Example of Cancer Imaging format
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Example of Cancer Imaging format Example of Cancer Imaging format Example of Cancer Imaging format Example of Cancer Imaging format Example of Cancer Imaging format Example of Cancer Imaging format Example of Cancer Imaging format Example of Cancer Imaging format Example of Cancer Imaging format Example of Cancer Imaging format Example of Cancer Imaging format Example of Cancer Imaging format Example of Cancer Imaging format Example of Cancer Imaging format Example of Cancer Imaging format Example of Cancer Imaging format Example of Cancer Imaging format Example of Cancer Imaging format
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Example of Cancer Imaging format Example of Cancer Imaging format Example of Cancer Imaging format Example of Cancer Imaging format Example of Cancer Imaging format Example of Cancer Imaging format Example of Cancer Imaging format Example of Cancer Imaging format Example of Cancer Imaging format Example of Cancer Imaging format Example of Cancer Imaging format Example of Cancer Imaging format Example of Cancer Imaging format Example of Cancer Imaging format Example of Cancer Imaging format Example of Cancer Imaging format Example of Cancer Imaging format Example of Cancer Imaging format
Sample paper formatted on SciSpace - SciSpace
This content is only for preview purposes. The original open access content can be found here.
open access Open Access

Cancer Imaging — Template for authors

Publisher: Springer
Categories Rank Trend in last 3 yrs
Radiological and Ultrasound Technology #20 of 51 down down by 8 ranks
Radiology, Nuclear Medicine and Imaging #111 of 288 down down by 51 ranks
Oncology #185 of 340 down down by 43 ranks
journal-quality-icon Journal quality:
Good
calendar-icon Last 4 years overview: 258 Published Papers | 907 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 08/07/2020
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Related Journals

open access Open Access

Springer

Quality:  
High
CiteRatio: 5.1
SJR: 0.6
SNIP: 1.254
open access Open Access

Springer

Quality:  
High
CiteRatio: 5.8
SJR: 1.147
SNIP: 0.995
open access Open Access
recommended Recommended

Springer

Quality:  
High
CiteRatio: 9.0
SJR: 2.558
SNIP: 2.194
open access Open Access

Springer

Quality:  
High
CiteRatio: 6.8
SJR: 1.055
SNIP: 1.846

Journal Performance & Insights

Impact Factor

CiteRatio

Determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

A measure of average citations received per peer-reviewed paper published in the journal.

2.193

30% from 2018

Impact factor for Cancer Imaging from 2016 - 2019
Year Value
2019 2.193
2018 3.153
2017 3.016
2016 2.404
graph view Graph view
table view Table view

3.5

3% from 2019

CiteRatio for Cancer Imaging from 2016 - 2020
Year Value
2020 3.5
2019 3.6
2018 4.3
2017 4.2
2016 3.8
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has decreased by 30% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has decreased by 3% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

1.017

8% from 2019

SJR for Cancer Imaging from 2016 - 2020
Year Value
2020 1.017
2019 0.939
2018 1.183
2017 1.012
2016 1.024
graph view Graph view
table view Table view

1.147

17% from 2019

SNIP for Cancer Imaging from 2016 - 2020
Year Value
2020 1.147
2019 1.382
2018 1.752
2017 1.321
2016 1.084
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has increased by 8% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has decreased by 17% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Cancer Imaging

Guideline source: View

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Springer

Cancer Imaging

Approved by publishing and review experts on SciSpace, this template is built as per for Cancer Imaging formatting guidelines as mentioned in Springer author instructions. The current version was created on and has been used by 364 authors to write and format their manuscripts to this journal.

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Last updated on
08 Jul 2020
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ISSN
1470-7330
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Open Access
Yes
i
Sherpa RoMEO Archiving Policy
White faq
i
Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Citation Type
Author Year
(Blonder et al, 1982)
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Bibliography Example
Beenakker CWJ (2006) Specular andreev reflection in graphene. Phys Rev Lett 97(6):067,007, URL 10.1103/PhysRevLett.97.067007

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.1102/1470-7330.2006.0021
Diffusion weighted magnetic resonance imaging and its application to cancer
13 Sep 2006 - Cancer Imaging

Abstract:

Diffusion-weighted magnetic resonance imaging (DW-MRI) provides image contrast through measurement of the diffusion properties of water within tissues. Application of diffusion sensitising gradients to the MR pulse sequence allows water molecular displacement over distances of 1–20 μm to be recognised. Diffusion can be predom... Diffusion-weighted magnetic resonance imaging (DW-MRI) provides image contrast through measurement of the diffusion properties of water within tissues. Application of diffusion sensitising gradients to the MR pulse sequence allows water molecular displacement over distances of 1–20 μm to be recognised. Diffusion can be predominantly unidirectional (anisotropic) or not (isotropic). Combining images obtained with different amounts of diffusion weighting provides an apparent diffusion coefficient (ADC) map. In cancer imaging DW-MRI has been used to distinguish brain tumours from peritumoural oedema. It is also increasingly exploited to differentiate benign and malignant lesions in liver, breast and prostate where increased cellularity of malignant lesions restricts water motion in a reduced extracellular space. It is proving valuable in monitoring treatment where changes due to cell swelling and apoptosis are measurable as changes in ADC at an earlier stage than subsequent conventional radiological response indicators. read more read less

