Partridge, Savannah C

603 Background: Accurate prediction of pathologic complete response (pCR) after preoperative therapy for HER2-positive (HER2+) breast cancer can help triage patients to immediate surgery vs additional preoperative therapy. Pre-surgery (i.e., after neoadjuvant therapy) imaging studies may help estimate the likelihood of pCR. In EA1181, patients (pts) with stage II-IIIA HER2+ breast cancer received four cycles of preoperative taxane, trastuzumab, and pertuzumab (THP×4). Pre-surgery imaging was required, though the specific imaging modality was not mandated. Here, we report the accuracy of pre-surgery breast MRI to predict pCR (ypT0/Tis, ypN0). Methods: Among 2,141 pts treated with THPx4, 1,378 had pre-surgery breast MRI and were representative of the overall cohort, although T3 tumors were more common (16% vs 9%). MRI reports were available for 1,351 pts; all reports were centrally reviewed and classified as radiologic complete response (rCR; no residual tumor enhancement and normalization of lymph nodes, if applicable) or radiologic residual disease (rRD). Reports with indeterminate radiologic response were independently reviewed by two study radiologists blinded to pathology outcomes. Among pts with rCR who had pathologic residual disease (pRD), we also assessed the extent of residual disease by residual cancer burden (RCB) score. Among pts with rRD who had a pCR, we determined the proportion who only had residual ductal carcinoma in situ (DCIS). Results: See Table. Conclusions: After neoadjuvant therapy, preoperative MRI accurately predicted pCR in 86% of patients with HER2+/ER− disease and may help identify patients who can proceed directly to surgery. MRI is insufficient to predict pCR for pts with HER2+/ER+ disease, as only 55% with complete response on MRI had a pCR at surgery. A pre-surgery MRI that indicated residual disease was accurate in 80% of pts with HER2+/ER+ disease, but in only 45% of pts with HER2+/ER- cancer. Accurately predicting residual disease after THPx4 allows for consideration of additional therapy that may increase the chance of pCR. Combining imaging like MRI with other biomarkers, such as tissue-based markers or circulating tumor DNA, may improve prediction of pCR and facilitate personalization of treatment decisions in HER2+ breast cancer. All pts with pre-surgery MRI (n=1351)pCR rate overall in this cohort = 47% MRI result Pathology pCR pRD rCR 70.6% (341/483) 29.4%(142/483) 78% had RCB1 rRD 34%(295/868) 21% of 295 had DCIS only 66.01% (573/868) ER- (n=589)*Overall pCR rate in ER- subset = 68% MRI result Pathology pCR pRD rCR 86.1% (205/238) 13.9% (33/238) 85% had RCB1 rRD 55.3% (194/351) 19% of 194 had only DCIS 44.7% (157/351) ER+ (n= 762)*Overall pCR rate in ER+ subset = 31% MRI result Pathology pCR pRD rCR 55.5% (136/245) 44.5% (109/245) 76% had RCB1 rRD 19.5% (101/517) 26% of 101 had only DCIS 80.5% (416/517)

AbstractPurpose has often been proposed as a quantitative imaging biomarker for diagnosis, prognosis, and treatment response assessment for various tumors. None of the many software tools for quantification are standardized. The ISMRM Open Science Initiative for Perfusion Imaging–Dynamic Contrast‐Enhanced (OSIPI‐DCE) challenge was designed to benchmark methods to better help the efforts to standardize measurement.MethodsA framework was created to evaluate values produced by DCE‐MRI analysis pipelines to enable benchmarking. The perfusion MRI community was invited to apply their pipelines for quantification in glioblastoma from clinical and synthetic patients. Submissions were required to include the entrants' values, the applied software, and a standard operating procedure. These were evaluated using the proposed score defined with accuracy, repeatability, and reproducibility components.ResultsAcross the 10 received submissions, the score ranged from 28% to 78% with a 59% median. The accuracy, repeatability, and reproducibility scores ranged from 0.54 to 0.92, 0.64 to 0.86, and 0.65 to 1.00, respectively (0–1 = lowest–highest). Manual arterial input function selection markedly affected the reproducibility and showed greater variability in analysis than automated methods. Furthermore, provision of a detailed standard operating procedure was critical for higher reproducibility.ConclusionsThis study reports results from the OSIPI‐DCE challenge and highlights the high inter‐software variability within estimation, providing a framework for ongoing benchmarking against the scores presented. Through this challenge, the participating teams were ranked based on the performance of their software tools in the particular setting of this challenge. In a real‐world clinical setting, many of these tools may perform differently with different benchmarking methodology.