Contrast-enhanced Magnetic Resonance Imaging Research Articles

Optimizing ultrafast dynamic contrast-enhanced MRI scan duration in the differentiation of benign and malignant breast lesions.

To determine the optimal scan duration for ultrafast DCE-MRI in effectively differentiating benign from malignant breast lesions. The study prospectively recruited participants who underwent breast ultrafast DCE-MRI from September 2021 to March 2023. A 30-phase breast ultrafast DCE-MRI on a 3.0-T MRI system was conducted with a 4.5-s temporal resolution. Scan durations ranged from 40.5 s to 135.0 s, during which the analysis is performed at three-phase intervals, forming eight dynamic sets (scan duration [SD]40.5s: 40.5 s, SD54s: 54.0 s, SD67.5s: 67.5 s, SD81s: 81.0 s, SD94.5s: 94.5 s, SD108s: 108.0 s, SD121.5s: 121.5 s, and SD135s: 135.0 s). Two ultrafast DCE-MRI parameters, maximum slope (MS) and initial area under the curve in 60 s (iAUC), were calculated for each dynamic set and compared between benign and malignant lesions. Areas under the receiver operating characteristic curve (AUCs) were used to assess their diagnostic performance. A total of 140 women (mean age, 47 ± 11 years) with 151 lesions were included. MS and iAUC from eight dynamic sets exhibited significant differences between benign and malignant lesions (all p < 0.05), except iAUC at SD40.5s. The AUC of MS (AUC = 0.804) and iAUC (AUC = 0.659) at SD67.5s were significantly higher than their values at SD40.5s (AUC = 0.606 and 0.516; corrected p < 0.05). No significant differences in AUCs for MS and iAUC were observed from SD67.5s to SD135s (all corrected p > 0.05). Ultrafast DCE-MRI with a 67.5-s scan duration appears optimal for effectively differentiating malignant from benign breast lesions. By evaluating scan durations (40.5-135 s) and analyzing two ultrafast DCE-MRI parameters, we found a scan duration of 67.5 s optimal for discriminating between these lesions and offering a balance between acquisition time and diagnostic efficacy. Ultrafast DCE-MRI can effectively differentiate malignant from benign breast lesions. A minimum of 67.5-sec ultrafast DCE-MRI scan duration is required to differentiate benign and malignant lesions. Extending the scan duration beyond 67.5 s did not significantly improve diagnostic accuracy.

Magnetic resonance imaging findings of radiation-induced breast angiosarcoma: A case report

BACKGROUND Breast conservation surgery (BCS) with adjuvant radiotherapy has become a gold standard in the treatment of early-stage breast cancer, significantly reducing the risk of tumor recurrence. However, this treatment is associated with adverse effects, including the rare but aggressive radiation-induced angiosarcoma (RIAS). Despite its rarity and nonspecific initial presentation, RIAS presents a challenging diagnosis, emphasizing the importance of imaging techniques for early detection and accurate diagnosis. CASE SUMMARY We present a case of a 48-year-old post-menopausal woman who developed skin ecchymosis on the right breast seven years after receiving BCS and adjuvant radiotherapy for breast cancer. Initial mammography and ultrasound were inconclusive, showing post-treatment changes but failing to identify the underlying angiosarcoma. Contrast-enhanced breast magnetic resonance imaging (MRI) revealed diffuse skin thickening and nodularity with distinctive enhancement kinetics, leading to the diagnosis of RIAS. This case highlights the crucial role of MRI in diagnosing and determining the extent of RIAS, facilitating timely and appropriate surgical intervention. CONCLUSION Breast MRI is crucial for detecting RIAS, especially when mammography and ultrasound are inconclusive.

Assessment of Hypoxia in Breast Cancer: Emerging Functional MR Imaging and Spectroscopy Techniques and Clinical Applications.

Breast cancer is one of the most prevalent forms of cancer affecting women worldwide. Hypoxia, a condition characterized by insufficient oxygen supply in tumor tissues, is closely associated with tumor aggressiveness, resistance to therapy, and poor clinical outcomes. Accurate assessment of tumor hypoxia can guide treatment decisions, predict therapy response, and contribute to the development of targeted therapeutic interventions. Over the years, functional magnetic resonance imaging (fMRI) and magnetic resonance spectroscopy (MRS) techniques have emerged as promising noninvasive imaging options for evaluating hypoxia in cancer. Such techniques include blood oxygen level-dependent (BOLD) MRI, oxygen-enhanced MRI (OE) MRI, chemical exchange saturation transfer (CEST) MRI, and proton MRS (1H-MRS). These may help overcome the limitations of the routinely used dynamic contrast-enhanced (DCE) MRI and diffusion-weighted imaging (DWI) techniques, contributing to better diagnosis and understanding of the biological features of breast cancer. This review aims to provide a comprehensive overview of the emerging functional MRI and MRS techniques for assessing hypoxia in breast cancer, along with their evolving clinical applications. The integration of these techniques in clinical practice holds promising implications for breast cancer management. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 1.

The discerning influence of dynamic contrast-enhanced MRI in anticipating molecular subtypes of breast cancer through the artistry of artificial intelligence - a narrative review.

Radio genomics is an exciting new area that uses diagnostic imaging to discover genetic features of diseases. In this review, we carefully examined existing literature to evaluate the role of artificial intelligence (AI) and machine learning (ML) on dynamic contrastenhanced MRI (DCE-MRI) data to distinguish molecular subtypes of breast cancer (BC). Implications to noninvasive assessment of molecular subtype include reduction in procedure risks, tailored treatment approaches, ability to examine entire lesion, follow-up of tumour biology in response to treatment and evaluation of treatment resistance and failure secondary to tumour heterogeneity. Recent studies leverage radiomics and AI on DCE-MRI data for reliable, non-invasive breast cancer subtype classification. This review recognizes the potential of AI to predict the molecular subtypes of breast cancer non-invasively.