Topics:

Effective diffusion coefficient (63%)63% related to the paper, Magnetic resonance imaging (53%)53% related to the paper, Whole body imaging (51%)51% related to the paper
317 Citations
open accessOpen access Journal Article DOI: 10.1102/1470-7330.2013.0015
Quantifying tumour heterogeneity with CT.
Balaji Ganeshan1, Kenneth A. Miles
26 Mar 2013 - Cancer Imaging

Abstract:

Heterogeneity is a key feature of malignancy associated with adverse tumour biology. Quantifying heterogeneity could provide a useful non-invasive imaging biomarker. Heterogeneity on computed tomography (CT) can be quantified using texture analysis which extracts spatial information from CT images (unenhanced, contrast-enhanc... Heterogeneity is a key feature of malignancy associated with adverse tumour biology. Quantifying heterogeneity could provide a useful non-invasive imaging biomarker. Heterogeneity on computed tomography (CT) can be quantified using texture analysis which extracts spatial information from CT images (unenhanced, contrast-enhanced and derived images such as CT perfusion) that may not be perceptible to the naked eye. The main components of texture analysis can be categorized into image transformation and quantification. Image transformation filters the conventional image into its basic components (spatial, frequency, etc.) to produce derived subimages. Texture quantification techniques include structural-, model- (fractal dimensions), statistical- and frequency-based methods. The underlying tumour biology that CT texture analysis may reflect includes (but is not limited to) tumour hypoxia and angiogenesis. Emerging studies show that CT texture analysis has the potential to be a useful adjunct in clinical oncologic imaging, providing important information about tumour characterization, prognosis and treatment prediction and response. read more read less

Topics:

Tumour heterogeneity (58%)58% related to the paper, Imaging biomarker (53%)53% related to the paper
311 Citations
open accessOpen access Journal Article DOI: 10.1102/1470-7330.2010.0021
Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage
Balaji Ganeshan1, Sandra Abaleke, Rupert Young, Chris Chatwin, Kenneth A. Miles
06 Jul 2010 - Cancer Imaging

Abstract:

The aim was to undertake an initial study of the relationship between texture features in computed tomography (CT) images of non-small cell lung cancer (NSCLC) and tumour glucose metabolism and stage. This retrospective pilot study comprised 17 patients with 18 pathologically confirmed NSCLC. Non-contrast-enhanced CT images o... The aim was to undertake an initial study of the relationship between texture features in computed tomography (CT) images of non-small cell lung cancer (NSCLC) and tumour glucose metabolism and stage. This retrospective pilot study comprised 17 patients with 18 pathologically confirmed NSCLC. Non-contrast-enhanced CT images of the primary pulmonary lesions underwent texture analysis in 2 stages as follows: (a) image filtration using Laplacian of Gaussian filter to differentially highlight fine to coarse textures, followed by (b) texture quantification using mean grey intensity (MGI), entropy (E) and uniformity (U) parameters. Texture parameters were compared with tumour fluorodeoxyglucose (FDG) uptake (standardised uptake value (SUV)) and stage as determined by the clinical report of the CT and FDG-positron emission tomography imaging. Tumour SUVs ranged between 2.8 and 10.4. The number of NSCLC with tumour stages I, II, III and IV were 4, 4, 4 and 6, respectively. Coarse texture features correlated with tumour SUV (E: r = 0.51, p = 0.03; U: r = -0.52, p = 0.03), whereas fine texture features correlated with tumour stage (MGI: rs = 0.71, p = 0.001; E: rs = 0.55, p = 0.02; U: rs = -0.49, p = 0.04). Fine texture predicted tumour stage with a kappa of 0.7, demonstrating 100% sensitivity and 87.5% specificity for detecting tumours above stage II ( p = 0.0001). This study provides initial evidence for a relationship between texture features in NSCLC on non-contrast-enhanced CT and tumour metabolism and stage. Texture analysis warrants further investigation as a potential method for obtaining prognostic information for patients with NSCLC undergoing CT. read more read less

Topics:

Positron emission tomography (50%)50% related to the paper
299 Citations
open accessOpen access Journal Article DOI: 10.1102/1470-7330.2013.9045
CT texture analysis using the filtration-histogram method: what do the measurements mean?
Kenneth A. Miles1, Balaji Ganeshan1, Michael P. Hayball
23 Sep 2013 - Cancer Imaging

Abstract:

