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Background Breast cancer is a heterogeneous disease and can be categorized into clinically or biologically meaningful subtypes. Predictive models built by MRI biomarkers performed better when they are optimized by breast cancer subtype than models optimized in the full cohort [1]. Functional tumor volume (FTV) measured from breast MRI has been used to assess tumor response to neoadjuvant therapy longitudinally in the I-SPY 2 TRIAL. Tumors show distinct morphological patterns, or phenotypes, on MRI. Previous studies demonstrated that either qualitative or quantitative measurements characterizing these phenotypes may provide additional information about treatment response [2,3]. In this study, we investigated if MRI morphologic phenotypes defined by unsupervised clustering is associated with breast cancer subtype and pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC). Methods A cohort of 990 patients enrolled in the I-SPY 2 TRIAL were included in this retrospective analysis. Patients were randomized to one of nine experimental drug arms or standard NAC, and pCR was assessed at surgery. DCE-MRI data acquired at pretreatment (T0) and early treatment (T1) were analyzed. Four subtypes of breast cancer were defined by immunohistochemistry (IHC) based on hormone receptor (HR) and HER2 status. Radiomic features were extracted by PyRadiomics [4] using FTV masks from DCE-MRI. MRI morphologic phenotypes were determined based on unsupervised hierarchical clustering approach on extracted radiomic shape features plus FTV using Pearson correlation with agglomerative ward linkage. The associations between the unsupervised clusters of radiomic features and FTV with four IHC subtypes and pCR were evaluated using χ2 test of independence. Cramer’s V [5] were computed to measure the strength of association (higher Cramer’s V means stronger association). P-value < 0.05 was considered statistically significant. Results Three clusters were generated by unsupervised hierarchical clustering in a population of 910 patients included in our analysis (80 patients excluded due to missing pCR or DCE-MRIs). At T0, the unsupervised clusters showed statistically significant but weak association with pCR (Cramer’s V = 0.088, p = 0.029), but the association between the clusters and HR/HER2 subtypes did not reach significance (Cramer’s V = 0.055, p = 0.48). The unsupervised clusters based on T1 shape radiomic features showed statistically significant association with both pCR and HR/HER2 subtypes (p < 0.001 for both) with Cramer’s V of 0.231 and 0.154, respectively. Our results showed stronger association between pCR and cancer subtypes with MRI shape radiomic features at T1 than at T0. Various pCR rates were observed in MRI clusters at T1. They were 56%, 36%, and 23% in Cluster 1, 2, 3, respectively. Table 1 shows pCR rates by HR/HER2 subtype in each cluster. In all sub-cohorts, pCR rate was highest in Cluster 1 and lowest in Cluster 3. In HR+/HER2-, the pCR rate in Cluster 1 was 2-fold of the pCR rates in Clusters 2 and 3-fold of Cluster 3. pCR rate was statistically significantly different depending on the MRI clusters in the sub-cohorts except for the HR/HER2+ sub-cohort: HR+/HER2-, p< 0.001; HR+/HER2+, p=0.021; HR-/HER2+, p=0.083; HR-/HER2-, p< 0.001. Conclusion MRI phenotype generated by unsupervised clustering using radiomic shape features at both pretreatment and early-treatment time points was associated with pCR outcome. Stronger association was observed at early-treatment time point. The association differed by subtype, with the strongest observed in HR+/HER2- and triple negative subtypes. Our results suggest that radiomic shape features derived from DCE-MRI may be helpful for early prediction of tumor response to NAC. Citations 1. npj Breast Cancer 6, (2020). 2. Tomography 6, (2020). 3. Annals of Surgical Oncology 20, 3823–3830 (2013). 4. Cancer Research 77, e104–e107 (2017). 5. Korean Stat Soc 42, 323–328 (2013). Table 1. pCR rate by HR/HER2 subtype in each MRI cluster at T1 Citation Format: Nu N. Le, Natsuko Onishi, David C. Newitt, Jessica E. Gibbs, Lisa J. Wilmes, Efstathios Gennatas, Barbara LeStage, Laura J. Esserman, Nola M. Hylton, Wen Li. Association of MRI morphologic phenotype from unsupervised learning with breast cancer subtypes and treatment response [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD16-07.