Abstract PS05-04: [89Zr]Trastuzumab-PET/MRI to Characterize HER2+ Breast Cancer: A Quantitative Approach on Tumor Heterogeneity

Abstract Introduction: Molecular imaging of human epidermal receptor 2 (HER2) aims to overcome limitations of traditional HER2 assessment through biopsy, including invasiveness and inability to detect intra and inter-tumoral spatial heterogeneity. Prior human studies suggest that [89Zr]Trastuzumab-positron emission tomography (PET) imaging can effectively differentiate HER2 lesions (Dehdashti et al, Breast Cancer Res. Treat. 2018). Commonly, CT complements PET for anatomical reference, however, integrating MRI enables the sensitivity of soft-tissue contrast required for anatomic and morphological breast imaging and can provide for quantitative multiparametric characterization. Diffusion weighted-MRI (DW-MRI) contributes to tumor characterization by assessing intratumoral cellularity via the apparent diffusion coefficient (ADC), and can be combined with molecular expression from HER2-PET. We present preliminary results in 13 patients with HER2 PET/MRI to define tumor and normal tissue imaging metrics. Methods: A phase II clinical trial assessing the feasibility of [89Zr]trastuzumab-PET with simultaneous quantitative DW-MRI to enhance understanding of tumor heterogeneity in patients with HER2-positive metastatic breast cancer. 13 patients (ages 40-70; mean 60) with HER2-positive breast cancer based on prior biopsy underwent whole-body [89Zr]trastuzumab-PET/MR imaging (5 ± 1 days post-injection of radiopharmaceutical) during the course of HER2-directed therapy. Normal organ and tumor regions of interest (ROI) were identified on concurrently acquired whole-body T1-weighted MRI for ADC (cellularity) and standardized uptake value (SUV, PET uptake) mean quantification. Tumor presence was confirmed via bone scintigraphy, FDG-PET/CT, or contrast-enhanced MRI. Non-parametric T-tests compared lesions to normal organs. Lesions greater than 30 mm in diameter underwent multiparametric intratumoral habitat analysis. Using the median HER2 values, tumors were evaluated for heterogeneity of high and low HER2 expression in conjunction with ADC. Long-term treatment response evaluation is ongoing. Results: Mean [89Zr]trastuzumab uptake, SUV, and ADC values for normal tissue were summarized in Table 1. All tumors demonstrated higher overall uptake of [89Zr]trastuzumab (bone: p=0.019, brain: p=0.014, breast: p=0.069, juxtapulmonary: p=0.026) and increased ADCmean values (bone: p=0.002, brain: p=0.5, breast: p=0.03, juxtapulmonary: p=0.037), in comparison to matched normal organs. Notably, one of five patients with a breast lesion, who completely responded to HER2 targeted therapy, exhibited the highest breast lesion SUVmean. Brain and lymph node lesions demonstrated intratumoral heterogeneity of HER2 expression. Conclusion: Our study demonstrates the potential value of quantitative MRI along with molecular imaging to characterize metastatic HER2+ breast cancer and evaluate intratumoral heterogeneity. We underscore the importance of standardizing processing techniques and contribute to this effort by summarizing [89Zr]trastuzumab uptake and cellularity values of normal physiological uptake. Higher SUVmean and ADC mean values observed in lesions, compared to normal tissue, highlight their potential roles in intratumoral classification. While studies have shown the utility of [89 Zr]trastuzumab-PET/CT, our findings demonstrate that integrated DW-MRI can aid in tumor classification. Table 1: Physiological 89Zr-Trastuzumab Uptake (SUVmean) and Apparent Diffusion Coefficient (ADCmean) in Healthy Tissues Citation Format: Ameer Mansur, Moozhan Nikpanah, Johnathan McConathy, Erica Stringer-Reasor, Gabrielle Rocque, Ahmed Elkhanany, Katia Khoury, Nusrat Jahan, Suzanne Lapi, Anna Sorace. [89Zr]Trastuzumab-PET/MRI to Characterize HER2+ Breast Cancer: A Quantitative Approach on Tumor Heterogeneity [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS05-04.

Abstract PO1-07-07: Radiomics features for distinguishing true recurrence versus new primary tumor following breast-conserving treatment

Abstract Background Despite modern surgical and irradiation techniques, ipsilateral breast tumor recurrence (IBTR) accounts for 5-15% of all cancer recurrences in women treated with breast conservative treatment. Historically, the methods to define true recurrence (TR) and new primary tumor (NP) for IBTR mainly rely on clinical and pathological criteria, limiting the accuracy of the discerption and causing misclassification. This study aimed to develop a preoperative, noninvasive model for distinguishing IBTR by integrating clinicopathological review with paired dynamic contrast-enhanced breast magnetic resonance imaging (DCE-MRI) at diagnosis and following in-breast recurrence. MethodsWe retrospectively extracted radiomics features from MRI to develop a radiomic cohort of IBTR (n =46), among which all patients underwent paired preoperative DCE-MRI. This radiomic cohort was divided into a training cohort (n =27) and a validation cohort (n =19) with stratified random sampling. Classification of IBTR as TR or NP on the basis of tumor location, histologic subtype, estrogen receptor, and HER2 status was set as the gold standard. The least absolute shrinkage and selection operator (LASSO) regression and logistic regression were utilized to perform radiomics feature selection and model training, respectively. The clinical utility of the model was determined via decision curve analysis (DCA). Results We selected three radiomics features (first-order feature Kurtosis from the first postcontrast phase on IBTR DCE-MRI, GLCM feature Imc2 from the first postcontrast phase on IBTR DCE-MRI, and the delta feature Correlation from the precontrast phase of the change between IBTR and primary tumor DCE-MRI) to develop an IBTR-classification predicting radiomic model, which performed well in the validation cohort (AUC 0.867, 95% confidence interval (CI) 0.694-1). Further investigation for sensitivity (73.3%) and specificity (100%) verified a favorable concordance between the radiomic classification and the conventional standard, with a diagnostic accuracy of 79%. Conclusions: Our study demonstrated the feasibility of the radiomics model in predicting IBTR classification and provided preoperative information about the nature of “recurrence”. This might have important implications in surgical approaches and multidisciplinary care for IBTR. Further efforts are needed to improve the reproducibility of radiomics features and models in multiple centers. Citation Format: Feilin Qu, Guan-Hua Su, Jin-Hui Li, Chao You, Zhi-Ming Shao. Radiomics features for distinguishing true recurrence versus new primary tumor following breast-conserving treatment [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-07-07.

