Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Machine Learning based Prediction of Pain Response to Palliative Radiation Therapy - is there a Role for Planning CT-based Radiomics and Semantic Imaging Features?

Oscar Llorián-Salvador
,
Joachim Akhgar
,
Steffi Pigorsch
ORCID logo ,
Kai Borm
,
Stefan Münch
,
Denise Bernhardt
ORCID logo ,
Burkhard Rost
,
Miguel Andrade-Navarro
ORCID logo ,
Stephanie Combs
,
Jan Peeken
* ORCID logo
Version 1 : Received: 9 December 2022 / Approved: 12 December 2022 / Online: 12 December 2022 (08:22:54 CET)

How to cite: Llorián-Salvador, O.; Akhgar, J.; Pigorsch, S.; Borm, K.; Münch, S.; Bernhardt, D.; Rost, B.; Andrade-Navarro, M.; Combs, S.; Peeken, J. Machine Learning based Prediction of Pain Response to Palliative Radiation Therapy - is there a Role for Planning CT-based Radiomics and Semantic Imaging Features?. Preprints 2022, 2022120195. https://doi.org/10.20944/preprints202212.0195.v1 Llorián-Salvador, O.; Akhgar, J.; Pigorsch, S.; Borm, K.; Münch, S.; Bernhardt, D.; Rost, B.; Andrade-Navarro, M.; Combs, S.; Peeken, J. Machine Learning based Prediction of Pain Response to Palliative Radiation Therapy - is there a Role for Planning CT-based Radiomics and Semantic Imaging Features?. Preprints 2022, 2022120195. https://doi.org/10.20944/preprints202212.0195.v1

Abstract

Background: Painful spinal bone metastases (PSBMs) patients regularly receive palliative radiation therapy (RT) with response rates in about 2 of 3 patients. In this exploratory study, we evaluated the value of machine learning (ML) models based on radiomic, semantic and clinical features to predict complete pain response. Methods: Gross tumour volumes (GTV) and clinical target volumes (CTV) of 261 PSBMs were segmented on planning computed tomography (CT) scans. Radiomic, semantic and clinical features were collected for all patients. Random forest (RFC) and support vector machine (SVM) classifiers were compared using repeated nested cross-validation.Results: The best radiomic classifier was trained on CTV with an area under the receiver-operator curve (AUROC) of 0.62 ± 0.01 (RFC; 95% confidence interval). The semantic model achieved a comparable AUROC of 0.63 ± 0.01 (RFC), significantly below the clinical model (SVM, AUROC: 0.80 ± 0.01); and slightly lower than the spinal instability neoplastic score (SINS; LR, AUROC: 0.65 ± 0.01). A combined model did not improve performance (AUROC: 0,74 ± 0,01).Conclusions: We could demonstrate that radiomic and semantic analyses of planning CTs allowed for limited prediction of therapy response to palliative RT. ML predictions based on established clinical parameters achieved the best results.

Keywords

radiomics; machine learning; radiation therapy; bone metastases; prediction

Subject

Medicine and Pharmacology, Oncology and Oncogenics

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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