- Email: [email protected]
In Response to Dr. Hayes and Colleagues
Int. J. Radiation Oncology Biol. Phys., Vol. 79, No. 5, pp. 1598–1602, 2011 Copyright Ó 2011 Elsevier Inc. Printed in the USA. All rights reserved 0360-3016/$ – see front matter
LETTERS TO THE EDITOR 2. Spevack L, Killion LT, West JC Jr., et al. Predicting the patient at low risk for lymph node metastasis with localized prostate cancer: An analysis of four statistical models. Int J Radiat Oncol Biol Phys 1996;34:543–547. 3. Medica M, Giglio M, Germinale F, et al. Roach’s mathematical equations in predicting pathological stage in men with clinically localized prostate cancer. Tumori 2001;87:130–133. 4. Yu JB, Makarov DV, Gross C. A new formula for prostate cancer lymph node risk. Int J Radiat Oncol Biol Phys. 2010 Jun 30. [Epub ahead of print] 5. Heidenreich A, Varga Z, Von Knobloch R. Extended pelvic lymphadenectomy in patients undergoing radical prostatectomy: High incidence of lymph node metastasis. J Urol 2002;167:1681–1686. 6. Briganti A, Chun FK, Salonia A, et al. Critical assessment of ideal nodal yield at pelvic lymphadenectomy to accurately diagnose prostate cancer nodal metastasis in patients undergoing radical retropubic prostatectomy. Urology 2007;69:147–151. 7. Pagliarulo V, Hawes D, Brands FH, et al. Detection of occult lymph node metastases in locally advanced node-negative prostate cancer. J Clin Oncol 2006;24:2735–2742. 8. Shariat SF, Kattan MW, Erdamar S, et al. Detection of clinically significant, occult prostate cancer metastases in lymph nodes using a splice variant-specific rt-PCR assay for human glandular kallikrein. J Clin Oncol 2003;21:1223–1231. 9. Ferrari AC, Stone NN, Kurek R, et al. Molecular load of pathologically occult metastases in pelvic lymph nodes is an independent prognostic marker of biochemical failure after localized prostate cancer treatment. J Clin Oncol 2006;24:3081–3088.
PREDICTING THE RISK OF PELVIC NODE INVOLVEMENT IN MEN WITH PROSTATE CANCER IN THE CONTEMPORARY ERA: CHANGE YOU CAN BELIEVE?: IN REGARD TO YU, ET AL. (INT J RADIAT ONCOL BIOL PHYS IN PRESS) To the Editor: Nearly 20 years ago, the ‘‘Roach formulas’’ (RF) were derived when it was observed that the shape of the risk curves in the Partin nomogram could be described using a simple equation: LNI = 2/3 (PSA) + (GS – 6) (1), where LNI is lymph node involvement, PSA is prostate-specific antigen, and GS is Glasgow scale. It is straightforward, validated, and predictive of outcomes (2, 3). In Yu et al.’s attempt to improve our ability to predict the risk of LNI (4), they arbitrarily decided to leave out patients with clinical T3 disease and PSA values .26 ng/mL—even though this represents nearly half of the patients treated on Radiation Therapy Oncology Group 9413! Beyond this drastic exclusion, Yu et al. based their ‘‘new formula’’ on the wrong ‘‘gold standard.’’ Compelling data exist suggesting that at least 40% of involved nodes could be missed by conventional lymph node dissection (CLND) compared with an extended lymph node dissection (ELND) (5, 6). Nevertheless, Yu et al. examined the accuracy of the RF using the pathological findings from the SEER database, wrongly assuming that this data set of CLND could serve as a ‘‘gold standard’’ for estimating the risk of LNI. Briganti et al. have shown the significant limitations of CLND (6). In a receiver operating characteristic analysis, they reported that 28 nodes yielded a 90% ability to detect LNI; conversely #10 nodes yielded a detection level close to nil. In fairness, Yu et al. attempt to address this problem by only including patients with at least 10 nodes sampled. However, the study overwhelmingly represents limited node dissections, with 64% of men having #15 nodes sampled. Thus, their false-negative rate is apt to be substantial. Finally, by relying on standard cytopathologic evaluation, Yu et al. ignore the literature showing that more sophisticated approaches to lymph node evaluation—approaches using reverse transcriptase polymerase chain reaction, assays based on PSA n-RNA copy number, and special cytological evaluation of nodal tissue—find occult cancer in 13–30% of histologically negative nodes (7–9). Their ‘‘new formula’’ is already substantially less sensitive than the RF, 39% vs 74.6%, but given its reliance on standard cytopathologic evaluation, its true sensitivity is likely to be much lower. In conclusion, we challenge Yu et al. to acknowledge their study’s severe limitations as discussed here. Until then, we are sticking with an equation that is supported by outcomes, and not derived from a tarnished ‘‘gold standard’’ that is based on data obtained from patients who underwent inadequate lymph node dissections.
