NORMAL TISSUE COMPLICATION PROBABILITY MODELLING OF FATAL ACUTE LUNG TOXICITY AFTER EXTERNAL RADIOTHERAPY TREATMENT OF LUNG TUMOURS

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1 SAHLGRENSKA ACADEMY DEPARTMENT OF RADIATION PHYSICS NORMAL TISSUE COMPLICATION PROBABILITY MODELLING OF FATAL ACUTE LUNG TOXICITY AFTER EXTERNAL RADIOTHERAPY TREATMENT OF LUNG TUMOURS A large-scale proof-of-concept study Louise Stervik Essay/Thesis: Program and/or course: Level: 30 hp Medical Physics Programme Second Cycle Semester/year: Autumn 2017 Supervisors: Anna Bäck 1, Niclas Pettersson 1, Crister Ceberg 2, Ivan Vogelius 3, Claus Behrens 4 Examiner: Magnus Båth 1 Sahlgrenska University Hospital, Sweden, 2 Lund University, Sweden, 3 Rigshospitalet, The Capital Region of Denmark, Denmark, 4 Herlev Hospital, The Capital Region of Denmark, Denmark

2 Abstract Essay/Thesis: 30 hp Program and/or course: Medical Physics Programme Level: Second Cycle Semester/year: Autumn 2017 Supervisors: Anna Bäck, Niclas Pettersson, Crister Ceberg, Ivan Vogelius, Claus Behrens Examiner: Magnus Båth Keywords: NTCP, Fatal acute lung toxicity, NSCLC, Radiotherapy, Data pooling Purpose: Methods: Results: Conclusions: The aims of this project were to study the relationship between the mean lung dose (MLD) and the risk of fatal acute lung toxicity for non-small-cell lung cancer (NSCLC) patients and to quantify the relation by normal tissue complication probability (NTCP) modelling based on data in radiotherapy databases for conventionally fractionated curative radiotherapy treatments of NSCLC. This work was done in a collaboration between Sahlgrenska University Hospital, Skåne University Hospital, Rigshospitalet and Herlev Hospital and the project also aimed to act as a proof-of-concept study for investigations of dose-response relationships using data from these hospitals. Scripting was used to extract the treatment related data from four hospitals belonging to the NSCLC patients assessed for eligibility. Before dose-response analysis, exclusion criteria were applied. Logistic regression and maximum likelihood estimation were used to model the risk of fatal acute lung toxicity. MLD, patient age and the volume of the gross tumour volume (GTV) were investigated as predictors in univariable logistic regression analyses. The analyses were performed on the data from the hospitals separately and merged, resulting in five groups used for modelling. For groups with a statistically significant relationship (p < 0.05) when using MLD as the predictor, confidence regions of D 50 and γ 50 for the confidence levels 68% and 95% as well as the 95% confidence intervals were calculated based on maximum likelihood estimation. The 95% confidence interval of the NTCP curve was determined using bootstrapping. Multivariable logistic regression was performed with predictor variables with a p-value less than 0.1 in any group. The MLD distributions were similar for hospitals 1, 3 and 4 while hospital 2 had lower MLDs in general. Since there was no permission to report mortality data from hospital 3 at the time of the study, the data from this hospital were not used in the subsequent analysis. For hospital 4, a statistically significant relationship between MLD and the risk of fatal acute lung toxicity (p = 0.020) was found. No statistically significant relationships were found when modelling with the data from hospital 1 and 2 separately. When using the merged data from all hospitals for modelling, the p-value for the relationship between MLD and the risk of fatal acute lung toxicity was Confidence regions and intervals were calculated for hospital 4 separately. From the univariable logistic regression with patient age and volume of GTV as predictor variables, only the patient age had a low p-value (p < 0.1). Multivariable logistic regression with MLD and patient age as predictor variables resulted in p-values for the regression coefficients less than 0.05 for the group with data from hospitals 1, 2 and 4. Modelling of the data from hospital 4 resulted in a statistically significant relationship between MLD and the risk of fatal acute lung toxicity which was quantified. By using pooled data from three hospitals, a statistically significant multivariable model of the risk of fatal acute lung toxicity with MLD and patient age as predictor variables was found. Scripting was successfully used in the study to extract data from the four hospitals.

3 Table of content 1. Background Introduction Aims Theoretical framework Maximum Likelihood Estimation Normal Tissue Complication Probability Materials and methods Selection of study population Data extraction and data management Automatic extraction by scripting Calculation of the mean lung dose Categorisation and exclusion of patients Collection of mortality data Modelling of complication risk Univariable analysis Multivariable analysis Results Data extraction and data management Automatic extraction by scripting Calculation of the mean lung dose Categorisation and exclusion of patients Collection of mortality data Modelling of complication risk Univariable analysis Multivariable analysis Discussion Conclusions Acknowledgements Reference list... 26

