# Radiotherapy and risks of tumor regrowth or inducing second cancer

- Emad Y. Moawad
^{1, 2}Email author

**2**:18

**DOI: **10.1007/s12645-011-0018-4

© Springer-Verlag 2011

**Received: **13 February 2011

**Accepted: **27 July 2011

**Published: **18 August 2011

## Abstract

Considerable research is aimed at determining the mechanism by which tumor cures, or regrows or second cancer develops, to be predictable and controllable. The wide range of doses, from low to very high, estimated statistically is responsible for such risks. A mathematical model is presented that describes both: the growth due to lower or over irradiated doses or the post therapy relapse of human cancer, and the shrinkage due to either of over irradiated doses, or appropriate irradiated doses. Simulations of the presented model showed that the initial tumor energy, administered dose energy, and their subsequent summation of tumor regrowth energy are always balanced with summation of Whole Body Cell Energy Burden during all treatment phases. Tumor regrows if its energy is higher than that of the dose, or if the increase of dose energy from that of the tumor is less than the one required to complete its shrinkage path. Patient-specific approaches that account for variations in tumor energies should enable more accurate dose estimates and, consequently, better protection against either lower or over irradiation that could lead to tumor regrowth and increase risks of second cancer.

### Keywords

Curing time Summation of tumor growth energy Whole Body Cell Energy Burden Lower irradiated dose treatment Over irradiated dose treatment## 1 Introduction

*A*

_{0}&

*G*

_{0}are the initial activities of each of administered doses and the tumor, respectively,

*Q*

_{ Iso }is the used isotope decay energy, t

_{1/2}&t

_{D}are the half-life time of the used radionuclide and the tumor doubling time, respectively, while E

_{Cell}&h% are the tumor cell growth energy in Emad and the percentage of the hypoxic cells, respectively (Gillies and Gatenby 2007; Sullivan and Graham 2007; Brown 1999). Knowing that a tumor of 1 g converted into 10

^{9}ng contains 10

^{9}cells, it would be more convenient to express the tumor cell growth energy by nanoscale as equivalent to the growth energy of a tumor of 1 ng or one nanoparticle investigating whether we can directly control matter on the molecular scale. Hereby, in all sections of the current approach the nanoparticle will be expressed by the cell itself (E

_{cell}= E

_{ng}), i.e.,

This relation enables us to test all the background of medical dosimetry experiments that, based on the statistical analysis as well as prior successful treatments, had been conducted in different schools of medicine. In an effort to assist in the understanding of recurrent cancer and the energy balance processes that mediate this disease, this approach provides a framework for using mathematical techniques to study novel therapeutic strategies aimed at controlling this disease and tries to relate the cancer therapeutic drugs course of phase I prior the treatment to tumor response of phases II and III.

## 2 Methods and materials

### 2.1 Mathematical model

_{0.Tumor}, initial drug energy E

_{0.Doses}, and, finally, amount of energy that the whole body disposed of by rate of radionuclide decay constant within the same duration, which is known by summation of Whole Body Cell Energy Burden ∑WBCEB, such that:

_{NBR}= 0.0000538132 Emad) (Moawad 2011). Thus, all body nanoparticles (cells) were involved in recovery burden, and then ∑WBCEB = the Whole Body Cell Energy Burden (WBCEB) gained due to radiotherapy × C

_{0}(the total number of the body nanoparticles (cells)). Negative or positive sign (∓) to cover all types of treatments with respect to dose energy, negative for either of the Over Irradiated Dose Treatment OIDT or treatments that follow work–energy principle WEPT (Moawad 2010), and positive for the Lower Irradiated Dose Treatment LIDT. The main features and assumptions of the mathematical model describing the response of the tumor to radiotherapy are as follows: the tumor is viewed as a densely packed, radially symmetric sphere. Cell movement is produced by the local volume changes that accompany cell proliferation and death. The spheroid expands or shrinks at a rate that depends on the balance between cell growth and division and cell death within the tumor volume (O’Donoghue 1997). Controlled tumors follow a growth curve by an exponential function of growth constant equivalent to ln2/

