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

Kraus, Kim Melanie; Oreshko, Maksym; Bernhardt, Denise; Combs, Stephanie E.; Peeken, Jan C.

doi:10.3389/fonc.2023.1124592

Cited by 7 publications

(5 citation statements)

References 38 publications

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“…Due to the high morbidity and potential mortality of RP in radiotherapy for thoracic tumors, it is particularly important to explore e cient and convenient predictive variables to reduce the incidence of RP and mitigate radiotherapy toxicity. Clinical parameters [15] , cytokines [16] , and dosimetric parameters [17] have been used in previous studies for prediction, which have poor predictive e cacy and stability.…”

Section: Discussionmentioning

confidence: 99%

Research on developing a predictive model for radiation pneumonitis risk based on radiomics and multiparameters

Zhu,

Song,

Zhang

et al. 2024

Preprint

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Objective A Nomogram model was constructed by combined pre-treatment CT radiomics, clinical characteristics, and lung dosimetry data of patients with non-small cell lung cancer,and to explore its predictive value of radiation pneumonitis. Methods A retrospective analysis was conducted on 104 non-surgical NSCLC patients who underwent chest intensity modulated radiation therapy(IMRT) at our center from January 2013 to December 2017. Intratumoral and peritumoral radiomics models were established using pre-radiotherapy CT images, and logistic regression was used to screen for the best clinical and dosimetric parameters. A combined Nomogram model was established by the above parameters, and receiver operating characteristic curve (ROC) analysis and area under the curve (AUC) was performed to estimate its predictive efficacy of radiation pneumonia. Results Among 104 patients, 59 cases developed radiopneumonia and 45 cases did not suffer from radiopneumonia within 6 months after radiotherapy, which were divided into 73 cases in the training set and 31 cases in the validation set. The AUC values of the intratumoral radiomics group model in the training and validation sets were 0.871 (95%CI 0.771~0.938) and 0.719 (95%CI 0.400~0.952), respectively, and the predictive efficacy was better than that of the peritumoral radiomics group model [0.798 (95%CI 0.629~0.921) and 0.714 (95%CI 0.500 ~0.857)]. Multifactorial regression analysis showed that patients' age, smoking, and pre-radiotherapy lymphocyte ratio were associated with radiation pneumonitis (P<0.05); physical dosimetric parameters of MLD and lung V20Gy were associated with radiation pneumonitis (P<0.05).The Nomogram model constructed by the intratumor radiomics model combined with clinical and dosimetric parameters had AUC values of 0.928 (95%CI 0.879~0.966) and 0.765 (95%CI 0.692~0.831) in the training and validation sets, respectively.It has the best prediction efficacy. Conclusion The Nomogram model based on the intratumoral radiomics features of pre-radiotherapy CT images, patient’s age, smoking, and pre-radiotherapy lymphocyte ratio combined with MLD and lung V20Gy has a better predictive accuracy for radiation pneumonitis in NSCLC, and it can be used as a quantitative model for the prediction of radiation pneumonitis in patients undergoing radiotherapy for NSCLC.

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Section: Discussionmentioning

confidence: 99%

Research on developing a predictive model for radiation pneumonitis risk based on radiomics and multiparameters

Zhu,

Song,

Zhang

et al. 2024

Preprint

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“…57 After the high radiation dose of radiotherapy (RT), incidence of ICI-pneumonitis increased. 58 Besides, the history of chronic lung disease, such as interstitial lung disease, especially lung fibrosis, chronic obstructive pulmonary disease (COPD) and asthma, was independently associated with an increased incidence of ICI-pneumonitis. 51,59,60 Thomas HMT et al…”

Section: Risk Factorsmentioning

confidence: 99%

“…Combination therapy with ipilimumab plus nivolumab resulted in the highest incidence of pneumonitis (5%–10%), and 2% were reported to be Grade 3 or 4 events 57 . After the high radiation dose of radiotherapy (RT), incidence of ICI‐pneumonitis increased 58 . Besides, the history of chronic lung disease, such as interstitial lung disease, especially lung fibrosis, chronic obstructive pulmonary disease (COPD) and asthma, was independently associated with an increased incidence of ICI‐pneumonitis 51,59,60 .…”

Section: Ici‐pneumonitismentioning

confidence: 99%

“… 57 After the high radiation dose of radiotherapy (RT), incidence of ICI‐pneumonitis increased. 58 Besides, the history of chronic lung disease, such as interstitial lung disease, especially lung fibrosis, chronic obstructive pulmonary disease (COPD) and asthma, was independently associated with an increased incidence of ICI‐pneumonitis. 51 , 59 , 60 Thomas HMT et al reported the role of novel functional lung radiomics for pneumonitis risk stratification in locally advanced NSCLC and found that the strongest predictor of ICI‐pneumonitis was the presence of baseline COPD.…”

Section: Ici ‐Pneumonitismentioning

confidence: 99%

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Radiological assessment of immunotherapy effects and immune checkpoint‐related pneumonitis for lung cancer

Wang

et al. 2023

J Cellular Molecular Medi

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Immune checkpoint inhibitors (ICIs) therapy have revolutionized advanced lung cancer care. Interestingly, the host responses for patients received ICIs therapy are distinguishing from those with cytotoxic drugs, showing potential initial transient worsening of disease burden, pseudoprogression and delayed time to treatment response. Thus, a new imaging criterion to evaluate the response for immunotherapy should be developed. ICIs treatment is associated with unique adverse events, including potential life‐threatening immune checkpoint inhibitor‐related pneumonitis (ICI‐pneumonitis) if treated patients are not managed promptly. Currently, the diagnosis and clinical management of ICI‐pneumonitis remain challenging. As the clinical manifestation is often nonspecific, computed tomography (CT) scan and X‐ray films play important roles in diagnosis and triage. This article reviews the complications of immunotherapy in lung cancer and illustrates various radiologic patterns of ICI‐pneumonitis. Additionally, it is tried to differentiate ICI‐pneumonitis from other pulmonary pathologies common to lung cancer such as radiation pneumonitis, bacterial pneumonia and coronavirus disease of 2019 (COVID‐19) infection in recent months. Maybe it is challenging to distinguish radiologically but clinical presentation may help.

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“…The role of treatment plan physical quantities including skin dose distribution and dose volume histograms (DVHs) [ 7 , 8 ], as well as patient’s demographic and clinical characteristics have been investigated in predicting AST in breast RT by several investigators [ 9 , 10 ]. However, DVHs lack spatial information of the dose of treatment plans [ 11 ]. A recent literature review of 38 studies has shown considerable heterogeneity in assessing acute radiation dermatitis and contributing risk factors [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

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

Cited by 7 publications

References 38 publications

Research on developing a predictive model for radiation pneumonitis risk based on radiomics and multiparameters

Research on developing a predictive model for radiation pneumonitis risk based on radiomics and multiparameters

Radiological assessment of immunotherapy effects and immune checkpoint‐related pneumonitis for lung cancer

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

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