JavaScript Access to DICOM Network and Objects in Web Browser

Drnasin, Ivan; Grgić, Mislav; Gogić, Goran

doi:10.1007/s10278-017-9956-7

Cited by 9 publications

(3 citation statements)

References 3 publications

(6 reference statements)

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“…The inclusion criteria were as follows: i) All patients were confirmed histologically with osteosarcoma; ii) patients were 9-35 years old; iii) patients underwent thin-slice CT examination, with a slice thickness of ≤3 mm; iv) the diameter range of all nodules was 2-150 mm; and v) there was no obvious cavity in the thin-slice CT images. The exclusion criteria were the following: i) Incomplete lung lobe scan; ii) serious artifacts in the image; iii) missing layers or faults in the image; and iv) the image did not conform to the DICOM 3.0 protocol (12). The images data of chest CT screening diagnosed with osteosarcoma was retrospectively collected from Hangzhou Third People's Hospital (Hangzhou, China) between March 2011 and February 2022.…”

Section: Methodsmentioning

confidence: 99%

Deep convolutional neural network based on CT images of pulmonary nodules in the lungs of adolescent and young adult patients with osteosarcoma

Xie

Shi

et al. 2023

Oncol Lett

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The aim of the present study was to explore the diagnostic value of a deep convolutional neural network (DCNN) model for the diagnosis of pulmonary nodules in adolescent and young adult patients with osteosarcoma. For the present study, 675 chest CT images were retrospectively collected from 109 patients with clinically confirmed osteosarcoma who underwent chest CT examination at Hangzhou Third People's Hospital (Hangzhou, China) from March 2011 to February 2022. CT images were then evaluated using the DCNN and manual models. Subsequently, pulmonary nodules of osteosarcoma were divided into calcified nodules, solid nodules, partially solid nodules and ground glass nodules using the DCNN model. Those patients with osteosarcoma who were diagnosed and treated were followed up to observe dynamic changes in the pulmonary nodules. A total of 3,087 nodules were detected, while 278 nodules were missed compared with those determined using the reference standard given by the consensus of three Experienced radiologists., which was analyzed by two diagnostic radiologists. In the manual model group, 2,442 nodules were detected, while 657 nodules were missed. The DCNN model showed significantly higher sensitivity and specificity compared with the manual model (sensitivity, 0.923 vs. 0.908; specificity, 0.552 vs. 0.351; P<0.05). In addition, the DCNN model yielded an area under the curve (AUC) value of 0.795 [95% confidence interval (CI), 0.743-0.846], outperforming that of the manual model (AUC, 0.687; 95% CI, 0.629-0.732; P<0.05). The film reading time of the DCNN model was also significantly shorter compared with that of the manual model [mean ± standard deviation (SD); 173.25±24.10 vs. 328.32±22.72 sec; P<0.05)]. The AUC of calcified nodules, solid nodules, partially solid nodules and ground glass nodules was calculated to be 0.766, 0.771, 0.761 and 0.796, respectively, using the DCNN model. Using this model, the majority of the pulmonary nodules were detected in patients with osteosarcoma at the initial diagnosis (69/109, 62.3%), and the majority of these were found with multiple pulmonary nodules instead of a single nodule (71/109, 65.1% vs. 38/109, 34.9%). These data suggest that, compared with the manual model, the DCNN model proved to be beneficial for the detection of pulmonary nodules in adolescent and young adult patients with osteosarcoma, which may reduce the time of artificial radiograph reading. In conclusion, the proposed DCNN model, developed using data from 675 chest CT images retrospectively collected from 109 patients with clinically confirmed osteosarcoma, may be used as an effective tool to evaluate pulmonary nodules in patients with osteosarcoma.

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Section: Methodsmentioning

confidence: 99%

Deep convolutional neural network based on CT images of pulmonary nodules in the lungs of adolescent and young adult patients with osteosarcoma

Xie

Shi

et al. 2023

Oncol Lett

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“…Recently, DICOM has added a protocol called DICOMweb built on top of the Hy-perText Transfer Protocol (HTTP) for using services via the web [17][18][19][20]. DICOMweb enables query, retrieval, storage, and worklist services.…”

Section: Table 2 Main Services Available On a Dicom Networkmentioning

confidence: 99%

Thirty Years of the DICOM Standard

Larobina

2023

Tomography

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Digital Imaging and Communications in Medicine (DICOM) is an international standard that defines a format for storing medical images and a protocol to enable and facilitate data communication among medical imaging systems. The DICOM standard has been instrumental in transforming the medical imaging world over the last three decades. Its adoption has been a significant experience for manufacturers, healthcare users, and research scientists. In this review, thirty years after introducing the standard, we discuss the innovation, advantages, and limitations of adopting the DICOM and its possible future directions.

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“…The QIDO-RS and WADO-RS services are simple Uniform Resource Locator requests and allow access to metadata in eXtensible Markup Language or JavaScript Object Notation formats and direct access to the compressed tile pixel data payload as ordinary JPEG or JPEG 2000 files. [ 21 22 23 ]…”

Section: T He C Onnectathonmentioning

confidence: 99%

Digital Imaging and Communications in Medicine Whole Slide Imaging Connectathon at Digital Pathology Association Pathology Visions 2017

Clunie¹,

Hosseinzadeh²,

Wintell

et al. 2018

Journal of Pathology Informatics

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As digital pathology systems for clinical diagnostic work applications become mainstream, interoperability between these systems from different vendors becomes critical. For the first time, multiple digital pathology vendors have publicly revealed the use of the digital imaging and communications in medicine (DICOM) standard file format and network protocol to communicate between separate whole slide acquisition, storage, and viewing components. Note the use of DICOM for clinical diagnostic applications is still to be validated in the United States. The successful demonstration shows that the DICOM standard is fundamentally sound, though many lessons were learned. These lessons will be incorporated as incremental improvements in the standard, provide more detailed profiles to constrain variation for specific use cases, and offer educational material for implementers. Future Connectathon events will expand the scope to include more devices and vendors, as well as more ambitious use cases including laboratory information system integration and annotation for image analysis, as well as more geographic diversity. Users should request DICOM features in all purchases and contracts. It is anticipated that the growth of DICOM-compliant manufacturers will likely also ease DICOM for pathology becoming a recognized standard and as such the regulatory pathway for digital pathology products.

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JavaScript Access to DICOM Network and Objects in Web Browser

Cited by 9 publications

References 3 publications

Deep convolutional neural network based on CT images of pulmonary nodules in the lungs of adolescent and young adult patients with osteosarcoma

Deep convolutional neural network based on CT images of pulmonary nodules in the lungs of adolescent and young adult patients with osteosarcoma

Thirty Years of the DICOM Standard

Digital Imaging and Communications in Medicine Whole Slide Imaging Connectathon at Digital Pathology Association Pathology Visions 2017

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