*t*

_{D}, where t

_{D}is the tumor doubling time; the initial tumor cell energy can be determined by Emad’s formula (Emad 2010):

*E*

_{ Tumor }along the WEPT is

_{NBR}is the curing duration. Then, in such a case, from Eq. (2.1.1), summation of Whole Body Cell Energy Burden is equivalent to the sum of energies of each of the dose and the tumor, i.e.,

_{0.Tumor}= E

_{0.Dose}in WEPT as shown in Eq. (2.1.3), then . Such curing time should be minimized as much as possible to reduce serious normal tissues toxicities (Schneider and Besserer 2010). Therefore, radionuclides with short half-lives offer advantages over those with longer lives; advantages over existing techniques include extremely low radiation dose because of the short half-life of the isotope ease. After passing

*n*radionuclide half-life times the WBCEB will be decreased to the

*E*

_{ NBR }. Accordingly, , and then curing time of the radiotherapy treatments is:

_{Tumor.Growth}must be known first, but it contributes to prove that the energy balances during radiotherapies for all types of tumor responses in accordance to the given experimental data. In addition, the physical quantity, ∑WBCEB, introduced in the presented mathematical model can be calculated according to the whole body measurement approach point of view, by considering that whole body cells gain energy after exposure to radiation, which leads to the increase of their growth energy exponentially by the growth constant of used radionuclide. Accordingly, if a healthy subject has been exposed to radiation dose for a certain duration (

*T*), then

_{0.Tumor}, and that of administered dose, E

_{LIDT}, tumor growth will be the resultant of the activated nuclear transmutations, as shown by Emad Moawad (2010); LIDT curve would be grown to a level of tumor energy equivalent to

_{Doses}is the difference between the initial tumor energy and that of insufficient dose administered in LIDT, i.e., . In addition, summation of tumor response growth energy after dose delivery would be: as postulated in Eq. (2.1.1). Then, from Eq. (2.1.12)

_{1/2.Shrinkage}> t

_{1/2.Isotope}, leading to faster shrinking than that of the WEPT as shown in Figs. 1 and 2, i.e.,

*T*in either WEPT or OIDT according to the following equation:

_{0.OIDT}≥ E

_{0.Tumor}. Hence, by trial and error method, assuming values of

*n*satisfies

### 2.2 Lower irradiated dose treatment

^{6}viable human prostate (DU145), breast (T47D), or colorectal cancer (LS174T) cells were implanted into nude mice in groups of ten mice each, and tumors were allowed to grow to (0.61 cm in diameter) 5 × 10

^{8}ng and treated with 16.7 MBq (450 μCi). 111In-oxine grew, on the average, only 17% irrespective of their type—breast, prostate, or colorectal, within 28 days after injection (Thakur and Ron Coss 2003), while those treated by 18.5 MBq (500 μCi) did not grow within the same duration as shown in Figs. 3 and 4.

_{0.Cell}, and its doubling time, t

_{D}, which is adequate for such a dose can be derived from the equations

_{o.Cell}= E

_{o.ng}= 4.385 Emad, corresponding to tumor doubling time, t

_{D}= 5384.51 s = 0.06 days, while the presented data shows that t

_{D}was equivalent to 28 days. This great difference in dose energy supply from that of the tumor allows tumor growth through the phenomenon of transmutation that permits transformation of elements in live organisms [9 s]. For growth calculations, Emad Moawad explained (2010) that those little doses were not sufficient. The growth energy of the untreated (controlled) tumor was 1.0091 J, while the decay energy of the insufficient dose from In-111 = 0.816 J only. Tumor growth energy (17%) + dose decay energy (insufficient dose) = 0.1717 + 0.8161 = 0.988 J, which achieves an accuracy of 98% of the growth energy of the untreated (control) tumor (1.0091 J). In addition, . At the same time, the regrowth energy, ΔE

