Biomarkers for site-specific response to neoadjuvant chemotherapy in epithelial ovarian cancer: relating MRI changes to tumour cell load and necrosis

Abstract

Background: Diffusion-weighted magnetic resonance imaging (DW-MRI) potentially interrogates site-specific response to neoadjuvant chemotherapy (NAC) in epithelial ovarian cancer (EOC).

Methods: Participants with newly diagnosed EOC due for platinum-based chemotherapy and interval debulking surgery were recruited prospectively in a multicentre study (n = 47 participants). Apparent diffusion coefficient (ADC) and solid tumour volume (up to 10 lesions per participant) were obtained from DW-MRI before and after NAC (including double-baseline for repeatability assessment in n = 19). Anatomically matched lesions were analysed after surgical excision (65 lesions obtained from 25 participants). A trained algorithm determined tumour cell fraction, percentage tumour and percentage necrosis on histology. Whole-lesion post-NAC ADC and pre/post-NAC ADC changes were compared with histological metrics (residual tumour/necrosis) for each tumour site (ovarian, omental, peritoneal, lymph node).

Results: Tumour volume reduced at all sites after NAC. ADC increased between pre- and post-NAC measurements. Post-NAC ADC correlated negatively with tumour cell fraction. Pre/post-NAC changes in ADC correlated positively with percentage necrosis. Significant correlations were driven by peritoneal lesions.

Conclusions: Following NAC in EOC, the ADC (measured using DW-MRI) increases differentially at disease sites despite similar tumour shrinkage, making its utility site-specific. After NAC, ADC correlates negatively with tumour cell fraction; change in ADC correlates positively with percentage necrosis.

Clinical trial registration: ClinicalTrials.gov NCT01505829.

Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1. Site-specific response of EOC to neoadjuvant chemotherapy.

Images in a 62-year-old woman with stage 3 high-grade serous epithelial ovarian cancer show differential response in primary and metastatic lesions: a axial T2-weighted magnetic resonance imaging (MRI) at baseline (pre-NAC), b corresponding axial high-b-value diffusion-weighted MRI (b = 900smm−2), c apparent diffusion coefficient (ADC) map and d–f matched sections of the same imaging series after three cycles of platinum-based chemotherapy (post-NAC preoperative). (Scalebar on the ADC map is in units of 10−5 mm2 s−1.) Delineation of regions of interest (ROIs) is shown in b, e for the left ovarian lesion (blue ROI) and peritoneal lesion (red ROI). The ovarian lesion remained measurable on MRI after three cycles of chemotherapy and was included in the imaging-pathology comparison, but the peritoneal lesion was nonmeasurable on MRI after three cycles of chemotherapy. MRI magnetic resonance imaging, NAC neoadjuvant chemotherapy, ADC apparent diffusion coefficient, ROI region of interest.

Fig. 2. Probability density functions for ADC estimates in all lesions at each anatomic site (ovarian, omental and peritoneal lesions and lymph nodes) at baseline (pre-NAC) and after three or four cycles of treatment (post-NAC preoperative).

Probability density functions have been normalised to aid comparison between pre- and post-treatment data. The same points and bandwidth were used for all lesions (bandwidths were determined for each lesion separately and the median bandwidth from all lesions estimated and applied to each lesion). ADC apparent diffusion coefficient, NAC neoadjuvant chemotherapy.

Fig. 3. Deep learning tumour and necrosis segmentation from pathologist annotations.

a Example of an H&E section from an omental lesion from a 63-year-old woman with stage 3 high-grade serous epithelial ovarian cancer; b shows tumour regions (red) and regions outlined as part of the necrosis (orange) delineated by a pathologist in the lower half of the section. The unannotated standard H&E stain is seen in the top half of the section. The same section after deep learning segmentation of the whole section (c), showing tumour (green) and necrosis (yellow) for comparison. The correlation between the deep learning segmentation and the ground-truth pathologist segmentation is high. The scalebar in a shows 100 microns. H&E haematoxylin and eosin.

Fig. 4. Site-specific correlations between imaging and histopathology metrics.

Comparison between a preoperative ADCmedian and tumour cell fraction, and b percentage change in ADCmedian and %necrosis, showing all lesions considered together and ovarian, omental, peritoneal lesions and lymph nodes considered separately. r = Spearman correlation coefficient, ADC apparent diffusion coefficient (where ADCmedian is defined as the median ADC of all fitted voxels in a lesion), tumour cell fraction = percentage of viable tumour cells to total cells in sample, %residual tumour = percentage area of whole section represented by viable tumour, %necrosis = percentage area of whole section represented by necrosis.