Clinical Results of Mean GTV Dose Optimized Robotic-Guided Stereotactic Body Radiation Therapy for Lung Tumors

Impact of setup and geometric uncertainties on the robustness of free-breathing photon radiotherapy of small lung tumors

PURPOSE

Respiratory motion and patient setup error both contribute to the dosimetric uncertainty in radiotherapy of lung tumors. Managing these uncertainties for free-breathing treatments is usually done by margin-based approaches or robust optimization. However, breathing motion can be irregular and concerns have been raised for the robustness of the treatment plans. We have previously reported the dosimetric effects of the respiratory motion, without setup uncertainties, in lung tumor photon radiotherapy using free-breathing images. In this study, we include setup uncertainty.

METHODS

Tumor positions from cine-CT images acquired in free-breathing were combined with per-fraction patient shifts to simulate treatment scenarios. A total of 14 patients with 300 tumor positions were used to evaluate treatment plans based on 4DCT. Four planning methods aiming at delivering 54 Gy as median tumor dose in three fractions were compared. The planning methods were denoted robust 4D (RB4), isodose to the PTV with a central higher dose (ISD), the ISD method normalized to the intended median tumor dose (IRN) and homogeneous fluence to the PTV (FLU).

RESULTS

For all planning methods 95% of the intended dose was achieved with at least 90% probability with RB4 and FLU having equal CTV D50% values at this probability. FLU gave the most consistent results in terms of CTV D50% spread and dose homogeneity.

CONCLUSIONS

Despite the simulated patient shifts and tumor motions being larger than observed in the 4DCTs the dosimetric impact was suggested to be small. RB4 or FLU are recommended for the planning of free-breathing treatments.

Predicting successful clinical candidates for fiducial-free lung tumor tracking with a deep learning binary classification model

OBJECTIVES

The CyberKnife system is a robotic radiosurgery platform that allows the delivery of lung SBRT treatments using fiducial-free soft-tissue tracking. However, not all lung cancer patients are eligible for lung tumor tracking. Tumor size, density, and location impact the ability to successfully detect and track a lung lesion in 2D orthogonal X-ray images. The standard workflow to identify successful candidates for lung tumor tracking is called Lung Optimized Treatment (LOT) simulation, and involves multiple steps from CT acquisition to the execution of the simulation plan on CyberKnife. The aim of the study is to develop a deep learning classification model to predict which patients can be successfully treated with lung tumor tracking, thus circumventing the LOT simulation process.

METHODS

Target tracking is achieved by matching orthogonal X-ray images with a library of digital radiographs reconstructed from the simulation CT scan (DRRs). We developed a deep learning model to create a binary classification of lung lesions as being trackable or untrackable based on tumor template DRR extracted from the CyberKnife system, and tested five different network architectures. The study included a total of 271 images (230 trackable, 41 untrackable) from 129 patients with one or multiple lung lesions. Eighty percent of the images were used for training, 10% for validation, and the remaining 10% for testing.

RESULTS

For all five convolutional neural networks, the binary classification accuracy reached 100% after training, both in the validation and the test set, without any false classifications.

CONCLUSIONS

A deep learning model can distinguish features of trackable and untrackable lesions in DRR images, and can predict successful candidates for fiducial-free lung tumor tracking.

Dosimetric robustness of lung tumor photon radiotherapy evaluated from multiple event CT imaging

PURPOSE

Intrafractional respiratory motion is a concern for lung tumor radiotherapy but full evaluation of its impact is hampered by the lack of images representing the true motion. This study presents a novel evaluation using free-breathing images acquired over realistic treatment times to study the dosimetric impact of respiratory motion in photon radiotherapy.

METHODS

Cine-CT images of 14 patients with lung cancer acquired during eight minutes of free-breathing at three occasions were used to simulate dose tracking of four different planning methods. These methods aimed to deliver 54 Gy in three fractions to D_50% of the target and were denoted as robust 4D (RB4), homogeneous fluence to the ITV (FLU) and an isodose prescription to the ITV with a high central dose (ISD), concurrently renormalized (IRN). Differences in dose coverage probability and homogeneity between the methods were quantified. Correlations between underdosage and attributes regarding the tumor and its motion were investigated.

