Prediction of femoral osteoporosis using machine-learning analysis with radiomics features and abdomen-pelvic CT: A retrospective single center preliminary study

Hyun Kyung Lim; Hong Il Ha; Sun-Young Park; Junhee Han

doi:10.1371/journal.pone.0247330

Peer Review History

Original SubmissionOctober 21, 2020
19 Jan 2021 Decision Letter - Alfredo Vellido, Editor PONE-D-20-33039 Prediction of femoral osteoporosis using machine-learning analysis with radiomics features and abdomen-pelvic CT: A retrospective single center preliminary study PLOS ONE Dear Dr. Ha, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. We suggest you pay special attention to address the experimental design concerns of reviewer #1. Please submit your revised manuscript by Mar 05 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. Please include the following items when submitting your revised manuscript: A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'. An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'. If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter. If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols We look forward to receiving your revised manuscript. Kind regards, Alfredo Vellido Academic Editor PLOS ONE Journal requirements: When submitting your revision, we need you to address these additional requirements. 1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf 2. Please state whether the data utilized in this study were de-identified/anonymised before access? 3. Thank you for stating the following in the Financial Disclosure section: 'Hyun Kyung Lim received the Soonchunhyang University research fund. This fund has no specific grant number. Hong Il Ha received fund from the DongKook Life Science. Co., Ltd., Republic of Korea (DK-IIT2019-03). These funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.' We note that you received funding from a commercial source: DongKook Life Science Co Ltd. a. Please provide an amended Competing Interests Statement that explicitly states this commercial funder, along with any other relevant declarations relating to employment, consultancy, patents, products in development, marketed products, etc. Within this Competing Interests Statement, please confirm that this does not alter your adherence to all PLOS ONE policies on sharing data and materials by including the following statement: "This does not alter our adherence to PLOS ONE policies on sharing data and materials.” (as detailed online in our guide for authors http://journals.plos.org/plosone/s/competing-interests). If there are restrictions on sharing of data and/or materials, please state these. Please note that we cannot proceed with consideration of your article until this information has been declared. b. Please include your amended Competing Interests Statement within your cover letter. We will change the online submission form on your behalf. Please know it is PLOS ONE policy for corresponding authors to declare, on behalf of all authors, all potential competing interests for the purposes of transparency. PLOS defines a competing interest as anything that interferes with, or could reasonably be perceived as interfering with, the full and objective presentation, peer review, editorial decision-making, or publication of research or non-research articles submitted to one of the journals. Competing interests can be financial or non-financial, professional, or personal. Competing interests can arise in relationship to an organization or another person. Please follow this link to our website for more details on competing interests: http://journals.plos.org/plosone/s/competing-interests 4. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Reviewer #2: Yes ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes ****** 3. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: No Reviewer #2: Yes ****** 4. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes ****** 5. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: " Prediction of femoral osteoporosis using machine-learning analysis with radiomics features and abdomen-pelvic CT: A retrospective single center preliminary study” It is very interesting that your academic paper evaluated the prediction performance of femoral osteoporosis using machine-learning analysis with radiomics features and APCT. This manuscript is very well written. However, there are some corrections that are essential to meet the standard for publication. Please refer to the following comments. 1. Did you evaluate the validity of the model by cross-validation? If not, please add it. If you have done so, please add it to the supplementary materials. 2. Please indicate the hyperparameters of Random Forest. Did you adjust the hyperparameters in this study? If so, please add the optimized method and result. If you haven't done so, expect to tune for this result. 