Background The Cures Act mandated immediately released health information. In this study, we investigated patient comprehension of mammography reports and the utility of online resources to aid report interpretation. Methods Patients who received a normal mammogram from February to April 2022 were invited to complete semi-structured interviews paired with health literacy questionnaires to assess patient’s report comprehension before and after internet search. Results Thirteen selected patients via purposeful sampling completed interviews. Most patients described their initial understanding of the mammography report as “good” and improved to between “good” and “very good” after an internet search. Patients suggested "a little column on the side" for medical terminology, “an extra prompt" for making an appointment, or a recommendation for "good sites” to improve mammography reports. Conclusion Patients varied in their ability to independently interpret medical reports and seek additional resources. While online resources marginally improved patient understanding, actionable and clear resources are needed.

Background Through online health portals, patients receive complex medical reports without interpretation from their healthcare provider. This study evaluated the usability of MedEd, a patient engagement tool providing definitions of medical terminology in breast pathology and radiology reports. Methods Individuals who underwent a normal screening mammogram were invited to complete semi-structured interviews where they downloaded MedEd and discussed their download experience. Acceptability, appropriateness, and feasibility of MedEd were evaluated. Results 143 individuals were invited to participate, and 14 semi-structured interviews were completed. Participants reported ease of downloading and navigating MedEd with concerns about privacy and others’ abilities to download. Participants demonstrated high acceptability (mean 4.48/5, SD 0.95), appropriateness (mean 4.66/5, SD 0.83), and feasibility (mean 4.48/5, SD 1.04) scores. Conclusion Participants expressed excitement for future use of MedEd and provided suggestions for improvements. Next steps include evaluating comprehension of real breast reports while using MedEd and expanding patient access.

INTRODUCTION:As part of the 21st Century Cures Act (April 2021), electronic health information (EHI) must be immediately released to patients. In this study, we sought to evaluate clinician and patient perceptions regarding this immediate release. METHODS:After surveying 33 clinicians and 30 patients, semi-structured interviews were conducted with a subset of the initial sample, comprising 8 clinicians and 12 patients. Open-ended questions explored clinicians' and patients' perceptions of immediate release of EHI and how they adjusted to this change. RESULTS:Ten themes were identified: Interpreting Results, Strategies for Patient Interaction, Patient Experiences, Communication Strategies, Provider Limitations, Provider Experiences, Health Information Interfaces, Barriers to Patient Understanding, Types of Results, and Changes due to Immediate Release. Interviews demonstrated differences in perceived patient distress and comprehension, emphasizing the impersonal nature of electronic release and necessity for therapeutic clinician-patient communication. CONCLUSIONS:Clinicians and patients have unique insights on the role of immediate release. Understanding these perspectives will help improve communication and develop patient-centered tools (glossaries, summary pages, additional resources) to aid patient understanding of complex medical information.

Introduction: Receiving a new breast cancer diagnosis can cause anxiety and distress, which can lead to psychologic morbidity, decreased treatment adherence, and worse clinical outcomes. Understanding sources of distress is crucial in providing comprehensive care. This study aims to evaluate the relationship between delays in breast cancer diagnosis and patient-reported distress. Secondary outcomes include assessing patient characteristics associated with delay. Methods: Newly diagnosed breast cancer patients who completed a distress screening tool at their initial evaluation at an academic institution between 2014 and 2019 were retrospectively evaluated. The tool captured distress levels in the emotional, social, health, and practical domains with scores of "high distress" defined by current clinical practice guidelines. Delay from mammogram to biopsy, whether diagnostic or screening mammogram, was defined as >30 days. Result: 745 newly diagnosed breast cancer patients met inclusion criteria. Median time from abnormal mammogram to core biopsy was 12 days, and 11% of patients experienced a delay in diagnosis. The non-delayed group had higher emotional (p = 0.04) and health (p = 0.03) distress than the delayed group. No statistically significant differences in social distress were found between groups. Additionally, patients with higher practical distress had longer time interval between mammogram and surgical intervention compared to those with lower practical distress. Older age, diagnoses of invasive lobular carcinoma or ductal carcinoma in situ, and clinical anatomic stages 0-I were associated with diagnostic delay. Conclusion: Patients with higher emotional or health-related distress were more likely to have timely diagnoses of breast cancer, suggesting that patients with higher distress may seek healthcare interventions more promptly. Improved understanding of sources of distress will permit early intervention regarding the devastating impact of breast cancer diagnosis.

<p>Instructional video for TCIApathfinder</p>

The NCCN Guidelines for Breast Cancer Screening and Diagnosis provide health care providers with a practical, consistent framework for screening and evaluating a spectrum of clinical presentations and breast lesions. The NCCN Breast Cancer Screening and Diagnosis Panel is composed of a multidisciplinary team of experts in the field, including representation from medical oncology, gynecologic oncology, surgical oncology, internal medicine, family practice, preventive medicine, pathology, diagnostic and interventional radiology, as well as patient advocacy. The NCCN Breast Cancer Screening and Diagnosis Panel meets at least annually to review emerging data and comments from reviewers within their institutions to guide updates to existing recommendations. These NCCN Guidelines Insights summarize the panel's decision-making and discussion surrounding the most recent updates to the guideline's screening recommendations.

