Deep learning based automatic internal gross target volume delineation from 4D-CT of hepatocellular carcinoma patients

BACKGROUND

The location and morphology of the liver are significantly affected by respiratory motion. Therefore, delineating the gross target volume (GTV) based on 4D medical images is more accurate than regular 3D-CT with contrast. However, the 4D method is also more time-consuming and laborious. This study proposes a deep learning (DL) framework based on 4D-CT that can achieve automatic delineation of internal GTV.

METHODS

The proposed network consists of two encoding paths, one for feature extraction of adjacent slices (spatial slices) in a specific 3D-CT sequence, and one for feature extraction of slices at the same location in three adjacent phase 3D-CT sequences (temporal slices), a feature fusion module based on an attention mechanism was proposed for fusing the temporal and spatial features. Twenty-six patients' 4D-CT, each consisting of 10 respiratory phases, were used as the dataset. The Hausdorff distance (HD95), Dice similarity coefficient (DSC), and volume difference (VD) between the manual and predicted tumor contour were computed to evaluate the model's segmentation accuracy.

RESULTS

The predicted GTVs and IGTVs were compared quantitatively and visually with the ground truth. For the test dataset, the proposed method achieved a mean DSC of 0.869 ± 0.089 and an HD95 of 5.14 ± 3.34 mm for all GTVs, with under-segmented GTVs on some CT slices being compensated by GTVs on other slices, resulting in better agreement between the predicted IGTVs and the ground truth, with a mean DSC of 0.882 ± 0.085 and an HD95 of 4.88 ± 2.84 mm. The best GTV results were generally observed at the end-inspiration stage.

CONCLUSIONS

Our proposed DL framework for tumor segmentation on 4D-CT datasets shows promise for fully automated delineation in the future. The promising results of this work provide impetus for its integration into the 4DCT treatment planning workflow to improve hepatocellular carcinoma radiotherapy.

Comparison of primary tumour volumes delineated on four-dimensional computed tomography maximum intensity projection and (18) F-fluorodeoxyglucose positron emission tomography computed tomography images of non-small cell lung cancer

INTRODUCTION

The study aims to compare the positional and volumetric differences of tumour volumes based on the maximum intensity projection (MIP) of four-dimensional CT (4DCT) and (18) F-fluorodexyglucose ((18) F-FDG) positron emission tomography CT (PET/CT) images for the primary tumour of non-small cell lung cancer (NSCLC).

METHODS

Ten patients with NSCLC underwent 4DCT and (18) F-FDG PET/CT scans of the thorax on the same day. Internal gross target volumes (IGTVs) of the primary tumours were contoured on the MIP images of 4DCT to generate IGTVMIP . Gross target volumes (GTVs) based on PET (GTVPET ) were determined with nine different threshold methods using the auto-contouring function. The differences in the volume, position, matching index (MI) and degree of inclusion (DI) of the GTVPET and IGTVMIP were investigated.

RESULTS

In volume terms, GTVPET 2.0 and GTVPET 20% approximated closely to IGTVMIP with mean volume ratio of 0.93 ± 0.45 and 1.06 ± 0.43, respectively. The best MI was between IGTVMIP and GTVPET 20% (0.45 ± 0.23). The best DI of IGTVMIP in GTVPET was IGTVMIP in GTVPET 20% (0.61 ± 0.26).

CONCLUSIONS

In 3D PET images, the GTVPET contoured by standardised uptake value (SUV) 2.0 or 20% of maximal SUV (SUVmax ) approximate closely to the IGTVMIP in target size, while the spatial mismatch is apparent between them. Therefore, neither of them could replace IGTVMIP in spatial position and form. The advent of 4D PET/CT may improve the accuracy of contouring the perimeter for moving targets.

Geometrical differences in target volumes based on 18F-fluorodeoxyglucose positron emission tomography/computed tomography and four-dimensional computed tomography maximum intensity projection images of primary thoracic esophageal cancer

The objective of the study was to compare geometrical differences of target volumes based on four-dimensional computed tomography (4DCT) maximum intensity projection (MIP) and 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) images of primary thoracic esophageal cancer for radiation treatment. Twenty-one patients with thoracic esophageal cancer sequentially underwent contrast-enhanced three-dimensional computed tomography (3DCT), 4DCT, and 18F-FDG PET/CT thoracic simulation scans during normal free breathing. The internal gross target volume defined as IGTVMIP was obtained by contouring on MIP images. The gross target volumes based on PET/CT images (GTVPET ) were determined with nine different standardized uptake value (SUV) thresholds and manual contouring: SUV≥2.0, 2.5, 3.0, 3.5 (SUVn); ≥20%, 25%, 30%, 35%, 40% of the maximum (percentages of SUVmax, SUVn%). The differences in volume ratio (VR), conformity index (CI), and degree of inclusion (DI) between IGTVMIP and GTVPET were investigated. The mean centroid distance between GTVPET and IGTVMIP ranged from 4.98 mm to 6.53 mm. The VR ranged from 0.37 to 1.34, being significantly (P<0.05) closest to 1 at SUV2.5 (0.94), SUV20% (1.07), or manual contouring (1.10). The mean CI ranged from 0.34 to 0.58, being significantly closest to 1 (P<0.05) at SUV2.0 (0.55), SUV2.5 (0.56), SUV20% (0.56), SUV25% (0.53), or manual contouring (0.58). The mean DI of GTVPET in IGTVMIP ranged from 0.61 to 0.91, and the mean DI of IGTVMIP in GTVPET ranged from 0.34 to 0.86. The SUV threshold setting of SUV2.5, SUV20% or manual contouring yields the best tumor VR and CI with internal-gross target volume contoured on MIP of 4DCT dataset, but 3DPET/CT and 4DCT MIP could not replace each other for motion encompassing target volume delineation for radiation treatment.