MRI T2WI-based radiomics combined with KRAS gene mutation constructed models for predicting liver metastasis in rectal cancer
Background The study aimed to identify the optimal model for predicting rectal cancer liver metastasis (RCLM). This involved constructing various prediction models to aid clinicians in early diagnosis and precise decision-making. Methods A retrospective analysis was conducted on 193 patients diagnos...
Saved in:
| Published in | BMC medical imaging Vol. 24; no. 1; pp. 262 - 13 |
|---|---|
| Main Authors | , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
BioMed Central
04.10.2024
BioMed Central Ltd Springer Nature B.V BMC |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1471-2342 1471-2342 |
| DOI | 10.1186/s12880-024-01439-6 |
Cover
| Abstract | Background
The study aimed to identify the optimal model for predicting rectal cancer liver metastasis (RCLM). This involved constructing various prediction models to aid clinicians in early diagnosis and precise decision-making.
Methods
A retrospective analysis was conducted on 193 patients diagnosed with rectal adenocarcinoma were randomly divided into training set (
n
= 136) and validation set (
n
= 57) at a ratio of 7:3. The predictive performance of three models was internally validated by 10-fold cross-validation in the training set. Delineation of the tumor region of interest (ROI) was performed, followed by the extraction of radiomics features from the ROI. The least absolute shrinkage and selection operator (LASSO) regression algorithm and multivariate Cox analysis were employed to reduce the dimensionality of radiomics features and identify significant features. Logistic regression was employed to construct three prediction models: clinical, radiomics, and combined models (radiomics + clinical). The predictive performance of each model was assessed and compared.
Results
KRAS mutation emerged as an independent predictor of liver metastasis, yielding an odds ratio (OR) of 8.296 (95%CI: 3.471–19.830;
p
< 0.001). 5 radiomics features will be used to construct radiomics model. The combined model was built by integrating radiomics model with clinical model. In both the training set (AUC:0.842, 95%CI: 0.778–0.907) and the validation set (AUC: 0.805; 95%CI: 0.692–0.918), the AUCs for the combined model surpassed those of the radiomics and clinical models.
Conclusions
Our study reveals that KRAS mutation stands as an independent predictor of RCLM. The radiomics features based on MR play a crucial role in the evaluation of RCLM. The combined model exhibits superior performance in the prediction of liver metastasis.
Clinical Trial Number
Not applicable. |
|---|---|
| AbstractList | The study aimed to identify the optimal model for predicting rectal cancer liver metastasis (RCLM). This involved constructing various prediction models to aid clinicians in early diagnosis and precise decision-making. A retrospective analysis was conducted on 193 patients diagnosed with rectal adenocarcinoma were randomly divided into training set (n = 136) and validation set (n = 57) at a ratio of 7:3. The predictive performance of three models was internally validated by 10-fold cross-validation in the training set. Delineation of the tumor region of interest (ROI) was performed, followed by the extraction of radiomics features from the ROI. The least absolute shrinkage and selection operator (LASSO) regression algorithm and multivariate Cox analysis were employed to reduce the dimensionality of radiomics features and identify significant features. Logistic regression was employed to construct three prediction models: clinical, radiomics, and combined models (radiomics + clinical). The predictive performance of each model was assessed and compared. KRAS mutation emerged as an independent predictor of liver metastasis, yielding an odds ratio (OR) of 8.296 (95%CI: 3.471-19.830; p < 0.001). 5 radiomics features will be used to construct radiomics model. The combined model was built by integrating radiomics model with clinical model. In both the training set (AUC:0.842, 95%CI: 0.778-0.907) and the validation set (AUC: 0.805; 95%CI: 0.692-0.918), the AUCs for the combined model surpassed those of the radiomics and clinical models. Our study reveals that KRAS mutation stands as an independent predictor of RCLM. The radiomics features based on MR play a crucial role in the evaluation of RCLM. The combined model exhibits superior performance in the prediction of liver metastasis. The study aimed to identify the optimal model for predicting rectal cancer liver metastasis (RCLM). This involved constructing various prediction models to aid clinicians in early diagnosis and precise decision-making. A retrospective analysis was conducted on 193 patients diagnosed with rectal adenocarcinoma were randomly divided into training set (n = 136) and validation set (n = 57) at a ratio of 7:3. The predictive performance of three models was internally validated by 10-fold cross-validation in the training set. Delineation of the tumor region of interest (ROI) was performed, followed by the extraction of radiomics features from the ROI. The least absolute shrinkage and selection operator (LASSO) regression algorithm and multivariate Cox analysis were employed to reduce the dimensionality of radiomics features and identify significant features. Logistic regression was employed to construct three prediction models: clinical, radiomics, and combined models (radiomics + clinical). The predictive performance of each model was assessed and compared. KRAS mutation emerged as an independent predictor of liver metastasis, yielding an odds ratio (OR) of 8.296 (95%CI: 3.471-19.830; p < 0.001). 