31 The present study resulted in a D50 value significantly lower than many other studies, which might be explained by differences in the definition of the end-point.
In this study only early pulmonary complications were evaluated.
In a continued work to reduce RP in BC patients treated with loco-regional RT at Södersjukhuset, the use of dose-volume constraints significantly reduced post-RT radiological changes on chest X-ray77. Symptomatic pneumonitis was very rare in this study. Only one patient of 88 developed a moderate reaction. Mild reactions were detected in 6 patients. Furthermore, no relationship was found between symptomatic RP and radiological RP on chest X-ray or CT, but only one patient was diagnosed with RP, so no statistically relevant conclusion can be made.
beam and superposition-convolution algorithms, compared to MC calculation, as shown in Figure 13 below.
Figure 13. Simulated dynamic case. Calculated longitudinal dose profiles for a simulated 2 cm tumor in lung tissue. Pencil beam algorithms, TMS and Eclipse, as well a superposition-convolution algorithm (CC) is compared to MC for both static and a dynamic case with a respiratory motion up to ±16 mm. Gray area represents the GTV and green square PTV.
At the periphery of the GTV, the dose planning algorithms overestimate the dose up to 10%, whereas in the lung tissue between PTV and GTV pencil beam algorithms generally overestimate the dose and the superposition-convolution underestimates it. Furthermore, there are differences between the two pencil beam algorithms as well as between the two superposition-convolution algorithms.
Two different respiratory motion patterns, representing different amplitudes, were included in the dose calculation with the MC. From the results of these calculations it was concluded that the dose to the GTV was considered relatively accurately estimated by the dose planning algorithms and the MC simulations for the static situation, as shown in Figure 13. On the other hand, the dose at the periphery of the GTV is overestimated, compared to the static case. A "narrowing" of the longitudinal dose profile of up to 20 mm (at about 90% dose level) is seen relative the static dose profile calculated with the pencil beam algorithms. An explanation for the relatively small impact on the dose to GTV, when breathing motions are included, is the inhomogeneous dose distribution in the PTV. The GTV is moving into higher and lower dose regions with the respiratory motion, which are partly compensating for each other.
An important conclusion from this investigation is that failure to consider breathing motions in the dose calculation will have a smaller impact than the choice of dose planning algorithm when one estimates the dose that will actually
33 distribution as used in conventional RT. Furthermore, the algorithm used must be reported together with dosimetric data in publications on follow-up data.
In paper IV, the impact of fractionation correction with the LQ and USC models was investigated for the modelling of lung toxicity after SBRT for lung tumors.
The toxicity data used were from 57 patients included in a phase II multicenter trial on SBRT for medically inoperable stage I NSCLC; of this group 10.5% were diagnosed with RP2+42. The prescribed dose to target was 22 Gy x 3 at the isocenter with 15 Gy per fraction at the periphery of the PTV. The dose planning involved two simple pencil beam algorithms implemented in two different TPSs, TMS-Helax and Eclipse. The dose to the high dose regions, in the volume between PTV and CTV was overestimated according to results in paper III and the dose to the lung volume outside the PTV was underestimated (Figure 13). Attempts were made to find a simplified dose correction algorithm to correct for the lack of accuracy of pencil beam algorithms to be applied on DVH data retrospectively.
Because these attempts were unsuccessful, original dose data were used in the NTCP modelling. To investigate the impact of fractionation correction two different models the LQ model81 and the USC model53, 54 were applied to dose data for each individual DVH for lung minus GTV. The NTCP-modelling was done using the Lyman-Kutcher-Burman (LKB) model63, 65.
Parameter values of D0 and in the USC model were determined from estimated values of and dT. In the LQ model a ratio of 3 Gy was used. In the fitting procedure, parameter values of n in the LKB model were determined for a range of values of m and D50, so the mean NTCP for the 57 patients was 10.5%, in accordance with the incidence.
The result of the NTCP modelling indicates a more serial like response of the lung with the USC correction for fractionation, as compared to that with LQ correction.
