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Individuals with disabilities often face discrimination due to negative attitudes from others around them. This is true for youth with specific learning disorders (SLD), whose experiences of discrimination can increase the risk for developing mental health concerns. The current study explored whether the presence of an SLD comorbid with mental health concerns and attitudes toward SLD may have an association with clinical decision-making in counselor trainees. The study additionally investigated the role of contact and experience in attitudes and decision-making patterns. Seventy graduate students enrolled in mental health-related programs at a public university in the southern United States participated in the survey study. Findings showed that SLD had an association with clinical decision-making, with counselor trainees rating a vignette depicting a youth with SLD as having more severe mental health concerns than a vignette without an SLD.

In low-resource settings, non-profit organisations play an essential role in providing services and support for families with young children with autism. However, resource constraints limit access to quality training in evidence-based interventions. Web-based training may help fill this gap. This study examined the feasibility, acceptability and appropriateness of the web-based Autism Navigator® JumpStart to Coaching in Everyday Activities course for 26 non-profit organisation providers (10 specialists and 16 non-specialists) in South Africa. Quantitative and qualitative methods were used including a post-training survey. Sixteen providers (62%) completed the course over an average period of 5.58 months, with many completing the course during lockdown restrictions, suggesting time as a barrier to course completion. No provider characteristics were related to attrition, nor to the time taken to complete the course. Better English proficiency, higher education levels and more positive attitudes to implementing an evidence-based intervention were associated with the number of attempts needed to pass some learner assessments, and to perceptions of course feasibility, acceptability, and appropriateness. Adaptations in the training delivery (using a group format and local group conference calls) and inclusion of South African video clips were identified as potential facilitators to acceptability and appropriateness.

BACKGROUND: This multicenter and retrospective study investigated the additive value of tumor morphologic features derived from the functional tumor volume (FTV) tumor mask at pre-treatment (T0) and the early treatment time point (T1) in the prediction of pathologic outcomes for breast cancer patients undergoing neoadjuvant chemotherapy. METHODS: A total of 910 patients enrolled in the multicenter I-SPY 2 trial were included. FTV and tumor morphologic features were calculated from the dynamic contrast-enhanced (DCE) MRI. A poor response was defined as a residual cancer burden (RCB) class III (RCB-III) at surgical excision. The area under the receiver operating characteristic curve (AUC) was used to evaluate the predictive performance. The analysis was performed in the full cohort and in individual sub-cohorts stratified by hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status. RESULTS: In the full cohort, the AUCs for the use of the FTV ratio and clinicopathologic data were 0.64 ± 0.03 (mean ± SD [standard deviation]). With morphologic features, the AUC increased significantly to 0.76 ± 0.04 (p < 0.001). The ratio of the surface area to volume ratio between T0 and T1 was found to be the most contributing feature. All top contributing features were from T1. An improvement was also observed in the HR+/HER2- and triple-negative sub-cohorts. The AUC increased significantly from 0.56 ± 0.05 to 0.70 ± 0.06 (p < 0.001) and from 0.65 ± 0.06 to 0.73 ± 0.06 (p < 0.001), respectively, when adding morphologic features. CONCLUSION: Tumor morphologic features can improve the prediction of RCB-III compared to using FTV only at the early treatment time point.

Only a subset of patients with breast cancer responds to immune checkpoint blockade (ICB). To better understand the underlying mechanisms, we analyze pretreatment biopsies from patients in the I-SPY 2 trial who receive neoadjuvant ICB using multiple platforms to profile the tumor microenvironment. A variety of immune cell populations and markers of immune/cytokine signaling associate with pathologic complete response (pCR). Interestingly, these differ by breast cancer receptor subtype. Measures of the spatial distributions of immune cells within the tumor microenvironment, in particular colocalization or close spatial proximity of PD-1+ T cells with PD-L1+ cells (immune and tumor cells), are significantly associated with response in the overall cohort as well as the in the triple negative (TN) and HR+HER2- subtypes. Our findings indicate that biomarkers associated with immune cell signaling, immune cell densities, and spatial metrics are predictive of neoadjuvant ICB efficacy in breast cancer.