RESULTS

Despite tumor motion amplitudes being larger than in the 4DCT all but FLU achieved the intended CTV D_50% for the cohort average. For all methods but IRN at least 93% of the patients would have received 95% of the intended dose. No differences in D_50% were found between RB4 and ISD nor IRN. However, RB4 led to better homogeneity.

CONCLUSIONS

Tumor motion in free-breathing not covered by the 4DCT had a small impact on dose. The RB4 is recommended for planning of free-breathing treatments. No factor was found that consistently correlated dose degradation with patient or motion attributes.

Harmonization of dose prescription for lung stereotactic radiotherapy

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Doses standardization achieved between dedicated linac and robotic-assisted unit.

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Both methods delivered 3×18.3 Gy to the near minimum dose of the tumor volume.

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Four-Dimensional deformable method allowed to estimate dose to a mobile tumor.

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The reliability of a double-check software using a Monte-Carlo algorithm was validated.

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Gross Tumor Volume-based prescription presented less dose heterogeneities to the tumor.

Background and purpose

Pulmonary stereotactic treatments can be performed using dedicated linear accelerators as well as robotic-assisted units, and different strategies can be used for dose prescription. This study aimed to compare the doses received by the tumor with a gross tumor volume (GTV)-based prescription on D_98%GTV using a robotic-assisted unit (method A) and planning target volume (PTV)-based prescription on D_95%PTV using a dedicated linac (method B).

Material & methods

Plans of 32 patients were collected for method A, and a dose of 3 × 18 Gy was prescribed using type A algorithm and recalculated using a Monte-Carlo (MC) algorithm. The plans were normalized to match D_98%GTV with the mean D98%GTV¯ of the cohort. The plans of 23 patients were collected for method B, and a dose of 3 × 18 Gy was prescribed to D_95%PTV using a MC algorithm. A 4D-sum method was developed to estimate doses for PTV and GTV. For validation, all plans were recalculated using an independent MC double-check software. A dose harmonization on D_{98% GTV} was determined for both methods.

Results

For method A, mean doses were D_2%GTV = 59.9 ± 2.1 Gy, D_50%GTV = 55.6 ± 1.2 Gy, D_98%GTV = 49.5 ± 0.0 Gy. For method B, the reported doses were D_2%GTV = 64.6 ± 2.1 Gy, D_50%GTV = 62.8 ± 1.7 Gy, and D_98%GTV = 60.0 ± 1.7 Gy. The dose trade-off of D_98%GTV = 55 Gy was obtained for both methods. For method A, it corresponded to a dose prescription of 3 × 20 Gy using type A algorithm, followed by rescaling to obtain D_98%GTV = 55 Gy. For method B, it corresponded to a dose prescription of D_95%PTV = 3 × 16.5 Gy using the MC algorithm.

Conclusions

This study determined similar near-minimum doses D_{98% GTV} of approximately 3 × 18.3 Gy (55 Gy) using a GTV-based prescription on a robotic-assisted unit (method A) and a PTV-based prescription on a dedicated linac (method B).

Dosimetric benefit of MR-guided online adaptive radiotherapy in different tumor entities: liver, lung, abdominal lymph nodes, pancreas and prostate

Background

Hybrid magnetic resonance (MR)-Linac systems have recently been introduced into clinical practice. The systems allow online adaption of the treatment plan with the aim of compensating for interfractional anatomical changes. The aim of this study was to evaluate the dose volume histogram (DVH)-based dosimetric benefits of online adaptive MR-guided radiotherapy (oMRgRT) across different tumor entities and to investigate which subgroup of plans improved the most from adaption.

