Biological dosiomic features for the prediction of radiation pneumonitis in esophageal cancer patients

Puttanawarut, Chanon; Sirirutbunkajorn, Nat; Khachonkham, Suphalak; Pattaranutaporn, Poompis; Wongsawat, Yodchanan

doi:10.1186/s13014-021-01950-y

Cited by 10 publications

(8 citation statements)

References 45 publications

(46 reference statements)

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“…As expected, results were comparable with a mean AUC of 0.7 and 0.68 for single dosiomics features analysis using physical dose and EQDs, respectively. This is well in line with findings in the literature ( 42 ). However, EQD2 could not further improve the prediction leading to the conclusion that conversion into EQD2 might be unnecessary for PTP prediction.…”

Section: Discussionsupporting

confidence: 94%

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: Discussionsupporting

confidence: 94%

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

et al. 2023

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“…In agreement with previous studies [ 53 , 54 ], our findings indicated that maintenance of spatial dose information results in a significant improvement ( p < 0.007) in the AUC of dosiomics features (up to 0.78) compared to DVH features (up to 0.71). Our results showed that the ET classifier achieved better performance than other models.…”

Section: Discussionsupporting

confidence: 93%

A dosiomics model for prediction of radiation-induced acute skin toxicity in breast cancer patients: machine learning-based study for a closed bore linac

Saadatmand,

Mahdavi,

Nikoofar

et al. 2024

Eur J Med Res

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Background Radiation induced acute skin toxicity (AST) is considered as a common side effect of breast radiation therapy. The goal of this study was to design dosiomics-based machine learning (ML) models for prediction of AST, to enable creating optimized treatment plans for high-risk individuals. Methods Dosiomics features extracted using Pyradiomics tool (v3.0.1), along with treatment plan-derived dose volume histograms (DVHs), and patient-specific treatment-related (PTR) data of breast cancer patients were used for modeling. Clinical scoring was done using the Common Terminology Criteria for Adverse Events (CTCAE) V4.0 criteria for skin-specific symptoms. The 52 breast cancer patients were grouped into AST 2 + (CTCAE ≥ 2) and AST 2 − (CTCAE < 2) toxicity grades to facilitate AST modeling. They were randomly divided into training (70%) and testing (30%) cohorts. Multiple prediction models were assessed through multivariate analysis, incorporating different combinations of feature groups (dosiomics, DVH, and PTR) individually and collectively. In total, seven unique combinations, along with seven classification algorithms, were considered after feature selection. The performance of each model was evaluated on the test group using the area under the receiver operating characteristic curve (AUC) and f1-score. Accuracy, precision, and recall of each model were also studied. Statistical analysis involved features differences between AST 2 − and AST 2 + groups and cutoff value calculations. Results Results showed that 44% of the patients developed AST 2 + after Tomotherapy. The dosiomics (DOS) model, developed using dosiomics features, exhibited a noteworthy improvement in AUC (up to 0.78), when spatial information is preserved in the dose distribution, compared to DVH features (up to 0.71). Furthermore, a baseline ML model created using only PTR features for comparison with DOS models showed the significance of dosiomics in early AST prediction. By employing the Extra Tree (ET) classifiers, the DOS + DVH + PTR model achieved a statistically significant improved performance in terms of AUC (0.83; 95% CI 0.71–0.90), accuracy (0.70), precision (0.74) and sensitivity (0.72) compared to other models. Conclusions This study confirmed the benefit of dosiomics-based ML in the prediction of AST. However, the combination of dosiomics, DVH, and PTR yields significant improvement in AST prediction. The results of this study provide the opportunity for timely interventions to prevent the occurrence of radiation induced AST.

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“…Combined radiotherapy and surgery can increase surgical resection rate and improve long-term survival rate [ 4 ]. However, radiation therapy easily damages the normal lung tissue within the radiation field, which in turn causes an inflammatory response in the body, resulting in acute radiation lung injury (acute RILI) [ 5 ]. Acute RILI not only affects the efficacy of radiotherapy but also reduces the quality of life of patients and even leads to death of patients.…”

Section: Introductionmentioning

confidence: 99%

Risk Factors of Acute Radiation-Induced Lung Injury Induced by Radiotherapy for Esophageal Cancer

Zhang¹,

Liu²,

Song³

et al. 2022

Computational and Mathematical Methods in Medicine

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Objective. To investigate the risk factors of acute radiation-induced lung injury (acute RILI) induced by radiotherapy for esophageal cancer. Methods. A total of 206 patients with esophageal cancer who received radiotherapy in our hospital from January 2017 to March 2020 were selected. The general data such as gender, age, and comorbidities of the patients were collected, as well as the levels of cytokines (TNF-α, TNF-β, and IL-6) in peripheral blood before radiotherapy; radiotherapy dose-related parameters were recorded during radiotherapy. Follow-up was 12 months after radiotherapy. The patients with induced acute RILI after radiotherapy were set as the observation group ( n = 75 ). Patients without acute RILI after radiotherapy were set as the control group ( n = 131 ). Univariate and multivariate logistic regression analysis was performed on the risk factors of acute RILI induced by radiotherapy for esophageal cancer. Results. Univariate analysis and multivariate logistic regression analysis showed that the combined diabetes, total radiation dose, combined lung disease, physical factors (V30, Dmean), and preradiotherapy cytokine (TNF-α, TNF-β, and IL-6) elevated level was an independent risk factor for radiotherapy-induced acute RILI in esophageal cancer ( P < 0.05 ).Conclusion. Concomitant diabetes, total radiation dose, lung disease, physical factors (V30, Dmean), and levels of cytokines (TNF-α, TNF-β, and IL-6) before radiation therapy are risk factors for acute RILI induced by radiation therapy in esophageal cancer. The possibility of acute RILI should be comprehensively assessed according to the patient’s condition, and the radiotherapy regimen should be adjusted to reduce and avoid the induction of acute radiation-induced lung injury.

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Biological dosiomic features for the prediction of radiation pneumonitis in esophageal cancer patients

Cited by 10 publications

References 45 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

A dosiomics model for prediction of radiation-induced acute skin toxicity in breast cancer patients: machine learning-based study for a closed bore linac

Risk Factors of Acute Radiation-Induced Lung Injury Induced by Radiotherapy for Esophageal Cancer

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