Intra- and inter-operator variability in MRI-based manual segmentation of HCC lesions and its impact on dosimetry

Abstract

Purpose: The aim was to quantify inter- and intra-observer variability in manually delineated hepatocellular carcinoma (HCC) lesion contours and the resulting impact on radioembolization (RE) dosimetry.

Methods: Ten patients with HCC lesions treated with Y-90 RE and imaged with post-therapy Y-90 PET/CT were selected for retrospective analysis. Three radiologists contoured 20 lesions manually on baseline multiphase contrast-enhanced MRIs, and two of the radiologists re-contoured at two additional sessions. Contours were transferred to co-registered PET/CT-based Y-90 dose maps. Volume-dependent recovery coefficients were applied for partial volume correction (PVC) when reporting mean absorbed dose. To understand how uncertainty varies with tumor size, we fit power models regressing relative uncertainty in volume and in mean absorbed dose on contour volume. Finally, we determined effects of segmentation uncertainty on tumor control probability (TCP), as calculated using logistic models developed in a previous RE study.

Results: The average lesion volume ranged from 1.8 to 194.5 mL, and the mean absorbed dose ranged from 23.4 to 1629.0 Gy. The mean inter-observer Dice coefficient for lesion contours was significantly less than the mean intra-observer Dice coefficient (0.79 vs. 0.85, p < 0.001). Uncertainty in segmented volume, as measured by the Coefficient of Variation (CV), ranged from 4.2 to 34.7% with an average of 17.2%. The CV in mean absorbed dose had an average value of 5.4% (range 1.2-13.1%) without PVC while it was 15.1% (range 1.5-55.2%) with PVC. Using the fitted models for uncertainty as a function of volume on our prior data, the mean change in TCP due to segmentation uncertainty alone was estimated as 16.2% (maximum 48.5%).

Conclusions: Though we find relatively high inter- and intra-observer reliability overall, uncertainty in tumor contouring propagates into non-negligible uncertainty in dose metrics and outcome prediction for individual cases that should be considered in dosimetry-guided treatment.

Keywords: Dosimetry; Observer studies; Radioembolization; Radionuclide therapy; Segmentation; Uncertainty analysis.

Fig. 1

Example HCC lesions as seen on dynamic post-contrast T1-weighted fat-saturated MRI images. a Large, well defined (code 46.1, corresponding to patient #46, lesion #1). b Small, well defined (39.3). c Large, poorly defined (49.1). d Small, poorly defined (55.4). Each image is displayed in the optimum visualization window and phase used for segmentation

Fig. 2

Radiologist-defined tumor contours on MRI corresponding to the four example lesions depicted in Fig. 1. Inserts show contours transferred to co-registered Y-90 PET/CT-based dose maps. Mean dose values are indicated before PVC. a Large, well defined (code 46.1). b Small, well defined (39.3). c Large, poorly defined (49.1). d Small, poorly defined (55.4). Contours drawn by the same radiologist are indicated in the same color (Pink = radiologist A, Yellow = radiologist B, Blue = radiologist C)

Fig. 3

Radiologist-defined RECIST measurements on two example lesions: Lesion codes 19.2 (top row) and 19.3 (bottom row). Radiologists were free to choose the MRI slice on which they indicated the diameter. Within each row, each of the three images depicts a different MRI slice. Diameters drawn by the same radiologist are indicated in the same color (Pink = radiologist A, Yellow = radiologist B, Blue = radiologist C). Lesion contours are included for context. Each image is displayed in the optimum visualization window and phase used for segmentation

Fig. 4

Volume uncertainty and mean absorbed dose uncertainty (with and without PVC) plotted as a function of volume

Fig. 5

Volume uncertainty propagated into tumor control probability (TCP), applied on the data and TCP curve fitted in the study by Dewaraja et al. [7]. a Procedure for computing Δ_TCP for an example patient. b Original TCP curve (black line) overlaid by Δ_TCP for 89 liver lesions