Background: As health care continues to evolve toward information transparency, an increasing number of patients have access to their medical records, including result reports that were not originally designed to be patient-facing. Previous studies have demonstrated that patients have poor under-standing of medical terminology. However, patient comprehension of terminology specific to breast pathology reports has not been well studied. We assessed patient understanding of common medical terms found in breast pathology reports. Methods: A survey was administered electronically to patients scheduled for a screening mammogram within a multisite health care system. Participants were asked to objectively define and interpret 8 medical terms common to breast biopsy pathology reports. Patient perception of the utility of various educational tools was also assessed. Demographic information including health literacy, education level, previous cancer diagnosis, and primary language was collected. Results: In total, 527 patients completed the survey. Terms including "malignant" and "benign" were the most correctly defined at 80% and 73%, respectively, whereas only 1% correctly defined "high grade." Factors including race/ethnicity and education level were correlated with more correct scores. Patients preferred educational tools that were specific to their diagnosis and available at the time they were reviewing their results. Conclusion: Patient comprehension of common medical terminology is poor. Potential assumptions of understanding based on patient factors including education, past medical history, and occupation are misinformed. With the newly mandated immediate release of information to patients, there is a pressing need to develop and integrate educational tools to support patients through all aspects of their care. (c) 2022 Elsevier Inc. All rights reserved.

Objectives: The 21st Century Cures Act requires that institutions release all electronic health information (EHI) to patients immediately. We aimed to understand patient and clinician attitudes toward the immediate release of EHI to patients. Methods: Patients and clinicians representing distinct specialties at a single academic medical center completed a survey to assess attitudes toward the immediate release of results. Differences between patient and clinician responses were compared using chi-square and student's t-test for categorical and continuous variables, respectively. A two-sided significance level of 0.05 was used for all statistical tests. Results: 69 clinicians and 57 patients completed the survey. Both patients (89.7%) and clinicians (80.6%) agreed or strongly agreed-here after referred to as agreed, that providing patients with access to their health information is necessary in delivering high-quality care. However, 62.7% of clinicians agreed that results released immediately would be more confusing than helpful, whereas the minority of patients agreed with this statement (15.8%) (p < 0.05). Providers were also more likely to disagree that patients are comfortable independently interpreting blood work results (p < 0.05), radiology results (p < 0.05) and pathology reports (p < 0.05). With regard to timing, the majority of patients (75.1%) felt their provider should contact them within 24 h of the release of an abnormal result, whereas only 9.0% of clinicians agreed with this timeframe (p < 0.05). Discussions: Patients and clinicians value information transparency. However, the immediate release of results is controversial, especially among clinicians. The discrepancy between patient and clinician perceptions underlines the importance of setting expectations about the communication of results. Additionally, our results emphasize the need to implement strategies to help improve patient comprehension, decrease patient distress and improve clinician workflows.

Receiving a new breast cancer (BC) diagnosis can cause significant patient anxiety, which is amplified by delays in diagnosis. There is a lack of defined time periods for delays in the workup of BC. This study aims to evaluate national variations in timing from first abnormal mammogram to first biopsy and to determine independent predictors of delay in diagnosis. Data were derived from SEER–Medicare linked claims database from 2007 to 2013. Time intervals from abnormal mammogram, either screening or diagnostic, to biopsy were assessed. The fourth quartile for timing from first mammogram to first biopsy was utilized to define delay in diagnosis. Multivariate analyses were used to evaluate the association between clinicopathologic variables and delays in diagnosis. We analyzed 53,758 patients with stage 0–II BC who underwent upfront surgery. Significant variations in timing of care were identified, with mean times from mammogram to biopsy, surgeon visit, and breast surgery of 23.3, 31.6, and 52.6 days, respectively. Over the study period, there was a decrease in delays from mammogram to biopsy. Non-White race, Northeast location, and earlier stage disease were found to be independent predictors of delays in the diagnosis of BC (p < 0.0001). The study demonstrates significant variations in time to diagnostic biopsy. More efficient processes of care to address these delays should be implemented, and further studies are needed to determine whether improved efficiency decreases patient anxiety. The large variations in time to diagnosis speak to the need for consensus guidelines to establish a standard of care.