_{Regrowth}, was , following a doubling time of 123.6156238 days. This indicates that the tumor regrowth energy due to LIDT is less than the difference between WEPT and LIDT dose energy. To check the hypothesis of the mathematical model: summation of tumor growth energy, , can be calculated along time of growth 28 days as presented in the experimental data as follows: from Eq. (2.1.1 & 2.1.14)

*n*that satisfies Eq. (2.1.18) is

*n*= 9.9 as it gives t

_{1/2.Shrinkage}equivalent to 2.83 days; this rate corresponds to the summation of tumor energy

### 2.3 Over irradiated dose treatment

^{11}ng) initial mass with the baseline response parameters. The t

_{D}of the tumor cells was taken as 4 days. This represents a central estimate of values measured by bromodeoxyuridine labeling in human tumors (Terry et al. 1995; Tsang et al. 1995; Bolger et al. 1996; Bourhis et al. 1996). The single, large administrations of LSA treatment consists of an administration of 8.25 GBq (223 mCi). A value of 3 days was used as an approximate value for the half-time of doomed cell loss (Ts). The time courses of tumor regression and recurrence for the treatment showed that the minimum tumor size reached was (7.2 × 10

^{8}ng) at 27.6 days for LSA. If remission duration is defined as the time to regrow to a tumor mass of (5 × 10

^{9}ng), then this was 53.2 days counted from the start of dose delivery (O’Donoghue et al. 2000); these experimental data are shown in Fig. 5.

_{D}= 4 days, from Eq. (2.1.13), the growth energy of tumor nanoparticle (cell), E

_{ng}, can be determined by Emad’s formula from Eq. (2.1.2) as follows:

*E*

_{0.Tumor}representing an OIDT, as from the point of view of this approach the initial decay energy of the administered dose was supposed to be 192.065 J, instead of 1284.837 J, and all such energy difference (1092.772 J) is considered an over irradiated dose, responsible for the consequent tumor regrowth. During this treatment, the initial tumor size shrunk, and the time courses of tumor regression and recurrence for the treatment showed that the minimum tumor size reached was 7.2 × 10

^{8}ng at 27.6 days. The corresponding half-life time of tumor decay was 3.87761 days. The first hypothesis of the OIDT mathematical model in which the accumulated difference of the tumor energy along OIDT from that of WEPT will be equivalent to the accumulated difference of energy of the administered doses in both treatments, i.e.,

*t*= 0 to

*t*= 27.6 days, the stage of the fast shrinkage, size of the over irradiated tumor decreased faster than the size of the one irradiated according to WEP, due to the over irradiated dose, the difference between its decay energy, and the decay energy administered by WEP released within this interval, Δ

*E*

_{ Doses }, where

*t*= 27.6 days to

*t*= 53.2 days, the time courses of the over irradiated treatment showed that the tumor size regrow from 7.2 × 10

^{8}ng and reached 5 × 10

^{9}ng. This shows that the regrowth doubling time was 9.1564 days. The WEP treatment showed that the tumor size decayed exponentially following the decay constant of the used radionuclide. Consequently, the time courses of both treatments showed that tumor energy would get the same energy in both treatments after their start by 43.419 days. At this balancing point, the accumulated differences between tumor energies along the same period [from start till balancing point] is , where

*t*= 0 to

*t*= 43.419 days, the difference between the decay energy of the over irradiated dose and that administered by the WEP that was released within this interval, ΔE

_{Doses}, is equivalent to the accumulated differences between tumor energies, , i.e., , along the same period [from start till balancing point] as previously postulated by Eq. (2.1.15). To check the second hypothesis of the OIDT mathematical model: summation of tumor growth energy, , can be calculated along the time of growth, 53.2 days, as shown by O’Donoghue et al. as follows: from Eqs. (2.1.1) and (2.1.19)

