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INTRODUCTION: Heart dose has emerged as an independent predictor of overall survival in patients with NSCLC treated with radiotherapy. Several studies have identified the base of the heart as a region of enhanced dose sensitivity and a potential target for cardiac sparing. We present a dosimetric analysis of overall survival in the multicenter, randomized PET-Plan trial (NCT00697333) and for the first time include left ventricular ejection fraction (EF) at baseline as a metric of cardiac function.METHODS: A total of 205 patients with inoperable stage II or III NSCLC treated with 60 to 72 Gy in 2 Gy fractions were included in this study. A voxel-wise image-based data mining methodology was used to identify anatomical regions where higher dose was significantly associated with worse overall survival. Univariable and multivariable Cox proportional hazards models tested the association of survival with dose to the identified region, established prognostic factors, and baseline cardiac function.RESULTS: A total of 172 patients remained after processing and censoring for follow-up. At 2-years posttreatment, a highly significant region was identified within the base of the heart (p < 0.005), centered on the origin of the left coronary artery and the region of the atrioventricular node. In multivariable analysis, the number of positron emission tomography-positive nodes (p = 0.02, hazard ratio = 1.13, 95% confidence interval: 1.02-1.25) and mean dose to the cardiac subregion (p = 0.02, hazard ratio = 1.11 Gy -1, 95% confidence interval: 1.02-1.21) were significantly associated with overall survival. There was a significant interaction between EF and region dose (p = 0.04) for survival, with contrast plots revealing a larger effect of region dose on survival in patients with lower EF values. CONCLUSIONS: This work validates previous image-based data mining studies by revealing a strong association between dose to the base of the heart and overall survival. For the first time, an interaction between baseline cardiac health and heart base dose was identified, potentially suggesting preexisting cardiac dysfunction exacerbates the impact of heart dose on survival.

Purpose To develop a machine learning (ML) model based on radiomic features (RF) extracted from whole prostate gland magnetic resonance imaging (MRI) for prediction of tumour hypoxia pre-radiotherapy. Material and methods Consecutive patients with high-grade prostate cancer and pre-treatment MRI treated with radiotherapy between 01/12/2007 and 1/08/2013 at two cancer centres were included. Cancers were dichotomised as normoxic or hypoxic using a biopsy-based 32-gene hypoxia signature (Ragnum signature). Prostate segmentation was performed on axial T2-weighted (T2w) sequences using RayStation (v9.1). Histogram standardisation was applied prior to RF extraction. PyRadiomics (v3.0.1) was used to extract RFs for analysis. The cohort was split 80:20 into training and test sets. Six different ML classifiers for distinguishing hypoxia were trained and tuned using five different feature selection models and fivefold cross-validation with 20 repeats. The model with the highest mean validation area under the curve (AUC) receiver operating characteristic (ROC) curve was tested on the unseen set, and AUCs were compared via DeLong test with 95% confidence interval (CI). Results 195 patients were included with 97 (49.7%) having hypoxic tumours. The hypoxia prediction model with best performance was derived using ridge regression and had a test AUC of 0.69 (95% CI: 0.14). The test AUC for the clinical-only model was lower (0.57), but this was not statistically significant (p = 0.35). The five selected RFs included textural and wavelet-transformed features. Conclusion Whole prostate MRI-radiomics has the potential to non-invasively predict tumour hypoxia prior to radiotherapy which may be helpful for individualised treatment optimisation.