4 1. Background 1.1. Introduction Before a patient with cancer receives an external radiotherapy treatment (RT), a patient-specific treatment plan is to be created. The RT plan is based on a computed tomography (CT) image of the patient and includes the three-dimensional absorbed dose distribution and the prescribed absorbed dose to the tumour. While high dose to the planning target volume (PTV) is needed to accomplish high tumour control, low doses to the organs at risk (OARs) are desired to keep the risk of complications low. Thus, an optimal risk-benefit balance is sought. Optimising the dose distribution to find an optimal risk-benefit balance however, requires knowledge about the risks of the complications [1]. Often the risks of radiation induced complications are described by the normal tissue complication probability (NTCP) for different OARs and different complications (endpoints). An NTCP model aims to quantify the dose-response relationship and to predict the probability of complication. Up till now, the three-dimensional dose distribution has commonly been described by dose volume histograms (DVHs) that do not include the spatial distribution. Furthermore, the DVH has often been reduced into a dose-distribution related parameter and used as input in the NTCP model [2]. If using this method, the three-dimensional dose distribution for the treatment plan is calculated into DVHs for the different organs of interest. The DVH for a specific organ can then be reduced into a single dose-related parameter such as the mean dose, the volume receiving more than a threshold dose D (V D) or the minimum dose to a threshold volume V (D V). The parameter that best correlates with the examined endpoint should be selected. Regardless of parameter choice, the parameter describes the generally inhomogeneous threedimensional dose distribution in the OARs with one value even though the distribution is complex. Today there are several NTCP models presented for numerous complications using this method, mainly from retrospective clinical studies [3]. For patients with non-small-cell lung cancer (NSCLC) receiving three-dimensional conformal RT, radiation pneumonitis (RP) is a common complication and occurs in approximately 17% of the patients [4]. This complication is well studied for conventionally fractionated RT ( Gy/fraction) with over 70 published papers identified by the 2010 QUANTEC report [3]. Some of the previous studies have modelled NTCP for RP with various dose-related parameters as input although the mean lung dose (MLD) and V 20 Gy have mainly been used. The studies have in common an endpoint with aggregated grades of RP [5] meaning that they assign an endpoint equal to 1 (the studied endpoint has occurred) to patients graded the chosen grade of RP or greater. There are different systems for grading of toxicity, three examples being the Radiation Therapy Oncology Group (RTOG), Common Terminology Criteria for Adverse Events (CTCAE) and the Southwest Oncology Group (SWOG) [6, 7, 8]. Usually, the grading ranges from 0 (no complication) to 5 (severe complication). Aggregated grades as endpoint rule out the possibility to distinguish the risk of high grade RP from the risk of low grade RP. This could be problematic since these risks potentially should affect the treatment plan differently. Lowering the risk of low grade RP by optimising the dose distribution outside the PTV is reasonable but the risk should probably not be lowered on behalf of reducing the prescribed dose to or decrease the dose coverage of the PTV as this would decrease the tumour control. That is, the risk of low grade RP should in most cases not compromise tumour control. In contrast to that, the risk of high grade RP is more relevant to minimize and compromised tumour control might be needed as the complication is lethal. This indicates the usefulness of modelling the risk of higher grades separately. 1

5 Fatal acute lung toxicity (CTCAE grade 5 RP) has been reported by Khalil et al. [4] who noticed a varying incidence when changing treatment technique and dose constraints in the treatment plan optimisation. In their study the incidence of fatal acute lung toxicity ranged between 2 % and 17 % with V 20 Gy < 40% leading to the lowest rate. They also noted that fatal acute lung toxicity occurred within 90 days from the treatment start. The same grade of the complication has been reported by Palma et al. [9] as well, who determined the incidence of fatal RP to be 1.9 % (16/836). However, these studies report incidences of fatal RP. To better understand the correlation between dose to the lungs and fatal acute lung toxicity and to predict the probability of complication, the dose-response relationship needs to be quantified. Studying correlations between high-grade toxicity such as fatal RP and dose-related parameters are challenging due to the low rate of occurrence. The low statistical power is an issue discussed by QUANTEC in their article about data pooling where co-operation between hospitals is encouraged [10] Aims The aims of this project were to study the relationship between MLD and the risk of fatal acute lung toxicity for NSCLC patients and to quantify the relation by NTCP modelling based on data in radiotherapy databases for conventionally fractionated curative radiotherapy treatments of NSCLC. This work was done in a collaboration between Sahlgrenska University Hospital, Skåne University Hospital, Rigshospitalet and Herlev Hospital and the project also aimed to act as a proof-of-concept study for investigations of dose-response relationships using data from these hospitals Theoretical framework Maximum Likelihood Estimation Maximum likelihood estimation (MLE) is a statistical method that can be used to calculate NTCP model parameters from a given set of data [11]. This is performed by analysing the log-likelihood (LL) values calculated with the LL function: N M LL(β 0, β 1 X i,j ) = i = 1 ln [NTCP i ( β 0, β 1 X i )] + j = 1 ln [1 NTCP j ( β 0, β 1 X j )], (1) where X i and X j are the known values for the predictor variables for N subjects with an endpoint equal to 1 and M subjects with an endpoint equal to 0. The model parameters (i.e. β 0, β 1, β 2 ) originate from the NTCP expression: NTCP(X) = e (β 0+β1 X1+β2 X2+ +β k X k ), (2) where k is the number of predictor variables and β k and X k their corresponding regression coefficient and variables respectively. Different combinations of the regression coefficients (i.e. β 0, β 1, β 2 ) will result in different LL values and the maximum LL value is denoted ML. That is, the combination of regression coefficients that best describes the observed outcome will receive the highest LL value. 2

