Multifactorial risk factors for mortality after chemotherapy and radiotherapy for non-small cell lung cancer

Dosimetric and NTCP advantages of robust proton therapy over robust VMAT for Stage III NSCLC in the immunotherapy era

AIMS

To assess the robustness and to define the dosimetric and NTCP advantages of pencil-beam-scanning proton therapy (PBSPT) compared with VMAT for unresectable Stage III non-small lung cancer (NSCLC) in the immunotherapy era.

MATERIAL AND METHODS

10 patients were re-planned with VMAT and PBSPT using: 1) ITV-based robust optimization with 0.5 cm setup uncertainties and (for PBSPT) 3.5 % range uncertainties on free-breathing CT 2) CTV-based RO including all 4DCTs anatomies. Target coverage (TC), organs at risk dose and TC robustness (TCR), set at V95%, were compared. The NTCP risk for radiation pneumonitis (RP), 24-month mortality (24MM), G2 + acute esophageal toxicity (ET), the dose to the immune system (EDIC) and the left anterior descending (LAD) coronary artery V15 < 10 % were registered. Wilcoxon test was used.

RESULTS

Both PBSPT methods improved TC and TCR (p < 0.01). The mean lung dose and lung V20 were lower with PBSPT (p < 0.01). Median mean heart dose reduction with PBSPT was 8 Gy (p < 0.001). PT lowered median LAD V15 (p = 0.004). ΔNTCP > 5 % with PBSPT was observed for two patients for RP and for five patients for 24 MM. ΔNTCP for ≥ G2 ET was not in favor of PBSPT for all patients. PBSPT halved median EDIC (4.9/5.1 Gy for ITV/CTV-based VMAT vs 2.3 Gy for both ITV/CTV-based PBSPT, p < 0.01).

CONCLUSIONS

PBSPT is a robust approach with significant dosimetric and NTCP advantages over VMAT; the EDIC reduction could allow for a better integration with immunotherapy. A clinical benefit for a subset of NSCLC patients is expected.

Federated Learning Survival Model and Potential Radiotherapy Decision Support Impact Assessment for Non-small Cell Lung Cancer Using Real-World Data

AIMS

The objective of this study was to develop a two-year overall survival model for inoperable stage I-III non-small cell lung cancer (NSCLC) patients using routine radiation oncology data over a federated (distributed) learning network and evaluate the potential of decision support for curative versus palliative radiotherapy.

METHODS

A federated infrastructure of data extraction, de-identification, standardisation, image analysis, and modelling was installed for seven clinics to obtain clinical and imaging features and survival information for patients treated in 2011-2019. A logistic regression model was trained for the 2011-2016 curative patient cohort and validated for the 2017-2019 cohort. Features were selected with univariate and model-based analysis and optimised using bootstrapping. System performance was assessed by the receiver operating characteristic (ROC) and corresponding area under curve (AUC), C-index, calibration metrics and Kaplan-Meier survival curves, with risk groups defined by model probability quartiles. Decision support was evaluated using a case-control analysis using propensity matching between treatment groups.

RESULTS

1655 patient datasets were included. The overall model AUC was 0.68. Fifty-eight percent of patients treated with palliative radiotherapy had a low-to-moderate risk prediction according to the model, with survival times not significantly different (p = 0.87 and 0.061) from patients treated with curative radiotherapy classified as high-risk by the model. When survival was simulated by risk group and model-indicated treatment, there was an estimated 11% increase in survival rate at two years (p < 0.01).

CONCLUSION

Federated learning over multiple institution data can be used to develop and validate decision support systems for lung cancer while quantifying the potential impact of their use in practice. This paves the way for personalised medicine, where decisions can be based more closely on individual patient details from routine care.

Comparison of deep inspiration breath hold and free breathing intensity modulated proton therapy of locally advanced lung cancer

Background and purpose

For locally advanced non-small cell lung cancer (LA-NSCLC), intensity-modulated proton therapy (IMPT) can reduce organ at risk (OAR) doses compared to intensity-modulated radiotherapy (IMRT). Deep inspiration breath hold (DIBH) reduces OAR doses compared to free breathing (FB) in IMRT. In IMPT, differences in dose distributions and robustness between DIBH and FB are unclear. In this study, we compare DIBH to FB in IMPT, and IMPT to IMRT.

