Multivariable Diagnostic Prediction Model to Detect Hormone Secretion Profile From T2W MRI Radiomics with Artificial Neural Networks in Pituitary Adenomas
Objective: This study aims to develop neural networks to detect hormone secretion profiles in the pituitary adenomas based on T2 weighted magnetic resonance imaging (MRI) radiomics. Methods: This retrospective model-development study included a cohort of patients with pituitary adenomas (n=130) from...
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Galenos Publishing House
2022-03-01
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| Series: | Medeniyet Medical Journal |
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| Online Access: | https://jag.journalagent.com/z4/download_fulltext.asp?pdir=medeniyet&un=MEDJ-58538 |
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| author | Begumhan BAYSAL Mehmet Bilgin ESER Mahmut Bilal DOGAN Muhammet Arif KURSUN |
| author_facet | Begumhan BAYSAL Mehmet Bilgin ESER Mahmut Bilal DOGAN Muhammet Arif KURSUN |
| author_sort | Begumhan BAYSAL |
| collection | DOAJ |
| description | Objective: This study aims to develop neural networks to detect hormone secretion profiles in the pituitary adenomas based on T2 weighted magnetic resonance imaging (MRI) radiomics.
Methods: This retrospective model-development study included a cohort of patients with pituitary adenomas (n=130) from January 2015 to January 2020 in one tertiary center. The mean age was 46.49+-13.69 years, and 76/130 (58.46%) were women. Three observers segmented lesions on coronal T2 weighted MRI, and an interrater agreement was evaluated using the Dice coefficient. Predictors were determined as radiomics features (n=851). Feature selection was based on intraclass correlation coefficient, coefficient variance, variance inflation factor, and LASSO regression analysis. Outcomes were identified as 7 hormone secretion profiles [non-functioning pituitary adenoma, growth hormone-secreting adenomas, prolactinomas, adrenocorticotropic hormone-secreting adenomas, pluri-hormonal secreting adenomas (PHA), follicle-stimulating hormone and luteinizing hormone-secreting adenomas, and thyroid-stimulating hormone adenomas]. A multivariable diagnostic prediction model was developed with artificial neural networks (ANN) for 7 outcomes. ANN performance was presented as an area under the receiver operating characteristic curve (AUC) and accepted as successful if the AUC was >0.85 and p-value was <0.01.
Results: The performance of the ANN distinguishing prolactinomas from other adenomas was validated (AUC=0.95, p<0.001, sensitivity: 91%, and specificity: 98%). The model distinguishing PHA had the lowest AUC (AUC=0.74 and p<0.001). The AUC values for the other five ANN were >0.85 and p values were <0.001.
Conclusions: This study was successful in training neural networks that could differentiate the hormone secretion profile of pituitary adenomas. |
| format | Article |
| id | doaj-art-78e46430c7764b34a5457a17f23a39d5 |
| institution | Kabale University |
| issn | 2149-2042 2149-4606 |
| language | English |
| publishDate | 2022-03-01 |
| publisher | Galenos Publishing House |
| record_format | Article |
| series | Medeniyet Medical Journal |
| spelling | doaj-art-78e46430c7764b34a5457a17f23a39d52025-01-30T07:15:46ZengGalenos Publishing HouseMedeniyet Medical Journal2149-20422149-46062022-03-01371364310.4274/MMJ.galenos.2022.58538MEDJ-58538Multivariable Diagnostic Prediction Model to Detect Hormone Secretion Profile From T2W MRI Radiomics with Artificial Neural Networks in Pituitary AdenomasBegumhan BAYSAL0Mehmet Bilgin ESER1Mahmut Bilal DOGAN2Muhammet Arif KURSUN3Istanbul Goztepe Prof. Dr. Suleyman Yalcin City Hospital, Clinic of Radiology, Istanbul, TurkeyIstanbul Goztepe Prof. Dr. Suleyman Yalcin City Hospital, Clinic of Radiology, Istanbul, TurkeyIstanbul