*n*that satisfies Eq. (2.17) is

*n*= 4.6 as it gives t

_{1/2.Shrinkage}equivalent to 3.877 days, which is also 100% identical to O’Donoghue et al.’s presentation (2000). As the goal of our model development is second cancer risk prevention, this approach, hereby suggests that WBCEB should be less than the Low Dose Radiation (LDR) effect that was settled by BEIR and that was shown by Emad Moawad to be equivalent to E

_{LDR}= 0.000538132 Emad or 12.503 MeV or 2.0030088 × 10

^{−12}J (Moawad 2011). In application 2.3-, ∑WBCEB has been increased from (2 × 192.065) × C

_{0}J in WEPT to in OIDT; this led to prolongation of the curing time shown in Eqs. (2.1.8) and (2.1.9) from 38.39 days in WEPT to 54.3 days in OIDT that could lead to serious normal tissue toxicities and contribute in increasing second cancer risks. Therefore, OIDT is also considered one cause of second cancer.

### 2.4 Estimating the WEPT from the tumor response through the LIDT or the OIDT

*nu*/

*nu*) mice, body weight , from their in-house nude mouse facility were injected with 10 × 10

^{6}SW1222 cells in the left thigh muscle. After 5–7 days, mice bearing tumors of 1.40–9.0 × 10

^{8}ng were selected. A total of 169 mice were divided into groups of 4–9 mice. Fourteen groups were administered varying amounts of mAb A33 labeled with 131I. The activities of 131I-A33 ranged from 0.925 to 18.5 MBq (0.025–0.5 mCi). Tumor size was measured bidimensionally with calipers, and the volume was calculated assuming elliptic geometry. Initial tumor sizes were between 0.14 and 0.90 cm

^{3}(mean, 0.44 cm

^{3}), i.e., initial tumor masses were between 1.4 and 9.0 × 10

^{8}ng, mean 4.4 × 10

^{8}ng. Mice with tumors of differing sizes were divided into groups such that the tumor size spectrum for each group was similar. The tumors were measured every 3 or 4 days for 100 days or until the death of the animal. Mice were killed when the tumor caused apparent discomfort in walking or when its volume exceeded 2 cm

^{3}, i.e., when tumor mass exceeded 2 × 10

^{9}ng. Observations showed that tumor growth was retarded after treatment to an extent that was dependent on the amount of activity administered. Barendswaard et al. showed that “tumors were considered cured if they failed to regrow over the period of observation (100 d after treatment), while occasional tumor cures were seen at intermediate administered activities of 131I (3.7–11.1 MBq), but a higher value (14.8 sMBq) did not produce any cures. Four of five tumors in this group became temporarily undetectable but subsequently recurred between day 40 and day 80. The highest activity of 131I administered (18.5 MBq) resulted in tumor cures in all four animals in that group.” (Barendswaard et al. 2001). Barendswaard et al. showed that the maximum tolerated activities of 131I were 18.5 MBq (0.5 mCi) in this model system. Activities of 18.5 MBq 131I caused petechiae, which became apparent after 2 days and confluent after 4 days; these activities also caused progressive weight loss. Median tumor volume, normalized to initial volume, as a function of time in nude mice bore SW1222 xenografts when treated and shown in Fig. 6 as presented by Barendswaard et al. (2001).

Checking the postulates of this approach:

^{8}ng, with a mean of 4.4 × 10

^{8}ng, as given by Barendswaard et al. (2001).

*n*that satisfies Eq. (2.1.18) is

*n*= 5.5 as it gives

*t*

_{1/2.Shrinkage}equivalent to 7.4 days. This rate causes the tumor energy to decrease from 1.2721 J to 0.028 J after 40 days as

*WBCEB*.

*n*that satisfies Eq. (2.1.18) is

*n*= 3 as it gives t

_{1/2.Shrinkage}equivalent to 6.72 days, which is also 97% identical to Barendswaard et al.’s presentation (6.5 days) (2001).

