scholarly journals Integration of Real-Time Image Fusion in the Robotic-Assisted Treatment of Hepatocellular Carcinoma

Biology ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 397
Author(s):  
Corina Radu ◽  
Petra Fisher ◽  
Delia Mitrea ◽  
Iosif Birlescu ◽  
Tiberiu Marita ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Image Fusion ◽  
Real Time ◽  
Detection System ◽  
Ct Images ◽  
Robotic System ◽  
Targeted Treatment ◽  
Tumor Segmentation ◽  
Tumor Visualization ◽  
Contrast Enhanced Ct

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide, with its mortality rate correlated with the tumor staging; i.e., early detection and treatment are important factors for the survival rate of patients. This paper presents the development of a novel visualization and detection system for HCC, which is a composing module of a robotic system for the targeted treatment of HCC. The system has two modules, one for the tumor visualization that uses image fusion (IF) between computerized tomography (CT) obtained preoperatively and real-time ultrasound (US), and the second module for HCC automatic detection from CT images. Convolutional neural networks (CNN) are used for the tumor segmentation which were trained using 152 contrast-enhanced CT images. Probabilistic maps are shown as well as 3D representation of HCC within the liver tissue. The development of the visualization and detection system represents a milestone in testing the feasibility of a novel robotic system in the targeted treatment of HCC. Further optimizations are planned for the tumor visualization and detection system with the aim of introducing more relevant functions and increase its accuracy.

CT-based radiomic analysis of hepatocellular carcinoma patients to predict key genomic information.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e15623-e15623 ◽  
Author(s):  
Derek L West ◽  
Aikaterini Kotrotsou ◽  
Andrew Scott Niekamp ◽  
Tagwa Idris ◽  
Dunia Giniebra Camejo ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Texture Features ◽  
Ct Images ◽  
The Cancer Genome Atlas ◽  
Tumor Segmentation ◽  
Full Potential ◽  
Imaging Features ◽  
Treatment Naïve

e15623 Background: The utilization of computed tomography (CT) has virtually replaced the need for tissue diagnosis in hepatocellular carcinoma (HCC). Imaging features (e.g. size, shape and vascularity) have been associated with patient survival. However, the full potential of CT in HCC diagnosis may not be reached, as high-throughput computing allows for extraction of quantitative features that are not part of radiologists’ lexicon. The purpose of this study was to investigate the ability of radiomic analysis to successfully identify specific doxorubicin chemoresistant genes on CT images of treatment-naïve hepatocellular carcinoma (HCC). Methods: We identified 27 treatment-naïve patients with a single HCC tumor from The Cancer Genome Atlas (TCGA) whom had gene expression profiles. Baseline CT images were obtained from The Cancer Imaging Archive (TCIA). 3D Slicer software was used for manual tumor segmentation and final segmented images were reviewed by a board-certified radiologist. Following tumor segmentation, texture analysis was performed on MATLAB environment. A total of 310 rotation invariant texture features, which measure tumor heterogeneity, were obtained (first-order histogram and grey level co-occurrence matrix). The mRMR method was used to select the most relevant radiomic features. ROC analysis and LOOCV were used to assess the performance of five specific genes known to confer doxorubicin chemoresistance (TP53, TOP2A, CTNNB1, CDKN2A and AKT1). Results: Radiomic analysis identified TP53 (AUC = 86.61%, Specificity = 92.31%, Sensitivity = 92.9%), TOP2A (AUC = 78.0%, Specificity = 69%, Sensitivity = 85.7%), CTNNB1 (AUC = 86.8%, Specificity = 92.3%, Sensitivity = 85.7%), CDKN2A (AUC = 76.9%, Specificity = 76.9%, Sensitivity = 78.6%) and AKT1 (AUC = 72.5%, Specificity = 69.2%, Sensitivity = 85.7%) in treatment-naïve HCC CT studies. Conclusions: The identification of specific genes that confer chemoresistance to doxorubicin can be reliably ascertained via the use of radiomic analysis. This study may help tailor future treatment paradigms via the ability to categorize HCC tumors on genetic level and identify tumors which may not have a favorable response to doxorubicin based therapies.

scholarly journals Combined Volumetric and Density Analyses of Contrast-Enhanced CT Imaging to Assess Drug Therapy Response in Gastroenteropancreatic Neuroendocrine Diffuse Liver Metastasis

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Yi Wang ◽  
Kun Huang ◽  
Jie Chen ◽  
Yanji Luo ◽  
Yu Zhang ◽  
...  
Keyword(s):  
Liver Metastases ◽  
Tumor Size ◽  
Therapy Response ◽  
Inverse Correlation ◽  
Ct Images ◽  
Ct Imaging ◽  
Tumor Segmentation ◽  
Gastroenteropancreatic Neuroendocrine Tumors ◽  
Axial Diameter ◽  
Contrast Enhanced Ct

Objective. We propose a computer-aided method to assess response to drug treatment, using CT imaging-based volumetric and density measures in patients with gastroenteropancreatic neuroendocrine tumors (GEP-NETs) and diffuse liver metastases. Methods. Twenty-five patients with GEP-NETs with diffuse liver metastases were enrolled. Pre- and posttreatment CT examinations were retrospectively analyzed. Total tumor volume (volume) and mean volumetric tumor density (density) were calculated based on tumor segmentation on CT images. The maximum axial diameter (tumor size) for each target tumor was measured on pre- and posttreatment CT images according to Response Evaluation Criteria In Solid Tumors (RECIST). Progression-free survival (PFS) for each patient was measured and recorded. Results. Correlation analysis showed inverse correlation between change of volume and density (Δ(V + D)), change of volume (ΔV), and change of tumor size (ΔS) with PFS (r = −0.653, P=0.001; r = −0.617, P=0.003; r = −0.548, P=0.01, respectively). There was no linear correlation between ΔD and PFS (r = −0.226, P=0.325). Conclusion. The changes of volume and density derived from CT images of all lesions showed a good correlation with PFS and may help assess treatment response.

