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.

scholarly journals The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 challenge

2021 ◽  
Vol 67 ◽  
pp. 101821
Author(s):  
Nicholas Heller ◽  
Fabian Isensee ◽  
Klaus H. Maier-Hein ◽  
Xiaoshuai Hou ◽  
Chunmei Xie ◽  
...  
Keyword(s):  
State Of The Art ◽  
Ct Imaging ◽  
The State ◽  
Tumor Segmentation ◽  
Kidney Tumor ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Imaging Results ◽  
Enhanced Ct

scholarly journals Noninvasive KRAS mutation estimation in colorectal cancer using a deep learning method based on CT imaging

2020 ◽  
Author(s):  
kan He ◽  
Xiaoming Liu ◽  
Mingyang Li ◽  
Xueyan Li ◽  
Hualin Yang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Deep Learning ◽  
Kras Mutation ◽  
Ct Images ◽  
Ct Imaging ◽  
Kras Mutations ◽  
Training Cohort ◽  
Portal Venous Phase ◽  
Contrast Enhanced Ct ◽  
Pre Treatment

Abstract ABSTRACT Background: The detection of Kirsten rat sarcoma viral oncogene homolog ( KRAS )gene mutations in colorectal cancer (CRC) is key to the optimal design of individualized therapeutic strategies. The noninvasive prediction of the KRAS status in CRC is challenging. Deep learning (DL) in medical imaging has shown its high performance in diagnosis, classification, and prediction in recent years. In this paper, we investigated predictive performance by using a DL method with a residual neural network ( ResNet ) to estimate the KRAS mutation status in CRC patients based on pre-treatment contrast-enhanced CT imaging. Methods: We have collected a dataset consisting of 157 patients with pathology-confirmed CRC who were divided into a training cohort (n = 117) and a testing cohort (n = 40). We developed an ResNet model that used portal venous phase CT images to estimate KRAS mutations in the axial, coronal, and sagittal directions of the training cohort and evaluated the model in the testing cohort. Several groups of expended region of interest （ROI）patches were generated for the ResNet model，to explore whether tissues around the tumor can contribute to cancer assessment. We also explored a radiomics model with the random forest classifier (RFC) to predict KRAS mutations and compared it with the DL model. Results: The ResNet model in the axial direction achieved the higher area under the curve (AUC) value (0.90) in the testing cohort and peaked at 0.93 with an input of “ROI and 20-pixel” surrounding area. AUC of radiomics model in testing cohorts were 0.818. In comparison, the ResNet model showed better predictive ability . Conclusions: Our experiments reveal that the computerized assessment of the pre-treatment CT images of CRC patients using a DL model has the potential to precisely predict KRAS mutations. This new model has the potential to assist in noninvasive KRAS mutation estimation. Keywords: Colorectal Neoplasm, Mutation, Deep Learning

scholarly journals Computed Tomography Imaging Findings of Pulmonary Chondroma

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Hexiang Wang ◽  
Pei Nie ◽  
Cheng Dong ◽  
Feng Hou ◽  
Peng Zhang ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Left Lung ◽  
Diagnostic Technique ◽  
Lesion Size ◽  
Ct Images ◽  
Ct Imaging ◽  
Imaging Findings ◽  
Contrast Enhanced Ct ◽  
Pulmonary Chondroma ◽  
The Right

Purpose. To characterize the computed tomography (CT) imaging findings in patients with pulmonary chondroma. Methods. We examined CT imaging findings of eight patients with histopathologically verified pulmonary chondroma. We assessed the location, size, shape, margins, amount of calcification, calcification pattern, and attenuation on precontrast and enhancement CT. Results. All patients exhibited solitary, mildly lobulated pulmonary masses, which were located in the right lung in four cases and the left lung in four cases. The mean lesion size was 3.7 cm (range 0.9–10.7 cm). All eight tumours had a well-defined margin. On plain CT images, seven of the cases (87.5%) showed a mass with varying degrees of calcification, which included strip-like punctate (n=5) and ring (n=2) patterns. One patient with a large lesion (10.7 cm) showed chest wall adhesion. On contrast-enhanced CT images, all lesions demonstrated slight inhomogeneous enhancement ≤14 HU. Conclusion. CT is the reference standard diagnostic technique for locating pulmonary chondroma. In most cases, CT findings show some characteristics that are important in the diagnosis, surgical planning, and follow-up of the tumour.

