Prediction of gastrointestinal toxicity after external beam radiotherapy for localized prostate cancer

Assessment of rectal toxicities after radiation therapy for localized prostate cancer: experience of the Akanda Cancer Institute in Gabon

Background

The purpose was to evaluate the incidence of acute and late rectal toxicities and their correlation with the clinical and dosimetric parameters of patients who underwent curative radiotherapy for localized prostate cancer at the Akanda Cancer Institute, Gabon.

Materials and methods

Between 2013 and 2021, a cohort of 46 patients with clinically localized stage cT1c-T4 prostate cancer was treated with three-dimensional conformal radiation therapy (3D-CRT) at the national cancer institute with doses ranging from 66 to 80 Gy. Post-radiation gastrointestinal (GI) toxicities were classified and graded according to the Common Terminology Criteria for Adverse Events CTCAE v4.0.

Results

In our study, 17.4% (8/46) developed acute GI. Grades 1 and 3 acute GI complications were seen in 13.0% (6/46) and 4.3% (2/46), respectively. No patient developed acute grade 2 or grade higher than 3 complications. Late GI side effects were limited. The median time to the development of late GI Grade ≥ 1 toxicities was 12 months (range: 9-19 months). 10.9% (5/46) had experience late GI. Among them, grade 1 and 2 were seen in 6.5% (3/46), and 4.3% (2/46), respectively. There was no grade 3 or higher complications. Statistically, we did not find any correlation between the presence of rectal toxicity and clinical factors or the presence of comorbidity. On the dosimetric level, the Mann-Whitney statistical test found a correlation between the presence of late GI toxicity and rectal volume irradiated at the prescribed dose (p = 0.02).

Conclusion

Despite the high radiation doses involved, our results showed an acceptable complication rate.

Association between gut microbial change and acute gastrointestinal toxicity in patients with prostate cancer receiving definitive radiation therapy

BACKGROUND

This prospective study investigated the association between gut microbial changes and acute gastrointestinal toxicities in prostate cancer patients receiving definitive radiation therapy (RT).

METHODS

Seventy-nine fecal samples were analyzed. Stool samples were collected at the following timepoints: pre-RT (prRT), 2 weeks after the start of RT (RT-2w), 5 weeks after the start of RT (RT-5w), 1 month after completion of RT (poRT-1 m), and 3 months after completion of RT (poRT-3 m). We computed the microbial community polarization index (MCPI) as an indicator of RT-induced dysbiosis.

RESULTS

Patients experiencing toxicity had lower alpha diversity, especially at RT-2w (p = 0.037) and RT-5w (p = 0.003). Compared to patients without toxicity, the MCPI in those experiencing toxicities was significantly elevated (p = 0.019). In terms of predicted metabolic pathways, we found linearly decreasing pathways, including carbon fixation pathways in prokaryotes (p = 0.035) and the bacterial secretion system (p = 0.005), in patients who experienced toxicities.

CONCLUSIONS

We showed RT-induced dysbiosis among patients who experienced toxicities. Reduced diversity and elevated RT-related MCPI could be helpfully used for developing individualized RT approaches.

Predictive modelling for late rectal and urinary toxicities after prostate radiotherapy using planned and delivered dose

Background and purpose

Normal tissue complication probability (NTCP) parameters derived from traditional 3D plans may not be ideal in defining toxicity outcomes for modern radiotherapy techniques. This study aimed to derive parameters of the Lyman-Kutcher-Burman (LKB) NTCP model using prospectively scored clinical data for late gastrointestinal (GI) and genitourinary (GU) toxicities for high-risk prostate cancer patients treated using volumetric-modulated-arc-therapy (VMAT). Dose-volume-histograms (DVH) extracted from planned (D_P) and accumulated dose (D_A) were used.