Materials and methods

Fortyone LA-NSCLC patients were prospectively included. 4D computed tomography images (4DCTs) and DIBH CTs were acquired for treatment planning and during weeks 1 and 3 of treatment. A new system for automated robust planning was developed and used to generate a FB and a DIBH IMPT plan for each patient. Plans were compared in terms of dose-volume parameters and normal tissue complication probabilities (NTCPs). Dose recalculations on repeat CTs were used to compare inter-fraction plan robustness.

Results

In IMPT, DIBH reduced median lungs Dmean from 9.3 Gy(RBE) to 8.0 Gy(RBE) compared to FB, and radiation pneumonitis NTCP from 10.9 % to 9.4 % (p < 0.001). Inter-fraction plan robustness for DIBH and FB was similar. Median NTCPs for radiation pneumonitis and mortality were around 9 percentage points lower with IMPT than IMRT (p < 0.001). These differences were much larger than between FB and DIBH within each modality.

Conclusion

DIBH IMPT resulted in reduced lung dose and radiation pneumonitis NTCP compared to FB IMPT. Inter-fraction robustness was comparable. OAR doses were far lower in IMPT than IMRT.

External validation of a lung cancer-based prediction model for two-year mortality in esophageal cancer patient cohorts

PURPOSE/OBJECTIVE

Chemo-radiotherapy can improve the oncological outcome of esophageal cancer (EC) patients, but may cause long term radiation-induced toxicity, including an increased risk of non-cancer related death. For lung cancer patients, a model to predict 2-year total mortality using mean heart dose (MHD) and gross tumor volume (GTV) has previously been developed and validated. This project aimed to externally validate this model in EC patients.

METHODS

Five EC patient cohorts from 3 different Dutch centres were used for model validation. External validity of the model was assessed separately in definitive (n = 170) and neo-adjuvant (n = 568) chemoradiotherapy (dCRT and nCRT) patients. External validity was assessed in terms of calibration by calibration plots, calibration-in-the-large (CITL) and calibration slope (CS), and discrimination by assessment of the c-statistic. If suboptimal model performance was observed, the model was further updated accordingly.

RESULTS

For the dCRT patients, good calibration was found after adjustment of the intercept (CITL 0.00; CS 1.08). The c-statistic of the adjusted model was 0.67 (95%CI: 0.58 to 0.75). For nCRT patients the model needed adjustment of both the slope and the intercept because of initial miscalibration in the validation population (CITL 0.00; CS 1.72). After recalibration, the model showed perfect calibration (i.e., CITL 0, CS 1), as is common after recalibration. The c-statistic of the recalibrated model equaled 0.62 (95%CI: 0.57 to 0.67).

CONCLUSION

The existing model for 2-year mortality prediction in lung cancer patients, based on the predictive factors MHD and GTV, showed good performance in EC patients after updating the intercept and/or slope of the original model.

Different benefits of adaptive radiotherapy for different histologies of NSCLC

BACKGROUND

Adenocarcinoma (AC) and squamous cell carcinoma (SCC) are the most frequent histological subtypes of non-small cell lung cancer (NSCLC). The aim of this study was to investigate how patients with AC and SCC benefit from image-guided adaptive radiotherapy (ART) with tumour match.

MATERIAL AND METHODS

Consecutive patients diagnosed with AC or SCC of the lung treated with definitive chemo-radiotherapy before and after the implementation of ART and tumour match were retrospectively included for analyses. Data collection included baseline patient and treatment characteristics in addition to clinical data on radiation pneumonitis (RP), failure, and survival. Patients were divided into four categories based on their histology and treatment before (n = 173 [89 AC and 84 SCC]) and after implementation of ART (n = 240 [141 AC and 99 SCC]).

RESULTS

Median follow-up was 5.7 years for AC and 6.3 years for SCC. Mean lung dose decreased for both histologies with ART, whereas mean heart dose only decreased for patients with AC. Incidences of grade 3 and 5 RP decreased for both histologies with ART. Loco-regional failure (LRF) rates decreased significantly for patients with SCC after ART (p = .04), no significant difference was observed for AC. Overall survival (OS) increased significantly for SCC after ART (p < .01): the 2-year OS increased from 31.0% (95% confidence interval [CI] [22.5-42.6]) to 54.5% (95% CI [45.6-65.3]). No significant effect on OS was observed for patients with AC.

CONCLUSION

ART and tumour match in the radiotherapeutic treatment of patients with locally advanced NSCLC primarily led to decreased LRF and improved OS for patients with SCC.

Comparison of photon intensity modulated, hybrid and volumetric modulated arc radiation treatment techniques in locally advanced non-small cell lung cancer

Background and purpose

There is no consensus on the best photon radiation technique for non-small cell lung cancer (NSCLC). This study quantified the differences between commonly used treatment techniques in NSCLC to find the optimal technique.