Background: Strong background parenchymal enhancement (BPE) may cause overestimation in tumor volume measured from dynamic contrast-enhanced (DCE) MRI, which may adversely affect the ability of MR tumor volume to predict treatment outcome for patients undergoing neoadjuvant chemotherapy (NAC). Specifically, an overestimation of tumor volume can result in misclassification of patients with complete pathologic response (pCR) as non-responders, leading to less confidence in MRI prediction. As well, overestimation of extent of disease might lead to more aggressive surgical therapy than necessary. This study investigated whether high BPE in the contralateral breast influences the predictive performance of MRI-measured functional tumor volume (FTV) for patients with locally advanced breast cancer undergoing NAC. Methods: patients (n=990) enrolled in the I-SPY 2 TRIAL who were randomized to the graduated experimental drug arms or controls from 2010 to 2016 were analyzed. Each patient had 4 MRI exams: pre-NAC (T0), after 3 weeks of NAC (T1), between NAC regimens (T2), and post-NAC (T3). FTV was calculated at each MRI exam by summing voxels meeting enhancement thresholds. Background parenchymal enhancement (BPE) in the contralateral breast was calculated automatically as mean percentage enhancement on the early (nominal 150sec post-contrast) image in the fibroglandular tissue segmented from 5 continuous axial slices centered in the inferior-to-superior stack. For each treatment time point, patients having both FTV and BPE measurements were included in the analysis. The area under the ROC curve (AUC) was estimated as the association between FTV and pCR at T1, T2, and T3. The analysis was conducted in the full patient cohort and in sub-cohorts defined by hormone receptor (HR) and HER2 status. In each patient cohort, a cut-off BPE value was selected to classify patients with high vs. low BPE by testing AUCs estimated with low-BPE patients reached maximum when the cut-off value varied from median to maximum in steps of 10%. Results: Out of 990 patients, 878 had pCR outcome data (pCR or non-pCR, pCR rate = 35%). Table 1 shows the number of patients, pCR rate, and AUC of FTV for predicting pCR using all patients available vs. a subset patients with low BPE ( Conclusions: High background parenchymal enhancement adversely affected the predictive performance of functional tumor volume measured by DCE-MRI, at early treatment time point for HR+/HER2- and across all time points for HR-/HER2+ cancer subtype. The adverse effect might be offset using subtype-optimized enhancement threshold in calculating functional tumor volume. Citation Format: Wen Li, Natsuko Onishi, David C Newitt, Roy Harnish, Ella F Jones, Lisa J Wilmes, Jessica Gibbs, Elissa Price, Bonnie N Joe, A. Jo Chien, Donald A Berry, Judy C Boughey, Kathy S Albain, Amy S Clark, Kirsten K Edmiston, Anthony D Elias, Erin D Ellis, David M Euhus, Heather S Han, Claudine Isaacs, Qamar J Khan, Julie E Lang, Janice Lu, Jane L Meisel, Zaha Mitri, Rita Nanda, Donald W Northfelt, Tara Sanft, Erica Stringer-Reasor, Rebecca K Viscusi, Anne M Wallace, Douglas Yee, Rachel Yung, Michelle E Melisko, Jane Perlmutter, Hope S Rugo, Richard Schwab, W. Fraser Symmans, Laura J van9t Veer, Christina Yau, Smita M Asare, Angela DeMichele, Sally Goudreau, Hiroyuki Abe, Deepa Sheth, Dulcy Wolverton, Kelly Fountain, Richard Ha, Ralph Wynn, Erin P Crane, Charlotte Dillis, Theresa Kuritza, Kevin Morley, Michael Nelson, An Church, Bethany Niell, Jennifer Drukteinis, Karen Y Oh, Neda Jafarian, Kathy Brandt, Sadia Choudhery, Dae Hee Bang, Christiane Mullins, Stefanie Woodard, Kathryn W Zamora, Haydee Ojeda-Fornier, Mohammad Eghedari, Pulin Sheth, Linda Hovanessian-Larsen, Mark Rosen, Elizabeth S McDonald, Michael Spektor, Marina Giurescu, Mary S Newell, Michael A Cohen, Elise Berman, Constance Lehman, William Smith, Kim Fitzpatrick, Marisa H Borders, Wei Yang, Basak Dogan, Laura J Esserman, Nola M Hylton. The effect of background parenchymal enhancement on the predictive performance of functional tumor volume measured in MRI [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P6-02-01.