As a consequence, low dose volumes were found to contribute less and high dose volumes more to the NTCP when using the USC model compared to the LQ model. Still, additional clinical data are needed in order to reach more reliable conclusions.
n
Figu PTV show The cons in which different with 2 to were stu First, th (denoted tissue we the OAR be propo point of here wit prescribe dose dist (NTCP) iso-effec reference In the al sensitivit values of was assu with a
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omogeneous ses (constan
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equal to rding that o r values as for the LQ m
d tumor for a s dose distrib
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mon in SBR s dose equ nt cell surv s, with 22 G bed dose, an d 2.2). Cell odels, for is ird, the OA med to be p ). Normal tis or fractionat
rs obtained h 15 Gy x 3, mor was assu
10 Gy in t of NSCLC a determined model.
a typical SBR bution from t
urther invest ere evaluate rection in a RT. Three d ual to the d vival) for tu Gy x 3 as a r
nd the respo survival wa soeffective t AR receives proportiona ssue compli tion with th in paper IV 22 Gy x 3 a umed to ha the LQ mod
s used in pa d for the lun
RT treatment the SBRT tec
igated in pa ed using th range of re different sce dose to the umor and n reference. Se onse is assum as calculated
tumor doses a fraction al to the com
ication prob e two mode V. Three dif and 29.3 Gy ve a fractio del and para aper IV. The g in paper I
t. GTV, chnique
aper V, he two egimes enarios e GTV normal econd, med to d at the s, also of the mplete bability els, for fferent y x 3.
onation ameter e OAR IV and Figu
PTV show The cons in which different with 2 to were stu First, th (denoted tissue we the OAR be propo point of here wit prescribe dose dist (NTCP) iso-effec reference In the al sensitivit values of was assu with a
re 14. Dose and OAR ar wing the centr sequences o h the impac t models, U o 20 fraction udied as illus he OAR rec d 2.1). Isoe
ere calculate R receives a
ortional to m f maximum
th 22 Gy x ed dose and tribution in
was calcula ctive tumor
e situations ll three scen ty correspon f the USC m umed to hav
ratio equ
eprofile throu re shown. Ins ral plane.
of the results ct on the t USC and LQ
ns at target strated in Fi ceives a ho
ffective dos ed with the fraction of maximum do dose, with
3 as a ref d the respon
the OAR (d ated, with c doses using were invest narios (2.1-2
nding to model, accor ve paramete ual to 3 Gy f
ugh lung and serted shows
s from pape herapeutic Q, for fractio
doses comm gure 14.
omogeneous ses (constan
two models the prescrib ose (denoted the two mo ference. Thi
nse is assum denoted 2.3) correction fo g parameter tigated with 2.3) the tum
equal to rding that o r values as for the LQ m
d tumor for a s dose distrib
er IV was fu window we onation corr
mon in SBR s dose equ nt cell surv s, with 22 G bed dose, an d 2.2). Cell odels, for is ird, the OA med to be p ). Normal tis or fractionat
rs obtained h 15 Gy x 3, mor was assu
10 Gy in t of NSCLC a determined model.
a typical SBR bution from t
urther invest ere evaluate rection in a RT. Three d ual to the d vival) for tu Gy x 3 as a r
nd the respo survival wa soeffective t AR receives proportiona ssue compli tion with th in paper IV 22 Gy x 3 a umed to ha the LQ mod
s used in pa d for the lun
RT treatment the SBRT tec
igated in pa ed using th range of re different sce dose to the umor and n reference. Se onse is assum as calculated
tumor doses a fraction al to the com
ication prob e two mode V. Three dif and 29.3 Gy ve a fractio del and para aper IV. The g in paper I
t. GTV, chnique
aper V, he two egimes enarios e GTV normal econd, med to d at the s, also of the mplete bability els, for fferent y x 3.
onation ameter e OAR IV and
35 Figure 15 Calculated isoeffect curves, normalized to 22 Gy x 3 for tumor and normal tissues using LQ or USC for fractionation correction. The number of fraction increases to the right on the dose/fraction axis.