The effectiveness of abdominal compression for motion management in hepatobiliary-pancreatic (HPB) radiotherapy has not been systematically evaluated.A systematic review was carried out using PubMed/Medline, Cochrane Library, Web of Science, and CINAHL databases up to 1 July 2021. No date restrictions were applied. Additional searches were carried out using the University of Manchester digital library, Google Scholar and of retrieved papers' reference lists. Studies conducted evaluating respiratory motion utilising imaging with and without abdominal compression in the same patients available in English were included. Studies conducted in healthy volunteers or majority non-HPB sites, not providing descriptive motion statistics or patient characteristics before and after compression in the same patients or published without peer-review were excluded. A narrative synthesis was employed by tabulating retrieved studies and organising chronologically by abdominal compression device type to help identify patterns in the evidence.The inclusion criteria were met by 6 studies with a total of 152 patients. Designs were a mix of retrospective and prospective quantitative designs with chronological, non-randomised recruitment. Abdominal compression reduced craniocaudal respiratory motion in the majority of patients, although in four studies there were increases seen in at least one direction. The influence of patient comorbidities on effectiveness of compression, and/or comfort with compression was not evaluated in any study.Abdominal compression may not be appropriate for all patients, and benefit should be weighed with potential increase in motion or discomfort in patients with small initial motion (5 mm). Patient factors including male sex, and high body mass index (BMI) were found to impact the effectiveness of compression, however with limited evidence. High-quality studies are warranted to fully assess the clinical impact of abdominal compression on treatment outcomes and toxicity prospective in comparison to other motion management strategies.

Purpose: Radiation cardiotoxicity (RC) is a clinically significant adverse effect of treatment for patients with thoracic malignancies. Clinical studies in lung cancer have indicated that heart substructures are not uniformly radiosensitive, and that dose to the heart base drives RC. In this study, we aimed to characterize late changes in gene expression using spatial transcriptomics in a mouse model of base regional radiosensitivity. Methods and Materials: An aged female C57BL/6 mouse was irradiated with 16 Gy delivered to the cranial third of the heart using a 6 × 9 mm parallel opposed beam geometry on a small animal radiation research platform, and a second mouse was sham-irradiated. After echocardiography, whole hearts were collected at 30 weeks for spatial transcriptomic analysis to map gene expression changes occurring in different regions of the partially irradiated heart. Cardiac regions were manually annotated on the capture slides and the gene expression profiles compared across different regions. Results: Ejection fraction was reduced at 30 weeks after a 16 Gy irradiation to the heart base, compared with the sham-irradiated controls. There were markedly more significant gene expression changes within the irradiated regions compared with nonirradiated regions. Variation was observed in the transcriptomic effects of radiation on different cardiac base structures (eg, between the right atrium [n = 86 dysregulated genes], left atrium [n = 96 dysregulated genes], and the vasculature [n = 129 dysregulated genes]). Disrupted biological processes spanned extracellular matrix as well as circulatory, neuronal, and contractility activities. Conclusions: This is the first study to report spatially resolved gene expression changes in irradiated tissues. Examination of the regional radiation response in the heart can help to further our understanding of the cardiac base's radiosensitivity and support the development of actionable targets for pharmacologic intervention and biologically relevant dose constraints.

Objective. Patients treated for cervical cancer exhibit large inter and intra-fraction anatomical changes. The Unity MR-Linac (MRL) can image these patients with MR prior to and during treatment which enables daily plan adaptation. However, the MRL has a limited treatment field in the sup/inf direction of 22 cm which can restrict the treatment of patients who require longer treatment fields. Here we explore potential adaptive workflows in combination with a dual isocentre approach, to widen the range of cervix patients that can benefit from this treatment. Approach. Ten cervical cancer patients were retrospectively planned with a dual isocentre technique to deliver 45 Gy in 25 fractions. 5 node-negative and 5 node-positive patients were planned using the EMBRACE II protocol. A 2 cm overlap region between the two isocentres was positioned entirely in the nodal region. A treatment workflow was simulated to account for inter-fraction anatomical change. Isocentre shifts of 3 and 6 mm were applied to investigate the effect of intra-fraction motion. Main results. Dual isocentre adapted plans ensured significantly better coverage than non-adapted (recalculated) plans with a larger benefit seen for the node-negative cases. The difference to the reference plan for the V4275 cGy to the ITV was −0.8 cGy and −8.2 cGy for the adapted and recalculated plans respectively. Movements superiorly did not affect the coverage of the ITV by more than 1%, but shifting it inferiorly caused the ITV coverage on the plan to reduce by ∼2.4% per mm. Significance. A dual isocentre technique for cervical cancer treatments and adaptive workflows have been demonstrated to recover the required plan quality for inter-fraction changes. This illustrates the feasibility of a dual isocentre technique for the MRL.

