Detection of mediastinal and hilar lymph nodes by 16-row MDCT: Is contrast material needed?

Takahashi, Masashi; Nitta, Norihisa; Takazakura, Ryutaro; Nagatani, Yukihiro; Ushio, Noritoshi; Murata, Kiyoshi

doi:10.1016/j.ejrad.2007.05.028

Cited by 14 publications

(11 citation statements)

References 12 publications

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“…If lymph node size is equivocal, contrast enhancement pattern may be helpful in differentiating between benign and aggressive disease. Though two reports in human literature specifically about tracheobronchial lymph nodes describe increased sensitivity to lymph node detection with contrast administration, 26,29 only a single report mentioned inhomogeneous contrast‐enhancement pattern, without indicating its significance 30 . Rim enhancement pattern has previously been significantly associated with metastasis in cervical lymph nodes 15–19 .…”

Section: Discussionmentioning

confidence: 99%

Computed Tomography Characteristics of Canine Tracheobronchial Lymph Node Metastasis

Ballegeer

Adams

Dubielzig

et al. 2010

Veterinary Radiology &Amp; Ultrasound

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Tracheobronchial lymph node evaluation is critical for accurate staging of canine thoracic neoplasia and is more accurately achieved with computed tomography (CT) than radiography. Thoracic CT scans of 18 canine patients with known tracheobronchial lymph node histopathology and 10 clinically normal dogs were compared to establish if enlargement or contrast enhancement pattern correlated with metastatic status. Absolute lymph node size and three anatomically normalized lymph node ratios were significantly correlated with metastasis or severe granulomatous lymphadenitis (P < 0.0003). Transverse maximum lymph node diameter of 12 mm or lymph node to thoracic body ratio of 1.05 are proposed cutoffs, above which metastatic involvement is very likely; however, only minimal accuracy was gained with normalized ratios. Lymph node contrast enhancement pattern was also significantly correlated to disease. A heterogenous and/or ring pattern was related to metastatic disease (P = 0.03). Recommended protocol for CT examination of the tracheobronchial lymph nodes is 1-1.5 mm slices and intervals, intravenous contrast, and control of respiratory motion.

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Section: Discussionmentioning

confidence: 99%

Computed Tomography Characteristics of Canine Tracheobronchial Lymph Node Metastasis

Ballegeer

Adams

Dubielzig

et al. 2010

Veterinary Radiology &Amp; Ultrasound

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“…Intravenous iodine contrast-enhanced computed tomography (CT) is usually used in radiotherapy to improve the contrast between tumors and normal tissues, allowing oncologists to accurately delineate the target region and normal tissues (1)(2)(3). A high-density contrast medium containing iodine is intravenously injected into the patient before scanning, followed by CT scanning to obtain enhanced CT images.…”

Section: Introductionmentioning

confidence: 99%

Generation of Virtual Non-Contrast CT From Intravenous Enhanced CT in Radiotherapy Using Convolutional Neural Networks

Gao

Xie

et al. 2020

Front. Oncol.

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Objective: To generate virtual non-contrast (VNC) computed tomography (CT) from intravenous enhanced CT through convolutional neural networks (CNN) and compare calculated dose among enhanced CT, VNC, and real non-contrast scanning. Method: 50 patients who accepted non-contrast and enhanced CT scanning before and after intravenous contrast agent injections were selected, and two sets of CT images were registered. A total of 40 and 10 groups were used as training and test datasets, respectively. The U-Net architecture was applied to learn the relationship between the enhanced and non-contrast CT. VNC images were generated in the test through the trained U-Net. The CT values of non-contrast, enhanced and VNC CT images were compared. The radiotherapy treatment plans for esophageal cancer were designed, and dose calculation was performed. Dose distributions in the three image sets were compared. Results: The mean absolute error of CT values between enhanced and non-contrast CT reached 32.3 ± 2.6 HU, and that between VNC and non-contrast CT totaled 6.7 ± 1.3 HU. The average CT values in enhanced CT of great vessels, heart, lungs, liver, and spinal cord were all significantly higher than those of non-contrast CT (p < 0.05), with the differences reaching 97, 83, 42, 40, and 10 HU, respectively. The average CT values of the organs in VNC CT showed no significant differences from those in non-contrast CT. The relative dose differences of the enhanced and non-contrast CT were −1.2, −1.3, −2.1, and −1.5% in the comparison of mean doses of planned target volume, heart, great vessels, and lungs, respectively. The mean dose calculated by VNC CT showed no significant difference from that by non-contrast CT. The average γ passing rate (2%, 2 mm) of VNC CT image was significantly higher than that of enhanced CT image (0.996 vs. 0.973, p < 0.05). Liugang et al. Non-contrast CT Generated From Enhanced Conclusion: Designing a treatment plan based on enhanced CT will enlarge the dose calculation uncertainty in radiotherapy. This paper proposed the generation of VNC CT images from enhanced CT images based on U-Net architecture. The dose calculated through VNC CT images was identical with that obtained through real non-contrast CT.

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“…Evenly distributing labels corresponding to contrast enhanced and non-contrast data, we selected only 5,10,15,20,25,30,35, and finally 39 label images for atlas generation.…”

Section: Dependency Of Atlas Generation On Number Of Label Imagesmentioning

confidence: 99%

“…However, it should be noted that even though the general guidelines specify lymph nodes to be pathological if their short axis is greater or equal than one centimeter [6], a differentiation of malignant from benign mediastinal lymph nodes by size alone may not be reliable [7,8]. Furthermore, the benefit of contrast administration during CT has been challenged for lymph node detection [9], in particular for mediastinal lymph nodes [10].…”

Section: Introductionmentioning

confidence: 99%

Mediastinal atlas creation from 3-D chest computed tomography images: Application to automated detection and station mapping of lymph nodes

Feuerstein

Glocker

Kitasaka

et al. 2012

Medical Image Analysis

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One important aspect of lung cancer staging is the assessment of mediastinal lymph nodes in 3-D chest computed tomography (CT) images. In the current clinical routine this is done manually by analyzing the 3-D CT image slice by slice to find nodes, evaluate them quantitatively, and assign labels to them for describing the clinical and pathologic extent of metastases. In this paper we present a method to automate the process of lymph node detection and labeling by creation of a mediastinal average image and a novel lymph node atlas containing probability maps for mediastinal, aortic, and N1 nodes. Utilizing a fast deformable registration approach to match the atlas with CT images of new patients, our method can maintain an acceptable runtime. In comparison to previously published methods for mediastinal lymph node detection and labeling it also shows a good sensitivity and positive predictive value.

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Detection of mediastinal and hilar lymph nodes by 16-row MDCT: Is contrast material needed?

Cited by 14 publications

References 12 publications

Computed Tomography Characteristics of Canine Tracheobronchial Lymph Node Metastasis

Computed Tomography Characteristics of Canine Tracheobronchial Lymph Node Metastasis

Generation of Virtual Non-Contrast CT From Intravenous Enhanced CT in Radiotherapy Using Convolutional Neural Networks

Mediastinal atlas creation from 3-D chest computed tomography images: Application to automated detection and station mapping of lymph nodes

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