Methods

Fifty patients treated with oMRgRT for five different tumor entities (liver, lung, multiple abdominal lymph nodes, pancreas, and prostate) were included in this retrospective analysis. Various target volume (gross tumor volume GTV, clinical target volume CTV, and planning target volume PTV) and organs at risk (OAR) related DVH parameters were compared between the dose distributions before and after plan adaption.

Results

All subgroups clearly benefited from online plan adaption in terms of improved PTV coverage. For the liver, lung and abdominal lymph nodes cases, a consistent improvement in GTV coverage was found, while many fractions of the prostate subgroup showed acceptable CTV coverage even before plan adaption. The largest median improvements in GTV near-minimum dose (D_98%) were found for the liver (6.3%, p < 0.001), lung (3.9%, p < 0.001), and abdominal lymph nodes (6.8%, p < 0.001) subgroups. Regarding OAR sparing, the largest median OAR dose reduction during plan adaption was found for the pancreas subgroup (-87.0%). However, in the pancreas subgroup an optimal GTV coverage was not always achieved because sparing of OARs was prioritized.

Conclusion

With online plan adaptation, it was possible to achieve significant improvements in target volume coverage and OAR sparing for various tumor entities and account for interfractional anatomical changes.

On the interplay effect for moving targets treated with the CyberKnife static tracking system

PURPOSE

To assess the interplay effect amplitude between different planned MU distributions and respiratory patterns in the CyberKnife system when treating moving targets with static tracking technique.

METHODS

Small- and Large-Respiratory Motions (SRM and LRM) differing in amplitude and frequency were simulated in a semi-anthropomorphic dynamic thorax phantom. The interplay effect was evaluated for both respiration motions in terms of GTV coverage and conformity for three plans designed with an increasing range of MU per beam (small, medium and large). Each plan was delivered three times changing the initial beam-on phase to assess the inter-fraction variation. Dose distributions were measured using radiochromic films placed in the GTV axial and sagittal planes.

RESULTS

Generally, SRM plans gave higher GTV coverage and were less dependent on beam-on phases than LRM plans. For SRM (LRM) plans, the GTV coverage ranged from 95.2% to 99.7% (85.9% to 99.8%). Maximum GTV coverage was found for large MU plans in SRM and for small MU plans in LRM. Minimum GTV coverage was found for medium MU plans for both SRM and LRM. For SRM plans, dose conformity decreased with increasing MU range while the variation was reduced for LRM plans. Large MU plans reduced the inter-fraction variation for SRM and LRM.

CONCLUSIONS

We confirmed the interplay effect between target motion and beam irradiation time for CyberKnife static tracking. Plans with large MU per beam improved the GTV coverage for small motion amplitude and the inter-fraction dose variation for large motion amplitude.

Combining computed tomography and biologically effective dose in radiomics and deep learning improves prediction of tumor response to robotic lung stereotactic body radiation therapy

PURPOSE

The aim of this study is to improve the performance of machine learning (ML) models in predicting response of non-small cell lung cancer (NSCLC) to stereotactic body radiation therapy (SBRT) by integrating image features from pre-treatment computed tomography (CT) with features from the biologically effective dose (BED) distribution.

MATERIALS AND METHODS

Image features, consisting of crafted radiomic features or machine-learned features extracted using a convolutional neural network, were calculated from pre-treatment CT data and from dose distributions converted into BED for 80 NSCLC lesions over 76 patients treated with robotic guided SBRT. ML models using different combinations of features were trained to predict complete or partial response according to response criteria in solid tumors, including radiomics CT (Rad_CT ), radiomics CT and BED (Rad_CT,BED ), deep learning (DL) CT (DL_CT ), and DL CT and BED (DL_CT,BED ). Training of ML included feature selection by neighborhood component analysis followed by ensemble ML using robust boosting. A model was considered as acceptable when the sum of average sensitivity and specificity on test data in repeated cross validations was at least 1.5.