6 Uncertainties associated with the parameter estimates such as confidence intervals (CIs), confidence regions (CRs) and confidence volumes (CVs) can also be calculated from the LL function. CRs can be sought using the value of the chi-square distribution with two degrees of freedom and the desired confidence levels, 1-α, as a criterion, according to: ML LL 0.5χ 2 (1 α, n), (3) where n is the degrees of freedom [11]. For the CR confidence levels 68% and 95%, 0.5χ 2 (1 α, 2) equals and 2.996, respectively. If calculating profile likelihood CIs, the chi-square distribution with one degree of freedom is used. 0.5χ 2 (1 α, 1) equals and for the CI confidence levels 68% and 95%, respectively. Lastly, the chi-square distribution with three degrees of freedom shall be used when calculating CVs. 0.5χ 2 (1 α, 3) equals and for the CV confidence levels 68% and 95%, respectively Normal Tissue Complication Probability The NTCP for a dose-related complication can be mathematically represented in various ways, one being the logistic function: NTCP(MLD) = 1 1+ e 4γ 50 (1 MLD D 50 ). (4) Here, MLD is the mean lung dose, D 50 the dose resulting in a complication risk of 50% and γ 50 the steepness (the normalized dose-response gradient) of the curve at D 50 [12]. If combining equation 2 used for one predictor variable (k = 1) and equation 4, the parameters D 50 and γ 50 can be determined using the regression coefficients β 0 and β 1 according to: γ 50 = β 0 4, D 50 = β 0 β 1. (5) 3

7 2. Materials and methods 2.1. Selection of study population All patients diagnosed with NSCLC receiving curative radiotherapy in the thoracic region at Sahlgrenska University Hospital (hospital 1), Skåne University Hospital (hospital 2), Rigshospitalet (hospital 3) or Herlev Hospital (hospital 4) with treatment start during the time periods seen in Table 1 were assessed for eligibility. Hospital 2 changed database in May Some patients fitting the criteria who had started their NSCLC RT between January 2010 and April 2012 at hospital 2 that had for some reason been imported to the newer database were included as well. Table 1. Time periods used for selection of patients. Time periods Hospital 1 January December 2016 Hospital 2 May December 2016* Hospital 3 January December 2016 Hospital 4 January December 2016 * Patients with a treatment starting between January 2010 and April 2012 that had been imported to the newer database were included as well Data extraction and data management Automatic extraction by scripting All four hospitals use the clinical oncology information system ARIA (Version 13.6; Varian Medical Systems, Palo Alto, CA, US) and ARIA s treatment planning system module Eclipse (Version 13.6; Varian Medical Systems, Palo Alto, CA, US). When using these systems, SQL queries and Eclipse scripting can be used to access and manage data stored in the database. To extract the desired data for this work, programs were written in Visual Studio (Version 14.0; Microsoft, 2015) using the programming language C# on a computer with both ARIA and Eclipse. In Eclipse, treatment courses are created in order to deliver RT to patients. Each course can be seen as a description of the treatment and contains at least one treatment plan outlining how the dose is to be delivered and distributed. Usually, the total dose is divided into a number of smaller doses called fractions. Multiple treatment plans in a course can occur due to plan revisions and new plans created during the treatment to modify the dose distribution, number of fractions etc. Each course and plan are uniquely recognizable by their treatment course ID and treatment plan ID, respectively. Also, a treatment course can include an intent but the use of this is optional. If used, information such as the intention of chemotherapy treatment, whether the treatment is preoperative or postoperative or if the treatment is curative or palliative could be written in the intent. It is also possible to specify a diagnosis associated with the patient by filling in a diagnosis code. At some hospitals the ICD-10-SE code is used as diagnosis code and at others the field is not used. The C# programs were designed to use SQL queries with hospital specific conditions to find the selected patients NSCLC treatment courses. For hospitals 1 and 2, the patients diagnosed with NSCLC were identified by the patient s diagnosis code (ICD-10-SE) in combination with the treatment course ID. For hospitals 3 and 4, the treatment plan ID was used to identify the treatment courses. Unfortunately, a few patients diagnosed with small-cell lung cancer (SCLC) could be included using these methods. These were handled as described later in section For hospitals 1 and 2, curative treatment courses were identified by a combination of the treatment course ID and the intent of the treatment course. For hospitals 3 and 4, these were identified by the treatment plan ID. 4

8 Only one NSCLC treatment course per patient was considered for the analysis and was denoted the NSCLC treatment course. For patients who received multiple curative NSCLC treatments during the time period (Table 1), the earliest delivered treatment course was chosen by the script as the NSCLC treatment course considered for the analysis. That is, henceforth each patient included in the study is associated with only one NSCLC treatment course which will be used in the analysis. The program extracted age and sex for all patients included in the study. In order to extract data concerning the structures of the lungs and the gross tumour volume (GTV) here denoted GTV, Right Lung and Left Lung, the structure names had to be known. Unfortunately, the naming of the structures was inconsistent and differed among hospitals and countries. To find the structures, several name suggestions of the structures were defined in the script. If the script still did not find one or more of the structures, further name suggestions were added manually by looking up the specific names for the patient in question. For each treatment plan in the NSCLC treatment course, the mean dose (D ) and volume (V) of the structures GTV, Right Lung and Left Lung were then collected using Eclipse scripting and built in functions. The total lungs were defined as the right plus the left lung with the GTV excluded. Both the volume of the total lungs as well as the corresponding DVH were extracted. All data extracted for the NSCLC treatment course are presented in Figure 1. In addition, for each patient the program also extracted data from all other existing treatment courses. Figure 2 shows the data extracted for the patient s other courses besides the NSCLC treatment course. Figure 1. Schematic illustration of the data extraction from the patient s non-small-cell lung cancer (NSCLC) treatment course. Figure 2. Schematic illustration of the data extraction from the patient s other courses. 5