5 radiomics features will be used to construct radiomics model. The combined model was built by integrating radiomics model with clinical model. In both the training set (AUC:0.842, 95%CI: 0.778-0.907) and the validation set (AUC: 0.805; 95%CI: 0.692-0.918), the AUCs for the combined model surpassed those of the radiomics and clinical models. Our study reveals that KRAS mutation stands as an independent predictor of RCLM. The radiomics features based on MR play a crucial role in the evaluation of RCLM. The combined model exhibits superior performance in the prediction of liver metastasis. Not applicable. BackgroundThe study aimed to identify the optimal model for predicting rectal cancer liver metastasis (RCLM). This involved constructing various prediction models to aid clinicians in early diagnosis and precise decision-making.MethodsA retrospective analysis was conducted on 193 patients diagnosed with rectal adenocarcinoma were randomly divided into training set (n = 136) and validation set (n = 57) at a ratio of 7:3. The predictive performance of three models was internally validated by 10-fold cross-validation in the training set. Delineation of the tumor region of interest (ROI) was performed, followed by the extraction of radiomics features from the ROI. The least absolute shrinkage and selection operator (LASSO) regression algorithm and multivariate Cox analysis were employed to reduce the dimensionality of radiomics features and identify significant features. Logistic regression was employed to construct three prediction models: clinical, radiomics, and combined models (radiomics + clinical). The predictive performance of each model was assessed and compared.ResultsKRAS mutation emerged as an independent predictor of liver metastasis, yielding an odds ratio (OR) of 8.296 (95%CI: 3.471–19.830; p < 0.001). 5 radiomics features will be used to construct radiomics model. The combined model was built by integrating radiomics model with clinical model. In both the training set (AUC:0.842, 95%CI: 0.778–0.907) and the validation set (AUC: 0.805; 95%CI: 0.692–0.918), the AUCs for the combined model surpassed those of the radiomics and clinical models.ConclusionsOur study reveals that KRAS mutation stands as an independent predictor of RCLM. The radiomics features based on MR play a crucial role in the evaluation of RCLM. The combined model exhibits superior performance in the prediction of liver metastasis.Clinical Trial NumberNot applicable. Abstract Background The study aimed to identify the optimal model for predicting rectal cancer liver metastasis (RCLM). This involved constructing various prediction models to aid clinicians in early diagnosis and precise decision-making. Methods A retrospective analysis was conducted on 193 patients diagnosed with rectal adenocarcinoma were randomly divided into training set (n = 136) and validation set (n = 57) at a ratio of 7:3. The predictive performance of three models was internally validated by 10-fold cross-validation in the training set. Delineation of the tumor region of interest (ROI) was performed, followed by the extraction of radiomics features from the ROI. The least absolute shrinkage and selection operator (LASSO) regression algorithm and multivariate Cox analysis were employed to reduce the dimensionality of radiomics features and identify significant features. Logistic regression was employed to construct three prediction models: clinical, radiomics, and combined models (radiomics + clinical). The predictive performance of each model was assessed and compared. Results KRAS mutation emerged as an independent predictor of liver metastasis, yielding an odds ratio (OR) of 8.296 (95%CI: 3.471–19.830; p < 0.001). 5 radiomics features will be used to construct radiomics model. The combined model was built by integrating radiomics model with clinical model. In both the training set (AUC:0.842, 95%CI: 0.778–0.907) and the validation set (AUC: 0.805; 95%CI: 0.692–0.918), the AUCs for the combined model surpassed those of the radiomics and clinical models. Conclusions Our study reveals that KRAS mutation stands as an independent predictor of RCLM. The radiomics features based on MR play a crucial role in the evaluation of RCLM. The combined model exhibits superior performance in the prediction of liver metastasis. Clinical Trial Number Not applicable. Background The study aimed to identify the optimal model for predicting rectal cancer liver metastasis (RCLM). This involved constructing various prediction models to aid clinicians in early diagnosis and precise decision-making. Methods A retrospective analysis was conducted on 193 patients diagnosed with rectal adenocarcinoma were randomly divided into training set ( n = 136) and validation set ( n = 57) at a ratio of 7:3. The predictive performance of three models was internally validated by 10-fold cross-validation in the training set. Delineation of the tumor region of interest (ROI) was performed, followed by the extraction of radiomics features from the ROI. The least absolute shrinkage and selection operator (LASSO) regression algorithm and multivariate Cox analysis were employed to reduce the dimensionality of radiomics features and identify significant features. Logistic regression was employed to construct three prediction models: clinical, radiomics, and combined models (radiomics + clinical). The predictive performance of each model was assessed and compared. Results KRAS mutation emerged as an independent predictor of liver metastasis, yielding an odds ratio (OR) of 8.296 (95%CI: 3.471–19.830; p < 0.001). 5 radiomics features will be used to construct radiomics model. The combined model was built by integrating radiomics model with clinical model. In both the training set (AUC:0.842, 95%CI: 0.778–0.907) and the validation set (AUC: 0.805; 95%CI: 0.692–0.918), the AUCs for the combined model surpassed those of the radiomics and clinical models. Conclusions Our study reveals that KRAS mutation stands as an independent predictor of RCLM. The radiomics features based on MR play a crucial role in the evaluation of RCLM. The combined model exhibits superior performance in the prediction of liver metastasis. Clinical Trial Number Not applicable. Background The study aimed to identify the optimal model for predicting rectal cancer liver metastasis (RCLM). This involved constructing various prediction models to aid clinicians in early diagnosis and precise decision-making. Methods A retrospective analysis was conducted on 193 patients diagnosed with rectal adenocarcinoma were randomly divided into training set (n = 136) and validation set (n = 57) at a ratio of 7:3. The predictive performance of three models was internally validated by 10-fold cross-validation in the training set. Delineation of the tumor region of interest (ROI) was performed, followed by the extraction of radiomics features from the ROI. The least absolute shrinkage and selection operator (LASSO) regression algorithm and multivariate Cox analysis were employed to reduce the dimensionality of radiomics features and identify significant features. Logistic regression was employed to construct three prediction models: clinical, radiomics, and combined models (radiomics + clinical). The predictive performance of each model was assessed and compared. Results KRAS mutation emerged as an independent predictor of liver metastasis, yielding an odds ratio (OR) of 8.296 (95%CI: 3.471-19.830; p < 0.001). 