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

Llorián-Salvador, Oscar; Akhgar, Joachim; Pigorsch, Steffi; Borm, Kai Joachim; Münch, Stefan; Bernhardt, Denise; Rost, Burkhard; Andrade‐Navarro, Miguel A.; Combs, Stephanie E.; Peeken, Jan C.

doi:10.20944/preprints202212.0195.v1

Cited by 3 publications

(1 citation statement)

References 37 publications

(45 reference statements)

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“…Apart from conventional dosimetric approaches, sophisticated methods such as machine learning gain more and more importance for radiation oncology. In recent years, it has been shown that spatial quantitative features assessing the image grey-level distribution extracted from medical imaging data (radiomics) allow for unprecedented predictions of clinical endpoints including patient survival, disease progression, tumor characterization, tumor response and tumor detection ( 9 – 17 ). Analysis using spatial features of the dose distribution or image grey-level distributions, referred to as dosiomics ( 18 – 22 ) or radiomics ( 23 – 25 ) and even the combination of both ( 26 , 27 ) have also been successfully investigated for prediction of lung toxicity after thoracic radiotherapy in previous studies.…”

Section: Introductionmentioning

confidence: 99%

Dosiomics and radiomics to predict pneumonitis after thoracic stereotactic body radiotherapy and immune checkpoint inhibition

et al. 2023

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IntroductionPneumonitis is a relevant side effect after radiotherapy (RT) and immunotherapy with checkpoint inhibitors (ICIs). Since the effect is radiation dose dependent, the risk increases for high fractional doses as applied for stereotactic body radiation therapy (SBRT) and might even be enhanced for the combination of SBRT with ICI therapy. Hence, patient individual pre-treatment prediction of post-treatment pneumonitis (PTP) might be able to support clinical decision making. Dosimetric factors, however, use limited information and, thus, cannot exploit the full potential of pneumonitis prediction.MethodsWe investigated dosiomics and radiomics model based approaches for PTP prediction after thoracic SBRT with and without ICI therapy. To overcome potential influences of different fractionation schemes, we converted physical doses to 2 Gy equivalent doses (EQD2) and compared both results. In total, four single feature models (dosiomics, radiomics, dosimetric, clinical factors) were tested and five combinations of those (dosimetric+clinical factors, dosiomics+radiomics, dosiomics+dosimetric+clinical factors, radiomics+dosimetric+clinical factors, radiomics+dosiomics+dosimetric+clinical factors). After feature extraction, a feature reduction was performed using pearson intercorrelation coefficient and the Boruta algorithm within 1000-fold bootstrapping runs. Four different machine learning models and the combination of those were trained and tested within 100 iterations of 5-fold nested cross validation.ResultsResults were analysed using the area under the receiver operating characteristic curve (AUC). We found the combination of dosiomics and radiomics features to outperform all other models with AUCradiomics+dosiomics, D = 0.79 (95% confidence interval 0.78-0.80) and AUCradiomics+dosiomics, EQD2 = 0.77 (0.76-0.78) for physical dose and EQD2, respectively. ICI therapy did not impact the prediction result (AUC ≤ 0.5). Clinical and dosimetric features for the total lung did not improve the prediction outcome.ConclusionOur results suggest that combined dosiomics and radiomics analysis can improve PTP prediction in patients treated with lung SBRT. We conclude that pre-treatment prediction could support clinical decision making on an individual patient basis with or without ICI therapy.

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Section: Introductionmentioning

confidence: 99%

Dosiomics and radiomics to predict pneumonitis after thoracic stereotactic body radiotherapy and immune checkpoint inhibition

et al. 2023

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Dosiomics and radiomics-based prediction of pneumonitis after radiotherapy and immune checkpoint inhibition: The relevance of fractionation

Kraus,

Oreshko,

Schnabel

et al. 2024

Lung Cancer

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

Llorián-Salvador,

Akhgar,

Pigorsch

et al. 2023

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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.

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

Cited by 3 publications

References 37 publications

Dosiomics and radiomics to predict pneumonitis after thoracic stereotactic body radiotherapy and immune checkpoint inhibition

Dosiomics and radiomics to predict pneumonitis after thoracic stereotactic body radiotherapy and immune checkpoint inhibition

Dosiomics and radiomics-based prediction of pneumonitis after radiotherapy and immune checkpoint inhibition: The relevance of fractionation

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

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