Materials and methods

Treatment plans were retrospectively generated according to clinical guidelines for 26 stage III NSCLC patients using intensity modulated radiation therapy (IMRT), hybrid, and volumetric modulated arc therapy (VMATC, and VMATV5 optimized for lower lung and heart dose). Plans were evaluated for target coverage, organs at risk dose (including heart substructures) and normal tissue complication probabilities (NTCP).

Results

The comparison showed significant and largest median differences (>1 Gy or >5%) in favor of IMRT for the mediastinal envelope and heart (maximum dose), in favor of the hybrid technique for the lungs (V5Gy of the total lungs and V5Gy of the contralateral lung) and in favor of VMATC for the heart (Dmean), most of the substructures of the heart, and the spinal cord (maximum dose). The VMATV5 technique had significantly lower heart dose compared to the hybrid technique and significantly lower lung dose compared to the VMATC, combining both advantages in one technique. The mean ΔNTCP did not exceed the 2 percent point (pp) for grade 5 (mortality), and 10 pp for grade ≥2 toxicities (radiation pneumonitis and acute esophageal toxicity), but ΔNTCP was mostly in favor of VMATC/V5 for individual patients.

Conclusion

This planning study showed that VMATV5 was preferred as it achieved low lung and heart doses, as well as low NTCPs, simultaneously.

Improving knowledge-based treatment planning for lung cancer radiotherapy with automatic multi-criteria optimized training plans

BACKGROUND

Knowledge-based planning (KBP) is a method for automated radiotherapy treatment planning where appropriate optimization objectives for new patients are predicted based on a library of training plans. KBP can save time and improve organ at-risk sparing and inter-patient consistency compared to manual planning, but its performance depends on the quality of the training plans. We used another system for automated planning, which generates multi-criteria optimized (MCO) plans based on a wish list, to create training plans for the KBP model, to allow seamless integration of knowledge from a new system into clinical routine. Model performance was compared for KBP models trained with manually created and automatic MCO treatment plans.

MATERIAL AND METHODS

Two RapidPlan models with the same 30 locally advanced non-small cell lung cancer patients included were created, one containing manually created clinical plans (RP_CLIN) and one containing fully automatic multi-criteria optimized plans (RP_MCO). For 15 validation patients, model performance was compared in terms of dose-volume parameters and normal tissue complication probabilities, and an oncologist performed a blind comparison of the clinical (CLIN), RP_CLIN, and RP_MCO plans.

RESULTS

The heart and esophagus doses were lower for RP_MCO compared to RP_CLIN, resulting in an average reduction in the risk of 2-year mortality by 0.9 percentage points and the risk of acute esophageal toxicity by 1.6 percentage points with RP_MCO. The oncologist preferred the RP_MCO plan for 8 patients and the CLIN plan for 7 patients, while the RP_CLIN plan was not preferred for any patients.

CONCLUSION

RP_MCO improved OAR sparing compared to RP_CLIN and was selected for implementation in the clinic. Training a KBP model with clinical plans may lead to suboptimal output plans, and making an extra effort to optimize the library plans in the KBP model creation phase can improve the plan quality for many future patients.

Clues to address barriers for access to proton therapy in the Netherlands

BACKGROUND AND PURPOSE

The Netherlands has National Indication Protocols on proton therapy (PT) to select patients who benefit most from PT. However, referrals to proton therapy centres (PTCs) are lagging. The objective of this research is to identify the barriers for access to PT and to design interventions to address these barriers.

MATERIAL AND METHODS

We conducted a nationwide survey among radiation oncologists (ROs), and semi- structured in-depth interviews with ROs and patients. Subsequently, four workshops were held, in which ROs from one PTC and ROs from referring hospitals participated. The workshops were based on design-thinking research, where ideas were co-created on a multidisciplinary basis to encourage joint problem ownership. Kruskal Wallis and X2 tests were used to analyze data.

RESULTS

The most prominent barriers mentioned by ROs were patient selection, poor logistics, and logistical worries about the combination of radiation treatment with chemotherapy. Patients pointed out the inefficient coordination between organisations, poor communication, travel issues and discomfort during treatment. Clues to increase referrals revealed the need for additional tools for patient selection and innovative ways to improve logistics. A case manager was identified as beneficial to the patients' journey as part of a multidisciplinary approach. Such an approach should include the active involvement of medical oncologists, surgeons and pulmonologists.

