LDeform: Longitudinal deformation analysis for adaptive radiotherapy of lung cancer

Nadeem, Saad; Zhang, Pengpeng; Rimner, Andreas; Sonke, Jan‐Jakob; Deasy, Joseph O.; Tannenbaum, Allen

doi:10.1002/mp.13907

Cited by 7 publications

(7 citation statements)

References 17 publications

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“…In lung cancer, various studies have utilized Jacobian maps using CT or PET/CT to assess lung volumetric shrinkage, 20 detect lung radiation‐induced disease, 21 visually identify temporal changes in lung nodules, 22 and correlate Jacobian map features and histopathologic response 23 . A recent study proposed a method to quantify surface changes of the tumor to predict tumor geometrical responses 24 . Similarly, Zhang et al 25 .…”

Section: Discussionmentioning

confidence: 99%

“…23 A recent study proposed a method to quantify surface changes of the tumor to predict tumor geometrical responses. 24 Similarly, Zhang et al 25 cross-validated and expanded the predictive atlas to estimate geometric patterns of lung shrinkage using data from two institutions. These works focused on the visible tumor or its boundaries (surface) and do not aim at distinguishing or accounting for modes of tumor regression, that is, elastic versus non-elastic regression.…”

Section: Clinical Evaluationmentioning

confidence: 99%

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Identification of modes of tumor regression in non‐small cell lung cancer patients during radiotherapy

et al. 2021

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Observed gross tumor volume (GTV) shrinkage during radiotherapy (RT) raises the question of whether to adapt treatment to changes observed on the acquired images. In the literature, two modes of tumor regression have been described: elastic and non-elastic. These modes of tumor regression will affect the safety of treatment adaptation. This study applies a novel approach, using routine cone-beam computed tomography (CBCT) and deformable image registration to automatically distinguish between elastic and non-elastic tumor regression. Methods: In this retrospective study, 150 locally advanced non-small cell lung cancer patients treated with 55 Gray of radiotherapy were included.First,the two modes of tumor regression were simulated. For each mode of tumor regression, one timepoint was simulated. Based on the results of simulated data, the approach used for analysis in real patients was developed. CBCTs were non-rigidly registered to the baseline CBCT using a cubic B-spline algorithm, NiftyReg. Next, the Jacobian determinants were computed from the deformation vector fields. To capture local volume changes, 10 Jacobian values were sampled perpendicular to the surface of the GTV, across the lung-tumor boundary. From the simulated data, we can distinguish elastic from non-elastic tumor regression by comparing the Jacobian values samples between 5 and 12.5 mm inside and 5 and 12.5 mm outside the planning GTV. Finally, morphometric results were compared between tumors of different histologies. Results: Most patients (92.3%) in our cohort showed stable disease in the first week of treatment and non-elastic shrinkage in the later weeks of treatment. At week 2, 125 patients (88%) showed stable disease, three patients (2.1%) disease progression, and 11 patients (8%) regression. By treatment completion, 91 patients (64%) had stable disease, one patient (0.7%) progression and 46 patients (32%) regression. A slight difference in the mode of tumor change was observed between tumors of different histologies. Conclusion: Our novel approach shows that it may be possible to automatically quantify and identify global changes in lung cancer patients during RT,using routine CBCT images. Our results show that different regions of the tumor change in different ways. Therefore, careful consideration should be taken when adapting RT.

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Section: Discussionmentioning

confidence: 99%

Section: Clinical Evaluationmentioning

confidence: 99%

Identification of modes of tumor regression in non‐small cell lung cancer patients during radiotherapy

et al. 2021

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“…While other studies have also identified pulmonary toxicities as predictors of DIR inaccuracy (48), the algorithms are continuously being improved to account for such changes and presence of similar conditions (49)(50)(51)(52), and even moving towards accurately predicting tumor responses (53). While utilizing DIR algorithms with CBCT imaging is a promising solution to a full re-simulation, not all treatment modalities allow for daily CBCT imaging.…”

