Deep Learning Radiomic Analysis of MRI Combined with Clinical Characteristics Diagnoses Placenta Accreta Spectrum and its Subtypes
Different placenta accreta spectrum (PAS) subtypes pose varying surgical risks to the parturient. Machine learning model has the potential to diagnose PAS disorder. To develop a cascaded deep semantic-radiomic-clinical (DRC) model for diagnosing PAS and its subtypes based on T2-weighted MRI. Retrosp...
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| Veröffentlicht in: | Journal of magnetic resonance imaging 2024-12, Vol.60 (6), p.2705-2715 |
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| creator | Zheng, Changye Zhong, Jian Wang, Ya Cao, Kangyang Zhang, Chang Yue, Peiyan Xu, Xiaoyang Yang, Yang Liu, Qinghua Zou, Yujian Huang, Bingsheng |
| description | Different placenta accreta spectrum (PAS) subtypes pose varying surgical risks to the parturient. Machine learning model has the potential to diagnose PAS disorder.
To develop a cascaded deep semantic-radiomic-clinical (DRC) model for diagnosing PAS and its subtypes based on T2-weighted MRI.
Retrospective.
361 pregnant women (mean age: 33.10 ± 4.37 years), suspected of PAS, divided into segment training cohort (N = 40), internal training cohort (N = 139), internal testing cohort (N = 60), and external testing cohort (N = 122).
Coronal T2-weighted sequence at 1.5 T and 3.0 T.
Clinical characteristics such as history of uterine surgery and the presence of placenta previa, complete placenta previa and dangerous placenta previa were extracted from clinical records. The DRC model (incorporating radiomics, deep semantic features, and clinical characteristics), a cumulative radiological score method performed by radiologists, and other models (including a radiomics and clinical, the clinical, radiomics and deep learning models) were developed for PAS disorder diagnosing (existence of PAS and its subtypes).
AUC, ACC, Student's t-test, the Mann-Whitney U test, chi-squared test, dice coefficient, intraclass correlation coefficients, least absolute shrinkage and selection operator regression, receiver operating characteristic curve, calibration curve with the Hosmer-Lemeshow test, decision curve analysis, DeLong test, and McNemar test. P |
| doi_str_mv | 10.1002/jmri.29317 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_3128306561</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3128306561</sourcerecordid><originalsourceid>FETCH-LOGICAL-c315t-3daf31e18a4fd42b3875d7593ec045c56600f12c5168a49c22d541b702236a03</originalsourceid><addsrcrecordid>eNpd0TtPwzAUBWALgXgv_ABkiQUhpfgR5zFWKS-pCNR2jxznBlwldrAdoa78cgIFBqZzh09nuAehM0omlBB2ve6cnrCc03QHHVLBWMREluyONxE8ohlJD9CR92tCSJ7HYh8d8IznJM_oIfqYAfR4DtIZbV7wQtbadlrhqZHtxmuPbYMfFw-4sF2lDdT4XYdXXLTaaCVbXLxKJ1UAp33QyuOZli_GevD4uZUKTJB4qpSDMZc9qOCGDktTYx08Xg5V2PTgT9BeI1sPpz95jFa3N6viPpo_3T0U03mkOBUh4rVsOAWaybipY1bxLBV1KnIOisRCiSQhpKFMCZqMJFeM1SKmVUoY44kk_Bhdbmt7Z98G8KHstFfQttKAHXzJKcs4SURCR3rxj67t4MaPbBVLUpbFo7raKuWs9w6asne6k25TUlJ-DVN-DVN-DzPi85_Koeqg_qO_S_BPS_yIow</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3128267284</pqid></control><display><type>article</type><title>Deep Learning Radiomic Analysis of MRI Combined with Clinical Characteristics Diagnoses Placenta Accreta Spectrum and its Subtypes</title><source>MEDLINE</source><source>Wiley Online Library Journals Frontfile Complete</source><creator>Zheng, Changye ; Zhong, Jian ; Wang, Ya ; Cao, Kangyang ; Zhang, Chang ; Yue, Peiyan ; Xu, Xiaoyang ; Yang, Yang ; Liu, Qinghua ; Zou, Yujian ; Huang, Bingsheng</creator><creatorcontrib>Zheng, Changye ; Zhong, Jian ; Wang, Ya ; Cao, Kangyang ; Zhang, Chang ; Yue, Peiyan ; Xu, Xiaoyang ; Yang, Yang ; Liu, Qinghua ; Zou, Yujian ; Huang, Bingsheng</creatorcontrib><description>Different placenta accreta spectrum (PAS) subtypes pose varying surgical risks to the parturient. Machine learning model has the potential to diagnose PAS disorder.
