Advances in the use of motion management and image guidance in radiation therapy treatment for lung cancer

Effects of electron density force to 1.0 and fill to 1.0 on VMAT treatment plans for lung SBRT

PURPOSE

The aim of this study is to determine the effect of forcing and filling the electron density (ED) to 1.0 of the planning target volume (PTV) overdose distribution in lung SBRT treatment leading to shortening patient treatment time and increasing patient comfort by reducing MU/fraction due to ED manipulation effect.

METHODS

In this study, 36 lung SBRT plans of 12 suitable patients who prescribed a total dose of 50 Gy in five fractions were generated with Monaco v.5.10 TPS using the Monte Carlo (MC) algorithm and volumetric modulated arc therapy (VMAT) technique by PTV ED values forcing as well as filling to 1.0 and comparatively assessed. The first group of plans was created by using the patient's original ED, second and third groups of plans were reoptimized by forcing and filling the ED of PTV to 1.0, respectively, therefore acquiring a new dose distribution which lead to comparatively assessment the effects of changes in ED on PTV and OAR doses.

RESULTS

Assessment of treatment plans revealed that mean MU/fx numbers were decreased by 76% and 75.25% between Groups 1 and 2, Groups 1 and 3, respectively. The number of segments was also reduced in Group 1 by up to 15% compared with Groups 2 and 3. Maximum HI and CI differences for PTV between Groups 1 and 2 were less than 1% and Groups 1 and 3 were 1.5% which indicates all 3 group plans were comparable in terms of dose distribution within PTV.

CONCLUSIONS

Forcing and filling the ED of PTV to 1.0 strategy has provided reduced a number of segments and MU/fx without a significant change in PTV mean and maximum doses, thereby decreasing treatment time and patient discomfort during treatment. This process should be considered in line of a potential number of patients as well as prescribed dose and MU/fx numbers.

Artificial intelligence-based motion tracking in cancer radiotherapy: A review

Radiotherapy aims to deliver a prescribed dose to the tumor while sparing neighboring organs at risk (OARs). Increasingly complex treatment techniques such as volumetric modulated arc therapy (VMAT), stereotactic radiosurgery (SRS), stereotactic body radiotherapy (SBRT), and proton therapy have been developed to deliver doses more precisely to the target. While such technologies have improved dose delivery, the implementation of intra-fraction motion management to verify tumor position at the time of treatment has become increasingly relevant. Artificial intelligence (AI) has recently demonstrated great potential for real-time tracking of tumors during treatment. However, AI-based motion management faces several challenges, including bias in training data, poor transparency, difficult data collection, complex workflows and quality assurance, and limited sample sizes. This review presents the AI algorithms used for chest, abdomen, and pelvic tumor motion management/tracking for radiotherapy and provides a literature summary on the topic. We will also discuss the limitations of these AI-based studies and propose potential improvements.

Prospective external validation of radiomics-based predictive model of distant metastasis after dynamic tumor tracking stereotactic body radiation therapy in patients with non-small-cell lung cancer: A multi-institutional analysis

BACKGROUND AND PURPOSE

This study aims to externally validate a predictive model for distant metastasis (DM) with computed tomography (CT)-based radiomics features in prospectively enrolled non-small-cell lung cancer patients undergoing dynamic tumor-tracking stereotactic body radiation therapy (DTT-SBRT).

MATERIALS AND METHODS

The study collected retrospective data from 567 patients across 11 institutions as the training dataset and prospectively enrolled 42 patients from four institutions as the external test dataset. Four clinical features were collected, and 944 CT-based radiomic features were extracted from gross tumor volumes. After standardization and feature selection, DM predictive models were developed using fine and gray regression (FG) and random survival forest (RSF), incorporating clinical and radiomic features, and their combinations within the training dataset. Then, the model was applied to the test dataset, dividing patients into high- and low-risk groups based on medians of risk scores. Model performance was assessed using the concordance index (C-index), and the statistical significance between groups was evaluated using Gray's test.

RESULTS

In the training dataset, 122 of 567 patients (21.5%) developed DM, compared to 9 of 42 patients (21.4%) in the test dataset. In the test dataset, the C-indices of the clinical, radiomics, and hybrid models with FG were 0.559, 0.544, and 0.560, respectively, whereas those with RSF were 0.576, 0.604, and 0.627, respectively. The hybrid model with RSF, which exhibited the best predictive performance of all models, identified 7 of 23 patients (30.4%) as high risk and 2 of 19 patients (10.5%) as low risk for DM incidence in the test dataset (p = 0.116).

CONCLUSION

Although predictive models for DM lack significance when applied to prospectively enrolled cases undergoing DTT-lung SBRT, the model with RSF exhibits a consistent capacity to effectively classify patients at a high risk of developing DM.

Towards more precise automatic analysis: a systematic review of deep learning-based multi-organ segmentation

Accurate segmentation of multiple organs in the head, neck, chest, and abdomen from medical images is an essential step in computer-aided diagnosis, surgical navigation, and radiation therapy. In the past few years, with a data-driven feature extraction approach and end-to-end training, automatic deep learning-based multi-organ segmentation methods have far outperformed traditional methods and become a new research topic. This review systematically summarizes the latest research in this field. We searched Google Scholar for papers published from January 1, 2016 to December 31, 2023, using keywords "multi-organ segmentation" and "deep learning", resulting in 327 papers. We followed the PRISMA guidelines for paper selection, and 195 studies were deemed to be within the scope of this review. We summarized the two main aspects involved in multi-organ segmentation: datasets and methods. Regarding datasets, we provided an overview of existing public datasets and conducted an in-depth analysis. Concerning methods, we categorized existing approaches into three major classes: fully supervised, weakly supervised and semi-supervised, based on whether they require complete label information. We summarized the achievements of these methods in terms of segmentation accuracy. In the discussion and conclusion section, we outlined and summarized the current trends in multi-organ segmentation.

Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation

Precise delineation of multiple organs or abnormal regions in the human body from medical images plays an essential role in computer-aided diagnosis, surgical simulation, image-guided interventions, and especially in radiotherapy treatment planning. Thus, it is of great significance to explore automatic segmentation approaches, among which deep learning-based approaches have evolved rapidly and witnessed remarkable progress in multi-organ segmentation. However, obtaining an appropriately sized and fine-grained annotated dataset of multiple organs is extremely hard and expensive. Such scarce annotation limits the development of high-performance multi-organ segmentation models but promotes many annotation-efficient learning paradigms. Among these, studies on transfer learning leveraging external datasets, semi-supervised learning including unannotated datasets and partially-supervised learning integrating partially-labeled datasets have led the dominant way to break such dilemmas in multi-organ segmentation. We first review the fully supervised method, then present a comprehensive and systematic elaboration of the 3 abovementioned learning paradigms in the context of multi-organ segmentation from both technical and methodological perspectives, and finally summarize their challenges and future trends.

