Mid-position treatment strategy for locally advanced lung cancer: a dosimetric study

"Mid-P strategy" versus "internal target volume strategy in locally advanced non small cell lung cancer: Clinical results from the randomized non-comparative phase II study Mid-P

BACKGROUND

Locally advanced non-small cell lung cancer (LA-NSCLC) reported poor 5-year survival rates with frequent local or regional recurrences. Personalized RT may contribute to improve control and clinical outcome. We investigated efficacy and tolerance of "Mid-position" (Mid-P) strategy versus the conventional Internal Target Volume (ITV) strategy in LA-NSCLC patients treated by definitive conformal radiotherapy.

METHODS

This prospective non-comparative randomized monocentric phase II trial included adult patients with non-resected, non-metastatic, non-previously irradiated proven LA-NSCLC treated with definitive normo-fractionated conformal radiotherapy (+/- chemotherapy). Allocated patients (randomisation 2:1) were treated using Mid-P or ITV strategy. A Fleming single-stage design (1-sided α = 0.1, 80 % power, P0 = 30 %, P1 = 50 %) planned enrolment of 36 patients in the Mid-P group. The ITV group ensured the absence of selection bias. The primary outcome was 1-year progression-free- survival (1y-PFS) rate.

RESULTS

Among 54 eligible patients included from September 2012 to May 2018, 51 patients were analyzed (Mid-P: N = 34; ITV: 17). The 1y-PFS was 38 % (1-sided 95 %CI 25 %-not reached) with Mid-P strategy, and 47 % (95 %CI [27 %-not reached[) with ITV. Loco-regional failure as first event mainly occurred within radiation-field regardless the strategy. Acute and middle-term radiation toxicities were observed with both strategies.

CONCLUSION

Local control and survival remain poor using the Mid-P strategy in this prospective randomized non-comparative monocentric study investigating Mid-P strategy versus ITV strategy in LA-NSCLC. Since the Mid-P strategy is not integrated into routine software, and perceived as a time-consuming method, Mid-P strategy cannot be recommended in LA-NSCLCC treated by definitive normo-fractionated conformal radiotherapy outside clinical trials.

An approach to generate synthetic 4DCT datasets to benchmark Mid-Position implementations

PURPOSE

The Mid-Position image is constructed from 4DCT data using Deformable Image Registration and can be used as planning CT with reduced PTV volumes. 4DCT datasets currently-available for testing do not provide the corresponding Mid-P images of the datasets. This work describes an approach to generate human-like synthetic 4DCT datasets with the associated Mid-P images that can be used as reference in the validation of Mid-P implementations.

METHODS

Twenty synthetic 4DCT datasets with the associated reference Mid-P images were generated from twenty clinical 4DCT datasets. Per clinical dataset, an anchor phase was registered to the remaining nine phases to obtain nine Deformable Vector Fields (DVFs). These DVFs were used to warp the anchor phase in order to generate the synthetic 4DCT dataset and the corresponding reference Mid-P image. Similarly, a reference 4D tumor mask dataset and its corresponding Mid-P tumor mask were generated. The generated synthetic datasets and masks were used to compare and benchmark the outcomes of three independent Mid-P implementations using a set of experiments.

RESULTS

The Mid-P images constructed by the three implementations showed high similarity scores when compared to the reference Mid-P images except for one noisy dataset. The biggest difference in the estimated motion amplitudes (-2.6 mm) was noticed in the Superior-Inferior direction. The statistical analysis showed no significant differences among the three implementations for all experiments.

CONCLUSION

The described approach and the proposed experiments provide an independent method that can be used in the validation of any Mid-P implementation being developed.

Respiratory motion management for external radiotherapy treatment

We present the update of the recommendations of the French society of oncological radiotherapy on respiratory motion management for external radiotherapy treatment. Since twenty years and the report 62 of ICRU, motion management during the course of radiotherapy treatment has become an increasingly significant concern, particularly with the development of hypofractionated treatments under stereotactic conditions, using reduced safety margins. This article related orders of motion amplitudes for different organs as well as the definition of the margins in radiotherapy. An updated review of the various movement management strategies is presented as well as main technological solutions enabling them to be implemented: when acquiring anatomical data, during planning and when carrying out treatment. Finally, the management of these moving targets, such as it can be carried out in radiotherapy departments, will be detailed for a few concrete examples of localizations (abdominal, thoracic and hepatic).