## 3 Results

Numerical simulations of Eqs. (2.1.1)–(2.1.19) are performed to investigate the tumor’s response to radiotherapy for various parameter values. The fit of the mathematical model to the experimental data [2.2-, 2.3-, 2.4-] is based on the tumor’s response to radiotherapy according to the balance of the dose released energy and the summation of tumor energy. During shrinkage stage, these energies are in equilibrium, and once balance is violated, tumor will be grown. During growing stage, summation of tumor growth energy
, results from the balance between initial tumor energy *E*_{0.Tumor}, initial drug energy *E*_{0.Doses}, and, finally, summation of Whole Body Cell Energy Burden ∑*WBCEB*, such that
. Negative or positive sign (∓) to cover all types of treatments with respect to dose energy: negative for the OIDT and positive for the LIDT. Despite OIDT may cure the primary tumor in certain cases as shown in 2.4- for the higher dose (18.5 MBq), it contributes in increasing WBCEB to levels higher than that tolerated. The best fit of the model to the experimental data allows for the estimation of the cure or the regrowth at both low and high radiation doses to be used for optimization of radiotherapy protocols.

### 3.1 Effects of changing model parameters

The stability of the conclusions of the modeling study was investigated by varying the radiobiologic and pharmacokinetic parameters associated with tumor response. The effects on tumor response of varying the radiobiologic parameters lower and over the initial tumor energy were covered. In all cases, cure responses were for WEPT and OIDT, which satisfied the model energy balances of Eq. (2.1.7), whereas remission responses were similar for all LIDT and OIDT that satisfied Eq. (2.1.1). This was done for both macroscopic and microscopic tumors. It should be noted that the tumor response model is applicable for all kinds of cancer radiotherapies. All variations of radiobiologic factors are explained in only four parameters (a) initial tumor energy, (b) initial dose energy, (c) summation of Whole Body Cell Energy Burden and (d) summation of tumor energy, which arises as a result of the unbalance between the sums of the first two parameters against the third one. The possibility of adaptation of shrinkage pathway is considered by changing the parameter of the drug released energy to maintain the equation of energy balances of this model. Notice that when Δ*E*_{
Doses
} → 0, which represents the amount by which the dose energy differs from that of WEPT, its resultant which is the difference in the summation of tumor energy of either OIDT or LIDT from that of WEPT
too, which is the optimal targeted cancer radiotherapy. Moreover, the model relates all types of tumor response to the difference between energies of OIDT or LIDT from that of WEPT, with the capability to predict the tumor pathway. This was done to keep the number of model parameters at a minimum, but at same time, this simulation shows that the model and so the tumor responses are completely controlled by energy balances. In addition, it is clear that there are specific times for which tumor response energy exceeds the difference of OIDT energy from that of WEPT, resulting in net growth. Further information regarding interval time of the tumor response, whether cure or regrowth, can be derived on the basis of the *n* half-life times needed for WBCEB to reach the NBR. During the regrowth period, the curve of tumor energy of OIDT surpasses that of WEPT resulting in a balance point at which the model predicts the level of tumor energy that can be reached above the curve of tumor energy of WEPT. The model also predicts that tumor relapse is associated with a decrease in released dose energy from the supposed quantity needed to allow the tumor to continue its shrinkage pathway.

## 4 Discussion

In such a case, tumor regrowth energy will vanish. Such an approach, unifying short- and long-term models, has some advantages over currently existing methods, as discussed in the previous articles (Moawad 2010; 2011). Reasons for tumor regrowth are either underestimation or overestimation of the administered dose. For underestimation, Emad Moawad showed that exposure to certain levels of radiation of energy less than that of the biological culture allows harmful nuclear transmutation in biological cultures, which contributes in their growth and, consequently, different kinds of cancerous tumors where growth energy gained is equivalent to energy gained of such elemental transmutations (Moawad 2011). While overestimation is the second reason for tumor regrowth or second cancer, it can be a reply for several questions like why might secondary rectal cancer rates be higher in prostate cancer patients who had conservative treatment (Harlan et al. 2001b). Rajendran et al. showed the statistical analysis to dose assessment by ignoring patient-specific factors and using standard models is responsible for a wide range of doses and, consequently, second cancer risks (Rajendran et al. 2004). Hence, significant differences are observed between the tumor response due to the physical approach and those obtained from the statistical standard models. This shows that ignoring patient-specific factors and tumor size that was handled by WEP and depending on statistical models, lead to either underestimation or overestimation of the true tumor energy of individual patients (Fisher 1994). Therefore, patient-specific approaches that account for variations in tumor sizes along with its growth doubling time should enable more accurate dose estimates and, consequently, better protection against lower or over irradiation that could lead to tumor growth or serious normal tissue toxicities and increasing the risks of second cancer.

