Site-specific dose-response relationships for cancer induction from the combined Japanese A-bomb and Hodgkin cohorts for doses relevant to radiotherapy
© Schneider et al; licensee BioMed Central Ltd. 2011
Received: 13 April 2011
Accepted: 26 July 2011
Published: 26 July 2011
Background and Purpose
Most information on the dose-response of radiation-induced cancer is derived from data on the A-bomb survivors. Since, for radiation protection purposes, the dose span of main interest is between zero and one Gy, the analysis of the A-bomb survivors is usually focused on this range. However, estimates of cancer risk for doses larger than one Gy are becoming more important for radiotherapy patients. Therefore in this work, emphasis is placed on doses relevant for radiotherapy with respect to radiation induced solid cancer.
Materials and methods
For various organs and tissues the analysis of cancer induction was extended by an attempted combination of the linear-no-threshold model from the A-bomb survivors in the low dose range and the cancer risk data of patients receiving radiotherapy for Hodgkin's disease in the high dose range. The data were fitted using organ equivalent dose (OED) calculated for a group of different dose-response models including a linear model, a model including fractionation, a bell-shaped model and a plateau-dose-response relationship.
The quality of the applied fits shows that the linear model fits best colon, cervix and skin. All other organs are best fitted by the model including fractionation indicating that the repopulation/repair ability of tissue is neither 0 nor 100% but somewhere in between. Bone and soft tissue sarcoma were fitted well by all the models. In the low dose range beyond 1 Gy sarcoma risk is negligible. For increasing dose, sarcoma risk increases rapidly and reaches a plateau at around 30 Gy.
In this work OED for various organs was calculated for a linear, a bell-shaped, a plateau and a mixture between a bell-shaped and plateau dose-response relationship for typical treatment plans of Hodgkin's disease patients. The model parameters (α and R) were obtained by a fit of the dose-response relationships to these OED data and to the A-bomb survivors. For any three-dimensional inhomogenous dose distribution, cancer risk can be compared by computing OED using the coefficients obtained in this work.
The dose-response relationship for radiation carcinogenesis up to one or two Gy has been quantified in several major analyses of the atomic bomb survivors data. Recent papers have been published, for example, by Preston et al. [1, 2] and Walsh et al. [3, 4]. This dose range is important for radiation protection purposes where low doses are of particular interest. However, it is also important to know the shape of the dose-response curve for radiation induced cancer for doses larger than one Gy. In patients who receive radiotherapy, parts of the patient volume can receive high doses and it is therefore of great importance to know the risk for the patient to develop a cancer which could have been caused by the radiation treatment.
There is currently much debate concerning the shape of the dose-response curve for radiation-induced cancer [5–17]. It is not known whether cancer risk as a function of dose continues to be linear or decreases at high dose due to cell killing or levels off due to, for example, a balance between cell killing and repopulation effects. The work presented here, aims to clarify the dose-response shape for the radiotherapy dose range. In this dose range, the linear-no-threshold model (LNT) derived from the atomic bomb survivors from Hiroshima and Nagasaki can be combined with cancer risk data available from about 30,000 patients with Hodgkin's disease who were irradiated with localized doses of up to around 40 Gy.
The usual method for obtaining empirical dose-response relationships for radiation associated cancer is to perform a case control study. For each patient with a second cancer the location of, and the point dose at the malignancy can be determined. If the dose is obtained also for a number of controls the dose-response relationship for radiation induced cancer can be obtained. The advantage of this method is a direct determination of risk as a function of point dose, the major disadvantage are the large errors involved when determining the location and dose to the origin of the tumor. In this work another method was used by assuming certain shapes of dose-response curves based on model assumptions. The free model parameters for each organ are adjusted in two steps. First, the models have to reproduce in the limit of low dose the risk coefficients of the A-bomb survivors. Second, by applying the models to typical dose-volume histograms of treated patients they have to predict the corresponding observed second cancer risk which was obtained from epidemiological studies. An advantage of this method is that no point dose estimates at the tumor origin are necessary; a disadvantage is that the obtained dose-response curve is dependent on the a priori model.