5 radiomics features will be used to construct radiomics model. The combined model was built by integrating radiomics model with clinical model. In both the training set (AUC:0.842, 95%CI: 0.778-0.907) and the validation set (AUC: 0.805; 95%CI: 0.692-0.918), the AUCs for the combined model surpassed those of the radiomics and clinical models. Conclusions Our study reveals that KRAS mutation stands as an independent predictor of RCLM. The radiomics features based on MR play a crucial role in the evaluation of RCLM. The combined model exhibits superior performance in the prediction of liver metastasis. Clinical Trial Number Not applicable. Keywords: RC, KRAS mutation, Radiomics, RCLM, MRI The study aimed to identify the optimal model for predicting rectal cancer liver metastasis (RCLM). This involved constructing various prediction models to aid clinicians in early diagnosis and precise decision-making.BACKGROUNDThe study aimed to identify the optimal model for predicting rectal cancer liver metastasis (RCLM). This involved constructing various prediction models to aid clinicians in early diagnosis and precise decision-making.A retrospective analysis was conducted on 193 patients diagnosed with rectal adenocarcinoma were randomly divided into training set (n = 136) and validation set (n = 57) at a ratio of 7:3. The predictive performance of three models was internally validated by 10-fold cross-validation in the training set. Delineation of the tumor region of interest (ROI) was performed, followed by the extraction of radiomics features from the ROI. The least absolute shrinkage and selection operator (LASSO) regression algorithm and multivariate Cox analysis were employed to reduce the dimensionality of radiomics features and identify significant features. Logistic regression was employed to construct three prediction models: clinical, radiomics, and combined models (radiomics + clinical). The predictive performance of each model was assessed and compared.METHODSA retrospective analysis was conducted on 193 patients diagnosed with rectal adenocarcinoma were randomly divided into training set (n = 136) and validation set (n = 57) at a ratio of 7:3. The predictive performance of three models was internally validated by 10-fold cross-validation in the training set. Delineation of the tumor region of interest (ROI) was performed, followed by the extraction of radiomics features from the ROI. The least absolute shrinkage and selection operator (LASSO) regression algorithm and multivariate Cox analysis were employed to reduce the dimensionality of radiomics features and identify significant features. Logistic regression was employed to construct three prediction models: clinical, radiomics, and combined models (radiomics + clinical). The predictive performance of each model was assessed and compared.KRAS mutation emerged as an independent predictor of liver metastasis, yielding an odds ratio (OR) of 8.296 (95%CI: 3.471-19.830; p < 0.001). 5 radiomics features will be used to construct radiomics model. The combined model was built by integrating radiomics model with clinical model. In both the training set (AUC:0.842, 95%CI: 0.778-0.907) and the validation set (AUC: 0.805; 95%CI: 0.692-0.918), the AUCs for the combined model surpassed those of the radiomics and clinical models.RESULTSKRAS mutation emerged as an independent predictor of liver metastasis, yielding an odds ratio (OR) of 8.296 (95%CI: 3.471-19.830; p < 0.001). 5 radiomics features will be used to construct radiomics model. The combined model was built by integrating radiomics model with clinical model. In both the training set (AUC:0.842, 95%CI: 0.778-0.907) and the validation set (AUC: 0.805; 95%CI: 0.692-0.918), the AUCs for the combined model surpassed those of the radiomics and clinical models.Our study reveals that KRAS mutation stands as an independent predictor of RCLM. The radiomics features based on MR play a crucial role in the evaluation of RCLM. The combined model exhibits superior performance in the prediction of liver metastasis.CONCLUSIONSOur study reveals that KRAS mutation stands as an independent predictor of RCLM. The radiomics features based on MR play a crucial role in the evaluation of RCLM. The combined model exhibits superior performance in the prediction of liver metastasis.Not applicable.CLINICAL TRIAL NUMBERNot applicable. |
| ArticleNumber | 262 |
| Audience | Academic |
| Author | Liu, Han Xiao, Lingqing Hu, Linlin Li, Xiaofu Wu, Yupeng Li, Na Shi, Shengming Ma, Jiaqi Nie, Xinsheng Kong, Xiangjiang |