3. Which data did you use when you took multiple CT scans during the survey period? Also, did any of the same patients have multiple BMD measurements? Please indicate whether the patient's data used either one or both were included in the analysis. 4. In recent years, deep learning has been attracting attention. The authors have shown very good results in machine learning. Make a comparison with deep learning and show your thoughts. Reviewer #2: Please use the following studies in your paper and compare the results: 1) Radiomics for classification of bone mineral loss: A machine learning study 2) Magnetic resonance imaging radiomic feature analysis of radiation-induced femoral head changes in prostate cancer radiotherapy ****** 6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No Reviewer #2: Yes:** I do not sign this review on behalf of another person [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0247330.r001
Revision 1
29 Jan 2021 Author Response Point by point response of Reviewer comment Response to the general comments by editor and editorial office: 1. We revised the financial disclosure section information. a. Please provide an amended Competing Interests Statement that explicitly states this commercial funder, along with any other relevant declarations relating to employment, consultancy, patents, products in development, marketed products, etc. � We provide an amended competing interest statement as following, “The DongKook Life Science. Co., Ltd. has no competing interests relating to employment, consultancy, patents, products in development, marketed products, etc. These funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. These do not alter our adherence to PLOS ONE policies on sharing data and materials.” b. Please include your amended Competing Interests Statement within your cover letter. We will change the online submission form on your behalf. � We add the amended Competing Interests Statement within our revised cover letter. 2. We include captions of supporting information files at the end of our manuscript. Supporting information files S1. Table of five-fold cross validation results of random forest model. S2. Appendix of graphical explanation of random forest model. 3. We added data availability statement in the cover letter and manuscript. Data Availability: The datasets in this paper can be fully accessed through the following address. “Ha, Hong Il; Lim, Hyun Kyung; Park, Sun-Young; & Han, Junhee (2021). Prediction of femoral osteoporosis using machine-learning analysis with radiomics features and abdomen-pelvic CT: A retrospective single center preliminary study. Zenodo. https://doi.org/10.5281/zenodo.4460972” Comment and response of Reviewer #1 1. Did you evaluate the validity of the model by cross-validation? If not, please add it. If you have done so, please add it to the supplementary materials. � We performed 5-fold cross validation and added it to supplementary material named as “S1. Table of five-fold cross validation results of random forest model”. 2. Please indicate the hyperparameters of Random Forest. Did you adjust the hyperparameters in this study? If so, please add the optimized method and result. If you haven't done so, expect to tune for this result. � We add the tuning of hyperparameters of random forest model as follows, “For the best hyperparameter tuning, five-fold cross validation using random search was performed and the result was summarized in supplementary file (S1. Table of five-fold cross validation results of random forest model). The hyperparameter of our RF algorithm were as follows: mtry=3, minimum nodal size=11, and splitrule=extratrees. The best model was selected and validated in the test cohort. Further explanation of RF model and features was summarized in supplementary file (S2. Appendix of graphical explanation of random forest model). 3. Which data did you use when you took multiple CT scans during the survey period? Also, did any of the same patients have multiple BMD measurements? Please indicate whether the patient's data used either one or both were included in the analysis. � No. 500 APCT cases were obtained from 500 non-duplicated patients and all patients had matched BMD test results. To avoid confusion, we add following sentence. “There were no duplicate patients enrolled.” 