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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.

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

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Heart disease is a significant adverse event caused by radiotherapy for some cancers. Identifying the origins of radiogenic heart disease will allow therapies to be developed. Previous studies showed non-targeted effects manifest as fibrosis in the non-irradiated heart after 120 days following targeted X-irradiation of the kidneys with 10 Gy in WAG/RijCmcr rats. To demonstrate the involvement of T cells in driving pathophysiological responses in the out-of-field heart, and to characterize the timing of immune cell engagement, we created and validated a T cell knock downrat on the WAG genetic backgrou nd. Irradiation of the kidneys with 10 Gy of X-rays in wild-type rats resulted in infiltration of T cells, natural killer cells, and macrophages after 120 days, and none of these after 40 days, suggesting immune cell engagement is a late response. The radiation nephropathy and cardiac fibrosis that resulted in these animals after 120 days was significantly decreased in irradiated T cell depleted rats. We conclude that T cells function as an effector cell in communicating signals from the irradiated kidneys which cause pathologic remodeling of non-targeted heart.

PurposeImage registration plays a vital role in spatially aligning multiple MRI scans for better longitudinal assessment of tumor morphological features. The objective was to evaluate the effect of registration accuracy of six established deformable registration methods(ANTs, DRAMMS, ART, NiftyReg, SSD-FFD, and NMI-FFD) on the predictive value of extracted radiomic features when modeling recurrence-free-survival(RFS) for women after neoadjuvant chemotherapy(NAC) for locally advanced breast cancer.Methods130 women had DCE-MRI scans available from the first two visits in the ISPY1/ACRIN-6657 cohort. We calculated the transformation field from each of the different deformable registration methods, and used it to compute voxel-wise parametric-response-maps(PRM) for established four kinetic features.104-radiomic features were computed from each PRM map to characterize intra-tumor heterogeneity. We evaluated performance for RFS using Cox-regression, C-statistic, and Kaplan-Meier(KM) plots.ResultsA baseline model(F1:Age, Race, and Hormone-receptor-status) had a 0.54 C-statistic, and model F2(baseline + functional-tumor-volume at early treatment visit(FTV2)) had 0.63. The F2+ANTs had the highest C-statistic(0.72) with the smallest landmark differences(5.40±4.40mm) as compared to other models. The KM curve for model F2 gave p=0.004 for separation between women above and below the median hazard compared to the model F1(p=0.31). A models augmented with radiomic features, also achieved significant KM curve separation(p

We analyzed DCE-MR images from 132 women with locally advanced breast cancer from the I-SPY1 trial to evaluate changes of intra-tumor heterogeneity for augmenting early prediction of pathologic complete response (pCR) and recurrence-free survival (RFS) after neoadjuvant chemotherapy (NAC). Utilizing image registration, voxel-wise changes including tumor deformations and changes in DCE-MRI kinetic features were computed to characterize heterogeneous changes within the tumor. Using five-fold cross-validation, logistic regression and Cox regression were performed to model pCR and RFS, respectively. The extracted imaging features were evaluated in augmenting established predictors, including functional tumor volume (FTV) and histopathologic and demographic factors, using the area under the curve (AUC) and the C-statistic as performance measures. The extracted voxel-wise features were also compared to analogous conventional aggregated features to evaluate the potential advantage of voxel-wise analysis. Voxel-wise features improved prediction of pCR (AUC = 0.78 (±0.03) vs 0.71 (±0.04), p  0.05). Furthermore, all selected voxel-wise features demonstrated significant association with outcome (p

ObjectiveThe aim of this study was to explore the contribution of blood and colon myeloid cells to HIV persistence during antiretroviral therapy (ART).DesignLeukapheresis was collected from HIV-infected individuals with undetectable plasma viral load during ART (HIV + ART; n = 15) and viremics untreated (HIV+; n = 6). Rectal sigmoid biopsies were collected from n = 8 HIV+ART.MethodsMyeloid cells (total monocytes (Mo), CD16/CD16 Mo, CD1c dendritic cells) and CD4 T cells were isolated by magnetic-activated cell sorting (MACS) and/or fluorescence-activated cell sorting (FACS) from peripheral blood. Matched myeloid and CCR6CD4 T cells were isolated from blood and rectal biopsies by FACS. Levels of early (RU5 primers), late (Gag primers) and/or integrated HIV-DNA (Alu/HIV primers) were quantified by nested real-time PCR. Replication-competent HIV was amplified by co-culturing cells from HIV-positive individuals with CD3/CD28-activated CD4 T cells from uninfected donors.ResultsEarly/late but not integrated HIV reverse transcripts were detected in blood myeloid subsets of four out of 10 HIV+ART; in contrast, integrated HIV-DNA was exclusively detected in CD4 T cells. In rectal biopsies, late HIV reverse transcripts were detected in myeloid cells and CCR6CD4 T cells from one out of eight and seven out of eight HIV+ART individuals, respectively. Replication-competent HIV was outgrown from CD4 T cells but not from myeloid of untreated/ART-treated HIV-positive individuals.ConclusionIn contrast to CD4 T cells, blood and colon myeloid cells carry detectable HIV only in a small fraction of HIV+ART individuals. This is consistent with the documented resistance of Mo to HIV infection and the rapid turnover of Mo-derived macrophages in the colon. Future assessment of multiple lymphoid and nonlymphoid tissues is required to include/exclude myeloid cells as relevant HIV reservoirs during ART.