Abstract Background: In an adaptive randomized trial, when new treatment combinations are being tested, it is important to be able to identify patients who are progressing on treatment so that they can be changed to a different therapeutic regimen. We know that even within the molecularly high risk patients in I-SPY 2, there is considerable variation in biology. In this study, we will present results of using MRI-calculated functional tumor volume (FTV) to identify tumor progression for each breast cancer subtype. Methods: Patients (n=990) enrolled in the I-SPY 2 TRIAL who were randomized to the graduated experimental drug arms or controls from 2010 to 2016 were analyzed. Four MRI exams were performed for each patient: pre-NAC (T0), after 3 weeks of NAC (T1), between regimens (T2), and post-NAC (T3). Functional tumor volume (FTV) was calculated at each exam by summing voxels meeting enhancement thresholds. Tumor progression at T1, T2 or T3 was identified by a positive FTV change relative to T0. Visual inspection was used to exclude false progression due to strong background parenchymal enhancement post-contrast, prominent vessels, motion, or insufficient image quality. pCR was defined as no invasive disease in the breast and lymph nodes. Negative predictive value for pCR was defined as:NPV=number of true non-pCRs / number of patients with MRI assessed tumor progressions, where “true non-pCRs” referred to patients who were non-pCRs at surgery and were assessed as progressors by MRI. The analysis was performed in the full cohort and in sub-cohorts defined by HR and HER2 statuses. Results: Out of 990 patients, 878 had pCR outcome data (pCR or non-pCR, pCR rate = 35%). Total and non-pCR numbers for each subtype, number of patients with tumor progression assessed by MRI at T1, T2, and T3, and NPVs, are shown in Table 1. In the full cohort, the NPV increased consistently over treatment, from T1 (NPV=83%) to T2 (93%), and to T3 (100%). The HER2+ cancer subtypes showed fewer MRI-assessed tumor progressions than HER2- subtypes: e.g. 10/209 (5%) vs. 108/669 (16%) at T1. NPV was 100% for HER2+ subtypes at T1 and T2 except for a single misclassification of a HR- tumor at T1. Only 6 tumor progressors, all HER2- were identified at T3, and all were confirmed at surgery as non-pCRs (NPV=100%). For HR+/HER2-, the NPV increased slightly from 89% at T1 to 91% at T2, while triple negative subtype had a more substantial increase, from 78% to 92%. Conclusions: Our study showed strong association between tumor progressors assessed by MRI with true non-pCRs after NAC. For HER2+ tumors, although MRI progressors are rare, they strongly indicate non-pCR at all treatment time points, while HER2- subtypes show more accurate results later in treatment. We are evaluating MRI change at 6 weeks to determine if that time point is sufficient to predict progressors. Table 1 MRI assessed tumor progression at different treatment time pointN/non-pCRs/%non-pCRMRI assessed tumor progressionT1 (after 3 weeks)T2 (inter-regimen)T3 (post-NAC)NNPV (%)NNPV (%)NNPV (%)Full cohort878/572/65%11883.14192.76100%HR+/HER2-344/280/81%4588.91190.93100%HR+/HER2+134/85/63%610021000N/AHR-/HER2+75/23/31%47521000N/Atriple negative325/184/57%6377.82692.33100% Citation Format: Wen Li, Natsuko Onishi, David C Newitt, Jessica Gibbs, Lisa J Wilmes, Ella F Jones, Bonnie N Joe, Laura S Sit, Christina Yau, A. Jo Chien, Elissa Price, Kathy S Albain, Theresa Kuritza, Kevin Morley, Judy C Boughey, Kathy Brandt, Sadia Choudhery, Amy S Clark, Mark Rosen, Elizabeth S McDonald, Anthony D Elias, Dulcy Wolverton, Kelly Fountain, David M Euhus, Heather S Han, Bethany Niell, Jennifer Drukteinis, Julie E Lang, Janice Lu, Jane L Meisel, Zaha Mitri, Rita Nanda, Donald W Northfelt, Tara Sanft, Erica Stringer-Reasor, Rebecca K Viscusi, Anne M Wallace, Douglas Yee, Rachel Yung, Smita M Asare, Michelle E Melisko, Jane Perlmutter, Hope S Rugo, Richard Schwab, W. Fraser Symmans, Laura J van't Veer, Donald A Berry, Angela DeMichele, Hiroyuki Abe, Deepa Sheth, Kirsten K Edmiston, Erin D Ellis, Richard Ha, Ralph Wynn, Erin P Crane, Charlotte Dillis, Michael Nelson, An Church, Claudine Isaacs, Qamar J Khan, Karen Y Oh, Neda Jafarian, Dae Hee Bang, Christiane Mullins, Stefanie Woodard, Kathryn W Zamora, Haydee Ojeda-Fornier, Pulin Sheth, Linda Hovanessian-Larsen, Mohammad Eghtedari, Michael Spektor, Marina Giurescu, Mary S Newell, Michael A Cohen, Elise Berman, Constance Lehman, William Smith, Kim Fitzpatrick, Marisa H Borders, Wei Yang, Basak Dogan, Sally Goudreau, Thelma Brown, Laura J Esserman, Nola M Hylton. Breast cancer subtype specific association of pCR with MRI assessed tumor volume progression during NAC in the I-SPY 2 trial [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr PD9-04.