9 Calculation of the mean lung dose Using a threshold on the Hounsfield scale, the lung structures had been segmented automatically in the CT images used for treatment planning. Voxels with high CT numbers inside or in the vicinity of the tumor are therefore excluded from the lung volume and in this study it was assumed that the automatically segmented lungs, used as the definition of the lung structures in the analysis, excluded the GTV. However, the GTV structure is delineated manually by an oncologist who may have a different opinion on where the tumour is. The result is a possible overlap between the automatically segmented lung and the manually segmented GTV structures. To preclude an effect on the result caused by the possible overlaps, a control calculation for 10 NSCLC treatment plans from each hospital was performed. This was manually done in Eclipse due to the inability to create structures using boolean operators through scripting. In Eclipse, the mean dose for the structures Right Lung OR Left Lung and ( Right Lung OR Left Lung ) SUB GTV were calculated for the 10 treatment plans. A difference less than 1 Gy was considered negligible. For all treatment plans in a NSCLC treatment course, the mean lung dose in the plan i (MLD i) was calculated according to: MLD i = D R V R + D L V L V R + V L, (6) where D is the mean dose to the structure, V the volume of the structure and subscripts R and L indicate the right and the left lung, respectively. To ensure that script based doses agreed with the ones in Eclipse, a control calculation was performed using the same 10 treatment plans from each hospital used previously for control calculation of the total lung structure. For these plans, the mean dose to the structure Right Lung OR Left Lung in Eclipse was compared to MLD i for the plan which is the mean lung dose as calculated by the script for the same structure. To calculate the total mean lung dose delivered in a NSCLC treatment course, all plans in the course must be taken into consideration. Since MLD i is the dose to the lungs from the plan if all prescribed fractions are delivered, the number of fractions that were actually delivered in the plan were taken into account as well. Therefore, the total mean lung dose (MLD) for the NSCLC treatment was calculated according to: Number of fractions delivered with plan i MLD = i MLD i. (7) Number of fractions prescribed in plan i The mean dose to GTV ( D GTV ) for the whole NSCLC treatment was calculated according to the same method Categorisation and exclusion of patients All patients assessed for eligibility were categorised according to the criteria in Table 2 using the information extracted as described in Figures 1 and 2. To do this, the other treatment courses, besides the NSCLC treatment course, that delivered dose in the thoracic region were identified. For hospitals 1 and 2, treatments in the thoracic region were identified using the treatment course ID and for hospitals 3 and 4, the treatment plan ID was used. Some of the plan IDs at hospitals 3 and 4 indicated a treatment in an unspecified body region. For hospital 4, treatment in the thoracic region was evaluated manually for these patients by visual examination of the treatment plans in Eclipse. For hospital 3, no manual evaluation was performed due to lack of time and the treatments were assumed not to deliver dose in the thoracic region. 6

10 Table 2. Patient categories and criteria used for categorisation. Category Criteria Patient who did not receive another RT within 180 days before or 90 days after the NSCLC treatment start and who have not A received another RT in the thoracic region before Patient who received RT in another body region within 180 B days before the NSCLC treatment start Patient who received RT in another body region concurrently C or within 90 days after the NSCLC treatment start Patient who received RT in the thoracic region before the D NSCLC treatment start Patient who received RT in the thoracic region concurrently or E within 90 days after the NSCLC treatment start Before the dose-response analysis, some patients were excluded. We excluded patients assigned category C, D or E and patients with a NSCLC treatment course with fraction dose lower than or equal to 1.9 Gy or larger than 2.2 Gy, a D GTV less than 55 Gy, a treatment longer than 70 days, only one lung delineated or no DVH for the lungs (i.e. if none of the lungs were delineated or if there were no calculated dose distribution in any treatment plan in the NSCLC treatment course). Excluding patients with category C, D or E was due to previous exposure in the thoracic region or concurrent radiation exposure. Patients with a NSCLC treatment course with fraction dose lower than or equal to 1.9 Gy or larger than 2.2 Gy were excluded to eliminate SCLC treatment courses falsely included and to discriminate hypofractionated RTs respectively. NSCLC treatment courses with a D GTV less than 55 Gy were excluded because of two reasons; to remove uncompleted treatment courses and to remove preoperative and postoperative treatments. Exclusion of NSCLC treatment courses with only one lung delineated was made since some of these patients actually had two lungs but only one was delineated, resulting in a misleading MLD. Patients with NSCLC treatment courses with no DVH for the lungs had to be removed from the material since no MLDs were possible to calculate. To analyse the data from the four hospitals both separately and merged, five groups were created. Four hospital specific groups included patients treated at the corresponding hospital. One group included all patients remaining after the exclusion from all hospitals (the hospital specific groups merged) Collection of mortality data The dates of death were collected manually at hospitals 1 and 4 in the patient administrative systems Elvis (Version , Sahlgrenska University Hospital), Epic (Version 2.8, Epic Systems Corporation) and Opus (Version , CSC). Due to a change of patient administrative system at hospital 4, the two systems Epic and Opus were needed to cover the whole time period (Table 1). Patients dates of death at hospital 3 were collected from PERSIMUNE s lung cancer database. At hospital 2 the death dates were collected through scripting from the ARIA database. ARIA automatically receives the mortality data from the Swedish population register. In this study, we used death within 90 days from the treatment start as a surrogate for fatal acute lung toxicity assuming that disease-related events for patients with curative intended treatments are rare at such an early stage. The binary endpoint death within 90 days from the start of the NSCLC treatment course was assessed for all patients. Relative risks, their 95% CIs and p-values were used to assess incidences between hospitals. 7