A model that predicts a real-time tumour surface using intra-treatment skin surface and end-of-expiration and end-of-inhalation planning CT images

OBJECTIVES

To develop a mapping model between skin surface motion and internal tumour motion and deformation using end-of-exhalation (EOE) and end-of-inhalation (EOI) 3D CT images for tracking lung tumours during respiration.

METHODS

Before treatment, skin and tumour surfaces were segmented and reconstructed from the EOE and the EOI 3D CT images. A non-rigid registration algorithm was used to register the EOE skin and tumour surfaces to the EOI, resulting in a displacement vector field that was then used to construct a mapping model. During treatment, the EOE skin surface was registered to the real-time, yielding a real-time skin surface displacement vector field. Using the mapping model generated, the input of a real-time skin surface can be used to calculate the real-time tumour surface. The proposed method was validated with and without simulated noise on 4D CT images from 15 patients at Léon Bérard Cancer Center and the 4D-lung dataset.

RESULTS

The average centre position error, dice similarity coefficient (DSC), 95%-Hausdorff distance and mean distance to agreement of the tumour surfaces were 1.29 mm, 0.924, 2.76 mm, and 1.13 mm without simulated noise, respectively. With simulated noise, these values were 1.33 mm, 0.920, 2.79 mm, and 1.15 mm, respectively.

CONCLUSIONS

A patient-specific model was proposed and validated that was constructed using only EOE and EOI 3D CT images and real-time skin surface images to predict internal tumour motion and deformation during respiratory motion.

ADVANCES IN KNOWLEDGE

The proposed method achieves comparable accuracy to state-of-the-art methods with fewer pre-treatment planning CT images, which holds potential for application in precise image-guided radiation therapy.

Dosimetric Evaluation and Reproducibility of Breath-hold Plans in Intensity Modulated Proton Therapy: An Initial Clinical Experience

Purpose

Breath-hold (BH) technique can mitigate target motion, minimize target margins, reduce normal tissue doses, and lower the effect of interplay effects with intensity-modulated proton therapy (IMPT). This study presents dosimetric comparisons between BH and nonbreath-hold (non-BH) IMPT plans and investigates the reproducibility of BH plans using frequent quality assurance (QA) computed tomography scans (CT).

Methods and Materials

Data from 77 consecutive patients with liver (n = 32), mediastinal/lung (n = 21), nonliver upper abdomen (n = 20), and malignancies in the gastroesophageal junction (n = 4), that were treated with a BH spirometry system (SDX) were evaluated. All patients underwent both BH CT and 4-dimensional CT simulations. Clinically acceptable BH and non-BH plans were generated on each scan, and dose-volume histograms of the 2 plans were compared. Reproducibility of the BH plans for 30 consecutive patients was assessed using 1 to 3 QA CTs per patient and variations in dose-volume histograms for deformed target and organs at risk (OARs) volumes were compared with the initial CT plan.

Results

Use of BH scans reduced initial and boost target volumes to 72% ± 20% and 70% ± 17% of non-BH volumes, respectively. Additionally, mean dose to liver, stomach, kidney, esophagus, heart, and lung V20 were each reduced to 71% to 79% with the BH technique. Similarly, small and large bowels, heart, and spinal cord maximum doses were each lowered to 68% to 84%. Analysis of 62 QA CT scans demonstrated that mean target and OAR doses using BH scans were reproducible to within 5% of their nominal plan values.

Conclusions

The BH technique reduces the irradiated volume, leading to clinically significant reductions in OAR doses. By mitigating tumor motion, the BH technique leads to reproducible target coverage and OAR doses. Its use can reduce motion-related uncertainties that are normally associated with the treatment of thoracic and abdominal tumors and, therefore, optimize IMPT delivery.

A Systematic Review of LET-Guided Treatment Plan Optimisation in Proton Therapy: Identifying the Current State and Future Needs

The well-known clinical benefits of proton therapy are achieved through higher target-conformality and normal tissue sparing than conventional radiotherapy. However, there is an increased sensitivity to uncertainties in patient motion/setup, proton range and radiobiological effect. Although recent efforts have mitigated some uncertainties, radiobiological effect remains unresolved due to a lack of clinical data for relevant endpoints. Therefore, RBE optimisations may be currently unsuitable for clinical treatment planning. LET optimisation is a novel method that substitutes RBE with LET, shifting LET hotspots outside critical structures. This review outlines the current status of LET optimisation in proton therapy, highlighting knowledge gaps and possible future research. Following the PRISMA 2020 guidelines, a search of the MEDLINE® and Scopus databases was performed in July 2023, identifying 70 relevant articles. Generally, LET optimisation methods achieved their treatment objectives; however, clinical benefit is patient-dependent. Inconsistencies in the reported data suggest further testing is required to identify therapeutically favourable methods. We discuss the methods which are suitable for near-future clinical deployment, with fast computation times and compatibility with existing treatment protocols. Although there is some clinical evidence of a correlation between high LET and adverse effects, further developments are needed to inform future patient selection protocols for widespread application of LET optimisation in proton therapy.

RMSim: controlled respiratory motion simulation on static patient scans

Objective.This work aims to generate realistic anatomical deformations from static patient scans. Specifically, we present a method to generate these deformations/augmentations via deep learning driven respiratory motion simulation that provides the ground truth for validating deformable image registration (DIR) algorithms and driving more accurate deep learning based DIR.Approach.We present a novel 3D Seq2Seq deep learning respiratory motion simulator (RMSim) that learns from 4D-CT images and predicts future breathing phases given a static CT image. The predicted respiratory patterns, represented by time-varying displacement vector fields (DVFs) at different breathing phases, are modulated through auxiliary inputs of 1D breathing traces so that a larger amplitude in the trace results in more significant predicted deformation. Stacked 3D-ConvLSTMs are used to capture the spatial-temporal respiration patterns. Training loss includes a smoothness loss in the DVF and mean-squared error between the predicted and ground truth phase images. A spatial transformer deforms the static CT with the predicted DVF to generate the predicted phase image. 10-phase 4D-CTs of 140 internal patients were used to train and test RMSim. The trained RMSim was then used to augment a public DIR challenge dataset for training VoxelMorph to show the effectiveness of RMSim-generated deformation augmentation.Main results.We validated our RMSim output with both private and public benchmark datasets (healthy and cancer patients). The structure similarity index measure (SSIM) for predicted breathing phases and ground truth 4D CT images was 0.92 ± 0.04, demonstrating RMSim's potential to generate realistic respiratory motion. Moreover, the landmark registration error in a public DIR dataset was improved from 8.12 ± 5.78 mm to 6.58mm ± 6.38 mm using RMSim-augmented training data.Significance.The proposed approach can be used for validating DIR algorithms as well as for patient-specific augmentations to improve deep learning DIR algorithms. The code, pretrained models, and augmented DIR validation datasets will be released athttps://github.com/nadeemlab/SeqX2Y.