To develop a cascaded deep semantic-radiomic-clinical (DRC) model for diagnosing PAS and its subtypes based on T2-weighted MRI.
Retrospective.
361 pregnant women (mean age: 33.10 ± 4.37 years), suspected of PAS, divided into segment training cohort (N = 40), internal training cohort (N = 139), internal testing cohort (N = 60), and external testing cohort (N = 122).
Coronal T2-weighted sequence at 1.5 T and 3.0 T.
Clinical characteristics such as history of uterine surgery and the presence of placenta previa, complete placenta previa and dangerous placenta previa were extracted from clinical records. The DRC model (incorporating radiomics, deep semantic features, and clinical characteristics), a cumulative radiological score method performed by radiologists, and other models (including a radiomics and clinical, the clinical, radiomics and deep learning models) were developed for PAS disorder diagnosing (existence of PAS and its subtypes).
AUC, ACC, Student's t-test, the Mann-Whitney U test, chi-squared test, dice coefficient, intraclass correlation coefficients, least absolute shrinkage and selection operator regression, receiver operating characteristic curve, calibration curve with the Hosmer-Lemeshow test, decision curve analysis, DeLong test, and McNemar test. P < 0.05 indicated a significant difference.
In PAS diagnosis, the DRC-1 outperformed than other models (AUC = 0.850 and 0.841 in internal and external testing cohorts, respectively). In PAS subtype classification (abnormal adherent placenta and abnormal invasive placenta), DRC-2 model performed similarly with radiologists (P = 0.773 and 0.579 in the internal testing cohort and P = 0.429 and 0.874 in the external testing cohort, respectively).
The DRC model offers efficiency and high diagnostic sensitivity in diagnosis, aiding in surgical planning.
3 TECHNICAL EFFICACY: Stage 2.</description><identifier>ISSN: 1053-1807</identifier><identifier>ISSN: 1522-2586</identifier><identifier>EISSN: 1522-2586</identifier><identifier>DOI: 10.1002/jmri.29317</identifier><identifier>PMID: 38390981</identifier><language>eng</language><publisher>United States: Wiley Subscription Services, Inc</publisher><subject>Adult ; Correlation coefficient ; Correlation coefficients ; Deep Learning ; Diagnosis ; Feature extraction ; Female ; Field strength ; Humans ; Machine learning ; Magnetic resonance imaging ; Magnetic Resonance Imaging - methods ; Placenta ; Placenta Accreta - diagnostic imaging ; Placenta Previa - diagnostic imaging ; Population studies ; Pregnancy ; Pregnancy complications ; Radiomics ; Retrospective Studies ; ROC Curve ; Semantics ; Sensitivity analysis ; Statistical analysis ; Statistical models ; Statistical tests</subject><ispartof>Journal of magnetic resonance imaging, 2024-12, Vol.60 (6), p.2705-2715</ispartof><rights>2024 International Society for Magnetic Resonance in Medicine.</rights><rights>2024 International Society for Magnetic Resonance in Medicine</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c315t-3daf31e18a4fd42b3875d7593ec045c56600f12c5168a49c22d541b702236a03</citedby><cites>FETCH-LOGICAL-c315t-3daf31e18a4fd42b3875d7593ec045c56600f12c5168a49c22d541b702236a03</cites><orcidid>0000-0002-1183-7506 ; 0000-0001-6335-5309</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38390981$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Zheng, Changye</creatorcontrib><creatorcontrib>Zhong, Jian</creatorcontrib><creatorcontrib>Wang, Ya</creatorcontrib><creatorcontrib>Cao, Kangyang</creatorcontrib><creatorcontrib>Zhang, Chang</creatorcontrib><creatorcontrib>Yue, Peiyan</creatorcontrib><creatorcontrib>Xu, Xiaoyang</creatorcontrib><creatorcontrib>Yang, Yang</creatorcontrib><creatorcontrib>Liu, Qinghua</creatorcontrib><creatorcontrib>Zou, Yujian</creatorcontrib><creatorcontrib>Huang, Bingsheng</creatorcontrib><title>Deep Learning Radiomic Analysis of MRI Combined with Clinical Characteristics Diagnoses Placenta Accreta Spectrum and its Subtypes</title><title>Journal of magnetic resonance imaging</title><addtitle>J Magn Reson Imaging</addtitle><description>Different placenta accreta spectrum (PAS) subtypes pose varying surgical risks to the parturient. Machine learning model has the potential to diagnose PAS disorder.