Abstract Background: Strong background parenchymal enhancement (BPE) may cause overestimation in tumor volume measured from dynamic contrast-enhanced (DCE) MRI, which may adversely affect the ability of MR tumor volume to predict treatment outcome for patients undergoing neoadjuvant chemotherapy (NAC). Specifically, an overestimation of tumor volume can result in misclassification of patients with complete pathologic response (pCR) as non-responders, leading to less confidence in MRI prediction. As well, overestimation of extent of disease might lead to more aggressive surgical therapy than necessary. This study investigated whether high BPE in the contralateral breast influences the predictive performance of MRI-measured functional tumor volume (FTV) for patients with locally advanced breast cancer undergoing NAC. Methods: patients (n=990) enrolled in the I-SPY 2 TRIAL who were randomized to the graduated experimental drug arms or controls from 2010 to 2016 were analyzed. Each patient had 4 MRI exams: pre-NAC (T0), after 3 weeks of NAC (T1), between NAC regimens (T2), and post-NAC (T3). FTV was calculated at each MRI exam by summing voxels meeting enhancement thresholds. Background parenchymal enhancement (BPE) in the contralateral breast was calculated automatically as mean percentage enhancement on the early (nominal 150sec post-contrast) image in the fibroglandular tissue segmented from 5 continuous axial slices centered in the inferior-to-superior stack. For each treatment time point, patients having both FTV and BPE measurements were included in the analysis. The area under the ROC curve (AUC) was estimated as the association between FTV and pCR at T1, T2, and T3. The analysis was conducted in the full patient cohort and in sub-cohorts defined by hormone receptor (HR) and HER2 status. In each patient cohort, a cut-off BPE value was selected to classify patients with high vs. low BPE by testing AUCs estimated with low-BPE patients reached maximum when the cut-off value varied from median to maximum in steps of 10%. Results: Out of 990 patients, 878 had pCR outcome data (pCR or non-pCR, pCR rate = 35%). Table 1 shows the number of patients, pCR rate, and AUC of FTV for predicting pCR using all patients available vs. a subset patients with low BPE (< BPE cut-off). In the full cohort, AUC increased slightly across all time points after patients with high BPE were removed. In the HR+/HER2- subtype, AUC increased at T1 after removal of cases with high BPE (0.65 vs. 0.71). For HR-/HER2+, AUC increased substantially after removal of high BPE cases (0.65 to 0.86 at T1, 0.71 to 0.87 at T2, and 0.71 to 0.89 at T3), with greater improvement at the early time point (T1) compared to later time points (T2 and T3). Only a slight improvement in the AUC was observed in the HR+/HER2+ and HR-/HER2- subtypes across all time points. Conclusions: High background parenchymal enhancement adversely affected the predictive performance of functional tumor volume measured by DCE-MRI, at early treatment time point for HR+/HER2- and across all time points for HR-/HER2+ cancer subtype. The adverse effect might be offset using subtype-optimized enhancement threshold in calculating functional tumor volume. Table 1 Effect of BPE on the prediction of pCR using FTV at various treatment time pointsT1T2T3npCR rateAUCBPE cut-offnpCR rateAUCBPE cut-offnpCR rateAUCBPE cut-offFullAll64734%0.662762334%0.701761134%0.6925Subset45334%0.6831133%0.7230534%0.72HR+/HER2-All26218%0.651924918%0.718225518%0.7519Subset13118%0.7124818%0.7120419%0.76HR+/HER2+All10636%0.642110538%0.62269634%0.7120Subset5332%0.668438%0.665740%0.73HR-/HER2+All5175%0.65204774%0.71204973%0.7116Subset3073%0.862871%0.872475%0.89HR-/HER2-All22842%0.682822243%0.751821143%0.6916Subset15940%0.7111137%0.7810540%0.75 Citation Format: Wen Li, Natsuko Onishi, David C Newitt, Roy Harnish, Ella F Jones, Lisa J Wilmes, Jessica Gibbs, Elissa Price, Bonnie N Joe, A. Jo Chien, Donald A Berry, Judy C Boughey, Kathy S Albain, Amy S Clark, Kirsten K Edmiston, Anthony D Elias, Erin D Ellis, David M Euhus, Heather S Han, Claudine Isaacs, Qamar J Khan, Julie E Lang, Janice Lu, Jane L Meisel, Zaha Mitri, Rita Nanda, Donald W Northfelt, Tara Sanft, Erica Stringer-Reasor, Rebecca K Viscusi, Anne M Wallace, Douglas Yee, Rachel Yung, Michelle E Melisko, Jane Perlmutter, Hope S Rugo, Richard Schwab, W. Fraser Symmans, Laura J van't Veer, Christina Yau, Smita M Asare, Angela DeMichele, Sally Goudreau, Hiroyuki Abe, Deepa Sheth, Dulcy Wolverton, Kelly Fountain, Richard Ha, Ralph Wynn, Erin P Crane, Charlotte Dillis, Theresa Kuritza, Kevin Morley, Michael Nelson, An Church, Bethany Niell, Jennifer Drukteinis, Karen Y Oh, Neda Jafarian, Kathy Brandt, Sadia Choudhery, Dae Hee Bang, Christiane Mullins, Stefanie Woodard, Kathryn W Zamora, Haydee Ojeda-Fornier, Mohammad Eghedari, Pulin Sheth, Linda Hovanessian-Larsen, Mark Rosen, Elizabeth S McDonald, Michael Spektor, Marina Giurescu, Mary S Newell, Michael A Cohen, Elise Berman, Constance Lehman, William Smith, Kim Fitzpatrick, Marisa H Borders, Wei Yang, Basak Dogan, Laura J Esserman, Nola M Hylton. The effect of background parenchymal enhancement on the predictive performance of functional tumor volume measured in MRI [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P6-02-01.

