Dataset Information

Therapeutic response assessment using 3D ultrasound for hepatic metastasis from colorectal cancer: Application of a personalized, 3D-printed tumor model using CT images.

ABSTRACT:

Background & aims

To evaluate accuracy and reliability of three-dimensional ultrasound (3D US) for response evaluation of hepatic metastasis from colorectal cancer (CRC) using a personalized 3D-printed tumor model.

Methods

Twenty patients with liver metastasis from CRC who underwent baseline and after chemotherapy CT, were retrospectively included. Personalized 3D-printed tumor models using CT were fabricated. Two radiologists measured volume of each 3D printing model using 3D US. With CT as a reference, we compared difference between CT and US tumor volume. The response evaluation was based on Response Evaluation Criteria in Solid Tumors (RECIST) criteria.

Results

3D US tumor volume showed no significant difference from CT volume (7.18 ± 5.44 mL, 8.31 ± 6.32 mL vs 7.42 ± 5.76 mL in CT, p>0.05). 3D US provided a high correlation coefficient with CT (r = 0.953, r = 0.97) as well as a high inter-observer intraclass correlation (0.978; 0.958-0.988). Regarding response, 3D US was in agreement with CT in 17 and 18 out of 20 patients for observer 1 and 2 with excellent agreement (? = 0.961).

Conclusions

3D US tumor volume using a personalized 3D-printed model is an accurate and reliable method for the response evaluation in comparison with CT tumor volume.

SUBMITTER: Choi YR

PROVIDER: S-EPMC5552302 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Therapeutic response assessment using 3D ultrasound for hepatic metastasis from colorectal cancer: Application of a personalized, 3D-printed tumor model using CT images.

Choi Ye Ra YR Kim Jung Hoon JH Park Sang Joon SJ Hur Bo Yun BY Han Joon Koo JK

PloS one 20170810 8

<h4>Background & aims</h4>To evaluate accuracy and reliability of three-dimensional ultrasound (3D US) for response evaluation of hepatic metastasis from colorectal cancer (CRC) using a personalized 3D-printed tumor model.<h4>Methods</h4>Twenty patients with liver metastasis from CRC who underwent baseline and after chemotherapy CT, were retrospectively included. Personalized 3D-printed tumor models using CT were fabricated. Two radiologists measured volume of each 3D printing model using 3D US. ...[more]

PMID: 28797089

Similar Datasets

Project description:Objective:To accurately drill the Kirschner wire with the help of the 3D-printed personalized guide and to evaluate the feasibility of the 3D technology as well as the outcome of the surgery. Methods:Patients' DICM data of ankle via CT examinations were introduced into the MIMICS software to design the personalized guides. Two 2mm Kirschner wires were drilled with the help of the guides; the C-arm fluoroscopy was used to confirm the position of the wires before applying the cannulated screws. The patients who underwent ankle arthrodesis were divided into two groups. The experimental group adopted the 3D-printed personalized guides, while the control group received traditional method, i.e., drilling the Kirschner wires according to the surgeon's previous experience. The times of completing drilling the Kirschner wires to correct position were compared between the two groups. Regular follow-ups were conducted to statistically analyze the differences in the ankle fusion time and AOFAS scores between the two groups. Results:3D-printed personalized guides were successfully prepared. A total of 29 patients were enrolled, 15 in the experimental group and 14 in the control group. It took 2.2 ± 0.8 minutes to drill the Kirschner wires to correct position in the experimental group and 4.5 ± 1.6 minutes in the control group (p=0.001). No obvious complications occurred in the two groups during and after surgery. Postoperative radiographs confirmed bony fusion in all cases. There were no significant differences in the fusion time (p=0.82) and AOFAS scores at 1 year postoperatively between the two groups (p=0.55). Conclusions:The application of 3D-printed personalized guide in assisting the accurate drilling of Kirschner wire in ankle arthrodesis can shorten the operation time and reduce the intraoperative radiation. This technique does not affect the surgical outcome. Trial Registration Number:This study is registered on www.clinicaltrials.gov with NCT03626935.

