Predicting response to somatostatin analogues in acromegaly: machine learning-based high-dimensional quantitative texture analysis on T2-weighted MRI.
Objective: To investigate the value of machine learning (ML)-based high-dimensional quantitative texture analysis (qTA) on T2-weighted magnetic resonance imaging (MRI) in predicting response to somatostatin analogues (SA) in acromegaly patients with growth hormone (GH)-secreting pituitary macroadeno...
| Publicado en: | European Radiology Vol. 29; no. 6; pp. 2731 - 2740 |
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| Autores principales: | , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jun2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=136405191&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 136405191 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Jun2019 vid: 29 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 136405191 136405191 NLM30506213 10.1007/s00330-018-5876-2 NLM30506213 136405191 ppf: 2731 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Predicting response to somatostatin analogues in acromegaly: machine learning-based high-dimensional quantitative texture analysis on T2-weighted MRI. aug: au: Kocak, Burak Durmaz, Emine Sebnem Kadioglu, Pinar Polat Korkmaz, Ozge Comunoglu, Nil Tanriover, Necmettin Kocer, Naci Islak, Civan Kizilkilic, Osman affil: Department of Radiology, Istanbul Training and Research Hospital, Istanbul, Turkey sug: subj: Magnetic Resonance Imaging Methods Somatostatin Analogs and Derivatives Adenoma Diagnosis Pituitary Neoplasms Diagnosis Acromegaly Diagnosis Algorithms ROC Curve Retrospective Design Young Adult Female Predictive Value of Tests Pituitary Neoplasms Complications Acromegaly Etiology Adenoma Complications Reproducibility of Results Middle Age Acromegaly Drug Therapy Male Adult Scales Middle Aged: 45-64 years Adult: 19-44 years Female Male ab: Objective: To investigate the value of machine learning (ML)-based high-dimensional quantitative texture analysis (qTA) on T2-weighted magnetic resonance imaging (MRI) in predicting response to somatostatin analogues (SA) in acromegaly patients with growth hormone (GH)-secreting pituitary macroadenoma, and to compare the qTA with quantitative and qualitative T2-weighted relative signal intensity (rSI) and immunohistochemical evaluation.Methods: Forty-seven patients (24 responsive; 23 resistant patients to SA) were eligible for this retrospective study. Coronal T2-weighted images were used for qTA and rSI evaluation. The immunohistochemical evaluation was based on the granulation pattern of the adenomas. Dimension reduction was carried out by reproducibility analysis and wrapper-based algorithm. ML classifiers were k-nearest neighbours (k-NN) and C4.5 algorithm. The reference standard was the biochemical response status. Predictive performance of qTA was compared with those of the quantitative and qualitative rSI and immunohistochemical evaluation.Results: Five hundred thirty-five out of 828 texture features had excellent reproducibility. For the qTA, k-NN correctly classified 85.1% of the macroadenomas regarding response to SAs with an area under the receiver operating characteristic curve (AUC-ROC) of 0.847. The accuracy and AUC-ROC ranges of the other methods were 57.4-70.2% and 0.575-0.704, respectively. Differences in predictive performance between qTA-based classification and the other methods were significant (p < 0.05).Conclusions: The ML-based qTA of T2-weighted MRI is a potential non-invasive tool in predicting response to SAs in patients with acromegaly and GH-secreting pituitary macroadenoma. The method performed better than the qualitative and quantitative rSI and immunohistochemical evaluation.Key Points: • Machine learning-based texture analysis of T2-weighted MRI can correctly classify response to somatostatin analogues in more than four fifths of the patients. • Machine learning-based texture analysis performs better than qualitative and quantitative evaluation of relative T2 signal intensity and immunohistochemical evaluation. • About one third of the texture features may not be excellently reproducible, indicating that a reliability analysis is necessary before model development. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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