IN RESPONSE TO DR. HAYES AND COLLEAGUES To the Editor: We appreciate Hayes et al.’s interest in our work, and the opportunity to reply. The ‘‘Roach Formula’’ (RF) was derived empirically (1) from the original Partin nomogram, circa 1993 (2). Since then, three updates of the Partin nomograms have been published (3–5), necessitated by significant stage migration (6). As more patients present with early stage disease, the prediction of lymph node involvement requires a tool with more precision within this group. Therefore, although Hayes et al. express concern that we exclude cT3 disease and prostate-specific antigen (PSA) values .26, they highlight an important point: in the era of Radiation Therapy Oncology Group 9413 and the RF, these findings were more commonplace (5–6% of patients) (3, 6), whereas in the contemporary era, these findings are so rare that the most recent version of the Partin tables excludes these patients (3). Our approach is therefore consistent with that employed in these revised Partin tables. The greater precision of a predictive model that is obtained by focusing on patients without T3 or PSA .26 should be balanced, however, by ensuring that clinicians do not apply our formula to patients who would have been excluded from the sample. Two critical questions arise: should clinical decision-making tools be revisited, and what are the desired test characteristics of such tools? New approaches to lymph node detection are an important breakthrough; however, it emphasizes the need to recreate and revalidate clinical decision tools using the newest data available. Refining predictive models is constantly iterative, with the ideal model having a balance between sensitivity and specificity. Overly sensitive tools (at the cost of specificity) would cause the overtreatment of patients at low risk of lymph node involvement. It is this poor specificity that limits the RF (74.6% vs. 94.9% for RF vs. our formula for patients at 15% risk or higher). The central question is whether, using current data and evolving analytic techniques, we can improve on the foundation created by prior investigators. Our study, based on a large national database using rigorous statistical
MELLODY HAYES, B.A UCSF Medical Student San Francisco, CA E-mail: [email protected] MACK ROACH III, M.D., F.A.C.R. Department of Radiation Oncology UCSF - Helen Diller Family NCI-Designated Comprehensive Cancer San Francisco, CA Reprints are not available from the authors. Conflict of interest: The submitted article is a response to a critique of Mack Roach’s formula, and a conflict—if any—may be present in the form of academic competition. doi:10.1016/j.ijrobp.2010.11.008 1. Roach M 3rd, DeSilvio M, Lawton C, et al. Phase III trial comparing whole-pelvic versus prostate-only radiotherapy and neoadjuvant versus adjuvant combined androgen suppression: Radiation Therapy Oncology Group 9413. J Clin Oncol 2003;21:1904–1911. 1598
Letters to the Editor
1599
techniques, has greater external validity than an empirical formula based on prior work. We commend Nguyen et al. for their work (7). We welcome other investigators to compare and test the current formulas. Most important, we hope future investigators will create new approaches that are even more accurate—ensuring that we can offer patients the best possible tools to make decisions about their care. JAMES B. YU, M.D. Yale School of Medicine Department of Therapeutic Radiology New Haven, CT, Cancer Outcomes, Policy and Effectiveness Research (COPPER) Center at Yale New Haven, CT DANIL V. MAKAROV, M.D. New York University School of Medicine Department of Urology New York, NY New York University School of Public Health New York, NY CARY P. GROSS, M.D. Cancer Outcomes, Policy and Effectiveness Research (COPPER) Center at Yale New Haven, CT Yale School of Medicine Department of Internal Medicine New Haven, CT doi:10.1016/j.ijrobp.2010.11.009 1. Roach M 3rd. Predicting pathologic stage, nomograms, and clinical judgment: don’t miss the forest for the trees!. Int J Radiat Oncol Biol Phys 2009;73:325–326. 2. Partin AW, Yoo J, Carter HB, et al. The use of prostate specific antigen, clinical stage and Gleason score to predict pathological stage in men with localized prostate cancer. J Urol 1993;150:110–114. 3. Makarov DV, Trock BJ, Humphreys EB, et al. Updated nomogram to predict pathologic stage of prostate cancer given prostate-specific antigen level, clinical stage, and biopsy Gleason score (Partin tables) based on cases from 2000 to 2005. Urology 2007;69:1095–1101. 4. Partin AW, Kattan MW, Subong EN, et al. Combination of prostatespecific antigen, clinical stage, and Gleason score to predict pathological stage of localized prostate cancer. A multi-institutional update. JAMA 1997;277:1445–1451. 5. Partin AW, Mangold LA, Lamm DM, Walsh PC, Epstein JI, Pearson JD. Contemporary update of prostate cancer staging nomograms (Partin Tables) for the new millennium. Urology 2001;58:843–848. 6. Han M, Partin AW, Piantadosi S, Epstein JI, Walsh PC. Era specific biochemical recurrence-free survival following radical prostatectomy for clinically localized prostate cancer. J Urol 2001;166: 416–419. 7. Nguyen PL, Chen MH, Hoffman KE, Katz MS, D’Amico AV. Predicting the risk of pelvic node involvement among men with prostate cancer in the contemporary era. Int J Radiat Oncol Biol Phys 2009;74: 104–109.