Abstract PO3-28-10: MRI Radiomic phenotypes derived from the ECOG-ACRIN E4112 Trial to assess high-risk ductal carcinoma in situ

Abstract INTRODUCTION Ductal carcinoma in situ (DCIS) is a non-lethal pre-invasive breast cancer that can co-exist with invasive disease. Dynamic contrast-enhanced (DCE) MRI is sensitive for the detection of high-grade DCIS and invasive cancer while Oncotype DCIS Score is a 12-gene assay that can assess recurrence risk. In practice, it is difficult to distinguish low- from high-risk DCIS, which leads to overtreatment for up to half of women diagnosed with DCIS. We hypothesize that radiomic phenotypes of DCIS derived from DCE-MRI data may serve as prognostic markers to improve risk stratification by capturing disease heterogeneity. Here we evaluate the ability of these phenotypes to predict DCIS Score and upstaging of DCIS to invasive disease on wide local excision in a multicenter trial. METHODS Data: DCE-MRI data from the ECOG-ACRIN E4112 trial were retrospectively analyzed. Primary analysis focused on participants with data on disease upstaging (N=295), with secondary analysis in a subset (N=174) of participants with DCIS Scores (dichotomized as &amp;gt;55 and 55) and pure DCIS. Clinical information included patient demographics, lesion morphology on MRI, background parenchymal enhancement, DCIS grade, central necrosis, and hormone receptor status. Data analysis: Radiologist-drawn lesion segmentations and publicly available software, CaPTk, were used to compute 64 radiomic features from first post-contrast images for each participant. Radiomic phenotypes were identified using hierarchical clustering on the extracted features. A Chi-square test was used to evaluate the association between radiomic phenotypes and each outcome. The likelihood ratio test was used to compare two logistic regression models: 1)clinical model using only clinical information as predictors and 2) clinical+phenotypes model using clinical information and phenotype assignment as predictors. Each model was used for the prediction of upstaging to invasive disease and DCIS score. Model performance was evaluated as the 10-fold cross-validated area under the receiver operator characteristic curve (AUC). A p&amp;lt; 0.05 was considered significant. RESULTS A total of 45 (15%) cases upstaged to invasive disease. Two radiomic phenotypes were identified: Phenotype 1 indicated greater lesion signal heterogeneity, while Phenotype 2 indicated lower heterogeneity. Radiomic phenotype was strongly associated with disease upstaging (p=0.0034) – with a higher rate of upstaging for Phenotype 1 – but not with DCIS score (p=0.1174, Table 1). For predicting disease upstaging, the clinical+phenotypes model yielded a higher AUC=0.72 and a significantly better fit to the data (p=0.0022) compared to the clinical model parameterized by clinical information alone (AUC=0.69). For predicting DCIS Score, the clinical+phenotypes model (AUC=0.77) showed similar performance compared to the clinical model (AUC=0.76) and no significant improvement in fit to the data (p=0.2920). CONCLUSION Radiomic phenotypes capturing disease heterogeneity show promise as prognostic predictors for predicting disease upstaging in DCIS compared to clinical information alone and may enable more efficient disease management. We observed that phenotypes did not have independent predictive outcome for DCIS score, suggesting that MRI and DCIS Score offer independent information and could be combined in future models to better predict disease recurrence or progression. Clinical applications of radiomic phenotypes may improve risk stratification and potentially result in decreased overtreatment of women diagnosed with DCIS. Table 1: Association between radiomic phenotypes and DCIS outcomes using a Chi square test. Citation Format: Kalina Slavkova, Ruya Kang, Vivian Belenky, Anum Kazerouni, Debosmita Biswas, Hannah Horng, Rhea Chitalia, Michael Hirano, Jennifer Xiao, Ralph Corsetti, Sarah Javid, Derrick Spell, Antonio Wolff, Joseph Sparano, Seema Khan, Christopher Comstock, Justin Romanoff, Jon Steingrimsson, Constantine Gatsonis, Constance Lehman, Savannah Partridge, Despina Kontos, Habib Rahbar. MRI Radiomic phenotypes derived from the ECOG-ACRIN E4112 Trial to assess high-risk ductal carcinoma in situ [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-28-10.

Abstract PO5-07-07: The biological basis of breast MRI background parenchymal enhancement in women with high breast cancer risk