Advances in treatment planning and management for the safety and accuracy of lung stereotactic body radiation therapy using proton pencil beam scanning: Simulation, planning, quality assurance, and delivery recommendations

This study presents the clinical experiences of the New York Proton Center in employing proton pencil beam scanning (PBS) for the treatment of lung stereotactic body radiation therapy. It encompasses a comprehensive examination of multiple facets, including patient simulation, delineation of target volumes and organs at risk, treatment planning, plan evaluation, quality assurance, and motion management strategies. By sharing the approaches of the New York Proton Center and providing recommendations across simulation, treatment planning, and treatment delivery, it is anticipated that the valuable experience will be provided to a broader proton therapy community, serving as a useful reference for future clinical practice and research endeavors in the field of stereotactic body proton therapy for lung tumors.

The future of MRI in radiation therapy: Challenges and opportunities for the MR community

Radiation therapy is a major component of cancer treatment pathways worldwide. The main aim of this treatment is to achieve tumor control through the delivery of ionizing radiation while preserving healthy tissues for minimal radiation toxicity. Because radiation therapy relies on accurate localization of the target and surrounding tissues, imaging plays a crucial role throughout the treatment chain. In the treatment planning phase, radiological images are essential for defining target volumes and organs-at-risk, as well as providing elemental composition (e.g., electron density) information for radiation dose calculations. At treatment, onboard imaging informs patient setup and could be used to guide radiation dose placement for sites affected by motion. Imaging is also an important tool for treatment response assessment and treatment plan adaptation. MRI, with its excellent soft tissue contrast and capacity to probe functional tissue properties, holds great untapped potential for transforming treatment paradigms in radiation therapy. The MR in Radiation Therapy ISMRM Study Group was established to provide a forum within the MR community to discuss the unmet needs and fuel opportunities for further advancement of MRI for radiation therapy applications. During the summer of 2021, the study group organized its first virtual workshop, attended by a diverse international group of clinicians, scientists, and clinical physicists, to explore our predictions for the future of MRI in radiation therapy for the next 25 years. This article reviews the main findings from the event and considers the opportunities and challenges of reaching our vision for the future in this expanding field.

Deep learning-based synthetization of real-time in-treatment 4D images using surface motion and pretreatment images: A proof-of-concept study

PURPOSE

To develop a deep learning model that maps body surface motion to internal anatomy deformation, which is potentially applicable to dose-free real-time 4D virtual image-guided radiotherapy based on skin surface data.

METHODS

Body contours were segmented out of 4DCT images. Deformable image registration algorithm was used to register the end-of-exhalation (EOE) phase to other phases. Deformation vector field was dimension-reduced to the first two principal components (PCs). A deep learning model was trained to predict the two PC scores of each phase from surface displacement. The instant deformation field can then be reconstructed, warping EOE image to obtain real-time CT image. This approach was validated on 4D XCAT phantom, the public DIR-Lab, and 4D-Lung dataset respectively, with and without simulated noise.

RESULTS

Validation accuracy of the tumor centroid trajectory was observed as 0.04 ± 0.02 mm on XCAT phantom. For the DIR-Lab dataset, 300 landmarks were annotated on the end-of-inhalation (EOI) images of each patient, and the mean displacements between their predicted and reference positions were below 2 mm for all studied cases. For the 4D-Lung dataset, the average dice coefficients ± std between predicted and reference tumor contours at EOI phase were 0.835 ± 0.092 for all studied cases.

CONCLUSIONS

A deep learning-based approach was proposed and validated to predict internal anatomy deformation from the surface motion, which is potentially applicable to on-line target navigation for accurate radiotherapy based on real-time 4D skin surface data and pretreatment images.

Adoption of respiratory motion management in radiation therapy

Background and Purpose

A survey on the patterns of practice of respiratory motion management (MM) was distributed to 111 radiation therapy facilities to inform the development of an end-to-end dosimetry audit including respiratory motion.

Materials and methods

The survey (distributed via REDCap) asked facilities to provide information specific to the combinations of MM techniques (breath-hold gating – BHG, internal target volume – ITV, free-breathing gating – FBG, mid-ventilation – MidV, tumour tracking – TT), sites treated (thorax, upper abdomen, lower abdomen), and fractionation regimes (conventional, stereotactic ablative body radiation therapy – SABR) used in their clinic.

Results

The survey was completed by 78% of facilities, with 98% of respondents indicating that they used at least one form of MM. The ITV approach was common to all MM-users, used for thoracic treatments by 89% of respondents, and upper and lower abdominal treatments by 38%. BHG was the next most prevalent (41% of MM users), with applications in upper abdominal and thoracic treatment sites (28% vs 25% respectively), but minimal use in the lower abdomen (9%). FBG and TT were utilised sparingly (17%, 7% respectively), and MidV was not selected at all.

Conclusions

Two distinct treatment workflows (including use of motion limitation, imaging used for motion assessment, dose calculation, and image guidance procedures) were identified for the ITV and BHG MM techniques, to form the basis of the initial audit. Thoracic SABR with the ITV approach was common to nearly all respondents, while upper abdominal SABR using BHG stood out as more technically challenging. Other MM techniques were sparsely used, but may be considered for future audit development.

Application of real-time MRI-guided linear accelerator in stereotactic ablative body radiotherapy for non-small cell lung cancer: one step forward to precise targeting

PURPOSE

Tumor motion is a major challenge in stereotactic ablative body radiotherapy (SABR) for non-small cell lung cancer (NSCLC), causing excessive irradiation to compensate for this motion. Real-time tumor tracking with a magnetic resonance imaging-guided linear accelerator (MR-Linac) could address this problem. This study aimed to assess the effects and advantages of MR-Linac in SABR for the treatment of lung tumors.

METHODS

Overall, 41 patients with NSCLC treated with SABR using MR-Linac between March 2019 and December 2021 were included. For comparison, 40 patients treated with SABR using computed tomography-based modalities were also enrolled. The SABR dose ranged from 48 to 60 Gy in 3-5 fractions. The primary endpoint was a lower radiation volume compared to CT-based SABR. The secondary endpoint was the local control rate of SABR using the MR-Linac.