Sequential adaptive trial designs can help accomplish the goals of personalized medicine, optimizing outcomes and avoiding unnecessary toxicity. Here we describe the results of incorporating a promising antibody-drug conjugate, datopotamab-deruxtecan (Dato-DXd) in combination with programmed cell death-ligand 1 inhibitor, durvalumab, as the first sequence of therapy in the I-SPY2.2 phase 2 neoadjuvant sequential multiple assignment randomization trial for high-risk stage 2/3 breast cancer. The trial includes three blocks of treatment, with initial randomization to different experimental agent(s) (block A), followed by a taxane-based regimen tailored to tumor subtype (block B), followed by doxorubicin-cyclophosphamide (block C). Subtype-specific algorithms based on magnetic resonance imaging volume change and core biopsy guide treatment redirection after each block, including the option of early surgical resection in patients predicted to have a high likelihood of pathologic complete response, which is the primary endpoint assessed when resection occurs. There are two primary efficacy analyses: after block A and across all blocks for six prespecified HER2-negative subtypes (defined by hormone receptor status and/or response-predictive subtypes). In total, 106 patients were treated with Dato-DXd/durvalumab in block A. In the immune-positive subtype, Dato-DXd/durvalumab exceeded the prespecified threshold for success (graduated) after block A; and across all blocks, pathologic complete response rates were equivalent to the rate expected for the standard of care (79%), but 54% achieved that result after Dato-DXd/durvalumab alone (block A) and 92% without doxorubicin-cyclophosphamide (after blocks A + B). The treatment strategy across all blocks graduated in the hormone-negative/immune-negative subtype. No new toxicities were observed. Stomatitis was the most common side effect in block A. No patients receiving block A treatment alone had adrenal insufficiency. Dato-DXd/durvalumab is a promising therapy combination that can eliminate standard chemotherapy in many patients, particularly the immune-positive subtype.ClinicalTrials.gov registration: NCT01042379 .

New approaches are needed to determine which ductal carcinoma in situ (DCIS) is at high risk for progression to invasive ductal carcinoma (IDC). We retrospectively studied DCIS patients who declined surgery (2002-2019), and received endocrine therapy (ET) and breast MRI. Baseline MRI and changes at 3 months and 6 months were analyzed by recursive partitioning to stratify IDC risk. Sixty-two patients (63 DCIS; 1 bilateral) with a mean follow-up of 8.5 years were included. Fifty-one percent remained on active surveillance (AS) without evidence of IDC, with a mean duration of 7.6 years. A decision tree based on MRI features of lesion distinctness and background parenchymal enhancement (BPE) at baseline and change after 3 months of ET stratified patients into low, intermediate, and high risk for progression to IDC. MRI imaging features in patients treated with ET and undergoing AS, may help determine which DCIS lesions are at low versus high risk for IDC.

PurposeWe previously demonstrated the clinical significance of circulating tumor DNA (ctDNA) in patients with HER2-negative breast cancer receiving neoadjuvant chemotherapy (NAC). Here, we compared its predictive and prognostic value with cell-free DNA (cfDNA) concentration measured in the same samples from the same patients.Experimental design145 patients with hormone receptor (HR)-positive/HER2-negative and 138 triple-negative breast cancer (TNBC) with ctDNA data from a previous study were included in the analysis. Associations of serial cfDNA concentration with residual cancer burden (RCB) and distant recurrence-free survival (DRFS) were examined.ResultsIn TNBC, we observed a modest negative correlation between cfDNA concentration 3 weeks after treatment initiation and RCB, but none of the other timepoints showed significant correlation. In contrast, ctDNA was significantly positively correlated with RCB at all timepoints (all R > 0.3 and P < 0.05). In the HR-positive/HER2-negative group, cfDNA concentration did not associate with response to NAC, but survival analysis showed that high cfDNA shedders at pretreatment had a significantly worse DRFS than low shedders (hazard ratio, 2.12; P = 0.037). In TNBC, the difference in survival between high versus low cfDNA shedders at all timepoints was not statistically significant. In contrast, as previously reported, ctDNA at all timepoints was significantly correlated with DRFS in both subtypes.ConclusionsIn TNBC, cfDNA concentrations during therapy were not strongly correlated with response or prognosis. In the HR-positive/HER2-negative group, pretreatment cfDNA concentration was prognostic for DRFS. Overall, the predictive and prognostic value of cfDNA concentration was more limited than that of ctDNA.