Abstract Introduction: The amount and degree of the fibroglandular tissue (FGT) enhancement on breast MRI, known as background parenchymal enhancement (BPE), is an emerging marker of breast cancer risk. Higher qualitative and quantitative BPE levels have been shown to correlate with both future risk of initial breast cancer diagnosis and breast cancer recurrence after treatment. Furthermore, adjuvant and preventative therapies such as radiation and endocrine therapy have been shown to lower BPE in many women, potentially serving as a marker of treatment efficacy. However, the biological basis of BPE remains unclear and elucidation of this mechanism could identify novel targeted prevention strategies. The goal of this study was to assess the association of BPE levels with markers of inflammation, vascularity, proliferation, and estrogen sensitivity in a cohort of high-risk women. Materials and Methods: In this IRB-approved retrospective study, we identified asymptomatic high-risk women who underwent at least one dynamic contrast-enhanced (DCE) breast MRI within one year of prophylactic bilateral mastectomy. Qualitative BPE assessments were obtained from clinical reports. Quantitative BPE measures were obtained using in-house semi-automated software: whole breast FGT was first segmented on pre-contrast images, percent enhancement (PE) was calculated for each voxel as (S1-S0)/S0 × 100%, where S0 and S1 are pre- and early post-contrast signal intensity, respectively, and quantitative BPE was then defined as the mean PE of the FGT. Additionally, the BPE volume ratio was calculated as the fraction of FGT that had a PE &amp;gt;50%. Histopathology markers of inflammation (COX-2), vascularity (VEGF), proliferation (Ki-67), and estrogen receptor status (ER) were prospectively measured on archived mastectomy specimens of each breast. BPE metrics were correlated with histopathological markers using Spearman’s rank correlation, with and without adjustment by menopausal status of the patient. All tests were based on generalized estimating equations models to account for the non-independence of the two breasts per patient. A p-value &amp;lt; 0.05 was considered significant. Results: We identified 56 women (median age: 39; range: 26-63 years) for this study, of which 39% were post-menopausal (N=22). Distribution of qualitative BPE levels were 41% minimal (N=23), 29% mild (N=16), 18% moderate (N=10), and 12% marked (N=7). Qualitative BPE and quantitative BPE markers were moderately positively correlated (r=0.37-0.54, p&amp;lt; 0.01). Table 1 lists the correlations between BPE metrics and histopathology markers. After adjusting for menopausal status, qualitative BPE was positively associated with ER (r=0.27, p=0.02) and VEGF (r=0.24, p&amp;lt; 0.01; r=0.21, p=0.02 adjusted). Quantitative BPE and BPE volume ratio were both positively correlated with VEGF (p&amp;lt; 0.04) and negatively correlated with COX2 (p&amp;lt; 0.05). No significant associations were observed between BPE and Ki-67. Discussion: Our study suggests that increased BPE on MRI positively correlates with increased vascularity and higher estrogen receptor expression but not inflammation or proliferation within normal breast tissue in high-risk women. These findings, along with established associations between BPE and cancer risk, could be useful to select and assess efficacy of targeted preventative treatments. Table. Spearman rank correlations between BPE metrics and histopathological markers. Citation Format: Pegah Khoshpouri, Anum Kazerouni, Sana Parsian, Daniel Hippe, Olivia Walsh, Lisa Koch, Savannah Partridge, Habib Rahbar. The biological basis of breast MRI background parenchymal enhancement in women with high breast cancer risk [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-07-07.

Abstract PO1-07-11: Radiogenomic-based imaging intratumor heterogeneity model predicts breast cancer prognosis and unveils therapeutic targets

Abstract Background: Intratumor heterogeneity (ITH) refers to variations observed among cancer cells within a single tumor, posing significant challenges in clinical practice due to its contribution to treatment resistance and unfavorable outcome. However, the inconvenience of multi-region biopsy and limitations of genomic sequencing based on limited tissue impede the practical detection of ITH. Consequently, there is an urgent need for a non-invasive method that comprehensively captures ITH from a holistic perspective of the entire tumor. Methods: We utilized a large multicenter dataset comprising dynamic contrast-enhanced magnetic resonance images from breast cancer patients (n = 1423) along with matched multiomics data (n = 468) to develop a non-invasive machine learning approach for measuring ITH. We extracted quantitative radiomic features from both the entire tumor and peritumor regions, with a specific focus on features associated with imaging heterogeneity. Using these radiomic features, we established an imaging ITH (IITH) model. The robustness of IITH evaluation was determined by assessing its correlation with genomic ITH (employing the mutant-allele tumor heterogeneity [MATH] algorithm) and pathological cellular ITH (analyzing variation in quantitative nuclear features extracted through digital pathology). Prognostic power was evaluated using Kaplan-Meier and multivariate Cox proportional hazards analyses. Integrated multiomics analyses were performed to investigate the molecular basis of different IITH subgroups. Results: We developed a non-invasive radiomic signature to quantify imaging ITH (IITH) in the FUSCC cohort. Breast cancer patients were retrospectively categorized into high and low IITH groups. Multivariate Cox analysis identified high IITH as an independent predictor of poor prognosis in breast cancer patients, even after adjusting for clinical risk factors such as tumor size, positive lymph nodes, clinical subtype and lymphovascular invasion status. These findings were further validated in the DUKE cohort, confirming the prognostic value of IITH. Moreover, we substantiated the robustness of IITH by demonstrating its association with genomic and pathological ITH. Multiomics analysis revealed the activation of oncogenic pathways and metabolic dysregulation in high-IITH tumors. Intriguingly, our investigation also highlighted ferroptosis as a vulnerability and potential therapeutic target in high-IITH tumors, which was further supported by evidence from the TCGA cohort. Conclusion: Radiomic-based assessment of ITH provides a non-invasive approach to comprehensively capture ITH and predict the prognosis of breast cancer patients. Targeting ferroptosis may hold promise as a treatment strategy for patients with high IITH. Citation Format: Guan-Hua Su, Yi Xiao, Chao You, Ren-Cheng Zheng, Shen Zhao, He Wang, Yi-Zhou Jiang, Ya-Jia Gu, Zhi-Ming Shao. Radiogenomic-based imaging intratumor heterogeneity model predicts breast cancer prognosis and unveils therapeutic targets [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-07-11.

Abstract PO3-07-12: Effect of neoadjuvant chemotherapy on peritumoral edema in early stage breast cancer and its prognostic value

Abstract Objectives: We aimed to evaluate the effect of neoadjuvant chemotherapy (NAC) on peritumoral edema (PE) and its prognostic value in operable breast cancer. Materials &amp; Methods: This retrospective study included 128 cT1-4N0-3M0 breast cancer patients who underwent contrast-enhanced magnetic resonance imaging (MRI) of the breast before and after anthracycline- and/or taxane-based NAC, followed by curative surgery at our hospital, between 2006 and 2016. T2-weighted MRI revealed PE, and PE before NAC (pre-PE) and PE after NAC (residual PE) were evaluated. The correlation between the presence or absence of PE and relapse-free survival was evaluated using a log-rank test and multivariable Cox regression analysis. The risk of recurrence was stratified according to the residual cancer burden (RCB). Results: Pre-PE was observed in 64 out of 128 patients (50%). Among 64 patients with pre-PE-positive breast cancer, residual PE was observed in 21 (32.8%). In the log-rank test, breast cancer with residual PE had a poorer prognosis than breast cancer with PE that had disappeared (p &amp;lt; 0.0001). In the multivariable Cox regression analysis, residual PE was a significant factor of poor prognosis (RR, 20.5; 95% confidence interval, 5.3–108.2; p &amp;lt; 0.0001). Regarding RCB stratification, compared to patients with breast cancer with disappeared PE, those with breast cancer with residual PE exhibited significantly poor prognosis in class II and class III (p = 0.006, 0.02). Conclusion: In breast cancer with PE, residual PE after NAC is a significant factor related to poor prognosis. Effect of neoadjuvant chemotherapy on peritumoral edema in patients with breast cancer and its clinical significance XX XX Citation Format: Hideo Shigematsu, Mutsumi Fujimoto, Yoshie Kobayashi, Tomoyuki Yoshiyama, Noriaki Matsuura, Daisuke Komoto, Kazuya Kuraoka. Effect of neoadjuvant chemotherapy on peritumoral edema in early stage breast cancer and its prognostic value [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-07-12.