RESULTS

The median follow-up time was 19 months (range: 3-105 months). There was no significant difference in the gross tumor volume between the MR and CT groups (7.1 ± 9.3 cm³ vs 8.0 ± 6.8 cm³, p = 0.643), but the planning target volume was significantly smaller in the MR group (20.8 ± 18.8 cm³ vs 34.1 ± 22.9 cm³, p = 0.005). The 1-year local control rates for the MR and CT groups were 92.1 and 75.4%, respectively (p = 0.07), and the 1-year overall survival rates were 87.4 and 87.0%, respectively (p = 0.30).

CONCLUSION

Lung SABR with MR-Linac can reduce the radiation field without compromising the local control rate. Further follow-up is needed to assess the long-term effects.

Advanced pencil beam scanning Bragg peak FLASH-RT delivery technique can enhance lung cancer planning treatment outcomes compared to conventional multiple-energy proton PBS techniques

PURPOSE

To investigate the dosimetric characteristics between an advanced proton pencil beam scanning (PBS) Bragg peak FLASH technique and conventional PBS planning technique in lung tumors. To evaluate the "FLASHness" of single-field in a multiple-field delivery scheme for a hypofractionation regimen and move a step forward to clinical application.

METHODS

Single-energy PBS Bragg peak FLASH treatment plans were optimized based on a novel Bragg peak tracking technique to enable Bragg peaks to stop at the distal edge of the target. Inverse treatment planning using multiple-field optimization (MFO) can achieve sufficient FLASH dose rate and intensity-modulated proton therapy (IMPT)-equivalent dosimetric quality. The dose rate of organs-at-risk (OARs) and the target were calculated under FLASH machine parameters. A group of 10 consecutive lung SBRT patients was optimized to 34 Gy/fraction using a standard treatment of PBS technique with multiple energy layers as references to the Bragg peak plans. The dosimetric quality was compared between Bragg peak FLASH and conventional plans based on RTOG0915 dose metrics. FLASH dose rate ratios (V_40Gy/s) were calculated as a metric of the FLASH-sparing effect.

RESULTS

For higher dose thresholds, the Bragg peak plans achieved greater V_40Gy/s FLASH coverage for all major OARs. The V40Gy/s was close to 100% for all OARs when the dose thresholds were > 5 Gy for full plan and single beam evaluations. The less "FLASHness" region was associated with a low dose distribution, mainly occurring in the PBS field penumbra region. The conventional IMPT treatment plans yielded slightly superior target dose uniformity with a D_2%(%) of 108.02% versus that of Bragg peak 300 MU plans of 111.81% (p < 0.01) and that of Bragg peak 1200 MU plans of 115.95% (p < 0.01). No significant difference in dose metrics was found between Bragg peak and IMPT treatment plans for the spinal cord, esophagus, heart, or lung-GTV (all p > 0.05).

CONCLUSION

Hypofractionated lung Bragg peak plans can maintain comparable plan quality to conventional PBS while achieving sufficient FLASH dose rate coverage for major OARs for each field under the multiple-field delivery scheme. The novel Bragg peak FLASH technique has the potential to enhance lung cancer planning treatment outcomes compared to standard PBS treatment techniques.

Use of single-energy proton pencil beam scanning Bragg peak for intensity-modulated proton therapy FLASH treatment planning in liver hypofractionated radiation therapy

PURPOSE

The transmission proton FLASH technique delivers high doses to the normal tissue distal to the target, which is less conformal compared to the Bragg peak technique. To investigate FLASH RT planning using single-energy Bragg peak beams with a similar beam arrangement as clinical intensity-modulated proton therapy (IMPT) in liver stereotactic body radiation therapy (SBRT) and to characterize the plan quality, dose sparing of organs-at-risk (OARs), and FLASH dose rate percentage.

MATERIALS AND METHODS

An in-house platform was developed to enable inverse IMPT-FLASH planning using single-energy Bragg peaks. A universal range shifter and range compensators were utilized to effectively align the Bragg peak to the distal edge of the target. Two different minimum MU settings of 400 and 800 MU/spot (Bragg-400MU and Bragg-800MU) plans were investigated on 10 consecutive hepatocellular carcinoma patients previously treated by IMPT-SBRT to evaluate the FLASH dose and dose rate coverage for OARs. The IMPT-FLASH using single-energy Bragg peaks delivered 50 Gy in 5 fractions with similar or identical beam arrangement to the clinical IMPT-SBRT plans. NRG GI003 dose constraint metrics were used. Three dose rate calculation methods, including average dose rate (ADR), dose threshold dose rate (DTDR), and dose-averaged dose rate (DADR), were all studied.

RESULTS

The novel spot map optimization can fulfill the inverse planning using single-energy Bragg peaks. All the Bragg peak FLASH plans achieved similar results for the liver-GTV D_mean and heart D_0.5cc , compared to SBRT-IMPT. The Bragg-800MU plans resulted in 18.3% higher CTV D_2cc compared with SBRT (p < 0.05), and no significant difference was found between Bragg-400MU and SBRT plans. For the CTV D_max , SBRT plans resulted in 10.3% (p<0.01) less than Bragg-400MU plans and 16.6% (p<0.01) less than Bragg-800MU plans. The Bragg-800MU plans generally achieved higher ADR, DADR, and DTDR dose rates than Bragg-400MU plans, and DADR mostly led to the highest V_40Gy/s compared to other dose rate calculation methods, whereas ADR led to the lowest. The lower dose rate portions in certain OARs are related to the lower dose deposited due to the farther distances from targets, especially in the penumbra of the beams.

CONCLUSION

Single-energy Bragg peak IMPT-FLASH plans eliminate the exit dose in normal tissues, maintaining comparable dose metrics to the conventional IMPT-SBRT plans while achieving a sufficient FLASH dose rate for liver cancers. This study demonstrates the feasibility of and sufficiently high dose rate when applying Bragg peak FLASH treatment for liver cancer hypofractionated FLASH therapy. The advancement of this novel method has the potential to optimize treatment for liver cancer patients. This article is protected by copyright. All rights reserved.

Erring Characteristics of Deformable Image Registration-Based Auto-Propagation for Internal Target Volume in Radiotherapy of Locally Advanced Non-Small Cell Lung Cancer

Purpose

Respiratory motion of locally advanced non-small cell lung cancer (LA-NSCLC) adds to the challenge of targeting the disease with radiotherapy (RT). One technique used frequently to alleviate this challenge is an internal gross tumor volume (IGTV) generated from manual contours on a single respiratory phase of the 4DCT via the aid of deformable image registration (DIR)-based auto-propagation. Through assessing the accuracy of DIR-based auto-propagation for generating IGTVs, this study aimed to identify erring characteristics associated with the process to enhance RT targeting in LA-NSCLC.