At a dose per fraction below about 6 Gy (case 2.1) the LQ and USC models predict the same fractionation sensitivity (slope) as shown in Figure 15. At a high dose per fraction, above about 15 Gy, the USC model predicts much lower fractionation sensitivity, compared to the LQ model. Further the therapeutic window was shown to increase with an increasing number of fractions in SBRT, compared to the commonly used three fractions. Both the LQ and USC models predicted this outcome, but generally a clearly greater gain is predicted with the USC model.
Specifically, the USC model predicts a higher sensitivity for fractionation than the LQ model (for iso effective tumor doses) if the OAR receives less than the dose given to the GTV. The greater gain predicted by the USC model applied to both cases, denoted as 2.2 and 2.3 in Figure 15.
The results from paper V have had an impact on clinical SBRT at Karolinska University Hospital in the sense that large tumors and centrally located lung tumors with adjacent sensitive structures are generally treated now with 8 to 10 fractions. Furthermore, a phase II multicenter trial on patients with lung tumors located less than 1 cm from the main/lobar bronchi has been initiated from Karolinska. Here, 8 fractions with 7 Gy/fraction are given at the periphery of the PTV. Other centres have also adopted a less extreme hypo fractionation than 3 for lung tumors at a central location. At VUMC in Amsterdam 8 x 7.5 Gy have been used and preliminary results on local control are similar to those from treating with 5 x 12 Gy or 3 x 20 Gy, with generally very low toxicity82. This preliminary clinical result indicates very low fractionation sensitivity at very high doses/fraction in correspondence to the results in Figure 12.
50 60 70 80 10090 200 300 400
2 4
6 8 10 30
LQ tumor LQ normal USC tumor USC normal
Total dose (Gy)
Dose/fraction (Gy)
7 CONCLUSION AND FUTURE POSSIBILITIES
In this thesis radiation induced side effects in lung tissue after radiotherapy in the thorax region were studied. The overall aim was to investigate how radiation induced side-effects correlates with and can be modelled in terms of the spatial and temporal distributions of the dose delivery in conventional RT for BC and hypofractionated SBRT for lung cancer.
The results showed, first, that short-term lung density changes and symptomatic RP were associated with RT techniques after RT for BC. The apical part of the lung appeared to be less radiation sensitive than the central part. Furthermore, RP grade I after RT for BC was accurately modelled with NTCP models and EUD and MLD are simple parameters that correlate with the risk of RP.
Second, for SBRT of lung tumors, the accuracy in dose to the GTV is relatively insensitive with respect to the algorithm used for dose planning, even considering breathing motions at dose delivery. However, at the periphery of the GTV and especially in the lung tissue outside but close to the GTV, the dose is considerably overestimated with the simplest algorithms. Improved algorithms that take into account the change in lateral transport of secondary particles is, however, relatively accurate in the estimation of dose to the lung.
From incidence data of RP grade 2 or more after SBRT of lung tumors, parameter values in the LKB NTCP model were determined for the lung using both the LQ and USC models for fractionation correction. The parameter values so determined were used to investigate the impact on the therapeutic window with increasing numbers of fractions in SBRT for lung tumors. When fractionation correction with the LQ and USC models were compared a larger gain was predicted by the USC model by increasing the number of fractions from 3 to about 10. At a very high dose per fraction the sensitivity for fractionation is considerably lower as predicted by the USC model compared to that for the LQ model.
To use dose/volume constraints that are available in the literature, knowledge of underlying data is important. Uncertainties in clinical response data stem partly from the difficulty of specifying end-points that are straightforward to quantify.
For the lungs, the response to radiation is to some extent a continuous effect with a gradual change in severity. In general, there is a lack of accurate data for the more severe end-points.
