Early Prediction of Acute Esophagitis for Adaptive Radiation Therapy

Alam, Sadegh R; Zhang, Pengpeng; Zhang, Siyuan; Chen, Ishita; Rimner, Andreas; Tyagi, Neetu; Hu, Yu‐Chi; Lu, Wei; Deasy, Joseph O.; Thor, Maria

doi:10.1016/j.ijrobp.2021.01.007

Cited by 11 publications

(26 citation statements)

References 21 publications

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“…Our dataset showed radiation toxicity (i.e. ARE) of 31.7% when using IMRT radiotherapy only, which falls into the previously reported range of 30–55% [ 12 , 13 , 18 , 26 , 43 ] for ARE grades ≥ 2. Apart from this, the incidence rates of ARE with a grade ≥ 3 were about 3.7% in our dataset, which was lower than that of the previous studies (11.4% and 10.3%) [ 14 , 42 ].…”

Section: Discussionsupporting

confidence: 64%

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Multi-omics to predict acute radiation esophagitis in patients with lung cancer treated with intensity-modulated radiation therapy

et al. 2023

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Purpose The study aimed to predict acute radiation esophagitis (ARE) with grade ≥ 2 for patients with locally advanced lung cancer (LALC) treated with intensity-modulated radiation therapy (IMRT) using multi-omics features, including radiomics and dosiomics. Methods 161 patients with stage IIIA−IIIB LALC who received chemoradiotherapy (CRT) or radiotherapy by IMRT with a prescribed dose from 45 to 70 Gy from 2015 to 2019 were enrolled retrospectively. All the toxicity gradings were given following the Common Terminology Criteria for Adverse Events V4.0. Multi-omics features, including radiomics, dosiomics (including dose−volume histogram dosimetric parameters), were extracted based on the planning CT image and three-dimensional dose distribution. All data were randomly divided into training cohorts (N = 107) and testing cohorts (N = 54). In the training cohorts, features with reliably high outcome relevance and low redundancy were selected under random patient subsampling. Four classification models (using clinical factors (CF) only, using radiomics features (RFs) only, dosiomics features (DFs) only, and the hybrid features (HFs) containing clinical factors, radiomics and dosiomics) were constructed employing the Ridge classifier using two-thirds of randomly selected patients as the training cohort. The remaining patient was treated as the testing cohort. A series of models were built with 30 times training–testing splits. Their performances were assessed using the area under the ROC curve (AUC) and accuracy. Results Among all patients, 51 developed ARE grade ≥ 2, with an incidence of 31.7%. Next, 8990 radiomics and 213 dosiomics features were extracted, and 3, 6, 12, and 13 features remained after feature selection in the CF, DF, RF and DF models, respectively. The RF and HF models achieved similar classification performance, with the training and testing AUCs of 0.796 ± 0.023 (95% confidence interval (CI [0.79, 0.80])/0.744 ± 0.044 (95% CI [0.73, 0.76]) and 0.801 ± 0.022 (95% CI [0.79, 0.81]) (p = 0.74), respectively. The model performances using CF and DF features were poorer, with training and testing AUCs of 0.573 ± 0.026 (95% CI [0.56, 0.58])/ 0.509 ± 0.072 (95% CI [0.48, 0.53]) and 0.679 ± 0.027 (95% CI [0.67, 0.69])/0.604 ± 0.041 (95% CI [0.53, 0.63]) compared with the above two models (p < 0.001), respectively. Conclusions In LALC patients treated with CRT IMRT, the ARE grade ≥ 2 can be predicted using the pretreatment radiotherapy image features. To predict ARE, the multi-omics features had similar predictability with radiomics features; however, the dosiomics features and clinical factors had a limited classification performance.

