0.857 (95% CI: 0.717-0.957) for triple-negative subtype. The AUCs between the semantic and the multiparametric models did not show significant differences (P range: 0.217-0.640). SHAP analysis revealed that lower iAUC, higher kurtosis, lower D*, and lower kurtosis were distinctive features for luminal A, luminal B, triple-negative breast cancer, and HER2-enriched subtypes, respectively., Conclusion: Multiparametric MRI is superior to semantic models to effectively predict the molecular subtypes of breast cancer., Competing Interests: Declaration of competing interest The authors declare no actual or potential conflict of interest related to this study., (Copyright © 2024 Société française de radiologie. Published by Elsevier Masson SAS. All rights reserved.)

Background: This study aims to investigate the efficacy of multifactorial MRI in diagnosing breast cancer, specifically in the context of predicting lymphovascular invasion (LVI). Materials & methods: The patients were stratified into two groups: the primary group (100 patients) and the validation group (100 patients), based on essential characteristics. Multifactorial MRI, encompassing tumor size evaluation, diffusion coefficient assessment and dynamic contrast enhancement, was employed for patient examination. Results: Statistically significant differences were observed in tumor size, diffusion coefficient and dynamic contrast enhancement between groups with LVI (LVI+) and those without (LVI-). Key parameters were identified for predicting the degree of invasion. Conclusion: The results affirm the effectiveness of multifactorial MRI in forecasting LVI.

Rationale and Objectives: To develop an easy-to-use model by combining pretreatment MRI and clinicopathologic features for early prediction of tumor regression pattern to neoadjuvant chemotherapy (NAC) in breast cancer., Materials and Methods: We retrospectively analyzed 420 patients who received NAC and underwent definitive surgery in our hospital from February 2012 to August 2020. Pathologic findings of surgical specimens were used as the gold standard to classify tumor regression patterns into concentric and non-concentric shrinkage. Morphologic and kinetic MRI features were both analyzed. Univariable and multivariable analyses were performed to select the key clinicopathologic and MRI features for pretreatment prediction of regression pattern. Logistic regression and six machine learning methods were used to construct prediction models, and their performance were evaluated with receiver operating characteristic curve., Results: Two clinicopathologic variables and three MRI features were selected as independent predictors to construct prediction models. The apparent area under the curve (AUC) of seven prediction models were in the range of 0.669-0.740. The logistic regression model yielded an AUC of 0.708 (95% confidence interval [CI]: 0.658-0.759), and the decision tree model achieved the highest AUC of 0.740 (95% CI: 0.691-0.787). For internal validation, the optimism-corrected AUCs of seven models were in the range of 0.592-0.684. There was no significant difference between the AUCs of the logistic regression model and that of each machine learning model., Conclusion: Prediction models combining pretreatment MRI and clinicopathologic features are useful for predicting tumor regression pattern in breast cancer, which can assist to select patients who can benefit from NAC for de-escalation of breast surgery and modify treatment strategy., Competing Interests: Declaration of Competing Interest None., (Copyright © 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Background Half of breast cancers exhibit low expression levels of human epidermal growth factor receptor 2 (HER2) and can be targeted by new antibody-drug conjugates. The imaging differences between HER2-zero (immunohistochemistry [IHC] score of 0), HER2-low (IHC score of 1+ or 2+ with negative findings at fluorescence in situ hybridization [FISH]), and HER2-positive (IHC score of 2+ with positive findings at FISH or IHC score of 3+) breast cancers were unknown. Purpose To assess whether multiparametric dynamic contrast-enhanced MRI-based radiomic features can help distinguish HER2 expressions in breast cancer. Materials and Methods This study included women with breast cancer who underwent MRI at two different centers between December 2020 and December 2022. Tumor segmentation and radiomic feature extraction were performed on T2-weighted and dynamic contrast-enhanced T1-weighted images. Unsupervised correlation analysis of reproducible features and least absolute shrinkage and selector operation were used for the selection of features to build a radiomics signature. The area under the receiver operating characteristic curve (AUC) was used to assess the performance of the radiomic signature. Multivariable logistic regression was used to identify independent predictors for distinguishing HER2 expressions in both the training and prospectively acquired external data set. Results The training set included 208 patients from center 1 (mean age, 53 years ± 14 [SD]), and the external test set included 131 patients from center 2 (mean age, 54 years ± 13). In the external test data set, the radiomic signature achieved an AUC of 0.80 (95% CI: 0.71, 0.89) for distinguishing HER2-low and -positive tumors versus HER2-zero tumors and was a significant predictive factor for distinguishing these two groups (odds ratio = 7.6; 95% CI: 2.9, 19.8; P < .001). Among HER2-low or -positive breast cancers, histology type, associated nonmass enhancement, and multiple lesions at MRI had an AUC of 0.77 (95% CI: 0.68, 0.86) in the external test set for the prediction of HER2-positive versus HER2-low cancers. Conclusion The radiomic signature and tumor descriptors from multiparametric breast MRI may predict distinct HER2 expressions of breast cancers with therapeutic implications. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Kataoka and Honda in this issue.