To safely use dose/volume constraints today only the context in which they were created may be relevant. However, this would restrict the use of dose-response relations in the generation of hypotheses for developments in RT. Thus, consistent modelling of dose/volume parameters in the determination of dose-response relations will be more fruitful for the future.
The causes of some uncertainties in dose delivery to the patients are set-up errors and organ motions, which can make the delivered dose unreliable compared to the calculated dose to an organ. Uncertainties in static dose calculations are today a minor problem because relatively accurate dose planning algorithms based on
37 and deformations must be implemented if uncertainties in calculated dose to OAR are to be reduced. Uncertainties in dose-response relations are highly dependent of how the correction for fractionation is done in the data gathered, especially for high doses per fraction.
To assure the reliable prediction of side-effects, more clinical studies are needed and the uncertainties in both the definition of clinical findings and in dose delivery have to be addressed and reduced.
8 ACKNOWLEDGEMENTS
Some of these studies were supported by grants. I wish to thank The Swedish Cancer Society, the King Gustaf Vth Jubilee Fund, the Alex and Eva Wallström Society and the Swedish Medical Society.
I would like to express my sincere gratitude to all those who directly or indirectly contributed to the completion of this work.
Special thanks to the Medical Physics department, Karolinska Sjukhuset who have supported this work through all the years.
To members of the study groups – both at Södersjukhuset (lung toxicity in BC) and the Nordic SBRT study group.
To my patient and understanding supervisor Ingmar Lax for all the invaluable advice.
To my late mentor, Lennart Sundbom, who initiated my interest for research– I am sure that he is still watching over me.
To all my friends and colleagues who have listened to my thoughts and encouraged me to continue. No one is forgotten.
Extra thanks to my supportive friends Giovanna, Bruno, Åsa, Younes, Massoud, Pierre, Mathias, Eija, Ann-Sofi and Ing-Marie. Without you nothing would have been possible.
And also to Anna and the Aria-group – keeping me sane.
To my fellow PhD students Vanessa, Pia and Ulla. We are soon all of us PhDs. Hooray!!
To all my friends and relatives – it is never too late …..
And finally to my family Emilia, Alex och Lars, who have waited for me to come home a million times. A zillion hugs and kisses for all the missed ones.
39
9 REFERENCES
1. P. Mayles, A. Nahum and Rosenwald, "Handbook of Radiotherapy Physics: Theory and Practice," edited by T. Francis (2007).
2. D. Hanahan and R. A. Weinberg, "The Hallmarks of Cancer," Cell 100, 57-70 (2000).
3. International Commission On Radiation Units And Measurement, "Prescribing, Recording and Reporting Photon Beam Therapy (ICRU)," Vol. 50, (1993).
4. H. Thames and J. Hendry, Fractionation in radiotherapy. (Taylor and Francis, London, 1987).
5. S. A. Bapat, "Evolution of cancer stem cells," Seminars in Cancer Biology 17, 204-213 (2007).
6. H. Withers, "Cell cycle redistribution as a factor in multifraction irradiation.,"
Radiology 114, 199-202 (1975).
7. H. R. Withers, "Lethal and sublethal cellular injury in multifraction irradiation,"
European Journal of Cancer (1965) 11, 581-583 (1975).
8. H. P. Rodemann and M. A. Blaese, "Responses of Normal Cells to Ionizing Radiation,"
Seminars in Radiation Oncology 17, 81-88 (2007).
9. M. Joiner and A. van der Kogel, "Basic Clinical Radiobiology ", (Hodder Arnold, 2009).
10. E. B. Podgorsak, "Radiation Oncology Physics: A Handbook for Teachers and Students," in Educational Report Series, (IAEA, Vienna, 2005).
11. Socialstyrelsen, "Cancer Incidence in Sweden 2009," in Sveriges officiella statistik, Hälso och Sjukvård, (2010).
12. M. Overgaard, P. S. Hansen, J. Overgaard, C. Rose, M. Andersson, F. Bach, M. Kjaer, C. C. Gadeberg, H. T. Mouridsen, M.-B. Jensen and K. Zedeler, "Postoperative radiotherapy in high-risk premenopausal women with breast cancer who receive adjuvant chemotherapy.," N Engl J Med 337, 949-955 (1997).