Material and methods

D_P and D_A obtained from the DVH of 150 prostate cancer patients with pelvic-lymph-nodes irradiation treated using VMAT were used to generate LKB-NTCP parameters using maximum likelihood estimations. Defined GI and GU toxicities were recorded up to 3-years post RT follow-up. Model performance was measured using Hosmer-Lemeshow goodness of fit test and the mean area under the receiver operating characteristics curve (AUC). Bootstrapping method was used for internal validation.

Results

For mild-severe (Grade ≥1) GI toxicity, the model generated similar parameters based on D_A and D_P DVH data (D_A-D₅₀:71.6 Gy vs D_P-D₅₀:73.4; D_A-m:0.17 vs D_P-m:0.19 and D_A/P-n 0.04). The 95% CI for D_A-D₅₀ was narrower and achieved an AUC of >0.6. For moderate-severe (Grade ≥2) GI toxicity, D_A-D₅₀ parameter was higher and had a narrower 95% CI (D_A-D₅₀:77.9 Gy, 95% CI:76.4-79.6 Gy vs D_P-D₅₀:74.6, 95% CI:69.1-85.4 Gy) with good model performance (AUC>0.7). For Grade ≥1 late GU toxicity, D₅₀ and n parameters for D_A and D_P were similar (D_A-D₅₀: 58.8 Gy vs D_P-D₅₀: 59.5 Gy; D_A-n: 0.21 vs D_P-n: 0.19) with a low AUC of<0.6. For Grade ≥2 late GU toxicity, similar NTCP parameters were attained from D_A and D_P DVH data (D_A-D₅₀:81.7 Gy vs D_P-D₅₀:81.9 Gy; D_A-n:0.12 vs D_P-n:0.14) with an acceptable AUCs of >0.6.

Conclusions

The achieved NTCP parameters using modern RT techniques and accounting for organ motion differs from QUANTEC reported parameters. D_A-D₅₀ of 77.9 Gy for GI and D_A/D_P-D₅₀ of 81.7-81.9 Gy for GU demonstrated good predictability in determining the risk of Grade ≥2 toxicities especially for GI derived D₅₀ and are recommended to incorporate as part of the DV planning constraints to guide dose escalation strategies while minimising the risk of toxicity.

Dose-volume analysis of planned versus accumulated dose as a predictor for late gastrointestinal toxicity in men receiving radiotherapy for high-risk prostate cancer

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Interfractional variations in organs at risk were observed in prostate radiotherapy.

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Rectal accumulated dose was significantly higher at the intermediate-high dose region.

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Rectal planned dose was significantly higher at the very high dose region.

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Dose>78.2 Gy to 0.03 cc of rectum was predictive of late Grade 2 toxicity.

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Patient age>72 years was predictive of late Grade 2 rectal toxicity.

Background and purpose

Significant dose deviations have been reported between planned (D_P) and accumulated (D_A) dose in prostate radiotherapy. This study aimed to develop multivariate analysis (MVA) models associating Grade 1 and 2 gastrointestinal (GI) toxicity with clinical and D_P or D_A dosimetric variables separately.

Materials and methods

Dose volume (DV) metrics were compared between D_A and D_P for 150 high-risk prostate cancer patients. MV models were generated from significant clinical and dosimetric variables (p < 0.05) at univariate level. Dose-based-region of interest (DB-ROI) metrics were included. Model performance was measured, and additional subgroup analysis were performed.

Results

Rectal D_A demonstrated a higher intermediate-high dose (V_{30-65 Gy} and DB-ROI at 15–50 mm) compared to D_P. Conversely, at the very high dose region, rectal D_A (V_{75 Gy} and DB-ROI at 5–10 mm) were significantly lower. In MVA, rectal DB-ROI at 10 mm was predictive for Grade ≥ 1 GI toxicity for D_A and D_P. Age, rectal D_A for D_{0.03 cc}, and rectal D_P for DB-ROI 10 mm were predictors for Grade 2 GI toxicity. Subgroup analysis revealed that patients ≥ 72 years old and a rectal D_A of ≥ 78.2 Gy were highly predictive of Grade 2 GI toxicity.