Abstract PO2-02-11: Radiomics and an Erosion Model to Predict Post-Neoadjuvant Therapy Tumor Characteristics for Personalized Breast Cancer Treatment

Abstract Background: Neoadjuvant therapy (NAT) has become a standard-of-care (SOC) for patients diagnosed with locally advanced or early-stage breast cancer. A critical step towards optimizing treatment selection and coordinating surgical care is to estimate the tumor’s response to NAT. While pathological complete response (pCR) is commonly used to measure NAT effectiveness, patients who do not achieve pCR still benefit from NAT depending on the tumor’s response. A reduction in tumor extent could alter the surgical approach from mastectomy to breast conserving therapy and improve recurrence and overall survival. In this study we used pre-treatment imaging data to provide a comprehensive prediction of clinically relevant metrics including tumor volume, tumor convex hull volume, and spatially derived metrics; this research represents an important advancement in predicting NAT effectiveness. Methods: 262 breast cancer patients from 5 institutions who underwent NAT with pre- and post-NAT dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) were included in this study. Each MRI was automatically segmented using a convolutional neural network with manual edits and verification. Radiomic features were extracted from the pre-NAT MRI and tumor segmentation. Significant radiomic features were selected based on their correlation with post-NAT volume, feature-to-feature correlation, and LASSO regression. Altogether, 12 and 17 radiomic features were selected for volume and convex hull volume prediction, respectively. The selected features were inputted into a Huber loss regression model to predict either the post-NAT tumor volume or the convex hull volume (surgical excision volume). To enable spatial and morphological comparison between the predicted and post-NAT tumor, an erosion model was developed in which the pre-NAT and post-NAT MRIs were co-registered, and the pre-NAT tumor was symmetrically eroded until its volume matched the predicted volume. Clinically relevant metrics such as the distance from the tumor to the nipple, skin, and chest were computed to assess the morphological and spatial prediction capabilities of the model. All models were tested on patients from cohort holdout sets (n=110) unseen during training. Results: The volume prediction analysis yielded promising results, with an R^2 value of 0.62 for post-NAT tumor volume prediction and an R^2 value of 0.56 for post-NAT tumor convex hull volume prediction. The mean volume and convex hull volume error was 1.1 cc and 1.3 cc, respectively. Table 1 provides an overview of the morphological and spatial comparisons between the predicted and post-NAT tumors. Overall, the predicted tumors demonstrated close agreement with their post-NAT counterparts. Conclusion: This study demonstrates a successful approach towards estimating post-NAT tumor volume, convex hull volume, and morphological and spatial characteristics. By providing reliable estimates of post-NAT tumor characteristics using SOC pre-NAT data, our predictive models enable personalized treatment planning and patient stratification to optimize patient care. Table 1. Predicted vs. post-NAT tumor comparison. Results demonstrate accurate predictions for clinically relevant distance metrics. Citation Format: Bradley Feiger, John Pfeiffer, John Cole, Anuja Antony, Joseph Peterson. Radiomics and an Erosion Model to Predict Post-Neoadjuvant Therapy Tumor Characteristics for Personalized Breast Cancer Treatment [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-02-11.

Abstract PO3-20-08: Case Studies on Detecting Ductal Carcinoma in Situ Using Contrast-Enhanced Spectral Mammography and Predictive Indicators for Underestimation