Methods

4DCTs of 19 patients with LA-NSCLC were acquired using retrospective gating with 10 respiratory phases (RPs). Ground-truth IGTVs (GT-IGTVs) were obtained through manual segmentation and union of gross tumor volumes (GTVs) in all 10 phases. IGTV auto-propagation was carried out using two distinct DIR algorithms for the manually contoured GTV from each of the 10 phases, resulting in 10 separate IGTVs for each patient per each algorithm. Differences between the auto-propagated IGTVs (AP-IGTVs) and their corresponding GT-IGTVs were assessed using Dice coefficient (DICE), maximum symmetric surface distance (MSSD), average symmetric surface distance (ASSD), and percent volume difference (PVD) and further examined in relation to anatomical tumor location, RP, and deformation index (DI) that measures the degree of deformation during auto-propagation. Furthermore, dosimetric implications due to the analyzed differences between the AP-IGTVs and GT-IGTVs were assessed.

Results

Findings were largely consistent between the two algorithms: DICE, MSSD, ASSD, and PVD showed no significant differences between the 10 RPs used for propagation (Kruskal–Wallis test, ps &gt; 0.90); MSSD and ASSD differed significantly by tumor location in the central–peripheral and superior–inferior dimensions (ps &lt; 0.0001) while only in the central–peripheral dimension for PVD (p &lt; 0.001); DICE, MSSD, and ASSD significantly correlated with the DI (Spearman’s rank correlation test, ps &lt; 0.0001). Dosimetric assessment demonstrated that 79% of the radiotherapy plans created by targeting planning target volumes (PTVs) derived from the AP-IGTVs failed prescription constraints for their corresponding ground-truth PTVs.

Conclusion

In LA-NSCLC, errors in DIR-based IGTV propagation present to varying degrees and manifest dependences on DI and anatomical tumor location, indicating the need for personalized consideration in designing RT internal target volume.

A standardized workflow for respiratory-gated motion management decision-making

Purpose

Motion management of tumors within the lung and abdomen is challenging because it requires balancing tissue sparing with accuracy of hitting the target, while considering treatment delivery efficiency. Physicists can play an important role in analyzing four‐dimensional computed tomography (4DCT) data to recommend the optimal respiratory gating parameters for a patient. The goal of this work was to develop a standardized procedure for making recommendations regarding gating parameters and planning margins for lung and gastrointestinal stereotactic body radiotherapy (SBRT) treatments. In doing so, we hoped to simplify decision‐making and analysis, and provide a tool for troubleshooting complex cases.

Methods

Factors that impact gating decisions and planning target volume (PTV) margins were identified. The gating options included gating on exhale with approximately a 50% duty cycle (Gate3070), exhale gating with a reduced duty cycle (Gate4060), and treating for most of respiration, excluding only extreme inhales and exhales (Gate100). A standard operating procedure was developed, as well as a physics consult document to communicate motion management recommendations to other members of the treatment team. This procedure was implemented clinically for 1 year and results are reported below.

Results

Identified factors that impact motion management included the magnitude of motion observed on 4DCT, the regularity of breathing and quality of 4DCT data, and ability to observe the target on fluoroscopy. These were collated into two decision tables—one specific to lung tumors and another for gastrointestinal tumors—such that a physicist could answer a series of questions to determine the optimal gating and PTV margin. The procedure was used clinically for 252 sites from 213 patients treated with respiratory‐gated SBRT and standardized practice across our 12‐member physics team.

Conclusion

Implementation of a standardized procedure for respiratory gating had a positive impact in our clinic, improving efficiency and ease of 4DCT analysis and standardizing gating decision‐making amongst physicists.

Real-time marker-less tumor tracking with TOF PET: in silico feasibility study

Purpose. Although positron emission tomography (PET) can provide a functional image of static tumors for RT guidance, it’s conventionally very challenging for PET to track a moving tumor in real-time with a multiple frame/s sampling rate. In this study, we developed a novel method to enable PET based three-dimension (3D) real-time marker-less tumor tracking (RMTT) and demonstrated its feasibility with a simulation study. Methods. For each line-of-response (LOR) acquired, its positron-electron annihilation position is calculated based on the time difference between the two gamma interactions detected by the TOF PET detectors. The accumulation of these annihilation positions from data acquired within a single sampling frame forms a coarsely measured 3D distribution of positron-emitter radiotracer uptakes of the lung tumor and other organs and tissues (background). With clinically relevant tumor size and sufficient differential radiotracer uptake concentrations between the tumor and background, the high-uptake tumor can be differentiated from the surrounding low-uptake background in the measured distribution of radiotracer uptakes. With a volume-of-interest (VOI) that closely encloses the tumor, the count-weighted centroid of the annihilation positions within the VOI can be calculated as the tumor position. All these data processes can be conducted online. The feasibility of the new method was investigated with a simulated cardiac-torso digital phantom and stationary dual-panel TOF PET detectors to track a 28 mm diameter lung tumor with a 4:1 tumor-to-background 18FDG activity concentration ratio. Results. The initial study shows TOF PET based RMTT can achieve <2.0 mm tumor tracking accuracy with 5 frame s−1 sampling rate under the simulated conditions. In comparison, using reconstructed PET images to track a similar size tumor would require >30 s acquisition time to achieve the same tracking accuracy. Conclusion. With the demonstrated feasibility, the new method may enable TOF PET based RMTT for practical RT applications.

GOECP/SEOR radiotheraphy guidelines for non-small-cell lung cancer

Non-small cell lung cancer (NSCLC) is a heterogeneous disease accounting for approximately 85% of all lung cancers. Only 17% of patients are diagnosed at an early stage. Treatment is multidisciplinary and radiotherapy plays a key role in all stages of the disease. More than 50% of patients with NSCLC are treated with radiotherapy (curative-intent or palliative). Technological advances-including highly conformal radiotherapy techniques, new immobilization and respiratory control systems, and precision image verification systems-allow clinicians to individualize treatment to maximize tumor control while minimizing treatment-related toxicity. Novel therapeutic regimens such as moderate hypofractionation and advanced techniques such as stereotactic body radiotherapy (SBRT) have reduced the number of radiotherapy sessions. The integration of SBRT into routine clinical practice has radically altered treatment of early-stage disease. SBRT also plays an increasingly important role in oligometastatic disease. The aim of the present guidelines is to review the role of radiotherapy in the treatment of localized, locally-advanced, and metastatic NSCLC. We review the main radiotherapy techniques and clarify the role of radiotherapy in routine clinical practice. These guidelines are based on the best available evidence. The level and grade of evidence supporting each recommendation is provided.