Analysis of texture within tumours on computed tomography (CT) is emerging as a potentially useful tool in assessing prognosis and treatment response for patients with cancer. This article illustrates the image and histological features that correlate with CT texture parameters obtained from tumours using the filtration-histo... Analysis of texture within tumours on computed tomography (CT) is emerging as a potentially useful tool in assessing prognosis and treatment response for patients with cancer. This article illustrates the image and histological features that correlate with CT texture parameters obtained from tumours using the filtration-histogram approach, which comprises image filtration to highlight image features of a specified size followed by histogram analysis for quantification. Computer modelling can be used to generate texture parameters for a range of simple hypothetical images with specified image features. The model results are useful in explaining relationships between image features and texture parameters. The main image features that can be related to texture parameters are the number of objects highlighted by the filter, the brightness and/or contrast of highlighted objects relative to background attenuation, and the variability of brightness/contrast of highlighted objects. These relationships are also demonstrable by texture analysis of clinical CT images. The results of computer modelling may facilitate the interpretation of the reported associations between CT texture and histopathology in human tumours. The histogram parameters derived during the filtration-histogram method of CT texture analysis have specific relationships with a range of image features. Knowledge of these relationships can assist the understanding of results obtained from clinical CT texture analysis studies in oncology. read more read less

Topics:

Texture (geology) (50%)50% related to the paper
257 Citations
open accessOpen access Journal Article DOI: 10.1102/1470-7330.2008.0006
Ultrasound of malignant cervical lymph nodes.
Anil T. Ahuja1, Michael Ying2, Ho Sy1, Gregory E. Antonio1, Yolanda Y. P. Lee1, Ann D. King1, K. T. Wong1
25 Mar 2008 - Cancer Imaging

Abstract:

Malignant lymph nodes in the neck include metastases and lymphoma. Cervical nodal metastases are common in patients with head and neck cancers, and their assessment is important as it affects treatment planning and prognosis. Neck nodes are also a common site of lymphomatous involvement and an accurate diagnosis is essential ... Malignant lymph nodes in the neck include metastases and lymphoma. Cervical nodal metastases are common in patients with head and neck cancers, and their assessment is important as it affects treatment planning and prognosis. Neck nodes are also a common site of lymphomatous involvement and an accurate diagnosis is essential as its treatment differs from other causes of neck lymphadenopathy. On ultrasound, grey scale sonography helps to evaluate nodal morphology, whilst power Doppler sonography is used to assess the vascular pattern. Grey scale sonographic features that help to identify metastatic and lymphomatous lymph nodes include size, shape and internal architecture (loss of hilar architecture, presence of intranodal necrosis and calcification). Soft tissue oedema and nodal matting are additional grey scale features seen in tuberculous nodes or in nodes that have been previously irradiated. Power Doppler sonography evaluates the vascular pattern of nodes and helps to identify the malignant nodes. In addition, serial monitoring of nodal size and vascularity are useful features in the assessment of treatment response. read more read less

Topics:

Cervical lymph nodes (70%)70% related to the paper
242 Citations
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SciSpace is a very innovative solution to the formatting problem and existing providers, such as Mendeley or Word did not really evolve in recent years.

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With SciSpace, you do not need a word template for Cancer Imaging.

It automatically formats your research paper to Springer formatting guidelines and citation style.

You can download a submission ready research paper in pdf, LaTeX and docx formats.

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Frequently asked questions

1. Can I write Cancer Imaging in LaTeX?

Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the Cancer Imaging guidelines and auto format it.

2. Do you follow the Cancer Imaging guidelines?

Yes, the template is compliant with the Cancer Imaging guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in Cancer Imaging?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the Cancer Imaging citation style.

4. Can I use the Cancer Imaging templates for free?

Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for Cancer Imaging.

5. Can I use a manuscript in Cancer Imaging that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper Cancer Imaging that you can download at the end.

6. How long does it usually take you to format my papers in Cancer Imaging?

It only takes a matter of seconds to edit your manuscript. Besides that, our intuitive editor saves you from writing and formatting it in Cancer Imaging.

7. Where can I find the template for the Cancer Imaging?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Cancer Imaging's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

8. Can I reformat my paper to fit the Cancer Imaging's guidelines?

Of course! You can do this using our intuitive editor. It's very easy. If you need help, our support team is always ready to assist you.

9. Cancer Imaging an online tool or is there a desktop version?

SciSpace's Cancer Imaging is currently available as an online tool. We're developing a desktop version, too. You can request (or upvote) any features that you think would be helpful for you and other researchers in the "feature request" section of your account once you've signed up with us.

10. I cannot find my template in your gallery. Can you create it for me like Cancer Imaging?

Sure. You can request any template and we'll have it setup within a few days. You can find the request box in Journal Gallery on the right side bar under the heading, "Couldn't find the format you were looking for like Cancer Imaging?”

11. What is the output that I would get after using Cancer Imaging?

After writing your paper autoformatting in Cancer Imaging, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Cancer Imaging's impact factor high enough that I should try publishing my article there?

To be honest, the answer is no. The impact factor is one of the many elements that determine the quality of a journal. Few of these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for Cancer Imaging?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Cancer Imaging. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In Cancer Imaging?

The 5 most common citation types in order of usage for Cancer Imaging are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

15. How do I submit my article to the Cancer Imaging?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Cancer Imaging's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download Cancer Imaging in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in Cancer Imaging Endnote style according to Elsevier guidelines.

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I spent hours with MS word for reformatting. It was frustrating - plain and simple. With SciSpace, I can draft my manuscripts and once it is finished I can just submit. In case, I have to submit to another journal it is really just a button click instead of an afternoon of reformatting.

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