Research Progress of Tumor Big Data Visualization

Round 1

Reviewer 1 Report

Some general comments:

• About the abstract. 

• First sentence should be rewritten. There are three ideas mixed and not well justified with technical elements. 
• Use quantitative elements to justify all your statements, otherwise the manuscript sounds as a collection of opinions, and this is not the intention. The paper should be raised at the level of a proper research contribution. 
• Same for all the sentences in the abstract and through all the manuscript. 
• The authors mention what they do, but as it is written it does not offer the level of details required for a summary. It is a very general overview, compatible with any review. You should use the abstract to highlight which technical elements you are reviewing and make emphasis on the main messages you would like to highlight after all the review. 
• The abstract must be rewritten completely, as it is now, it sounds as a template for the abstract of a review manuscript. Fit with your research. 
  
   

• Section 2: 

• Why big data associated only with internet facilities? Extend the discussion, consider other reasoning’s pointing as big data supported by the exponential grown of storage capacity, and more important as old as the mankind since we were always interested and developing methods/infrastructures to work with such high amount of data. 
  
   

• Section 3: 

• The relation between such biological process and bid data treatments are properly discussed, as well as inputs from other areas. However, two elements are missed: 
  
  3.1. Try to make some diagram/picture to visualize how data collection is organized in such context. 
  
  3.2. Make emphasis on contributions/abilities coming from other technical areas. Highlight the issue that this research is extremely interdisciplinary, and many different sources of efforts are needed. For instance, consider contributions from Artificial Intelligence as such that follows: 
  
   

• Maahi Amit Khemchandani, Shivajirao Manikrao Jadhav, B. R. Iyer (2022). "Brain Tumor Segmentation and Identification Using Particle Imperialist Deep Convolutional Neural Network in MRI Images", International Journal of Interactive Multimedia and Artificial Intelligence, vol. 7, issue Regular Issue, no. 7, pp. 38-47. https://doi.org/10.9781/ijimai.2022.10.006 
  
   
• Manuel Martín Merino, Alfonso José López Rivero, Vidal Alonso, Marcelo Vallejo, Antonio Ferreras (2022). "A Clustering Algorithm Based on an Ensemble of Dissimilarities: An Application in the Bioinformatics Domain", International Journal of Interactive Multimedia and Artificial Intelligence, vol. 7, issue Special Issue on New Trends in Disruptive Technologies, Tech Ethics and Artificial Intelligence, no. 6, pp. 6-13. https://doi.org/10.9781/ijimai.2022.09.007 
  
   
• Satheshkumar Kaliyugarasan, Arvid Lundervold, Alexander Selvikvåg Lundervold (2021). "Pulmonary Nodule Classification in Lung Cancer from 3D Thoracic CT Scans Using fastai and MONAI", International Journal of Interactive Multimedia and Artificial Intelligence, vol. 6, issue Special Issue on Current Trends in Intelligent Multimedia Processing Systems, no. 7, pp. 83-89. https://doi.org/10.9781/ijimai.2021.05.002 
  
   
• Loay Hassan, Adel Saleh, Mohamed Abdel-Nasser, Osama A. Omer, Domenec Puig (2021). "Promising Deep Semantic Nuclei Segmentation Models for Multi-Institutional Histopathology Images of Different Organs", International Journal of Interactive Multimedia and Artificial Intelligence, vol. 6, issue Regular Issue, no. 6, pp. 35-45. https://doi.org/10.9781/ijimai.2020.10.004 

 

• Review insets and figures edition. Some of them are not readable. 
• Also, about figures/pictures in general. Include a complete caption: number, title and make emphasis on why this picture/result is relevant to understand your contribution. 
  
   

• Section 4. 

• Sometimes very technical and leaving the general reader quite apart from a needed understanding. Pay attention to the issue that you are planning a “review”. Then, put the focus on clarity, be didactic and try to connect with other general fields. For instance, Fig. 4 saturates in a first view and do not leave a clear message. 
• Fig.´s 5-7 should be re-elaborated to gain in clarity.  
• Fig. 8. Complete axis and other elements already mentioned. 
• Fig. 9, add legends 
• The authors dilucidated the role of big data either on detection, evaluation, and attention. Such efforts should be clearly transmitted through the manuscript. In general, all the figures should be reviewed.  
• Fig. 14. Insets not readable. Soundless as it is now. 
• Why a circular coordinate system, any technical reason/experience beside this selection? 
  