Project description:ObjectivesTo develop a convolutional neural network system to jointly segment and classify a hepatic lesion selected by user clicks in ultrasound images.MethodsIn total, 4309 anonymized ultrasound images of 3873 patients with hepatic cyst (n = 1214), hemangioma (n = 1220), metastasis (n = 1001), or hepatocellular carcinoma (HCC) (n = 874) were collected and annotated. The images were divided into 3909 training and 400 test images. Our network is composed of one shared encoder and two inference branches used for segmentation and classification and takes the concatenation of an input image and two Euclidean distance maps of foreground and background clicks provided by a user as input. The performance of hepatic lesion segmentation was evaluated based on the Jaccard index (JI), and the performance of classification was based on accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC).ResultsWe achieved performance improvements by jointly conducting segmentation and classification. In the segmentation only system, the mean JI was 68.5%. In the classification only system, the accuracy of classifying four types of hepatic lesions was 79.8%. The mean JI and classification accuracy were 68.5% and 82.2%, respectively, for the proposed joint system. The optimal sensitivity and specificity and the AUROC of classifying benign and malignant hepatic lesions of the joint system were 95.0%, 86.0%, and 0.970, respectively. The respective sensitivity, specificity, and the AUROC for classifying four hepatic lesions of the joint system were 86.7%, 89.7%, and 0.947.ConclusionsThe proposed joint system exhibited fair performance compared to segmentation only and classification only systems.Key points• The joint segmentation and classification system using deep learning accurately segmented and classified hepatic lesions selected by user clicks in US examination. • The joint segmentation and classification system for hepatic lesions in US images exhibited higher performance than segmentation only and classification only systems. • The joint segmentation and classification system could assist radiologists with minimal experience in US imaging by characterizing hepatic lesions.

Project description:ObjectiveThe objective of this study was to evaluate the robustness and reproducibility of computed tomography-based texture analysis (CTTA) metrics extracted from CT images of a customized texture phantom built for assessing the association of texture metrics to three-dimensional (3D) printed progressively increasing textural heterogeneity.Materials and methodsA custom-built 3D-printed texture phantom comprising of six texture patterns was used to evaluate the robustness and reproducibility of a radiomics panel under a variety of routine abdominal imaging protocols. The phantom was scanned on four CT scanners (Philips, Canon, GE, and Siemens) to assess reproducibility. The robustness assessment was conducted by imaging the texture phantom across different CT imaging parameters such as slice thickness, field of view (FOV), tube voltage, and tube current for each scanner. The texture panel comprised of 387 features belonging to 15 subgroups of texture extraction methods (e.g., Gray-level Co-occurrence Matrix: GLCM). Twelve unique image settings were tested on all the four scanners (e.g., FOV125). Interclass correlation two-way mixed with absolute agreement (ICC3) was used to assess the robustness and reproducibility of radiomic features. Linear regression was used to test the association between change in radiomic features and increased texture heterogeneity. Results were summarized in heat maps.ResultsA total of 5612 (23.2%) of 24 090 features showed excellent robustness and reproducibility (ICC ≥ 0.9). Intensity, GLCM 3D, and gray-level run length matrix (GLRLM) 3D features showed best performance. Among imaging variables, changes in slice thickness affected all metrics more intensely compared to other imaging variables in reducing the ICC3. From the analysis of linear trend effect of the CTTA metrics, the top three metrics with high linear correlations across all scanners and scanning settings were from the GLRLM 2D/3D and discrete cosine transform (DCT) texture family.ConclusionThe choice of scanner and imaging protocols affect texture metrics. Furthermore, not all CTTA metrics have a linear association with linearly varying texture patterns.

Dataset Information

Therapeutic response assessment using 3D ultrasound for hepatic metastasis from colorectal cancer: Application of a personalized, 3D-printed tumor model using CT images.

Background & aims

Methods

Results

Conclusions

Publications

Therapeutic response assessment using 3D ultrasound for hepatic metastasis from colorectal cancer: Application of a personalized, 3D-printed tumor model using CT images.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets