Repeated Stereotactic Radiotherapy for Local Brain Metastases Failure or Distant Brain Recurrent: A Retrospective Study of 184 Patients.

BACKGROUND: The main advantages of stereotactic radiotherapy (SRT) are to delay whole-brain radiotherapy (WBRT) and to deliver ablative doses. Despite this efficacy, the risk of distant brain metastases (BM) one year after SRT ranges from 26% to 77% and 20 to 40% of patients required salvage treatment. The role and consequences of reirradiation remain unclear, particularly in terms of survival. The objective was to study overall survival (OS) and neurological death-free survival (NDFS) and to specify the prognostic factors of long-term survival. METHODS: we retrospectively reviewed the data of patients treated between 2010 and 2020 with at least two courses of SRT without previous WBRT. RESULTS: In total, 184 patients were treated for 915 BMs with two-to-six SRT sessions. Additional SRT sessions were provided for local (5.6%) or distant (94.4%) BM recurrence. The median number of BMs treated per SRT was one with a median of four BMs in total. The mean time between the two SRT sessions was 8.9 months (95%CI 7.7-10.1) and there was no significant difference in the delay between the two sessions. The 6-, 12- and 24-month NDFS rates were 97%, 82% and 52%, respectively. The 6-, 12- and 24-month OS rates were 91%, 70% and 38%, respectively. OS was statistically related to the number of SRT sessions (HR = 0.48; p < 0.01), recursive partitioning analysis (HR = 1.84; p = 0.01), salvage WBRT (HR = 0.48; p = 0.01) and brain metastasis velocity (high: HR = 13.83; p < 0.01; intermediate: HR = 4.93; p < 0.01). CONCLUSIONS: Lung cancer and melanoma were associated with a lower NDFS compared to breast cancer. A low KPS, a low number of SRT sessions, synchronous extracerebral metastases, synchronous BMs, extracerebral progression at SRT1, a high BMV grade, no WBRT and local recurrence were also associated with a lower NDFS. A high KPS at SRT1 and low BMV grade are prognostic factors for better OS, regardless of the number of BM recurrence events.

SurVolT: Surface to Volume conversion Tool. A proof of concept.

PURPOSE: To develop SurVolT, a conversion tool able to apply volumetric changes to DICOM Computed Tomography (CT) data using daily surface (obj) data acquired with AlignRT® (VisionRT Ltd.), primarily designed and validated for breast treatments. MATERIALS AND METHODS: SurVolT proceeds in 4 steps: 1. AlignRT .obj files extraction, 2. Contour deformation where the surface data points are matched to the initial external contour on a Region Of Interest, ROImatch, on which the anatomy is supposed to be unchanged. Then, external contour substitution is performed on the ROIttt covering the treated breast area. This is validated on a female torso phantom with a tissue-equivalent bolus mimicking an edema. The Planning Treatment Volume (PTV) contour from the initial CT is also deformed according to the new external contour in the ROIttt. 3. Volumetric data estimation according to the new external contour, validated on an anthropomorphic pelvis phantom. 4. Import of new DICOM data into the Treatment Planning System (TPS). Finally, the workflow is applied on a first patient presenting an anatomical change during the treatment. RESULTS: The validation of step 2 and 3 shows a bolus thickness estimation of 5.8±1.2mm (expected 5 mm) and the non-rigid deformation of initial CT images follows the new external contour at the ROIttt bolus site while revealing negligible deformation elsewhere. CONCLUSION: This first proof of concept introducing a Surface Guided Radiotherapy (SGRT) tool allowing daily surface data to volume conversion is a fundamental step toward SGRT-based adaptive radiotherapy.

177Lu-DOTA-0-Tyr3-octreotate infusion modeling for real-time detection and characterization of extravasation during PRRT.

PURPOSE: Given the recent and rapid development of peptide receptor radionuclide therapy (PRRT), increasing emphasis should be placed on the early identification and quantification of therapeutic radiopharmaceutical (thRPM) extravasation during intravenous administration. Herein, we provide an analytical model of 177Lu-DOTA0-Tyr3-octreotate (Lutathera®) infusion for real-time detection and characterization of thRPM extravasation. METHODS: For 33 Lutathera®-based PRRT procedures using the gravity infusion method, equivalent dose rates (EDRs) were monitored at the patient's arm. Models of flow dynamics for nonextravasated and extravasated infusions were elaborated and compared to experimental data through an equivalent dose rate calibration. Nonextravasated infusion was modeled by assuming constant volume dilution of 177Lu activity concentration in the vial and Poiseuille-like laminar flow through the tubing and patient vein. Extravasated infusions were modeled according to their onset times by considering elliptically shaped extravasation region with different aspect ratios. RESULTS: Over the 33 procedures, the peak of the median EDR was reached 14 min after the start of the infusion with a value of 450 µSv h-1. On the basis of experimental measurements, 1 mSv h-1 was considered the empirical threshold for Lutathera® extravasation requiring cessation of the infusion and start again with a new route of injection. According to our model, the concentration of extravascular activity was directly related to the time of extravasation onset and its duration, a finding inherent in the gravity infusion method. This result should be considered when planning therapeutic strategy in the case of RPM extravasation because the local absorbed dose for ß-emitters is closely linked to activity concentration. For selected EDR values, charts of extravasated activity, volume, and activity concentration were computed for extravasation characterization. CONCLUSION: We proposed an analytical model of Lutathera® infusion and extravasation (gravity method) based on EDR monitoring. This approach could be useful for the early detection of thRPM extravasation and for the real-time assessment of activity concentration and volume accumulation in the extravascular medium.

Survey on deep learning for radiotherapy.

More than 50% of cancer patients are treated with radiotherapy, either exclusively or in combination with other methods. The planning and delivery of radiotherapy treatment is a complex process, but can now be greatly facilitated by artificial intelligence technology. Deep learning is the fastest-growing field in artificial intelligence and has been successfully used in recent years in many domains, including medicine. In this article, we first explain the concept of deep learning, addressing it in the broader context of machine learning. The most common network architectures are presented, with a more specific focus on convolutional neural networks. We then present a review of the published works on deep learning methods that can be applied to radiotherapy, which are classified into seven categories related to the patient workflow, and can provide some insights of potential future applications. We have attempted to make this paper accessible to both radiotherapy and deep learning communities, and hope that it will inspire new collaborations between these two communities to develop dedicated radiotherapy applications.

Megavoltage 2D topographic imaging: An attractive alternative to megavoltage CT for the localization of breast cancer patients treated with TomoDirect.

PURPOSE: To show the usefulness of topographic 2D megavoltage images (MV2D) for the localization of breast cancer patients treated with TomoDirect (TD), a radiotherapy treatment technique with fixed-angle beams performed on a TomoTherapy system. METHODS: A method was developed to quickly localize breast cancer patients treated with TD by registering the MV2D images produced before a TD treatment with reference images reconstructed from a kilovoltage CT simulation scanner and by using the projection of the beam-eye-view TD treatment field. Dose and image quality measurements were performed to determine the optimal parameters for acquiring MV2D images. A TD treatment was simulated on a chest phantom equipped with a breast attachment. MVCT and MV2D images were performed for 7 different shifted positions of the phantom and registered by 10 different operators with the simulation kilovoltage CT images. RESULTS: Compared to MVCT, MV2D imaging reduces the dose by a factor of up to 45 and the acquisition time by a factor of up to 49. Comparing the registration shift values obtained for the phantom images obtained with MVCT in the coarse mode to those obtained with MV2D, the mean difference is 1.0±1.1mm, -1.1mm±1.1, and -0.1±2.2mm, respectively, in the lateral, longitudinal, and vertical directions. CONCLUSIONS: With dual advantages (very fast imaging and a potentially reduced dose to the heart and contralateral organs), MV2D topographic images may be an attractive alternative to MVCT for the localization of breast cancer patients treated with TomoDirect.