13. M. Overgaard, M. B. Jensen, J. Overgaard, P. S. Hansen, C. Rose, M. Andersson, C.
Kamby, M. Kjaer, C. C. Gadeberg, B. B. Rasmussen, M. Blichert-Toft and H. T.
Mouridsen, "Postoperative radiotherapy in high-risk postmenopausal breast-cancer patients given adjuvant tamoxifen: Danish Breast Cancer Cooperative Group DBCG 82c randomised trial [see comments]," Lancet 353, 1641-1648 (1999).
14. J. Ragaz, S. M. Jackson, N. Le, I. H. Plenderleith, J. J. Spinelli, V. E. Basco, K. S.
Wilson, M. A. Knowling, C. M. L. Coppin, M. Paradis, A. J. Coldman and I. A.
Olivotto, "Adjuvant radiotherapy and chemotherapy in node-positive premenopausal women with breast cancer," N Engl J Med 337, 956-962 (1997).
15. P. McGale, S. C. Darby, P. Hall, J. Adolfsson, N.-O. Bengtsson, A. M. Bennet, T.
Fornander, B. Gigante, M.-B. Jensen, R. Peto, K. Rahimi, C. W. Taylor and M. Ewertz,
"Incidence of heart disease in 35,000 women treated with radiotherapy for breast cancer in Denmark and Sweden," Radiotherapy and Oncology 100, 167-175 (2011).
16. N. Martini, M. S. Bains, M. E. Burt, M. F. Zakowski, P. McCormack, V. W. Rusch and R. J. Ginsberg, "Incidence of local recurrence and second primary tumors in resected stage I lung cancer," The Journal of Thoracic and Cardiovascular Surgery 109, 120-129 (1995).
17. X. Qiao, O. Tullgren, I. Lax, F. Sirzen and R. Lewensohn, "The role of radiotherapy in treatment of stage I non-small cell lung cancer," Lung Cancer 41, 1-11 (2003).
18. G. S. Sibley, "Radiotherapy for patients with medically inoperable Stage I nonsmall cell lung carcinoma: smaller volumes and higher doses - a review.," Cancer 82, 433-438 (1998).
19. M. Werner-Wasik, E. Yorke, J. Deasy, J. Nam and L. B. Marks, "Radiation Dose-Volume Effects in the Esophagus," International Journal of Radiation
Oncology*Biology*Physics 76, S86-S93 (2010).
20. International Commission On Radiation Units And Measurement, "Prescribing, Recording and Reporting Photon Beam Therapy," Vol. 50, (1993).
21. S. M. Bentzen, L. S. Constine, J. O. Deasy, A. Eisbruch, A. Jackson, L. B. Marks, R. T.
Haken and E. D. Yorke, "Quantitative Analyses of Normal Tissue Effects in the Clinic (QUANTEC)," Int J Radiat Oncol Biol Phys 76 (2010).
22. L. B. Marks, X. Yu, Z. Vujaskovic, W. Small, R. Folz and M. S. Anscher, "Radiation-induced lung injury," Seminars in Radiation Oncology 13, 333-345 (2003).
23. S. Delanian and J.-L. Lefaix, "Current Management for Late Normal Tissue Injury:
Radiation-Induced Fibrosis and Necrosis," Seminars in Radiation Oncology 17, 99-107 (2007).
24. D. Stanley, "The uniform reporting of treatment-related morbidity," Seminars in Radiation Oncology 4, 112-118 (1994).
25. P. Lind, G. Svane, G. Gagliardi and C. Svensson, "Abnormalities by pulmonary regions studied with computer tomography following local or local-regional radiotherapy for breast cancer," International Journal of Radiation Oncology*Biology*Physics 43, 489-496 (1999).