Goztepe Prof. Dr. Suleyman Yalcin City Hospital, Clinic of Radiology, Istanbul, TurkeyIstanbul Goztepe Prof. Dr. Suleyman Yalcin City Hospital, Clinic of Radiology, Istanbul, TurkeyObjective: This study aims to develop neural networks to detect hormone secretion profiles in the pituitary adenomas based on T2 weighted magnetic resonance imaging (MRI) radiomics. Methods: This retrospective model-development study included a cohort of patients with pituitary adenomas (n=130) from January 2015 to January 2020 in one tertiary center. The mean age was 46.49+-13.69 years, and 76/130 (58.46%) were women. Three observers segmented lesions on coronal T2 weighted MRI, and an interrater agreement was evaluated using the Dice coefficient. Predictors were determined as radiomics features (n=851). Feature selection was based on intraclass correlation coefficient, coefficient variance, variance inflation factor, and LASSO regression analysis. Outcomes were identified as 7 hormone secretion profiles [non-functioning pituitary adenoma, growth hormone-secreting adenomas, prolactinomas, adrenocorticotropic hormone-secreting adenomas, pluri-hormonal secreting adenomas (PHA), follicle-stimulating hormone and luteinizing hormone-secreting adenomas, and thyroid-stimulating hormone adenomas]. A multivariable diagnostic prediction model was developed with artificial neural networks (ANN) for 7 outcomes. ANN performance was presented as an area under the receiver operating characteristic curve (AUC) and accepted as successful if the AUC was >0.85 and p-value was <0.01. Results: The performance of the ANN distinguishing prolactinomas from other adenomas was validated (AUC=0.95, p<0.001, sensitivity: 91%, and specificity: 98%). The model distinguishing PHA had the lowest AUC (AUC=0.74 and p<0.001). The AUC values for the other five ANN were >0.85 and p values were <0.001. Conclusions: This study was successful in training neural networks that could differentiate the hormone secretion profile of pituitary adenomas.https://jag.journalagent.com/z4/download_fulltext.asp?pdir=medeniyet&un=MEDJ-58538pituitary adenomamagnetic resonance imagingmachine learningartificial intelligenceradiomics |
| spellingShingle | Begumhan BAYSAL Mehmet Bilgin ESER Mahmut Bilal DOGAN Muhammet Arif KURSUN Multivariable Diagnostic Prediction Model to Detect Hormone Secretion Profile From T2W MRI Radiomics with Artificial Neural Networks in Pituitary Adenomas Medeniyet Medical Journal pituitary adenoma magnetic resonance imaging machine learning artificial intelligence radiomics |
| title | Multivariable Diagnostic Prediction Model to Detect Hormone Secretion Profile From T2W MRI Radiomics with Artificial Neural Networks in Pituitary Adenomas |
| title_full | Multivariable Diagnostic Prediction Model to Detect Hormone Secretion Profile From T2W MRI Radiomics with Artificial Neural Networks in Pituitary Adenomas |
| title_fullStr | Multivariable Diagnostic Prediction Model to Detect Hormone Secretion Profile From T2W MRI Radiomics with Artificial Neural Networks in Pituitary Adenomas |
| title_full_unstemmed | Multivariable Diagnostic Prediction Model to Detect Hormone Secretion Profile From T2W MRI Radiomics with Artificial Neural Networks in Pituitary Adenomas |
| title_short | Multivariable Diagnostic Prediction Model to Detect Hormone Secretion Profile From T2W MRI Radiomics with Artificial Neural Networks in Pituitary Adenomas |
| title_sort | multivariable diagnostic prediction model to detect hormone secretion profile from t2w mri radiomics with artificial neural networks in pituitary adenomas |
| topic | pituitary adenoma magnetic resonance imaging machine learning artificial intelligence radiomics |
| url | https://jag.journalagent.com/z4/download_fulltext.asp?pdir=medeniyet&un=MEDJ-58538 |
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