Conclusions

The dosimetric impact of a higher dose rectal dose in D_A due to volumetric changes was minimal and was not predictive of detrimental clinical toxicity apart from rectal D_{0.03 cc} ≥ 78.2 Gy for Grade 2 GI toxicity. The use of the DB-ROI method can provide equivalent predictive power as the DV method in toxicity prediction.

Radiation-Induced Esophagitis in Non-Small-Cell Lung Cancer Patients: Voxel-Based Analysis and NTCP Modeling

Simple Summary

Radiation-induced esophagitis (RE) is a common dose-limiting complication associated with concurrent chemoradiation therapy for Non-Small-Cell Lung Cancer (NSCLC), and a wide range of esophageal dosimetric parameters have been described as predictive of RE. In this study, we characterize the risk of RE for NSCLC patients enrolled in a prospective trial comparing intensity-modulated RT versus passive scattering proton therapy for locally advanced NSCLC. Dose patterns associated with RE were analyzed by applying voxel-based analysis approaches, and predictive models for RE were finally investigated. Two predictive models for acute RE with good cross-validated predictive performances and discrimination capability were developed (thoracic esophageal model: ROC-AUC = 0.73; whole esophagus model: ROC-AUC = 0.70).

Abstract

The aim of our study is to characterize the risk of radiation-induced esophagitis (RE) in a cohort of Non-Small-Cell Lung Cancer (NSCLC) patients treated with concurrent chemotherapy and photon/proton therapy. For each patient, the RE was graded according to the CTCAE v.3. The esophageal dose-volume histograms (DVHs) were extracted. Voxel-based analyses (VBAs) were performed to assess the spatial patterns of the dose differences between patients with and without RE of grade ≥ 2. Two hierarchical NTCP models were developed by multivariable stepwise logistic regression based on non-dosimetric factors and on the DVH metrics for the whole esophagus and its anatomical subsites identified by the VBA. In the 173 analyzed patients, 76 (44%) developed RE of grade ≥ 2 at a median follow-up time of 31 days. The VBA identified regions of significant association between dose and RE in a region encompassing the thoracic esophagus. We developed two NTCP models, including the RT modality and a dosimetric factor: V_55Gy for the model related to the whole esophagus, and the mean dose for the model designed on the thoracic esophagus. The cross-validated performance showed good predictions for both models (ROC-AUC of 0.70 and 0.73, respectively). The only slight improvement provided by the analysis of the thoracic esophageal subsites might be due to the relevant sparing of cervical and lower thoracic esophagus in the analyzed cohort. Further studies on larger cohorts and a more heterogeneous set of dose distributions are needed to validate these preliminary findings and shed further light on the spatial patterns of RE development.

MRI-Based Radiotherapy Planning to Reduce Rectal Dose in Excess of Tolerance

Materials and Methods

This prospective single-arm study enrolled 15 men treated with IG-IMRT for localized prostate cancer. All participants received a dedicated 3 Tesla MRI examination of the prostate in addition to a pelvic CT examination for treatment planning. Two volumetric modulated arc therapy (VMAT) plans with a prescription dose of 79.2 Gy were designed using identical constraints based on CT- and MRI-defined consensus volumes. The volume of rectum exposed to 70 Gy or more was compared using the Wilcoxon paired signed rank test.

Results

For CT-based treatment plans, the median volume of rectum receiving 70 Gy or more was 9.3 cubic centimeters (cc) (IQR 7.0 to 10.2) compared with 4.9 cc (IQR 4.1 to 7.8) for MRI-based plans. This resulted in a median volume reduction of 2.1 cc (IQR 0.5 to 5.3, P < .001).

Conclusions

Using MRI to plan prostate IG-IMRT to a dose of 79.2 Gy reduces the volume of rectum receiving radiation dose in excess of tolerance (70 Gy or more) and should be considered in men who are at high risk for late rectal toxicity and are not good candidates for other rectal sparing techniques such as hydrogel spacer. This trial is registered with NCT02470910.

