ARRS 2022 Abstracts

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1830. Correlation Between CTC Counts and Radiomic Metrics from Multiple Osseous Lesions in Metastatic Prostate Cancer
Authors * Denotes Presenting Author
  1. Bino Varghese; Keck School of Medicine of the University of Southern California
  2. Steven Cen; Keck School of Medicine of the University of Southern California
  3. Gareth Morrison; Keck School of Medicine of the University of Southern California
  4. Jonathan Buckley; Keck School of Medicine of the University of Southern California
  5. Redmond-Craig Anderson *; Keck School of Medicine of the University of Southern California
  6. Amir Goldkorn; Keck School of Medicine of the University of Southern California
  7. Vinay Duddalwar; Keck School of Medicine of the University of Southern California
Objective:
The purpose of this study is identify CT radiomic correlates for liquid biopsy metrics such as circulating tumor cells (CTC) counts in patients with metastatic prostate cancer. Together, CT radiomics and liquid biopsy could detect clonal heterogeneity and aid in improved patient management.

Materials and Methods:
Under IRB-approved informed consent, blood was collected from 22 patients with metastatic castrate-resistant prostate cancer (mCRPC). CTC counts were enumerated using the CellSearch platforms. Five osseous metastatic lesions in each patient were manually segmented from CT scans in the same patients using ITK-SNAP software by an experienced radiologist. Cancer imaging phenomics toolkit (CaPTk) was used for radiomics analysis. A panel of six metrics represented first-order statistical measures. Intensity and second-order statistical measures were calculated using grey level co-occurrence matrix (GLCM) for each segmented lesion per patient. Mixed model correlation was used to examine correlation between the weighted average of radiomic features from the five bone lesions per patient and their CTC counts. The Benjamini-Hochberg procedure was used to control false discovery rate from multiple comparisons.

Results:
The distribution of CTC counts was 102±198 (min: 0, max: 692). Among the 69 texture features extracted from the bone lesions, the weighted average of intensity-based inter-quartile-range and GLCM-based kurtosis, variance and standard-deviation extracted from the five bone lesions per patient showed a statistically significant correlation with CellSearch enumerated CTC counts ranged between 0.71 - 0.79 with adjusted p-value < 0.01). Radiomics and liquid biopsy have theoretically comparable advantages: they are both noninvasive, can be quantified and followed serially to evaluate disease progression. While their exact role in clinical practice is yet to be established, we conducted a preliminary integrative assessment of the two, within a small cohort. We show that the weighted average of select radiomic metrics extracted from multiple bone lesions (N=5) provides strong correlates for CTC counts.

Conclusion:
Post validation in larger cohorts, radiomic corelates of liquid biopsy parameters such as CTC counts may provide surrogates for assessment of the disease and prognostication. Using artificial intelligence techniques, the complex relationships between tumors molecular makeup and its imaging phenotype may provide further insight into a patient's prognosis.