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.

scholarly journals Noninvasive KRAS mutation estimation in colorectal cancer using a deep learning method based on routine CT imaging

2020 ◽  
Author(s):  
kan He ◽  
Xiaoming Liu ◽  
Mingyang Li ◽  
Xueyan Li ◽  
Hualin Yang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Deep Learning ◽  
Kras Mutation ◽  
Validation Cohort ◽  
Ct Images ◽  
Ct Imaging ◽  
Kras Mutations ◽  
Training Cohort ◽  
Contrast Enhanced Ct ◽  
Pre Treatment

Abstract Background: The detection of KRAS gene mutations in colorectal cancer (CRC) is key to the optimal design of individualized therapeutic strategies. The noninvasive prediction of the KRAS status in CRC is challenging. Deep learning (DL) in medical imaging has shown its high performance in diagnosis, classification, and prediction in recent years. In this paper, we investigated predictive performance by using a DL method with a residual neural network ( ResNet ) to estimate the KRAS mutation status in CRC patients based on routine pre-treatment contrast-enhanced CT imaging. Methods: We have collected a dataset consisting of 157 patients with pathology-confirmed CRC who were randomly divided into a training cohort (n = 117) and a validation cohort (n = 40). We developed an ResNet model that used portal venous phase CT images to estimate KRAS mutations in the axial, coronal, and sagittal directions of the training cohort and validated the model in the validation cohort. Several groups of expended ROI patches were generated for the ResNet model, to explore whether tissues around the tumor can contribute to cancer assessment. We also explored a radiomics model with the random forest classifier (RFC) to predict KRAS mutations and compared it with the DL model. Results: The ResNet model in the axial direction achieved the higher area under the curve (AUC) value (0.90) in the validation cohort and peaked at 0.93 with an input of “ROI and 20-pixel” surrounding area. In the training cohort, the AUC was 0.945 (sensitivity: 0.75; specificity: 0.94), and in the validation cohort, the AUC was0.818 (sensitivity: 0.70; specificity: 0.85). In comparison, the ResNet model showed better predictive ability . Conclusions: Our experiments reveal that the computerized assessment of the pre-treatment CT images of CRC patients using a DL model has the potential to precisely predict KRAS mutations. This new model has the potential to assist in noninvasive KRAS mutation estimation. Keywords: Colorectal Neoplasm, Mutation, Deep Learning

scholarly journals Multi-factorial considerations for intra-thoracic lymph node evaluations of healthy cats on computed tomographic images

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Ninlawan Thammasiri ◽  
Chutimon Thanaboonnipat ◽  
Nan Choisunirachon ◽  
Damri Darawiroj
Keyword(s):  
Lymph Node ◽  
Lymph Nodes ◽  
Ct Images ◽  
Slice Thickness ◽  
Maximum Width ◽  
Post Contrast ◽  
Computed Tomographic ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct

Abstract Background It is difficult to examine mild to moderate feline intra-thoracic lymphadenopathy via and thoracic radiography. Despite previous information from computed tomographic (CT) images of intra-thoracic lymph nodes, some factors from animals and CT setting were less elucidated. Therefore, this study aimed to investigate the effect of internal factors from animals and external factors from the CT procedure on the feasibility to detect the intra-thoracic lymph nodes. Twenty-four, client-owned, clinically healthy cats were categorized into three groups according to age. They underwent pre- and post-contrast enhanced CT for whole thorax followed by inter-group evaluation and comparison of sternal, cranial mediastinal, and tracheobronchial lymph nodes. Results Post contrast-enhanced CT appearances revealed that intra-thoracic lymph nodes of kittens were invisible, whereas the sternal, cranial mediastinal, and tracheobronchial nodes of cats aged over 7 months old were detected (6/24, 9/24 and 7/24, respectively). Maximum width of these lymph nodes were 3.93 ± 0.74 mm, 4.02 ± 0.65 mm, and 3.51 ± 0.62 mm, respectively. By age, lymph node sizes of these cats were not significantly different. Transverse lymph node width of males was larger than that of females (P = 0.0425). Besides, the detection score of lymph nodes was affected by slice thickness (P < 0.01) and lymph node width (P = 0.0049). Furthermore, an irregular, soft tissue structure, possibly the thymus, was detected in all juvenile cats and three mature cats. Conclusions Despite additional information on intra-thoracic lymph nodes in CT images, which can be used to investigate lymphatic-related abnormalities, age, sex, and slice thickness of CT images must be also considered.

