Michael H. Schild

Patient Specific Characteristics Are an Important Factor That Determines the Risk of Acute Grade ≥ 2 Rectal Toxicity in Patients Treated for Prostate Cancer with IMRT and Daily Image Guidance Based on Implanted Gold Markers

Aim To model acute rectal toxicity in Intensity Modulated Radiation Therapy (IMRT) for prostate cancer using dosimetry and patient specific characteristics. Methods A database of 79 prostate cancer patients treated with image guided IMRT was used to fit parameters of Lyman-Kutcher-Burman (LKB) and logistic regression Normal Tissue Complications Probability (NTCP) models to acute grade ≥ 2 rectal toxicities. We used a univariate regression model to find the dosimetric index which was most correlated with toxicity and a multivariate logistic regression model with machine learning algorithm to integrate dosimetry with patient specific characteristics. We used Receiver Operating Characteristics (ROC) analysis and the area under the ROC curve (AUC) to quantify the predictive power of models. Results Sixteen patients (20.3%) developed acute grade≥2 rectal toxicity. Our best estimate (95% confidence interval) of LKB model parameters for acute rectal toxicity are exponent n=0.13 (0.1–0.16), slope m=0.09 (0.08–0.11), and threshold dose TD50=56.8 (53.7–59.9) Gy. The best dosimetric indices in the univariate logistic regression NTCP model were D25% and V50Gy. The best AUC of dosimetry only modeling was 0.67 (0.54, 0.8). In the multivariate logistic regression two patient specific variables were particularly strongly correlated with acute rectal toxicity, the use of statin drugs and PSA level prior to IMRT, while two additional variables, age and diabetes were weakly correlated. The AUC of the logistic regression NTCP model improved to 0.88 (0.8, 0.96) when patient specific characteristics were included. In a group of 79 patients, 40 took Statins and 39 did not. Among patients who took statins, (4/40)=10% developed acute grade ≥2 rectal toxicity, compared to (12/39)=30.8% who did not take statins (p=0.03). The average and standard deviation of PSA distribution for patients with acute rectal toxicity was PSAtox = 5.77 ± 2.27 and it was PSAnotox = 9.5 ± 7.8 for the remainder (p=0.01). Conclusions Patient specific characteristics strongly influence the likelihood of acute grade ≥ 2 rectal toxicity in radiation therapy for prostate cancer.

Statins and Metformin Use Is Associated with Lower PSA Levels in Prostate Cancer Patients Presenting for Radiation Therapy

Background A possible association between the level of prostate specific antigen (PSA) and the use of some commonly prescribed medications has been reported in recent studies. Most of these studies were carried out in general populations of men who were screened for prostate cancer using the PSA test. We reported on the association between the initial PSA level and the use of statins, metformin and alpha-blockers in patients who were diagnosed with prostate cancer and presented for radiation therapy. Methods Three hundred and eighty one patients treated between the years of 2000-2005 and 2009-2012 were included in this retrospective study. The information about statin, metformin and alpha-blockers use was recorded immediately prior to treatment. Differences in PSA levels prior to treatment by medication status were estimated using univa-riate and multivariate linear regression on log PSA values. Results Compared with men who were not on these medications, the PSA level at presentation was 20% lower for statin users (p = 0.002) and 33% lower for metformin users (p = 0.004). We did not observe statistically significant associations between the use of statins or metformin and cancer stage, National Comprehensive Cancer Network (NCCN) risk score, or therapy outcome. A statistically significant association between the NCCN risk score and the use of alpha-blockers was observed (p = 0.002). Conclusions We found that statins and metformin were associated with lower PSA levels in prostate cancer patients to an extent that could influence management decisions. We found no statistically significant associations between the use of these medications and treatment outcomes.

Primary pulmonary classical Hodgkin lymphoma: a case report.

Primary pulmonary Hodgkin lymphoma (PPHL) is a rare entity. Most reported cases occurred before the availability of PET scan for accurate staging of the disease. We report a case of PPHL for which PET/CT scan was used and surgery was performed to confirm the diagnosis. A review of cases of PPHL since 1990 suggests that adjuvant chemotherapy and/or radiation therapy after surgical resection of the lung lesions achieve better disease control than surgical resection alone. J. Surg. Oncol. 2014 110:341–344.

Langerhan’s Cell Sarcoma: Two Case Reports

Langerhan’s cell sarcoma (LCS) is a rare neoplasm with a poor prognosis. To our knowledge, only sixty-six cases have been published. We discuss two patients who presented very differently with LCS, as well as a recently published review of all sixty-six cases. Our first case had a complicated history of metastatic, high-grade myxofibrosarcomas and presented with a single skin lesion of LCS which was treated with resection to a positive margin and adjuvant radiotherapy. The LCS recurred locoregionally and was again resected. The patient is alive two years after initial diagnosis. The second case presented with bone marrow and splenic involvement, leukocytosis, and thrombocytopenia. This patient had an excellent response to etoposide, prednisone, oncovorin, cyclophosphamide, and adriamycin, with normalization of the complete blood count, negative bone marrow biopsy at follow up, and splenectomy without viable neoplasm. This patient is alive without signs of disease at 16 months after initial diagnosis.

SU-D-204-03: Comparison of Patient Positioning Methods Through Modeling of Acute Rectal Toxicity in Intensity Modulated Radiation Therapy for Prostate Cancer. Does Quality of Data Matter More Than the Quantity?