This study tested the hypothesis that a change in the apparent diffusion coefficient (ADC) measured in diffusion-weighted MRI (DWI) is an independent imaging marker, and ADC performs better than functional tumor volume (FTV) for assessing treatment response in patients with locally advanced breast cancer receiving neoadjuvant immunotherapy. A total of 249 patients were randomized to standard neoadjuvant chemotherapy with pembrolizumab (pembro) or without pembrolizumab (control). DCE-MRI and DWI, performed prior to and 3 weeks after the start of treatment, were analyzed. Percent changes of tumor ADC metrics (mean, 5th to 95th percentiles of ADC histogram) and FTV were evaluated for the prediction of pathologic complete response (pCR) using a logistic regression model. The area under the ROC curve (AUC) estimated for the percent change in mean ADC was higher in the pembro cohort (0.73, 95% confidence interval [CI]: 0.52 to 0.93) than in the control cohort (0.63, 95% CI: 0.43 to 0.83). In the control cohort, the percent change of the 95th percentile ADC achieved the highest AUC, 0.69 (95% CI: 0.52 to 0.85). In the pembro cohort, the percent change of the 25th percentile ADC achieved the highest AUC, 0.75 (95% CI: 0.55 to 0.95). AUCs estimated for percent change of FTV were 0.61 (95% CI: 0.39 to 0.83) and 0.66 (95% CI: 0.47 to 0.85) for the pembro and control cohorts, respectively. Tumor ADC may perform better than FTV to predict pCR at an early treatment time-point during neoadjuvant immunotherapy.

In diffusion-weighted MRI (DW-MRI), choice of

Background: The apparent diffusion coefficient (ADC) presents a biomarker that is sensitive to tumor cellularity. ADC maps can be calculated from non-contrast diffusion-weighted magnetic resonance imaging (DW-MRI) measurements. ACRIN 6698, a sub-study of clinical trial I-SPY 2, investigated mean ADC – averaged over the whole tumor – as a marker to predict pathologic complete response (pCR) [1]. This work compares a group of histogram-based ADC metrics in addition to mean ADC for early prediction of pCR in patients stratified by breast cancer subtype. Methods: We performed a retrospective analysis of DW-MRI, dynamic-contrast enhanced (DCE) MRI, and clinical outcome (i.e., pCR at surgery) in a cohort of 79 female patients who were diagnosed with high-risk, stage II/III breast cancer. Patients underwent neoadjuvant chemotherapy (NAC) with paclitaxel (12 weeks), followed by doxorubicin plus cyclophosphamide (12 weeks). The included population represents a subset of the I-SPY 2 cohort and comprises 48 patients with hormone receptor [HR]+/HER2-, and 31 patients with HR-/HER2-. DW- and DCE-MRI acquisitions were performed according to the I-SPY 2 protocol at pretreatment (T0) and after three weeks (T1) and were analyzed to find early treatment percentage (%) change (T0 to T1) in any metric M; where %-change = 100 × (M(T1) – M(T0))/M(T0). Histogram analysis provided nine region-of-interest (ROI)-based ADC metrics (Table 1). ROIs were manually delineated by expert observers in three-dimensional ADC maps, focusing on diffusion-restricted regions [2]. DCE-MRI was analyzed for the integral I-SPY 2 imaging marker of %-change in functional tumor volume (FTV) between T0 and T1. Statistical analysis compared the predictive power of ADC metrics and FTV, including: the receiver-operating-characteristic (ROC) curve from