CONCLUSION

Barriers for access to PT were identified and prioritized in the inter-organisational care- pathway of proton therapy patients in The Netherlands. Innovative solutions were co- designed to solve the barriers.

The impact of organ motion and the appliance of mitigation strategies on the effectiveness of hypoxia-guided proton therapy for non-small cell lung cancer

BACKGROUND AND PURPOSE

To investigate the impact of organ motion on hypoxia-guided proton therapy treatments for non-small cell lung cancer (NSCLC) patients.

MATERIALS AND METHODS

Hypoxia PET and 4D imaging data of six NSCLC patients were used to simulate hypoxia-guided proton therapy with different motion mitigation strategies including rescanning, breath-hold, respiratory gating and tumour tracking. Motion-induced dose degradation was estimated for treatment plans with dose painting of hypoxic tumour sub-volumes at escalated dose levels. Tumour control probability (TCP) and dosimetry indices were assessed to weigh the clinical benefit of dose escalation and motion mitigation. In addition, the difference in normal tissue complication probability (NTCP) between escalated proton and photon VMAT treatments has been assessed.

RESULTS

Motion-induced dose degradation was found for target coverage (CTV V_95% up to -4%) and quality of the dose-escalation-by-contour (Q_RMS up to 6%) as a function of motion amplitude and amount of dose escalation. The TCP benefit coming from dose escalation (+4-13%) outweighs the motion-induced losses (<2%). Significant average NTCP reductions of dose-escalated proton plans were found for lungs (-14%), oesophagus (-10%) and heart (-16%) compared to conventional VMAT plans. The best plan dosimetry was obtained with breath hold and respiratory gating with rescanning.

CONCLUSION

NSCLC affected by hypoxia appears to be a prime target for proton therapy which, by dose-escalation, allows to mitigate hypoxia-induced radio-resistance despite the sensitivity to organ motion. Furthermore, substantial reduction in normal tissue toxicity can be expected compared to conventional VMAT. Accessibility and standardization of hypoxia imaging and clinical trials are necessary to confirm these findings in a clinical setting.

Deep learning-based 4D-synthetic CTs from sparse-view CBCTs for dose calculations in adaptive proton therapy

BACKGROUND

Time-resolved 4D cone beam-computed tomography (4D-CBCT) allows a daily assessment of patient anatomy and respiratory motion. However, 4D-CBCTs suffer from imaging artifacts that affect the CT number accuracy and prevent accurate proton dose calculations. Deep learning can be used to correct CT numbers and generate synthetic CTs (sCTs) that can enable CBCT-based proton dose calculations.

PURPOSE

In this work, sparse view 4D-CBCTs were converted into 4D-sCT utilizing a deep convolutional neural network (DCNN). 4D-sCTs were evaluated in terms of image quality and dosimetric accuracy to determine if accurate proton dose calculations for adaptive proton therapy workflows of lung cancer patients are feasible.

METHODS

A dataset of 45 thoracic cancer patients was utilized to train and evaluate a DCNN to generate 4D-sCTs, based on sparse view 4D-CBCTs reconstructed from projections acquired with a 3D acquisition protocol. Mean absolute error (MAE) and mean error were used as metrics to evaluate the image quality of single phases and average 4D-sCTs against 4D-CTs acquired on the same day. The dosimetric accuracy was checked globally (gamma analysis) and locally for target volumes and organs-at-risk (OARs) (lung, heart, and esophagus). Furthermore, 4D-sCTs were also compared to 3D-sCTs. To evaluate CT number accuracy, proton radiography simulations in 4D-sCT and 4D-CTs were compared in terms of range errors. The clinical suitability of 4D-sCTs was demonstrated by performing a 4D dose reconstruction using patient specific treatment delivery log files and breathing signals.

RESULTS

4D-sCTs resulted in average MAEs of 48.1 ± 6.5 HU (single phase) and 37.7 ± 6.2 HU (average). The global dosimetric evaluation showed gamma pass ratios of 92.3% ± 3.2% (single phase) and 94.4% ± 2.1% (average). The clinical target volume showed high agreement in D₉₈ between 4D-CT and 4D-sCT, with differences below 2.4% for all patients. Larger dose differences were observed in mean doses of OARs (up to 8.4%). The comparison with 3D-sCTs showed no substantial image quality and dosimetric differences for the 4D-sCT average. Individual 4D-sCT phases showed slightly lower dosimetric accuracy. The range error evaluation revealed that lung tissues cause range errors about three times higher than the other tissues.

