Magnetic resonance image guided radiation therapy (MRIgRT) is a relatively new technology that has already shown outcomes benefits but that has not yet reached its clinical potential. The improved soft-tissue contrast provided with MR, coupled with the immediacy of image acquisition with respect to the treatment, enables expansion of on-table adaptive protocols, currently at a cost of increased treatment complexity, use of human resources, and longer treatment slot times, which translate to decreased throughput. Many approaches are being investigated to meet these challenges, including the development of artificial intelligence (AI) algorithms to accelerate and automate much of the workflow and improved technology that parallelizes workflow tasks, as well as improvements in image acquisition speed and quality. This article summarizes limitations of current available integrated MRIgRT systems and gives an outlook about scientific developments to further expand the use of MRIgRT. 

The Future of MR-Guided Radiation Therapy.

Background and aim Radiation pneumonitis (RP) is a critical pulmonary toxicity following dose delivery to the lung, and it is usually diagnosed after radiotherapy courses are completed. Because RP may result in a lethal complication, a practical method for detecting early-phase RP is awaited. In this article, we describe our experience through a variety of clinical cases and discuss treatment decisions and lessons we have made and learned. Materials and methods A daily cone-beam computed tomography (CBCT) scan was employed with a lung window setting to detect the early-phase RP during treatment courses. For the past five years, thirty patients were diagnosed with RP, and eight patients were detected during radiotherapy courses on the CBCT images. Our best efforts were made in detecting early ground-glass opacity and early RP on CBCT images prior to symptoms. The eight cases were described in more detail with CBCT or CT images. Results and discussion Initially, RP was detected at 50 Gy or greater. However, more careful CBCT observation resulted in earlier detection at around 40 Gy. Then, a new problem arose whether the treatment should be terminated. It was reported that early RP development was associated with higher-grade complications, and therefore it is preferable to terminate radiotherapy once we detect even early-phase RP. However, termination in the middle of the treatment course may significantly reduce the therapeutic effect. In our experience, patients with favorable clinical status may continue to receive radiotherapy with careful observation of lung parenchyma on CBCT images and clinical data, such as Krebs Von den Lungen-6 (KL-6) and C-reactive protein (CRP). Conclusion We have shown that early detection of RP may be feasible during radiotherapy courses by daily monitoring of CBCT lung images. Further studies are awaited to proceed.

Early Detection of Radiation Pneumonitis on Cone-Beam CT Images During a Course of Radiotherapy: A Case Series Report

<h3>Purpose/Objective(s)</h3> Delta radiomics which refers to longitudinal changes of radiomic features over time has shown the potential to predict treatment response. However, its role in predicting normal lung toxicity has not been studied extensively. This study evaluates the potential for CBCT based delta radiomics in predicting radiotherapy induced lung parenchymal changes during thoracic radiotherapy. <h3>Materials/Methods</h3> Sixty-two patients were selected who underwent thoracic radiation with volume-modulated-arc-therapy. CBCT images from treatment weeks 1, 3 and 6 (CBCTfirst, CBCTmid, CBCTend) were included. An experienced radiologist reviewed the serial lung changes and geographic location. 19 patients had radiological confirmed early pneumonitis. The normal lung was delineated into regions receiving dose between 5-10 Gy(V5-10), 10-20 Gy(V10-20), above 20 Gy (V20) on the planning CT and structures were transferred to CBCT following rigid registration using the treatment planning system. Each CBCT was processed with resampling (2mm3 isotropic), feature extraction (103 Pyradiomic features) and normalization (z-score). Optimal delta features were selected following a forward sequential selection method. Dataset was resampled with synthetic minority oversampling technique. Support vector machine classifier was used to build a machine-learning-based model to pick up incidence of early pneumonitis. The models used were built using 80:20 training/testing data split and performance was evaluated by the area-under-the-receiver-operating-characteristic-curve (AUC). <h3>Results</h3> The median target dose received by the patients during thoracic radiation was 54 Gy. The delta features selected for model training following the sequential feature selection technique were SurfaceArea, Contrast, RootMeanSquared, MeanAbsoluteDeviation, SmallAreaHighGrayLevelEmphasis (GLZSM). At CBCTmid, support vector machine classifier resulted in the best performance in predicting early pneumonitis in the region V5-10, AUC being 0.89 (95% CI 0.89-0.903) and 0.76 (0.74-0.78) in the training and testing datasets, respectively. This was followed by V20 region in the CBCTend with AUC of 0.904(0.89-0.92) and 0.71(0.69-0.74) in the training and testing datasets, respectively. <h3>Conclusion</h3> This study supports a potential role of delta radiomics to predict incidence of early pneumonitis changes with reasonable accuracy in patients undergoing thoracic radiation. Future studies with more patients and external validation are needed to confirm these results and for clinical implementation. In future, prediction models for clinical pneumonitis using CBCT based delta radiomics will be explored. 

Can CBCT-Based Delta Radiomics Predict Normal Lung Toxicity during Thoracic Radiation?

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Can CBCT-Based Delta Radiomics Predict Normal Lung Toxicity during Thoracic Radiation?