Dynamic contrast-enhanced (DCE) MRI provides both morphological and functional information regarding breast tumor response to neoadjuvant chemotherapy (NAC). The purpose of this retrospective study is to test if prediction models combining multiple MRI features outperform models with single features. Four features were quantitatively calculated in each MRI exam: functional tumor volume, longest diameter, sphericity, and contralateral background parenchymal enhancement. Logistic regression analysis was used to study the relationship between MRI variables and pathologic complete response (pCR). Predictive performance was estimated using the area under the receiver operating characteristic curve (AUC). The full cohort was stratified by hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status (positive or negative). A total of 384 patients (median age: 49 y/o) were included. Results showed analysis with combined features achieved higher AUCs than analysis with any feature alone. AUCs estimated for the combined versus highest AUCs among single features were 0.81 (95% confidence interval [CI]: 0.76, 0.86) versus 0.79 (95% CI: 0.73, 0.85) in the full cohort, 0.83 (95% CI: 0.77, 0.92) versus 0.73 (95% CI: 0.61, 0.84) in HR-positive/HER2-negative, 0.88 (95% CI: 0.79, 0.97) versus 0.78 (95% CI: 0.63, 0.89) in HR-positive/HER2-positive, 0.83 (95% CI not available) versus 0.75 (95% CI: 0.46, 0.81) in HR-negative/HER2-positive, and 0.82 (95% CI: 0.74, 0.91) versus 0.75 (95% CI: 0.64, 0.83) in triple negatives. Multi-feature MRI analysis improved pCR prediction over analysis of any individual feature that we examined. Additionally, the improvements in prediction were more notable when analysis was conducted according to cancer subtype.

Abstract Background In clinically node-negative (cN0) breast cancer patients with triple negative (TN) and HER2+ disease and breast pathological complete response (breast pCR), low rates of nodal positivity after neoadjuvant chemotherapy (NAC) have been demonstrated. In these patients, the omission of surgical axillary staging has been proposed. However, this information is not routinely known preoperatively. We aimed to validate the correlation between pathologic breast response and pathologic nodal status, and evaluate the relationship between response of the breast tumor on MRI and pathologic nodal status after NAC in cN0 patients in the I-SPY2 trial. Methods We identified all patients with cT1-4 cN0 breast cancer prior to NAC from graduated arms of the I-SPY2 trial, a prospective neoadjuvant chemotherapy trial. Absence of residual disease post-NAC was defined as longest diameter (LD) of 0 mm on MRI. Breast pCR was defined as the absence of invasive tumor in the breast at surgery. Associations between ypN0 and patient, MRI, and tumor characteristics were assessed using chi-square tests and univariate regression. Results Of 365 cT1-4 cN0 patients included, 128 had HR+/HER2- tumors (35%), 60 HR+/HER2+ tumors (16%), 34 HR-/HER2+ tumors (9%) and 143 TN tumors (39%). Overall, 283 patients (78%) were ypN0 after NAC and 152 patients (42%) had a breast pCR. ypN0 rate was higher in patients with a breast pCR than those with residual disease (93% vs 66%, p<0.001). Patients with HR-/HER2+ and TN tumors were more likely to be ypN0 (97% and 87% respectively) than patients with HR+/HER2- and HR+/HER2+ disease (66% and 71% respectively, p<0.001). Other characteristics associated with ypN0 were tumor grade (grade I 57%, grade II 66%, grade III 84%; p=0.002), MammaPrint Classification (High Risk 1 68% and High Risk 2 87%; p<0.001) and absence of residual tumor in the breast on MRI (87% vs 72% in patients with evidence of tumor on MRI post-NAC/pre-surgery; p=0.003). In patients with HR-/HER2+, HR+/HER2+, HR-/HER2+ or TN disease and a breast pCR, ypN0 rate was respectively 82%, 96%, 96% and 97% (table 1). In patients with HR+/HER2-, HR+/HER2+, HR-/HER2+ or TN disease and with no evidence of residual disease in the breast on MRI, rate of ypN0 was 71%, 80%, 94% and 96% respectively. Conclusion In cT1-4 cN0 breast cancer patients with HR+/HER2+, HR-/HER2+ and TN tumors and a breast pCR, ypN0 rates after NAC are extremely high. In patients with HR-/HER2+ and TN tumors with no residual breast disease on MRI after NAC and pre-surgery, ypN0 rates are high enough to consider omission of axillary surgery. In patients with HR+ tumors, MRI is unsufficiently predictive for pathological response and can therefore not be used to select ypN0 patients. Research on the prediction of ypN0 in cN+ I-SPY2 patients is ongoing. Nodal status in patients with pCR and absence of residual disease on MRI Number of positive nodesBreast Cancer Subtype0123AllBreast pCR HR+/HER2-27(82)2(6)4(12)033(100)HR+/HER2+24(96)01(4)025(100)HR-/HER2+24(96)1(4)0025(100)TN67(97)2(3)0069(100)Absence of residual disease on MRI HR+/HER2-24(71)7(21)3(9)034(100)HR+/HER2+16(80)3(15)01(5)20(100)HR-/HER2+15(94)1(6)0016(100)TN54(96)2(4)0056(100) Citation Format: van der Noordaa ME, Esserman L, Yau C, Mukhtar R, Price E, Hylton N, Abe H, Wolverton D, Crane EP, Ward KA, Nelson M, Niell BL, Oh K, Brandt KR, Bang DH, Ojeda-Fournier H, Eghtedari M, Sheth PA, Bernreuter WK, Umphrey H, Rosen MA, Dogan B, Yang W, Joe B, van 't Veer L, Hirst G, Lancaster R, Wallace A, Alvaredo M, Symmans F, Asare S, Boughey JC, I-SPY2 Consortium. Role of breast MRI in predicting pathologically negative nodes after neoadjuvant chemotherapy in cN0 patients in the I-SPY2 trial [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr PD4-04.