Evaluation of patient-specific motion management for radiotherapy planning computed tomography using a statistical method

We evaluated the probabilistic randomness of predictions by using individual numerical data based on general data for treatment planning computed tomography (CT) and evaluated the importance of patient-specific management through statistical analysis of our facility's data in lung stereotactic body radiotherapy (SBRT) and prostate volumetric modulated arc therapy (VMAT). The subjects were 30 patients who underwent lung SBRT with fiducial markers and 24 patients who underwent prostate VMAT. The average 3-dimensional (3D) displacement error between the fiducial marker and lung mass in 4DCT of lung SBRT was calculated and then compared with the 3D displacement error between the upper-lobe group (UG) and middle- or lower-lobe group (LG). The duty cycles between the lung tumor and fiducial marker at the <2-mm³ ambush area were compared between the UG and LG. In the prostate VMAT, the Shewhart control chart was analyzed by comparing multiple acquisition planning CT (MPCT) and cone-beam CT (CBCT) during the treatment period. The average 3D displacement errors in 4DCT for the lung tumor and fiducial marker were significantly different between the UG and middle- or lower-lobe group, but there was no correlation with the duty cycle. The Shewhart control chart for 3D displacement errors of the prostate for MPCT and CBCT showed that errors of >8 mm exceeded the control limit. In lung SBRT and prostate VMAT, overall statistical data from planning CT showed probabilistic randomness in predictions during the treatment period, and patient-specific motion management was needed to increase accuracy. A radiotherapy planning CT report showing a statistical analysis graph would be useful to objective share with staff.

A novel hardware duo of beam modulation and shielding to reduce scatter acquisition and dose in cone-beam breast CT

PURPOSE

In cone-beam breast CT, scattered photons form a large portion of the acquired signal, adversely impacting image quality throughout the frequency response of the imaging system. Prior simulation studies provided proof of concept for utilization of a hardware solution to prevent scatter acquisition. Here, we report the design, implementation, and characterization of an auxiliary apparatus of fluence modulation and scatter shielding that does indeed lead to projections with a reduced level of scatter.

METHODS

An apparatus was designed for permanent installation within an existing cone-beam CT system. The apparatus is composed of two primary assemblies: a "Fluence Modulator" (FM) and a "Scatter Shield" (SS). The design of the assemblies enables them to operate in synchrony during image acquisition, converting the sourced x-rays into a moving narrow beam. During a projection, this narrow beam sweeps the entire fan angle coverage of the imaging system. As the two assemblies are contingent on one another, their joint implementation is described in the singular as apparatus FM-SS. The FM and the SS assemblies are each comprised a metal housing, a sensory system, and a robotic system. A controller unit handles their relative movements. A series of comparative studies were conducted to evaluate the performance of a cone-beam CT system in two "modes" of operation: with and without FM-SS installed, and to compare the results of physical implementation with those previously simulated. The dynamic range requirements of the utilized detector in the cone-beam CT imaging system were first characterized, independent of the mode of operation. We then characterized and compared the spatial resolution of the imaging system with, and without, FM-SS. A physical breast phantom, representative of an average size breast, was developed and imaged. Actual differences in signal level obtained with, versus without, FM-SS were then compared to the expected level gains based on previously reported simulations. Following these initial assessments, the scatter acquisition in each projection in both modes of operation was investigated. Finally, as an initial study of the impact of FM-SS on radiation dose in an average size breast, a series of Monte Carlo simulations were coupled with physical measurements of air kerma, with and without FM-SS.

RESULTS

With implementation of FM-SS, the detector's required dynamic range was reduced by a factor of 5.5. Substantial reduction in the acquisition of the scattered rays, by a factor of 5.1 was achieved. With the implementation of FM-SS, deposited dose was reduced by 27% in the studied breast.

CONCLUSIONS

The disclosed implementation of FM-SS, within a cone-beam breast CT system, results in reduction of scatter-components in acquired projections, reduction of dose deposit to the breast, and relaxation of requirements for the detector's dynamic range. Controlling or correcting for patient motion occurring during image acquisition remains an open problem to be solved prior to practical clinical usage of FM-SS cone-beam breast CT.

Simultaneous Multiple Liver Metastasis Treated with Pencil Beam Proton Stereotactic Body Radiotherapy (SBRT)

Compared with photon stereotactic body radiotherapy (SBRT) plans that may have to use many more penetrating x-ray beams for each isocenter, proton SBRT with ultrahypofractionated doses use fewer beam angles and offer significantly reduced low-dose radiation bath to normal liver tissue. We demonstrate techniques to deliver safe and effective proton SBRT, where planning and organ motion complexity further increased with multiple liver lesions. For treatment planning, we recommend robust and logical beam angles, avoiding devices and encouraging entry perpendicular to the dominant motion, as well as volumetric repainting to mitigate the interplay effect to clinically acceptable levels. This report highlights the significant technical challenges with ultrahypofractionated proton pencil beam scanning liver therapy, how they are managed, and the effectiveness of this treatment.

Analysis of normal lung irradiation in radiosurgery treatments: a comparison of lung optimized treatment (LOT) on cyberknife, 4D target volume on helical tomotherapy, and DIBH on linear accelerator

Quantitative retrospective analysis of the normal lung irradiation due to the variations of the ITV volume based on the techniques used for upper lobe (UL), mid lobe (ML), and lower lobe (LL) lung tumours when used with 2-view, 1-view, 0-view based LOT technique on Cyberknife, AveIP on Helical Tomotherapy, and DIBH on VMAT systems. In the treatment of lung tumours, patients medically inoperable or those who are unwilling to undergo surgery have the option to be treated using radiation therapy. There are many motion control techniques available for the treatment of the moving target, such as movement encompassment, respiratory gating, breath-hold, motion reduction, and tumour monitoring. ITV generation is dependent on technique and hence the volume of the PTVs will differ based on the technique used. This study aimed to determine the influence of these ITVs on the irradiated normal lung volume for UL, ML, and LL lung tumours for 23 patients. The mean difference in the PTV volumes generated with the 0-view technique was significant with that of 2-view and DIBH techniques (p-value < 0.04). The mean difference in the PTV volumes generated by 2-view and DIBH was small for UL, ML, and LL tumours. V₅ of the combined lung with the 0-view method was 5% compared to the 2-view method for UL tumours (p-value = 0.04) and the same was 9.5%, and 16.8% for ML and LL tumours (p-value < 0.04). In contrast to all other techniques, lung volume parameters V₅, V₁₀, V_20, and V₃₀ for the 0-view technology were consistently higher irrespective of the tumour location in the lung. The observed maximum mean lung dose (MLD) was 6.2 Gy ± 2.7 Gy with the 0-view technique and the minimum was 3.85 Gy ± 1.75 Gy with the DIBH technique. The difference in MLD between DIBH and 2-view was negligible (p-value = 0.67). The MLD increased for LL tumours from 4 Gy to 6.5 Gy from the 2-view to 0-view technique (p-value = 0.009). There was a significant increase in MLD for LL tumours with the 0-view technique compared to AveIP (1.9 Gy, p-value = 0.04) and DIBH (2.0 Gy, p-value = 0.003) technique. For ML and UL tumours, except for 0-view and 1-view, the difference in the MLD between the rest of the methods was not significant (p-value > 0.11). In the treatment of lung tumour patients with SBRT, this study has demonstrated 2-view with Cyberknife and DIBH with VMAT treatment techniques have optimal normal lung tissue sparing. There was a significant increase in the average lung volume receiving 5%,10%, 20%, and 30% dose when comparing the 1-view, 0-view, AveIP, and DIBH techniques to the 2-view technique. However, DIBH with VMAT was dosimetrically advantageous for ML and LL tumours, while providing significantly shorter treatment times than any other technique studied.