| Author_xml | – sequence: 1 givenname: Jiaqi surname: Ma fullname: Ma, Jiaqi organization: Department of Magnetic Resonance Imaging Diagnostic, The 2nd Affiliated Hospital of Harbin Medical University – sequence: 2 givenname: Xinsheng surname: Nie fullname: Nie, Xinsheng organization: Medical Imaging Center, the Xinjiang Production and Construction Corps Tenth Division Beitun Hospital – sequence: 3 givenname: Xiangjiang surname: Kong fullname: Kong, Xiangjiang organization: Medical Imaging Center, the Xinjiang Production and Construction Corps Tenth Division Beitun Hospital – sequence: 4 givenname: Lingqing surname: Xiao fullname: Xiao, Lingqing organization: Medical Imaging Center, the Xinjiang Production and Construction Corps Tenth Division Beitun Hospital – sequence: 5 givenname: Han surname: Liu fullname: Liu, Han organization: Department of Magnetic Resonance Imaging Diagnostic, The 2nd Affiliated Hospital of Harbin Medical University – sequence: 6 givenname: Shengming surname: Shi fullname: Shi, Shengming organization: Department of Magnetic Resonance Imaging Diagnostic, The 2nd Affiliated Hospital of Harbin Medical University – sequence: 7 givenname: Yupeng surname: Wu fullname: Wu, Yupeng organization: Department of Magnetic Resonance Imaging Diagnostic, The 2nd Affiliated Hospital of Harbin Medical University – sequence: 8 givenname: Na surname: Li fullname: Li, Na organization: Medical Imaging Center, the Xinjiang Production and Construction Corps Tenth Division Beitun Hospital – sequence: 9 givenname: Linlin surname: Hu fullname: Hu, Linlin organization: Medical Imaging Center, the Xinjiang Production and Construction Corps Tenth Division Beitun Hospital – sequence: 10 givenname: Xiaofu surname: Li fullname: Li, Xiaofu email: drxiaofuli@hrbmu.edu.cn organization: Department of Magnetic Resonance Imaging Diagnostic, The 2nd Affiliated Hospital of Harbin Medical University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39367333$$D View this record in MEDLINE/PubMed |
| BookMark | eNqNkktv1DAUhSNURB_wB1ggS2zYpPiV1wqNKh4jipBKEUvLcW5SjxJ7sJNWXfDfudMMQ6dCFUqkRNffOfbxvcfJgfMOkuQlo6eMlfnbyHhZ0pRymVImRZXmT5IjJguWciH5wb3_w-Q4xhWlrCiFfJYcikrkhRDiKPn15WJJLvmPZVrrCA0JurF-sCYS44faOizd2PGKfL5YfCMdOCDDNOrReoeAi2OYzIjM4BvoI2l9IOsAjTWjdR3p7TUEMsCoI742EutIADPqnhjtDITnydNW9xFebL8nyfcP7y_PPqXnXz8uzxbnqclpOaZ5BqKmmWmLXDaSssYAUFm2La9xnRcNpbXmlJcZgMxyjFYBF0xryvMSpBQnyXL2bbxeqXWwgw63ymur7go-dEqH0ZoelBEMqoqaDLJWihY96qoGWgspGtbkLXqJ2Wtya317o_t-Z8io2jRGzY1R2Bh11xiVo-rdrFpP9QAYwI1B93tH2V9x9kp1_hoNZSZoztHhzdYh-J8TxFENNhroe-3AT1EJxgTjuRAb9PUDdOWn4PCGN1TGOC-Q21GdxtzWtR43NhtTtSgZ4wyzbKjTf1D4NIBjggPZWqzvCV7dT7qL-GfmEOAzYIKPMUD7f_dXPhAZO88hHsf2j0u3DYu4j-sg_L2NR1S_AaRZCUk |
| CitedBy_id | crossref_primary_10_3389_fonc_2025_1511260 |
| Cites_doi | 10.1038/sj.bjc.6605164 10.1158/1078-0432.CCR-09-2446 10.3390/cancers11101545 10.1016/j.acra.2018.12.019 10.1007/s00330-018-5802-7 10.1093/annonc/mdw261 10.1007/s00261-018-1740-8 10.1109/ACCESS.2023.3272987 10.1007/s00261-015-0438-4 10.1186/s12859-020-03686-0 10.1200/JCO.2010.30.1366 10.1007/s00261-020-02624-1 10.3322/caac.21660 10.2214/AJR.14.14201 10.1038/s41598-022-16828-6 10.1148/radiol.2015151169 10.7717/peerj.9149 10.1186/1741-7015-10-135 10.3389/fonc.2020.00457 10.1200/JCO.2016.71.4394 10.1186/s12885-016-2141-4 10.1200/JCO.2015.65.9128 10.1002/cam4.6089 10.1186/s12935-020-1117-2 10.1136/gutjnl-2019-319292 10.3390/cancers12092665 10.1007/s00261-018-01891-3 10.1016/j.ejrad.2012.09.024 10.1016/j.artmed.2023.102690 10.18632/aging.202752 10.21037/qims.2019.12.16 10.3389/fonc.2022.1087882 10.5005/jp-journals-10018-1241 10.1007/s00268-010-0881-x 10.1097/01.sla.0000217629.94941.cf 10.1186/s12880-022-00825-2 10.1109/ICACA.2016.7887916 10.7717/peerj.8301 10.1016/j.compbiomed.2022.106478 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2024 2024. The Author(s). COPYRIGHT 2024 BioMed Central Ltd. 2024. This work is licensed under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. The Author(s) 2024 2024 |
| Copyright_xml | – notice: The Author(s) 2024 – notice: 2024. The Author(s). – notice: COPYRIGHT 2024 BioMed Central Ltd. – notice: 2024. This work is licensed under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: The Author(s) 2024 2024 |
| DBID | C6C AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7QO 7RV 7X7 7XB 88E 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABUWG AFKRA ARAPS AZQEC BBNVY BENPR BGLVJ BHPHI CCPQU DWQXO FR3 FYUFA GHDGH GNUQQ HCIFZ K9. KB0 LK8 M0S M1P M7P NAPCQ P5Z P62 P64 PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS 7X8 5PM ADTOC UNPAY DOA |
| DOI | 10.1186/s12880-024-01439-6 |
| DatabaseName | Springer Nature OA Free Journals CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Biotechnology Research Abstracts Nursing & Allied Health Database Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Journals Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central ProQuest Advanced Technologies & Aerospace Database ProQuest Central Essentials Biological Science Collection ProQuest Central Technology Collection (ProQuest) Natural Science Collection (ProQuest) ProQuest One Community College ProQuest Central Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) Nursing & Allied Health Database (Alumni Edition) Biological Sciences Health & Medical Collection (Alumni Edition) Medical Database Biological science database Nursing & Allied Health Premium Advanced Technologies & Aerospace Collection ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts Proquest Central Premium ProQuest One Academic Publicly Available Content Database (Proquest) ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic PubMed Central (Full Participant titles) Unpaywall for CDI: Periodical Content Unpaywall DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database ProQuest Central Student Technology Collection Technology Research Database ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest Health & Medical Research Collection Health Research Premium Collection Biotechnology Research Abstracts Health and Medicine Complete (Alumni Edition) Natural Science Collection ProQuest Central Korea Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) Advanced Technologies & Aerospace Collection ProQuest Biological Science Collection ProQuest One Academic Eastern Edition ProQuest Nursing & Allied Health Source ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest SciTech Collection ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts Advanced Technologies & Aerospace Database Nursing & Allied Health Premium ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest Nursing & Allied Health Source (Alumni) Engineering Research Database ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE Publicly Available Content Database MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: C6C name: Springer Nature OA Free Journals url: http://www.springeropen.com/ sourceTypes: Publisher – sequence: 2 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 3 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 4 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 5 dbid: UNPAY name: Unpaywall url: https://proxy.k.utb.cz/login?url=https://unpaywall.org/ sourceTypes: Open Access Repository – sequence: 6 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 1471-2342 |