Real-Time Image Fusion for Estimating the Therapeutic Effects of Radiofrequency Ablation on Hepatocellular Carcinoma

2017 ◽  
Vol 152 (5) ◽  
pp. S1173
Author(s):  
Nobuyuki Toshikuni ◽  
Yasuhiro Matsue ◽  
Kazuaki Ozaki ◽  
Nobuhiko Hayashi ◽  
Mutsumi Tsuchishima ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Radiofrequency Ablation ◽  
Image Fusion ◽  
Real Time ◽  
Therapeutic Effects ◽  
Real Time Image ◽  
Time Image

scholarly journals In-Series U-Net Network to 3D Tumor Image Reconstruction for Liver Hepatocellular Carcinoma Recognition

Diagnostics ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 11
Author(s):  
Wen-Fan Chen ◽  
Hsin-You Ou ◽  
Keng-Hao Liu ◽  
Zhi-Yun Li ◽  
Chien-Chang Liao ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Standard Of Care ◽  
Clinical Decision ◽  
Liver Lesion ◽  
Ct Images ◽  
Tumor Segmentation ◽  
Automatic Interpretation ◽  
In Series ◽  
High Expenditure ◽  
Liver Hepatocellular Carcinoma

Cancer is one of the common diseases. Quantitative biomarkers extracted from standard-of-care computed tomography (CT) scan can create a robust clinical decision tool for the diagnosis of hepatocellular carcinoma (HCC). According to the current clinical methods, the situation usually accounts for high expenditure of time and resources. To improve the current clinical diagnosis and therapeutic procedure, this paper proposes a deep learning-based approach, called Successive Encoder-Decoder (SED), to assist in the automatic interpretation of liver lesion/tumor segmentation through CT images. The SED framework consists of two different encoder-decoder networks connected in series. The first network aims to remove unwanted voxels and organs and to extract liver locations from CT images. The second network uses the results of the first network to further segment the lesions. For practical purpose, the predicted lesions on individual CTs were extracted and reconstructed on 3D images. The experiments conducted on 4300 CT images and LiTS dataset demonstrate that the liver segmentation and the tumor prediction achieved 0.92 and 0.75 in Dice score, respectively, by as-proposed SED method.

Automatic image fusion of real-time ultrasound with computed tomography images: a prospective comparison between two auto-registration methods

2017 ◽  
Vol 58 (11) ◽  
pp. 1349-1357 ◽  
Author(s):  
Dong Ik Cha ◽  
Min Woo Lee ◽  
Ah Yeong Kim ◽  
Tae Wook Kang ◽  
Young-Taek Oh ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Fusion ◽  
Real Time ◽  
Ct Images ◽  
Rank Test ◽  
Initial Image ◽  
Major Drawback ◽  
Registration Error ◽  
Computed Tomography Images ◽  
Prospective Comparison

Background A major drawback of conventional manual image fusion is that the process may be complex, especially for less-experienced operators. Recently, two automatic image fusion techniques called Positioning and Sweeping auto-registration have been developed. Purpose To compare the accuracy and required time for image fusion of real-time ultrasonography (US) and computed tomography (CT) images between Positioning and Sweeping auto-registration. Material and Methods Eighteen consecutive patients referred for planning US for radiofrequency ablation or biopsy for focal hepatic lesions were enrolled. Image fusion using both auto-registration methods was performed for each patient. Registration error, time required for image fusion, and number of point locks used were compared using the Wilcoxon signed rank test. Results Image fusion was successful in all patients. Positioning auto-registration was significantly faster than Sweeping auto-registration for both initial (median, 11 s [range, 3–16 s] vs. 32 s [range, 21–38 s]; P < 0.001] and complete (median, 34.0 s [range, 26–66 s] vs. 47.5 s [range, 32–90]; P = 0.001] image fusion. Registration error of Positioning auto-registration was significantly higher for initial image fusion (median, 38.8 mm [range, 16.0–84.6 mm] vs. 18.2 mm [6.7–73.4 mm]; P = 0.029), but not for complete image fusion (median, 4.75 mm [range, 1.7–9.9 mm] vs. 5.8 mm [range, 2.0–13.0 mm]; P = 0.338]. Number of point locks required to refine the initially fused images was significantly higher with Positioning auto-registration (median, 2 [range, 2–3] vs. 1 [range, 1–2]; P = 0.012]. Conclusion Positioning auto-registration offers faster image fusion between real-time US and pre-procedural CT images than Sweeping auto-registration. The final registration error is similar between the two methods.

scholarly journals Full-Automatic Image Fusion of Real-Time Ultrasound and MR/CT Images for Radiofrequency Ablation of Hepatic Malignancies

2017 ◽  
Vol 43 ◽  
pp. S162
Author(s):  
Moon Hyung Choi ◽  
Joon Il Choi ◽  
Young Joon Lee ◽  
Sung Eun Rha ◽  
Jae Young Byun
Keyword(s):  
Radiofrequency Ablation ◽  
Image Fusion ◽  
Real Time ◽  
Ct Images ◽  
Hepatic Malignancies
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