RESULTS

Complete or partial response occurred in 58 out of 80 lesions. The best models to predict the tumor response were those using BED variables, achieving significantly better area under curve (AUC) and accuracy than those using only features from CT, including a Rad_CT,BED model using three radiomic features from BED, which scored an accuracy of 0.799 (95% confidence intervals (0.75-0.85)) and AUC of 0.773 (0.688-0.846), and a DL_CT,BED model also using three variables with an accuracy of 0.798 (0.649-0.829) and AUC of 0.812 (0.755-0.867).

CONCLUSION

According to our results, the inclusion of BED features improves the response prediction of ML models for lung cancer patients undergoing SBRT, regardless of the use of radiomic or DL features.

Improving interinstitutional and intertechnology consistency of pulmonary SBRT by dose prescription to the mean internal target volume dose

Purpose

Dose, fractionation, normalization and the dose profile inside the target volume vary substantially in pulmonary stereotactic body radiotherapy (SBRT) between different institutions and SBRT technologies. Published planning studies have shown large variations of the mean dose in planning target volume (PTV) and gross tumor volume (GTV) or internal target volume (ITV) when dose prescription is performed to the PTV covering isodose. This planning study investigated whether dose prescription to the mean dose of the ITV improves consistency in pulmonary SBRT dose distributions.

Materials and methods

This was a multi-institutional planning study by the German Society of Radiation Oncology (DEGRO) working group Radiosurgery and Stereotactic Radiotherapy. CT images and structures of ITV, PTV and all relevant organs at risk (OAR) for two patients with early stage non-small cell lung cancer (NSCLC) were distributed to all participating institutions. Each institute created a treatment plan with the technique commonly used in the institute for lung SBRT. The specified dose fractionation was 3 × 21.5 Gy normalized to the mean ITV dose. Additional dose objectives for target volumes and OAR were provided.

Results

In all, 52 plans from 25 institutions were included in this analysis: 8 robotic radiosurgery (RRS), 34 intensity-modulated (MOD), and 10 3D-conformal (3D) radiation therapy plans. The distribution of the mean dose in the PTV did not differ significantly between the two patients (median 56.9 Gy vs 56.6 Gy). There was only a small difference between the techniques, with RRS having the lowest mean PTV dose with a median of 55.9 Gy followed by MOD plans with 56.7 Gy and 3D plans with 57.4 Gy having the highest. For the different organs at risk no significant difference between the techniques could be found.

Conclusions

This planning study pointed out that multiparameter dose prescription including normalization on the mean ITV dose in combination with detailed objectives for the PTV and ITV achieve consistent dose distributions for peripheral lung tumors in combination with an ITV concept between different delivery techniques and across institutions.

Supplementary Information

The online version of this article (10.1007/s00066-021-01799-w) contains supplementary material, which is available to authorized users.

Radiomics for prediction of radiation-induced lung injury and oncologic outcome after robotic stereotactic body radiotherapy of lung cancer: results from two independent institutions

OBJECTIVES

To generate and validate state-of-the-art radiomics models for prediction of radiation-induced lung injury and oncologic outcome in non-small cell lung cancer (NSCLC) patients treated with robotic stereotactic body radiation therapy (SBRT).

METHODS

Radiomics models were generated from the planning CT images of 110 patients with primary, inoperable stage I/IIa NSCLC who were treated with robotic SBRT using a risk-adapted fractionation scheme at the University Hospital Cologne (training cohort). In total, 199 uncorrelated radiomic features fulfilling the standards of the Image Biomarker Standardization Initiative (IBSI) were extracted from the outlined gross tumor volume (GTV). Regularized models (Coxnet and Gradient Boost) for the development of local lung fibrosis (LF), local tumor control (LC), disease-free survival (DFS) and overall survival (OS) were built from either clinical/ dosimetric variables, radiomics features or a combination thereof and validated in a comparable cohort of 71 patients treated by robotic SBRT at the Radiosurgery Center in Northern Germany (test cohort).