Machine learning (ML) and artificial intelligence (AI) have demonstrated potential to improve the care of radiation oncology patients. Here we review recent advances applicable to the care of bladder cancer, with an eye towards studies that may suggest next steps in clinical implementation. Algorithms have been applied to clinical records, pathology, and radiology data to generate accurate predictive models for prognosis and clinical outcomes. AI has also shown increasing utility for auto-contouring and efficient creation of workflows involving multiple treatment plans. As technologies progress towards routine clinical use for bladder cancer patients, we also discuss emerging methods to improve interpretability and reliability of algorithms.

Purpose: Adaptive radiotherapy relies on rapid recontouring for replanning. Contour propagation offers workflow efficiencies, but the impact of using unedited propagated OAR contours directly during re-optimisation is unclear. Methods: Plans for ten head and neck patients were created on the planning CT scan. OAR contours for the spinal cord, brainstem, parotids and larynx were then propagated to five shading-corrected CBCTs equally spaced throughout treatment using five commercial packages. Two reference contours were created on the CBCTs by (1) a clinician and (2) a geometric consensus from the propagated contours. Treatment plans were re-optimised on each CBCT for each set of contours, and the DVH statistic differences to the reference contours were calculated. The spread of DVH statistic differences between the 5th and 95th percentiles was quantified. Results: The spread of DVH statistic differences was 3.7 Gy compared to the clinician contour and 3.3 Gy compared to the consensus contour for the brainstem (and PRV) and 2.4 Gy and 2 Gy for the spinal cord (and PRV), across all 5 auto-contouring solutions. The parotids and larynx showed differences of 3.7 Gy compared to the clinician and 0.9 Gy to the consensus contour, with the larger difference for the clinician possibly caused by uncertainty in the clinician standard due to poor image quality on the CBCTs. Conclusions: Propagated OAR contours can be used safely for adaptive radiotherapy replanning, however, where organ doses are close to clinical tolerance then the contours should be reviewed for accuracy regardless of the propagation software used.

Image-based data mining (IBDM) and voxel-based analysis (VBA) have shown great promise in the retrospective analysis of routine clinical data, offering a way to analyze a patient population without selection. In radiation therapy, IBDM analyzes whole dose distributions for their effect on a given outcome with no prior assumptions. Recent extensions in IBDM methodologies include per voxel survival analyses, allowing the generation of more robust and testable hypotheses. An essential aspect to produce solid conclusions includes applying the correct statistical techniques to account for multiple testing. There is a growing interest in applying IBDM/VBA techniques in radiation therapy, which is a relatively new area with limited examples in the literature. We present this editorial to discuss guidelines for best practice. In doing so, we highlight 3 recently published papers applying IBDM techniques to radiation therapy data 1 , 2 , 3 and suggest alternative, more robust analysis methods.

The impact of radiotherapy on the heart has become an area of interest in recent years. Many different cardiac dose-volume constraints have been associated with cardiac toxicity and survival; however, no consistent constraint has been found. Many patients undergoing treatment for lung cancer have risk factors for cardiovascular disease or known cardiac comorbidities; however, there is little evidence on the effects of radiotherapy on the heart in these patients. We aim to provide a summary of the existing literature on cardiac toxicity of lung cancer radiotherapy, propose strategies to avoid and manage cardiac toxicity, and suggest avenues for future research.

