Evaluation of the radiomics model.

<p>(A) ROC curves of the radiomics model for the training and testing sets. (B) Calibration curves of the radiomics model for the training and testing sets. (C) DCA curve of the radiomics model for the training set. (D) DCA curve of the radiomics model for the testing set.</p>

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Main Author: Pan Tang (4411270) (author)
Other Authors: Qi Zhang (28502) (author), Ling-cui Meng (22386132) (author), Miao Chen (213356) (author), Sai-Feng He (22386135) (author), Jian-Xing Zhang (22386138) (author)
Published: 2025
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_version_ 1852015980777046016
author Pan Tang (4411270)
author2 Qi Zhang (28502)
Ling-cui Meng (22386132)
Miao Chen (213356)
Sai-Feng He (22386135)
Jian-Xing Zhang (22386138)
author2_role author
author
author
author
author
author_facet Pan Tang (4411270)
Qi Zhang (28502)
Ling-cui Meng (22386132)
Miao Chen (213356)
Sai-Feng He (22386135)
Jian-Xing Zhang (22386138)
author_role author
dc.creator.none.fl_str_mv Pan Tang (4411270)
Qi Zhang (28502)
Ling-cui Meng (22386132)
Miao Chen (213356)
Sai-Feng He (22386135)
Jian-Xing Zhang (22386138)
dc.date.none.fl_str_mv 2025-10-07T17:30:57Z
dc.identifier.none.fl_str_mv 10.1371/journal.pone.0333172.g004
dc.relation.none.fl_str_mv https://figshare.com/articles/figure/Evaluation_of_the_radiomics_model_/30298062
dc.rights.none.fl_str_mv CC BY 4.0
info:eu-repo/semantics/openAccess
dc.subject.none.fl_str_mv Medicine
Cancer
Biological Sciences not elsewhere classified
Mathematical Sciences not elsewhere classified
Information Systems not elsewhere classified
probability threshold range
personalized treatment planning
offering valuable guidance
least absolute shrinkage
invasive predictive tool
independent risk factors
guiding treatment strategies
3 positive alns
755 – 0
678 – 0
05 – 0
integrating clinical data
reported aln status
nomogram model vs
derived radiomics features
based radiomics nomogram
radiomics nomogram model
ultrasound imaging features
based nomogram
nomogram model
radiomics features
clinical model
radiomics model
ultrasound imaging
best features
clinical pathology
radiomics score
combined model
xlink ">
ultrasound images
tumor burden
training set
testing set
statistically significant
serologic markers
selection operator
selected using
roc curve
results showed
calibration curves
4161 ).
dc.title.none.fl_str_mv Evaluation of the radiomics model.
dc.type.none.fl_str_mv Image
Figure
info:eu-repo/semantics/publishedVersion
image
description <p>(A) ROC curves of the radiomics model for the training and testing sets. (B) Calibration curves of the radiomics model for the training and testing sets. (C) DCA curve of the radiomics model for the training set. (D) DCA curve of the radiomics model for the testing set.</p>
eu_rights_str_mv openAccess
id Manara_2fe2d134a2c4eed0ba5f98a28f372ef2
identifier_str_mv 10.1371/journal.pone.0333172.g004
network_acronym_str Manara
network_name_str ManaraRepo
oai_identifier_str oai:figshare.com:article/30298062
publishDate 2025
repository.mail.fl_str_mv
repository.name.fl_str_mv
repository_id_str
rights_invalid_str_mv CC BY 4.0
spelling Evaluation of the radiomics model.Pan Tang (4411270)Qi Zhang (28502)Ling-cui Meng (22386132)Miao Chen (213356)Sai-Feng He (22386135)Jian-Xing Zhang (22386138)MedicineCancerBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedprobability threshold rangepersonalized treatment planningoffering valuable guidanceleast absolute shrinkageinvasive predictive toolindependent risk factorsguiding treatment strategies3 positive alns755 – 0678 – 005 – 0integrating clinical datareported aln statusnomogram model vsderived radiomics featuresbased radiomics nomogramradiomics nomogram modelultrasound imaging featuresbased nomogramnomogram modelradiomics featuresclinical modelradiomics modelultrasound imagingbest featuresclinical pathologyradiomics scorecombined modelxlink ">ultrasound imagestumor burdentraining settesting setstatistically significantserologic markersselection operatorselected usingroc curveresults showedcalibration curves4161 ).<p>(A) ROC curves of the radiomics model for the training and testing sets. (B) Calibration curves of the radiomics model for the training and testing sets. (C) DCA curve of the radiomics model for the training set. (D) DCA curve of the radiomics model for the testing set.</p>2025-10-07T17:30:57ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0333172.g004https://figshare.com/articles/figure/Evaluation_of_the_radiomics_model_/30298062CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/302980622025-10-07T17:30:57Z
spellingShingle Evaluation of the radiomics model.
Pan Tang (4411270)
Medicine
Cancer
Biological Sciences not elsewhere classified
Mathematical Sciences not elsewhere classified
Information Systems not elsewhere classified
probability threshold range
personalized treatment planning
offering valuable guidance
least absolute shrinkage
invasive predictive tool
independent risk factors
guiding treatment strategies
3 positive alns
755 – 0
678 – 0
05 – 0
integrating clinical data
reported aln status
nomogram model vs
derived radiomics features
based radiomics nomogram
radiomics nomogram model
ultrasound imaging features
based nomogram
nomogram model
radiomics features
clinical model
radiomics model
ultrasound imaging
best features
clinical pathology
radiomics score
combined model
xlink ">
ultrasound images
tumor burden
training set
testing set
statistically significant
serologic markers
selection operator
selected using
roc curve
results showed
calibration curves
4161 ).
status_str publishedVersion
title Evaluation of the radiomics model.
title_full Evaluation of the radiomics model.
title_fullStr Evaluation of the radiomics model.
title_full_unstemmed Evaluation of the radiomics model.
title_short Evaluation of the radiomics model.
title_sort Evaluation of the radiomics model.
topic Medicine
Cancer
Biological Sciences not elsewhere classified
Mathematical Sciences not elsewhere classified
Information Systems not elsewhere classified
probability threshold range
personalized treatment planning
offering valuable guidance
least absolute shrinkage
invasive predictive tool
independent risk factors
guiding treatment strategies
3 positive alns
755 – 0
678 – 0
05 – 0
integrating clinical data
reported aln status
nomogram model vs
derived radiomics features
based radiomics nomogram
radiomics nomogram model
ultrasound imaging features
based nomogram
nomogram model
radiomics features
clinical model
radiomics model
ultrasound imaging
best features
clinical pathology
radiomics score
combined model
xlink ">
ultrasound images
tumor burden
training set
testing set
statistically significant
serologic markers
selection operator
selected using
roc curve
results showed
calibration curves
4161 ).