CONCLUSION

In this study, we have investigated the accuracy of deep learning-based 4D-sCTs for daily dose calculations in adaptive proton therapy. Despite image quality differences between 4D-sCTs and 3D-sCTs, comparable dosimetric accuracy was observed globally and locally. Further improvement of 3D and 4D lung sCTs could be achieved by increasing CT number accuracy in lung tissues.

Heart dose and cardiac comorbidities influence death with a cardiac cause following hypofractionated radiotherapy for lung cancer

Background

There is increasing evidence of cardiac toxicity of thoracic radiotherapy however, it is difficult to draw conclusions on cardiac dose constraints due to the heterogeneity of published studies. Moreover, few studies record data on cause of death. The aim of this paper is to investigate the relationship between conventional cardiac dosimetric parameters and death with cardiac causes using data from the UK national cause of death registry.

Methods

Data on cancer diagnosis, treatment and cause of death following radical lung cancer radiotherapy were obtained from Public Health England for all patients treated at the Christie NHS Foundation Trust between 1/1/10 and 31/12/16. Individuals with metastatic disease and those who received multiple courses of thoracic radiotherapy where excluded. All patients who received &gt; 45Gy in 20 fractions were included. Cardiac cause of death was defined as the following ICD-10 codes on death certificate: I20-I25; I30-I32; I34-I37; I40-I52. Cardiac V5Gy, V30Gy, V50Gy and mean heart dose (MHD) were extracted. Cumulative incidence of death with cardiac causes were plotted for each cardiac dosimetric parameter. Multi-variable Fine and Gray competing risk analysis was used to model predictors for cardiac death with non-cardiac death as a competing risk.

Results

Cardiac dosimetric parameters were available for 967 individuals, 110 died with a cardiac cause (11.4%). Patients with a cardiac comorbidity had an increased risk of death with a cardiac cause compared with those without a cardiac comorbidity (2-year cumulative incidence 21.3% v 6.2%, p&lt;0.001). In patients with a pre-existing cardiac comorbidity, heart V30Gy ≥ 15% was associated with higher cumulative incidence of death with a cardiac cause compared to patients with heart V30Gy &lt;15% (2-year rate 25.8% v 17.3%, p=0.05). In patients without a cardiac comorbidity, after adjusting for tumour and cardiac risk factors, MHD (aHR 1.07, 1.01-1.13, p=0.021), heart V5Gy (aHR 1.01, 1-1.13, p=0.05) and heart V30Gy (aHR 1.04, 1-1.07, p=0.039) were associated with cardiac death.

Conclusion

The effect of cardiac radiation dose on cardiac-related death following thoracic radiotherapy is different in patients with and without cardiac comorbidities. Therefore patients’ cardiovascular risk factors should be identified and managed alongside radiotherapy for lung cancer.

Patient selection, inter-fraction plan robustness and reduction of toxicity risk with deep inspiration breath hold in intensity-modulated radiotherapy of locally advanced non-small cell lung cancer

Background

State-of-the-art radiotherapy of locally advanced non-small cell lung cancer (LA-NSCLC) is performed with intensity-modulation during free breathing (FB). Previous studies have found encouraging geometric reproducibility and patient compliance of deep inspiration breath hold (DIBH) radiotherapy for LA-NSCLC patients. However, dosimetric comparisons of DIBH with FB are sparse, and DIBH is not routinely used for this patient group. The objective of this simulation study was therefore to compare DIBH and FB in a prospective cohort of LA-NSCLC patients treated with intensity-modulated radiotherapy (IMRT).

Methods

For 38 LA-NSCLC patients, 4DCTs and DIBH CTs were acquired for treatment planning and during the first and third week of radiotherapy treatment. Using automated planning, one FB and one DIBH IMRT plan were generated for each patient. FB and DIBH was compared in terms of dosimetric parameters and NTCP. The treatment plans were recalculated on the repeat CTs to evaluate robustness. Correlations between ΔNTCPs and patient characteristics that could potentially predict the benefit of DIBH were explored.

Results

DIBH reduced the median D_mean to the lungs and heart by 1.4 Gy and 1.1 Gy, respectively. This translated into reductions in NTCP for radiation pneumonitis grade ≥2 from 20.3% to 18.3%, and for 2-year mortality from 51.4% to 50.3%. The organ at risk sparing with DIBH remained significant in week 1 and week 3 of treatment, and the robustness of the target coverage was similar for FB and DIBH. While the risk of radiation pneumonitis was consistently reduced with DIBH regardless of patient characteristics, the ability to reduce the risk of 2-year mortality was evident among patients with upper and left lower lobe tumors but not right lower lobe tumors.