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Section: Discussionsupporting

confidence: 64%

“…Alam et al . [ 43 ] reported the potential predictability using CBCT and MR images by evaluating the esophagus volume changes in ARE. Another study demonstrated that the SUV peak of PET images correlated significantly with the ARE [ 44 ].…”

Section: Discussionmentioning

confidence: 99%

Multi-omics to predict acute radiation esophagitis in patients with lung cancer treated with intensity-modulated radiation therapy

et al. 2023

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“…One study used magnetic resonance imaging (MRI) in their work. Multi‐modalities such as the use of CT with MRI were reported by Alam et al 33 . Uses of CT and MRI in order to compare the performance of the CT and MRI‐based radiomics models was carried out by Wang et al 32 .…”

Section: Resultsmentioning

confidence: 99%

“…In order to compare between non‐delta and delta‐radiomics models, articles reported radiomics model development in both single‐time point features (non‐delta radiomics) and multiple‐time points features (delta‐radiomics), were included. Four studies that met the criteria, and reported AUCs and confidence intervals of 95% (95% CI) were included in the meta‐analysis 28,29,33,35 . A comparison of delta radiomics with non‐delta radiomics models reported in these selected studies was carried out using a random effects model and plotted in Figure 3.…”

Section: Resultsmentioning

confidence: 99%

Delta‐radiomics‐based models for toxicity prediction in radiotherapy: A systematic review and meta‐analysis

et al. 2023

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IntroductionDelta‐radiomics models are potentially able to improve the treatment assessment than single‐time point features. The purpose of this study is to systematically synthesize the performance of delta‐radiomics‐based models for radiotherapy (RT)‐induced toxicity.MethodsA literature search was performed following the PRISMA guidelines. Systematic searches were performed in PubMed, Scopus, Cochrane and Embase databases in October 2022. Retrospective and prospective studies on the delta‐radiomics model for RT‐induced toxicity were included based on predefined PICOS criteria. A random‐effect meta‐analysis of AUC was performed on the performance of delta‐radiomics models, and a comparison with non‐delta radiomics models was included.ResultsOf the 563 articles retrieved, 13 selected studies of RT‐treated patients on different types of cancer (HNC = 571, NPC = 186, NSCLC = 165, oesophagus = 106, prostate = 33, OPC = 21) were eligible for inclusion in the systematic review. Included studies show that morphological and dosimetric features may improve the predictive model performance for the selected toxicity. Four studies that reported both delta and non‐delta radiomics features with AUC were included in the meta‐analysis. The AUC random effects estimate for delta and non‐delta radiomics models were 0.80 and 0.78 with heterogeneity, I2 of 73% and 27% respectively.ConclusionDelta‐radiomics‐based models were found to be promising predictors of predefined end points. Future studies should consider using standardized methods and radiomics features and external validation to the reviewed delta‐radiomics model.

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“…The Jacobian integral defined as [(Mean J – 1) × structure volume] measured the net local volume change. The correlation between the local volume change of each structure obtained using Jacobian integral and the volume change obtained from the ground-truth contours by the physician was calculated [22] , [26] by generating scatter plots.…”

Section: Methodsmentioning

confidence: 99%

Inter- and intrafraction motion assessment and accumulated dose quantification of upper gastrointestinal organs during magnetic resonance-guided ablative radiation therapy of pancreas patients

Alam

Veeraraghavan

Tringale

et al. 2022

Physics and Imaging in Radiation Oncology

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Early Prediction of Acute Esophagitis for Adaptive Radiation Therapy

Cited by 11 publications

References 21 publications

Multi-omics to predict acute radiation esophagitis in patients with lung cancer treated with intensity-modulated radiation therapy

Multi-omics to predict acute radiation esophagitis in patients with lung cancer treated with intensity-modulated radiation therapy

Delta‐radiomics‐based models for toxicity prediction in radiotherapy: A systematic review and meta‐analysis

Inter- and intrafraction motion assessment and accumulated dose quantification of upper gastrointestinal organs during magnetic resonance-guided ablative radiation therapy of pancreas patients

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