Next Article in Journal
Serum Asprosin Concentrations in Children with Prader–Willi Syndrome: Correlations with Metabolic Parameters
Next Article in Special Issue
Machine Learning Based on Diffusion Kurtosis Imaging Histogram Parameters for Glioma Grading
Previous Article in Journal
Prediction of Poststroke Depression Based on the Outcomes of Machine Learning Algorithms
Previous Article in Special Issue
Distinct Functional Cortico-Striato-Thalamo-Cerebellar Networks in Genetic Generalized and Focal Epilepsies with Generalized Tonic-Clonic Seizures
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JCM
Volume 11
Issue 8
10.3390/jcm11082267
Review Report
jcm-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
Article
Peer-Review Record

Classification of Chemotherapy-Related Subjective Cognitive Complaints in Breast Cancer Using Brain Functional Connectivity and Activity: A Machine Learning Analysis

J. Clin. Med. 2022, 11(8), 2267; https://doi.org/10.3390/jcm11082267
by Lei Wang 1,2, Yanyan Zhu 1,2, Lin Wu 1,2, Ying Zhuang 3,*, Jinsheng Zeng 4 and Fuqing Zhou 1,2,*
Reviewer 1:
Thomas Lindner
Reviewer 2: Anonymous
J. Clin. Med. 2022, 11(8), 2267; https://doi.org/10.3390/jcm11082267
Submission received: 16 February 2022 / Revised: 4 April 2022 / Accepted: 14 April 2022 / Published: 18 April 2022
(This article belongs to the Special Issue Computational Neuroradiology and AI in Neuroradiology)

Round 1

Reviewer 1 Report

The authors present a manuscript on machine learning analyzing brain functionality in breast cancer students undergoing chemotherapy.

Generally, the manuscript is well written, the message is clear. The methods are sound and the conclusion warranted by the data. The number of patients included is on the lower edge when considering machine learning approaches.

My major concerns are:

  • I am missing the duration and dose as well as type of chemotherapy used. Assumingly, the different tumor stages as well as the age, weight, etc. of the patients gives the need of using different chemotherapy regimes and dosages. The authors did not include this information.
  • Again with chemotherapy, it is not clear if the pateitns udnerwent chemo while the scanning took place or if they already finished it and if yes, for how long

Minor points:

Introduction: Are there any studies conducted that the brain recovers after chemotherapy? e.g. a normalization after 10 years or similar?

Materials and Methods: How many breast cancer patients did you have overall to end up with 46 (i.e. how many percent did you remove?)

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 2 Report

This article is shown the classification of resting-state functional MRI features for chemotherapy-related subjective cognitive complaints in breast cancer or breast cancer or healthy control.  This is an exciting and well-written paper. I have the following minor comments:

1) Were the rs-fMRI features between BC and HC groups the same?  It is needed to describe whether are different features between BC and HC in the Result section. 

2) Is it correlated between different rs-fMRI features (SCC vs. BC) and FACT-Cog scores?

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Back to TopTop