STEREOTACTIC ABLATIVE RADIOTHERAPY IN THE FRAMEWORK OF CLASSICAL RADIOBIOLOGY: RESPONSE TO DRS. BROWN, DIEHN, AND LOO To the Editor: We read the recent commentary in the IJROBP by Brown, Diehn, and Loo with great interest (1). The authors discuss one of the fundamental tenets of radiobiology, hypoxia, and its impact on radioresistance, in the context of the increasing interest in stereotactic body radiotherapy (SBRT)/stereotactic ablative radiotherapy (SABR). The biology of SABR effects is an exciting and new area of study (2, 3). Brown et al. emphasize that classical contributors to radioresistance, in particular hypoxia, should not be forgotten. In contrast to previous experiences with hypoxic radiosensitizers, we are optimistic about the utility of their recommendation especially given that cumulative toxicity seen in historical experiences using protracted conventionally fractionated courses would not be an issue with a single fraction (4). We would like to add some points to the discussion. In vitro cell survival curves frequently used in linear quadratic and universal survival curve
Fig. 1. Biphasic in vivo cell survival curve, showing a hypoxia transition dose around 9 Gy. Reproduced, with permission, from (5).
modeling do not implicitly incorporate hypoxia. Actual in vivo cell tumor cell survival curves can reveal a relatively radioresistant portion of the curve representing the response of the hypoxic fraction of cells (Fig. 1) (5). The most ‘‘efficient’’ dose per fraction for a fractionated course would be to use the ‘‘hypoxia transition dose,’’ which likely varies widely for various tumors and interfraction intervals (allowing for reoxygenation) (6). Calling on the principle of reoxygenation, which makes the hypoxic proportion relatively constant between fractions, this dose could be repeated to yield efficient cell kill without the need for a hypoxic radiosensitizer (6). In the example, 9 Gy times four fractions yields in excess of 12 logs of cell kill (assuming reoxygenation). Of note, 9 Gy is well above standard fractionation sizes and would still require conformal and accurate dose delivery common to SABR treatments. Treating at the hypoxia transition dose (for ‘‘optimum’’ treatment fractionation) necessitates data about the hypoxic fraction preceding treatment fractions (to calculate appropriate treatment doses as well as to ensure adequate reoxygenation) (6). Multiple imaging technologies, such as blood oxygen level dependent 64Cu-ATSM: 64Cu-diacetyl-bis(N4methylthiosemicarbazone) magnetic resonance imaging and 64Cu-ATSM positron emission tomography imaging may shed light on the degree of hypoxia before treatment (7, 8). In ‘‘dose painting’’ techniques used to overcome hypoxia, the spatial distribution of hypoxia is important because extra dose is used to overcome hypoxic radioresistance (9). In the context of our discussion using the ‘‘efficient’’ transition dose via oligofractionation to overcome hypoxia, the spatial distribution is not particularly relevant so long as the hypoxic proportion is constant (or less) between fractions. JEFFREY MEYER, M.D. ROBERT TIMMERMAN, M.D. Department of Radiation Oncology UT-Southwestern Medical Center Dallas TX 75390-9183 doi:10.1016/j.ijrobp.2010.11.014 1. Brown JM, Diehn M, Loo BW Jr. Stereotactic ablative radiotherapy should be combined with a hypoxic cell radiosensitizer. Int J Radiat Oncol Biol Phys 2010;78:323–327.
