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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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 ). |