Introduction: This study evaluated the performance of primary foci of breast cancer on multiparametric magnetic resonance imaging (MRI) contributing to establish and validate radiomics-based nomograms for predicting the different pathological outcome of breast cancer patients after neoadjuvant chemotherapy (NAC)., Materials and Methods: Retrospectively collected 387 patients with locally advanced breast cancer, all treated with NAC and received breast dynamic contrast-enhanced MRI (DCE-MRI) before NAC. Radiomics signatures were extracted from region of interest (ROI) on multiparametric MRI to build rad score. Clinical-pathologic data and radiological features established the clinical model. The comprehensive model featured rad-score, predictive clinical-pathologic data and radiological features, which was ultimately displayed as a nomogram. Patients were grouped in 2 different ways in accordance with the Miller-Payne (MP) grading of surgical specimens. The first grouping method: 181 patients with pathological reaction grades Ⅳ∼Ⅴ were included in the significant remission group, while 206 patients with pathological reaction grades Ⅰ∼Ⅲ were included in the nonsignificant remission group. The second grouping method: 117 patients with pathological complete response (pCR) were assigned to the pCR group, and 270 patients who failed to meet pCR were assigned to in the non-pCR group. Two combined nomograms are created from 2 grouped data for predicting different pathological responses to NAC. The area under the curves (AUC) of the receiver operating characteristic curves (ROC) were used to evaluate the performance of each model. While decision curve analysis (DCA) and calibration curves were used for estimating the clinical application value of the nomogram., Results: Two combined nomograms embodying rad score and clinical-pathologic data outperformed, showing good calibrations for predicting response to NAC. The combined nomogram predicting pCR showed the best performance with the AUC values of 0.97, 0.90 and 0.86 in the training, testing, and external validation cohorts respectively. The AUC values of another combined nomogram predicting significant remission: 0.98, 0.88 0.80 in the training, testing and external validation cohorts. DCA showed the comprehensive model nomogram obtained the most clinical benefit., Conclusions: The combined nomogram could preoperatively predict significant remission or even pCR to NAC in breast cancer based on multiparametric MRI and clinical-pathologic data., Competing Interests: Disclosure The authors have stated that they have no conflicts of interest., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: While the Oncotype DX 21-gene recurrence score (RS) has been recommended for guiding ER+/HER2- breast cancer treatment decisions, it is limited by cost and availability., Purpose: To develop a multiparametric MRI-based radiomics model for assessing ER+/HER2- breast cancer patients' 21-gene RS., Study Type: Retrospective., Subjects: A total of 151 patients with pathologically confirmed ER+/HER2- breast cancers, who underwent preoperative breast MR examinations and 21-gene expression assays, divided into training (n = 106) and validation (n = 45) cohorts., Field Strength/sequence: T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhancement (DCE) sequence at 1.5 T or 3 T., Assessment: A total of 1046 radiomics features were extracted from each MRI sequence with a manual lesion segmentation method. After feature dimension reduction by the recursive feature elimination method and dataset balance by the synthetic minority oversampling technique, linear support vector machine classifier models were built to distinguish high RS (RS ≥ 26) from low RS (RS < 26) from T2WI, DWI apparent diffusion coefficient (ADC) maps, DCE and their combination (multiparametric). A model based on clinical characteristics and a fusion model combining clinical characteristics and multiparametric MRI were also built., Statistical Tests: Receiver operating characteristic (ROC) curve analysis and De Long's test with Bonferroni correction were used. A P value <0.01 was considered statistically significant., Results: The area under the ROC curve (AUC) value of multiparametric radiomics model was 0.92, significantly higher than DCE (0.83), T2WI (0.78), and ADC (0.77) models in the training cohort. The radiomics model also achieved good performance in the validation cohort (AUC = 0.77). The fusion model had significantly higher performance than the clinical model in both the training (AUC = 0.92 and 0.64, respectively) and validation cohorts (AUC = 0.78 and 0.62, respectively)., Data Conclusion: The proposed multiparametric MRI-based radiomics models may have potential to help distinguish ER+/HER2- breast cancer patients' recurrence risk., Evidence Level: 3., Technical Efficacy: Stage 2., (© 2022 International Society for Magnetic Resonance in Medicine.)