RESULTS

Oncologic outcome did not differ significantly between the two cohorts (OS at 36 months 56% vs. 43%, p = 0.065; median DFS 25 months vs. 23 months, p = 0.43; LC at 36 months 90% vs. 93%, p = 0.197). Local lung fibrosis developed in 33% vs. 35% of the patients (p = 0.75), all events were observed within 36 months. In the training cohort, radiomics models were able to predict OS, DFS and LC (concordance index 0.77-0.99, p < 0.005), but failed to generalize to the test cohort. In opposite, models for the development of lung fibrosis could be generated from both clinical/dosimetric factors and radiomic features or combinations thereof, which were both predictive in the training set (concordance index 0.71- 0.79, p < 0.005) and in the test set (concordance index 0.59-0.66, p < 0.05). The best performing model included 4 clinical/dosimetric variables (GTV-D_mean, PTV-D_95%, Lung-D_1ml, age) and 7 radiomic features (concordance index 0.66, p < 0.03).

CONCLUSION

Despite the obvious difficulties in generalizing predictive models for oncologic outcome and toxicity, this analysis shows that carefully designed radiomics models for prediction of local lung fibrosis after SBRT of early stage lung cancer perform well across different institutions.

Liver SBRT with active motion-compensation results in excellent local control for liver oligometastases: An outcome analysis of a pooled multi-platform patient cohort

BACKGROUND

Local treatment of metastases in combination with systemic therapy can prolong survival of oligo-metastasized patients. To fully exploit this potential, safe and effective treatments are needed to ensure long-term metastases control. Stereotactic body radiotherapy (SBRT) is one means, however, for moving liver tumors correct delivery of high doses is challenging. After validating equal in-vivo treatment accuracy, we analyzed a pooled multi-platform liver-SBRT-database for clinical outcome.

METHODS

Local control (LC), progression-free interval (PFI), overall survival (OS), predictive factors and toxicity was evaluated in 135 patients with 227 metastases treated by gantry-based SBRT (deep-inspiratory breath-hold-gating; n = 71) and robotic-based SBRT (fiducial-tracking, n = 156) with mean gross tumor volume biological effective dose (GTV-BED_α/β=10Gy) of 146.6 Gy₁₀.

RESULTS

One-, and five-year LC was 90% and 68.7%, respectively. On multivariate analysis, LC was significantly predicted by colorectal histology (p = 0.006). Median OS was 20 months with one- and two-year OS of 67% and 37%. On multivariate analysis, ECOG-status (p = 0.003), simultaneous chemotherapy (p = 0.003), time from metastasis detection to SBRT-treatment (≥2months; p = 0.021) and LC of the treated metastases (≥12 months, p < 0.009) were significant predictors for OS. One- and two-year PFI were 30.5% and 14%. Acute toxicity was mild and rare (14.4% grade I, 2.3% grade II, 0.6% grade III). Chronic °III/IV toxicities occurred in 1.1%.

CONCLUSIONS

Patient selection, time to treatment and sufficient doses are essential to achieve optimal outcome for SBRT with active motion compensation. Local control appears favorable compared to historical control. Long-term LC of the treated lesions was associated with longer overall survival.

Current status and future directions in unresectable stage III non-small cell lung cancer

BACKGROUND

Patients with unresectable stage III non-small-cell lung cancer constitute a heterogeneous group in which the available treatments may range from radical therapies with radio-chemotherapy to supportive treatments depending on the extent of the disease and comorbidities present. For years the standard treatment based on the combination of chemotherapy and radiotherapy (RT) has remained unchanged and survival outcomes have been poor.

AIM

Recent advances in molecular biology and RT technology have resulted in improved survival. This article reviews the treatments that constitute current standard treatment in unresectable advanced lung cancer and the situations and indications for the management of patients who are not candidates for radical therapy.

RELEVANCE FOR PATIENTS

Although unresectable lung cancer does not have a good prognosis, new drugs and new technologies in radiation oncology can offer treatment options adapted to the patient's clinical situation, ranging from therapies administered with radical intent to others aimed mainly at improving the patient's quality of life, which, judiciously chosen, will provide optimal management of the patient.