PURPOSE Patients with early-stage lung cancer undergoing stereotactic ablative radiotherapy receive four-dimensional computed tomography (4D-CT) for treatment planning. Often, an internal gross target volume (iGTV), which approximates the motion envelope of a tumor over the breathing cycle, is delineated without defining a gross tumor volume (GTV). However, the GTV volume and shape are important parameters for prognostic and dose modelling, and there is interest in radiomic features extracted from the GTV and surrounding tissue. We demonstrate and validate a method to generate the GTV from an iGTV contour to aid retrospective analysis on routine data. METHOD It is possible to reconstruct the geometry of a tumor with knowledge of tumor motion and the motion envelope formed during respiration. To demonstrate this, the tumor motion path was estimated with local rigid registration, and the iGTV positioned incrementally at stations along the reverse path. It is shown that the tumor volume is the largest set common to the intersection of the iGTV at these positions, hence can be derived. This was implemented for 521 lung lesions on 4D-CT. Eleven patients with a GTV delineation performed by a radiation oncologist on a reference phase (50%) were used for validation. The generated GTV was compared to that delineated by the expert using distance-to-agreement (DTA), volume, and distance between centres of mass. An overall success rate was determined by detecting registration inaccuracy and performing a quality check on the routine iGTV. For successfully generated contours, GTV volume was compared to iGTV volume in a prognostic model for overall survival. RESULTS For the validation dataset, DTA mean (0.79 - 1.55 mm) and standard deviation (0.68 - 1.51 mm) were comparable to expected observer variation. Difference in volume was < 5 cm3 , and average difference in position was 1.21 mm. Deviations in shape and position were mainly caused by observer differences in iGTV and GTV interpretation as opposed to algorithm performance. For the complete dataset, an acceptable contour was generated for 94% of patients using statistical and visual assessment to detect failures. Generated GTV volumes improved prognostic model performance over iGTV volumes. CONCLUSION A method to generate a GTV from an iGTV and 4D-CT dataset was developed. This method facilitates data analysis of patients with early-stage lung cancer treated in the routine setting, that is, data mining, prognostic modeling, and radiomics. Generation failure detection removes the need for visual assessment of all contours, reducing a time-consuming aspect of big-data analysis. Favorable prognostic performance of generated GTV volumes over iGTV ones demonstrates opportunities to use this methodology for future study.

For patients with lung cancer treated with radiation therapy, a dose to the heart is associated with excess mortality; however, it is often not feasible to spare the whole heart. Our aim is to define cardiac substructures and dose thresholds that optimally reduce early mortality.Fourteen cardiac substructures were delineated on 5 template patients with representative anatomies. One thousand one hundred sixty-one patients with non-small cell lung cancer were registered nonrigidly to these 5 template anatomies, and their radiation therapy doses were mapped. Mean and maximum dose to each substructure were extracted, and the means were evaluated as input to prediction models. The cohort was bootstrapped into 2 variable reduction techniques: elastic net least absolute shrinkage and selection operator and the random survival forest model. Each method was optimized to extract variables contributing most to overall survival, and model coefficients were evaluated to select these substructures. The most important variables common to both models were selected and evaluated in multivariable Cox-proportional hazard models. A threshold dose was defined, and Kaplan-Meier survival curves plotted.Nine hundred seventy-eight patients remained after visual quality assurance of the registration. Ranking the model coefficients across the bootstraps selected the maximum dose to the right atrium, right coronary artery, and ascending aorta as the most important factors associated with survival. The maximum dose to the combined cardiac region showed significance in the multivariable model, a hazard ratio of 1.01/Gy, and P = .03 after accounting for tumor volume (P.001), N stage (P.01), and performance status (P = .01). The optimal threshold for the maximum dose, equivalent dose in 2-Gy fractions, was 23 Gy. Kaplan-Meier survival curves showed a significant split (log-rank P = .008).The maximum dose to the combined cardiac region encompassing the right atrium, right coronary artery, and ascending aorta was found to have the greatest effect on patient survival. A maximum equivalent dose in 2-Gy fractions of 23 Gy was identified for consideration as a dose limit in future studies.