Background: Preoperative pathological grading assessment is important for patients with breast phyllodes tumors (PTs)., Purpose: To develop and validate a clinical-radiomics model based on multiparametric MRI and clinical information for the pretreatment differential diagnosis of PTs., Study Type: Retrospective., Population: A total of 216 patients with PTs, 133 in the training cohort (55 benign PTs [BPTs] and 78 borderline/malignant PTs [BMPTs]) and 83 in the validation cohort (28 BPTs and 55 BMPTs)., Field Strength/sequence: 1.5 T and 3 T; T2-weighted imaging (T2WI), precontrast T1-weighted imaging (T1WI) and dynamic contrast-enhanced T1-weighted imaging (DCE-T1WI)., Assessment: A total of 3138 radiomics features were computed to decode the imaging phenotypes of PTs. To build the classification models, the following workflow was followed: minimum-maximum scaling normalization method, recursive feature elimination based on ridge regression (Ridge-RFE), synthetic minority oversampling technique, and support vector machine classifier. We established several models based on the statistically significant features (Ridge-RFE selected) of each sequence to distinguish BPTs from BMPTs, including precontrast T1WI model, DCE-T1WI phase 1 model, T1WI feature fusion model, T2WI model, T1WI + T2WI model, clinical feature model, conventional MRI characteristics model, and combined clinical-radiomics model., Statistical Tests: Univariate analysis was utilized to compare variables between the BPT and BMPT groups. The receiver operating characteristic curve (ROC) analysis was used to evaluate the diagnostic performance of these models., Results: In the training cohort, the clinical-radiomics model had excellent diagnostic efficiency, with an area under ROC (AUC) of 0.91 ± 0.02 (95% CI: 0.87-0.94). In the validation cohort, the AUCs were 0.79 ± 0.05 (95% CI: 0.70-0.87) for the combined model and 0.77 ± 0.05 (95% CI: 0.67-0.85) for the radiomics model., Data Conclusion: Compared with conventional MRI characteristics, radiomics features extracted from multiparametric MRI are helpful for improving the accuracy of differentiating the pathological grades of PTs preoperatively. The model based on radiomics and clinical information is expected to become a potential noninvasive tool for the assessment of PTs grades., Evidence Level: 4 TECHNICAL EFFICACY: Stage 2., (© 2022 International Society for Magnetic Resonance in Medicine.)

