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The importance of planning CT-based imaging features for machine learning-based prediction of pain response.

ABSTRACT: Patients suffering from painful spinal bone metastases (PSBMs) often undergo palliative radiation therapy (RT), with an efficacy of approximately two thirds of patients. In this exploratory investigation, we assessed the effectiveness of machine learning (ML) models trained on radiomics, semantic and clinical features to estimate complete pain response. Gross tumour volumes (GTV) and clinical target volumes (CTV) of 261 PSBMs were segmented on planning computed tomography (CT) scans. Radiomics, 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. The best radiomics 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). We could demonstrate that radiomics 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.

SUBMITTER: Llorian-Salvador O

PROVIDER: S-EPMC10576053 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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The importance of planning CT-based imaging features for machine learning-based prediction of pain response.

Llorián-Salvador Óscar Ó Akhgar Joachim J Pigorsch Steffi S Borm Kai K Münch Stefan S Bernhardt Denise D Rost Burkhard B Andrade-Navarro Miguel A MA Combs Stephanie E SE Peeken Jan C JC

Scientific reports 20231013 1

Patients suffering from painful spinal bone metastases (PSBMs) often undergo palliative radiation therapy (RT), with an efficacy of approximately two thirds of patients. In this exploratory investigation, we assessed the effectiveness of machine learning (ML) models trained on radiomics, semantic and clinical features to estimate complete pain response. Gross tumour volumes (GTV) and clinical target volumes (CTV) of 261 PSBMs were segmented on planning computed tomography (CT) scans. Radiomics, ...[more]

PMID: 37833283

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The importance of planning CT-based imaging features for machine learning-based prediction of pain response.

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The importance of planning CT-based imaging features for machine learning-based prediction of pain response.

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