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International Conference on Image Analysis and Processing

ICIAP 2022: Image Analysis and Processing. ICIAP 2022 Workshops pp 431–440Cite as

A Predictive System to Classify Preoperative Grading of Rectal Cancer Using Radiomics Features

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13373))

Abstract

Although preoperative biopsy of rectal cancer (RC) is an essential step for confirmation of diagnosis, it currently fails to provide prognostic information to the clinician beyond a rough estimation of tumour grade. In this study we used a risk classification to stratified patient in low-risk and high-risk patients in relation to the disease free survival and the overall survival using histopathological post-operative features. The purpose of this study was to evaluate if low-risk and high-risk RC can be distinguished using a CT-based radiomics model. We retrospectively reviewed the preoperative abdominal contrast-enhanced CT of 40 patients with RC. CT portal-venous phase was used for manual RC segmentation by two radiologists. Radiomics parameters were extracted by using PyRadiomics (3.0) software, which automatically obtained a total of 120 radiomics features. An operator-independent statistical hybrid method was adopted for the selection and reduction of features, while discriminant analysis was used to construct the predictive model. Postoperative histopathological report was used as reference standard. Receiver operating characteristics (ROC) and areas under the ROC curve (AUROC) were calculated to evaluate the diagnostic performance of the most dis-criminating selected parameters. Sensitivity, specificity, and accuracy were calculated. In our study cohort, the original_shape_Maximum3DDiameter and origi-nal_shape_MajorAxisLength demonstrated a good performance in the differentiation between preoperative degree of RC, with an AUROC of 0.680%, sensitivity of 74.02%, specificity of 73.45%, positive predictive value of 81.47%, and accuracy of 73.71%. In conclusion, this preliminary analysis showed statistically significant differences in radiomics features between low-risk and high-risk RC.

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Authors and Affiliations

  1. Oncological and Stomatological Sciences, University of Palermo, Via del Vespro 129, 90127, Palermo, Italy

    Ilaria Canfora

  2. Section of Radiology - Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University Hospital “Paolo Giaccone”, Via del Vespro 129, 90127, Palermo, Italy

    Giuseppe Cutaia, Mauro Calamia, Roberta Faraone, Roberto Cannella, Lo Re Giuseppe & Giuseppe Salvaggio

  3. Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Molecular and Clinical Medicine, University of Palermo, 90127, Palermo, Italy

    Giuseppe Cutaia, Roberto Cannella & Viviana Benfante

  4. Department of Surgical, Oncological and Stomatological Sciences, University of Palermo, Palermo, Via del Vespro 129, 90127, Palermo, Italy

    Marco Marcianò & Giovanni Guercio

  5. Ri.MED Foundation, Via Bandiera 11, 90133, Palermo, Italy

    Viviana Benfante & Albert Comelli

Authors
  1. Ilaria Canfora
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  2. Giuseppe Cutaia
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  3. Marco Marcianò
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  4. Mauro Calamia
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  5. Roberta Faraone
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  6. Roberto Cannella
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  7. Viviana Benfante
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  8. Albert Comelli
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  9. Giovanni Guercio
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  10. Lo Re Giuseppe
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  11. Giuseppe Salvaggio
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Corresponding author

Correspondence to Albert Comelli .

Editor information

Editors and Affiliations

  1. National Research Council, Lecce, Italy

    Pier Luigi Mazzeo

  2. Università Politecnica delle Marche, Ancona, Italy

    Emanuele Frontoni

  3. Boston University, Boston, MA, USA

    Stan Sclaroff

  4. National Research Council, Lecce, Italy

    Cosimo Distante

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Canfora, I. et al. (2022). A Predictive System to Classify Preoperative Grading of Rectal Cancer Using Radiomics Features. In: Mazzeo, P.L., Frontoni, E., Sclaroff, S., Distante, C. (eds) Image Analysis and Processing. ICIAP 2022 Workshops. ICIAP 2022. Lecture Notes in Computer Science, vol 13373. Springer, Cham. https://doi.org/10.1007/978-3-031-13321-3_38

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  • DOI: https://doi.org/10.1007/978-3-031-13321-3_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-13320-6

  • Online ISBN: 978-3-031-13321-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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