4. In recent years, deep learning has been attracting attention. The authors have shown very good results in machine learning. Make a comparison with deep learning and show your thoughts. � We add following paragraph in discussion section. “Furthermore, we researched the prediction of osteoporosis by applying machine learning analysis using specific radiomics features and HUHA due to the limited number of patients in this study. Recent AI research is moving to deep learning, which is the evolution of machine learning and it helps in making better precision medicine than machine learning. Deep learning is similar to machine learning, but it does not require artificial intervention. However, it requires big data to train the model otherwise it won’t work as expected. If osteoporosis screening can be implemented by deep learning using wide and easily accessible plain radiographs or large number of CT images in the future, great progress can be expected in the prevention and treatment of osteoporosis.” Comment and response of Reviewer #2 Please use the following studies in your paper and compare the results: 1) Radiomics for classification of bone mineral loss: A machine learning study Rastegar S, Vaziri M, Qasempour Y, Akhash MR, Abdalvand N, Shiri I, Abdollahi H, Zaidi H. Radiomics for classification of bone mineral loss: A machine learning study. Diagn Interv Imaging. 2020 Sep;101(9):599-610. doi: 10.1016/j.diii.2020.01.008. Epub 2020 Feb 4. PMID: 32033913. 2) Magnetic resonance imaging radiomic feature analysis of radiation-induced femoral head changes in prostate cancer radiotherapy Abdollahi H, Mahdavi SR, Shiri I, Mofid B, Bakhshandeh M, Rahmani K. Magnetic resonance imaging radiomic feature analysis of radiation-induced femoral head changes in prostate cancer radiotherapy. J Cancer Res Ther. 2019 Mar;15(Supplement):S11-S19. doi: 10.4103/jcrt.JCRT_172_18. PMID: 30900614. � We thoroughly reviewed the papers presented. However, we found that the experimental methods and the subject of the study differed greatly from our study. Therefore, the comparison and discussion between the results of these papers and the results of our research does not seem to fit the subject of this research theme. However, since both of these papers are valuable papers that analyzed femoral structural changes through radiomics analysis, we cite these papers as references to the radiomics analysis description mentioned in the introduction as #14 and #15, respectively. Attachments Attachment Submitted filename: Point by point response of Reviewer comment_plos One.docx https://doi.org/10.1371/journal.pone.0247330.r002
2 Feb 2021 Decision Letter - Alfredo Vellido, Editor PONE-D-20-33039R1 Prediction of femoral osteoporosis using machine-learning analysis with radiomics features and abdomen-pelvic CT: A retrospective single center preliminary study PLOS ONE Dear Dr. Ha, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript by Mar 19 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. Please include the following items when submitting your revised manuscript: A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'. An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'. If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter. If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols We look forward to receiving your revised manuscript. Kind regards, Alfredo Vellido Academic Editor PLOS ONE Additional Editor Comments (if provided): Dear authors, one of the reviewers is already happy with the current version of your manuscript, but you still need to carefully address the concerns raised by the other reviewer. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #1: All comments have been addressed Reviewer #2: All comments have been addressed ******** 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: (No Response) Reviewer #2: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: No Reviewer #2: Yes ****** 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes Reviewer #2: Yes ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes ****** 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: Thank you for giving me this opportunity to re-review your revised manuscript. I am happy that almost all of the suggested corrections have been made. Please refer only to the minor comments below. Thank you for spending so much time for revised manuscript. 1. Did you evaluate the validity of the model by cross-validation? If not, please add it. If you have done so, please add it to the supplementary materials. The authors explained that they performed cross-validation to fine-tune the hyperparameters. Cross-validation is generally an analysis performed to ensure generalization. Is the result of this analysis of the gini coefficient etc. the holdout method? Please show the results of cross-validation. In addition, please show me how to split data for cross-validation. Results may vary depending on the proportion of test data. Additionally, did you change the hyperparameter adjustment from the default value? Also show the changes from the initial values of hyperparameters. Reviewer #2: It is an interesting study on the bone radiomics analysis. ****** 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review?** For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No Reviewer #2: No [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0247330.r003
Revision 2