Purpose To compare quantitative measures of tumor metabolism and perfusion using fluorine 18 (18F) fluorodeoxyglucose (FDG) dedicated breast PET (dbPET) and breast dynamic contrast-enhanced (DCE) MRI during early treatment with neoadjuvant chemotherapy (NAC). Materials and Methods Prospectively collected DCE MRI and 18F-FDG dbPET examinations were analyzed at baseline (T0) and after 3 weeks (T1) of NAC in 20 participants with 22 invasive breast cancers. FDG dbPET-derived standardized uptake value (SUV), metabolic tumor volume, and total lesion glycolysis (TLG) and MRI-derived percent enhancement (PE), signal enhancement ratio (SER), and functional tumor volume (FTV) were calculated at both time points. Differences between FDG dbPET and MRI parameters were evaluated after stratifying by receptor status, Ki-67 index, and residual cancer burden. Parameters were compared using Wilcoxon signed rank and Mann-Whitney U tests. Results High Ki-67 tumors had higher baseline SUVmean (difference, 5.1; P = .01) and SUVpeak (difference, 5.5; P = .04). At T1, decreases were observed in FDG dbPET measures (pseudo-median difference T0 minus T1 value [95% CI]) of SUVmax (-6.2 [-10.2, -2.6]; P < .001), SUVmean (-2.6 [-4.9, -1.3]; P < .001), SUVpeak (-4.2 [-6.9, -2.3]; P < .001), and TLG (-29.1 mL3 [-71.4, -6.8]; P = .005) and MRI measures of SERpeak (-1.0 [-1.3, -0.2]; P = .02) and FTV (-11.6 mL3 [-22.2, -1.7]; P = .009). Relative to nonresponsive tumors, responsive tumors showed a difference (95% CI) in percent change in SUVmax of -34.3% (-55.9%, 1.5%; P = .06) and in PEpeak of -42.4% (95% CI: -110.5%, 8.5%; P = .08). Conclusion 18F-FDG dbPET was sensitive to early changes during NAC and provided complementary information to DCE MRI that may be useful for treatment response evaluation. Keywords: Breast, PET, Dynamic Contrast-enhanced MRI Clinical trial registration no. NCT01042379 Supplemental material is available for this article. © RSNA, 2024.