Abstract Background: Detecting residual disease accurately using MRI after NAC to identify both responders and non-responders is essential for de-escalating therapy or redirecting patients to more effective treatment. The purpose of this study is to determine if the combination of longest diameter (LD) and functional tumor volume (FTV) from dynamic contrast enhanced (DCE-) MRI is superior to FTV alone or LD alone for assessing treatment response after neoadjuvant therapy in breast cancer patients. Methods: Data from patients in the graduated drug arms of the I-SPY 2 trial were included in the analysis. Both LD and FTV were assessed using DCE-MRI after neoadjuvant therapy. LD was measured by the site radiologist as the longest dimension of the enhanced area on early post-contrast images. Functional tumor volume (FTV) was assessed as the sum of voxels with enhancement above specific thresholds within the pre-defined region-of-interest (ROI). A linearized variable was derived to represent the combination of FTV and LD. The area under the receiver operating characteristic curve (AUC) was used to evaluate the assessment of treatment response, pathologic complete response (pCR), defined as no invasive disease in the breast and lymph nodes, and in-breast pCR, defined as no invasive disease in the breast only. The analysis was performed in the full cohort and in breast cancer subtype defined by hormone receptor status and HER2 status. Results: Among the patient cohort of N=675 with FTV and LD, 247 (37%) did and 428 (41%) did not achieve pCR after neoadjuvant therapy. pCR rates varied among HR/HER2 subtypes (HR+/HER2-: 19%; HR+/HER2+: 38%; HR-/HER2+: 71%; HR-/HER2- (triple negative, TN): 43%). In-breast pathologic complete response rates were slightly higher in each group (full: 41%; HR+/HER2-: 23%; HR+/HER2+: 43%; HR-/HER2+: 72%; HR-/HER2-: 49%). Table 1 shows AUCs for assessing pCR using FTV alone, LD alone, and the variable combining FTV and LD. Higher AUCs were observed in all patient groups using the combined variable. AUC of 0.79 (95% CI: 0.77, 0.81) was observed for the combined variable to assess pCR in the full cohort. AUCs varied from 0.69 to 0.86 among HR/HER2 subgroups (HR+/HER2-: 0.69; HR+/HER2+: 0.74; HR-/HER2+: 0.86; HR-/HER2-: 0.80), with no difference in assessing pCR or in-breast pCR. The performance is best for the HR- subtypes. Conclusions: Both FTV and LD can be used in the assessment of invasive disease residual after neoadjuvant therapy. The combined variable of FTV and LD achieved highest AUCs, compared to using individual variable alone. Tools to improve performance in the HR+ subsets are underway. AUCs of MR measurements for identifying pCR FTV alone (95% CI)LD alone (95% CI)Combined (95% CI)FullWith subtype adj.0.73 (0.71, 0.75)0.77 (0.74, 0.79)0.79 (0.77, 0.81)FullWithout subtype adj0.69 (0.65, 0.73)0.72 (0.68, 0.76)0.75 (0.71, 0.79)HR+/HER2- 0.68 (0.60, 0.77)0.68 (0.59, 0.77)0.69 (0.61, 0.77)HR+/HER2+ 0.65 (0.56, 0.75)0.72 (0.64, 0.80)0.74 (0.66, 0.82)HR-/HER2+ 0.69 (0.55, 0.83)0.82 (0.71, 0.92)0.86 (0.77, 0.95)HR-/HER2- (TN) 0.72 (0.66, 0.79)0.73 (0.67, 0.80)0.80 (0.74, 0.85) Citation Format: Li W, Newitt D, Yun BL, Kornak J, Joe B, Yau C, Abe H, Wolverton D, Crane E, Ward K, Nelson M, Niell B, Drukteinis J, Oh K, Brandt K, Bang DH, Ojeda H, Eghtedari M, Sheth P, Bernreuter W, Umphrey H, Rosen M, Dogan B, Yang W, Esserman L, Hylton N. MRI detection of residual disease following neoadjuvant chemotherapy (NAC) in the I-SPY 2 TRIAL [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr PD4-03.

