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CT-based radiomics prediction of complete response after stereotactic body radiation therapy for patients with lung metastases

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Abstract

Purpose

Stereotactic body radiotherapy (SBRT) is a key treatment modality for lung cancer patients. This study aims to develop a machine learning-based prediction model of complete response for lung oligometastatic cancer patients undergoing SBRT.

Materials and methods

CT images of 80 pulmonary oligometastases from 56 patients treated with SBRT were analyzed. The gross tumor volumes (GTV) were contoured on CT images. Patients that achieved complete response (CR) at 4 months were defined as responders. For each GTV, 107 radiomic features were extracted using the Pyradiomics software. The concordance correlation coefficients (CCC) between the region of interest (ROI)-based radiomics features obtained by the two segmentations were calculated. Pairwise feature interdependencies were evaluated using the Spearman rank correlation coefficient. The association of clinical variables and radiomics features with CR was evaluated with univariate logistic regression. Two supervised machine learning models, the logistic regression (LR) and the classification and regression tree analysis (CART), were trained to predict CR. The models were cross-validated using a five-fold cross-validation. The performance of models was assessed by receiver operating characteristic curve (ROC) and class-specific accuracy, precision, recall, and F1-measure evaluation metrics.

Results

Complete response was associated with four radiomics features, namely the surface to volume ratio (SVR; p = 0.003), the skewness (Skew; p = 0.027), the correlation (Corr; p = 0.024), and the grey normalized level uniformity (GNLU; p = 0.015). No significant relationship between clinical parameters and CR was found. In the validation set, the developed LR and CART machine learning models had an accuracy, precision, and recall of 0.644 and 0.750, 0.644 and 0.651, and 0.635 and 0.754, respectively. The area under the curve for CR prediction was 0.707 and 0.753 for the LR and CART models, respectively.

Conclusion

This analysis demonstrates that radiomics features obtained from pretreatment CT could predict complete response of lung oligometastases following SBRT.

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Funding

The authors received no specific financial support for the research, authorship, and/or publication of this article.

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Authors and Affiliations

  1. Gemelli Molise Hospital, Medical Physics Unit, Largo Gemelli 1, 86100, Campobasso, Italy

    Savino Cilla, Domenico Pistilli & Carmela Romano

  2. Radiation Oncology Unit, Gemelli Molise Hospital, Campobasso, Italy

    Gabriella Macchia & Francesco Deodato

  3. Radiology Unit, Gemelli Molise Hospital, Campobasso, Italy

    Antonio Pierro

  4. Radiation Oncology Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy

    Alessandra Arcelli, Milly Buwenge & Alessio Giuseppe Morganti

  5. Department of Experimental, Diagnostic, and Specialty Medicine—DIMES, Alma Mater Studiorum, Università di Bologna, Diagnostic, Italy

    Alessio Giuseppe Morganti

  6. Istituto di Radiologia, Università Cattolica del Sacro Cuore, Roma, Italy

    Francesco Deodato

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  1. Savino Cilla
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Contributions

Concept and design: SC. Conduct: SC, DP, FD, CR, GM. Supervision: AGM. Data acquisition: DP, CR, AP, AA, MB. Statistical analysis: SC. Critical review: SC, FD, GM, AGM. Manuscript drafting, editing: all authors. Revision and final approval: all authors.

Corresponding author

Correspondence to Savino Cilla.

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Conflict of interest

S. Cilla, D. Pistilli, C. Romano, G. Macchia, A. Pierro, A. Arcelli, M. Buwenge, A.G. Morganti, and F. Deodato declare that they have no competing interests.

Ethical standards

All procedures performed in studies involving human participants or on human tissue were in accordance with the ethical standards of the institutional and/or national research committee and with the 1975 Helsinki declaration and its later amendments or comparable ethical standards. The study received approval at the Gemelli Molise Hospital Institutional Review Board. Informed consent: not applicable; patients signed informed consent to the treatment procedure, but the study was retrospective.

Additional information

Two authors share the seniorship: Alessio Giuseppe Morganti and Francesco Deodato.

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Cilla, S., Pistilli, D., Romano, C. et al. CT-based radiomics prediction of complete response after stereotactic body radiation therapy for patients with lung metastases. Strahlenther Onkol 199, 676–685 (2023). https://doi.org/10.1007/s00066-023-02086-6

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