On the interplay between dosiomics and genomics in radiation-induced lymphopenia of lung cancer patients

PURPOSE

To investigate the interplay between spatial dose patterns and single nucleotide polymorphisms in the development of radiation-induced lymphopenia (RIL) in 186 non-small-cell lung cancer (NSCLC) patients undergoing chemo-radiotherapy (RT).

METHODS

This study included NSCLC patients enrolled in a randomized trial of protons vs. photons with available absolute lymphocyte counts at baseline and during RT and XRCC1-rs25487 genotyping data. After masking the GTV, planning CT scans and dose maps were spatially normalized to a common anatomical reference. A Voxel-Based Analysis (VBA) was performed to assess voxel-wise relationships of dosiomic and genomic explanatory variables with RIL. The underlying generalized linear model was designed to include both the explanatory variables (3D dose distributions and the XRCC1-rs25487 genotypes) and possible nuisance variables significantly correlated with RIL. The maps of model coefficients as well as their significance maps were generated.

RESULTS

Measures for RIL definition during RT were characterized, including kinetic parameters for lymphocyte loss. The VBA generated three-dimensional maps of correlation between RIL and dose in lymphoid organs as well as organs with abundant blood pools. The identified voxel-wise relationships account for XRCC1-rs25487 polymorphism and demonstrate the variant AA genotype being detrimental to lymphocyte depletion (p = 0.03).

CONCLUSION

The performed analyses blindly highlighted relevant anatomical regions that contributed most to lymphocyte depletion during RT and the interplay of the variant XRCC1-rs25487 AA genotype with the dose delivered to the primary lymphoid organs. These findings may help to guide the development of dosimetric RIL mitigation strategies for the application of effective individualized RT.

Normal tissue complication probability models for prospectively scored late rectal and urinary morbidity after proton therapy of prostate cancer

Background and purpose

Photons and protons have fundamentally different properties, i.e. protons have a reduced dose bath but a higher relative biological effectiveness. Photon-based normal tissue complication probability (NTCP) models may therefore not immediately be applicable to proton therapy (PT). The aim was to derive parameters of the Lyman-Kutcher-Burman (LKB) NTCP model using prospectively recorded late morbidity data from PT, focusing on rectal morbidity and prostate cancer.

Materials and methods

Prospectively collected data were available for 1151 prostate cancer patients treated with passive scattering PT and prescribed target doses of 78–82 Gy (RBE = 1.1) in 2 Gy fractions. Morbidity data (CTCAE v3.0) consisted of two alternative late grade 2 rectal bleeding endpoints: Medical Grade2A (GR2A) and procedural Grade2B (GR2B), as well as late grade 3 + urinary morbidity. GR2A + 2B were observed in 156/1047 patients (15%), GR2B in 45/1047 patients (4%), and urinary grade 3 + in 51/1151 patients (4%). LKB NTCP model parameters (D50, m, and n) were derived by maximum likelihood estimation.

Results

For the rectum/rectal wall the volume parameter n was low (0.07–0.14) for both GR2A + 2B and GR2B, as was the m parameter (range: 0.16–0.20). For the bladder/bladder wall both parameters were high (n-range: 0.20–0.36; m-range: 0.32–0.36). D50 parameters were higher for GR2B of the rectum/rectal wall (95.9–98.0 Gy) and bladder/bladder wall (118.1–119.9 Gy), but lower for GR2A2B (71.7–73.6 Gy).

Conclusion

PT specific LKB NTCP model parameters were derived from a population of more than 1000 patients. The D50 parameter differed for all structures and endpoints and deviated from typical photon-based LKB model values.

Radiation-Induced Dyspnea in Lung Cancer Patients Treated with Stereotactic Body Radiation Therapy

Simple Summary

Dyspnea is a common symptomatic side-effect of thoracic radiation therapy. The aim of this study is to build a predictive model of any-grade radiation-induced dyspnea within six months after stereotactic body radiation therapy in patients treated for non-small cell lung cancer. The occurrence of pre-treatment chronic obstructive pulmonary disease and higher relative lungs volume receiving more than 15 Gy as well as heart volume were shown to be risk factors for dyspnea. The obtained results encourage further studies on the topic, which could validate the present organ-based findings and explore the voxel-based landscape of radiation dose sensitivity in the development of dyspnea.