The aim of this paper is to attempt a combination of the LNT model derived from the atomic bomb survivors and cancer risk data from a Hodgkin cohort treated with radiotherapy, in order to determine possible dose-response relationships for radiation associated site specific solid cancers for radiotherapy doses. This work is an extension of recently published results on possible dose-response relationships for radiation induced solid cancers for all organs combined [11, 14, 18]. The main difference to previous work is the use of a more realistic dose-response relationship including fractionation effects which is more suitable for radiotherapy applications. Many problems and uncertainties are involved in combing these two data-sets. However, since very little is currently known about the shape of dose-response relationships for radiation-induced cancer in the radiotherapy dose range, this approach could be regarded as an attempt to acquire more information in this area.
Materials and methods
Cancer risk from the Atomic bomb survivor data
Initial slopes β (in brackets 95% confidence interval) of the A-bomb survivors for age at exposure of 30 and attained age of 70 years and age modifying parameters γ e and γ a for different sites.
β EAR - Japan *
β EAR - UK *
Mouth and pharynx◇
Brain and CNS
β ERR /β EAR - Japan
β ERR /β EAR - UK
w ERR /w EAR
β weighted - UK/ β EAR - Japan
Mouth and pharynx†
Brain and CNS
Application of cancer risk models to radiotherapy patients
where V T is the total organ volume and the sum is taken over all bins of the dose-volume histogram V(D). For a completely homogenously irradiated organ with a dose D hom excess absolute risk is simply EAR org = EAR(D hom ).
It becomes instantly clear that risk ratios for different radiotherapy treatment plans are equivalent to OED ratios which can be simply determined on the basis of an organ specific dose-response relationship (RED) and dose volume histogram (V(D)). OED values are independent of the initial slope β and the modifying function μ and are thus keeping the necessary variables and the corresponding uncertainties at a minimum.
It should be noted here that for highly inhomogeneous dose distributions, cancer risk is proportional to average dose only for a linear dose-response relationship. For any other dose-response relationship, cancer risk is proportional to OED.
Dose-response models for carcinoma induction
where D T and d T is the prescribed dose to the target volume with the corresponding fractionation dose, respectively. For analyzing the Hodgkin data from Dores et al.  we used for D T = 40 Gy and for d T = 2 Gy. The repopulation/repair parameter R characterizes the repopulation/repair-ability of the tissue between two dose fractions and is 0 if no and 1 if full repopulation/repair occurs. It is assumed here an α/β = 3 Gy for all tissues, since analysis of breast cancer data has shown that the dose-response model is robust with variations in α/β .
Although the case R = 0 represents an acute dose exposure, repopulation/repair effects are certainly important. However, any repopulated cell is not irradiated (as long as the time scale of irradiation is small) and thus, in the context of carcinogenesis, repopulation/repair effects are in this case irrelevant.
Hence OED is, in the case of a homogenous distribution of small dose, average absorbed organ dose D;-. Thus in the limit of small dose all proposed dose-response relationships approach the LNT model and the initial slope β can be obtained from the most recent data for solid cancer incidence. Here the data for a follow-up period from 1958 to 1998 was used from a publication of Preston et al. .
Dose-response models for sarcoma induction
Sarcoma risk from a homogenous distribution of small dose is proportional to the cube of dose and thus results in a much lower cancer risk than expected from a linear model. This is consistent with the observations of the A-bomb survivors.
Modeling of the Hodgkin's patients
Cancer risk is only proportional to average organ dose as long as the dose-response curve is linear. At high dose it could be that the dose-response relationship is non-linear and as a consequence, OED replaces average dose to quantify radiation induced cancer. In order to calculate OED in radiotherapy patients, information on the three-dimensional dose distribution is necessary. This information is usually not provided in epidemiological studies on second cancers after radiotherapy. However, in Hodgkin's patients the three-dimensional dose distribution can be reconstructed.