26. M. Guckenberger, K. Heilman, J. Wulf, G. Mueller, G. Beckmann and M. Flentje,
"Pulmonary injury and tumor response after stereotactic body radiotherapy (SBRT):
Results of a serial follow-up CT study," Radiotherapy and Oncology 85, 435-442 (2007).
27. P. Lind, H. Bylund, B. Wennberg, C. Svensson and G. Svane, "Abnormalities on chest radiographs following radiation therapy for breast cancer," European Radiol 10, 484-489 (2000).
28. K. L. Stephans, T. Djemil, S. M. Gajdos, C. A. Reddy and G. M. M. Videtic,
"Dosimetric Variables Predictive of Pulmonary Function Test (PFT) Changes After Stereotactic Body Radiotherapy (SBRT) for Stage I Lung Cancer," International Journal of Radiation Oncology*Biology*Physics 69, S496-S497 (2007).
29. M. T. Milano, L. S. Constine and P. Okunieff, "Normal Tissue Tolerance Dose Metrics for Radiation Therapy of Major Organs," Seminars in Radiation Oncology 17, 131-140 (2007).
30. L. B. Marks, S. M. Bentzen, J. O. Deasy, F.-M. Kong, J. D. Bradley, I. S. Vogelius, I.
El Naqa, J. L. Hubbs, J. V. Lebesque, R. D. Timmerman, M. K. Martel and A. Jackson,
"Radiation Dose–Volume Effects in the Lung," International Journal of Radiation Oncology*Biology*Physics 76, S70-S76 (2010).
31. A. Jackson, L. B. Marks, S. M. Bentzen, A. Eisbruch, E. D. Yorke, R. K. Ten Haken, L. S. Constine and J. O. Deasy, "The Lessons of QUANTEC: Recommendations for Reporting and Gathering Data on Dose–Volume Dependencies of Treatment Outcome," International Journal of Radiation Oncology*Biology*Physics 76, S155-S160 (2010).
32. National Cancer Institute, "Common Toxicity Criteria NCI-CTC v2," in http://www.eortc.be/services/doc/ctc/ctcv20_4-30-992.pdf, (1999).
33. Radiation Oncology Group and European Organization for Research and Treatment of Cancer, "RTOG/EORTC Acute and Late Radiation Morbidity Scoring schema," in http://www.rtog.org/ResearchAssociates/AdverseEventReporting/RTOGEORTCLateRa diationMorbidityScoringSchema.aspx
http://www.rtog.org/ResearchAssociates/AdverseEventReporting/AcuteRadiationMorbidit yScoringCriteria.aspx.
34. S. L. Faria, M. Aslani, F. S. Tafazoli, L. Souhami and C. R. Freeman, "The Challenge of Scoring Radiation-induced Lung Toxicity," Clinical Oncology 21, 371-375 (2009).
35. S. M. Bentzen, J. Z. Skoczylas, M. Overgaard and J. Overgaard, "Radiotherapy-related lung fibrosis enhanced by tamoxifen," J Natl Cancer Inst 88, 918-922 (1996).
36. S. Johansson, L. Bjermer, L. Franzen and R. Henriksson, "Effects of ongoing smoking on the development of radiation-induced pneumonitis in breast cancer and oesophagus cancer patients [see comments]," Radiother Oncol 49, 41-47 (1998).
37. P. Lind, B. Wennberg, G. Gagliardi and T. Fornander, "Pulmonary complications following different radiotherapy techniques for breast cancer, and the association to irradiated lung volume and dose," Breast Cancer Res Treat 68, 199-210 (2001).
38. P. Lind, S. Rosfors, B. Wennberg, U. Glas, S. Bevegard and T. Fornander, "Pulmonary function following adjuvant chemotherapy and radiotherapy for breast cancer and the issue of three-dimensional treatment planning," Radiotherapy and Oncology 49, 245-254 (1998).
39. I. Lax, H. Blomgren, I. Näslund and R. Svanström, "Stereotactic Radiotherapy of