Abstract

In this study, we investigated the prognostic factors for radiation-induced dyspnea after hypo-fractionated radiation therapy (RT) in 106 patients treated with Stereotactic Body RT for Non-Small-Cell Lung Cancer (NSCLC). The median prescription dose was 50 Gy (range: 40–54 Gy), delivered in a median of four fractions (range: 3–12). Dyspnea within six months after SBRT was scored according to CTCAE v.4.0. Biologically Effective Dose (α/β = 3 Gy) volume histograms for lungs and heart were extracted. Dosimetric parameters along with patient-specific and treatment-related factors were analyzed, multivariable logistic regression method with Leave-One-Out (LOO) internal validation applied. Model performance was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC) and calibration plot parameters. Fifty-seven patients (53.8%) out of 106 developed dyspnea of any grade after SBRT (25/57 grade ≥ 2 cases). A three-variable predictive model including patient comorbidity (COPD), heart volume and the relative lungs volume receiving more than 15 Gy was selected. The model displays an encouraging performance given by a training ROC-AUC = 0.71 [95%CI 0.61–0.80] and a LOO-ROC-AUC = 0.64 [95%CI 0.53–0.74]. Further modeling efforts are needed for dyspnea prediction in hypo-fractionated treatments in order to identify patients at high risk for developing lung toxicity more accurately.

Dose Calculation Algorithms for External Radiation Therapy: An Overview for Practitioners

Radiation therapy (RT) is a constantly evolving therapeutic technique; improvements are continuously being introduced for both methodological and practical aspects. Among the features that have undergone a huge evolution in recent decades, dose calculation algorithms are still rapidly changing. This process is propelled by the awareness that the agreement between the delivered and calculated doses is of paramount relevance in RT, since it could largely affect clinical outcomes. The aim of this work is to provide an overall picture of the main dose calculation algorithms currently used in RT, summarizing their underlying physical models and mathematical bases, and highlighting their strengths and weaknesses, referring to the most recent studies on algorithm comparisons. This handy guide is meant to provide a clear and concise overview of the topic, which will prove useful in helping clinical medical physicists to perform their responsibilities more effectively and efficiently, increasing patient benefits and improving the overall quality of the management of radiation treatment.

Sample sizes of prediction model studies in prostate cancer were rarely justified and often insufficient

OBJECTIVE

Developing clinical prediction models (CPMs) on data of sufficient sample size is critical to help minimize overfitting. Using prostate cancer as a clinical exemplar, we aimed to investigate to what extent existing CPMs adhere to recent formal sample size criteria, or historic rules of thumb of events per predictor parameter (EPP)≥10.

STUDY DESIGN AND SETTING

A systematic review to identify CPMs related to prostate cancer, which provided enough information to calculate minimum sample size. We compared the reported sample size of each CPM against the traditional 10 EPP rule of thumb and formal sample size criteria.

RESULTS

About 211 CPMs were included. Three of the studies justified the sample size used, mostly using EPP rules of thumb. Overall, 69% of the CPMs were derived on sample sizes that surpassed the traditional EPP≥10 rule of thumb, but only 48% surpassed recent formal sample size criteria. For most CPMs, the required sample size based on formal criteria was higher than the sample sizes to surpass 10 EPP.

CONCLUSION

Few of the CPMs included in this study justified their sample size, with most justifications being based on EPP. This study shows that, in real-world data sets, adhering to the classic EPP rules of thumb is insufficient to adhere to recent formal sample size criteria.