Conclusion

Compared to FB, DIBH allowed for smaller target volumes and similar target coverage. DIBH reduced the lung and heart dose, as well as the risk of radiation pneumonitis and 2-year mortality, for 92% and 74% of LA-NSCLC patients, respectively. However, the advantages varied considerably between patients, and the ability to reduce the risk of 2-year mortality was dependent on tumor location. Evaluation of repeat CTs showed similar robustness of the dose distributions with each technique.

Predicting 2-year survival in stage I-III non-small cell lung cancer: the development and validation of a scoring system from an Australian cohort

Background

There are limited data on survival prediction models in contemporary inoperable non-small cell lung cancer (NSCLC) patients. The objective of this study was to develop and validate a survival prediction model in a cohort of inoperable stage I-III NSCLC patients treated with radiotherapy.

Methods

Data from inoperable stage I-III NSCLC patients diagnosed from 1/1/2016 to 31/12/2017 were collected from three radiation oncology clinics. Patient, tumour and treatment-related variables were selected for model inclusion using univariate and multivariate analysis. Cox proportional hazards regression was used to develop a 2-year overall survival prediction model, the South West Sydney Model (SWSM) in one clinic (n = 117) and validated in the other clinics (n = 144). Model performance, assessed internally and on one independent dataset, was expressed as Harrell’s concordance index (c-index).

Results

The SWSM contained five variables: Eastern Cooperative Oncology Group performance status, diffusing capacity of the lung for carbon monoxide, histological diagnosis, tumour lobe and equivalent dose in 2 Gy fractions. The SWSM yielded a c-index of 0.70 on internal validation and 0.72 on external validation. Survival probability could be stratified into three groups using a risk score derived from the model.

Conclusions

A 2-year survival model with good discrimination was developed. The model included tumour lobe as a novel variable and has the potential to guide treatment decisions. Further validation is needed in a larger patient cohort.

The barriers and facilitators of radical innovation implementation in secondary healthcare: a systematic review

PURPOSE

This study aimed to identify the barriers and facilitators related to the implementation of radical innovations in secondary healthcare.

DESIGN/METHODOLOGY/APPROACH

A systematic review was conducted and presented in accordance with a PRISMA flowchart. The databases PubMed and Web of Science were searched for original publications in English between the 1st of January 2010 and 6th of November 2020. The level of radicalness was determined based on five characteristics of radical innovations. The level of evidence was classified according to the level of evidence scale of the University of Oxford. The Consolidated Framework for Implementation Research was used as a framework to classify the barriers and facilitators.

FINDINGS

Based on the inclusion and exclusion criteria, nine publications were included, concerning six technological, two organizational and one treatment innovation. The main barriers for radical innovation implementation in secondary healthcare were lack of human, material and financial resources, and lack of integration and organizational readiness. The main facilitators included a supportive culture, sufficient training, education and knowledge, and recognition of the expected added value.

ORIGINALITY/VALUE

To our knowledge, this is the first systematic review examining the barriers and facilitators of radical innovation implementation in secondary healthcare. To ease radical innovation implementation, alternative performance systems may be helpful, including the following prerequisites: (1) Money, (2) Added value, (3) Timely knowledge and integration, (4) Culture, and (5) Human resources (MATCH). This study highlights the need for more high-level evidence studies in this area.

Clinical suitability of deep learning based synthetic CTs for adaptive proton therapy of lung cancer

Purpose

Adaptive proton therapy (APT) of lung cancer patients requires frequent volumetric imaging of diagnostic quality. Cone‐beam CT (CBCT) can provide these daily images, but x‐ray scattering limits CBCT‐image quality and hampers dose calculation accuracy. The purpose of this study was to generate CBCT‐based synthetic CTs using a deep convolutional neural network (DCNN) and investigate image quality and clinical suitability for proton dose calculations in lung cancer patients.

Methods

A dataset of 33 thoracic cancer patients, containing CBCTs, same‐day repeat CTs (rCT), planning‐CTs (pCTs), and clinical proton treatment plans, was used to train and evaluate a DCNN with and without a pCT‐based correction method. Mean absolute error (MAE), mean error (ME), peak signal‐to‐noise ratio, and structural similarity were used to quantify image quality. The evaluation of clinical suitability was based on recalculation of clinical proton treatment plans. Gamma pass ratios, mean dose to target volumes and organs at risk, and normal tissue complication probabilities (NTCP) were calculated. Furthermore, proton radiography simulations were performed to assess the HU‐accuracy of sCTs in terms of range errors.

