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Radiomics based predictive modeling of rectal toxicity in prostate cancer patients undergoing radiotherapy: CT and MRI comparison

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Abstract

Background

Rectal toxicity is one of the common side effects after radiotherapy in prostate cancer patients. Radiomics is a non-invasive and low-cost method for developing models of predicting radiation toxicity that does not have the limitations of previous methods. These models have been developed using individual patients’ information and have reliable and acceptable performance. This study was conducted by evaluating the radiomic features of computed tomography (CT) and magnetic resonance (MR) images and using machine learning (ML) methods to predict radiation-induced rectal toxicity.

Methods

Seventy men with pathologically confirmed prostate cancer, eligible for three-dimensional radiation therapy (3DCRT) participated in this prospective trial. Rectal wall CT and MR images were used to extract first-order, shape-based, and textural features. The least absolute shrinkage and selection operator (LASSO) was used for feature selection. Classifiers such as Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), and K-Nearest Neighbors (KNN) were used to create models based on radiomic, dosimetric, and clinical data alone or in combination. The area under the curve (AUC) of the receiver operating characteristic curve (ROC), accuracy, sensitivity, and specificity were used to assess each model’s performance.

Results

The best outcomes were achieved by the radiomic features of MR images in conjunction with clinical and dosimetric data, with a mean of AUC: 0.79, accuracy: 77.75%, specificity: 82.15%, and sensitivity: 67%.

Conclusions

This research showed that as radiomic signatures for predicting radiation-induced rectal toxicity, MR images outperform CT images.

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Data Availability

In the present study, the datasets are in the possession of the corresponding author and can be accessed if needed.

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Acknowledgements

The authors are appreciative of the cooperation of Isfahan Milad Hospital for data collection.

Funding

This article was conducted with the financial support of the Isfahan University of Medical Sciences, Isfahan, I.R. Iran (grant number 399646).

Author information

Authors and Affiliations

  1. Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, 81746-73461, Iran

    Hossein Hassaninejad, Iraj Abedi & Mohamad Bagher Tavakoli

  2. Department of Radiology Technology, Faculty of Allied Medical Sciences, Kerman University of Medical Sciences, Kerman, Iran

    Hamid Abdollahi

  3. Department of Radiology, University of British Columbia, Vancouver, BC, Canada

    Hamid Abdollahi

  4. Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada

    Hamid Abdollahi

  5. Isfahan Milad Hospital, Isfahan, Iran

    Alireza Amouheidari

Authors
  1. Hossein Hassaninejad
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  4. Alireza Amouheidari
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  5. Mohamad Bagher Tavakoli
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Contributions

All authors contributed to the study conception and design. Data curation was performed by Hossein Hassaninejad, Hamid Abdollahi, Iraj Abedi and Alireza Amouheidari. Methodology, formal analysis and investigation were performed by Hossein Hassaninejad and Hamid Abdollahi. Software was performed by Hossein Hassaninejad. Funding acquisition, supervision, project administration was performed by Mohamad Bagher Tavakoli. Writing – Original Draft Preparation were performed by All authors. Writing – Review & Editing were performed by Hossein Hassaninejad, Hamid Abdollahi, Iraj Abedi and Mohamad Bagher Tavakoli.

Corresponding author

Correspondence to Mohamad Bagher Tavakoli.

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Conflict of interest

This article has no conflict of interest and this issue is approved by the authors.

Ethics approval

This investigation was following the Helsinki Declaration. This prospective research was carried out with the agreement of the local ethics committee (Isfahan University of Medical Sciences, Isfahan, Iran, IR.MUI.MED.REC.1399.731).

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Informed consent was obtained from all individual participants included in the study.

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Hassaninejad, H., Abdollahi, H., Abedi, I. et al. Radiomics based predictive modeling of rectal toxicity in prostate cancer patients undergoing radiotherapy: CT and MRI comparison. Phys Eng Sci Med 46, 1353–1363 (2023). https://doi.org/10.1007/s13246-023-01260-5

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  • DOI: https://doi.org/10.1007/s13246-023-01260-5

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