For this purpose data on secondary cancer incidence rates in various organs for Hodgkin's patients treated with radiation were included in this analysis. Data on Hodgkin's patients treated with radiation seem to be ideal for an attempted combination with the A-bomb data. These patients were treated at a relatively young age, with curative intent and hence secondary cancer incidence rates for various organs are known with a good degree of precision. Since the treatment of Hodgkin's disease with radiotherapy has been highly successful in the past, the treatment techniques have not been modified very much over the last 30 years. This can be verified, for example, by a comparison of the treatment planning techniques used from 1960 to 1970  with those used from 1980 until 1990 . Additionally, the therapy protocols do not differ very much between the institutions that apply this form of treatment. These factors make it possible to reconstruct a statistically averaged OED for each dose-response model RED(D), which is characteristic for a large patient collective of Hodgkin's disease patients.
Observed excess absolute risk of site-specific radiation induced cancer from the study of Dores et al. .
agex= 37 and agea= 45
agex= 30 and agea= 70
Mouth and pharynx
Brain and CNS
Typical treatment techniques for Hodgkin's disease radiotherapy were reconstructed in an Alderson Rando Phantom with a 200 ml breast attachment. Treatment planning was performed on the basis of the review by Hoppe  and the German Hodgkin disease study protocols http://www.ghsg.org. We used for treatment planning the Eclipse External Beam Planning system version 8.6 (Varian Oncology Systems, Palo Alto, CA) using the AAA-algorithm (version 8.6.14) with corrected dose distributions for head-, phantom- and collimator-scatter. Three different treatment plans were computed which included a mantle field, an inverted-Y field and a para-aortic field. All plans were calculated with 6 MV photons and consisted of two opposed fields. The technique for shaping large fields included divergent lead blocks. Treatment was performed at a distance of 100 cm (SSD). Anterior-posterior (ap/pa) opposed field treatment techniques were applied to insure dose homogeneity.
The dose-volume histograms of the organs and tissues (exclusive of bone and soft tissue) which are listed in Table 1 were converted into OED according to the dose-response relationships for carcinomas (Eqs. 6, 7, 9 and 10). A statistically averaged OED was then obtained by combining the OED from different plans with respect to the statistical weight of the involvement of the individual lymph nodes . The same was executed with bone and soft tissue using the sarcoma dose-response relationships from Eqs. 12 and 13. Here it is assumed that radiation causes in bone and soft tissue exclusively sarcomas, in all other organs which are listed in Table 1 carcinomas.
Combined fit of A-bomb survivor and Hodgkin's patients
Since the dose distribution in a Hodgkin's patient is highly inhomogenous and the dose-response relationships as described by Eqs. 7, 9, 10, 12 and 13 are non-linear, it is not appropriate to apply a straight forward fit to the data. An iterative fitting procedure needs to be used instead. For this purpose, as described in the previous section, the dose-volume histograms for the different organs of interest were converted into OED for given model parameters α and R. The initial slope β was taken from Table 1 for carcinoma induction and kept fix. For sarcoma dose-response curves the parameters β and α were varied and R was kept fix at 0.1, 0.5 and 1, respectively.
A fit was accepted as significant good when CV < 0.05.
The procedure described above was slightly varied to fit all solid cancers, since for all solid cancers combined statistically significant A-bomb data up to approximately 5 Gy are available. Thus the α- value for all solid cancers combined could be obtained using the A-bomb data and was fixed at 0.089 .
Results of the fits to the Hodgkin data for the different dose-response models for carcinoma induction.
No fractionation (bell shape) R = 0
Full tissue recovery (plateau) R = 1
Mouth and pharynx
Brain and CNS
Results of the fit to the Hodgkin data for the different dose-response models for sarcoma induction.
