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Radiomics textural features by MR imaging to assess clinical outcomes following liver resection in colorectal liver metastases

  • Abdominal Radiology
  • Published:
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Abstract

Purpose

To assess the efficacy of radiomics features obtained by T2-weighted sequences to predict clinical outcomes following liver resection in colorectal liver metastases patients.

Methods

This retrospective analysis was approved by the local Ethical Committee board and radiological databases were interrogated, from January 2018 to May 2021, to select patients with liver metastases with pathological proof and MRI study in pre-surgical setting. The cohort of patients included a training set and an external validation set. The internal training set included 51 patients with 61 years of median age and 121 liver metastases. The validation cohort consisted a total of 30 patients with single lesion with 60 years of median age. For each volume of interest, 851 radiomics features were extracted as median values using PyRadiomics. Nonparametric test, intraclass correlation, receiver operating characteristic (ROC) analysis, linear regression modelling and pattern recognition methods (support vector machine (SVM), k-nearest neighbours (KNN), artificial neural network (NNET) and decision tree (DT)) were considered.

Results

The best predictor to discriminate expansive versus infiltrative front of tumour growth was obtained by wavelet_LHL_gldm_DependenceNonUniformityNormalized with an accuracy of 82%; to discriminate high grade versus low grade or absent was the wavelet_LLH_glcm_Imc1 with accuracy of 88%; to differentiate the mucinous type of tumour was the wavelet_LLH_glcm_JointEntropy with accuracy of 92% while to identify tumour recurrence was the wavelet_LLL_glcm_Correlation with accuracy of 85%. Linear regression model increased the performance obtained with respect to the univariate analysis exclusively in the discrimination of expansive versus infiltrative front of tumour growth reaching an accuracy of 90%, a sensitivity of 95% and a specificity of 80%. Considering significant texture metrics tested with pattern recognition approaches, the best performance was reached by the KNN in the discrimination of the tumour budding considering the four textural predictors obtaining an accuracy of 93%, a sensitivity of 81% and a specificity of 97%.

Conclusions

Ours results confirmed the capacity of radiomics to identify as biomarkers, several prognostic features that could affect the treatment choice in patients with liver metastases, in order to obtain a more personalized approach.

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Data Availability

Data are available at the link https://zenodo.org/record/6374234#.YjjY3erMK3A.

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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”, Naples, I-80131, Italy

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The authors have not disclosed any funding.

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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, Napoli, Italy

    Roberta Fusco

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

    Federica De Muzio & Maria Chiara Brunese

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

    Carmen Cutolo & Vincenzo Pilone

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

    Roberta Grassi & Francesca Grassi

  6. Division of Abdominal Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale – IRCCS di Napoli, Naples, Italy

    Alessandro Ottaiano & Guglielmo Nasti

  7. Division of Pathology, Istituto Nazionale Tumori IRCCS Fondazione Pascale – IRCCS di Napoli, Naples, Italy

    Fabiana Tatangelo

  8. Division of Radiology, Azienda Ospedaliera Universitaria Careggi, Florence, Italy

    Vittorio Miele

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

    Vittorio Miele

  10. Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale – IRCCS di Napoli, Naples, Italy

    Francesco Izzo

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  1. Vincenza Granata
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Correspondence to Vincenza Granata.

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Granata, V., Fusco, R., De Muzio, F. et al. Radiomics textural features by MR imaging to assess clinical outcomes following liver resection in colorectal liver metastases. Radiol med 127, 461–470 (2022). https://doi.org/10.1007/s11547-022-01477-6

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