11 2.4. Modelling of complication risk To investigate if the shapes of the lungs DVHs differed among the hospitals, the DVH parameters V 5 Gy, V 20 Gy, V 35 Gy and V 50 Gy were calculated. As V Ds from more than one treatment plan cannot be united into one value without considering the three-dimensional dose distribution, the DVH parameters were calculated only for patients with one treatment plan in the NSCLC treatment course Univariable analysis For all patients, MLD, patient age, volume of GTV and the endpoint were exported to MATLAB (Version R2017b, MathWorks, Natick, MA, USA). Following analysis was performed for the five groups separately. To model the complication risk, univariable logistic regression (one predictor variable, k = 1) was performed with the function glmfit in MATLAB for a binomial distribution with MLD as investigated predictor. With the aid of glmfit, the regression coefficients β 0 and β 1 in equation 2 were estimated using MLE (equation 1) and the p-value for β 1 was assessed. In this context, a low β 1 p-value implies that the predictor variable affects the complication risk (i.e. that β 1 0). If the β 1 p-value was less than 0.05, there was a statistically significant relationship between the investigated predictor and the risk of fatal acute lung toxicity. Using the estimated regression coefficients, D 50 and γ 50 were calculated according to equation 5. To visually compare the model to the extracted data, the observed rate of fatal acute lung toxicity in the material was calculated by binning the data. In addition, binomial CIs for the confidence level 95% were calculated for the binned data using the function fitdist in MATLAB. For groups with a β 1 p-value less than 0.05 when using MLD as the predictor, the uncertainties of the calculated D 50 and γ 50 were analysed. Equations 1 and 3 were used to evaluate the CRs and CIs. The intervals used for D 50 and γ 50 were 0 to D Gy and 0 to γ with a step length of 0.5 Gy and 0.01 respectively. CRs were determined for the confidence levels 68% and 95%. We determined the 95% CIs for D 50 and γ 50. Bootstrapping was used to determine the uncertainty of the NTCP curve. Bootstrapping is random sampling with replacement and can be used to empirically determine the uncertainty of an NTCP curve. To do this, data sets of the same size as the original were created in MATLAB by randomly selecting patients from the original data set. To avoid situations where glmfit cannot find a proper solution, data sets sorted by ascending MLDs perfectly separating patients with an endpoint equal to 0 (i.e. patients who did not die within 90 days from the treatment start) from patients with an endpoint equal to 1 (i.e. patients who died within 90 days from the treatment start) were not included. The same concerns data sets containing less than 4 patients with an endpoint equal to 1. The previously described method to estimate D 50 and γ 50 was used on 2000 bootstrapped data sets and their NTCP curves were calculated. To get the 95% CI of the NTCP curve, the 2.5 th and 97.5 th percentile of the NTCP curves from the bootstrapped data sets were calculated and used as the limit of the interval Multivariable analysis To investigate if patient age and the volume of GTV had an impact on the dose-response relationship, univariable logistic regressions were performed for these predictors separately. For predictor variables with a p-value less than 0.1 in any group from the univariable logistic regression, a multivariable logistic regression (k = 2 or 3) was performed using equations 1 and 2. The regression coefficients and their p- values were assessed. Multivariable models where all predictor variables had p-values less than 0.05 were considered statically significant. For such models, the NTCP curves were calculated for all combinations of the predictor variables (X p) and the estimated regression coefficients (β p) according to equation 2. Furthermore, the p-values of these multivariable models were calculated using the function fitglm in MATLAB. 8

12 3. Results 3.1. Data extraction and data management Automatic extraction by scripting The extraction resulted in 708 patients from hospital 1, 613 from hospital 2 (whereof 7 were imported from the older database), 985 from hospital 3 and 519 from hospital 4 with a total of 2825 patients that were assessed for eligibility Calculation of the mean lung dose The control calculation performed at all hospitals showed a negligible difference (i.e. < 1 Gy) between the mean dose to the structure Right Lung OR Left Lung compared to the mean dose to the structure ( Right Lung OR Left Lung ) SUB GTV. The second control calculation confirmed that the script based MLD i corresponded with the ones in Eclipse for the same plans with no difference larger than 0.01 Gy Categorisation and exclusion of patients Exclusion of patients was performed according to the described method in section and groups were created. Specific numbers concerning the hospital specific groups are shown in the consort diagrams in Figure 3. Since there was no permission to report mortality data or date of fractions from hospital 3 at the time of the study, factors affected by this are marked with a question mark in the consort diagram. Due to these circumstances the data from hospital 3 were not included in the group with merged data resulting in a total of 848 patients in the merged group (hospitals 1, 2 and 4). The distributions of MLDs of the groups are shown in Figure 4. 9