Abstract Background: Patients achieving a pathologic complete response (pCR) following neoadjuvant therapy have significantly improved event-free survival relative to those who do not; and pCR is an FDA-accepted endpoint to support accelerated approval of novel agents/combinations in the neoadjuvant treatment of high risk early stage breast cancer. Previous studies have shown that recurrence risk increased with increasing burden of residual disease (as assessed by the RCB index). As well, these studies suggest that patients with minimum residual disease (RCB-I class) also have favorable outcomes (comparable to those achieving a pCR) within high risk tumor subtypes. In this study, we assess whether integrating RCB with MRI functional tumor volume (FTV), which in itself is prognostic, can improve prediction of distant recurrence free survival (DRFS); and identify a subset of patients with minimal residual disease with comparable DRFS as those who achieved a pCR. Imaging tools can then be used to identify the subset that will do well early and guide the timing of surgical therapy. Method: We performed a pooled analysis of 596 patients from the I-SPY2 TRIAL with RCB, pre-surgical MRI FTV data and known follow-up (median 2.5 years). We first assessed whether FTV predicts residual disease (pCR or pCR/RCB-I) using ROC analysis. We applied a power transformation to normalize the pre-surgical FTV distribution; and assessed its association with DRFS using a bi-variate Cox proportional hazard model adjusting for HR/HER2 subtype. We also fitted a bivariate Cox model of RCB index adjusting for subtype; and assessed whether adding pre-surgical FTV to this model further improves association with DRFS using a likelihood ratio (LR) test. For the Cox modeling, penalized splines approximation of the transformed FTV and RCB index with 2 degrees of freedom was used to allow for non-linear effects of FTV and RCB on DRFS. Result: Pre-surgical MRI FTV is significantly associated with DRFS (Wald p<0.00001), and more effective at predicting pCR/RCB-I than predicting pCR alone (AUC: 0.72 vs. 0.65). Larger pre-surgical FTV remains associated with worse DRFS adjusting for subtype (Wald p <0.00001). The RCB index is also significantly associated with DRFS adjusting for subtype (Wald p<0.00001). Adding FTV to a model containing RCB and subtype further improves association with DRFS (LR p=0.0007). RCB-I patients have excellent DRFS (94% at 3 years compared to 95% in the pCR group). Efforts are underway to identify an optimal threshold for dichotomizing pre-surgical FTV and FTV change measures for use in combination with pCR/RCB-I class to generate integrated RCB (iRCB) groups as a composite predictor of DRFS. Conclusion: Pre-surgical MRI FTV is effective at predicting minimal residual disease (RCB0/I) in the I-SPY 2 TRIAL. Despite the association between FTV and RCB, FTV appears to provide independent added prognostic value (to RCB and subtype), suggesting that integrating MRI volume measures and RCB into a composite predictor may improve DRFS prediction. Citation Format: Hylton NM, Symmans WF, Yau C, Li W, Hatzis C, Isaacs C, Albain KS, Chen Y-Y, Krings G, Wei S, Harada S, Datnow B, Fadare O, Klein M, Pambuccian S, Chen B, Adamson K, Sams S, Mhawech-Fauceglia P, Magliocco A, Feldman M, Rendi M, Sattar H, Zeck J, Ocal I, Tawfik O, Grasso LeBeau L, Sahoo S, Vinh T, Yang S, Adams A, Chien AJ, Ferero-Torres A, Stringer-Reasor E, Wallace A, Boughey JC, Ellis ED, Elias AD, Lang JE, Lu J, Han HS, Clark AS, Korde L, Nanda R, Northfelt DW, Khan QJ, Viscusi RK, Euhus DM, Edmiston KK, Chui SY, Kemmer K, Wood WC, Park JW, Liu MC, Olopade O, Tripathy D, Moulder SL, Rugo HS, Schwab R, Lo S, Helsten T, Beckwith H, Haugen PK, van't Veer LJ, Perlmutter J, Melisko ME, Wilson A, Peterson G, Asare AL, Buxton MB, Paoloni M, Clennell JL, Hirst GL, Singhrao R, Steeg K, Matthews JB, Sanil A, Berry SM, Abe H, Wolverton D, Crane EP, Ward KA, Nelson M, Niell BL, Oh K, Brandt KR, Bang DH, Ojeda-Fournier H, Eghtedari M, Sheth PA, Bernreuter WK, Umphrey H, Rosen MA, Dogan B, Yang W, Joe B, I-SPY 2 TRIAL Consortium, Yee D, Pusztai L, DeMichele A, Asare SM, Berry DA, Esserman LJ. Refining neoadjuvant predictors of three year distant metastasis free survival: Integrating volume change as measured by MRI with residual cancer burden [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P2-07-03.

Abstract The Cancer Imaging Archive (TCIA) hosts publicly available deidentified medical images of cancer from over 25 body sites and over 30,000 patients. Over 400 published studies have utilized freely available TCIA images. Images and metadata are available for download through a web interface or a REST API. Here, we present TCIApathfinder, an R client for the TCIA REST API. TCIApathfinder wraps API access in user-friendly R functions that can be called interactively within an R session or easily incorporated into scripts. Functions are provided to explore the contents of the large database and to download image files. TCIApathfinder provides easy access to TCIA resources in the highly popular R programming environment. TCIApathfinder is freely available under the MIT license as a package on CRAN (https://cran.r-project.org/web/packages/TCIApathfinder/index.html) and from https://github.com/pamelarussell/TCIApathfinder. Significance: These findings present a new tool, TCIApathfinder, the first client for The Cancer Imaging Archive (TCIA) for use in the highly popular R computing environment, that will dramatically lower the barrier of access to the valuable tools in TCIA. Cancer Res; 78(15); 4424–6. ©2018 AACR.