LETTERS TO THE EDITOR 2. Spevack L, Killion LT, West JC Jr., et al. Predicting the patient at low risk for lymph node metastasis with localized prostate cancer: An analysis of four statistical models. Int J Radiat Oncol Biol Phys 1996;34:543–547. 3. Medica M, Giglio M, Germinale F, et al. Roach’s mathematical equations in predicting pathological stage in men with clinically localized prostate cancer. Tumori 2001;87:130–133. 4. Yu JB, Makarov DV, Gross C. A new formula for prostate cancer lymph node risk. Int J Radiat Oncol Biol Phys. 2010 Jun 30. [Epub ahead of print] 5. Heidenreich A, Varga Z, Von Knobloch R. Extended pelvic lymphadenectomy in patients undergoing radical prostatectomy: High incidence of lymph node metastasis. J Urol 2002;167:1681–1686. 6. Briganti A, Chun FK, Salonia A, et al. Critical assessment of ideal nodal yield at pelvic lymphadenectomy to accurately diagnose prostate cancer nodal metastasis in patients undergoing radical retropubic prostatectomy. Urology 2007;69:147–151. 7. Pagliarulo V, Hawes D, Brands FH, et al. Detection of occult lymph node metastases in locally advanced node-negative prostate cancer. J Clin Oncol 2006;24:2735–2742. 8. Shariat SF, Kattan MW, Erdamar S, et al. Detection of clinically significant, occult prostate cancer metastases in lymph nodes using a splice variant-specific rt-PCR assay for human glandular kallikrein. J Clin Oncol 2003;21:1223–1231. 9. Ferrari AC, Stone NN, Kurek R, et al. Molecular load of pathologically occult metastases in pelvic lymph nodes is an independent prognostic marker of biochemical failure after localized prostate cancer treatment. J Clin Oncol 2006;24:3081–3088.
PREDICTING THE RISK OF PELVIC NODE INVOLVEMENT IN MEN WITH PROSTATE CANCER IN THE CONTEMPORARY ERA: CHANGE YOU CAN BELIEVE?: IN REGARD TO YU, ET AL. (INT J RADIAT ONCOL BIOL PHYS IN PRESS) To the Editor: Nearly 20 years ago, the ‘‘Roach formulas’’ (RF) were derived when it was observed that the shape of the risk curves in the Partin nomogram could be described using a simple equation: LNI = 2/3 (PSA) + (GS – 6) (1), where LNI is lymph node involvement, PSA is prostate-specific antigen, and GS is Glasgow scale. It is straightforward, validated, and predictive of outcomes (2, 3). In Yu et al.’s attempt to improve our ability to predict the risk of LNI (4), they arbitrarily decided to leave out patients with clinical T3 disease and PSA values .26 ng/mL—even though this represents nearly half of the patients treated on Radiation Therapy Oncology Group 9413! Beyond this drastic exclusion, Yu et al. based their ‘‘new formula’’ on the wrong ‘‘gold standard.’’ Compelling data exist suggesting that at least 40% of involved nodes could be missed by conventional lymph node dissection (CLND) compared with an extended lymph node dissection (ELND) (5, 6). Nevertheless, Yu et al. examined the accuracy of the RF using the pathological findings from the SEER database, wrongly assuming that this data set of CLND could serve as a ‘‘gold standard’’ for estimating the risk of LNI. Briganti et al. have shown the significant limitations of CLND (6). In a receiver operating characteristic analysis, they reported that 28 nodes yielded a 90% ability to detect LNI; conversely #10 nodes yielded a detection level close to nil. In fairness, Yu et al. attempt to address this problem by only including patients with at least 10 nodes sampled. However, the study overwhelmingly represents limited node dissections, with 64% of men having #15 nodes sampled. Thus, their false-negative rate is apt to be substantial. Finally, by relying on standard cytopathologic evaluation, Yu et al. ignore the literature showing that more sophisticated approaches to lymph node evaluation—approaches using reverse transcriptase polymerase chain reaction, assays based on PSA n-RNA copy number, and special cytological evaluation of nodal tissue—find occult cancer in 13–30% of histologically negative nodes (7–9). Their ‘‘new formula’’ is already substantially less sensitive than the RF, 39% vs 74.6%, but given its reliance on standard cytopathologic evaluation, its true sensitivity is likely to be much lower. In conclusion, we challenge Yu et al. to acknowledge their study’s severe limitations as discussed here. Until then, we are sticking with an equation that is supported by outcomes, and not derived from a tarnished ‘‘gold standard’’ that is based on data obtained from patients who underwent inadequate lymph node dissections.