Background: Accurate assessment of the axillary lymph nodes (aLNs) in breast cancer patients is essential for prognosis and treatment planning. Current radiological staging of nodal metastasis has poor accuracy. This study aimed to investigate the machine learning convolutional neural networks (CNNs) on multiparametric MRI to detect nodal metastasis with 18FDG-PET as ground truths., Materials and Methods: Data were obtained via a retrospective search. Inclusion criteria were patients with bilateral breast MRI and 18FDG-PETand/or CT scans obtained before neoadjuvant chemotherapy. In total, 238 aLNs were obtained from 56 breast cancer patients with 18FDG-PET and/or CT and breast MRI data. Radiologists scored each node based on all MRI as diseased and non-diseased nodes. Five models were built using T1-W MRI, T2-W MRI, DCE MRI, T1-W + T2-W MRI, and DCE + T2-W MRI model. Performance was evaluated using receiver operating curve (ROC) analysis, including area under the curve (AUC)., Results: All CNN models yielded similar performance with an accuracy ranging from 86.08% to 88.50% and AUC ranging from 0.804 to 0.882. The CNN model using T1-W MRI performed better than that using T2-W MRI in detecting nodal metastasis. CNN model using combined T1- and T2-W MRI performed the best compared to all other models (accuracy = 88.50%, AUC = 0.882), but similar in AUC to the DCE + T2-W MRI model (accuracy = 88.02%, AUC = 0.880). All CNN models performed better than radiologists in detecting nodal metastasis (accuracy = 65.8%)., Conclusion: xxxxxx., (Copyright © 2021. Published by Elsevier Inc.)

Background: The sensitivity of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for detecting breast cancer was high and the specificity was relatively low. However, diffusion-weighted imaging (DWI) has a high specificity in the diagnosis of malignant lesions., Purpose: To evaluate the accuracy of the multiparametric MRI (mp-MRI) in distinguishing the breast malignant lesions from the benign lesions., Material and Methods: A comprehensive search of the PubMed, Embase, and Cochrane Library electronic databases was conducted up to March 2020. Data were analyzed for the following indexes: pooled sensitivity and specificity; positive likelihood ratio; negative likelihood ratio; diagnostic odds ratio; and the area under the curve., Results: A total of 2356 patients with 1604 malignant and 967 benign breast lesions were included from 22 studies. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and area under the curve for mp-MRI were 0.93, 0.85, 6.3, 0.08, 81, and 0.96, respectively. The pooled sensitivity, specificity, and area under the curve for DCE-MRI alone were 0.95, 0.71, and 0.92, respectively. The pooled sensitivity, specificity, and area under the curve for DWI alone were 0.88, 0.84, and 0.93, respectively., Conclusion: The mp-MRI did not improve the sensitivity but increased the specificity for the diagnosis of breast malignant lesions.

Purpose: To assess the additional value of quantitative tCho evaluation to diagnose malignancy and lymph node metastases in suspicious lesions on multiparametric breast MRI (mpMRI, BI-RADS 4, and BI-RADS 5)., Methods: One hundred twenty-one patients that demonstrated suspicious multiparametric breast MRI lesions using DCE, T2w, and diffusion-weighted (DW) images were prospectively enrolled in this IRB-approved study. All underwent single-voxel proton MR spectroscopy ( 1 H-MRS, point-resolved spectroscopy sequence, TR 2000 ms, TE 272 ms) with and without water suppression. The total choline (tCho) amplitude was measured and normalized to millimoles/liter according to established methodology by two independent readers (R1, R2). ROC-analysis was employed to predict malignancy and lymph node status by tCho results., Results: One hundred three patients with 74 malignant and 29 benign lesions had full 1 H-MRS data. The area under the ROC curve (AUC) for prediction of malignancy was 0.816 (R1) and 0.809 (R2). A cutoff of 0.8 mmol/l tCho could diagnose malignancy with a sensitivity of > 95%. For prediction of lymph node metastases, tCho measurements achieved an AUC of 0.760 (R1) and 0.788 (R2). At tCho levels < 2.4 mmol/l, no metastatic lymph nodes were found., Conclusion: Quantitative tCho evaluation from 1 H-MRS allowed diagnose malignancy and lymph node status in breast lesions suspicious on multiparametric breast MRI. tCho therefore demonstrated the potential to downgrade suspicious mpMRI lesions and stratify the risk of lymph node metastases for improved patient management., Key Points: • Quantitative tCho evaluation can distinguish benign from malignant breast lesions suspicious after multiparametric MRI assessment. • Quantitative tCho levels are associated with lymph node status in breast cancer. • Quantitative tCho levels are higher in hormonal receptor positive compared to hormonal receptor negative lesions.