PURPOSE To determine if differences in patient positioning methods have an impact on the incidence and modeling of grade >=2 acute rectal toxicity in prostate cancer patients who were treated with Intensity Modulated Radiation Therapy (IMRT). METHODS We compared two databases of patients treated with radiation therapy for prostate cancer: a database of 79 patients who were treated with 7 field IMRT and daily image guided positioning based on implanted gold markers (IGRTdb), and a database of 302 patients who were treated with 5 field IMRT and daily positioning using a trans-abdominal ultrasound system (USdb). Complete planning dosimetry was available for IGRTdb patients while limited planning dosimetry, recorded at the time of planning, was available for USdb patients. We fit Lyman-Kutcher-Burman (LKB) model to IGRTdb only, and Univariate Logistic Regression (ULR) NTCP model to both databases. We perform Receiver Operating Characteristics analysis to determine the predictive power of NTCP models. RESULTS The incidence of grade >= 2 acute rectal toxicity in IGRTdb was 20%, while the incidence in USdb was 54%. Fits of both LKB and ULR models yielded predictive NTCP models for IGRTdb patients with Area Under the Curve (AUC) in the 0.63 - 0.67 range. Extrapolation of the ULR model from IGRTdb to planning dosimetry in USdb predicts that the incidence of acute rectal toxicity in USdb should not exceed 40%. Fits of the ULR model to the USdb do not yield predictive NTCP models and their AUC is consistent with AUC = 0.5. CONCLUSION Accuracy of a patient positioning system affects clinically observed toxicity rates and the quality of NTCP models that can be derived from toxicity data. Poor correlation between planned and clinically delivered dosimetry may lead to erroneous or poorly performing NTCP models, even if the number of patients in a database is large.

SU‐E‐T‐803: Verification of QUANTEC Lyman Kutcher Burman (LKB) Model for Grade>=2(2+) Late Rectal Complication Rates Using a Database of 79 Prostate Patients Treated with IMRT

Purpose: QUANTEC review of best parameters for the LKB NTCP model of rectal complications is based exclusively on datasets obtained with 3D conformal techniques. The report suggests that inherent differences in rectal dose distributions obtained with IMRT techniques could require modification of the parameters which provide the best fit to clinical data. Methods: We compiled a database of 79 prostate patients who were treated with an IMRT technique to a dose of 77.4 Gy, 1.8Gy/fx, with an integrated boost to 81–83Gy in a sub-volume of a prostate which was identified on a pre-treatment MRI study. Rectal toxicities were graded according to CTCAE v4 by a physician who retrospectively reviewed patient’s medical records. Late grade 2+ toxicities were defined as toxicities occurring later than 90 days following the end of treatment. We defined the model in terms of parameters, m,n and TD_50, as recommended in the Quantec report. We converted the dose to 2Gy equivalent dose using linear-quadratic model with alpha over beta of 3Gy. We applied QUANTEC model to our data and compared results to our own fit of LKB model using two sample t-test. Results: Grade 2+ late rectal toxicity occurred in 4% (3/79 patients). The best fit of LKB model to data was obtained for n = 0.26, m = 0.25, and TD_50 = 81.53Gy. The two sample t-test yielded p value of 0.67. Average NTCP predicted by our parametrization is 3.8% while average NTCP predicted by QUANTEC parametrization is 3.5%. Conclusion: Predictions of late rectal toxicities in IMRT patients using LKB model with parameters from QUANTEC report match observed toxicity rates. Independent fit of LKB model to IMRT data produces somewhat different parameter set, but two parametrizations are equivalent within statistical uncertainties.

TH‐AB‐304‐02: Fitting Grade>=2(2+) Acute Rectal Complication Rates in Prostate Cancer Patients to Lyman Kutcher Burman (LKB) and Logistic Regression NTCP Models Using Dosimetry and Patient Specific Characteristics

Purpose: Models of rectal toxicity which include dosimetry only are known to have a relatively low predictive power, at least as measured by the area under the ROC curve (AUC). It has been suggested that the predictive power of models can be improved by including non-dosimetric patient specific characteristics. Methods: We compiled a database of 79 prostate patients who were treated with an IMRT technique to a dose of 77.4 Gy, 1.8Gy/fx, with an integrated boost to 81–83Gy in a sub-volume of a prostate which was identified on a pre-treatment MRI study. Acute grade 2+ rectal toxicities were graded according to CTCAE v4 by a physician who retrospectively reviewed patient’s medical records. We modified the LKB model to include one patient specific variable at a time, and we also used an NTCP model based on logistic regression to perform multi-variate analysis. We used patient specific variables available to us in a retrospective study: age, diabetes, hormonal treatment, Gleason Score, PSA, Statin use, prostate volume, boost volume and rectal volume. Results: Grade 2+ acute rectal toxicity occurred in 20% (16/79 patients). The LKB model with dosimetry alone gives AUC=0.65. Four variables, age, diabetes, PSA, Statin use, increase the AUC in LKB model to a maximum of 0.79. The same four variables in the logistic regression model increase AUC to 0.87. The most significant correlations are with PSA and with Statin use. Conclusion: Including patient specific variables in toxicity models can significantly increase apparent predictive power of a model. Somewhat surprising finding of a strong correlation between rectal toxicity and PSA in our dataset suggests that conclusions from each individual study should be treated with caution, until independently confirmed. Larger databases from prospective studies or meta-analysis of multiple studies may be needed to find patient characteristics that are truly predictive.