IN RESPONSE TO DR. HAYES AND COLLEAGUES To the Editor: We appreciate Hayes et al.’s interest in our work, and the opportunity to reply. The ‘‘Roach Formula’’ (RF) was derived empirically (1) from the original Partin nomogram, circa 1993 (2). Since then, three updates of the Partin nomograms have been published (3–5), necessitated by significant stage migration (6). As more patients present with early stage disease, the prediction of lymph node involvement requires a tool with more precision within this group. Therefore, although Hayes et al. express concern that we exclude cT3 disease and prostate-specific antigen (PSA) values .26, they highlight an important point: in the era of Radiation Therapy Oncology Group 9413 and the RF, these findings were more commonplace (5–6% of patients) (3, 6), whereas in the contemporary era, these findings are so rare that the most recent version of the Partin tables excludes these patients (3). Our approach is therefore consistent with that employed in these revised Partin tables. The greater precision of a predictive model that is obtained by focusing on patients without T3 or PSA .26 should be balanced, however, by ensuring that clinicians do not apply our formula to patients who would have been excluded from the sample. Two critical questions arise: should clinical decision-making tools be revisited, and what are the desired test characteristics of such tools? New approaches to lymph node detection are an important breakthrough; however, it emphasizes the need to recreate and revalidate clinical decision tools using the newest data available. Refining predictive models is constantly iterative, with the ideal model having a balance between sensitivity and specificity. Overly sensitive tools (at the cost of specificity) would cause the overtreatment of patients at low risk of lymph node involvement. It is this poor specificity that limits the RF (74.6% vs. 94.9% for RF vs. our formula for patients at 15% risk or higher). The central question is whether, using current data and evolving analytic techniques, we can improve on the foundation created by prior investigators. Our study, based on a large national database using rigorous statistical
MELLODY HAYES, B.A UCSF Medical Student San Francisco, CA E-mail: [email protected] MACK ROACH III, M.D., F.A.C.R. Department of Radiation Oncology UCSF - Helen Diller Family NCI-Designated Comprehensive Cancer San Francisco, CA Reprints are not available from the authors. Conflict of interest: The submitted article is a response to a critique of Mack Roach’s formula, and a conflict—if any—may be present in the form of academic competition. doi:10.1016/j.ijrobp.2010.11.008 1. Roach M 3rd, DeSilvio M, Lawton C, et al. Phase III trial comparing whole-pelvic versus prostate-only radiotherapy and neoadjuvant versus adjuvant combined androgen suppression: Radiation Therapy Oncology Group 9413. J Clin Oncol 2003;21:1904–1911. 1598
Letters to the Editor
1599
techniques, has greater external validity than an empirical formula based on prior work. We commend Nguyen et al. for their work (7). We welcome other investigators to compare and test the current formulas. Most important, we hope future investigators will create new approaches that are even more accurate—ensuring that we can offer patients the best possible tools to make decisions about their care. JAMES B. YU, M.D. Yale School of Medicine Department of Therapeutic Radiology New Haven, CT, Cancer Outcomes, Policy and Effectiveness Research (COPPER) Center at Yale New Haven, CT DANIL V. MAKAROV, M.D. New York University School of Medicine Department of Urology New York, NY New York University School of Public Health New York, NY CARY P. GROSS, M.D. Cancer Outcomes, Policy and Effectiveness Research (COPPER) Center at Yale New Haven, CT Yale School of Medicine Department of Internal Medicine New Haven, CT doi:10.1016/j.ijrobp.2010.11.009 1. Roach M 3rd. Predicting pathologic stage, nomograms, and clinical judgment: don’t miss the forest for the trees!. Int J Radiat Oncol Biol Phys 2009;73:325–326. 2. Partin AW, Yoo J, Carter HB, et al. The use of prostate specific antigen, clinical stage and Gleason score to predict pathological stage in men with localized prostate cancer. J Urol 1993;150:110–114. 3. Makarov DV, Trock BJ, Humphreys EB, et al. Updated nomogram to predict pathologic stage of prostate cancer given prostate-specific antigen level, clinical stage, and biopsy Gleason score (Partin tables) based on cases from 2000 to 2005. Urology 2007;69:1095–1101. 4. Partin AW, Kattan MW, Subong EN, et al. Combination of prostatespecific antigen, clinical stage, and Gleason score to predict pathological stage of localized prostate cancer. A multi-institutional update. JAMA 1997;277:1445–1451. 5. Partin AW, Mangold LA, Lamm DM, Walsh PC, Epstein JI, Pearson JD. Contemporary update of prostate cancer staging nomograms (Partin Tables) for the new millennium. Urology 2001;58:843–848. 6. Han M, Partin AW, Piantadosi S, Epstein JI, Walsh PC. Era specific biochemical recurrence-free survival following radical prostatectomy for clinically localized prostate cancer. J Urol 2001;166: 416–419. 7. Nguyen PL, Chen MH, Hoffman KE, Katz MS, D’Amico AV. Predicting the risk of pelvic node involvement among men with prostate cancer in the contemporary era. Int J Radiat Oncol Biol Phys 2009;74: 104–109.
