Radiomic measurements of tumor-associated vasculature morphology and function on pretreatment dynamic MRI identifies responders to neoadjuvant chemotherapy

Background: Angiogenesis is crucial to a tumor9s growth and an important factor in therapeutic outcome. Although quantitative analysis of tumors on dynamic contrast enhanced (DCE) MRI can provide indirect characterization of a tumor9s vascularization, direct computational analysis of the tumor-associated vessel network remains a promising, but under-explored potential marker of therapeutic response. For instance, surrounding vasculature with a convoluted 3-dimensional shape and poor blood flow may indicate a more aggressive tumor and poorly facilitate delivery of therapeutic agents. In this work, we present a computational approach for the prediction of neoadjuvant chemotherapy response using quantitative imaging features describing the morphology and function of tumor associated vasculature on pretreatment MRI. Methods: 243 patients who received DCE-MRI scans prior to neo-adjuvant chemotherapy (NAC) at institution A [n=83], B [n=76], or one of nine other institutions as part of the ISPY1 Trial [n=84] were divided randomly into training (n=123) and testing (n=120) sets. 148 patients were HER2- and received neoadjuvant AC-T, while the 95 HER2+ patients were treated with TCHP (ISPY predates anti-HER2 therapy and HER2+ ISPY patients were excluded). 79 patients achieved pathological complete response [pCR, ypT0/is] following NAC. MRI exams were collected with a 1.5 or 3 Tesla scanner in the axial or sagittal plane. A baseline scan and 2-5 scans after injection of a gadolinium-based contrast agent with a median temporal resolution of 2.5 minutes were acquired. A portion of the tumor was manually delineated, then semi-automatically expanded to 3D. Vasculature was isolated from subtraction images with a specialized filtering approach to detect vessel-shaped objects. Features describing the 3D shape and architecture of the tumor-associated vessel network (e.g. curvature, torsion, and local orientation) and functional semi-quantitative pharmacokinetic (PK) measurements of temporal contrast enhancement changes (e.g. signal enhancement ratio, time to peak enhancement, and rates of uptake and washout) were calculated. Performance was assessed by area under the receiver operating characteristic curve (AUC), as well as the accuracy, sensitivity, and specificity at the operating point corresponding to the Youden Index. The most discriminating features were determined based on frequency of selection by the random forest classifier. Results: Within the training set, PK parameters of the vessels (AUC=.66) outperformed relative to the PK of tumor (AUC=.63) and the PK of peritumoral regions (AUC=.64); however, a combination of the three yielded best performance (AUC=.75). Vessel shape features alone achieved AUC=.67 in the training set. When multi-region PK features and tumor shape features were combined and applied to the 120-patient independent testing set, the random forest classifier achieved an AUC of 0.70 and identified 81% of patients who would achieve pCR. Non-pCR was best characterized by increased vessel curvature and PK parameters indicating poor perfusion, such as greater time to peak enhancement, slower uptake rate, and quicker washout. Conclusions: Our findings suggest that properties of the tumor-associated vessel network, such as its shape and enhancement profile, might provide value in identifying patients who will respond to NAC before administration of treatment. Citation Format: Nathaniel Braman, Prateek Prasanna, Kaustav Bera, Mehdi Alilou, Manasa Vulchi, Maryam Etesami, Paulette Turk, Jame Abraham, Donna Plecha, Anant Madabhushi. Radiomic measurements of tumor-associated vasculature morphology and function on pretreatment dynamic MRI identifies responders to neoadjuvant chemotherapy [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 P1-10-06.