Low repopulation R = 0.1
Intermediate repopulation R = 0.5
Full tissue recovery R = 1
All dose-response models are plotted for a age at diagnosis of 30 and an attained age of 70 years, but they can easily converted to other ages by using the temporal patterns described by Eq. 2 with the parameters listed in Table 1.
From the analysis excluded were Esophagus and Thyroid, since theses organs were covered by a limited dose range of 30-55 Gy and 44-46 Gy, respectively.
Figure 1 shows the dose-response models fitted to the whole body structure (the complete Alderson phantom). The initial slope β of the A-bomb survivor data is that for all solid tumors. The linear model was not converging, all other models could be fitted. There is strong variation for intermediate dose levels around 10 Gy. It should be noted here that for inhomogenous dose distributions a dose response relationship for the whole body should be used with extreme care, as two completely different distributions of dose in the organs could result in the same OED for the whole body. The dose-response relationships for the whole body obtained in this report should be therefore used only for Hodgkin's patients treated with mantle fields. In contrast the dose-response relationships for single organs can be used generally for analyzing any dose distribution.
The quality of the applied fits shows that the linear model fits best colon, cervix and skin. All other organs are best fitted by the full model indicating that the repopulation/repair ability of tissue is neither 0 nor 100% but somewhere in between. It seems that for most organs at large doses the dose-response relationship is flattening or decreasing.
It should be noted that Mouth and Pharynx was covered by a limited dose range from 16-45 Gy. Thus the resulting dose-response relationship for dose levels outside that range should be used with care. For rectum none of the dose response models could predict the Hodgkin data. The linear model fitted colon, liver, cervix, skin and Brain/CNS. The model with full repopulation/repair did not fit rectum, cervix and skin.
Bone and soft tissue sarcoma were fitted by all the models well. In the low dose range beyond 1 Gy sarcoma risk is negligible. For increasing dose sarcoma risk increases rapidly and reaches a plateau at around 30 Gy. This is in agreement with observations which demonstrate small sarcoma risk at low dose from the A-bomb survivors and significant sarcoma risk in the high dose regions of radiotherapy patients.
The results of this study can be compared to EAR-modeling based on case control studies. In two recent publications excess absolute risk of breast and lung cancer was fitted to the model including fractionation [23, 27]. For breast cancer the obtained model parameters where α = 0.067 Gy-1 and R = 0.62 and for lung α = 0.061 Gy-1 and R = 0.84. The corresponding dose-response curves are plotted in Figures 2 and 3 as the magenta lines for comparison with the results obtained in this study. If it is considered that the dose-response relationships were derived from two completely different data sets with two different methods the agreement is satisfying.
The epidemiological data from the Atomic-bomb survivors and the Hodgkin's patients are associated with large errors as discussed below. Nevertheless some basic conclusion can be tentatively drawn from the analysis presented here.
Increased risks of solid cancers after Hodgkin's disease have been generally attributed to radiotherapy. An important question is whether chemotherapy for Hodgkin's disease also adds to the solid cancer risk, and if so, at which sites. If chemotherapy indeed affects induction of solid tumors, one would expect that patients receiving combined modality treatment would have a greater relative risk than patients treated solely with radiotherapy. In several studies, no increased risk of solid cancers overall was observed after the application of chemotherapy alone. Dores et al.  calculated both the risk after radiotherapy alone and the solid cancer risk after combined modality therapy and found an excess absolute risk of 39 and 43 per 10,000 patients per year, respectively. As a consequence, the difference in risk between combined modality treatment and radiotherapy alone (4 per 10,000 patients per year) can be tentatively attributed to either chemotherapy or a genetic susceptibility of the Hodgkin patient population with regard to cancer or both. The risk difference accounts approximately for 10% of all solid cancers and can be regarded as not substantial when compared to other errors involved for risk estimation and is also not statistically significant (see Table 1).