Highlights • Residual errors following treatment setup result in a change in dose from planned. • The change in dose in a region in the heart is related to overall survival. • No relationship between the change in dose and other clinical variables was found. • Increased heart dose explains the relationship between residual errors and survival., Background and purpose A recent study of NSCLC patients showed small residual setup errors (shifts) in the direction of the heart following image-guidance were significantly related to overall survival. This study of the dosimetric effects of these residual shifts investigates the hypothesis that observed survival differences were related to a change in heart dose. Materials and methods Accumulated doses including shifts for each fraction were determined for 475 NSCLC patients. Planning CTs and corresponding dose distributions were deformed to a reference. Image-based data-mining techniques were then applied to the difference between the planned and accumulated dose (Δdose) to determine where Δdose relates to 1-year survival. The significance of Δdose in the identified region was assessed using multivariable Cox analysis. The cohort was then split into octiles, based upon planned dose to the region, and multivariable Cox analysis performed for each sub-cohort to explore the dose response relationship. The identified dose threshold for damage was then tested in an independent validation cohort of 1482 NSCLC patients from the same institution. Results Permutation testing identified a small region in the heart base where Δdose significantly correlated with 1-year survival. Δdose in this region showed no correlation with common clinical variables, and was significant in multivariable Cox regression (p

Purpose Emerging evidence suggests that the heart is more radiosensitive than previously assumed; therefore, accounting for heart motion in radiation therapy planning is becoming more critical. In this study, we determined how much heart delineations based on 3-dimensional (3D) computed tomography (CT), 4-dimensional (4D) average projection (AVG), and maximum intensity projection (MIP) images should be extended to represent the full extent of heart motion during 4D imaging acquisition. Methods and Materials The 3D and 4D CT scans of 10 lung cancer patients treated with stereotactic ablative radiation therapy were used. Median surfaces were derived from heart delineations of 3 observers on the 3D CT, AVG, MIP, and 25% exhale scans. Per patient, the 25% exhale contour was propagated on every phase of the 4D scan. The union of all 4D phase delineations (U4D) represented the full extent of heart motion during imaging acquisition. Surface distances from U4D to 3D, AVG, and MIP volumes were calculated. Distances in the most extreme surface points (1.5 cm most superoinferior, 10% most right/left/anteroposterior) were used to derive margins accounting only for systematic (delineation) errors. Results Heart delineations on the MIP were the closest to the full extent of motion, requiring only ≤2.5-mm margins. Delineations on the AVG and 3D scans required margins up to 3.4 and 7.1 mm, respectively. The largest margins were for the inferior, right, and anterior aspects for the delineations on the 3D, AVG, and MIP scans, respectively. Conclusion Delineations on 3D, AVG, or MIP scans required extensions for representing the heart’s full extent of motion, with the MIP requiring the smallest margins. Research including daily imaging to determine the random components for the margins and dosimetric measurements to determine the relevance of creating a planning organ at risk volume of the heart is required.

Purpose: The aim was to identify vascular calcification in 4DCT scan of lung cancer patients and establish the association between overall survival (OS) and vascular calcification, as surrogate for vascular health. Methods: Vascular calcification within the thoracic cavity were segmented in 334 lung cancer patients treated with stereotactic body radiation therapy (SBRT). This has been done automatically on 4D planning CT and average reconstruction scans. Correlation between cardiac comorbidity and calcification volumes was evaluated for patients with recorded Adult Co-Morbidity Evaluation (n = 303). Associations between the identified calcifications and OS were further investigated. Results: The volume of calcification from the average scan was significantly lower than from each phase (p < 0.001). The highest level of correlations between cardiac comorbidity and volume of the calcifications were found for one phase representing inhale and two phases representing exhale with the least motion blurring due to respiration (p < 0.005). The volume of the calcifications was subsequently averaged over these three phases. The average of calcification volumes over the three phases (denoted by inhale-exhale) showed the highest likelihood in univariate analysis and was chosen as vascular calcification measure. Cox-model suggested that tumor volume (Hazard Ratio [HR] = 1.46, p < 0.01) and inhale-exhale volume (HR = 1.05, p < 0.05) are independent factors predicting OS after adjusting for age, sex, and performance status. Conclusion: It was feasible to use. It 4DCT scan for identifying thoracic calcifications in lung cancer patients treated with SBRT. Calcification volumes from inhale-exhale phases had the highest correlation with overall cardiac comorbidity and the average of the calcification volume obtained from these phases was an independent predictive factor for OS.

