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Radiomics and machine learning analysis based on magnetic resonance imaging in the assessment of liver mucinous colorectal metastases

  • Diagnostic Imaging in Oncology
  • Published:
La radiologia medica Aims and scope Submit manuscript

Abstract

Purpose

The purpose of this study is to evaluate the Radiomics and Machine Learning Analysis based on MRI in the assessment of Liver Mucinous Colorectal Metastases.Query

Methods

The cohort of patients included a training set (121 cases) and an external validation set (30 cases) with colorectal liver metastases with pathological proof and MRI study enrolled in this approved study retrospectively. About 851 radiomics features were extracted as median values by means of the PyRadiomics tool on volume on interest segmented manually by two expert radiologists. Univariate analysis, linear regression modelling and pattern recognition methods were used as statistical and classification procedures.

Results

The best results at univariate analysis were reached by the wavelet_LLH_glcm_JointEntropy extracted by T2W SPACE sequence with accuracy of 92%. Linear regression model increased the performance obtained respect to the univariate analysis. The best results were obtained by a linear regression model of 15 significant features extracted by the T2W SPACE sequence with accuracy of 94%, a sensitivity of 92% and a specificity of 95%. The best classifier among the tested pattern recognition approaches was k-nearest neighbours (KNN); however, KNN achieved lower precision than the best linear regression model.

Conclusions

Radiomics metrics allow the mucinous subtype lesion characterization, in order to obtain a more personalized approach. We demonstrated that the best performance was obtained by T2-W extracted textural metrics.

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Data availability

The data presented in this study are available at link https://zenodo.org/record/6589924#.YpI6vGhBy3A.

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Acknowledgements

The authors are grateful to Alessandra Trocino, librarian at the National Cancer Institute of Naples, Italy. Moreover, for the collaboration, authors are grateful for the research support to Paolo Pariate, Martina Totaro and Andrea Esposito of Radiology Division, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Napoli, Italy.

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Authors and Affiliations

  1. Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS Di Napoli, Naples, Italy

    Vincenza Granata, Sergio Venanzio Setola & Antonella Petrillo

  2. Medical Oncology Division, Igea SpA, Naples, Italy

    Roberta Fusco

  3. Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100, Campobasso, Italy

    Federica De Muzio

  4. Department of Medicine, Surgery and Dentistry, University of Salerno, 84084, Fisciano, Italy

    Carmen Cutolo

  5. Division of Radiology, Università Degli Studi Della Campania Luigi Vanvitelli, Naples, Italy

    Federica Dell’Aversana & Francesca Grassi

  6. Division of Hepatobiliary Surgery, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS Di Napoli, Naples, Italy

    Andrea Belli & Francesco Izzo

  7. Division of Abdominal Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale, Naples, Italy

    Lucrezia Silvestro, Alessandro Ottaiano, Guglielmo Nasti & Antonio Avallone

  8. Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy

    Vittorio Miele

  9. Division of Radiology, Azienda Ospedaliera Universitaria Careggi, 50134, Florence, Italy

    Federica Flammia & Vittorio Miele

  10. Division of Pathology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS Di Napoli, 80131, Naples, Italy

    Fabiana Tatangelo

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  1. Vincenza Granata
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  2. Roberta Fusco
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Each author has participated sufficiently to take public responsibility for the content of the manuscript.

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Correspondence to Roberta Fusco.

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Ethical standards This article does not contain any studies with human participants or animals performed by any of the authors.

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This study aligned with National appropriate guidelines and procedures. The Local Ethical Committee board approved this retrospective study.

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This study aligned with National appropriate guidelines and procedures. Renouncing the need for informed patient consent given the study nature.

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Granata, V., Fusco, R., De Muzio, F. et al. Radiomics and machine learning analysis based on magnetic resonance imaging in the assessment of liver mucinous colorectal metastases. Radiol med 127, 763–772 (2022). https://doi.org/10.1007/s11547-022-01501-9

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