scholarly journals Integrating Machine/Deep Learning Methods and Filtering Techniques for Reliable Mineral Phase Segmentation of 3D X-ray Computed Tomography Images

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4595
Author(s):  
Parisa Asadi ◽  
Lauren E. Beckingham
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Random Forest ◽  
Ct Images ◽  
Ct Imaging ◽  
Learning Method ◽  
Learning Methods ◽  
X Ray ◽  
Machine Learning Methods ◽  
Filtering Techniques

X-ray CT imaging provides a 3D view of a sample and is a powerful tool for investigating the internal features of porous rock. Reliable phase segmentation in these images is highly necessary but, like any other digital rock imaging technique, is time-consuming, labor-intensive, and subjective. Combining 3D X-ray CT imaging with machine learning methods that can simultaneously consider several extracted features in addition to color attenuation, is a promising and powerful method for reliable phase segmentation. Machine learning-based phase segmentation of X-ray CT images enables faster data collection and interpretation than traditional methods. This study investigates the performance of several filtering techniques with three machine learning methods and a deep learning method to assess the potential for reliable feature extraction and pixel-level phase segmentation of X-ray CT images. Features were first extracted from images using well-known filters and from the second convolutional layer of the pre-trained VGG16 architecture. Then, K-means clustering, Random Forest, and Feed Forward Artificial Neural Network methods, as well as the modified U-Net model, were applied to the extracted input features. The models’ performances were then compared and contrasted to determine the influence of the machine learning method and input features on reliable phase segmentation. The results showed considering more dimensionality has promising results and all classification algorithms result in high accuracy ranging from 0.87 to 0.94. Feature-based Random Forest demonstrated the best performance among the machine learning models, with an accuracy of 0.88 for Mancos and 0.94 for Marcellus. The U-Net model with the linear combination of focal and dice loss also performed well with an accuracy of 0.91 and 0.93 for Mancos and Marcellus, respectively. In general, considering more features provided promising and reliable segmentation results that are valuable for analyzing the composition of dense samples, such as shales, which are significant unconventional reservoirs in oil recovery.

scholarly journals Value of contrast-enhanced CT texture analysis in predicting IDH mutation status of intrahepatic cholangiocarcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yong Zhu ◽  
Yingfan Mao ◽  
Jun Chen ◽  
Yudong Qiu ◽  
Yue Guan ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Ct Images ◽  
Textural Features ◽  
Mutation Status ◽  
Portal Venous Phase ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Venous Phase ◽  
Ct Texture Analysis

AbstractTo explore the value of contrast-enhanced CT texture analysis in predicting isocitrate dehydrogenase (IDH) mutation status of intrahepatic cholangiocarcinomas (ICCs). Institutional review board approved this study. Contrast-enhanced CT images of 138 ICC patients (21 with IDH mutation and 117 without IDH mutation) were retrospectively reviewed. Texture analysis was performed for each lesion and compared between ICCs with and without IDH mutation. All textural features in each phase and combinations of textural features (p < 0.05) by Mann–Whitney U tests were separately used to train multiple support vector machine (SVM) classifiers. The classification generalizability and performance were evaluated using a tenfold cross-validation scheme. Among plain, arterial phase (AP), portal venous phase (VP), equilibrium phase (EP) and Sig classifiers, VP classifier showed the highest accuracy of 0.863 (sensitivity, 0.727; specificity, 0.885), with a mean area under the receiver operating characteristic curve of 0.813 in predicting IDH mutation in validation cohort. Texture features of CT images in portal venous phase could predict IDH mutation status of ICCs with SVM classifier preoperatively.

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