## 5 Conclusions

Radiotherapy and its subsequent are an energy balance process; tumor regrows if its energy is higher than that of the dose, or if the increase of dose energy from that of the tumor is less than the required one to complete its shrinkage path. Patient-specific approaches that account for variations in tumor energies should enable more accurate dose estimates and, consequently, better protection against either lower or over irradiation that could lead to tumor regrowth and increase risks of second cancer.

## Declarations

### Conflict of interest

The author declares that there is no conflict of interest concerning this paper.

## Authors’ Affiliations

## References

- Balog J, Lucas D, DeSouza C et al (2005) Helical tomotherapy radiation leakage and shielding considerations. Med Phys 32:710–719View ArticleGoogle Scholar
- Barendswaard EC, Humm JL, O’Donoghue JA et al (2001) Relative therapeutic efficacy of
^{125}I- and^{131}I-labeled monoclonal antibody A33 in a human colon cancer xenograft. J Nucl Med 42:1251–1256, Abstract/Free Full TextGoogle Scholar - Bolger BS, Symonds RP, Stanton PD et al (1996) Prediction of radiotherapy response of cervical carcinoma through measurement of proliferation rate. Br J Cancer 74:1223–1226View ArticleGoogle Scholar
- Bourhis J, Dendale R, Hill C etal (1996) Potential doubling time and clinical out come in head and neck squamous cell carcinoma treated with 70 Gy in 7 weeks. In: J Radiat OncolBiol Phys 35:47l–476Google Scholar
- Brenner DJ, Curtis RE, Hall EJ, Ron E (2000) Second malignancies in prostate carcinoma patients after radiotherapy compared with surgery. Cancer 88:398–406View ArticleGoogle Scholar
- Brenner DJ, Shuryak I, Russo S, Sachs RK (2007) Reducing second breast cancers: a potential role for prophylactic mammary irradiation. J Clin Oncol 25:4868–4872View ArticleGoogle Scholar
- Brown JM (1999) The hypoxic cell: a target for selective cancer therapy—Eighteenth Bruce F. Cain Memorial Award lecture. Cancer Res 59:5863–5870Google Scholar
- Coselmon MM, Moran JM, Radawski JD et al (2005) Improving IMRT delivery efficiency using intensity limits during inverse planning. Med Phys 32:1234–1245View ArticleGoogle Scholar
- Crooks SM, McAven LF, Robinson DF et al (2002) Minimizing delivery time and monitor units in static IMRT by leaf-sequencing. Phys Med Biol 47:3105–3116View ArticleGoogle Scholar
- Fisher DR (1994) Radiation dosimetry for radioimmunotherapy: an overview of current capabilities and limitations. Cancer 73:905–911View ArticleGoogle Scholar
- Fu W, Dai J, Hu Y et al (2004) Delivery time comparison for intensity-modulated radiation therapy with/without flattening filter: a planning study. Phys Med Biol 49:1535–1547View ArticleGoogle Scholar
- Gillies RJ, Gatenby RA (2007) Hypoxia and adaptive landscapes in the evolution of carcinogenesis. Cancer Metastasis Rev. 26(2):311–317Google Scholar
- Gold DG, Neglia JP, Dusenbery KE (2003) Second neoplasms after megavoltage radiation for pediatric tumors. Cancer 97:2588–2596View ArticleGoogle Scholar
- Harlan LC, Potosky A, Gilliland FD et al (2001a) Factors associated with initial therapy for clinically localized prostate cancer: prostate cancer outcomes study. J Natl Cancer Inst 93:1864–1871View ArticleGoogle Scholar