Abstract Purpose: To introduce clinical cases of ductal carcinoma in situ (DCIS) underestimation focusing on additional benefits of contrast-enhanced spectral mammography (CESM). Background: DCIS, a noninvasive breast cancer, is often underestimated during diagnosis. Predicting which cases will upgrade to invasive cancer is crucial. CESM offers superior diagnostic performance compared to mammography and mammography with targeted ultrasound (US). Its low-energy images improve microcalcification conspicuity, enable accurate tumor extent measurement, and provide information on enhancements associated with nonmass microcalcifications. Enhanced areas on CESM can be easily compared with mammographic microcalcifications, for they are obtained during the same session. Indicators heralding DCIS underestimation 1. Detectability: underestimated DCIS is more likely to be detected on CESM and mammogram than pure DICS, whereas detection rates do not statistically differ on US and MRI. 2. Lesion type: underestimated DCIS mostly presents as enhancing mass or nonmass lesions on CESM and calcifications on low-energy conventional mammography. 3. Disease extent: on CESM, pure DCIS show smaller tumor extent than the underestimated ones. 4. CESM gray value (CGV) of the lesion on recombined image: the pure DCIS types show lower mean, standard deviation, maximum, and difference values, whereas the underestimated types demonstrate lower minimum values, and vice versa. Case 1: Screen detected pure DCIS without postsurgical upgrade in an asymptomatic 44-year-old woman. US-guided core biopsy yielded DCIS, intermediate nuclear grade with necrosis. Postsurgical pathology confirmed a 1.5 cm pure ductal carcinoma in situ, intermediate nuclear grade with necrosis. (A) US shows a 1.3 cm irregular hypoechoic mass in the right breast. (B) Routine mammography demonstrates no definite abnormality in the right breast. (C) CESM shows a 1.2 cm irregular enhancing mass in the upper central portion of right breast and CGV was 2034 AU. (D) Contrast-enhanced MR images show a 2.1 cm irregular enhancing mass in 12 o’clock direction of the right breast. Case 2: Screen detected DCIS with postsurgical upgrade to microinvasive ductal carcinoma in a 64-year-old woman with nonspecific discomfort in her right breast. US-guided core biopsy yielded DCIS, high nuclear grade with necrosis. Postsurgical pathology confirmed a 2.8 cm sized microinvasive ductal carcinoma, high nuclear grade, histologic grade 3 with necrosis. (A) US demonstrates a 1.9 cm irregular hypoechoic mass with calcifications in 9 o’clock direction 2.1 cm from the nipple in the right breast. (B) Right magnification view shows a 2.5 cm-extent segmentally distributed fine pleomorphic calcifications in the right breast. (C) CESM shows a 3 cm irregular enhancing mass with calcifications in the right breast, and CGV was 2045 AU. (D) Contrast-enhanced MR images shows a 2.3 cm irregular enhancing mass in the right breast. Case 3: Screen detected DCIS with postsurgical upgrade to invasive ductal carcinoma with DCIS in a 47-year-old woman who had a family history of breast cancer. US-guided core biopsy yielded DCIS, intermediate nuclear grade with necrosis. Postsurgical pathology confirmed a 0.2 cm invasive ductal carcinoma with 2.4 cm DCIS. (A) US demonstrates a 2.3 cm irregular hypoechoic mass in the left breast. (B) Routine mammography shows asymmetry in the upper portion of the left breast. (C) CESM shows a 3 cm enhancing mass in the left breast, and CGV of the lesion on MLO-recombined image was 2072 AU. (D) Contrast-enhanced MR image shows a 6 cm irregular enhancing mass in the upper portion of the left breast. Conclusion: CESM provides added advantages over mammography and breast US in detecting underestimated DCIS, and it is comparable to breast MRI. Key indicators to consider include detectability, enhancing lesion, larger tumor extent, higher mean CGV on CESM. Citation Format: Minji Song, Hee Jung Shin. Case Studies on Detecting Ductal Carcinoma in Situ Using Contrast-Enhanced Spectral Mammography and Predictive Indicators for Underestimation [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-20-08.

Abstract PO5-07-11: Use of 64Cu-DOTA trastuzumab -PET to predict response to trastuzumab-deruxtecan (TDXd) in patients with metastatic disease to the brain: Study in Progress

Abstract Background: We have utilized 64Cu-DOTA trastuzumab-PET imaging in patients with advanced breast cancer. In our experience, uptake on 64Cu-DOTA trastuzumab-PET correlated with the qualitative assessment of HER2 by IHC. In women treated with the antibody-drug conjugate (ADC), ado-trastuzumab emtansine (TDM1), we identified a threshold level of 64Cu-DOTA trastuzumab-PET uptake that predicted for lack of response to TDM1. As patients are living longer with advanced breast cancer, the incidence of brain metastasis has increased. The ADC, trastuzumab-deruxtecan (T-DXd) has demonstrated activity in patients with metastatic HER2 positive breast cancer with brain metastases. The DESTINY 04 trial included patients with HER2 1+ and 2+ disease and demonstrated superior survival compared to standard of care. It is not feasible to biopsy all brain metastases to determine tumor markers. Given the increased incidence of brain metastases in this population and the efficacy of T-DXd in patients with HER2+ metastatic disease to the brain, we initiated a study of 64Cu-DOTA trastuzumab-PET/MRI of the brain prior to institution of T-Dxd. Endpoints Primary: 1. 64Cu-DOTA-trastuzumab PET-CT will identify tumor heterogeneity of trastuzumab uptake in women with metastatic breast cancers. 2. The degree of uptake of 64Cu-DOTA-trastuzumab PET-CT will predict for response to Enhertu. Secondary: 1. Uptake of 64Cu-DOTA-trastuzumab PET-CT will be predictive of response duration to Enhertu. Eligibility: Patients must have documented recurrent breast cancer that is HER2 1+, 2+ or 3+, evidence of brain metastases by CT or MRI of the brain and are candidate for treatment with trastuzumab-deruxtecan. The CNS disease must be stable and not in need of intervention. Methods: Prior to treatment staging workups includes pathologic review of HER2 status, disease staging with 18FDG-PET or CT of the chest/abdomen/pelvis and bone scan, and 64Cu-DOTA-trastuzumab PET/MRI of the brain. After initiation of T-Dxd, MRI of the brain is repeated every 6 weeks for the first 24 weeks and every 9 weeks thereafter. Progress: We have enrolled 1 patients to date, received PET and dynamic contrast-enhanced MRI. Four lesions were analyzed in this patient. The mean values of Ktrans (volumetric perfusion to tumor, s−1) in these lesions near a prior resection cavity were 0.210 s−1, indicating sufficient perfusion for ADC uptake. Corresponding to this perfusion data, the mean SUVmax of these lesions on the 64Cu-DOTA-trastuzumab PET scans was 5.38. Citation Format: Joanne Mortimer, Kofi Poku, Jessica Liu, Russell Rockne, Chen Bihong, Ryan Woodall, Vikram Adhikarla. Use of 64Cu-DOTA trastuzumab -PET to predict response to trastuzumab-deruxtecan (TDXd) in patients with metastatic disease to the brain: Study in Progress [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-07-11.

