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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. [ABSTRACT FROM AUTHOR]

Background: Contouring of internal gross target volume (iGTV) is an essential part of treatment planning in radiotherapy to mitigate the impact of intrafractional target motion. However, it is usually time-consuming and easily subjected to intra-observer and inter-observer variability.So far,few studies have been explored to directly predict iGTV by deep learning technique, because the iGTV contains not only the gross target volume (GTV) but also the motion information of the GTV. Purpose: This work was an exploratory study to present a deep learning-based framework to segment iGTV rapidly and accurately in 4D CT images for lung cancers. Methods: Five models, including 3D UNet, mmUNet with point-wise add merging approach (mmUNet-add), mmUNet with concatenate fusion strategy (mmUNet-cat), gruUNet with point-wise add fusion approach (gruUNet-add), and gruUNet with concatenate method (gruUNet-cat), were adopted for iGTV segmentation. All the models originated from the 3D UNet network, with multichannel multi-path and convolutional gated recurrent unit (GRU) added in the mmUNet and gruUNet networks, respectively. Seventy patients with lung cancers were collected and 55 cases were randomly selected as the training set, and 15 cases as the testing set. In addition, the segmentation results of the five models were compared with the ground truths qualitatively and quantitatively. Results: In terms of Dice Similarity Coefficient (DSC), the proposed four networks (mmUNet-add, mmUNet-cat, gruUNet-add, and gruUNet-cat) increased the DSC score of 3D UNet from 0.6945 to 0.7342, 0.7253, 0.7405, and 0.7365, respectively. However, the differences were not statistically significant (p > 0.05). After a simple post-processing to remove the small isolated connected regions, the mean 95th percentile Hausdorff distances (HD_95s) of the 3D UNet, mmUNet-add, mmUNet-cat, gruUNet-add, and gruUNet-cat networks were 19.70, 15.75, 15.84, 15.61, and 15.83 mm, respectively, corresponding to 25.35, 25.96, 25.11, 28.23, and 24.47 mm before the post-processing. With regard to runtime, significant elapsed time growths (about 70s and 230s) were observed both in the mmUNet and gruUNet architectures due to the increasing parameters. But the mmUNet structure showed less growth. Conclusion: Our study demonstrated the ability of the deep learning technique to predict iGTVs directly.With the introduction of multi-channel multi-path and convolutional GRU, the segmentation accuracy was improved under certain conditions with a reduced segmentation efficiency and a further research topic when the 3D UNet network would lead to poor performance is elicited. Less efficiency degradation was observed in the mmUNet structure. Besides, the element-wise add fusing strategy was favorable to increase DSC, whereas HD_95 benefited from the concentrate merging approach. Nevertheless, the segmentation accuracy by deep learning still remains to be improved. [ABSTRACT FROM AUTHOR]

In this paper, we study the reliability of a novel deep learning framework for internal gross target volume (IGTV) delineation from 4-D computed tomography (4DCT), which is applied to patients with lung cancer treated by stereotactic body radiation therapy (SBRT). Seventy seven patients who underwent SBRT followed by 4DCT scans were incorporated in this retrospective study. The IGTV_DL was delineated using a novel deep machine learning algorithm with a linear exhaustive optimal combination framework. For the purpose of comparison, three other IGTVs based on common methods was also delineated. We compared the relative volume difference (RVI), matching index (MI), and encompassment index (EI) for the above IGTVs. Then, multiple parameter regression analysis was performed to assess the tumor volume and motion range as clinical influencing factors in the MI variation. The results demonstrated that the deep learning algorithm with linear exhaustive optimal combination framework has a higher probability of achieving optimal MI compared with other currently widely used methods. For patients after simple breathing training by keeping the respiratory frequency in 10 breath per minute (BPM), the four phase combinations of 0%, 30%, 50% and 90% can be considered as a potential solution for an optimal combination to synthesize IGTV in all respiration amplitudes.