a logistic regression model to predict pCR as ‘positive’, area-under-the-curve (AUC) assessment, and rank-sum Wilcoxon test (p< 0.05: statistically significant). Results (Table 1): 16 out of 79 (20.3%) patients reached pCR at surgery, with 18.8% pCR among HR+/HER- and 22.6% among HR-/HER2- groups. For all nine computed ADC statistics (listed as median [Q1, Q3], across all patients), %-change was higher in patients who reached pCR than patients with non-pCR (highest value for metric ‘MIN’: 23.9% [-0.9%, 52.5] vs. 16.6% [0.4%, 27.6%], though without statistical significance: p=0.237). Likewise, %-change of FTV was also stronger in pCR patients than non-pCR patients (-58.8% [-80.6%, -22.5%] vs. -28.2% [54.2%, -2.7%], with statistical significance: p=0.036). For all patients combined (n=79), among the various reported ADC metrics, %-change in ‘PCTL_95’ (95th percentile of histogram) yielded the highest AUC (0.7; 95% CI = [0.56, 0.83]; p=0.012). %-change in FTV showed the second highest AUC (0.67; 95% CI = [0.52, 0.82]; p=0.036). By subtype, AUC was highest for %-change of ‘PCTL_95’ (0.69; 95% CI = [0.5, 0.87]; p=0.072) in the HR+/HER2- subgroup; and highest for both %-change of ‘MEAN’ (AUC = 0.73; 95% CI = [0.49, 0.94]; p=0.065) and ‘PCTL_75’ (AUC = 0.73; 95% CI = [0.49, 0.94]; p=0.073) triple negative (HR-/HER2-) subgroup. By comparison, %-change of FTV yielded AUCs of 0.64 (95% CI = [0.41, 0.85]; p=0.191) and 0.71 (95% CI = [0.51, 0.9]; p=0.098) in the HR+/HER2- and triple-negative subgroups, respectively. Conclusion: Various tumor ADC metrics from non-contrast DW-MRI demonstrate potential biomarkers for assessing responsiveness to NAC at an early treatment timepoint. ADC may have predictive performance that is comparable to FTV, depending on the breast cancer subtype. Observations for %-change in ‘MEAN’ ADC at T1 differed from previous reports [1], which may be explained by the small sample size and single (paclitaxel) drug arm. Additional studies are warranted to include patients of experimental arms and of HER2+ subtypes. [1] Partridge et al., Radiology 289(3):618-27 (2018) [2] Nu et al., Tomography 8: 1208-20 (2022) Results of statistical analysis of ADC-based and FTV markers for predicting treatment response at 3 weeks into NAC. Median [Q1, Q3] values represent the median and interquartile range over the respective patient population regarding the %-change of the respective ADC (FTV) metric from T0 to T1. T0: pretreatment, T1: 3-week timepoint, ADC: apparent diffusion coefficient, MRI: magnetic resonance imaging, DW: diffusion-weighted, DCE: dynamic-contrast enhanced, pCR: pathologic complete response, AUC: area under the ROC curve, 95% CI [LL, UL]: confidence interval lower and upper limit, PCTL_x: xth-percentile of tumor ADC histogram, MEAN: mean of ADC within ROI, MIN: minimum of ADC within ROI, MAX: maximum of ADC within ROI, FTV: functional tumor volume, **: statistically significant. Citation Format: Judith Zimmermann, Julia Carmona-Bozo, Nu N. Le, Natsuko Onishi, Lisa J. Wilmes, Jessica E. Gibbs, Jiachao Liang, David C. Newitt, Savannah Partridge, Patrick Bolan, Bonnie N. Joe, Elissa R. Price, Barbara LeStage, Nola M. Hylton, Wen Li. Diffusion-weighted magnetic resonance imaging for subtype-specific prediction of pathologic complete response in neoadjuvant chemotherapy [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P6-01-33.