| EndPage | 13 |
| ExternalDocumentID | oai_doaj_org_article_c31e990c5e5f43faa0b9be0b343d1d6f 10.1186/s12880-024-01439-6 PMC11453062 A811211183 39367333 10_1186_s12880_024_01439_6 |
| Genre | Journal Article |
| GrantInformation_xml | – fundername: Xinjiang Production and Construction Corps grantid: 2021AB029 funderid: http://dx.doi.org/10.13039/501100009967 – fundername: Xinjiang Production and Construction Corps grantid: 2021AB029 |
| GroupedDBID | --- 0R~ 23N 2WC 53G 5VS 6J9 7RV 7X7 88E 8FE 8FG 8FH 8FI 8FJ AAFWJ AAJSJ AASML ABUWG ACGFO ACGFS ACIHN ACIWK ACPRK ADBBV ADRAZ ADUKV AEAQA AENEX AFKRA AFPKN AFRAH AHBYD AHMBA AHYZX ALMA_UNASSIGNED_HOLDINGS AMKLP AMTXH AOIJS ARAPS BAPOH BAWUL BBNVY BCNDV BENPR BFQNJ BGLVJ BHPHI BMC BPHCQ BVXVI C6C CCPQU CS3 DIK DU5 E3Z EBD EBLON EBS EMB EMOBN F5P FYUFA GROUPED_DOAJ GX1 HCIFZ HMCUK HYE IAO IHR INH INR ITC KQ8 LK8 M1P M48 M7P M~E NAPCQ O5R O5S OK1 OVT P2P P62 PGMZT PHGZM PHGZT PIMPY PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO PUEGO RBZ RNS ROL RPM RSV SMD SOJ SV3 TR2 UKHRP W2D WOQ WOW XSB AAYXX CITATION -A0 3V. ACRMQ ADINQ ALIPV C24 CGR CUY CVF ECM EIF NPM 7QO 7XB 8FD 8FK AZQEC DWQXO FR3 GNUQQ K9. P64 PKEHL PQEST PQUKI PRINS 7X8 5PM 123 2VQ 4.4 ADTOC AHSBF C1A EJD H13 IPNFZ RIG UNPAY |
| ID | FETCH-LOGICAL-c608t-65e3b05cf764d401dcee048ff2b60827d00ba20285ee4566739e231aa0268e443 |
| IEDL.DBID | M48 |
| ISSN | 1471-2342 |
| IngestDate | Fri Oct 03 12:40:59 EDT 2025 Sun Oct 26 01:55:53 EDT 2025 Tue Sep 30 17:07:06 EDT 2025 Fri Sep 05 06:39:15 EDT 2025 Tue Oct 07 05:30:27 EDT 2025 Mon Oct 20 22:49:03 EDT 2025 Mon Oct 20 16:58:12 EDT 2025 Wed Feb 19 02:03:41 EST 2025 Thu Apr 24 23:02:05 EDT 2025 Wed Oct 01 04:39:23 EDT 2025 Sat Sep 06 07:26:53 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | RC KRAS mutation RCLM MRI Radiomics |
| Language | English |
| License | 2024. The Author(s). Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c608t-65e3b05cf764d401dcee048ff2b60827d00ba20285ee4566739e231aa0268e443 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| OpenAccessLink | https://doi.org/10.1186/s12880-024-01439-6 |
| PMID | 39367333 |
| PQID | 3115122763 |
| PQPubID | 44833 |
| PageCount | 13 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_c31e990c5e5f43faa0b9be0b343d1d6f unpaywall_primary_10_1186_s12880_024_01439_6 pubmedcentral_primary_oai_pubmedcentral_nih_gov_11453062 proquest_miscellaneous_3113126332 proquest_journals_3115122763 gale_infotracmisc_A811211183 gale_infotracacademiconefile_A811211183 pubmed_primary_39367333 crossref_primary_10_1186_s12880_024_01439_6 crossref_citationtrail_10_1186_s12880_024_01439_6 springer_journals_10_1186_s12880_024_01439_6 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2024-10-04 |
| PublicationDateYYYYMMDD | 2024-10-04 |
| PublicationDate_xml | – month: 10 year: 2024 text: 2024-10-04 day: 04 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England |
| PublicationTitle | BMC medical imaging |
| PublicationTitleAbbrev | BMC Med Imaging |
| PublicationTitleAlternate | BMC Med Imaging |
| PublicationYear | 2024 |
| Publisher | BioMed Central BioMed Central Ltd Springer Nature B.V BMC |
| Publisher_xml | – name: BioMed Central – name: BioMed Central Ltd – name: Springer Nature B.V – name: BMC |
| References | W Mao (1439_CR9) 2019; 44 S Manfredi (1439_CR3) 2006; 244 C Reichling (1439_CR25) 2020; 69 MY Ansari (1439_CR14) 2023; 146 SE Baldus (1439_CR42) 2010; 16 C Zilkens (1439_CR12) 2013; 82 JC Jones (1439_CR7) 2017; 35 G Hutchins (1439_CR29) 2011; 29 JL Bos (1439_CR5) 1989; 49 D Dai (1439_CR22) 2020; 8 H Li (1439_CR39) 2022; 12 Y Zhang (1439_CR37) 2020; 10 R Shi (1439_CR23) 2020; 10 MG Lubner (1439_CR28) 2015; 40 SC Chuang (1439_CR32) 2011; 35 J Souglakos (1439_CR11) 2009; 101 M Liang (1439_CR36) 2019; 26 M Li (1439_CR35) 2020; 10 V Chandrasekar (1439_CR13) 2023; 11 MY Ansari (1439_CR15) 2023; 153 H Sung (1439_CR1) 2021; 71 H Liu (1439_CR31) 2019; 29 Y Li (1439_CR34) 2019; 9 MY Ansari (1439_CR16) 2022; 12 HS Kim (1439_CR30) 2016; 16 S Lee (1439_CR41) 2020; 21 DP Modest (1439_CR10) 2016; 27 AI Valderrama-Treviño (1439_CR4) 2017; 7 R Fusco (1439_CR20) 2020; 12 F Xue (1439_CR27) 2020; 8 P Rai (1439_CR18) 2023; 12 SJ Kim (1439_CR21) 2019; 44 MY Ansari (1439_CR17) 2022; 22 M Taghavi (1439_CR33) 2021; 46 T Machackova (1439_CR2) 2019; 11 YQ Huang (1439_CR24) 2016; 34 KS Jhaveri (1439_CR40) 2015; 205 G Bruera (1439_CR6) 2012; 10 RJ Gillies (1439_CR19) 2016; 278 H Zeng (1439_CR38) 2021; 13 1439_CR26 GN Fanelli (1439_CR8) 2020; 20 |
| References_xml | – volume: 101 start-page: 465 issue: 3 year: 2009 ident: 1439_CR11 publication-title: Br J Cancer doi: 10.1038/sj.bjc.6605164 – volume: 16 start-page: 790 issue: 3 year: 2010 ident: 1439_CR42 publication-title: Clin Cancer Res off J Am Assoc Cancer Res doi: 10.1158/1078-0432.CCR-09-2446 – volume: 11 start-page: E1545 issue: 10 year: 2019 ident: 1439_CR2 publication-title: Cancers doi: 10.3390/cancers11101545 – volume: 26 start-page: 1495 issue: 11 year: 2019 ident: 1439_CR36 publication-title: Acad Radiol doi: 10.1016/j.acra.2018.12.019 – volume: 29 start-page: 4418 issue: 8 year: 2019 ident: 1439_CR31 publication-title: Eur Radiol doi: 10.1007/s00330-018-5802-7 – volume: 27 start-page: 1746 issue: 9 year: 2016 ident: 1439_CR10 publication-title: Ann Oncol doi: 10.1093/annonc/mdw261 – volume: 9 start-page: 2482 issue: 11 year: 2019 ident: 1439_CR34 publication-title: Am J Cancer Res – volume: 44 start-page: 2059 issue: 6 year: 2019 ident: 1439_CR9 publication-title: Abdom Radiol N Y doi: 10.1007/s00261-018-1740-8 – volume: 11 start-page: 52726 year: 2023 ident: 1439_CR13 publication-title: IEEE Access doi: 10.1109/ACCESS.2023.3272987 – volume: 40 start-page: 2331 issue: 7 year: 2015 ident: 1439_CR28 publication-title: Abdom Imaging doi: 10.1007/s00261-015-0438-4 – volume: 21 start-page: 