Core samples for radiomics features that are insensitive to tumor segmentation: method and pilot study using CT images of hepatocellular carcinoma

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.

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Globally Optimal Tumor Segmentation in PET-CT Images: A Graph-Based Co-segmentation Method

Lecture Notes in Computer Science - Information Processing in Medical Imaging ◽

10.1007/978-3-642-22092-0_21 ◽

2011 ◽

pp. 245-256 ◽

Cited By ~ 45

Author(s):

Dongfeng Han ◽

John Bayouth ◽

Qi Song ◽

Aakant Taurani ◽

Milan Sonka ◽

...

Keyword(s):

Ct Images ◽

Tumor Segmentation ◽

Segmentation Method ◽

Pet Ct ◽

Optimal Tumor

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

Diagnostics ◽

10.3390/diagnostics11010011 ◽

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.

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Registration-Based Organ Positioning and Joint Segmentation Method for Liver and Tumor Segmentation

BioMed Research International ◽

10.1155/2018/8536854 ◽

2018 ◽

Vol 2018 ◽

pp. 1-11

Author(s):

Huiyan Jiang ◽

Shaojie Li ◽

Siqi Li

Keyword(s):

Active Contour Model ◽

Automatic Segmentation ◽

Ct Images ◽

Initial Contour ◽

Tumor Segmentation ◽

Segmentation Method ◽

Anatomical Structures ◽

Low Contrast ◽

Medical Diagnoses ◽

Learning Machine

The automated segmentation of liver and tumor from CT images is of great importance in medical diagnoses and clinical treatment. However, accurate and automatic segmentation of liver and tumor is generally complicated due to the complex anatomical structures and low contrast. This paper proposes a registration-based organ positioning (ROP) and joint segmentation method for liver and tumor segmentation from CT images. First, a ROP method is developed to obtain liver’s bounding box accurately and efficiently. Second, a joint segmentation method based on fuzzy c-means (FCM) and extreme learning machine (ELM) is designed to perform coarse liver segmentation. Third, the coarse segmentation is regarded as the initial contour of active contour model (ACM) to refine liver boundary by considering the topological information. Finally, tumor segmentation is performed using another ELM. Experiments on two datasets demonstrate the performance advantages of our proposed method compared with other related works.

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

Biology ◽

10.3390/biology9110397 ◽

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.

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