Section: Use Of Deformable Image Registration Based Algorithms In Place Of Re-simulationmentioning

confidence: 99%

Adaptive Radiation Therapy in the Treatment of Lung Cancer: An Overview of the Current State of the Field

et al. 2021

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Lung cancer treatment is constantly evolving due to technological advances in the delivery of radiation therapy. Adaptive radiation therapy (ART) allows for modification of a treatment plan with the goal of improving the dose distribution to the patient due to anatomic or physiologic deviations from the initial simulation. The implementation of ART for lung cancer is widely varied with limited consensus on who to adapt, when to adapt, how to adapt, and what the actual benefits of adaptation are. ART for lung cancer presents significant challenges due to the nature of the moving target, tumor shrinkage, and complex dose accumulation because of plan adaptation. This article presents an overview of the current state of the field in ART for lung cancer, specifically, probing topics of: patient selection for the greatest benefit from adaptation, models which predict who and when to adapt plans, best timing for plan adaptation, optimized workflows for implementing ART including alternatives to re-simulation, the best radiation techniques for ART including magnetic resonance guided treatment, algorithms and quality assurance, and challenges and techniques for dose reconstruction. To date, the clinical workflow burden of ART is one of the major reasons limiting its widespread acceptance. However, the growing body of evidence demonstrates overwhelming support for reduced toxicity while improving tumor dose coverage by adapting plans mid-treatment, but this is offset by the limited knowledge about tumor control. Progress made in predictive modeling of on-treatment tumor shrinkage and toxicity, optimizing the timing of adaptation of the plan during the course of treatment, creating optimal workflows to minimize staffing burden, and utilizing deformable image registration represent ways the field is moving toward a more uniform implementation of ART.

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“…According to retrospective studies, the setup variation can be characterized early on during the treatment process 5,12 . Therefore, ART may potentially benefit from predictions of patient's anatomical deformation in the later timepoints since these predictions could directly be used in the replanning process and improve the therapeutic outcome 10,13,14 . In this paper, we present a novel 3D deep learning sequence‐to‐sequence model (Seq2Seq) using ConvLSTM to predict patient anatomy deformation (with reference to the planning/pre‐treatment image) given any number of input/output sequence timepoints.…”

Section: Introduction and Purposementioning

confidence: 99%

Deformation driven Seq2Seq longitudinal tumor and organs‐at‐risk prediction for radiotherapy

et al. 2021

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Purpose Radiotherapy presents unique challenges and clinical requirements for longitudinal tumor and organ‐at‐risk (OAR) prediction during treatment. The challenges include tumor inflammation/edema and radiation‐induced changes in organ geometry, whereas the clinical requirements demand flexibility in input/output sequence timepoints to update the predictions on rolling basis and the grounding of all predictions in relationship to the pre‐treatment imaging information for response and toxicity assessment in adaptive radiotherapy. Methods To deal with the aforementioned challenges and to comply with the clinical requirements, we present a novel 3D sequence‐to‐sequence model based on Convolution Long Short‐Term Memory (ConvLSTM) that makes use of series of deformation vector fields (DVFs) between individual timepoints and reference pre‐treatment/planning CTs to predict future anatomical deformations and changes in gross tumor volume as well as critical OARs. High‐quality DVF training data are created by employing hyper‐parameter optimization on the subset of the training data with DICE coefficient and mutual information metric. We validated our model on two radiotherapy datasets: a publicly available head‐and‐neck dataset (28 patients with manually contoured pre‐, mid‐, and post‐treatment CTs), and an internal non‐small cell lung cancer dataset (63 patients with manually contoured planning CT and 6 weekly CBCTs). Results The use of DVF representation and skip connections overcomes the blurring issue of ConvLSTM prediction with the traditional image representation. The mean and standard deviation of DICE for predictions of lung GTV at weeks 4, 5, and 6 were 0.83 ± 0.09, 0.82 ± 0.08, and 0.81 ± 0.10, respectively, and for post‐treatment ipsilateral and contralateral parotids, were 0.81 ± 0.06 and 0.85 ± 0.02. Conclusion We presented a novel DVF‐based Seq2Seq model for medical images, leveraging the complete 3D imaging information of a relatively large longitudinal clinical dataset, to carry out longitudinal GTV/OAR predictions for anatomical changes in HN and lung radiotherapy patients, which has potential to improve RT outcomes.

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LDeform: Longitudinal deformation analysis for adaptive radiotherapy of lung cancer

Abstract: This is the author manuscript accepted for publication and has undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as

Cited by 7 publications

References 17 publications

Identification of modes of tumor regression in non‐small cell lung cancer patients during radiotherapy

Identification of modes of tumor regression in non‐small cell lung cancer patients during radiotherapy

Adaptive Radiation Therapy in the Treatment of Lung Cancer: An Overview of the Current State of the Field

Deformation driven Seq2Seq longitudinal tumor and organs‐at‐risk prediction for radiotherapy

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