382 issue: Suppl 13 year: 2020 ident: 1439_CR41 publication-title: BMC Bioinformatics doi: 10.1186/s12859-020-03686-0 – volume: 29 start-page: 1261 issue: 10 year: 2011 ident: 1439_CR29 publication-title: J Clin Oncol off J Am Soc Clin Oncol doi: 10.1200/JCO.2010.30.1366 – volume: 46 start-page: 249 issue: 1 year: 2021 ident: 1439_CR33 publication-title: Abdom Radiol N Y doi: 10.1007/s00261-020-02624-1 – volume: 71 start-page: 209 issue: 3 year: 2021 ident: 1439_CR1 publication-title: CA Cancer J Clin doi: 10.3322/caac.21660 – volume: 205 start-page: W42 issue: 1 year: 2015 ident: 1439_CR40 publication-title: AJR Am J Roentgenol doi: 10.2214/AJR.14.14201 – volume: 12 start-page: 14153 issue: 1 year: 2022 ident: 1439_CR16 publication-title: Sci Rep doi: 10.1038/s41598-022-16828-6 – volume: 278 start-page: 563 issue: 2 year: 2016 ident: 1439_CR19 publication-title: Radiology doi: 10.1148/radiol.2015151169 – volume: 8 start-page: e9149 year: 2020 ident: 1439_CR22 publication-title: PeerJ doi: 10.7717/peerj.9149 – volume: 10 start-page: 135 issue: 1 year: 2012 ident: 1439_CR6 publication-title: BMC Med doi: 10.1186/1741-7015-10-135 – volume: 10 start-page: 457 year: 2020 ident: 1439_CR37 publication-title: Front Oncol doi: 10.3389/fonc.2020.00457 – volume: 10 start-page: 4513 issue: 12 year: 2020 ident: 1439_CR23 publication-title: Am J Cancer Res – volume: 35 start-page: 2624 issue: 23 year: 2017 ident: 1439_CR7 publication-title: J Clin Oncol doi: 10.1200/JCO.2016.71.4394 – volume: 16 start-page: 120 issue: 1 year: 2016 ident: 1439_CR30 publication-title: BMC Cancer doi: 10.1186/s12885-016-2141-4 – volume: 34 start-page: 2157 issue: 18 year: 2016 ident: 1439_CR24 publication-title: J Clin Oncol off J Am Soc Clin Oncol doi: 10.1200/JCO.2015.65.9128 – volume: 12 start-page: 14225 issue: 13 year: 2023 ident: 1439_CR18 publication-title: Cancer Med doi: 10.1002/cam4.6089 – volume: 20 start-page: 30 issue: 1 year: 2020 ident: 1439_CR8 publication-title: Cancer Cell Int doi: 10.1186/s12935-020-1117-2 – volume: 69 start-page: 681 issue: 4 year: 2020 ident: 1439_CR25 publication-title: Gut doi: 10.1136/gutjnl-2019-319292 – volume: 12 start-page: E2665 issue: 9 year: 2020 ident: 1439_CR20 publication-title: Cancers doi: 10.3390/cancers12092665 – volume: 44 start-page: 1703 issue: 5 year: 2019 ident: 1439_CR21 publication-title: Abdom Radiol doi: 10.1007/s00261-018-01891-3 – volume: 82 start-page: e81 issue: 2 year: 2013 ident: 1439_CR12 publication-title: Eur J Radiol doi: 10.1016/j.ejrad.2012.09.024 – volume: 146 start-page: 102690 year: 2023 ident: 1439_CR14 publication-title: Artif Intell Med doi: 10.1016/j.artmed.2023.102690 – volume: 13 start-page: 9960 issue: 7 year: 2021 ident: 1439_CR38 publication-title: Aging doi: 10.18632/aging.202752 – volume: 10 start-page: 397 issue: 2 year: 2020 ident: 1439_CR35 publication-title: Quant Imaging Med Surg doi: 10.21037/qims.2019.12.16 – volume: 12 start-page: 1087882 year: 2022 ident: 1439_CR39 publication-title: Front Oncol doi: 10.3389/fonc.2022.1087882 – volume: 7 start-page: 166 issue: 2 year: 2017 ident: 1439_CR4 publication-title: Euroasian J Hepato-Gastroenterol doi: 10.5005/jp-journals-10018-1241 – volume: 35 start-page: 424 issue: 2 year: 2011 ident: 1439_CR32 publication-title: World J Surg doi: 10.1007/s00268-010-0881-x – volume: 244 start-page: 254 issue: 2 year: 2006 ident: 1439_CR3 publication-title: Ann Surg doi: 10.1097/01.sla.0000217629.94941.cf – volume: 22 start-page: 97 issue: 1 year: 2022 ident: 1439_CR17 publication-title: BMC Med Imaging doi: 10.1186/s12880-022-00825-2 – volume: 49 start-page: 4682 issue: 17 year: 1989 ident: 1439_CR5 publication-title: Cancer Res – ident: 1439_CR26 doi: 10.1109/ICACA.2016.7887916 – volume: 8 start-page: e8301 year: 2020 ident: 1439_CR27 publication-title: PeerJ doi: 10.7717/peerj.8301 – volume: 153 start-page: 106478 year: 2023 ident: 1439_CR15 publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2022.106478 |
| SSID | ssj0017834 |
| Score | 2.3559616 |
| Snippet | Background
The study aimed to identify the optimal model for predicting rectal cancer liver metastasis (RCLM). This involved constructing various prediction... The study aimed to identify the optimal model for predicting rectal cancer liver metastasis (RCLM). This involved constructing various prediction models to aid... Background The study aimed to identify the optimal model for predicting rectal cancer liver metastasis (RCLM). This involved constructing various prediction... BackgroundThe study aimed to identify the optimal model for predicting rectal cancer liver metastasis (RCLM). This involved constructing various prediction... Abstract Background The study aimed to identify the optimal model for predicting rectal cancer liver metastasis (RCLM). This involved constructing various... |
| SourceID | doaj unpaywall pubmedcentral proquest gale pubmed crossref springer |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 262 |
| SubjectTerms | Ablation Adenocarcinoma Adenocarcinoma - diagnostic imaging Adenocarcinoma - genetics Adenocarcinoma - secondary Adult Aged Algorithms Cancer Cancer therapies Clinical decision making Colorectal cancer Decision making Deep learning Female Gene mutations Genetic aspects Health aspects Humans Imaging K-Ras protein KRAS mutation Liver Liver cancer Liver Neoplasms - diagnostic imaging Liver Neoplasms - genetics Liver Neoplasms - secondary Machine learning Magnetic resonance imaging Magnetic Resonance Imaging - methods Male Medical prognosis Medicine Medicine & Public Health Metastases Metastasis Middle Aged MRI Mutation Performance evaluation Performance prediction Point mutation Prediction models Predictive Value of Tests Proto-Oncogene Proteins p21(ras) - genetics Radiology Radiomics RCLM Rectal Neoplasms - diagnostic imaging Rectal Neoplasms - genetics Rectal Neoplasms - pathology Rectum Regression analysis Retrospective Studies |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwELZQDzwOiDeBgoyExIFaTWzHSY4LompB5VBa0ZtlJ2N1pW262uwKceC_M5MXG5AKB67xOIo9n2fG8fgbxl4XCiri_RaglcQNSgLCF4kXAb2R0UZmvmoTZD-bwzP98Tw93yr1RTlhHT1wN3H7pUoALWaZQhq0Cs7FvvAQe6VVlVQmkPWN82LYTPXnB1Q-Yrgik5v9Bq1wHgv0R4L47AphJm6oZev_0yZvOaXfEybHU9M77NamXrrv39xiseWYDu6xu31EyWfdSO6zG1A_YDeP-zPzh-zH8ckRP5VfjwR5rIqvXDWnq8gNR7Dhvhgf0c9Y_ulk9oUjnIBfbrrzeRTo-WVRpi2Z03CMcflyRS-nfGm-oLQOfglrh1FmM2_4vOZkQ_GDSsLT6hE7O_hw-v5Q9EUXRGnifC1MCsrHaRkyoyvUHY4ZcJWHID22y6yKY-8kRiUpAAZfJlMFYIyIepEmB63VY7ZTX9XwlHEdAHC3lBbelzo4T7UDMx_QazrpUxVHLBl0YMuekZwKYyxsuzPJje30ZlFvttWbNRF7O_ZZdnwc10q_I9WOksSl3T5AhNkeYfZvCIvYGwKGpRWPn1e6_uICDpK4s-wsT4gnD21jxHYnkrhSy2nzAC3bW4rGEttRIiWa-Yi9GpupJ2W_1XC1aWVUIo1SMmJPOiSOQ1I4q5lS2DufYHQy5mlLPb9oecRxK4xaMPjSvQHOv77rukndGyH_Dzp49j908JzdlrR6KXND77IdxD68wGhw7V-2C_8nW3lYdQ priority: 102 providerName: Directory of Open Access Journals – databaseName: ProQuest Central dbid: BENPR link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3Nb9MwFLdGJ8E4IL4XGMhISByYtcR2nOSAUIc2baBVqGxiN8tOHKjUpaVphTjwv_Oem2QLSBXX-DmK_b7j598j5HUmXIG438xJwSFBiRyzWWRZCd5IScUTW_gC2ZE6uZAfL-PLLTJq78JgWWVrE72hLmY5_iM_QFSYiHNQh_fzHwy7RuHpattCwzStFYp3HmLsFtnmiIw1INuHR6PP4-5cAdtKtFdnUnVQg3VOQwZ-iiHOXcZUzz15FP9_bfUNZ_V3IWV3mnqX3FlVc_Prp5lObzis4_vkXhNp0uFaNB6QLVc9JLfPmrP0R-T32fiUnvOvpww9WUEXppjgFeWawoZAvgyP8Cct_TQefqEgZo5erdbn9kDQ4M4CjW-lU1OIfel8gS_HOmo6xXIPeuWWBqLPelLTSUXRtsIH5Shni8fk4vjo_MMJa5oxsFyF6ZKp2AkbxnmZKFkAT2HNDrS_LLmFcZ4UYWgNh2gldg6CMpWIzEHsaAwkeamTUjwhg2pWuV1CZekcZFFxZm0uS2Oxp2BiS_CmhttYhAGJWh7ovEEqx4YZU-0zllTpNd808E17vmkVkLfdnPkap2Mj9SGytqNEjG3_YLb4phuV1bmIHPjqPHZxKUUJK7GZdaEVUhRRocqAvEHB0GgJ4PNy01xogEUippYephHi54HNDMhejxI0OO8Pt6KlGwtS62t5D8irbhhnYlVc5WYrTyMiroTgAXm6lsRuSQJ2NRECZqc9Ge2tuT9STb57fHFIkYELCl6634rz9Xdt2tT9TuT_gwfPNq_6OdnhqJdYqyH3yACk2r2A-G9pXzZK_QdLhVU8 priority: 102 providerName: ProQuest – databaseName: Springer Nature OA Free Journals dbid: C6C link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9QwELagSDwOiHcDBRkJiQONmtiOkxyXiqoFlUNpRW-WnYzFStt0tdlVxYH_zoyTDRtAFVzjsRVnvnllxmPG3pQSaur7HYOSAgOUFGJXpi72aI200iJ3dSiQ_awPz9TH8-y8b5NDZ2E28_dpofda1J9FEqMliakTXRnrm-wWGikdErN6f8gY0IUR60Mxf503MjyhP_-fWnjDDP1eIjnkSe-xO6tmbr9f2dlswxQdPGD3ex-STzqmP2Q3oHnEbh_3WfLH7MfxyRE_FV-PYrJRNV_YekqHj1uO8MJIGB_R71f-6WTyhSOAgF-suow8EvQdZZEmXJLTcvRq-XxBi1OFNJ9RIQe_gKVFv7KdtnzacNKa-EIVIWjxhJ0dfDjdP4z7axbiSifFMtYZSJdklc-1qpFbuGdAufZeOBwXeZ0kzgr0QzIAdLd0LktAr9BaDN8KUEo-ZVvNZQPbjCsPgPFRVjpXKW8d3RaYO4920gqXySRi6ZoHpup7kNNVGDMTYpFCm45vBvlmAt-Mjti7Yc6868BxLfV7Yu1ASd2zwwMElemF0VQyBbTCVQaZV9LjTlzpIHFSyTqttY_YWwKGIRnH16tsf1QBN0ndssykSKkzHmrDiO2MKFE2q_HwGlqm1w2tof5GqRCo2CP2ehimmVTv1sDlKtBIArkUEXvWIXHYksSvmkuJs4sRRkd7Ho8002-hczgGv8gFjYvuruH8672u-6i7A-T_gQfP_2_1F-yuIDmlqgy1w7YQ5fASPb2lexVE_CdwY0eo priority: 102 providerName: Springer Nature – databaseName: Unpaywall dbid: UNPAY link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3db9MwELegk_h4GF8Dsg1kJCQemEdiO07yWBDTBtqExirGkxUntqjosqpphUDif9-dk4ZmoGm8xmfLPp_vI777mZCXmbAl4n4zKwWHACWyzGSRYQ6skZKKJ6b0CbJHan8kP5zGpy1MDtbCrN7fR6l6U4P-TEMGloQhEl3G1E2ypmLwuwdkbXT0afjVlw8lEeNC8mVVzD879iyPB-j_Ww2v2KHLOZLdReldcntRTfOfP_LJZMUW7d1rHjWqPYQhpqB8313MzW7x6xLA4_WWeZ-sty4pHTYy9IDcsNVDcuuwvXR_RH4fHh_QE_7lgKHJK-ksL8dYy1xTkFYIrOET_s2lH4-HnynIo6Vni-aCHwhagFqg8W_u1BScZDqd4eCYcE0nmBdCz-w8Bze1Htd0XFFUwjChAgVytkFGe-9P3u2z9tUGVqgwnTMVW2HCuHCJkiVsPnDQgppwjhto50kZhibn4NbE1oL3phKRWXAy8xyiwdRKKR6TQXVe2aeESmcthFtxZkwhXW7w8cHEODC7OTexCAMSLXdUFy2kOb6sMdE-tEmVbviqga_a81WrgLzu-kwbQI8rqd-ioHSUCMbtP8Dm6fZs60JEFox6EdvYSeFgJSYzNjRCijIqlQvIKxQzjSoDplfkbeUDLBLBt_QwjRBoD5RrQLZ7lHDUi37zUlB1q2pqjXBJEedgJwLyomvGnpg-V9nzhacREVdC8IA8aeS6W5IAriZCQO-0J_G9NfdbqvE3D0QOsTTsgoJBd5aH48-8rmLqTneArrEHm_9HvkXucDxHmOQht8kApNw-A8dxbp63GuMCT8lgDg priority: 102 providerName: Unpaywall |
| Title | MRI T2WI-based radiomics combined with KRAS gene mutation constructed models for predicting liver metastasis in rectal cancer |
| URI | https://link.springer.com/article/10.1186/s12880-024-01439-6 https://www.ncbi.nlm.nih.gov/pubmed/39367333 https://www.proquest.com/docview/3115122763 https://www.proquest.com/docview/3113126332 https://pubmed.ncbi.nlm.nih.gov/PMC11453062 https://doi.org/10.1186/s12880-024-01439-6 https://doaj.org/article/c31e990c5e5f43faa0b9be0b343d1d6f |
| UnpaywallVersion | publishedVersion |
| Volume | 24 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVADU databaseName: BioMed Central customDbUrl: eissn: 1471-2342 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017834 issn: 1471-2342 databaseCode: RBZ dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.biomedcentral.com/search/ providerName: BioMedCentral – providerCode: PRVAFT databaseName: Open Access Digital Library customDbUrl: eissn: 1471-2342 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017834 issn: 1471-2342 databaseCode: KQ8 dateStart: 20010101 isFulltext: true titleUrlDefault: http://grweb.coalliance.org/oadl/oadl.html providerName: Colorado Alliance of Research Libraries – providerCode: PRVAFT databaseName: Open Access Digital Library customDbUrl: eissn: 1471-2342 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017834 issn: 1471-2342 databaseCode: KQ8 dateStart: 20011101 isFulltext: true titleUrlDefault: http://grweb.coalliance.org/oadl/oadl.html providerName: Colorado Alliance of Research Libraries – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1471-2342 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017834 issn: 1471-2342 databaseCode: DOA dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVBFR databaseName: Free Medical Journals customDbUrl: eissn: 1471-2342 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017834 issn: 1471-2342 databaseCode: DIK dateStart: 20010101 isFulltext: true titleUrlDefault: http://www.freemedicaljournals.com providerName: Flying Publisher – providerCode: PRVFQY databaseName: GFMER Free Medical Journals customDbUrl: eissn: 1471-2342 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017834 issn: 1471-2342 databaseCode: GX1 dateStart: 0 isFulltext: true titleUrlDefault: http://www.gfmer.ch/Medical_journals/Free_medical.php providerName: Geneva Foundation for Medical Education and Research – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1471-2342 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017834 issn: 1471-2342 databaseCode: M~E dateStart: 20010101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVAQN databaseName: PubMed Central customDbUrl: eissn: 1471-2342 