STEREOTACTIC ABLATIVE RADIOTHERAPY IN THE FRAMEWORK OF CLASSICAL RADIOBIOLOGY: RESPONSE TO DRS. BROWN, DIEHN, AND LOO To the Editor: We read the recent commentary in the IJROBP by Brown, Diehn, and Loo with great interest (1). The authors discuss one of the fundamental tenets of radiobiology, hypoxia, and its impact on radioresistance, in the context of the increasing interest in stereotactic body radiotherapy (SBRT)/stereotactic ablative radiotherapy (SABR). The biology of SABR effects is an exciting and new area of study (2, 3). Brown et al. emphasize that classical contributors to radioresistance, in particular hypoxia, should not be forgotten. In contrast to previous experiences with hypoxic radiosensitizers, we are optimistic about the utility of their recommendation especially given that cumulative toxicity seen in historical experiences using protracted conventionally fractionated courses would not be an issue with a single fraction (4). We would like to add some points to the discussion. In vitro cell survival curves frequently used in linear quadratic and universal survival curve
Fig. 1. Biphasic in vivo cell survival curve, showing a hypoxia transition dose around 9 Gy. Reproduced, with permission, from (5).
modeling do not implicitly incorporate hypoxia. Actual in vivo cell tumor cell survival curves can reveal a relatively radioresistant portion of the curve representing the response of the hypoxic fraction of cells (Fig. 1) (5). The most ‘‘efficient’’ dose per fraction for a fractionated course would be to use the ‘‘hypoxia transition dose,’’ which likely varies widely for various tumors and interfraction intervals (allowing for reoxygenation) (6). Calling on the principle of reoxygenation, which makes the hypoxic proportion relatively constant between fractions, this dose could be repeated to yield efficient cell kill without the need for a hypoxic radiosensitizer (6). In the example, 9 Gy times four fractions yields in excess of 12 logs of cell kill (assuming reoxygenation). Of note, 9 Gy is well above standard fractionation sizes and would still require conformal and accurate dose delivery common to SABR treatments. Treating at the hypoxia transition dose (for ‘‘optimum’’ treatment fractionation) necessitates data about the hypoxic fraction preceding treatment fractions (to calculate appropriate treatment doses as well as to ensure adequate reoxygenation) (6). Multiple imaging technologies, such as blood oxygen level dependent 64Cu-ATSM: 64Cu-diacetyl-bis(N4methylthiosemicarbazone) magnetic resonance imaging and 64Cu-ATSM positron emission tomography imaging may shed light on the degree of hypoxia before treatment (7, 8). In ‘‘dose painting’’ techniques used to overcome hypoxia, the spatial distribution of hypoxia is important because extra dose is used to overcome hypoxic radioresistance (9). In the context of our discussion using the ‘‘efficient’’ transition dose via oligofractionation to overcome hypoxia, the spatial distribution is not particularly relevant so long as the hypoxic proportion is constant (or less) between fractions. JEFFREY MEYER, M.D. ROBERT TIMMERMAN, M.D. Department of Radiation Oncology UT-Southwestern Medical Center Dallas TX 75390-9183 doi:10.1016/j.ijrobp.2010.11.014 1. Brown JM, Diehn M, Loo BW Jr. Stereotactic ablative radiotherapy should be combined with a hypoxic cell radiosensitizer. Int J Radiat Oncol Biol Phys 2010;78:323–327.