To develop a cascaded deep semantic-radiomic-clinical (DRC) model for diagnosing PAS and its subtypes based on T2-weighted MRI.
Retrospective.
361 pregnant women (mean age: 33.10 ± 4.37 years), suspected of PAS, divided into segment training cohort (N = 40), internal training cohort (N = 139), internal testing cohort (N = 60), and external testing cohort (N = 122).
Coronal T2-weighted sequence at 1.5 T and 3.0 T.
Clinical characteristics such as history of uterine surgery and the presence of placenta previa, complete placenta previa and dangerous placenta previa were extracted from clinical records. The DRC model (incorporating radiomics, deep semantic features, and clinical characteristics), a cumulative radiological score method performed by radiologists, and other models (including a radiomics and clinical, the clinical, radiomics and deep learning models) were developed for PAS disorder diagnosing (existence of PAS and its subtypes).
AUC, ACC, Student's t-test, the Mann-Whitney U test, chi-squared test, dice coefficient, intraclass correlation coefficients, least absolute shrinkage and selection operator regression, receiver operating characteristic curve, calibration curve with the Hosmer-Lemeshow test, decision curve analysis, DeLong test, and McNemar test. P < 0.05 indicated a significant difference.
In PAS diagnosis, the DRC-1 outperformed than other models (AUC = 0.850 and 0.841 in internal and external testing cohorts, respectively). In PAS subtype classification (abnormal adherent placenta and abnormal invasive placenta), DRC-2 model performed similarly with radiologists (P = 0.773 and 0.579 in the internal testing cohort and P = 0.429 and 0.874 in the external testing cohort, respectively).
The DRC model offers efficiency and high diagnostic sensitivity in diagnosis, aiding in surgical planning.
3 TECHNICAL EFFICACY: Stage 2.</description><subject>Adult</subject><subject>Correlation coefficient</subject><subject>Correlation coefficients</subject><subject>Deep Learning</subject><subject>Diagnosis</subject><subject>Feature extraction</subject><subject>Female</subject><subject>Field strength</subject><subject>Humans</subject><subject>Machine learning</subject><subject>Magnetic resonance imaging</subject><subject>Magnetic Resonance Imaging - methods</subject><subject>Placenta</subject><subject>Placenta Accreta - diagnostic imaging</subject><subject>Placenta Previa - diagnostic imaging</subject><subject>Population studies</subject><subject>Pregnancy</subject><subject>Pregnancy complications</subject><subject>Radiomics</subject><subject>Retrospective Studies</subject><subject>ROC Curve</subject><subject>Semantics</subject><subject>Sensitivity analysis</subject><subject>Statistical analysis</subject><subject>Statistical models</subject><subject>Statistical tests</subject><issn>1053-1807</issn><issn>1522-2586</issn><issn>1522-2586</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpd0TtPwzAUBWALgXgv_ABkiQUhpfgR5zFWKS-pCNR2jxznBlwldrAdoa78cgIFBqZzh09nuAehM0omlBB2ve6cnrCc03QHHVLBWMREluyONxE8ohlJD9CR92tCSJ7HYh8d8IznJM_oIfqYAfR4DtIZbV7wQtbadlrhqZHtxmuPbYMfFw-4sF2lDdT4XYdXXLTaaCVbXLxKJ1UAp33QyuOZli_GevD4uZUKTJB4qpSDMZc9qOCGDktTYx08Xg5V2PTgT9BeI1sPpz95jFa3N6viPpo_3T0U03mkOBUh4rVsOAWaybipY1bxLBV1KnIOisRCiSQhpKFMCZqMJFeM1SKmVUoY44kk_Bhdbmt7Z98G8KHstFfQttKAHXzJKcs4SURCR3rxj67t4MaPbBVLUpbFo7raKuWs9w6asne6k25TUlJ-DVN-DVN-DzPi85_Koeqg_qO_S_BPS_yIow</recordid><startdate>20241201</startdate><enddate>20241201</enddate><creator>Zheng, Changye</creator><creator>Zhong, Jian</creator><creator>Wang, Ya</creator><creator>Cao, Kangyang</creator><creator>Zhang, Chang</creator><creator>Yue, Peiyan</creator><creator>Xu, Xiaoyang</creator><creator>Yang, Yang</creator><creator>Liu, Qinghua</creator><creator>Zou, Yujian</creator><creator>Huang, Bingsheng</creator><general>Wiley Subscription Services, Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>7TK</scope><scope>8FD</scope><scope>FR3</scope><scope>K9.