# Imaging-based assessment of response to olaparib in platinum-sensitive relapsed ovarian cancer patients

## TL;DR

This study explores how imaging can help assess treatment response in ovarian cancer patients receiving PARP inhibitors and immune checkpoint inhibitors.

## Contribution

The study introduces noninvasive imaging-based metrics to evaluate treatment response in ovarian cancer patients.

## Key findings

- Changes in volume at week 4 better predict long-term response than standard RECIST criteria.
- Responders and non-responders showed significant differences in the radiomic feature Energy.
- Anatomical network metrics like average number of edges and total volumetric burden help differentiate responders from non-responders.

## Abstract

High-grade serous carcinoma is a highly metastatic disease with a limited longterm disease control from systemic anti-cancer treatment, for which the radiological treatment response assessment metrics are imprecise. In this work, we developed noninvasive imagingbased measurements of spatial and longitudinal heterogeneity in a retrospective analysis of a phase 2 non-randomized study of germline BRCA1/BRCA2 mutated (gBRCAm) ovarian cancer patients treated with combination of PARP inhibitors (PARPi) and immune checkpoint inhibitors (ICIs).

Lesions identified in CT images at baseline, week 4 (after PARPi only) and week 12 (after 8 weeks of PARPi + ICIs) were manually segmented. Anatomical networks of the metastatic sites were constructed to represent patterns of disease distribution. Volume and first-order radiomic features were computed and compared to different assessments of treatment response.

The average number of edges per patient in the anatomical networks and total volumetric burden decreased with treatment were measured, differentiating between responders and nonresponders. Changes in volume at week 4 provided better indication of long-term response than the default RECIST assessment at the same time-point. Significant differences were also found between responders and non-responders in the first-order radiomic feature Energy.

In this feasibility study, we have demonstrated that noninvasive image-based analysis can identify quantitative imaging features associated with the response to the combination of PARPi and ICIs. These can be used to identify markers of response to ICIs from negative trials of a disease with limited response to ICIs.