Results

On average, sCTs without correction resulted in a MAE of 34 ± 6 HU and ME of 4 ± 8 HU. The correction reduced the MAE to 31 ± 4HU (ME to 2 ± 4HU). Average 3%/3 mm gamma pass ratios increased from 93.7% to 96.8%, when the correction was applied. The patient specific correction reduced mean proton range errors from 1.5 to 1.1 mm. Relative mean target dose differences between sCTs and rCT were below ± 0.5% for all patients and both synthetic CTs (with/without correction). NTCP values showed high agreement between sCTs and rCT (<2%).

Conclusion

CBCT‐based sCTs can enable accurate proton dose calculations for APT of lung cancer patients. The patient specific correction method increased the image quality and dosimetric accuracy but had only a limited influence on clinically relevant parameters.

Treatment planning and 4D robust evaluation strategy for proton therapy of lung tumors with large motion amplitude

Purpose

Intensity‐modulated proton therapy (IMPT) for lung tumors with a large tumor movement is challenging due to loss of robustness in the target coverage. Often an upper cut‐off at 5‐mm tumor movement is used for proton patient selection. In this study, we propose (1) a robust and easily implementable treatment planning strategy for lung tumors with a movement larger than 5 mm, and (2) a four‐dimensional computed tomography (4DCT) robust evaluation strategy for evaluating the dose distribution on the breathing phases.

Materials and methods

We created a treatment planning strategy based on the internal target volume (ITV) concept (aim 1). The ITV was created as a union of the clinical target volumes (CTVs) on the eight 4DCT phases. The ITV expanded by 2 mm was the target during robust optimization on the average CT (avgCT). The clinical plan acceptability was judged based on a robust evaluation, computing the voxel‐wise min and max (VWmin/max) doses over 28 error scenarios (range and setup errors) on the avgCT. The plans were created in RayStation (RaySearch Laboratories, Stockholm, Sweden) using a Monte Carlo dose engine, commissioned for our Mevion S250i Hyperscan system (Mevion Medical Systems, Littleton, MA, USA). We developed a new 4D robust evaluation approach (4DRobAvg; aim 2). The 28 scenario doses were computed on each individual 4DCT phase. For each scenario, the dose distributions on the individual phases were deformed to the reference phase and combined to a weighted sum, resulting in 28 weighted sum scenario dose distributions. From these 28 scenario doses, VWmin/max doses were computed. This new 4D robust evaluation was compared to two simpler 4D evaluation strategies: re‐computing the nominal plan on each individual 4DCT phase (4DNom) and computing the robust VWmin/max doses on each individual phase (4DRobInd). The treatment planning and dose evaluation strategies were evaluated for 16 lung cancer patients with tumor movement of 4–26 mm.

Results

The ratio of the ITV and CTV volumes increased linearly with the tumor amplitude, with an average ratio of 1.4. Despite large ITV volumes, a clinically acceptable plan fulfilling all target and organ at risk (OAR) constraints was feasible for all patients. The 4DNom and 4DRobInd evaluation strategies were found to under‐ or overestimate the dosimetric effect of the tumor movement, respectively. 4DRobInd showed target underdosage for five patients, not observed in the robust evaluation on the avgCT or in 4DRobAvg. The accuracy of dose deformation used in 4DRobAvg was quantified and found acceptable, with differences for the dose‐volume parameters below 1 Gy in most cases.

Conclusion

The proposed ITV‐based planning strategy on the avgCT was found to be a clinically feasible approach with adequate tumor coverage and no OAR overdosage even for large tumor movement. The new proposed 4D robust evaluation, 4DRobAvg, was shown to give an easily interpretable understanding of the effect of respiratory motion dose distribution, and to give an accurate estimate of the dose delivered in the different breathing phases.

Probing thoracic dose patterns associated to pericardial effusion and mortality in patients treated with photons and protons for locally advanced non-small-cell lung cancer

PURPOSE

To investigate thoracic dose-response patterns for pericardial effusion (PCE) and mortality in patients treated for locally advanced Non-Small-Cell Lung Cancer (NSCLC) by Intensity Modulated RT (IMRT) or Passive-Scattering Proton Therapy (PSPT).