Highlights • Creation of a modified cylindrical coordinate system to map cardiac surface dose. • Validation of mapping using internal anatomical sub-structures. • Identification of surface regions were excess dose is associated with poorer survival., Background and purpose For lung cancer patients treated with radiotherapy, radiation dose to the heart has been associated with overall survival, with volumetric dose statistics widely presented. However, critical cardiac structures are present on the hearts surface, where this approach may be sub-optimal. In this work we present a methodology for creating cardiac surface dose maps and identify regions where excess dose is associated with in worse overall survival. Material and methods A modified cylindrical coordinate system was implemented to map the cardiac surface dose for lung cancer patients. Validation was performed by mapping the cardiac chambers for 55 patients, fitting a point spread function (PSF) to the blurred edge. To account for this uncertainty, dose maps were blurred by a 2D-Gaussian with width described by the PSF. Permutation testing identified regions where excess dose was associated with worse patient survival. The 99th percentile of the max t-value then defined a cardiac surface region to extract dose, from each patient, to be analysed in a multivariable cox-proportional hazards survival model. Results Cardiac surface maps were created for 648 lung cancer patients. Cardiac surface dose maps were blurred with a 2D- Gaussian filter of size σφ = 4.3° and σy = 1.3units to account for mapping uncertainties. Permutation testing identified significant differences across the surface of the right atria, p

Background: Skeletal muscle segmentation is an important procedure for assessing sarcopenia, an emerging imaging biomarker of patient frailty. Data annotation remains the bottleneck for training deep learning auto-segmentation models. Purpose: There is a need to define methodologies for applying models to different domains (e.g., anatomical regions or imaging modalities) without dramatically increasing data annotation. Methods: To address this problem, we empirically evaluate the generalizability of various source tasks for transfer learning: natural image classification, natural image segmentation, unsupervised image reconstruction, and self-supervised jigsaw solving. Axial CT slices at L3 were extracted from PET-CT scans for 204 oesophago-gastric cancer patients and the skeletal muscle manually delineated by an expert. Features were transferred and segmentation models trained on subsets ((Formula presented.)) of the manually annotated training set. Four-fold cross-validation was performed to evaluate model generalizability. Human-level performance was established by performing an inter-observer study consisting of ten trained radiographers. Results: We find that accurate segmentation models can be trained on a fraction of the data required by current approaches. The Dice similarity coefficient and root mean square distance-to-agreement were calculated for each prediction and used to assess model performance. Models pre-trained on a segmentation task and fine-tuned on 10 images produce delineations that are comparable to those from trained observers and extract reliable measures of muscle health. Conclusions: Appropriate transfer learning can generate convolutional neural networks for abdominal muscle segmentation that achieve human-level performance while decreasing the required data by an order of magnitude, compared to previous methods ((Formula presented.)). This work enables the development of future models for assessing skeletal muscle at other anatomical sites where large annotated data sets are scarce and clinical needs are yet to be addressed.

PURPOSE Observed gross tumor volume (GTV) shrinkage during radiotherapy (RT) raises the question of whether to adapt treatment to changes observed on the acquired images. In the literature, two modes of tumor regression have been described: elastic and non-elastic. These modes of tumor regression will affect the safety of treatment adaptation. This study applies a novel approach, using routine cone-beam computed tomography (CBCT) and deformable image registration to automatically distinguish between elastic and non-elastic tumor regression. METHODS In this retrospective study, 150 locally advanced non-small cell lung cancer patients treated with 55 Gray of radiotherapy were included. First, the two modes of tumor regression were simulated. For each mode of tumor regression, one timepoint was simulated. Based on the results of simulated data, the approach used for analysis in real patients was developed. CBCTs were non-rigidly registered to the baseline CBCT using a cubic B-spline algorithm, NiftyReg. Next, the Jacobian determinants were computed from the deformation vector fields. To capture local volume changes, 10 Jacobian values were sampled perpendicular to the surface of the GTV, across the lung-tumor boundary. From the simulated data, we can distinguish elastic from non-elastic tumor regression by comparing the Jacobian values samples between 5 and 12.5 mm inside and 5 and 12.5 mm outside the planning GTV. Finally, morphometric results were compared between tumors of different histologies. RESULTS Most patients (92.3%) in our cohort showed stable disease in the first week of treatment and non-elastic shrinkage in the later weeks of treatment. At week 2, 125 patients (88%) showed stable disease, three patients (2.1%) disease progression, and 11 patients (8%) regression. By treatment completion, 91 patients (64%) had stable disease, one patient (0.7%) progression and 46 patients (32%) regression. A slight difference in the mode of tumor change was observed between tumors of different histologies. CONCLUSION Our novel approach shows that it may be possible to automatically quantify and identify global changes in lung cancer patients during RT, using routine CBCT images. Our results show that different regions of the tumor change in different ways. Therefore, careful consideration should be taken when adapting RT.