It is well known that genetic susceptibility underlies Hodgkin's disease . It is not clear whether this genetic susceptibility would also affect the development of other cancers. There is the possibility of a cancer diathesis, the prospect that, for some reasons related to genetic makeup, a person who developed one cancer has an inherently increased risk of developing another. However, such cancer susceptibility would result in a minimal excess cancer incidence compared to the incidence of radiation related tumors, since such an excess cancer incidence of solid tumors should also be seen in Hodgkin's patients after treatment with chemotherapy alone. However, there is no statistically significant increase for all solid tumors combined. Therefore, such an effect will be hidden in the 95% confidence interval of the observed cancer incidence after chemotherapy.
In this work EAR has been used to quantify radiation-induced cancer. EAR is used here, since the risk calculations of the Hodgkin's cohort are based on extremely inhomogenous dose distributions. It is assumed that the total absolute risk in an organ is the volume weighted sum of the risks of the partial volumes which are irradiated homogenously. Currently there is no available method for obtaining analogous organ risks using ERR without modeling the underlying baseline risk. Shuryak et al. [16, 17] recently published a model including the description of typical background carcinogenesis in addition to radiation induced cancer. They could thus obtain a microscopic ERR model. The advantage of their model in comparison to our approach is that they could determine directly ERR the disadvantage is a larger number of adjustable parameters (three more parameters) which must be introduced to model background cancer risk.
The A-bomb survivor data used in this work were taken from a recent report from Preston et al. . Preston determined the initial slope of the dose-response relationship by using an RBE of 10 for the neutrons. Recent research by Sasaki et al. however indicated that the neutron RBE might by larger and varying with dose . It could be important to determine site specific cancer induction also for a dose varying RBE similar to the work which was done for all solid cancers combined .
A comparison of dose distributions in humans, for example in radiotherapy treatment planning, with regard to cancer incidence or mortality can be performed by computing OED, which can be based on any dose-response relationship. In this work OED for various organs was calculated for a linear, a bell-shaped, a plateau and a mixture between a bell-shaped and plateau dose-response relationship for typical treatment plans of Hodgkin's disease patients. The model parameters (α and R) were obtained by a fit of the dose-response relationships to these OED data and to the A-bomb survivors. For any three-dimensional inhomogenous dose distribution, cancer risk can be compared by computing OED using the coefficients obtained in this work.
For absolute risk estimates, EAR org can be determined by taking additionally the initial slope β from Table 1 and multiplying it with the population-dependent modifying function using the coefficients of Table 1. However, absolute risk estimates must be viewed with care, since the errors involved are large.
This study was supported in part financially by the European Commission with ALLEGRO grant No. 231965.
- Preston DL, Ron E, Tokuoka S, Funamoto S, Nishi N, Soda M, Mabuchi K, Kodama K: Solid cancer incidence in atomic bomb survivors: 1958-1998. Radiat Res. 2007, 168 (1): 1-64. 10.1667/RR0763.1.View ArticlePubMedGoogle Scholar
- Preston DL, Pierce DA, Shimizu Y, Cullings HM, Fujita S, Funamoto S, Kodama K: Effects of recent changes in Atomic bomb survivor dosimetry on cancer mortality risk estimated. Radiat Res. 2004, 162: 377-389. 10.1667/RR3232.View ArticlePubMedGoogle Scholar