2 Feb 2021 Author Response Response to Reviewers Reviewer #1: Thank you for giving me this opportunity to re-review your revised manuscript. I am happy that almost all of the suggested corrections have been made. Please refer only to the minor comments below. Thank you for spending so much time for revised manuscript. 1. Did you evaluate the validity of the model by cross-validation? If not, please add it. If you have done so, please add it to the supplementary materials. The authors explained that they performed cross-validation to fine-tune the hyperparameters. Cross-validation is generally an analysis performed to ensure generalization. Is the result of this analysis of the gini coefficient etc. the holdout method? Please show the results of cross-validation. In addition, please show me how to split data for cross-validation. Results may vary depending on the proportion of test data. Additionally, did you change the hyperparameter adjustment from the default value? Also show the changes from the initial values of hyperparameters � We attach the original results of 5-folds cross validation confusion matrix. Supplementary file named “S1. Table” is summary of this results. We divided the total 500 cases into 70% of the train set and 30% of the validation set. And hyperparameter tuning was performed while performing 5-fold cross validation on the train set. The test set result was the validation result of RF model using a tuned hyperparameter by 5-fold cross validation. In addition, we attach the our R-statistical code. This code contains all information of our random forest model about data split, train and test set composition and hyperparameter searching algorithm, etc. This is critical information of this research because we have 2nd phase study related this research. So, we can't publish this "R-code data". However, we already submit our full data set so anyone can test and validate our research. A. Five-fold cross validation confusion matrix results 1) Fold 1. 2) Fold 2 3) Fold 3 4) fold 4 5) Fold 5 B. R- code for random forest model library(randomForest) library(MASS) library(caret) library(dplyr) library(caTools) library(e1071) library(tidyverse) library(tictoc) library(janitor) library(doSNOW) library(ranger) library(BradleyTerry2) library(randomForestExplainer) setwd() getwd() data <- read.csv(file="data.csv", header=T) data["Dx"] <- as.factor(data$Dx) levels(data$Dx)=c("negative","positive") set.seed(123) trainIndex <- createDataPartition(data$Dx, p=.7, list=FALSE, times=1) train = data[trainIndex,] test = data[-trainIndex,] print(table(train$Dx) / nrow(train)) print(table(test$Dx) / nrow(test)) # K-folds set.seed(123) cv_folds_lst <- createFolds(train$Dx, k=5, list=FALSE) set.seed(123) cv_folds <- createFolds(train$Dx, k=5, list=TRUE) ranger_tune_grid <- expand.grid( .mtry = c(2:32), .splitrule = c("gini","extratrees"), .min.node.size = c(2:30) ) fit_ctrl <-trainControl(method = "repeatedcv", number = 5, repeats = 5, index = cv_folds, summaryFunction=twoClassSummary, classProbs = TRUE, verboseIter = TRUE) set.seed(123) gc_grid_ranger_model <- train(Dx ~., train, method = "ranger", metric = "AUC", preProcess = c("zv", "center", "scale", "spatialSign"), tuneGrid = ranger_tune_grid, #tuneLength = 15, trControl = fit_ctrl) gc_grid_ranger_model # The final values used for the model were mtry = 10, splitrule = extratrees and min.node.size = 3. fit_ctrl <- trainControl(method = "adaptive_cv", number = 5, repeats = 1, index = cv_folds, search = "random", adaptive = list(min = 3, alpha = 0.05, method = "BT", complete = FALSE), summaryFunction = twoClassSummary, classProbs = TRUE, verboseIter = TRUE) gc_ranger_model <- train(Dx ~., train, method = "ranger", metric = "Sens", preProcess = c("zv", "center", "scale", "spatialSign"), trControl = fit_ctrl, tuneLength = 7) gc_ranger_model for (ntrees in c(25, 50, 100, 250, 500, 750, 1000, 2000)){ print(ntrees) acc_vec <- c() for (idx in c(1, 2, 3, 4, 5)){ num <- numeric(idx) #print(idx) cv_train <- train[cv_folds_lst != idx,] cv_validation <- train[cv_folds_lst == idx,] X_validation <- cv_validation[, -33] y_validation <- cv_validation[, 33] formula.init <- "Dx ~ ." formula.init <- as.formula(formula.init) set.seed(123) rf.model <- randomForest(formula=formula.init, data=cv_train, proximity=T, ntree=ntrees,mtry = 3 , splitrule="extratrees", min.node.size=11) rf.predictions <- predict(rf.model,X_validation, type="class") #print(rf.model) #print(confusionMatrix(data=rf.predictions, reference =y_validation, positive="No")) acc_vec <- append(acc_vec,mean(rf.predictions == y_validation)) } print(acc_vec) print(mean(acc_vec)) } for (idx in c(1, 2, 3, 4, 5)){ num <- numeric(idx) print(idx) cv_train <- train[cv_folds_lst != idx,] cv_validation <- train[cv_folds_lst == idx,] X_validation <- cv_validation[, -33] y_validation <- cv_validation[, 33] formula.init <- "Dx ~ ." formula.init <- as.formula(formula.init) rf.model <- randomForest(formula=formula.init, data=cv_train, proximity=T,ntree=500,mtry = 3, splitrule="extratrees", min.node.size=11) rf.predictions <- predict(rf.model,X_validation, type="class") print(confusionMatrix(data=rf.predictions, reference =y_validation, positive="positive")) } X_test <- test[, -33] y_test <- test[, 33] formula.init <- "Dx ~ ." formula.init <- as.formula(formula.init) set.seed(123) rf.model <- randomForest(formula=formula.init, data=train, proximity=T,ntree=500,mtry =3, splitrule="extratrees", min.node.size=11) rf.predictions <- predict(rf.model,X_test, type="class") confusion <- confusionMatrix(data=rf.predictions, reference =y_test, positive="?