Tumor-dose-rate variations during robotic radiosurgery of oligo and multiple brain metastases

Purpose

For step-and-shoot robotic stereotactic radiosurgery (SRS) the dose delivered over time, called local tumor-dose-rate (TDR), may strongly vary during treatment of multiple lesions. The authors sought to evaluate technical parameters influencing TDR and correlate TDR to clinical outcome.

Material and methods

A total of 23 patients with 162 oligo (1–3) and multiple (>3) brain metastases (OBM/MBM) treated in 33 SRS sessions were retrospectively analyzed. Median PTV were 0.11 cc (0.01–6.36 cc) and 0.50 cc (0.12–3.68 cc) for OBM and MBM, respectively. Prescription dose ranged from 16 to 20 Gy prescribed to the median 70% isodose line. The maximum dose-rate for planning target volume (PTV) percentage p in time span s during treatment (TDR_s,p) was calculated for various p and s based on treatment log files and in-house software.

Results

TDR_60min,98% was 0.30 Gy/min (0.23–0.87 Gy/min) for OBM and 0.22 Gy/min (0.12–0.63 Gy/min) for MBM, respectively, and increased by 0.03 Gy/min per prescribed Gy. TDR_60min,98% strongly correlated with treatment time (ρ = −0.717, p < 0.001), monitor units (MU) (ρ = −0.767, p < 0.001), number of beams (ρ = −0.755, p < 0.001) and beam directions (ρ = −0.685, p < 0.001) as well as lesions treated per collimator (ρ = −0.708, P < 0.001). Median overall survival (OS) was 20 months and 1‑ and 2‑year local control (LC) was 98.8% and 90.3%, respectively. LC did not correlate with any TDR, but tumor response (partial response [PR] or complete response [CR]) correlated with all TDR in univariate analysis (e.g., TDR_60min,98%: hazard ration [HR] = 0.974, confidence interval [CI] = 0.952–0.996, p = 0.019). In multivariate analysis only concomitant targeted therapy or immunotherapy and breast cancer tumor histology remained a significant factor for tumor response. Local grade ≥2 radiation-induced tissue reactions were noted in 26.3% (OBM) and 5.2% (MBM), respectively, mainly influenced by tumor volume (p < 0.001).

Conclusions

Large TDR variations are noted during MBM-SRS which mainly arise from prolonged treatment times. Clinically, low TDR corresponded with decreased local tumor responses, although the main influencing factor was concomitant medication.

Large-scale dosimetric assessment of Monte Carlo recalculated doses for lung robotic stereotactic body radiation therapy

Owing to its short computation time and simplicity, the Ray-Tracing algorithm (RAT) has long been used to calculate dose distributions for the CyberKnife system. However, it is known that RAT fails to fully account for tissue heterogeneity and is therefore inaccurate in the lung. The aim of this study is to make a dosimetric assessment of 219 non-small cell lung cancer CyberKnife plans by recalculating their dose distributions using an independent Monte Carlo (MC) method. For plans initially calculated by RAT without heterogeneity corrections, target coverage was found to be significantly compromised when considering MC doses. Only 35.4% of plans were found to comply to their prescription doses. If the normal tissue dose limits were respected in the treatment planning dose, the MC recalculated dose did not exceed these limits in over 97% of the plans. Comparison of RAT and recalculated-MC doses confirmed the overestimation of RAT doses observed in previous studies. An inverse correlation between the RAT/MC dose ratio and the target size was also found to be statistically significant (p<10^-4), consistent with other studies. In addition, the inaccuracy and variability in target coverage incurred from dose calculations using RAT without heterogeneity corrections was demonstrated. On average, no clinically relevant differences were observed between MC-calculated dose-to-water and dose-to-medium for all tissues investigated (⩽1%). Patients receiving a dose D_95% larger than 119 Gy in EQD2₁₀ (or ≈52 Gy in 3 fractions) as recalculated by MC were observed to have significantly superior loco-regional progression-free survival rates (p=0.02) with a hazard ratio of 3.45 (95%CI: 1.14-10.5).