- Walsh L, Rühm W, Kellerer AM: Cancer risk estimates for X-rays with regard to organ specific doses, part I: All solid cancers combined. Radiat Environ Biophys. 2004, 43: 145-151. 10.1007/s00411-004-0248-5.View ArticlePubMedGoogle Scholar
- Walsh L, Rühm W, Kellerer AM: Cancer risk estimates for γ-rays with regard to organ specific doses, part II: Site specific solid cancers. Radiat Environ Biophys. 2004, 43: 225-231. 10.1007/s00411-004-0263-6.View ArticlePubMedGoogle Scholar
- Lindsay KA, Wheldon EG, Deehan C, Wheldon TE: Radiation carcinogenesis modelling for risk of treatment-related second tumours following radiotherapy. Br J Radiol. 2001, 74 (882): 529-36.View ArticlePubMedGoogle Scholar
- Hall EJ, Wuu CS: Radiation-induced second cancers: the impact of 3D-CRT and IMRT. Int J Radiat Oncol Biol Phys. 2003, 56 (1): 83-8. 10.1016/S0360-3016(03)00073-7.View ArticlePubMedGoogle Scholar
- Davis RH: Production and killing of second cancer precursor cells in radiation therapy: in regard to Hall and Wuu (Int J Radiat Oncol Biol Phys 2003;56:83-88). Int J Radiat Oncol Biol Phys. 2004, 59 (3): 916-10.1016/j.ijrobp.2003.09.076. and author reply Int J Radiat Oncol Biol Phys. 2005 Jan 1;61(1):312-3View ArticlePubMedGoogle Scholar
- Schneider U, Besserer J, Mack A: Hypofractionated radiotherapy has the potential for second cancer reduction. Theor Biol Med Model. 2010, 7: 4-10.1186/1742-4682-7-4.PubMed CentralView ArticlePubMedGoogle Scholar
- Dasu A, Toma-Dasu I: Dose-effect models for risk-relationship to cell survival parameters. Acta Oncol. 2005, 44 (8): 829-35. 10.1080/02841860500401159.View ArticlePubMedGoogle Scholar
- Dasu A, Toma-Dasu I, Olofsson J, Karlsson M: The use of risk estimation models for the induction of secondary cancers following radiotherapy. Acta Oncol. 2005, 44 (4): 339-47. 10.1080/02841860510029833.View ArticlePubMedGoogle Scholar
- Schneider U, Zwahlen D, Ross D, Kaser-Hotz B: Estimation of radiation-induced cancer from three-dimensional dose distributions: Concept of organ equivalent dose. Int J Radiat Oncol Biol Phys. 2005, 61 (5): 1510-5. 10.1016/j.ijrobp.2004.12.040.View ArticlePubMedGoogle Scholar
- Schneider U, Kaser-Hotz B: A simple dose-response relationship for modeling secondary cancer incidence after radiotherapy. Z Med Phys. 2005, 15 (1): 31-7.View ArticlePubMedGoogle Scholar
- Sachs RK, Brenner DJ: Solid tumor risks after high doses of ionizing radiation. Proc Natl Acad Sci USA. 2005, 102 (37): 13040-5. 10.1073/pnas.0506648102.PubMed CentralView ArticlePubMedGoogle Scholar
- Schneider U, Kaser-Hotz B: Radiation risk estimates after radiotherapy: application of the organ equivalent dose concept to plateau dose-response relationships. Radiat Environ Biophys. 2005, 44 (3): 235-9. 10.1007/s00411-005-0016-1.View ArticlePubMedGoogle Scholar
- Pfaffenberger A, Schneider U, Poppe B, Oelfke U: Phenomenological modelling of second cancer incidence for radiation treatment planning. Z Med Phys. 2009, 19 (4): 236-50.View ArticlePubMedGoogle Scholar
- Shuryak I, Hahnfeldt P, Hlatky L, Sachs RK, Brenner DJ: A new view of radiation-induced cancer: integrating short- and long-term processes. Part I: approach. Radiat Environ Biophys. 2009, 48 (3): 263-74. 10.1007/s00411-009-0230-3.PubMed CentralView ArticlePubMedGoogle Scholar
- Shuryak I, Hahnfeldt P, Hlatky L, Sachs RK, Brenner DJ: A new view of radiation-induced cancer: integrating short- and long-term processes. Part II: second cancer risk estimation. Radiat Environ Biophys. 2009, 48 (3): 275-86. 10.1007/s00411-009-0231-2.PubMed CentralView ArticlePubMedGoogle Scholar
- Schneider U, Walsh L: Cancer risk estimates from the combined Japanese A-bomb and Hodgkin cohorts for doses relevant to radiotherapy. Radiat Environ Biophys. 2008, 47 (2): 253-63. 10.1007/s00411-007-0151-y.View ArticlePubMedGoogle Scholar
- The 2007 Recommendations of the International Commission on Radiological Protection. ICRP publication 103. Ann ICRP. 2007, 37 (2-4): 1-332.