Background and Purpose: Multiparametric radiological imaging is vital for detection, characterization, and diagnosis of many different diseases. Radiomics provide quantitative metrics from radiological imaging that may infer potential biological meaning of the underlying tissue. However, current methods are limited to regions of interest extracted from a single imaging parameter or modality, which limits the amount of information available within the data. This limitation can directly affect the integration and applicable scope of radiomics into different clinical settings, since single image radiomics are not capable of capturing the true underlying tissue characteristics in the multiparametric radiological imaging space. To that end, we developed a multiparametric imaging radiomic (mpRad) framework for extraction of first and second order radiomic features from multiparametric radiological datasets., Methods: We developed five different radiomic techniques that extract different aspects of the inter-voxel and inter-parametric relationships within the high-dimensional multiparametric magnetic resonance imaging breast datasets. Our patient cohort consisted of 138 breast patients, where, 97 patients had malignant lesions and 41 patients had benign lesions. Sensitivity, specificity, receiver operating characteristic (ROC) and areas under the curve (AUC) analysis were performed to assess diagnostic performance of the mpRad parameters. Statistical significance was set at p < 0.05., Results: The mpRad features successfully classified malignant from benign breast lesions with excellent sensitivity and specificity of 82.5% and 80.5%, respectively, with Area Under the receiver operating characteristic Curve (AUC) of 0.87 (0.81-0.93). mpRad provided a 9-28% increase in AUC metrics over single radiomic parameters., Conclusions: We have introduced the mpRad framework that extends radiomic analysis from single images to multiparametric datasets for better characterization of the underlying tissue biology.

Purpose: To evaluate the value of multiparametric magnetic resonance imaging (MRI) in pretreatment prediction of breast cancers insensitive to neoadjuvant chemotherapy (NAC)., Methods: A total of 125 breast cancer patients (63 in the primary cohort and 62 in the validation cohort) who underwent MRI before receiving NAC were enrolled. All patients received surgical resection, and Miller-Payne grading system was applied to assess the response to NAC. Grade 1-2 cases were classified as insensitive to NAC. We extracted 1941 features in the primary cohort. After feature selection, the optimal feature set was used to construct a radiomic signature using machine learning. We built a combined prediction model incorporating the radiomic signature and independent clinical risk factors selected by multivariable logistic regression. The performance of the combined model was assessed with the results of independent validation., Results: Four features were selected for the construction of the radiomic signature based on the primary cohort. Combining with independent clinical factors, the combined prediction model for identifying the Grade 1-2 group reached a better discrimination power than the radiomic signature, with an area under the receiver operating characteristic curve of 0.935 (95% confidence interval 0.848-1) in the validation cohort, and its clinical utility was confirmed by the decision curve analysis., Conclusion: The combined model based on radiomics and clinical variables has potential in predicting drug-insensitive breast cancers.