缺") rf.model$importance varImpPlot(rf.model) X_test <- test[, -33] y_test <- test[, 33] test_predict <- predict(rf.model,X_test, type="class") levels(y_test)=c(0,1) levels(test_predict)=c(0,1) y_test <- as.numeric(levels(y_test))[y_test] test_predict <- as.numeric(levels(test_predict))[test_predict] train_model <- rf.model$predicted train_model <- as.factor(as.numeric(train_model)) levels(train_model)=c(0,1) library(pROC) par(pty="s") trainROC <- roc(y_train ~ as.numeric(levels(train_model))[train_model],plot=TRUE,print.auc=TRUE,col="blue", lwd =4,legacy.axes=TRUE,main="ROC Curves", percent=TRUE) #ylab="False Positive Percentage", xlab = "True Positive Percentage" ## Setting levels: control = 0, case = 1 ## Setting direction: controls < cases testROC <- roc(y_test ~ test_predict,plot=TRUE,print.auc=TRUE,col="red",lwd = 4,print.auc.y=45,legacy.axes=TRUE,add = TRUE, percent=TRUE) ## Setting levels: control = 0, case = 1 ## Setting direction: controls < cases legend("bottomright",legend=c("Training set","Test set"),col=c("blue","red"),lwd=4) library(ROCR) library(pROC) library(randomForest) #data dependent variable set set.seed(123) train$Dx = as.factor(train$Dx) data1.rf <- randomForest(Dx ~., data=train, proximity=T,ntree=500,mtry = 3, splitrule="extratrees", min.node.size=11) test$Dx = as.factor(test$Dx) data2.rf <- randomForest(Dx ~., data=test,proximity=T,ntree=500,mtry = 3, splitrule="extratrees", min.node.size=11) par(pty="s") set.seed(123) require(pROC) rf.roc1 <-roc(train$Dx,data1.rf$votes[,2], plot=TRUE, legacy.axes=TRUE, percent = TRUE, xlab="1-Specificity",ylab='Sensitivity',col='#FF0000', lwd=4, print.auc=TRUE,print.auc.y=25) rf.roc2 <-roc(test$Dx,data2.rf$votes[,2], plot=TRUE, legacy.axes=TRUE, percent = TRUE, xlab="1-Specificity",ylab='Sensitivity',col='#0000FF', lwd=4, print.auc=TRUE,add=TRUE, print.auc.y=20) ci(rf.roc1) ci(rf.roc2) roc.test(rf.roc1, rf.roc2, method=c("delong", "bootstrap", "venkatraman", "sensitivity", "specificity"), sensitivity = NULL, specificity = NULL, alternative = c("two.sided", "less", "greater"), paired=NULL, reuse.auc=TRUE, boot.n=2000, boot.stratified=TRUE, ties.method="first", progress=getOption("pROCProgress")$name, parallel=FALSE) library(randomForestExplainer) library(randomForest) library(tidyverse) set.seed(123) forest <- randomForest::randomForest(Dx ~ ., data = data, localImp = TRUE, proximity=T,mtry = 3, splitrule="extratrees", min.node.size=11) suppressPackageStartupMessages(suppressMessages(suppressWarnings(explain_forest(forest, interactions = TRUE)))) Attachments Attachment Submitted filename: point by point response of Reviewer comment2 Plos one.docx https://doi.org/10.1371/journal.pone.0247330.r004
5 Feb 2021 Decision Letter - Alfredo Vellido, Editor Prediction of femoral osteoporosis using machine-learning analysis with radiomics features and abdomen-pelvic CT: A retrospective single center preliminary study PONE-D-20-33039R2 Dear Dr. Ha, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org. If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Alfredo Vellido Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #1: All comments have been addressed ******** 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes ****** 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes ****** 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: Thank you for giving me this opportunity to re-review your revised manuscript. I am happy that all of the suggested corrections have been made. Thank you for spending so much effort. ****** 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review?** For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No https://doi.org/10.1371/journal.pone.0247330.r005
Formally Accepted
10 Feb 2021 Acceptance Letter - Alfredo Vellido, Editor PONE-D-20-33039R2 Prediction of femoral osteoporosis using machine-learning analysis with radiomics features and abdomen-pelvic CT: A retrospective single center preliminary study Dear Dr. Ha: I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org. If we can help with anything else, please email us at plosone@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Alfredo Vellido Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0247330.r006

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