- UNSCEAR: United Nations Scientific Committee on the Effects of Atomic Radiation (2006) Effects of ionizing radiation. UNSCEAR. 2006, United Nations, New York, Report to the General Assembly, with Scientific AnnexGoogle Scholar
- Schneider U: Mechanistic model of radiation-induced cancer after fractionated radiotherapy using the linear-quadratic formula. Med Phys. 2009, 36 (4): 1138-43. 10.1118/1.3089792.View ArticlePubMedGoogle Scholar
- Dores GM, Metayer C, Curtis RE, Lynch CF, Clarke EA, Glimelius B, Storm H, Pukkala E, van Leeuwen FE, Holowaty EJ, Andersson M, Wiklund T, Joensuu T, van't Veer MB, Stovall M, Gospodarowicz M, Travis LB: Second malignant neoplasms among long-term survivors of Hodgkin's disease: a population-based evaluation over 25 years. J Clin Oncol. 2002, 20 (16): 3484-94. 10.1200/JCO.2002.09.038.View ArticlePubMedGoogle Scholar
- Schneider U, Sumila M, Robotka J, Gruber G, Mack A, Besserer J: Dose-response relationship for breast cancer induction at radiotherapy dose. Radiat Oncol. 2011, 6 (1): 67-10.1186/1748-717X-6-67.PubMed CentralView ArticlePubMedGoogle Scholar
- Carmel RJ, Kaplan HS: Mantle irradiation in Hodgkin's disease. An analysis of technique, tumor eradication, and complications. Cancer. 1976, 37 (6): 2813-25. 10.1002/1097-0142(197606)37:6<2813::AID-CNCR2820370637>3.0.CO;2-S.View ArticlePubMedGoogle Scholar
- Hoppe RT: Radiation therapy in the management of Hodgkin's disease. Semin Oncol. 1990, 704-15. 6
- Mauch PM, Kalish LA, Kadin M, Coleman CN, Osteen R, Hellman S: Patterns of presentation of Hodgkin disease. Implications for etiology and pathogenesis. Cancer. 1993, 71 (6): 2062-71. 10.1002/1097-0142(19930315)71:6<2062::AID-CNCR2820710622>3.0.CO;2-0.View ArticlePubMedGoogle Scholar
- Schneider U, Stipper A, Besserer J: Dose-response relationship for lung cancer induction at radiotherapy dose. Z Med Phys. 2010, 20 (3): 206-14.View ArticlePubMedGoogle Scholar
- Mack TM, Cozen W, Shibata DK, Weiss LM, Nathwani BN, Hernandez AM, Taylor CR, Hamilton AS, Deapen DM, Rappaport EB: Concordance for Hodgkin's disease in identical twins suggesting genetic susceptibility to the young-adult form of the disease. N Engl J Med. 1995, 332 (7): 413-8. 10.1056/NEJM199502163320701.View ArticlePubMedGoogle Scholar
- Sasaki MS, Nomura T, Ejima Y, Utsumi H, Endo S, Saito I, Itoh T, Hoshi M: Experimental derivation of relative biological effectiveness of A-bomb neutrons in Hiroshima and Nagasaki and implications for risk assessment. Radiat Res. 2008, 170 (1): 101-17. 10.1667/RR1249.1.View ArticlePubMedGoogle Scholar
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