BACKGROUND Lung cancer survival after radiotherapy remains poor and greater knowledge of normal tissue risk factors is needed to further optimise treatments. In this work, we investigate tumour laterality in a large cohort of patients treated with curative-intent radiotherapy, including the effect of dose on the right and left lungs. METHODS 1101 NSCLC patients were included in the analysis, treated with 55 Gy in 20 fractions. Tumour laterality was determined by comparing the centre of mass of the tumour volume with the centre of mass of the lung. Right and left lungs were segmented from the whole lung contour and the mean dose to each volume calculated. Laterality and mean lung doses were included in multi-variable cox-regression survival models. RESULTS 1026 patients were eligible for inclusion; 579 right-sided and 447 left-sided tumours were identified. All tumour and patient characteristics were balanced with laterality. The multi-variable models were controlled for known clinical factors: tumour volume (p

Introduction: Post-treatment lymphocytopaenia is a recognized complication of thoracic radiotherapy likely due to irradiation of a large volume of circulatory blood. We hypothesize that post-treatment absolute lymphocyte count (ALC) is associated with integral body dose and overall survival (OS) in lung cancer patients treated with radical radiotherapy. Materials and methods: Data on clinicopathological variables, dosimetric parameters, and pre and post-treatment blood counts were collected retrospectively in 217 lung cancer patients (131 with non-small cell lung cancer and 86 with small cell lung cancer) treated with radical radiotherapy. Induction chemotherapy followed by radiotherapy and concurrent chemoradiotherapy were delivered in 89 (42%) and 99 (47%) patients respectively. Multiple stepwise regression analysis was performed separately for ALC and absolute neutrophil count (ANC) to derive a model for prediction of post-treatment count and multivariate analysis was performed for OS using a Cox regression model. Results: There was a significant decline in post-treatment counts for both ANC and ALC (p < 0.001). Multiple stepwise linear regression analysis confirmed pre-treatment ALC, body integral dose and use of concurrent chemotherapy as significant predictors of post-treatment ALC (R 2 = 0.33, F(4,212) = 26.6 p < 0.001). Pre-treatment ANC, integral heart dose and number of fractions were significant predictors of post-treatment ANC (R 2 = 0.18, F(3,213) = 16.38 p < 0.001). Low post-treatment ALC, high pre-treatment ANC, high planning target volume integral dose and lower number of fractions were predictive of inferior OS. Conclusions: There is a negative correlation between integral body dose and post-treatment ALC which is an adverse prognostic factor in lung cancer patients treated with radical radiotherapy.

Learning health systems and rapid-learning are well developed at the conceptual level. The promise of rapidly generating and applying evidence where conventional clinical trials would not usually be practical is attractive in principle. The connectivity of modern digital healthcare information systems and the increasing volumes of data accrued through patients’ care pathways offer an ideal platform for the concepts. This is particularly true in radiotherapy where modern treatment planning and image guidance offers a precise digital record of the treatment planned and delivered. The vision is of real-world data, accrued by patients during their routine care, being used to drive programmes of continuous clinical improvement as part of standard practice. This vision, however, is not yet a reality in radiotherapy departments. In this article we review the literature to explore why this is not the case, identify barriers to its implementation, and suggest how wider clinical application might be achieved.