Improving motion-mask segmentation in thoracic CT with multiplanar U-nets

PURPOSE

Motion-mask segmentation from thoracic computed tomography (CT) images is the process of extracting the region that encompasses lungs and viscera, where large displacements occur during breathing. It has been shown to help image registration between different respiratory phases. This registration step is, for example, useful for radiotherapy planning or calculating local lung ventilation. Knowing the location of motion discontinuity, that is, sliding motion near the pleura, allows a better control of the registration preventing unrealistic estimates. Nevertheless, existing methods for motion-mask segmentation are not robust enough to be used in clinical routine. This article shows that it is feasible to overcome this lack of robustness by using a lightweight deep-learning approach usable on a standard computer, and this even without data augmentation or advanced model design.

METHODS

A convolutional neural-network architecture with three 2D U-nets for the three main orientations (sagittal, coronal, axial) was proposed. Predictions generated by the three U-nets were combined by majority voting to provide a single 3D segmentation of the motion mask. The networks were trained on a database of nonsmall cell lung cancer 4D CT images of 43 patients. Training and evaluation were done with a K-fold cross-validation strategy. Evaluation was based on a visual grading by two experts according to the appropriateness of the segmented motion mask for the registration task, and on a comparison with motion masks obtained by a baseline method using level sets. A second database (76 CT images of patients with early-stage COVID-19), unseen during training, was used to assess the generalizability of the trained neural network.

RESULTS

The proposed approach outperformed the baseline method in terms of quality and robustness: the success rate increased from 53 % to 79 % without producing any failure. It also achieved a speed-up factor of 60 with GPU, or 17 with CPU. The memory footprint was low: less than 5 GB GPU RAM for training and less than 1 GB GPU RAM for inference. When evaluated on a dataset with images differing by several characteristics (CT device, pathology, and field of view), the proposed method improved the success rate from 53 % to 83 % .

CONCLUSION

With 5-s processing time on a mid-range GPU and success rates around 80 % , the proposed approach seems fast and robust enough to be routinely used in clinical practice. The success rate can be further improved by incorporating more diversity in training data via data augmentation and additional annotated images from different scanners and diseases. The code and trained model are publicly available.

Incorporation of tumor motion directionality in margin recipe: The directional MidP strategy

PURPOSE

Planning target volume (PTV) definition based on Mid-Position (Mid-P) strategy typically integrates breathing motion from tumor positions variances along the conventional axes of the DICOM coordinate system. Tumor motion directionality is thus neglected even though it is one of its stable characteristics in time. We therefore propose the directional MidP approach (MidP dir), which allows motion directionality to be incorporated into PTV margins. A second objective consists in assessing the ability of the proposed method to better take care of respiratory motion uncertainty.

METHODS

11 lung tumors from 10 patients with supra-centimetric motion were included. PTV were generated according to the MidP and MidP dir strategies starting from planning 4D CT.

RESULTS

PTV_{MidP dir} volume didn't differ from the PTV_MidP volume: 31351 mm³ IC95% [17242-45459] vs. 31003 mm³ IC95% [ 17347-44659], p = 0.477 respectively. PTV_{MidP dir} morphology was different and appeared more oblong along the main motion axis. The relative difference between 3D and 4D doses was on average 1.09%, p = 0.011 and 0.74%, p = 0.032 improved with directional MidP for D_99% and D_95%. D_2% was not significantly different between both approaches. The improvement in dosimetric coverage fluctuated substantially from one lesion to another and was all the more important as motion showed a large amplitude, some obliquity with respect to conventional axes and small hysteresis.

CONCLUSIONS

Directional MidP method allows tumor motion to be taken into account more tightly as a geometrical uncertainty without increasing the irradiation volume.