METHODS

Among 178 patients, 43.5% developed grade ≥ 2 PCE. Clinical and dosimetric factors associated with PCE or overall survival (OS) were identified via multi-variable Cox proportional hazards modeling. The Voxel-Based Analyses (VBAs) of local dose differences between patients with and without PCE and mortality was performed. The robustness of VBA results was assessed by a novel characterization of spatial properties of dose distributions based on probabilistic independent component analysis (PICA) and connectograms.

RESULTS

Several non-dosimetric variables were selected by the multivariable analysis for the considered outcomes, while the time-dependent PCE onset was uncorrelated with the OS (p = 0.34) at a multi-variable Cox analysis. Despite the significant PSPT dosimetric advantage, the RT technique did not affect the occurrence of PCE or OS. VBAs highlighted largely overlapping clusters significantly associated with PCE endpoints in heart and lungs. No significant dosimetric patterns related to mortality endpoints were found. PICA identified 43 components homogeneously scattered within thorax, while connectograms showed modest correlations between doses in main cardio-pulmonary substructures.

CONCLUSIONS

Spatially resolved analysis highlighted dose patterns related to radiation-induced cardiac toxiciy and the observed organ-based dose-response mismatch in PSPT and IMRT. Indeed, the thoracic regions spared by PSPT poorly overlapped with the areas involved in PCE development, as highlited by VBA. PICA and connectograms proved valuable tools for assessing the robusteness of obtained VBA inferences.

Are patients with stage III non-small cell lung cancer treated with chemoradiotherapy at risk for cardiac events? Results from a retrospective cohort study

OBJECTIVES

Dyspnoea is one of the symptoms frequently encountered after treatment with chemoradiotherapy (CRT) in stage III non-small cell lung cancer (NSCLC). Long-term data on mild to moderately severe cardiac events as underlying cause of dyspnoea in patients with stage III NSCLC are lacking. Therefore, the incidence of new cardiac events, with a common terminology criteria for adverse events (CTCAE) score of ≥2 within 5 years after diagnosis, were analysed.

DESIGN

Retrospective multicentre cohort study of patients with stage III NSCLC treated with CRT from 2006 to 2013. The medical files of the treated patients were reviewed.

OUTCOME MEASURES

The primary endpoint of the study was the incidence of new cardiac events with a CTCAE score of ≥2 within 5 years after diagnosis. Secondary endpoint was to identify risk factors associated with the development of a cardiac event.

RESULTS

Four hundred and sixty patients were included in the study. Of all patients, 150 (32.6%) developed a new cardiac event. In patients with a known cardiac history (n=138), 44.2% developed an event. The most common cardiac events were arrhythmia (14.6%), heart failure (7.6%) and symptomatic coronary artery disease (6.8%). Pre-existent cardiac comorbidity (HR 1.96; p<0.01) and WHO-performance score ≥2 (HR 2.71; p<0.01) were significantly associated with developing a cardiac event. The majority of patients did not have pre-existent cardiac comorbidity (n=322). Elevated WHO/International Society of Hypertension score was not identified as a significant predictor for cardiac events.

CONCLUSION

One-third of patients with stage III NSCLC treated in daily clinical practice develop a new cardiac event within 5 years after CRT. All physicians confronted with patients with NSCLC should take cardiac comorbidity as a serious possible explanation for dyspnoea after treatment with CRT.

Harnessing data science to advance radiation oncology

Radiation oncology, a major treatment modality in the care of patients with malignant disease, is a technology‐ and computer‐intensive medical specialty. As such, it should lend itself ideally to data science methods, where computer science, statistics, and clinical knowledge are combined to advance state‐of‐the‐art care. Nevertheless, data science methods in radiation oncology research are still in their infancy and successful applications leading to improved patient care remain scarce. Here, we discuss data interoperability issues within and across organizational boundaries that hamper the introduction of big data and data science techniques in radiation oncology. At the semantic level, creating common underlying models and codification of the data, including the use of data elements with standardized definitions, an ontology, remains a work in progress. Methodological issues in data science and in the use of large population‐based health data registries are identified. We show that data science methods and big data cannot replace randomized clinical trials in comparative effectiveness research by reviewing a series of instances where the outcomes of big data analyses and randomized trials are at odds. We also discuss the modern wave of machine learning and artificial intelligence as represented by deep learning and convolutional neural networks. Finally, we identify promising research avenues and remain optimistic that the data sources in radiation oncology can be linked to yield important insights in the near future. We argue that data science will be a valuable complement to, but not a replacement of, the traditional hypothesis‐driven translational research chain and the randomized clinical trials that form the backbone of evidence‐based medicine.