PURPOSE: The neutralizing peptibody trebananib prevents angiopoietin-1 and angiopoietin-2 from binding with Tie2 receptors, inhibiting angiogenesis and proliferation. Trebananib was combined with paclitaxel±trastuzumab in the I-SPY2 breast cancer trial. PATIENTS AND METHODS: I-SPY2, a phase II neoadjuvant trial, adaptively randomizes patients with high-risk, early-stage breast cancer to one of several experimental therapies or control based on receptor subtypes as defined by hormone receptor (HR) and HER2 status and MammaPrint risk (MP1, MP2). The primary endpoint is pathologic complete response (pCR). A therapy graduates if/when it achieves 85% Bayesian probability of success in a phase III trial within a given subtype. Patients received weekly paclitaxel (plus trastuzumab if HER2-positive) without (control) or with weekly intravenous trebananib, followed by doxorubicin/cyclophosphamide and surgery. Pathway-specific biomarkers were assessed for response prediction. RESULTS: There were 134 participants randomized to trebananib and 133 to control. Although trebananib did not graduate in any signature [phase III probabilities: Hazard ratio (HR)-negative (78%), HR-negative/HER2-positive (74%), HR-negative/HER2-negative (77%), and MP2 (79%)], it demonstrated high probability of superior pCR rates over control (92%-99%) among these subtypes. Trebananib improved 3-year event-free survival (HR 0.67), with no significant increase in adverse events. Activation levels of the Tie2 receptor and downstream signaling partners predicted trebananib response in HER2-positive disease; high expression of a CD8 T-cell gene signature predicted response in HR-negative/HER2-negative disease. CONCLUSIONS: The angiopoietin (Ang)/Tie2 axis inhibitor trebananib combined with standard neoadjuvant therapy increased estimated pCR rates across HR-negative and MP2 subtypes, with probabilities of superiority >90%. Further study of Ang/Tie2 receptor axis inhibitors in validated, biomarker-predicted sensitive subtypes is warranted.

High-density lipoproteins (HDLs) are key regulators of cellular cholesterol homeostasis but are functionally altered in many chronic diseases. The factors that cause HDL functional loss in chronic disease are not fully understood. It is also unknown what roles antioxidant carotenoids play in protecting HDL against functional loss. The aim of this study was to measure how various disease-associated chemical factors including exposure to (1) Cu2+ ions, (2) hypochlorous acid (HOCL), (3) hydrogen peroxide (H2O2), (4) sialidase, (5) glycosidase, (6) high glucose, (7) high fructose, and (8) acidic pH, and the carotenoid antioxidants (9) lutein and (10) zeaxanthin affect HDL functionality. We hypothesized that some of the modifications would have stronger impacts on HDL particle structure and function than others and that lutein and zeaxanthin would improve HDL function. HDL samples were isolated from generally healthy human plasma and incubated with the corresponding treatments listed above. Cholesterol efflux capacity (CEC), lecithin-cholesterol acyl transferase (LCAT) activity, and paraoxonase-1 (PON1) activity were measured in order to determine changes in HDL functionality. Median HDL particle diameter was increased by acidic pH treatment and reduced by HOCl, high glucose, high fructose, N-glycosidase, and lutein treatments. Acidic pH, oxidation, and fructosylation all reduced HDL CEC, whereas lutein, zeaxanthin, and sialidase treatment improved HDL CEC. LCAT activity was reduced by acidic pH, oxidation, high fructose treatments, and lutein. PON1 activity was reduced by sialidase, glycosidase, H2O2, and fructose and improved by zeaxanthin and lutein treatment. These results show that exposure to oxidizing agents, high fructose, and low pH directly impairs HDL functionality related to cholesterol efflux and particle maturation, whereas deglycosylation impairs HDL antioxidant capacity. On the other hand, the antioxidants lutein and zeaxanthin improve or preserve both HDL cholesterol efflux and antioxidant activity but have no effect on particle maturation.

Purpose To describe the design, conduct, and results of the Breast Multiparametric MRI for prediction of neoadjuvant chemotherapy Response (BMMR2) challenge. Materials and Methods The BMMR2 computational challenge opened on May 28, 2021, and closed on December 21, 2021. The goal of the challenge was to identify image-based markers derived from multiparametric breast MRI, including diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) MRI, along with clinical data for predicting pathologic complete response (pCR) following neoadjuvant treatment. Data included 573 breast MRI studies from 191 women (mean age [±SD], 48.9 years ± 10.56) in the I-SPY 2/American College of Radiology Imaging Network (ACRIN) 6698 trial (ClinicalTrials.gov: NCT01042379). The challenge cohort was split into training (60%) and test (40%) sets, with teams blinded to test set pCR outcomes. Prediction performance was evaluated by area under the receiver operating characteristic curve (AUC) and compared with the benchmark established from the ACRIN 6698 primary analysis. Results Eight teams submitted final predictions. Entries from three teams had point estimators of AUC that were higher than the benchmark performance (AUC, 0.782 [95% CI: 0.670, 0.893], with AUCs of 0.803 [95% CI: 0.702, 0.904], 0.838 [95% CI: 0.748, 0.928], and 0.840 [95% CI: 0.748, 0.932]). A variety of approaches were used, ranging from extraction of individual features to deep learning and artificial intelligence methods, incorporating DCE and DWI alone or in combination. Conclusion The BMMR2 challenge identified several models with high predictive performance, which may further expand the value of multiparametric breast MRI as an early marker of treatment response. Clinical trial registration no. NCT01042379 Keywords: MRI, Breast, Tumor Response Supplemental material is available for this article. © RSNA, 2024.