Preclinical imaging is critical in the development of translational strategies to detect diseases and monitor response to therapy. The National Cancer Institute Co-Clinical Imaging Resource Program was launched, in part, to develop best practices in preclinical imaging. In this context, the objective of this work was to develop a 1-hour, multiparametric magnetic resonance image-acquisition pipeline with triple-negative breast cancer patient-derived xenografts (PDXs). The 1-hour, image-acquisition pipeline includes T1- and T2-weighted scans, quantitative T1, T2, and apparent diffusion coefficient (ADC) parameter maps, and dynamic contrast-enhanced (DCE) time-course images. Quality-control measures used phantoms. The triple-negative breast cancer PDXs used for this study averaged 174 ± 73 μL in volume, with region of interest-averaged T1, T2, and ADC values of 1.9 ± 0.2 seconds, 62 ± 3 milliseconds, and 0.71 ± 0.06 μm 2 /ms (mean ± SD), respectively. Specific focus was on assessing the within-subject test-retest coefficient-of-variation (CV WS ) for each of the magnetic resonance imaging metrics. Determination of PDX volume via manually drawn regions of interest is highly robust, with ∼1% CV WS . Determination of T2 is also robust with a ∼3% CV WS . Measurements of T1 and ADC are less robust with CV WS values in the 6%-11% range. Preliminary DCE test-retest time-course determinations, as quantified by area under the curve and K trans from 2-compartment exchange (extended Tofts) modeling, suggest that DCE is the least robust protocol, with ∼30%-40% CV WS ., Competing Interests: Conflict of Interest: The authors have no conflict of interest to declare., (© 2019 The Authors. Published by Grapho Publications, LLC.)

It is well-recognized that solid tumors are genomically, anatomically, and physiologically heterogeneous. In general, more heterogeneous tumors have poorer outcomes, likely due to the increased probability of harboring therapy-resistant cells and regions. It is hypothesized that the genomic and physiologic heterogeneity are related, because physiologically distinct regions will exert variable selection pressures leading to the outgrowth of clones with variable genomic/proteomic profiles. To investigate this, methods must be in place to interrogate and define, at the microscopic scale, the cytotypes that exist within physiologically distinct subregions ("habitats") that are present at mesoscopic scales. MRI provides a noninvasive approach to interrogate physiologically distinct local environments, due to the biophysical principles that govern MRI signal generation. Here, we interrogate different physiologic parameters, such as perfusion, cell density, and edema, using multiparametric MRI (mpMRI). Signals from six different acquisition schema were combined voxel-by-voxel into four clusters identified using a Gaussian mixture model. These were compared with histologic and IHC characterizations of sections that were coregistered using MRI-guided 3D printed tumor molds. Specifically, we identified a specific set of MRI parameters to classify viable-normoxic, viable-hypoxic, nonviable-hypoxic, and nonviable-normoxic tissue types within orthotopic 4T1 and MDA-MB-231 breast tumors. This is the first coregistered study to show that mpMRI can be used to define physiologically distinct tumor habitats within breast tumor models. SIGNIFICANCE: This study demonstrates that noninvasive imaging metrics can be used to distinguish subregions within heterogeneous tumors with histopathologic correlation., (©2019 American Association for Cancer Research.)

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 HER2-negative 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., (©2019 American Association for Cancer Research.)

Objectives: We investigated artificial intelligence (AI)-based classification of benign and malignant breast lesions imaged with a multiparametric breast magnetic resonance imaging (MRI) protocol with ultrafast dynamic contrast-enhanced MRI, T2-weighted, and diffusion-weighted imaging with apparent diffusion coefficient mapping., Materials and Methods: We analyzed 576 lesions imaged with MRI, including a consecutive set of biopsied malignant (368) and benign (149) lesions, and an additional set of 59 benign lesions proven by follow-up. We used deep learning methods to interpret ultrafast dynamic contrast-enhanced MRI and T2-weighted information. A random forests classifier combined the output with patient information (PI; age and BRCA status) and apparent diffusion coefficient values obtained from diffusion-weighted imaging to perform the final lesion classification. We used receiver operating characteristic (ROC) analysis to evaluate our results. Sensitivity and specificity were compared with the results of the prospective clinical evaluation by radiologists., Results: The area under the ROC curve was 0.811 when only ultrafast dynamics was used. The final AI system that combined all imaging information with PI resulted in an area under the ROC curve of 0.852, significantly higher than the ultrafast dynamics alone (P = 0.002). When operating at the same sensitivity level of radiologists in this dataset, this system produced 19 less false-positives than the number of biopsied benign lesions in our dataset., Conclusions: Use of adjunct imaging and PI has a significant contribution in diagnostic performance of ultrafast breast MRI. The developed AI system for interpretation of multiparametric ultrafast breast MRI may improve specificity.