Automatic liver tumor localization using deep learning-based liver boundary motion estimation and biomechanical modeling (DL-Bio)

PURPOSE

Recently, two-dimensional-to-three-dimensional (2D-3D) deformable registration has been applied to deform liver tumor contours from prior reference images onto estimated cone-beam computed tomography (CBCT) target images to automate on-board tumor localizations. Biomechanical modeling has also been introduced to fine-tune the intra-liver deformation-vector-fields (DVFs) solved by 2D-3D deformable registration, especially at low-contrast regions, using tissue elasticity information and liver boundary DVFs. However, the caudal liver boundary shows low contrast from surrounding tissues in the cone-beam projections, which degrades the accuracy of the intensity-based 2D-3D deformable registration there and results in less accurate boundary conditions for biomechanical modeling. We developed a deep-learning (DL)-based method to optimize the liver boundary DVFs after 2D-3D deformable registration to further improve the accuracy of subsequent biomechanical modeling and liver tumor localization.

METHODS

The DL-based network was built based on the U-Net architecture. The network was trained in a supervised fashion to learn motion correlation between cranial and caudal liver boundaries to optimize the liver boundary DVFs. Inputs of the network had three channels, and each channel featured the 3D DVFs estimated by the 2D-3D deformable registration along one Cartesian direction (x, y, z). To incorporate patient-specific liver boundary information into the DVFs, the DVFs were masked by a liver boundary ring structure generated from the liver contour of the prior reference image. The network outputs were the optimized DVFs along the liver boundary with higher accuracy. From these optimized DVFs, boundary conditions were extracted for biomechanical modeling to further optimize the solution of intra-liver tumor motion. We evaluated the method using 34 liver cancer patient cases, with 24 for training and 10 for testing. We evaluated and compared the performance of three methods: 2D-3D deformable registration, 2D-3D-Bio (2D-3D deformable registration with biomechanical modeling), and DL-Bio (DL model prediction with biomechanical modeling). The tumor localization errors were quantified through calculating the center-of-mass-errors (COMEs), DICE coefficients, and Hausdorff distance between deformed liver tumor contours and manually segmented "gold-standard" contours.

RESULTS

The predicted DVFs by the DL model showed improved accuracy at the liver boundary, which translated into more accurate liver tumor localizations through biomechanical modeling. On a total of 90 evaluated images and tumor contours, the average (± sd) liver tumor COMEs of the 2D-3D, 2D-3D-Bio, and DL-Bio techniques were 4.7 ± 1.9 mm, 2.9 ± 1.0 mm, and 1.7 ± 0.4 mm. The corresponding average (± sd) DICE coefficients were 0.60 ± 0.12, 0.71 ± 0.07, and 0.78 ± 0.03; and the average (± sd) Hausdorff distances were 7.0 ± 2.6 mm, 5.4 ± 1.5 mm, and 4.5 ± 1.3 mm, respectively.

CONCLUSION

DL-Bio solves a general correlation model to improve the accuracy of the DVFs at the liver boundary. With improved boundary conditions, the accuracy of biomechanical modeling can be further increased for accurate intra-liver low-contrast tumor localization.

Using 4DCBCT simulation and guidance to evaluate inter-fractional tumor variance during SABR for lung tumor within the lower lobe

Inter-fractional tumor variance would lead to insufficient dosage or overdose in tumor region during lung cancer radiotherapy. However, previous works have not considered influence of inter-fractional tumor amplitude variance at treatment position due to lack of effective evaluation method during radiotherapy, especially for lung tumor within the lower lobe. Our objective was to investigate inter-fractional tumor baseline shift and amplitude variance due to respiratory motion with 4DCBCT simulation and guidance during stereotactic ablative body radiotherapy (SABR) for lung tumor. Subject included 19 patients with lung tumor within the lower lobe. 4DCBCT-simulated images at treatment position were acquired sequentially to determine internal tumor volume (ITV) and reference tumor motion at simulation process. Compared with reference tumor motion, 95 4DCBCT-guided images were acquired during each treatment to evaluate inter-fractional tumor baseline shift and amplitude variance, which were − 0.0 ± 1.3 mm and − 0.2 ± 1.4 mm in left–right(LR) direction, 0.9 ± 2.3 mm and 0.4 ± 2.9 mm in superior-inferior (SI) direction, 0.1 ± 1.5 mm and − 0.4 ± 2.0 mm in anterior–posterior (AP) direction. ITV margin were 3.5 mm, 7.5 mm and 5.3 mm in LR, SI and AP directions with van Herk’s (Int J Radiat Oncol Biol Phys 52(5):1407–1422, 2002) formula. 4DCBCT simulation and guidance is a reliable method to evaluate inter-fractional tumor variance during SABR for lung tumor within the lower lobe. ITV margin of 3.5 mm, 7.5 mm and 5.3 mm in LR, SI and AP directions would ensure greater tumor coverage during SABR for lung tumor within the lower lobe.

A review of deep learning based methods for medical image multi-organ segmentation

Deep learning has revolutionized image processing and achieved the-state-of-art performance in many medical image segmentation tasks. Many deep learning-based methods have been published to segment different parts of the body for different medical applications. It is necessary to summarize the current state of development for deep learning in the field of medical image segmentation. In this paper, we aim to provide a comprehensive review with a focus on multi-organ image segmentation, which is crucial for radiotherapy where the tumor and organs-at-risk need to be contoured for treatment planning. We grouped the surveyed methods into two broad categories which are 'pixel-wise classification' and 'end-to-end segmentation'. Each category was divided into subgroups according to their network design. For each type, we listed the surveyed works, highlighted important contributions and identified specific challenges. Following the detailed review, we discussed the achievements, shortcomings and future potentials of each category. To enable direct comparison, we listed the performance of the surveyed works that used thoracic and head-and-neck benchmark datasets.

