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      Prediction of pituitary adenoma surgical consistency: radiomic data mining and machine learning on T2-weighted MRI

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          Abstract

          Purpose

          Pituitary macroadenoma consistency can influence the ease of lesion removal during surgery, especially when using a transsphenoidal approach. Unfortunately, it is not assessable on standard qualitative MRI. Radiomic texture analysis could help in extracting mineable quantitative tissue characteristics. We aimed to assess the accuracy of texture analysis combined with machine learning in the preoperative evaluation of pituitary macroadenoma consistency in patients undergoing endoscopic endonasal surgery.

          Methods

          Data of 89 patients (68 soft and 21 fibrous macroadenomas) who underwent MRI and transsphenoidal surgery at our institution were retrospectively reviewed. After manual segmentation, radiomic texture features were extracted from original and filtered MR images. Feature stability analysis and a multistep feature selection were performed. After oversampling to balance the classes, 80% of the data was used for hyperparameter tuning via stratified 5-fold cross-validation, while a 20% hold-out set was employed for its final testing, using an Extra Trees ensemble meta-algorithm. The reference standard was based on surgical findings.

          Results

          A total of 1118 texture features were extracted, of which 741 were stable. After removal of low variance ( n = 4) and highly intercorrelated ( n = 625) parameters, recursive feature elimination identified a subset of 14 features. After hyperparameter tuning, the Extra Trees classifier obtained an accuracy of 93%, sensitivity of 100%, and specificity of 87%. The area under the receiver operating characteristic and precision-recall curves was 0.99.

          Conclusion

          Preoperative T2-weighted MRI texture analysis and machine learning could predict pituitary macroadenoma consistency.

          Electronic supplementary material

          The online version of this article (10.1007/s00234-020-02502-z) contains supplementary material, which is available to authorized users.

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          Most cited references38

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          Radiomics: Images Are More than Pictures, They Are Data

          Robert Gillies, Paul E Kinahan, Hedvig Hricak (2015)
          This report describes the process of radiomics, its challenges, and its potential power to facilitate better clinical decision making, particularly in the care of patients with cancer.
          Article 0 comments Cited 1970 times – based on 0 reviews     Review now
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            Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images.

            Duc Fehr, Harini Veeraraghavan, Andreas Wibmer … (2015)
            Noninvasive, radiological image-based detection and stratification of Gleason patterns can impact clinical outcomes, treatment selection, and the determination of disease status at diagnosis without subjecting patients to surgical biopsies. We present machine learning-based automatic classification of prostate cancer aggressiveness by combining apparent diffusion coefficient (ADC) and T2-weighted (T2-w) MRI-based texture features. Our approach achieved reasonably accurate classification of Gleason scores (GS) 6(3 + 3) vs. ≥7 and 7(3 + 4) vs. 7(4 + 3) despite the presence of highly unbalanced samples by using two different sample augmentation techniques followed by feature selection-based classification. Our method distinguished between GS 6(3 + 3) and ≥7 cancers with 93% accuracy for cancers occurring in both peripheral (PZ) and transition (TZ) zones and 92% for cancers occurring in the PZ alone. Our approach distinguished the GS 7(3 + 4) from GS 7(4 + 3) with 92% accuracy for cancers occurring in both the PZ and TZ and with 93% for cancers occurring in the PZ alone. In comparison, a classifier using only the ADC mean achieved a top accuracy of 58% for distinguishing GS 6(3 + 3) vs. GS ≥7 for cancers occurring in PZ and TZ and 63% for cancers occurring in PZ alone. The same classifier achieved an accuracy of 59% for distinguishing GS 7(3 + 4) from GS 7(4 + 3) occurring in the PZ and TZ and 60% for cancers occurring in PZ alone. Separate analysis of the cancers occurring in TZ alone was not performed owing to the limited number of samples. Our results suggest that texture features derived from ADC and T2-w MRI together with sample augmentation can help to obtain reasonably accurate classification of Gleason patterns.
            Article 0 comments Cited 126 times – based on 0 reviews     Review now
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              Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules.

              Fatima-Zohra Mokrane, Lin Lu, Adrien Vavasseur … (2020)
              To enhance clinician's decision-making by diagnosing hepatocellular carcinoma (HCC) in cirrhotic patients with indeterminate liver nodules using quantitative imaging features extracted from triphasic CT scans.
              Article 0 comments Cited 61 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Lorenzo Ugga:
                ORCID: http://orcid.org/0000-0001-7811-4612
                lorenzo.ugga@unina.it
                Journal
                Journal ID (nlm-ta): Neuroradiology
                Journal ID (iso-abbrev): Neuroradiology
                Title: Neuroradiology
                Publisher: Springer Berlin Heidelberg (Berlin/Heidelberg )
                ISSN (Print): 0028-3940
                ISSN (Electronic): 1432-1920
                Publication date (Electronic): 23 July 2020
                Publication date PMC-release: 23 July 2020
                Publication date (Print): 2020
                Volume: 62
                Issue: 12
                Pages: 1649-1656
                Affiliations
                [1 ]GRID grid.4691.a, ISNI 0000 0001 0790 385X, Department of Advanced Biomedical Sciences, , University of Naples “Federico II”, ; Via Pansini, 5, 80131 Naples, Italy
                [2 ]GRID grid.4691.a, ISNI 0000 0001 0790 385X, Department of Neurosciences, Reproductive and Odontostomatological Sciences, Division of Neurosurgery, , University of Naples “Federico II”, ; Naples, Italy
                Author information
                http://orcid.org/0000-0001-7811-4612
                Article
                Publisher ID: 2502
                DOI: 10.1007/s00234-020-02502-z
                PMC ID: 7666676
                PubMed ID: 32705290
                SO-VID: 3ef2ad3d-193c-46db-8b4f-a71bc1a6519a
                Copyright © © The Author(s) 2020
                License:

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, 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 changes were made. 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/4.0/.

                History
                Date received : 19 May 2020
                Date accepted : 17 July 2020
                Funding
                Funded by: Università degli Studi di Napoli Federico II
                Open Access :

                Open access funding provided by Università degli Studi di Napoli Federico II within the CRUI-CARE Agreement.

                Categories
                Subject: Diagnostic Neuroradiology
                Custom metadata
                issue-copyright-statement © Springer-Verlag GmbH Germany, part of Springer Nature 2020

                ScienceOpen disciplines: Radiology & Imaging
                Keywords: machine learning,radiomics,magnetic resonance imaging,pituitary adenoma,consistency
                Data availability:
                ScienceOpen disciplines: Radiology & Imaging
                Keywords: machine learning, radiomics, magnetic resonance imaging, pituitary adenoma, consistency

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