   
  
  The summary should revisited main results in the area and once again be supported on measurable elements to sustain this technical work. 
  
               

Author Response

Dear Editor and Reviewer:

Thank you for your letter and for the reviewers’ comments concerning our manuscript entitled “Research progress of tumor big data visualization” (ID: 2178028). Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made correction which we hope meet with approval. Revised portion are marked in red in the paper. The main corrections in the paper and the responds to the reviewer’s comments are as flowing:

About the abstract.

First sentence should be rewritten. There are three ideas mixed and not well justified with technical elements.

Use quantitative elements to justify all your statements, otherwise the manuscript sounds as a collection of opinions, and this is not the intention. The paper should be raised at the level of a proper research contribution.

Same for all the sentences in the abstract and through all the manuscript.

The authors mention what they do, but as it is written it does not offer the level of details required for a summary. It is a very general overview, compatible with any review. You should use the abstract to highlight which technical elements you are reviewing and make emphasis on the main messages you would like to highlight after all the review.

The abstract must be rewritten completely, as it is now, it sounds as a template for the abstract of a review manuscript. Fit with your research.

Response: I have rewritten the abstract according to your suggestion, and hope to get your approval.

Section 2:

Why big data associated only with internet facilities? Extend the discussion, consider other reasoning’s pointing as big data supported by the exponential grown of storage capacity, and more important as old as the mankind since we were always interested and developing methods/infrastructures to work with such high amount of data.

Response: Thank you for expanding my thinking. I only mention the Internet facilities here because most of the data come from the Internet. Due to the limited number of words in the article, I hope to conduct further research about it in the future.

Section 3:

The relation between such biological process and bid data treatments are properly discussed, as well as inputs from other areas. However, two elements are missed:

3.1. Try to make some diagram/picture to visualize how data collection is organized in such context.

3.2. Make emphasis on contributions/abilities coming from other technical areas. Highlight the issue that this research is extremely interdisciplinary, and many different sources of efforts are needed. For instance, consider contributions from Artificial Intelligence as such that follows.

Response: I didn't mention data collection in 3.1, I’m sorry that I don't know how to add some charts. I have marked in red the parts you mentioned that need to be added in 3.2. I hope it is reasonable to add in this way.

Review insets and figures edition. Some of them are not readable.

Also, about figures/pictures in general. Include a complete caption: number, title and make emphasis on why this picture/result is relevant to understand your contribution.

Section 4.

Sometimes very technical and leaving the general reader quite apart from a needed understanding. Pay attention to the issue that you are planning a “review”. Then, put the focus on clarity, be didactic and try to connect with other general fields. For instance, Fig. 4 saturates in a first view and do not leave a clear message.

Fig.´s 5-7 should be re-elaborated to gain in clarity. 

Fig. 8. Complete axis and other elements already mentioned.

Fig. 9, add legends

The authors dilucidated the role of big data either on detection, evaluation, and attention. Such efforts should be clearly transmitted through the manuscript. In general, all the figures should be reviewed. 

Fig. 14. Insets not readable. Soundless as it is now.

Why a circular coordinate system, any technical reason/experience beside this selection?

Response: I added a note after the caption of each image, hoping to make the meaning of the image clearer. Because my framework belongs to the big data section, but the content of my article is mainly to provide oncology researchers with ideas of new data visualization methods. Therefore, in my revision, the introduction related to big data is relatively simple, and the introduction related to oncology is more in-depth. I hope this modification will make it easier for the reader to understand. Finally, the choice of the parallel coordinate system is mainly due to the consideration of the circular coordinate system, which is relatively rare, so here is a picture for readers to understand. For the selection of images, I generally choose a more typical image under each sub-type so that readers can understand the specific form of data visualization.

The summary should revisited main results in the area and once again be supported on measurable elements to sustain this technical work.

Response: I have added two tables in the final summary section as a summary of data visualization methods, hoping that it can meet your requirements.

Special thanks to you for your good comments.

Author Response File: Author Response.pdf

Reviewer 2 Report

Dear Authors,
Your paper seems good as a Review Paper for giving an understanding of tumor Data visualization, please find my note below:

1- Abstract, please revise to clarify the reason to do visualization on Big data, as the size could be reached a billion records,s and may your method could not able to provide a comprehensive view of it, also pay attention to that in the introduction part.

2. Figure 1 result could be affected when other source data is used in further research work,  however, let me know why we shall do visualization for your data, and if the subject got attention internationally either than China.