13 Figure 3. Consort diagrams of the hospital specific groups. * Not available at the time of the study. ** Mortality data are not reported since permission had not been obtained at the time of the study. 10

14 Figure 4. Histograms of the mean lung dose (MLD) distribution at each hospital, for all hospitals and for hospitals 1, 2 and 4. The dose bin size is 1 Gy. 11

15 3.2. Collection of mortality data The endpoint death within 90 days from the treatment start for all 1299 patients was retrieved. However, due to lack of permission the mortality data from hospital 3 could not be reported here, thus hospital 3 is not included in any of the following results affecting mortality data. In the group with data from hospitals 1, 2 and 4, 32 patients of 848 died within 90 days from the start of the NSCLC treatment, resulting in an incidence of 3.8%. The incidence at each hospital was 2.2% (7/312) at hospital 1, 1.8% (2/110) at hospital 2 and 5.4% (23/426) at hospital 4. The difference in incidence between hospitals 1 and 4 was statistically significant with a relative risk of 2.4 (p = 0.04). Table 3 shows the characteristics of the patients in the hospital specific groups and the group with data from hospitals 1, 2 and 4. Table 3. Characteristics of patients in the groups. In the group Hospitals 124, only data from hospitals 1, 2 and 4 are included. Hospital 1 Patients deceased within 90 days Patients not deceased within 90 days Category A Category B 0 5 Male Female Age (median, range) [years] 76.7 ( ) 68.2 ( ) V GTV (median, range) [cm 3 ] ( ) 70.7 ( ) D GTV (mean ± SD) [Gy] 67.5 ± ± 3.7 MLD (mean ± SD) [Gy] 18.0 ± ± 5.4 Hospital 2 Patients deceased within 90 days Patients not deceased within 90 days Category A Category B 0 0 Male 2 57 Female 0 51 Age (median, range) [years] 73.1 ( ) 70.4 ( ) V GTV (median, range) [cm 3 ] 59.3 ( ) 52.2 ( ) D GTV (mean ± SD) [Gy] 60.8 ± ± 1.8 MLD (mean ± SD) [Gy] 12.1 ± ± 3.6 Hospital 4 Patients deceased within 90 days Patients not deceased within 90 days Category A Category B 0 3 Male Female Age (median, range) [years] 69.0 ( ) 66.9 ( ) V GTV (median, range) [cm 3 ] 31.8 ( ) 45.1 ( ) D GTV (mean ± SD) [Gy] 66.0 ± ± 2.5 MLD (mean ± SD) [Gy] 18.4 ± ± 4.6 Hospitals 124 Patients deceased within 90 days Patients not deceased within 90 days Category A Category B 0 8 Male Female Age (median, range) [years] 72.6 ( ) 67.9 ( ) V GTV (median, range) [cm 3 ] 51.9 ( ) 56.0 ( ) D GTV (mean ± SD) [Gy] 66.0 ± ± 4.2 MLD (mean ± SD) [Gy] 17.9 ± ±

16 3.3. Modelling of complication risk The DVH parameters V 5 Gy, V 20 Gy, V 35 Gy and V 50 Gy for each hospital are shown in a boxplot together with the mortality data in Figure 5. Only patients with one treatment plan in the NSCLC treatment course were used for the boxplot, resulting in 181 patients from hospital 1, 59 from hospital 2, 219 from hospital 3 and 297 from hospital 4. In the boxplot, the vertical lines represent the median and the bottom and top of the boxes indicate 25 th and 75 th percentiles, respectively. The whiskers include the end data points not considered outliers and the outliers are plotted individually outside the whiskers in the boxplot. Of these patients with only one treatment plan in the NSCLC treatment course, 5 patients from hospital 1 died within 90 days from the treatment start, 2 patients from hospital 2 and 19 patients from hospital 4. V 5 Gy V 20 Gy V 35 Gy V 50 Gy Figure 5. Boxplot of the calculated dose volume histogram (DVH) parameters for each hospital. On each box, the central mark represents the median and the bottom and top indicate the 25 th and 75 th percentiles, respectively. The whiskers extend to the most extreme data points not considered outliers and the outliers are plotted individually ( ). DVH parameters belonging to patients deceased within 90 days from the treatment start are marked with red crosses ( ). 13

17 Univariable analysis The result from the univariable modelling with MLD as the predictor is shown in Table 4 where β 0 and β 1 are the estimated regression coefficients which D 50 and γ 50 originate from. D 50 and γ 50 are only presented for groups with a p-value for β 1 less than The hospital specific group with data from hospital 4 had a p-value for β 1 less than 0.05, confirming a correlation between MLD and fatal acute lung toxicity. The mortality rates and their 95% binomial CIs for all groups as well as the NTCP curves for the statistically significant dose-response relationships are presented in Figure 6. Table 4. Estimated parameter values and p-values from the univariable modelling with MLD as the predictor for the different groups. The intervals of D 50 and γ 50 are the 95% confidence intervals. In the group Hospitals 124, data from hospitals 1, 2 and 4 are included. No. of patients in group Deceased within 90 days β1 p-value D50 [Gy] (95% CI) Group β0 [Gy -1 ] of β1 Hospital Hospital Hospital Hospitals ( ) γ50 (95% CI) 1.20 ( ) Figure 6. Normal tissue complication probability (NTCP) as a function of the mean lung dose (MLD) (solid line) and observed complication rates ( ). The position of the rates on the x-axis is the average MLD in the bin. The vertical error bars represent the 95% binomial confidence intervals for the observed outcome. The dose bin size is 5 Gy and absolute patient numbers in each dose interval are indicated. 14