Background: Contouring of internal gross target volume (iGTV) is an essential part of treatment planning in radiotherapy to mitigate the impact of intra-fractional target motion. However, it is usually time-consuming and easily subjected to intra-observer and inter-observer variability. So far, few studies have been explored to directly predict iGTV by deep learning technique, because the iGTV contains not only the gross target volume (GTV) but also the motion information of the GTV., Purpose: This work was an exploratory study to present a deep learning-based framework to segment iGTV rapidly and accurately in 4D CT images for lung cancers., Methods: Five models, including 3D UNet, mmUNet with point-wise add merging approach (mmUNet-add), mmUNet with concatenate fusion strategy (mmUNet-cat), gruUNet with point-wise add fusion approach (gruUNet-add), and gruUNet with concatenate method (gruUNet-cat), were adopted for iGTV segmentation. All the models originated from the 3D UNet network, with multi-channel multi-path and convolutional gated recurrent unit (GRU) added in the mmUNet and gruUNet networks, respectively. Seventy patients with lung cancers were collected and 55 cases were randomly selected as the training set, and 15 cases as the testing set. In addition, the segmentation results of the five models were compared with the ground truths qualitatively and quantitatively., Results: In terms of Dice Similarity Coefficient (DSC), the proposed four networks (mmUNet-add, mmUNet-cat, gruUNet-add, and gruUNet-cat) increased the DSC score of 3D UNet from 0.6945 to 0.7342, 0.7253, 0.7405, and 0.7365, respectively. However, the differences were not statistically significant (p > 0.05). After a simple post-processing to remove the small isolated connected regions, the mean 95th percentile Hausdorff distances (HD_95s) of the 3D UNet, mmUNet-add, mmUNet-cat, gruUNet-add, and gruUNet-cat networks were 19.70, 15.75, 15.84, 15.61, and 15.83 mm, respectively, corresponding to 25.35, 25.96, 25.11, 28.23, and 24.47 mm before the post-processing. With regard to runtime, significant elapsed time growths (about 70s and 230s) were observed both in the mmUNet and gruUNet architectures due to the increasing parameters. But the mmUNet structure showed less growth., Conclusion: Our study demonstrated the ability of the deep learning technique to predict iGTVs directly. With the introduction of multi-channel multi-path and convolutional GRU, the segmentation accuracy was improved under certain conditions with a reduced segmentation efficiency and a further research topic when the 3D UNet network would lead to poor performance is elicited. Less efficiency degradation was observed in the mmUNet structure. Besides, the element-wise add fusing strategy was favorable to increase DSC, whereas HD_95 benefited from the concentrate merging approach. Nevertheless, the segmentation accuracy by deep learning still remains to be improved., (© 2022 American Association of Physicists in Medicine.)

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. [ABSTRACT FROM AUTHOR]

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 18 F-fluorodeoxyglucose positron emission tomography/computed tomography (18 F-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 18 F-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 ( SUV n); ≥20%, 25%, 30%, 35%, 40% of the maximum (percentages of SUVmax, SUV n%). 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. [ABSTRACT FROM AUTHOR]

Background: To explore the volume and localization of the internal gross target volume defined using the seroma and/or surgical clips based on the four-dimensional computed tomography (4DCT) during free-breathing. Methods: Fifteen breast cancer patients after breast-conserving surgery (BCS) were recruited for EB-PBI. On the ten sets CT images, the gross target volume formed by the clips, the seroma, both the clips and seroma delineated by one radiation oncologist and defined as GTVc, GTVs and GTVc + s, respectively. The ten GTVc, GTVs and GTVc + s on the ten sets CT images produced the IGTVc, IGTVs, IGTVc + s, respectively. The IGTV volume and the distance between the center of IGTVc, IGTVs, IGTVc + s were all recorded. Conformity index (CI), degree of inclusion (DI) were calculated for IGTV/IGTV, respectively. Results: The volume of IGTVc + s were significantly larger than the IGTVc and IGTVs (p < 0.05). There was significant difference between the DIs of IGTVc vs IGTVc + s, the DIs of IGTVs vs IGTVc + s. There was significant difference among the CIs of IGTV/IGTV. The DIs and CIs of IGTV/IGTV were negatively correlated with their centroid distance (r < 0, p < 0.05). Conclusions: There were volume difference and spatial mismatch between the IGTVs delineated based on the surgical clips and seroma. The IGTV defined as the seroma and surgical clips provided the best overall representation of the ‘true’ moving GTV. [ABSTRACT FROM AUTHOR]

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., (© 2014 International Society for Diseases of the Esophagus.)