3. Some Data Visualization Algorithm could be used behind the Applications that
you have mentioned in Figure 3, Such as PCA, NMF, T-SNE, etc. please explain how to handle the missing data as most of your presented applications are licensed software, and mostly they have limits on that, how do you handle this issue?

4. Please give your contributions and modification on this subject, as many scholars would like to visualize their medical data, give the sources of this software and improve the result discussion part in each figure.

5. I could not able to find any conclusion result in your paper, what are the challenges, limitations, features, advantages, and drawbacks of using these software to represent Tumor data?

6. Improve the quality and resolutions of some figures e.g. figure 14.

7. Explain also how your paper differs from Reference no. 8, 11and 31, based on presented techniques. 

8. Most of the well-known visualization algorithms and software work based on Clustering, Classification, and Dimension reduction of data, give a description of your Tumor data and their features, to be clear for other readers to know the best software that is able to work enough on it.

All the best

Author Response

Dear Editor and Reviewer:

Thank you for your letter and for the reviewers’ comments concerning our manuscript entitled “Research progress of tumor big data visualization” (ID: 2178028). Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made correction which we hope meet with approval. Revised portion are marked in red in the paper. The main corrections in the paper and the responds to the reviewer’s comments are as flowing:

1- Abstract, please revise to clarify the reason to do visualization on Big data, as the size could be reached a billion records and may your method could not able to provide a comprehensive view of it, also pay attention to that in the introduction part.

Response: I've added the reasons for the visualization in the abstract and introduction, and I hope this change can meet your requirements.

2. Figure 1 result could be affected when other source data is used in further research work, however, let me know why we shall do visualization for your data, and if the subject got attention internationally either than China.

Response: It is really true as reviewer suggested that result could be affected. The actual number of articles is probably higher, but I would like to choose a more widely used site as a typical introduction. The reasons for this visualization are as follows: The visualization methods of tumor big data can well show the key information in a large amount of data and facilitate the human brain to receive information. Data Visualization has almost 35,000 articles in the last five years in the web of science. There are also about 3000 articles on big data visualization in the past 5 years. So it should be said that the direction of big data visualization is also concerned internationally. The reason why I pointed out that there are few articles on tumor big data visualization in this article is to show that there has not been more research in this subdivision field. Therefore, my article is not only a summary and review, but also hopes to provide some new ideas for oncology researchers.

3. Some Data Visualization Algorithm could be used behind the Applications that you have mentioned in Figure 3, Such as PCA, NMF, T-SNE, etc. please explain how to handle the missing data as most of your presented applications are licensed software, and mostly they have limits on that, how do you handle this issue?
4. Please give your contributions and modification on this subject, as many scholars would like to visualize their medical data, give the sources of this software and improve the result discussion part in each figure.
5. Most of the well-known visualization algorithms and software work based on Clustering, Classification, and Dimension reduction of data, give a description of your Tumor data and their features, to be clear for other readers to know the best software that is able to work enough on it.

Response: The visualization algorithm you mentioned is limited by the number of words in the article and the article mainly hopes to introduce readers to visualization methods. I hope I can have the opportunity to study the visualization algorithm carefully in the future, but please forgive me for not being able to explain it in this article. I have added a fifth section to the article to introduce the features and scope of the software available for data visualization. The software is easily accessible to the readers. For the visualization software mentioned in my previous articles, if readers are interested in one of the software that requires licensing, they can find the corresponding article in References.

5. I could not able to find any conclusion result in your paper, what are the challenges, limitations, features, advantages, and drawbacks of using these software to represent Tumor data?

Response: Thank you for your suggestions, but the main content of my article is the specific presentation form of the data visualization method. So I did not add the introduction of the software mentioned in the reference at the end, but added the introduction of the characteristics and application scenarios of the data visualization method as a summary and conclusion.

6. Improve the quality and resolutions of some figures e.g. figure 14.

Response: I added a note after the caption of each image, hoping to make the meaning of the image clearer.

7. Explain also how your paper differs from Reference no. 8, 11 and 31, based on presented techniques.

Response: We are writing in the same direction. However, the classification of data visualization methods in my article is more complete, the literature cited is more recent, and our specific application scenarios are different.

Special thanks to you for your good comments.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

I appreciate that the authors have attended the reviewers' comments satisfactorily. 

If paper finally accepted, two minor things should be attended:

1.Delete  [background] and [process] in the abstract.

2.Some references are not complete, for example refs 20-23 do not include the name of the journal, vol, no., pp.