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Subpopulations of Similarly-Responding Lesions in Metastatic Prostate Cancer

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C Lin

C Lin*, S Harmon , T Perk, R Jeraj , University of Wisconsin - Madison, Madison, WI


TH-E-BRF-8 Thursday 1:00PM - 2:50PM Room: Ballroom F

In patients with multiple lesions, resistance to cancer treatments and subsequent disease recurrence may be due to heterogeneity of response across lesions. This study aims to identify subpopulations of similarly-responding metastatic prostate cancer lesions in bone using quantitative PET metrics.

Seven metastatic prostate cancer patients treated with AR-directed therapy received pre-treatment and mid-treatment [F-18]NaF PET/CT scans. Images were registered using an articulated CT registration algorithm and transformations were applied to PET segmentations. Mid-treatment response was calculated on PET-based texture features. Hierarchical agglomerative clustering was used to form groups of similarly-responding lesions, with the number of natural clusters (K) determined by the inconsistency coefficient. Lesion clustering was performed within each patient, and for the pooled population. The cophenetic coefficient (C) quantified how well the data was clustered. The Jaccard Index (JI) assessed similarity of cluster assignments from patient clustering and from population clustering.

188 lesions in seven patients were identified for analysis (between 6 to 53 lesions per patient). Lesion response was defined as percent change relative to pre-treatment for 23 uncorrelated PET-based feature identifiers. . High response heterogeneity was found across all lesions (i.e. range ΔSUVmax =-95.98% to 775.00%). For intra-patient clustering, K ranged from 1-20. Population-based clustering resulted in 75 clusters, of 1-6 lesions each. Intra-patient clustering resulted in higher quality clusters than population clustering (mean C=0.95, range=0.89 to 1.00). For all patients, cluster assignments from population clustering showed good agreement to intra-patient clustering (mean JI=0.87, range=0.68 to 1.00).

Subpopulations of similarly-responding lesions were identified in patients with multiple metastatic lesions. Good agreement was found between population- and patient-level clustering, indicating that PET-based response features may be used to quantify disease heterogeneity in advanced metastatic disease. This method for categorizing lesion response could help in identifying lesions with similar phenotypic- or genetic-based resistances to therapy.

Funding Support, Disclosures, and Conflict of Interest: Research Supported by the Prostate Cancer Foundation.

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