External Validation of a Predictive Model for Acute Skin Radiation Toxicity in the REQUITE Breast Cohort

Background: Acute skin toxicity is a common and usually transient side-effect of breast radiotherapy although, if sufficiently severe, it can affect breast cosmesis, aftercare costs and the patient's quality-of-life. The aim of this study was to develop predictive models for acute skin toxicity using published risk factors and externally validate the models in patients recruited into the prospective multi-center REQUITE (validating pREdictive models and biomarkers of radiotherapy toxicity to reduce side-effects and improve QUalITy of lifE in cancer survivors) study.

Methods: Patient and treatment-related risk factors significantly associated with acute breast radiation toxicity on multivariate analysis were identified in the literature. These predictors were used to develop risk models for acute erythema and acute desquamation (skin loss) in three Radiogenomics Consortium cohorts of patients treated by breast-conserving surgery and whole breast external beam radiotherapy (n = 2,031). The models were externally validated in the REQUITE breast cancer cohort (n = 2,057).

Results: The final risk model for acute erythema included BMI, breast size, hypo-fractionation, boost, tamoxifen use and smoking status. This model was validated in REQUITE with moderate discrimination (AUC 0.65), calibration and agreement between predicted and observed toxicity (Brier score 0.17). The risk model for acute desquamation, excluding the predictor tamoxifen use, failed to validate in the REQUITE cohort.

Conclusions: While most published prediction research in the field has focused on model development, this study reports successful external validation of a predictive model using clinical risk factors for acute erythema following radiotherapy after breast-conserving surgery. This model retained discriminatory power but will benefit from further re-calibration. A similar model to predict acute desquamation failed to validate in the REQUITE cohort. Future improvements and more accurate predictions are expected through the addition of genetic markers and application of other modeling and machine learning techniques.

Multi-view radiomics and dosiomics analysis with machine learning for predicting acute-phase weight loss in lung cancer patients treated with radiotherapy

We propose a multi-view data analysis approach using radiomics and dosiomics (R&D) texture features for predicting acute-phase weight loss (WL) in lung cancer radiotherapy. Baseline weight of 388 patients who underwent intensity modulated radiation therapy (IMRT) was measured between one month prior to and one week after the start of IMRT. Weight change between one week and two months after the commencement of IMRT was analyzed, and dichotomized at 5% WL. Each patient had a planning CT and contours of gross tumor volume (GTV) and esophagus (ESO). A total of 355 features including clinical parameter (CP), GTV and ESO (GTV&ESO) dose-volume histogram (DVH), GTV radiomics, and GTV&ESO dosiomics features were extracted. R&D features were categorized as first- (L1), second- (L2), higher-order (L3) statistics, and three combined groups, L1 + L2, L2 + L3 and L1 + L2 + L3. Multi-view texture analysis was performed to identify optimal R&D input features. In the training set (194 earlier patients), feature selection was performed using Boruta algorithm followed by collinearity removal based on variance inflation factor. Machine-learning models were developed using Laplacian kernel support vector machine (lpSVM), deep neural network (DNN) and their averaged ensemble classifiers. Prediction performance was tested on an independent test set (194 more recent patients), and compared among seven different input conditions: CP-only, DVH-only, R&D-only, DVH + CP, R&D + CP, R&D + DVH and R&D + DVH + CP. Combined GTV L1 + L2 + L3 radiomics and GTV&ESO L3 dosiomics were identified as optimal input features, which achieved the best performance with an ensemble classifier (AUC = 0.710), having statistically significantly higher predictability compared with DVH and/or CP features (p < 0.05). When this performance was compared to that with full R&D-only features which reflect traditional single-view data, there was a statistically significant difference (p < 0.05). Using optimized multi-view R&D input features is beneficial for predicting early WL in lung cancer radiotherapy, leading to improved performance compared to using conventional DVH and/or CP features.