18 Since the hospital specific group with data from hospital 4 had a p-value for β 1 less than 0.05 from the modelling with MLD as the predictor, the uncertainties of the calculated D 50 and γ 50 were analysed. The 68% and 95% CRs as well as the 95% CIs of the calculated D 50 and γ 50 from the hospital specific group with data from hospital 4 are shown in Figure 7. The 95% CIs of D 50 and γ 50 ranged from 31.4 to Gy and 0.77 to 1.68 respectively. The NTCP curve assessed from modelling with the data from hospital 4 and the 95% CI of the curve determined from bootstrapping are presented in Figure 8. Hospital 4 Figure 7. The estimated dose resulting in a complication risk of 50% (D 50) and the steepness of the curve at D 50 (γ 50) for hospital 4 (o), 68% and 95% confidence regions of D 50 and γ 50 (solid black and blur lines) and 95% confidence intervals of D 50 and γ 50 (solid straight line). Hospital 4 Figure 8. The calculated normal tissue complication probability (NTCP) as a function of the mean lung dose (MLD) for hospital 4 (solid line). The 95% confidence interval of the curve is shown as the grey region and the 95% binomial confidence intervals of the observed complication rates ( ) are shown with vertical error bars. The position of the complication rates on the x-axis is the average MLD in the bin. The dose bin size is 5 Gy and absolute patient numbers in each dose interval are indicated. 15

19 Multivariable analysis The result from the univariable modelling with patient age as the predictor is shown in Table 5 where β 0 and β 1 are the estimated regression coefficients. The same result from univariable modelling with the volume of GTV as the predictor is shown in Table 6. Using the volume of GTV as a predictor when modelling fatal acute lung toxicity did not result in a statistically significant relationship in any group. Table 5. Estimated parameter values and p-values from the univariable modelling with patient age as the predictor for the groups. In the group Hospitals 124, data from hospitals 1, 2 and 4 are included. No. of patients in group Deceased within 90 days β1 p-value of β1 Group β0 [years -1 ] Hospital Hospital Hospital Hospitals Table 6. Estimated parameter values and p-values from the univariable modelling with volume of GTV as the predictor for the groups. In the group Hospitals 124, data from hospitals 1, 2 and 4 are included. No. of patients in group Deceased within 90 days β1 p-value of β1 Group β0 [cm -3 ] Hospital Hospital Hospital Hospitals The predictor variables MLD and patient age were used for multivariable logistic regression (k = 2) and the results are presented in Table 7. Because of few events in the hospital specific group with data from hospital 2 (2 patients), no multivariable logistic regression was performed for this group. For the multivariable model from data from hospitals 1, 2 and 4, the p-value for the model was Different presentations of the multivariable model for the group with data from hospitals 1, 2 and 4 when using MLD and patient age as predictor variables are shown in Figure 9. Table 7. Estimated parameter values and p-values from the multivariable modelling. β 1 is related to the predictor variable MLD and β 2 to patient age. In the group Hospitals 124, data from hospitals 1, 2 and 4 are included. No. of patients in group Deceased within 90 days β1 p-value β2 p-value of β2 Group β0 [Gy -1 ] of β1 [years -1 ] Hospital Hospital Hospitals

20 Hospitals 124 a) Hospitals 124 Hospitals 124 b) c) Figure 9. Multivariable analysis using the group with data from hospitals 1, 2 and 4. Normal tissue complication probability (NTCP) curves for all combinations of mean lung doses (MLD) and patient ages are presented in a) and b) presents all combinations of MLD for some chosen ages. c) The NTCP curve for the median age 68.1 years and the 95% confidence interval of the curve is shown as the grey region. In figure b) and c), the solid lines cover the range of MLDs in the group and the dashed lines larger MLDs. 17

21 The combinations of MLDs and patient ages resulting in 1%, 2.5%, 5% and 10% risk of fatal acute lung toxicity are shown in Figure 10 together with the observed combinations belonging to the patients in the group with data from hospitals 1, 2 and 4. An illustration of the correspondence between the calculated incidence according to the multivariable model and the observed incidence for the group with data from hospitals 1, 2 and 4 is presented in Figure 11. Hospitals 124 Figure 10. Patient ages and mean lung doses are shown with red asterisks (*) for the combinations belonging to patients deceased within 90 days from the treatment start and with grey asterisks for those not deceased within 90 days. The diagonal lines (-) represent the calculated 1%, 2.5%, 5% and 10% risks of fatal acute lung toxicity. Hospitals 124 Hospitals 124 Figure 11. A calibration plot illustrating the correspondence between calculated incidence according to the multivariable model and observed incidence with vertical error bars representing the 95% binomial confidence intervals. Absolute patient numbers in each interval are indicated. 18