Abstract PS05-08: Restriction Spectrum Imaging MRI for automated evaluation of response to neoadjuvant therapy in breast cancer

Abstract Introduction: Dynamic contrast-enhanced MRI (DCE) is currently used to evaluate neoadjuvant therapy response of breast cancer (1). However, DCE requires expert radiologist readers to assess the change in longest tumor dimension during therapy, as well as administration of Gadolinium contrast agents. One MRI modality that does not require contrast agents is diffusion-weighted MRI (DWI), a method that detects the microscopic diffusion of water molecules. However, the commonly used DWI method apparent diffusion coefficient (ADC) is not fully optimised in the breast (2). The purpose of the current study was to evaluate the recent DWI method Restriction Spectrum Imaging (RSI) (2) to automatically monitor breast tumor size during neoadjuvant therapy. Methods: Twenty-seven women underwent 3T MRI at four time points during therapy at University of California San Diego; 17 received all four scans (see Table 1 for patient details). Inclusion criteria included biopsy-proven unilateral invasive breast cancer ≥2.5 cm (defined on clinical examination/imaging) with indication for neoadjuvant therapy. The therapy used was primarily paclitaxel (+/-experimental agent) followed by anthracycline. The MRI protocol included Gadolinium DCE and DWI (b-values 0, 500, 1500, 4000 s/mm2); TE/TR = 82/9000 ms. ADC was calculated using b-values &amp;lt; 1000 s/mm2 while signal from all available b-values were fitted to the previously-developed three-component RSI model (2). The tumor size by RSI was assessed against manual DCE tumor size and mean ADC values. Prediction of therapy response during therapy and residual tumor post-therapy were assessed using non-pathological complete response (non-pCR) as endpoint. pCR was defined as ypT0/is or ypN0. Results: Ten patients experienced pCR. Prediction of non-pCR by ROC AUC at the early-therapy time point was 0.65 for RSI, 0.64 for DCE and 0.45 for ADC (Table 2). Prediction of post-therapy residual tumor is given in Table 3. Discussion: The novel RSI cancer tissue classifier predicted response to neoadjuvant therapy after only 19 days. RSI could also identify 71% of cases with residual tumor at surgery with 90% specificity post-therapy. RSI performance was similar to performance by standard MRI by manual tumor measurement on DCE. In contrast to standard-of-care MRI by using DCE and ADC that requires manual user input, the RSI classifier is automatic. This suggests that RSI may aid to cost-efficiently evaluate neoadjuvant therapy of breast cancer, with the aim to help guide clinical decision-making and enable tailored therapy regimens.

Diffusion probabilistic versus generative adversarial models to reduce contrast agent dose in breast MRI

BackgroundTo compare denoising diffusion probabilistic models (DDPM) and generative adversarial networks (GAN) for recovering contrast-enhanced breast magnetic resonance imaging (MRI) subtraction images from virtual low-dose subtraction images.MethodsRetrospective, ethically approved study. DDPM- and GAN-reconstructed single-slice subtraction images of 50 breasts with enhancing lesions were compared to original ones at three dose levels (25%, 10%, 5%) using quantitative measures and radiologic evaluations. Two radiologists stated their preference based on the reconstruction quality and scored the lesion conspicuity as compared to the original, blinded to the model. Fifty lesion-free maximum intensity projections were evaluated for the presence of false-positives. Results were compared between models and dose levels, using generalized linear mixed models.ResultsAt 5% dose, both radiologists preferred the GAN-generated images, whereas at 25% dose, both radiologists preferred the DDPM-generated images. Median lesion conspicuity scores did not differ between GAN and DDPM at 25% dose (5 versus 5, p = 1.000) and 10% dose (4 versus 4, p = 1.000). At 5% dose, both readers assigned higher conspicuity to the GAN than to the DDPM (3 versus 2, p = 0.007). In the lesion-free examinations, DDPM and GAN showed no differences in the false-positive rate at 5% (15% versus 22%), 10% (10% versus 6%), and 25% (6% versus 4%) (p = 1.000).ConclusionsBoth GAN and DDPM yielded promising results in low-dose image reconstruction. However, neither of them showed superior results over the other model for all dose levels and evaluation metrics. Further development is needed to counteract false-positives.Relevance statementFor MRI-based breast cancer screening, reducing the contrast agent dose is desirable. Diffusion probabilistic models and generative adversarial networks were capable of retrospectively enhancing the signal of low-dose images. Hence, they may supplement imaging with reduced doses in the future.Key points• Deep learning may help recover signal in low-dose contrast-enhanced breast MRI.• Two models (DDPM and GAN) were trained at different dose levels.• Radiologists preferred DDPM at 25%, and GAN images at 5% dose.• Lesion conspicuity between DDPM and GAN was similar, except at 5% dose.• GAN and DDPM yield promising results in low-dose image reconstruction.Graphical

MRI Scoring Systems for Predicting Isocitrate Dehydrogenase Mutation and Chromosome 1p/19q Codeletion in Adult-type Diffuse Glioma Lacking Contrast Enhancement.

Background According to 2021 World Health Organization criteria, adult-type diffuse gliomas include glioblastoma, isocitrate dehydrogenase (IDH)-wildtype; oligodendroglioma, IDH-mutant and 1p/19q-codeleted; and astrocytoma, IDH-mutant, even when contrast enhancement is lacking. Purpose To develop and validate simple scoring systems for predicting IDH and subsequent 1p/19q codeletion status in gliomas without contrast enhancement using standard clinical MRI sequences. Materials and Methods This retrospective study included adult-type diffuse gliomas lacking contrast at contrast-enhanced MRI from two tertiary referral hospitals between January 2012 and April 2022 with diagnoses confirmed at pathology. IDH status was predicted primarily by using T2-fluid-attenuated inversion recovery (FLAIR) mismatch sign, followed by 1p/19q codeletion prediction. A visual rating of MRI features, apparent diffusion coefficient (ADC) ratio, and relative cerebral blood volume was measured. Scoring systems were developed through univariable and multivariable logistic regressions and underwent calibration and discrimination, including internal and external validation. Results For the internal validation cohort, 237 patients were included (mean age, 44.4 years ± 14.4 [SD]; 136 male patients; 193 patients in IDH prediction and 163 patients in 1p/19q prediction). For the external validation cohort, 35 patients were included (46.1 years ± 15.3; 20 male patients; 28 patients in IDH prediction and 24 patients in 1p/19q prediction). The T2-FLAIR mismatch sign demonstrated 100% specificity and 100% positive predictive value for IDH mutation. IDH status prediction scoring system for tumors without mismatch sign included age, ADC ratio, and morphologic characteristics, whereas 1p/19q codeletion prediction for IDH-mutant gliomas included ADC ratio, cortical involvement, and mismatch sign. For IDH status and 1p/19q codeletion prediction, bootstrap-corrected areas under the receiver operating characteristic curve were 0.86 (95% CI: 0.81, 0.90) and 0.73 (95% CI: 0.65, 0.81), respectively, whereas at external validation they were 0.99 (95% CI: 0.98, 1.0) and 0.88 (95% CI: 0.63, 1.0). Conclusion The T2-FLAIR mismatch sign and scoring systems using standard clinical MRI predicted IDH and 1p/19q codeletion status in gliomas lacking contrast enhancement. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Badve and Hodges in this issue.

