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Machine learning and radiomics analysis by computed tomography in colorectal liver metastases patients for RAS mutational status prediction

  • Abdominal Radiology
  • Published:
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Abstract

Purpose

To assess the efficacy of machine learning and radiomics analysis by computed tomography (CT) in presurgical setting, to predict RAS mutational status in colorectal liver metastases.

Methods

Patient selection in a retrospective study was carried out from January 2018 to May 2021 considering the following inclusion criteria: patients subjected to surgical resection for liver metastases; proven pathological liver metastases; patients subjected to enhanced CT examination in the presurgical setting with a good quality of images; and RAS assessment as standard reference. A total of 851 radiomics features were extracted using the PyRadiomics Python package from the Slicer 3D image computing platform after slice-by-slice segmentation on CT portal phase by two expert radiologists of each individual liver metastasis performed first independently by the individual reader and then in consensus. Balancing technique was performed, and inter- and intraclass correlation coefficients were calculated to assess the between-observer and within-observer reproducibility of features. Receiver operating characteristics (ROC) analysis with the calculation of area under the ROC curve (AUC), sensitivity (SENS), specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV) and accuracy (ACC) were assessed for each parameter. Linear and non-logistic regression model (LRM and NLRM) and different machine learning-based classifiers were considered. Moreover, features selection was performed before and after a normalized procedure using two different methods (3-sigma and z-score).

Results

Seventy-seven liver metastases in 28 patients with a mean age of 60 years (range 40–80 years) were analyzed. The best predictors, at univariate analysis for both normalized procedures, were original_shape_Maximum2DDiameter and wavelet_HLL_glcm_InverseVariance that reached an accuracy of 80%, an AUC ≥ 0.75, a sensitivity ≥ 80% and a specificity ≥ 70% (p value <  < 0.01). However, a multivariate analysis significantly increased the accuracy in RAS prediction when a linear regression model (LRM) was used. The best performance was obtained using a LRM combining linearly 12 robust features after a z-score normalization procedure: AUC of 0.953, accuracy 98%, sensitivity 96%, specificity of 100%, PPV 100% and NPV 96% (p value <  < 0.01). No statistically significant increase was obtained considering the tested machine learning both without normalization and with normalization methods.

Conclusions

Normalized approach in CT radiomics analysis allows to predict RAS mutational status in colorectal liver metastases patients.

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Data availability

Data are reported in the manuscript and at link https://zenodo.org/records/10820402

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Acknowledgements

The authors are grateful to Alessandra Trocino, librarian at the National Cancer Institute of Naples, Italy.

Funding

This work was supported by the Italian Ministry of Health Ricerca Corrente funds.

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Author notes

  1. Vincenza Granata and Roberta Fusco contributed equally in the study.

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

    Maria Chiara Brunese

  4. Pathological Anatomy and Cytopathology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, 80131, Naples, Italy

    Annabella Di Mauro

  5. Clinical Sperimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, 80131, Naples, Italy

    Antonio Avallone & Alessandro Ottaiano

  6. IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) “Dino Amadori”, 47014, Mendola, Italy

    Nicola Normanno

  7. Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131, Naples, Italy

    Francesco Izzo

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  1. Vincenza Granata
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Contributions

Each author contributed to methodology, enrollment of patient and investigation. Each author written, revised and approved the manuscript.

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Correspondence to Vincenza Granata.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Roberta Fusco and Vincenza Granata are editors in Radiologia Medica journal.

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The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee National Cancer Institute of Naples Pascale Foundation.

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Granata, V., Fusco, R., Setola, S.V. et al. Machine learning and radiomics analysis by computed tomography in colorectal liver metastases patients for RAS mutational status prediction. Radiol med (2024). https://doi.org/10.1007/s11547-024-01828-5

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