Tumour motion management in lung cancer: a narrative review

Botticella, Angela; Lévy, Antonin; Auzac, G.; Chabert, I.; Berthold, C.; Péchoux, C. Le

doi:10.21037/tlcr-20-856

Cited by 16 publications

(3 citation statements)

References 46 publications

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“…In turn, this enlargement of the irradiated volume may lead to additional radiation exposure for healthy tissues, increasing the likelihood of radiation-induced side effects, such as radiation pneumonitis and acute esophageal toxicity [5] , [6] , [7] . Several types of management strategies have been developed to mitigate the effects of breathing-induced motion, such as respiratory gating, breath-hold techniques or patient coaching [8] . However, these methods highly rely on patient compliance and may therefore be challenging to perform, particularly for patients with underlying lung disease.…”

Section: Introductionmentioning

confidence: 99%

Continuous and bilevel positive airway pressure may improve radiotherapy delivery in patients with intra-thoracic tumors

Elshof,

Steenstra,

Niezink

et al. 2024

Clinical and Translational Radiation Oncology

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Section: Introductionmentioning

confidence: 99%

Continuous and bilevel positive airway pressure may improve radiotherapy delivery in patients with intra-thoracic tumors

Elshof,

Steenstra,

Niezink

et al. 2024

Clinical and Translational Radiation Oncology

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“…Intra‐fractional tumor motion due to breathing can negatively influence the precision of dose delivered to a patient and can lead to cold spots in the tumor as well as hot spots in healthy tissue and thus reduce the effectiveness of the treatment while increasing toxicity to the patients 1–5 . Real‐time tumor motion management can reduce this uncertainty by accounting for the motion of the target and therefore improve the accuracy of the delivered dose 6,7 …”

Section: Introductionmentioning

confidence: 99%

“…[1][2][3][4][5] Real-time tumor motion management can reduce this uncertainty by accounting for the motion of the target and therefore improve the accuracy of the delivered dose. 6,7 There are two approaches to tackle intra-fractional tumor motion: gating where the beam is only turned on when the target is in the desired location 8,9 and tracking where the beam is continuously realigned to the target position. [10][11][12] A prerequisite for both approaches is a fast and accurate registration between the reference and real tumor position over the time of irradiation.…”

Section: Introductionmentioning

confidence: 99%

A novel bone suppression algorithm in intensity‐based 2D/3D image registration for real‐time tumor motion monitoring: Development and phantom‐based validation

et al. 2022

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Background Real‐time tumor motion monitoring (TMM) is a crucial process for intra‐fractional respiration management in lung cancer radiotherapy. Since the tumor can be partly or fully located behind the ribs, the TMM is challenging. Purpose The aim of this work was to develop a bone suppression (BS) algorithm designed for real‐time 2D/3D marker‐less TMM to increase the visibility of the tumor when overlapping with bony structures and consequently to improve the accuracy of TMM. Method A BS method was implemented in the in‐house developed software for ultrafast intensity‐based 2D/3D tumor registration (Fast Image‐based Registration [FIRE]). The method operates on both, digitally reconstructed radiograph (DRR) and intra‐fractional X‐ray images. The bony structures are derived from computed tomography data by thresholding during ray‐casting, and the resulting bone DRR is subtracted from intra‐fractional X‐ray images to obtain a soft‐tissue‐only image for subsequent tumor registration. The accuracy of TMM utilizing BS was evaluated within a retrospective phantom study with nine different 3D‐printed tumor phantoms placed in the in‐house developed Advanced Radiation DOSimetry (ARDOS) breathing phantom. A 24 mm craniocaudal tumor motion, including rib eclipses, was simulated, and X‐ray images were acquired on the Elekta Versa HD Linac in the lateral and posterior–anterior directions. An error assessment for BS images was evaluated with respect to the ground truth tumor position. Results A total error (root mean square error) of 0.87 ± 0.23 mm and 1.03 ± 0.26 mm was found for posterior–anterior and lateral imaging; the mean time for BS was 8.03 ± 1.54 ms. Without utilizing BS, TMM failed in all X‐ray images since the registration algorithm focused on the rib position due to the predominant intensity of this tissue within DRR and X‐ray images. Conclusion The BS algorithm developed and implemented improved the accuracy, robustness, and stability of real‐time TMM in lung cancer in a phantom study, even in the case of rib interlude where normal tumor registration fails.

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Predicting early stage lung cancer recurrence and survival from combined tumor motion amplitude and radiomics on free‐breathing 4D‐CT

Ouraou,

Tonneau,

et al. 2024

Medical Physics

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BackgroundCancer control outcomes of lung cancer are hypothesized to be affected by several confounding factors, including tumor heterogeneity and patient history, which have been hypothesized to mitigate the dose delivery effectiveness when treated with radiation therapy. Providing an accurate predictive model to identify patients at risk would enable tailored follow‐up strategies during treatment.PurposeOur goal is to demonstrate the added prognostic value of including tumor displacement amplitude in a predictive model that combines clinical features and computed tomography (CT) radiomics for 2‐year recurrence and survival in non‐small‐cell lung cancer (NSCLC) patients treated with curative‐intent stereotactic body radiation therapy.MethodsA cohort of 381 patients treated for primary lung cancer with radiotherapy was collected, each including a planning CT with a dosimetry plan, 4D‐CT, and clinical information. From this cohort, 101 patients (26.5%) experienced cancer progression (locoregional/distant metastasis) or death within 2 years of the end of treatment. Imaging data was analyzed for radiomics features from the tumor segmented image, as well as tumor motion amplitude measured on 4D‐CT. A random forest (RF) model was developed to predict the overall outcomes, which was compared to three other approaches — logistic regression, support vector machine, and convolutional neural networks.ResultsA 6‐fold cross‐validation study yielded an area under the receiver operating characteristic curve of 72% for progression‐free survival when combining clinical data with radiomics features and tumor motion using a RF model (72% sensitivity and 81% specificity). The combined model showed significant improvement compared to standard clinical data. Model performances for loco‐regional recurrence and overall survival sub‐outcomes were established at 73% and 70%, respectively. No comparative methods reached statistical significance in any data configuration.ConclusionsCombined tumor respiratory motion and radiomics features from planning CT showed promising predictive value for 2‐year tumor control and survival, indicating the potential need for improving motion management strategies in future studies using machine learning‐based prognosis models.

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Tumour motion management in lung cancer: a narrative review

Cited by 16 publications

References 46 publications

Continuous and bilevel positive airway pressure may improve radiotherapy delivery in patients with intra-thoracic tumors

Continuous and bilevel positive airway pressure may improve radiotherapy delivery in patients with intra-thoracic tumors

A novel bone suppression algorithm in intensity‐based 2D/3D image registration for real‐time tumor motion monitoring: Development and phantom‐based validation

Predicting early stage lung cancer recurrence and survival from combined tumor motion amplitude and radiomics on free‐breathing 4D‐CT

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