</scope><scope>P64</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-1183-7506</orcidid><orcidid>https://orcid.org/0000-0001-6335-5309</orcidid></search><sort><creationdate>20241201</creationdate><title>Deep Learning Radiomic Analysis of MRI Combined with Clinical Characteristics Diagnoses Placenta Accreta Spectrum and its Subtypes</title><author>Zheng, Changye ; 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Machine learning model has the potential to diagnose PAS disorder.
To develop a cascaded deep semantic-radiomic-clinical (DRC) model for diagnosing PAS and its subtypes based on T2-weighted MRI.
Retrospective.
361 pregnant women (mean age: 33.10 ± 4.37 years), suspected of PAS, divided into segment training cohort (N = 40), internal training cohort (N = 139), internal testing cohort (N = 60), and external testing cohort (N = 122).
Coronal T2-weighted sequence at 1.5 T and 3.0 T.
Clinical characteristics such as history of uterine surgery and the presence of placenta previa, complete placenta previa and dangerous placenta previa were extracted from clinical records. The DRC model (incorporating radiomics, deep semantic features, and clinical characteristics), a cumulative radiological score method performed by radiologists, and other models (including a radiomics and clinical, the clinical, radiomics and deep learning models) were developed for PAS disorder diagnosing (existence of PAS and its subtypes).
AUC, ACC, Student's t-test, the Mann-Whitney U test, chi-squared test, dice coefficient, intraclass correlation coefficients, least absolute shrinkage and selection operator regression, receiver operating characteristic curve, calibration curve with the Hosmer-Lemeshow test, decision curve analysis, DeLong test, and McNemar test. P < 0.05 indicated a significant difference.
In PAS diagnosis, the DRC-1 outperformed than other models (AUC = 0.850 and 0.841 in internal and external testing cohorts, respectively). In PAS subtype classification (abnormal adherent placenta and abnormal invasive placenta), DRC-2 model performed similarly with radiologists (P = 0.773 and 0.579 in the internal testing cohort and P = 0.429 and 0.874 in the external testing cohort, respectively).
The DRC model offers efficiency and high diagnostic sensitivity in diagnosis, aiding in surgical planning.
3 TECHNICAL EFFICACY: Stage 2.</abstract><cop>United States</cop><pub>Wiley Subscription Services, Inc</pub><pmid>38390981</pmid><doi>10.1002/jmri.29317</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0002-1183-7506</orcidid><orcidid>https://orcid.org/0000-0001-6335-5309</orcidid></addata></record> |
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| subjects | Adult Correlation coefficient Correlation coefficients Deep Learning Diagnosis Feature extraction Female Field strength Humans Machine learning Magnetic resonance imaging Magnetic Resonance Imaging - methods Placenta Placenta Accreta - diagnostic imaging Placenta Previa - diagnostic imaging Population studies Pregnancy Pregnancy complications Radiomics Retrospective Studies ROC Curve Semantics Sensitivity analysis Statistical analysis Statistical models Statistical tests |
| title | Deep Learning Radiomic Analysis of MRI Combined with Clinical Characteristics Diagnoses Placenta Accreta Spectrum and its Subtypes |
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