Molecular subtyping of breast cancer is based mostly on HR/HER2 and gene expression-based immune, DNA repair deficiency, and luminal signatures. We extend this description via functional protein pathway activation mapping using pre-treatment, quantitative expression data from 139 proteins/phosphoproteins from 736 patients across 8 treatment arms of the I-SPY 2 Trial (ClinicalTrials.gov: NCT01042379). We identify predictive fit-for-purpose, mechanism-of-action-based signatures and individual predictive protein biomarker candidates by evaluating associations with pathologic complete response. Elevated levels of cyclin D1, estrogen receptor alpha, and androgen receptor S650 associate with non-response and are biomarkers for global resistance. We uncover protein/phosphoprotein-based signatures that can be utilized both for molecularly rationalized therapeutic selection and for response prediction. We introduce a dichotomous HER2 activation response predictive signature for stratifying triple-negative breast cancer patients to either HER2 or immune checkpoint therapy response as a model for how protein activation signatures provide a different lens to view the molecular landscape of breast cancer and synergize with transcriptomic-defined signatures.

Caring for children with developmental delays or disabilities places significant stress on caregivers, which is often exacerbated in low-resource settings. The World Health Organization developed a caregiver skills training for families of children with developmental delays or disabilities, which includes a three-session caregiver well-being module based on Acceptance and Commitment Therapy, that aims to help caregivers build psychological flexibility (i.e. the ability to focus on the present moment, make space for difficult thoughts and emotions and commit to value-driven actions that enrich their lives). We investigated whether this brief caregiver well-being programme was feasible, acceptable and contributed to positive outcomes for caregivers in a rural South African town. We adapted the intervention to the local context using a participatory approach. The 'Well-Beans for Caregivers' adaptation of the World Health Organization Caregiver Skills Training Caregiver Wellbeing module was then delivered by trained facilitators to a group of 10 caregivers in three weekly, 2-h sessions. Most caregivers were single and unemployed. Feedback from the facilitators, caregivers and trainee observers was obtained before, during and after the programme. Results suggested that this brief programme shows promise as a feasible and acceptable intervention, which may lead to improved caregiver well-being and mental health in communities in South Africa. [This article was written with the WHO CST Team.]