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017834 issn: 1471-2342 databaseCode: RPM dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.ncbi.nlm.nih.gov/pmc/ providerName: National Library of Medicine – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: http://www.proquest.com/pqcentral?accountid=15518 eissn: 1471-2342 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017834 issn: 1471-2342 databaseCode: BENPR dateStart: 20090101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Health & Medical Collection customDbUrl: eissn: 1471-2342 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017834 issn: 1471-2342 databaseCode: 7X7 dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Technology Collection customDbUrl: eissn: 1471-2342 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017834 issn: 1471-2342 databaseCode: 8FG dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/technologycollection1 providerName: ProQuest – providerCode: PRVFZP databaseName: Scholars Portal Journals: Open Access customDbUrl: eissn: 1471-2342 dateEnd: 20250131 omitProxy: true ssIdentifier: ssj0017834 issn: 1471-2342 databaseCode: M48 dateStart: 20011101 isFulltext: true titleUrlDefault: http://journals.scholarsportal.info providerName: Scholars Portal – providerCode: PRVAVX databaseName: Springer Nature HAS Fully OA customDbUrl: eissn: 1471-2342 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017834 issn: 1471-2342 databaseCode: AAJSJ dateStart: 20011201 isFulltext: true titleUrlDefault: https://www.springernature.com providerName: Springer Nature – providerCode: PRVAVX databaseName: Springer Nature OA Free Journals customDbUrl: eissn: 1471-2342 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017834 issn: 1471-2342 databaseCode: C6C dateStart: 20010112 isFulltext: true titleUrlDefault: http://www.springeropen.com/ providerName: Springer Nature |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjR1db9Mw0NqHxMcD4pvAqIyE4IEZEttxkgeE2mplA3WayioKL1GcOFCpS7t-CPbAf-fOTbIFpmkvkRKfLZ_vzncXn-8IeRkJk2Heb2ak4OCgeIbpyNMsB22kpOKBzmyA7KHaH8pPI3-0Qapw23IBF5e6dlhPajifvP19evYBBP69FfhQvVvAHhu6DLQNw2x1EVOvZqcMC0vhAWxZZWOTbIPyirC6Q1-eHzRgnQl7ASnwGBeSV_dqLh22obtsiv__N_ILmuzfKMv6qPU2ubkqZsnZr2QyuaDNenfJndIMpe0139wjG6a4T270y4P2B-RPf3BAj_nXA4ZqLqPzJBvj_eUFBQ4FZxo-4R9c-nnQ_kKBBw09Wa0P9QGgTEoLMLbOzoKCYUxncxwcg6zpBGNB6IlZJmCaLsYLOi4obrwwoRSZcP6QDHt7x919VlZqYKlywyVTvhHa9dM8UDIDggPOBraGPOca2nmQua5OOJgyvjFgsalARAYMyyQBDzA0UopHZKuYFuYJoTI3BlwsP9I6lXmiseBgoHNQtQnXvnAd4lU0iNMyjTlW05jE1p0JVbymWwx0iy3dYuWQN3Wf2TqJx5XQHSRtDYkJuO2H6fxHXMpznArPgCJPfePnUuSAiY60cbWQIvMylTvkNTJGjIwL00uT8rYDIIkJt-J26GFyPdhQHbLTgATxTpvNFWvFlXTEmCLJ4xx0g0Ne1M3YE0PmCjNdWRjhcSUEd8jjNSfWKAlY1UAI6B02eLSBc7OlGP-0ycfBfwYqKBh0t2Ln83ldtai7NctfgwZPr4HWM3KLo3BiNIfcIVvA2uY5WIhL3SKbwSiAZ9j72CLbnb3DowG8dVW3Zf-5tOweAM9B5zu0Dw-P2t_-Aop3ZUY |
| linkProvider | Scholars Portal |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwELZKkSgcEG8CBYwE4kCtJrbjJAeElke1y3Z7KK26NxMnDqy0zS6bXVU98Jf4jczk1QakFZde47Flez7PIx7PEPIqEjbFvN_MSsHBQfEsM5FnWAbaSEnFA5OWAbIHqn8sv4z98Qb53byFwbDKRiaWgjqdJfiPfBezwnicw3F4P__JsGoU3q42JTQqWAzt-Rm4bMW7wSfg72vO9z4ffeyzuqoAS5QbLpnyrTCun2SBkilMLgU1ATDOMm6gnQep65qYg9r1rQXrQgUismAExTF4K6GVUsC418h1KUCWwPkJxq2D52HRiuZhTqh2C5D9octACzLMohcx1VF-ZY2AfzXBJVX4d5hme1d7i2yt8nl8fhZPp5fU4d4dcru2Y2mvAt5dsmHze-TGqL6pv09-jQ4H9IifDBjqyZQu4nSCD6ALCtsN3jh8wl_AdHjY-0oBxJaerqqoACCos9oCTVmop6BgWdP5AgfHKG06xWASemqXMdi2xaSgk5yi5IYJJYjixQNyfCVMeUg281luHxMqM2vBR_MjYxKZxQYrFgYmA10dc-ML1yFewwOd1HnQsRzHVJf-UKh0xTcNfNMl37RyyNu2z7zKArKW-gOytqXEDN7lh9niu64Fgk6EZ8ESSHzrZ1JksBITGesaIUXqpSpzyBsEhkY5A9NL4vq5BCwSM3bpXuhhdj6QyA7Z7lCCfEi6zQ20dC2fCn1xmhzysm3Gnhhzl9vZqqQRHldCcIc8qpDYLknArgZCQO-wg9HOmrst-eRHmb0cHHDggoJBdxo4X8xr3abutJD_Dx48Wb_qF2SrfzTa1_uDg-FTcpPjGcWoELlNNgHh9hlYmkvzvDzelHy7annyBy3wihU |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3Nb9MwFLdgSAMOiO8FBhgJiQOzltiOkxxLoVoZm9DYxG5WnNisUpdWTSvEgf-d95w0NIAmuMbPVpz3HT__HiGvMmFLxP1mVgoOCUpkmckiwxx4IyUVT0zpC2SP1cGZ_HAen2_c4vfV7usjyeZOA6I0Vcv9eekaFU_Vfg1WNQ0Z-BeG-HQZU9fJDQneDXsYDNWwO0fANhLrqzJ_nddzRx61_0_bvOGcfi-c7E5Pb5Obq2qef_-WT6cbDmp0l9xpI0s6aEThHrlmq_tk-6g9O39AfhydjOkp_zJm6LlKusjLCV5JrinsH_JjeIQ_ZenhyeAzBbGy9HLVnNMDQYszCzS-dU5NIdal8wUujnXTdIrlHfTSLnOINutJTScVRVsKL1SgXC0ekrPR-9PhAWubL7BChemSqdgKE8aFS5QsgYewZwva7hw3MM6TMgxNziE6ia2FIEwlIrMQK-Y5JHWplVI8IlvVrLI7hEpnLWRNcWZMIV1usIdgYhx4z5ybWIQBidY80EWLTI4NMqbaZyip0g3fNPBNe75pFZA33Zx5g8txJfVbZG1HiZja_sFs8VW3KqoLEVnwzUVsYyeFg52YzNjQCCnKqFQuIK9RMDRqPrxekbcXGGCTiKGlB2mEeHlgIwOy26MEjS36w2vR0q3FqDWiHkWcg7kPyMtuGGdiFVxlZytPIyKuhOABedxIYrclAV81EQJmpz0Z7e25P1JNLjyeOKTEwAUFi-6txfnXe131Ufc6kf8HHjz5v9VfkO1P70b64_j48Cm5xVFlsWxD7pItEHj7DELBpXnutf0n88JS3g |
| linkToUnpaywall | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3db9MwELegk_h4GF8Dsg1kJCQemEdiO07yWBDTBtqExirGkxUntqjosqpphUDif9-dk4ZmoGm8xmfLPp_vI777mZCXmbAl4n4zKwWHACWyzGSRYQ6skZKKJ6b0CbJHan8kP5zGpy1MDtbCrN7fR6l6U4P-TEMGloQhEl3G1E2ypmLwuwdkbXT0afjVlw8lEeNC8mVVzD879iyPB-j_Ww2v2KHLOZLdReldcntRTfOfP_LJZMUW7d1rHjWqPYQhpqB8313MzW7x6xLA4_WWeZ-sty4pHTYy9IDcsNVDcuuwvXR_RH4fHh_QE_7lgKHJK-ksL8dYy1xTkFYIrOET_s2lH4-HnynIo6Vni-aCHwhagFqg8W_u1BScZDqd4eCYcE0nmBdCz-w8Bze1Htd0XFFUwjChAgVytkFGe-9P3u2z9tUGVqgwnTMVW2HCuHCJkiVsPnDQgppwjhto50kZhibn4NbE1oL3phKRWXAy8xyiwdRKKR6TQXVe2aeESmcthFtxZkwhXW7w8cHEODC7OTexCAMSLXdUFy2kOb6sMdE-tEmVbviqga_a81WrgLzu-kwbQI8rqd-ioHSUCMbtP8Dm6fZs60JEFox6EdvYSeFgJSYzNjRCijIqlQvIKxQzjSoDplfkbeUDLBLBt_QwjRBoD5RrQLZ7lHDUi37zUlB1q2pqjXBJEedgJwLyomvGnpg-V9nzhacREVdC8IA8aeS6W5IAriZCQO-0J_G9NfdbqvE3D0QOsTTsgoJBd5aH48-8rmLqTneArrEHm_9HvkXucDxHmOQht8kApNw-A8dxbp63GuMCT8lgDg |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=MRI+T2WI-based+radiomics+combined+with+KRAS+gene+mutation+constructed+models+for+predicting+liver+metastasis+in+rectal+cancer&rft.jtitle=BMC+medical+imaging&rft.au=Ma%2C+Jiaqi&rft.au=Nie%2C+Xinsheng&rft.au=Kong%2C+Xiangjiang&rft.au=Xiao%2C+Lingqing&rft.date=2024-10-04&rft.issn=1471-2342&rft.eissn=1471-2342&rft.volume=24&rft.issue=1&rft.spage=262&rft_id=info:doi/10.1186%2Fs12880-024-01439-6&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1471-2342&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1471-2342&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1471-2342&client=summon |