Normal tissue complication probability (NTCP) models for modern radiation therapy

Mathematical models of normal tissue complication probability (NTCP) able to robustly predict radiation-induced morbidities (RIM) play an essential role in the identification of a personalized optimal plan, and represent the key to maximizing the benefits of technological advances in radiation therapy (RT). Most modern RT techniques pose, however, new challenges in estimating the risk of RIM. The aim of this report is to schematically review NTCP models in the framework of advanced radiation therapy techniques. Issues relevant to hypofractionated stereotactic body RT and ion beam therapy are critically reviewed. Reirradiation scenarios for new or recurrent malignances and NTCP are also illustrated. A new phenomenological approach to predict RIM is suggested.

A new formalism of Dose Surface Histograms for robust modeling of skin toxicity in radiation therapy

PURPOSE

To present a new formalism for a robust computation of Dose-Surface Histograms (DSHs) to be exploited in the analysis of surface effects in radiation induced toxicity phenomena.

METHODS

A new formal recipe for the DSH extraction is described. It is based on the computation of the Dose-Volume Histogram (DVH) on a 3D structure in the limit of vanishing thickness to approach the two-dimensional organ manifold. The theory is customized for the application to skin description.

RESULTS

The derived formalism resulted in a redefinition of the generalized equivalent uniform dose (gEUD) and, accordingly, in an extension of the scope of the classical Lyman-Kutcher-Burman (LKB) Normal Tissue Complication Probability (NTCP) to a DSH-based toxicity modeling.

CONCLUSIONS

Our approach properly fits the intrinsic 3D nature of the DSH computation issue, and guarantees the rotational invariance and the robustness of the results. The proposed formalism can be easily implemented in treatment planning systems for dose optimization and potentially paves the way to a consistent analysis of radiation-induced morbidity endpoints related to surface effects in hollow organs.

Complementary Relation Between the Improvement of Dose Delivery Technique and PTV Margin Reduction in Dose-Escalated Radiation Therapy for Prostate Cancer

PURPOSE

The purpose of this study is to demonstrate quantitatively the complementary relationship between the introduction of intensity modulated radiation therapy (IMRT) and planning target volume (PTV) margin reduction with an image guided technique in reducing the risk of rectal toxicity in dose-escalating prostate radiation therapy.

METHODS AND MATERIALS

Three-dimensional conformal radiation therapy (CRT) and IMRT plans were generated for 10 patients with prostate cancer based on 2 PTV margin protocols (10/8 mm and 6/5 mm) and 2 dose prescriptions (70 Gy and 78 Gy). The normal tissue complication probability (NTCP) for each of the 8 scenarios was calculated using the Lyman-Kutcher-Burman model to estimate the risk of rectal and bladder late toxicity. The conformity and homogeneity indices of PTVs were calculated for each plan.

RESULTS

The IMRT plans showed superiority in conformity and inferiority in homogeneity over 3-dimensional CRT plans. The rectal NTCPs were increased 3.5 to 4.1 times when the prescribed total dose was changed from 70 Gy to 78 Gy and the dose delivery and the image guided radiation therapy techniques remained unchanged. PTV margin reduction was shown to reduce the value of rectal NTCP significantly. Overall, implementing the IMRT technique alone could reduce the NTCP values only by 2.1% to 7.3% from those of 3-dimensional CRT. The introduction of both IMRT and PTV margin reduction was found to be necessary for rectal NTCP to remain <5% in the dose escalation from 70 to 78 Gy.

CONCLUSIONS

The complementary relationship between the introduction of IMRT and PTV margin reduction was proven. We found that both approaches need to be implemented to safely deliver a curative dose in dose-escalating prostate radiation therapy.

Texture analysis of 3D dose distributions for predictive modelling of toxicity rates in radiotherapy

BACKGROUND AND PURPOSE

To explore the use of texture analysis (TA) features of patients' 3D dose distributions to improve prediction modelling of treatment complication rates in prostate cancer radiotherapy.