Purpose To investigate the impact of longitudinal variation in functional tumor volume (FTV) underestimation and overestimation in predicting pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC). Materials and Methods Women with breast cancer who were enrolled in the prospective I-SPY 2 TRIAL (Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging and Molecular Analysis 2) from May 2010 to November 2016 were eligible for this retrospective analysis. Participants underwent four MRI examinations during NAC treatment. FTV was calculated based on automated segmentation. Baseline FTV before treatment (FTV0) and the percentage of FTV change at early treatment and inter-regimen time points relative to baseline (∆FTV1 and ∆FTV2, respectively) were classified into high-standard or standard groups based on visual assessment of FTV under- and overestimation. Logistic regression models predicting pCR using single predictors (FTV0, ∆FTV1, and ∆FTV2) and multiple predictors (all three) were developed using bootstrap resampling with out-of-sample data evaluation with the area under the receiver operating characteristic curve (AUC) independently in each group. Results This study included 432 women (mean age, 49.0 years ± 10.6 [SD]). In the FTV0 model, the high-standard and standard groups showed similar AUCs (0.61 vs 0.62). The high-standard group had a higher estimated AUC compared with the standard group in the ∆FTV1 (0.74 vs 0.63), ∆FTV2 (0.79 vs 0.62), and multiple predictor models (0.85 vs 0.64), with a statistically significant difference for the latter two models (P = .03 and P = .01, respectively). Conclusion The findings in this study suggest that longitudinal variation in FTV estimation needs to be considered when using early FTV change as an MRI-based criterion for breast cancer treatment personalization. Keywords: Breast, Cancer, Dynamic Contrast-enhanced, MRI, Tumor Response ClinicalTrials.gov registration no. NCT01042379 Supplemental material is available for this article. © RSNA, 2023 See also the commentary by Ram in this issue.

Background Although several clinical breast cancer risk models are used to guide screening and prevention, they have only moderate discrimination. Purpose To compare selected existing mammography artificial intelligence (AI) algorithms and the Breast Cancer Surveillance Consortium (BCSC) risk model for prediction of 5-year risk. Materials and Methods This retrospective case-cohort study included data in women with a negative screening mammographic examination (no visible evidence of cancer) in 2016, who were followed until 2021 at Kaiser Permanente Northern California. Women with prior breast cancer or a highly penetrant gene mutation were excluded. Of the 324 009 eligible women, a random subcohort was selected, regardless of cancer status, to which all additional patients with breast cancer were added. The index screening mammographic examination was used as input for five AI algorithms to generate continuous scores that were compared with the BCSC clinical risk score. Risk estimates for incident breast cancer 0 to 5 years after the initial mammographic examination were calculated using a time-dependent area under the receiver operating characteristic curve (AUC). Results The subcohort included 13 628 patients, of whom 193 had incident cancer. Incident cancers in eligible patients (additional 4391 of 324 009) were also included. For incident cancers at 0 to 5 years, the time-dependent AUC for BCSC was 0.61 (95% CI: 0.60, 0.62). AI algorithms had higher time-dependent AUCs than did BCSC, ranging from 0.63 to 0.67 (Bonferroni-adjusted P < .0016). Time-dependent AUCs for combined BCSC and AI models were slightly higher than AI alone (AI with BCSC time-dependent AUC range, 0.66-0.68; Bonferroni-adjusted P < .0016). Conclusion When using a negative screening examination, AI algorithms performed better than the BCSC risk model for predicting breast cancer risk at 0 to 5 years. Combined AI and BCSC models further improved prediction. © RSNA, 2023 Supplemental material is available for this article.

Circulating tumor DNA (ctDNA) analysis may improve early-stage breast cancer treatment via non-invasive tumor burden assessment. To investigate subtype-specific differences in the clinical significance and biology of ctDNA shedding, we perform serial personalized ctDNA analysis in hormone receptor (HR)-positive/HER2-negative breast cancer and triple-negative breast cancer (TNBC) patients receiving neoadjuvant chemotherapy (NAC) in the I-SPY2 trial. ctDNA positivity rates before, during, and after NAC are higher in TNBC than in HR-positive/HER2-negative breast cancer patients. Early clearance of ctDNA 3 weeks after treatment initiation predicts a favorable response to NAC in TNBC only. Whereas ctDNA positivity associates with reduced distant recurrence-free survival in both subtypes. Conversely, ctDNA negativity after NAC correlates with improved outcomes, even in patients with extensive residual cancer. Pretreatment tumor mRNA profiling reveals associations between ctDNA shedding and cell cycle and immune-associated signaling. On the basis of these findings, the I-SPY2 trial will prospectively test ctDNA for utility in redirecting therapy to improve response and prognosis.