22 4. Discussion The relationship between MLD and the risk of fatal acute lung toxicity for NSCLC patients was studied. The study showed a statistically significant relationship in the data from hospital 4 which was quantified by NTCP-modelling. By using pooled data from three hospitals, a statistically significant multivariable model with MLD and patient age as predictor variables was found. Also, we managed to pool data from the four hospitals in order to investigate an endpoint with low rate of occurrence. Patient identification and data extraction by scripting We successfully extracted treatment-related data for 1299 patients, demonstrating that data pooling is possible through scripting. The script based data extraction was time efficient and the method consistent in this study. The fact that all hospitals used the same treatment planning system (Eclipse) facilitated the data extraction. In order to successfully extract data at all hospitals, programs accounting for inter-institutional differences were created. Between the programs, the biggest differences were the modifications compensating for inter-institutional differences in structure naming. This issue could be solved be using a standardized nomenclature such as the nomenclature developed by the Swedish Radiation Safety Authority [13]. During our work other inter-institutional differences were found as well regarding the accessibility of data. Aside from the mortality data being available in Eclipse at hospital 2, it turned out that hospitals 3 and 4 did not use the ability to write an intent for their treatments nor the ability to use an ICD-SE-10 code for specification of the diagnosis. Patient inclusion and exclusion In this project, we investigated conventionally fractionated curative radiotherapy treatments of NSCLC. A conventionally fractionated RT is commonly defined as Gy/fraction but in the material used for modelling only courses including treatment plans with fraction dose over 1.9 Gy and below or equal to 2.2 Gy were used. Even though we used the diagnosis code and the treatment course ID or the treatment plan ID to include only NSCLC patients, a few SCLC patients could have been included in the material. Knowing that the majority of patients diagnosed with SCLC receives RT with fractions doses < 1.6 Gy at the hospitals included in the study, treatment courses with fraction doses lower than or equal to 1.9 Gy were removed. Doing this, it was believed that the patients diagnosed with SCLC were discriminated without removing many patients diagnosed with NSCLC. Still it is possible that a few undetected SCLC patients remained in the materials used for modelling. Excluding patients receiving treatment plans with a fraction dose over 2.2 Gy was to acquire a material of patients treated with conventional fractionation. Considering that hypofractionated and stereotactical treatments are common for patients diagnosed with NSCLC, the size of the material is scaled down when performing this exclusion. In this study we investigated only conventionally treated NSCLC patients to analyze a homogenously treated patient group, but we are interested in including hypofractionated and stereotactical treatments in further studies. 19

23 Regarding patients with category D (patients who received RT in the thoracic region before the NSCLC treatment start), it is reasonable to assume that previous dose to the lungs induces a higher risk of developing fatal RP this time around. It is also reasonable to claim that concurrent radiation exposure during the time period between the NSCLC treatment start and when the endpoint is evaluated could affect the dose-response relationship. This concerns patients categorised C (patients who received RT in another body region concurrently or within 90 days after the NSCLC treatment start) or E (patients who received RT in the thoracic region concurrently or within 90 days after the NSCLC treatment start). Therefore, all patients labelled category C, D or E were excluded. Given that the categorisation was based only on the data in the currently used database of history of treatments received at the corresponding hospital, there is a possibility that patients could have had prior thoracic RT elsewhere or registered in an earlier database. In that case, the patient should have been labelled category D but was not. Considering the poor prognosis of lung cancer and that only curative RT are included in this study, we consider these patient cases to be few. To do a more thorough investigation of this, the national cancer registries could be queried. This was outside the scope of this study. Patients with a D GTV less than 55 Gy were excluded due to two reasons; uncompleted treatment courses, and preoperative and postoperative treatments. An uncompleted course signals a possibly reduced general condition which could be a reason unrelated to radiation for death within 90 days from the treatment start. Sometimes, preoperative and postoperative RT have a lower prescribed dose to the PTV Preoperative and postoperative treatments were removed arguing that surgical procedures are known non-dose-related risks of complication which affects the dose-response relationship. It is possible that a patient receives RT with higher prescribed dose than 55 Gy even though the treatment is preoperative or postoperative. Bearing this in mind, preoperative and postoperative treatments included in the modelling are a potential source of error. As well as preoperative and postoperative RT, chemotherapy in combination with RT could also affect the dose-response relationship between MLD and fatal acute lung toxicity. Information on chemotherapy treatments is not collected nor considered in this study due to lack of time. Both surgery and chemotherapy are parameters that could be possible to include but this data collection implies manual handling of each patient individually and the work is therefore time-consuming. Patients with treatments longer than 70 days were excluded from the material for two reasons. Having a treatment longer than 70 days indicates a gap, for example in between plans, since no general RT is that long. Unsure of whether the MLD could still be used to describe the dose even though it does not take repair into account, these treatment courses were excluded. Also, a long treatment could mean that the treatment is not completed during the time the endpoint is evaluated (90 days). If that is the case, the calculated MLD will be misleading since all fractions had not been delivered at the time we evaluated the endpoint. The occurrence of patients with treatments longer than 70 days was low, four patients from hospital 2 and zero patients from hospitals 1 and 4. The number of patients with treatments longer than 70 days from hospital 3 was not available at the time of the study. As discussed, there are possible source of errors in the inclusion and exclusion criteria. Furthermore, there are other parameters that could affect the dose-response relationship such as concurrent chemotherapy treatment. These parameters are not considered in this study since we evaluated the patients based on the data accessible through scripting only. Still, we believe that an eligible patient group was used for analysis considering the large scale of patients included. 20