MATERIAL AND METHODS

Late toxicity scores, dose distributions, and non-treatment related (NTR) predictors for late toxicity, such as age and baseline symptoms, of 351 patients of the hypofractionation arm of the HYPRO randomized trial were used in this study. Apart from DVH parameters, also TA features of rectum and bladder 3D dose distributions were used for predictive modelling of gastrointestinal (GI) and genitourinary (GU) toxicities. Logistic Normal Tissue Complication Probability (NTCP) models were derived, using only NTR parameters, NTR + DVH, NTR + TA, and NTR + DVH + TA.

RESULTS

For rectal bleeding, the area under the curve (AUC) for using only NTR parameters was 0.58, which increased to 0.68, and 0.73, when adding DVH or TA parameters respectively. For faecal incontinence, the AUC went up from 0.63 (NTR only), to 0.68 (+DVH) and 0.73 (+TA). For nocturia, adding TA features resulted in an AUC increase from 0.64 to 0.66, while no improvement was seen when including DVH parameters in the modelling. For urinary incontinence, the AUC improved from 0.68 to 0.71 (+DVH) and 0.73 (+TA). For GI, model improvements resulting from adding TA parameters to NTR instead of DVH were statistically significant (p < 0.04).

CONCLUSION

Inclusion of 3D dosimetric texture analysis features in predictive modelling of GI and GU toxicity rates in prostate cancer radiotherapy improved prediction performance, which was statistically significant for GI.

Data Analysis in Radiotherapy Treatments

Radiotherapy is one of the main cancer treatments available today, together with chemotherapy and surgery. Radiotherapy treatments have to be planned for each patient in an individualized manner. The knowledge acquired from one single treatment can be used to improve the treatment planning and outcome of several other patients. In the last years, attention has been drawn to the added value of using data analysis for radiotherapy treatment planning, prediction of treatment outcomes, survival analysis and quality assurance. In this article, existing literature is reviewed.

Predicting toxicity in radiotherapy for prostate cancer

This comprehensive review addresses most organs at risk involved in planning optimization for prostate cancer. It can be considered an update of a previous educational review that was published in 2009 (Fiorino et al., 2009). The literature was reviewed based on PubMed and MEDLINE database searches (from January 2009 up to September 2015), including papers in press; for each section/subsection, key title words were used and possibly combined with other more general key-words (such as radiotherapy, dose-volume effects, NTCP, DVH, and predictive model). Publications generally dealing with toxicity without any association with dose-volume effects or correlations with clinical risk factors were disregarded, being outside the aim of the review. A focus was on external beam radiotherapy, including post-prostatectomy, with conventional fractionation or moderate hypofractionation (<4Gy/fraction); extreme hypofractionation is the topic of another paper in this special issue. Gastrointestinal and urinary toxicity are the most investigated endpoints, with quantitative data published in the last 5years suggesting both a dose-response relationship and the existence of a number of clinical/patient related risk factors acting as dose-response modifiers. Some results on erectile dysfunction, bowel toxicity and hematological toxicity are also presented.

Intra-fraction motion of the prostate is not increased by patient couch shifts

BACKGROUND

During a fraction of external beam radiotherapy for prostate cancer, a mismatch between target volume and dose coverage may accumulate over time due to intra-fraction motion. One way to remove the residual error is to perform a couch shift in opposite direction. In principle, such couch shifts could cause secondary displacements of the patient and prostate. Hence it is interesting to investigate if couch shifts might amplify intra-fraction motion.

FINDINGS

Intra-fraction motion of the prostate and patient couch position were simultaneously recorded during 359 fractions in 15 patients. During this time, a total of 22 couch shifts of up to 31.5 mm along different axes were recorded. Prostate position and couch position were plotted before, during and after each couch shift. There was no visible impact of couch shifts on prostate motion. The standard deviation of prostate position was calculated before, during and after each couch shift. The standard deviation did not significantly increase during couch shifts (by 3 % on average, p = 0.88) and even slightly decreased after a couch shift (by 37 % on average; p = 0.02).

CONCLUSIONS

Shifts of the patient couch did not adversely affect the motion of the prostate relative to the patient couch. Hence, shifts of the patient couch may be a viable way to correct the position of the prostate relative to the dose distribution.