Dose prescription in SBRT for early-stage non-small cell lung cancer: are we all speaking the same language?

INTRODUCTION

Stereotactic body radiation therapy is increasingly used in the treatment of early-stage lung cancers. Guidelines provide indications regarding the constraints to the organs at risk (OARs) and the minimum coverage of the planning target volume but do not suggest optimal dose distribution. Data on dose distribution from the different published series are not comparable due to different prescription modalities and reported dose parameters.

METHODS

We conducted a review of the published data on dose prescription, focusing on the role of homogeneity on local tumor control, and present suggestions on how to specify and report the prescriptions to permit comparisons between studies or between cases from different centers.

CONCLUSIONS

To identify the dose-prescription modality that better correlates with oncologic outcomes, future studies should guarantee a close uniformity of dose distribution between cases and complete dose parameters reporting for treatment volumes and OARs.

On the pitfalls of PTV in lung SBRT using type-B dose engine: an analysis of PTV and worst case scenario concepts for treatment plan optimization

Background

PTV concept is presumed to introduce excessive and inconsistent GTV dose in lung stereotactic body radiotherapy (SBRT). That GTV median dose prescription (D₅₀) and robust optimization are viable PTV–free solution (ICRU 91 report) to harmonize the GTV dose was investigated by comparisons with PTV–based SBRT plans.

Methods

Thirteen SBRT plans were optimized for 54 Gy / 3 fractions and prescribed (i) to 95% of the PTV (D₉₅) expanded 5 mm from the ITV on the averaged intensity project (AIP) CT, i.e., PTV_ITV, (ii) to D₉₅ of PTV derived from the van Herk (VH)‘s margin recipe on the mid–ventilation (MidV)–CT, i.e., PTV_VH, (iii) to ITV D₉₈ by worst case scenario (WCS) optimization on AIP,i.e., WCS_ITV and (iv) to GTV D₉₈ by WCS using all 4DCT images, i.e., WCS_GTV. These plans were subsequently recalculated on all 4DCT images and deformably summed on the MidV–CT. The dose differences between these plans were compared for the GTV and selected normal organs by the Friedman tests while the variability was compared by the Levene’s tests. The phase–to–phase changes of GTV dose through the respiration were assessed as an indirect measure of the possible increase of photon fluence owing to the type–B dose engine. Finally, all plans were renormalized to GTV D₅₀ and all the dosimetric analyses were repeated to assess the relative influences of the SBRT planning concept and prescription method on the variability of target dose.

Results

By coverage prescriptions (i) to (iv), significantly smaller chest wall volume receiving ≥30 Gy (CW_V30) and normal lung ≥20 Gy (NL_V20Gy) were achieved by WCS_ITV and WCS_GTV compared to PTV_ITV and PTV_VH (p > 0.05). These plans differed significantly in the recalculated and summed GTV D₂, D₅₀ and D₉₈ (p <  0.05). The inter–patient variability of all GTV dose parameters is however equal between these plans (Levene’s tests; p > 0.05). Renormalizing these plans to GTV D₅₀ reduces their differences in GTV D₂, and D₉₈ to insignificant level (p > 0.05) and their inter–patient variability of all GTV dose parameters. None of these plans showed significant differences in GTV D₂, D₅₀ and D₉₈ between respiratory phases, nor their inter–phase variability is significant.

Conclusion

Inconsistent GTV dose is not unique to PTV concept but occurs to other PTV–free concept in lung SBRT. GTV D₅₀ renormalization effectively harmonizes the target dose among patients and SBRT concepts of geometric uncertainty management.

Comparative analysis between interstitial brachytherapy and stereotactic body irradiation for local ablation in liver malignancies

PURPOSE

Interstitial high-dose-rate brachytherapy (BT) is an alternative treatment option to stereotactic body radiotherapy (SBRT) for the ablative treatment of liver malignancies. The aim of the present comparative planning study was to reveal the possibilities and limitations of both techniques with regard to dosimetric properties.