- Harlan LC, Potosky A, Gilliland FD et al (2001b) Factors associated with initial therapy for clinically localized prostate cancer: prostate cancer outcomes study. J Natl Cancer Inst 93:1864–1871View ArticleGoogle Scholar
- Miralbell R, Lomax A, Cella L et al (2002) Potential reduction of the incidence of radiation-induced second cancers by using proton beams in the treatment of pediatric tumors. Int J Radiat Oncol Biol Phys 54:824–829View ArticleGoogle Scholar
- Moawad E (2010) Isolated system towards a successful radiotherapy treatment. NMMI 44:123–136View ArticleGoogle Scholar
- Moawad EY, Mechanism of nuclear transmutations in the biological culture. MPM-D.3 15:30 HPS 2011 Annual Meeting hpschapters.org/2011AM/program/singlesession.php3?sessid=MPM-D
- Mohan R, Arnfield M, Tong S et al (2000) The impact of fluctuations in intensity patterns on the number of monitor units and the quality and accuracy of intensity modulated radiotherapy. Med Phys 27:1226–1237View ArticleGoogle Scholar
- O’Donoghue JA (1997) The response of tumors with Gompertzian growth kinetics to fractionated radiotherapy. Int J Radiat Biol 72:325–339View ArticleGoogle Scholar
- O’Donoghue JA, Sgouros G, Divgi CR, Humm JL (2000) Single-dose versus fractionated radioimmunotherapy: model comparisons for uniform tumor dosimetry. J Nucl Med 41:538–547, Abstract/Free Full TextGoogle Scholar
- Rajendran JG, Fisher DR, Gopal AK, Durack LD, Press OW, Eary JF (2004) High-dose
^{131}I-tositumomab (anti-CD20) radioimmunotherapy for non-Hodgkin’s lymphoma: adjusting radiation absorbed dose to actual organ volumes. J Nucl Med 45:1059–1064, Abstract/Free Full TextGoogle Scholar - Ron E (2006) Childhood cancer—treatment at a cost. J Natl Cancer Inst 98:1510–1511View ArticleGoogle Scholar
- Sachs RK, Brenner DJ (2005) Solid tumor risks after high doses of ionizing radiation. Proc Natl Acad Sci USA 102:13040–13045View ArticleGoogle Scholar
- Schneider U, Besserer J (2010) Hypofractionated radiotherapy has the potential for second cancer reduction. Theor Biol Med Model 7:4View ArticleGoogle Scholar
- Stabin MG (1999) Internal dosimetry in the use of radiopharmaceuticals in therapy: science at a crossroads? Cancer Biother Radiopharm 14:81–89, MedlineView ArticleGoogle Scholar
- Sullivan R, Graham CH (2007) Hypoxia-driven selection of the metastatic phenotype. Cancer Metastasis Rev. 26(2):319–331Google Scholar
- Terry NH, Meistrich ML, Roubein LD, Lynch PM, Dubrow RA, Rich TA (1995) Cellular kinetics in rectal cancer. BrJ Cancer 72:435–441View ArticleGoogle Scholar
- Thakur ML, Ron Coss (2003) Role of lipid-soluble complexes in targeted tumor therapy. J Nucl Med 44:1293–1300Google Scholar
- Tsang RW, Fyles A W, Kirk bride P et al (1995) Proliferation measurements with flow cytometry Tpot in cancer of the uterine cervix: correlation between two laboratories and preliminary clinical results. In: J Radiat Oncol Biol Phys 32:1319–1329
- Tubiana M (2009) Can we reduce the incidence of second primary malignancies occurring after radiotherapy? A critical review. Radiother Oncol 91(1):4–15View ArticleGoogle Scholar