The family resemblance approach to nature of science is receiving increasing attention by science educators since its inception about a decade ago. Many scholars of science education have contributed and continue to contribute to it not only theoretically but also by applying it empirically to a wide range of areas such as curriculum and textbook analyses, pre-service teacher training, undergraduate teaching and, STEM education. This article aims to develop the family resemblance approach further. We do this in several ways. First, we clarify its foundations in a way to reveal that it provides not only a domain-specific, but at the same time a domain-general conceptualization of nature of science. Second, we expand the structure of science as a social institution by adding a new category to it, i.e., the reward system, and justify it. Third, we show that two of the most common elements of the category "practices," namely, observation and experimentation, display the character of family resemblance. Then, we explore this for methods and values in science. Finally, we discuss the possibility of a rapprochement between the family resemblance approach and the consensus view.

Background There is increasing interest in noncontrast breast MRI alternatives for tumor visualization to increase the accessibility of breast MRI. Purpose To evaluate the feasibility and accuracy of generating simulated contrast-enhanced T1-weighted breast MRI scans from precontrast MRI sequences in biopsy-proven invasive breast cancer with use of deep learning. Materials and Methods Women with invasive breast cancer and a contrast-enhanced breast MRI examination that was performed for initial evaluation of the extent of disease between January 2015 and December 2019 at a single academic institution were retrospectively identified. A three-dimensional, fully convolutional deep neural network simulated contrast-enhanced T1-weighted breast MRI scans from five precontrast sequences (T1-weighted non-fat-suppressed [FS], T1-weighted FS, T2-weighted FS, apparent diffusion coefficient, and diffusion-weighted imaging). For qualitative assessment, four breast radiologists (with 3-15 years of experience) blinded to whether the method of contrast was real or simulated assessed image quality (excellent, acceptable, good, poor, or unacceptable), presence of tumor enhancement, and maximum index mass size by using 22 pairs of real and simulated contrast-enhanced MRI scans. Quantitative comparison was performed using whole-breast similarity and error metrics and Dice coefficient analysis of enhancing tumor overlap. Results Ninety-six MRI examinations in 96 women (mean age, 52 years ± 12 [SD]) were evaluated. The readers assessed all simulated MRI scans as having the appearance of a real MRI scan with tumor enhancement. Index mass sizes on real and simulated MRI scans demonstrated good to excellent agreement (intraclass correlation coefficient, 0.73-0.86; P < .001) without significant differences (mean differences, -0.8 to 0.8 mm; P = .36-.80). Almost all simulated MRI scans (84 of 88 [95%]) were considered of diagnostic quality (ratings of excellent, acceptable, or good). Quantitative analysis demonstrated strong similarity (structural similarity index, 0.88 ± 0.05), low voxel-wise error (symmetric mean absolute percent error, 3.26%), and Dice coefficient of enhancing tumor overlap of 0.75 ± 0.25. Conclusion It is feasible to generate simulated contrast-enhanced breast MRI scans with use of deep learning. Simulated and real contrast-enhanced MRI scans demonstrated comparable tumor sizes, areas of tumor enhancement, and image quality without significant qualitative or quantitative differences. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Slanetz in this issue. An earlier incorrect version appeared online. This article was corrected on January 17, 2023.

BackgroundEarly changes in breast intratumor heterogeneity during neoadjuvant chemotherapy may reflect the tumor's ability to adapt and evade treatment. We investigated the combination of precision medicine predictors of genomic and MRI data towards improved prediction of recurrence free survival (RFS).MethodsA total of 100 women from the ACRIN 6657/I-SPY 1 trial were retrospectively analyzed. We estimated MammaPrint, PAM50 ROR-S, and p53 mutation scores from publicly available gene expression data and generated four, voxel-wise 3-D radiomic kinetic maps from DCE-MR images at both pre- and early-treatment time points. Within the primary lesion from each kinetic map, features of change in radiomic heterogeneity were summarized into 6 principal components.ResultsWe identify two imaging phenotypes of change in intratumor heterogeneity (p