METHODS AND MATERIALS

Eighty-five consecutive patients with liver malignancy diagnosis were treated with interstitial BT between 12/2008 and 09/2009. The prescription dose of BT varied between 15 and 20 Gy, depending on histology. For dosimetric comparison, virtual SBRT treatment plans were generated using the original BT planning CTs. Additional margins reflecting the respiratory tumor motion were added to the target volumes for SBRT planning.

RESULTS

The mean PTV_BT was 34.7 cm³ (0.5-410.0 cm³) vs. a mean PTV_SBRT of 73.2 cm³ (6.1-593.4 cm³). Regarding the minimum peripheral dose (D_99.9), BT achieved the targeted prescription dose of 15 Gy/20 Gy better without violating organ at risk constraints. The dose exposure of the liver was significantly influenced by treatment modality. The liver exposure to 5 Gy was statistically lower with 611 ± 43 cm³ for BT as compared with 694 ± 37 cm³ for SBRT plans (20-Gy group, p = 0.001), corresponding to 41.8% vs. 45.9% liver volume, respectively.

CONCLUSIONS

To the best of our knowledge, this is the first report on the comparison of clinically treated liver BT treatments with virtually planned SBRT treatments. The planning study showed a superior outcome of BT regarding dose coverage of the target volume and exposed liver volume. Nevertheless, further studies are needed to determine ideal applicability for each treatment approach.

Radiomic analysis of planning computed tomograms for predicting radiation-induced lung injury and outcome in lung cancer patients treated with robotic stereotactic body radiation therapy

OBJECTIVES

To predict radiation-induced lung injury and outcome in non-small cell lung cancer (NSCLC) patients treated with robotic stereotactic body radiation therapy (SBRT) from radiomic features of the primary tumor.

METHODS

In all, 110 patients with primary stage I/IIa NSCLC were analyzed for local control (LC), disease-free survival (DFS), overall survival (OS) and development of local lung injury up to fibrosis (LF). First-order (histogram), second-order (GLCM, Gray Level Co-occurrence Matrix) and shape-related radiomic features were determined from the unprocessed or filtered planning CT images of the gross tumor volume (GTV), subjected to LASSO (Least Absolute Shrinkage and Selection Operator) regularization and used to construct continuous and dichotomous risk scores for each endpoint.

RESULTS

Continuous scores comprising 1-5 histogram or GLCM features had a significant (p = 0.0001-0.032) impact on all endpoints that was preserved in a multifactorial Cox regression analysis comprising additional clinical and dosimetric factors. At 36 months, LC did not differ between the dichotomous risk groups (93% vs. 85%, HR 0.892, 95%CI 0.222-3.590), while DFS (45% vs. 17%, p < 0.05, HR 0.457, 95%CI 0.240-0.868) and OS (80% vs. 37%, p < 0.001, HR 0.190, 95%CI 0.065-0.556) were significantly lower in the high-risk groups. Also, the frequency of LF differed significantly between the two risk groups (63% vs. 20% at 24 months, p < 0.001, HR 0.158, 95%CI 0.054-0.458).

CONCLUSION

Radiomic analysis of the gross tumor volume may help to predict DFS and OS and the development of local lung fibrosis in early stage NSCLC patients treated with stereotactic radiotherapy.

ICRU report 91 on prescribing, recording, and reporting of stereotactic treatments with small photon beams : Statement from the DEGRO/DGMP working group stereotactic radiotherapy and radiosurgery

The International Commission on Radiation Units and Measurements (ICRU) report 91 with the title "prescribing, recording, and reporting of stereotactic treatments with small photon beams" was published in 2017. This extensive publication covers different relevant aspects of stereotactic radiotherapy such as small field dosimetry, accuracy requirements for volume definition and planning algorithms, and the precise application of treatment by means of image guidance. Finally, recommendations for prescribing, recording and reporting are given.