Radiation Modalities Used in Lung Cancer: An Overview for Thoracic Surgeons

Radiation is a constantly evolving technology which plays a role in the management of lung cancer in a variety of settings: as an adjunct to surgery, definitively, and palliatively. Key aspects of radiation oncology-including acute and chronic toxicities of thoracic radiation and rationale for choosing one modality of radiation over another-may be obscure to those outside the field. We aim to provide a useful overview relevant for the thoracic surgeon of radiation technology and delivery. A review was performed of salient articles identifying radiation technologies used in lung cancer which were summarized and expounded upon with focus on integrating their history, evolution, and landmark trials establishing basis of their use. This article reviews the four fundamental means of external beam radiation employed in managing lung cancer and provides visual examples of comparison plans. We also touch on potential practice-changing developments in regards to proton therapy and radiation in the era of immunotherapy. Radiation oncology has evolved considerably over time to become a critical part of lung cancer management, particularly in early-stage inoperable disease and locally advanced disease. Maximizing tumor control while minimizing toxicity drives treatment strategies. Knowledge of these fundamentals will help the thoracic surgeon answer many questions patients pose regarding radiation.

Proton therapy for thoracic malignancies: a review of oncologic outcomes

Introduction: Radiotherapy is an integral component in the treatment of the majority of thoracic malignancies. By taking advantage of the steep dose fall-off characteristic of protons combined with modern optimization and delivery techniques, proton beam therapy (PBT) has emerged as a potential tool to improve oncologic outcomes while reducing toxicities from treatment.Areas covered: We review the physical properties and treatment techniques that form the basis of PBT as applicable for thoracic malignancies, including a brief discussion on the recent advances that show promise to enhance treatment planning and delivery. The dosimetric advantages and clinical outcomes of PBT are critically reviewed for each of the major thoracic malignancies, including lung cancer, esophageal cancer, mesothelioma, thymic cancer, and primary mediastinal lymphoma.Expert opinion: Despite clear dosimetric benefits with PBT in thoracic radiotherapy, the improvement in clinical outcomes remains to be seen. Nevertheless, with the incorporation of newer techniques, PBT remains a promising modality and ongoing randomized studies will clarify its role to determine which patients with thoracic malignancies receive the most benefit. Re-irradiation, advanced disease requiring high cardio-pulmonary irradiation volume and younger patients will likely derive maximum benefit with modern PBT.

The assessment of consecutive 4D-CT scans during simulation for lung stereotactic body radiation therapy patients

Purpose: To evaluate the breathing amplitude, tumor motion, patient positioning, and treatment volumes among consecutive four-dimensional computed tomography (4D-CT) scans, during the simulation for lung stereotactic body radiation therapy (SBRT).

Material and methods: The variation and shape of the breathing amplitude, patient positioning, and treatment volumes were evaluated for 55 lung cancer patients after consecutive 4D-CT acquisitions, scanned at one-week intervals. The impact of variation in the breathing amplitude on lung tumor motion was determined for 20 patients. The gross tumor volume (GTV) was contoured from a free-breathing CT scan and at ten phases of the respiratory cycle, for both 4D-CTs (440 phases in total).

Results: Breathing amplitude decreased by 3.6 (3.4-4.9) mm, tumor motion by 3.2 (0.4-5.0) mm while breathing period increased by 4 (2-6) s, inter-scan for 20 patients. Intra-scan variation was 4 times greater for the breathing amplitude, 5 times for the breathing period, and 8 times for the breathing cycle, comparing irregular versus regular breathing patterns for 55 patients. Using coaching, the breathing amplitude increases 3 to 8 mm, and the breathing period 2 to 6 s. Differences in the contoured treatment volumes were less than 10% between consecutive scans. Patient positioning remained stable, with a small inter-scan difference of 1.1 (0.6-1.4) mm.

Conclusion: Decreasing the inter-scan breathing amplitude decreases the tumor motion reciprocally. When the breathing amplitude decreases, the breathing period increases at inter- and intra-scan, especially during irregular breathing. Coaching improves respiration, keeping the initial shape of the breathing amplitude. Contoured treatment volumes and patient positioning were reproducible through successive scans.

Online adaptive dose restoration in intensity modulated proton therapy of lung cancer to account for inter-fractional density changes

BACKGROUND AND PURPOSE

In proton therapy, inter-fractional density changes can severely compromise the effective delivery of the planned dose. Such dose distortion effects can be accounted for by treatment plan adaptation, that requires considerable automation for widespread implementation in clinics. In this study, the clinical benefit of an automatic online adaptive strategy called dose restoration (DR) was investigated. Our objective was to assess to what extent DR could replace the need for a comprehensive offline adaptive strategy.

MATERIALS AND METHODS

The fully automatic and robust DR workflow was evaluated in a cohort of 14 lung IMPT patients that had a planning-CT and two repeated 4D-CTs (rCT1,rCT2). Initial plans were generated using 4D-robust optimization (including breathing-motion, setup and range errors). DR relied on isodose contours generated from the initial dose and associated patient specific weighted objectives to mimic this initial dose in repeated-CTs. These isodose contours, with their corresponding objectives, were used during re-optimization to compensate proton range distortions disregarding re-contouring. Robustness evaluations were performed for the initial, not-adapted and restored (adapted) plans.

RESULTS

The resulting DVH-bands showed overall improvement in DVH metrics and robustness levels for restored plans, with respect to not-adapted plans. According to CTV coverage criteria (D95%>95%Dprescription) in not-adapted plans, 35% (5/14) of the cases needed offline adaptation. After DR, Median(D95%) was increased by 1.1 [IQR,0.4] Gy and only one patient out of 14 (7%) still needed offline adaptation because of important anatomical changes.

CONCLUSIONS

DR has the potential to improve CTV coverage and reduce offline adaptation rate.

Circular RNA hsa_circ_0004015 regulates the proliferation, invasion, and TKI drug resistance of non-small cell lung cancer by miR-1183/PDPK1 signaling pathway

Circular RNAs (circRNAs) were recently reported to be involved in the pathogenesis of Non-small cell lung cancer (NSCLC), however, the molecular mechanisms of circRNAs in cell proliferation, invasion and TKI drug resistance remain largely undetermined. Here, we identified hsa_circ_0004015 was upregulated in NSCLC tissues, and was associated with the poor overall survival rate of NSCLC patients. Knockdown of hsa_circ_0004015 significantly decreased cell viability, proliferation, and invasion, whereas overexpression exhibited opposed effects in vivo and in vitro. Furthermore, hsa_circ_0004015 could enhance the resistance of HCC827 to gefitinib. In mechanism, hsa_circ_0004015 acted as a sponge for miR-1183, and PDPK1 was revealed to be target gene of miR-1183. Subsequently, functional assays illustrated that the oncogenic effects of hsa_circ_0004015 was attributed to the regulation of miR-1183/PDPK1 axis. In conclusion, circ_0016760/miR-1183/PDPK1 signaling pathway might play vital roles in the tumorigenesis of NSCLC.