A watch, wait, and rescan approach for incidental benign-appearing notochordal lesions of the skull base.

The aim of this study was to describe the natural history of incidental benign-appearing notochordal lesions of the skull base with specific attention to features that can make differentiation from low-grade chordoma more difficult, namely contrast uptake and bone erosion. In this retrospective case series, the authors describe the clinical outcomes of 58 patients with incidental benign-appearing notochordal lesions of the clivus, including those with minor radiological features of bone erosion or contrast uptake. All lesions remained stable during a median follow-up of almost 3 years. Thirty-seven (64%) patients underwent contrast-enhanced MRI; lesions in 14 (38%) of these patients exhibited minimal contrast enhancement. Twenty-seven (47%) patients underwent CT; lesions in 6 (22%) of these patients exhibited minimal bone erosion. These data make the case for monitoring selected cases of benign-appearing notochordal lesions of the clivus in the first instance even when there is minor contrast uptake or minimal bone erosion.

Intensity Score of Vessel Wall Contrast Enhancement in MRI Allows Prediction of Disease Progression in Moyamoya Angiopathy.

The underlying pathophysiological cause of moyamoya angiopathy (MMA) is still unclear. High-resolution vessel wall imaging has become a useful tool. The aim was to study vessel wall contrast-enhancement (VW-CE) as an imaging marker to predict disease progression in MMA. Patients with MMA, who had undergone serial contrast-enhanced high-resolution MRI with concomitant and follow-up digital subtraction angiography, were analyzed retrospectively. VW-CE was semiquantified by measurement of the signal intensity of the vessel wall in in contrast-enhanced high-resolution MRI. A comparative quotient with the contrast-intensity of the pituitary stalk was calculated and graded accordingly from grade 1 to 5. VW-CE status was correlated with disease status, stroke, cerebrovascular reactivity in CO2-triggered blood-oxygen level-dependent MRI, angiographic disease progression, revascularization surgery, and follow-up imaging. Forty eight patients met the inclusion criteria. N = 56 MRI and digital subtraction angiography time-intervals were evaluated for 12 vessel sections per hemisphere each (N = 1344). N = 38 (79%) patients showed VW-CE and N = 10 (21%) did not. VW-CE was only observed in the terminal internal carotid artery and the proximal circle of Willis (N = 96/1344). Notably, patients with VW-CE significantly more often presented with acute infarction in the concomitant MRI. The incidence of angiographically proven disease progression was significantly associated with the incidence of VW-CE, and time to disease progression was earlier in higher grades of VW-CE compared with lower grades. VW-CE is a semiquantifiable marker for disease activity in patients with MMA and associated with disease progression and increased risk of stroke. VW-CE analysis can be routinely performed in patients with MMA to estimate the risk for disease progression and stroke.

DIAGNOSTIC PERFORMANCE OF CONTRAST-ENHANCED SPECTRAL MAMMOGRAPHY: COMPARISON WITH CONTRAST-ENHANCED DYNAMIC MR IMAGING IN PATIENTS WITH SUSPECTED BREAST CANCER

OBJECTIVE: To compare the diagnostic performance of contrast-enhanced spectral mammography (CESM) with dynamic contrast-enhanced magnetic resonance (MR) imaging in terms of the detection of BI-RADS 4 and 5 lesions suspected of breast cancer. MATERIAL AND METHODS: 92 patients with ACR BI-RADS 4 and 5 lesions underwent CESM, MR Imaging, and consequent core biopsy. Two readers assessed the index lesions which were classified as mass lesions, non-mass lesions, and microcalcifications, and scored using a 7-point scoring system. RESULTS: A total of 98 index lesions were detected, including bilateral lesions in six patients. In histopathological analysis, 56 of the lesions were benign (56/98, 57%,) and 42 of the lesions were malignant (42/98, 43%). 55 of the lesions were classified as mass lesions, 18 as non-mass lesions, and 25 as microcalcifications. CESM scored 28 of the lesions (28,6%) as benign, and 70 (71,4%) of the lesions were malignant whereas these results were 30 (30,6%) and 68 (69,4%) for MR Imaging examinations, respectively. The sensitivity of both CESM and MR imaging for depicting the index cancer was 95 % for both modalities. In ROC (Receiver Operating Characteristic) analysis, AUC (Area Under the Curve) was 0.93 (%95 CI:0.870-0.977) for CESM and 0.94 (%95 CI:0.882-0.982) for MR Imaging. There was no statistically significant difference in AUC values between CESM and MR Imaging (p=0.332; p&amp;gt;0.05). CONCLUSIONS: The diagnostic performance of CESM is similar when compared to MR imaging in the detection of index cancers in patients with BI-RADS 4 and 5 lesions. CESM may be used as a confidential diagnostic tool in this regard.

Model discovery approach enables noninvasive measurement of intra-tumoral fluid transport in dynamic MRI

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a routine method to noninvasively quantify perfusion dynamics in tissues. The standard practice for analyzing DCE-MRI data is to fit an ordinary differential equation to each voxel. Recent advances in data science provide an opportunity to move beyond existing methods to obtain more accurate measurements of fluid properties. Here, we developed a localized convolutional function regression that enables simultaneous measurement of interstitial fluid velocity, diffusion, and perfusion in 3D. We validated the method computationally and experimentally, demonstrating accurate measurement of fluid dynamics in situ and in vivo. Applying the method to human MRIs, we observed tissue-specific differences in fluid dynamics, with an increased fluid velocity in breast cancer as compared to brain cancer. Overall, our method represents an improved strategy for studying interstitial flows and interstitial transport in tumors and patients. We expect that our method will contribute to the better understanding of cancer progression and therapeutic response.