Purpose. Hormonal status significantly affects background normal parenchymal enhancement (BPE) degree on breast MRI. A previous study reported the association between lack of BPE suppression and non pathologic complete response (pCR) after neoadjuvant chemotherapy for HR+ invasive cancer1. For DCIS, local control is the treatment goal2,3. Surgical outcomes which might be related to local control include presence of invasive components at surgery, re-excision after breast conserving surgery (BCS), and residual DCIS extent. We hypothesized that imaging outcomes (pre-surgery MRI volume and volume reduction from baseline) might also be related to local control. We investigated the association between BPE degree/suppression and 5 surgical/imaging outcomes after neoadjuvant endocrine therapy (NET) for DCIS. Methods. CALGB 40903 was a single arm trial of NET for ER+ DCIS without invasion4. Clinical and image data from a subset of 59 post-menopausal patients from this trial was available for analysis. Patients took letrozole 2.5 mg/day for 6 months before surgery (BCS, 53; mastectomy, 6). Dynamic contrast-enhanced MRIs were obtained at baseline, 3 months, and 6 months (pre-surgery). MRI volume was measured by summing all voxels with pre-defined level of enhancement within a 3D bounding box. MRI volume reduction was measured by percent change from baseline. BPE degree was calculated as the mean early percent enhancement of the central 50% axial slices on a contralateral breast. BPE suppression was dichotomized as suppressed or non-suppressed relative to baseline. For analyses on re-excision, only BCS patients were included. For analyses on residual DCIS extent, patients who achieved pCR or had invasive components were excluded. Mann-Whitney, Spearman’s rho, and Fisher’s exact tests were used for analyses, with P Table 1.Association of BPE Degree with Surgical and Imaging OutcomesOutcomeTime pointOutcome statusNo. of patientsMedian [1st, 3rd quartiles]P-valuePresence of invasive componentBaselineAbsent47 (92)25.0 [19.6, 30.0]0.282Present4 (8)17.2 [15.3, 25.5]3 monthsAbsent48 (91)23.5 [17.8, 31.0]0.668Present5 (9)16.6 [16.3, 34.7]6 monthsAbsent45 (90)24.6 [18.1, 33.0]0.343Present5 (10)17.2 [12.8, 31.2]OutcomeTime pointOutcome statusNo. of patientsMedian [1st, 3rd quartiles]P-valueRe-excision after BCSBaselineNot performed40 (87)22.6 [19.0, 27.1]0.019*Performed6 (13)43.0 [29.3, 53.9]3 monthsNot performed40 (85)22.1 [16.5, 29.5]0.018*Performed7 (15)36.7 [29.2, 40.3]6 monthsNot performed37 (84)19.8 [16.5, 31.4]0.009*Performed7 (16)38.3 [31.4, 41.0]OutcomeTime pointOutcome statusNo. of patientsSpearman’s rhoP-valueResidual DCIS extentBaselineNA3800.9973 monthsNA38-0.270.1016 monthsNA37-0.230.164MRI volumeBaselineNA510.130.3733 monthsNA530.170.2326 monthsNA500.420.003*MRI volume reductionBaselineNA510.070.6093 monthsNA530.290.0336 monthsNA500.50< 0.001** P Table 2.Association of BPE suppression with Surgical and Imaging OutcomesOutcomeTime pointBPE suppression groupNo. of patientsNo. of patients w/o invasive componentNo. of patients w/invasive componentP-valuePresence of invasive component3 monthssuppressed26 (53)24 (53)2 (50)1.000non-suppressed23 (47)21 (47)2 (50)6 monthssuppressed25 (53)22 (51)3 (75)0.611non-suppressed22 (47)21 (49)1 (25)OutcomeTime pointBPE suppression groupNo. of patientsNo. of patients w/o re-excisionNo. of patients w/re-excisionP-valueRe-excision after BCS3 monthssuppressed26 (53)22 (51)4 (67)0.671non-suppressed23 (47)21 (49)2 (33)6 monthssuppressed25 (53)21 (51)4 (67)0.670non-suppressed22 (47)20 (49)2 (33)OutcomeTime pointBPE suppression groupNo. of patientsMedian[1st, 3rd quartiles]P-valueResidual DCIS extent3 monthssuppressed19 (53)23mm[7, 36]0.172non-suppressed17 (47)11mm[4, 22]6 monthssuppressed18 (51)20mm[6.5, 34]0.487non-suppressed17 (49)18mm[4, 24]MRI volume3 monthssuppressed26 (53)0.4cc[0.2, 1.9]0.613non-suppressed23 (47)0.6cc[0.1, 1.6]6 monthssuppressed25 (53)0.3cc[0.1, 0.8]0.015*non-suppressed22 (47)1.1cc[0.4, 2.8]MRI volume reduction3 monthssuppressed26 (53)-73.0%[-91.4, -45.0]0.684non-suppressed23 (47)-71.1%[-92.1, -46.7]6 monthssuppressed25 (53)-84.8%[-96.2, -66.5]0.001*non-suppressed22 (47)-48.6%[-75.0, 57.3]* P Citation Format: Natsuko Onishi, David C Newitt, Jessica E Gibbs, Alex A Nguyen, Rita Freimanis, Elissa R Price, Shelly Hwang, Nola M Hylton. Background parenchymal enhancement as an imaging biomarker in neoadjuvant endocrine therapy for DCIS [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P3-03-08.