Image-guided radiation therapy (IGRT) in prostate cancer in México, survey of SOMERA (Sociedad Mexicana de Radioterapeutas/Mexican Society of Radiation Oncologists) with recommendations on its implementation and process.

Background: Prostate cancer is one of the main tumors worldwide, its treatment is multidisciplinary, includes radiotherapy in all stages: curative, radical, adjuvant, salvage and palliative. Technological advances in planning systems, image acquisition and treatment equipment have allowed the delivery of higher doses limiting toxicity in healthy tissues, distributing radiation optimally and ensuring reproducibility of conditions. Image-guided radiotherapy (IGRT) is not standard in guidelines, only recommended with heterogeneity in its own process. Materials and methods: A survey was conducted to members of the Mexican Society of Radiation Oncologists (SOMERA), to know the current status and make recommendations about its implementation and use, taking into account existing resources. Results: Responses of 541 patients were evaluated, 85% belonged to the intermediate-high risk group, 65% received adjuvant or salvage radiotherapy (RT), 80% received intensity-modulated radiation therapy (IMRT) using doses up to 80 Gy/2 Gy. Cone beam computed tomography (CBCT) was performed on 506 (93.5%), (100% IMRT) and 90% at a periodicity of 3-5/week. 3D treatment with 42% portal images 1/week. Online correction strategies (36% changes before treatment), following a diet and bladder and rectal control. Evidence and recommendations are reviewed. Conclusions: IGRT should be performed in patients with prostate cancer. In Mexico, despite limitations in the distribution of human and technological resources, it is routinely applied. More information is still needed on clinical evidence of its benefits and the process should be implemented according to infrastructure, following institutional guidelines, recommending to report the initial experience that helps to standardize national conduct.

Association between acute histopathological changes of rectal walls and late radiation proctitis following radiotherapy for prostate cancer.

PURPOSE: The impact of acute histopathological changes (HC) of the rectum on development of late clinical proctitis (LCP) after external radiotherapy (RT) for prostate cancer is poorly explored and was the primary end point of this prospective study. METHODS: In 70 patients, 15 HC of early rectal biopsies after RT were identified, whereby RT was conventional 2D RT in 41 cases and conformational 3D RT in 29. Associations of HC in anterior and posterior rectal walls (ARW, PRW) with LCP, acute endoscopic (AEP) and acute clinical proctitis (ACP) were statistically evaluated considering as explicative variables the patient general characteristics and the HC. RESULTS: The mean patients' follow-up was 123.5 months (24-209). The median prostatic dose was 72 Gy (2 Gy/fraction). For the 41 and 29 patients the ARW and PRW doses were 64 and 49 Gy vs. 63 and 50 Gy, respectively. The incidence of LCP ≥ grade 2 at 10 years was 12.9%. The univariate (p = 0.02) and Kaplan-Meyer methods (p = 0.007) showed that the gland (or crypts) loss in the ARW was significantly associated with LCP. AEP and ACP occurred in 14.3 and 55.7% of cases. At multivariate level AEP significantly correlated with hemorrhoids (p = 0.014) and neutrophilia in ARW (p = 0.042). CONCLUSIONS: Early after RT, substantial gland loss in ARW is predictive of LCP. To reduce this complication with conventional fractionation, we suggest keeping the mean dose to ARW ≤48-52 Gy.

Three-dimensional ultrasound-based image-guided hypofractionated radiotherapy for intermediate-risk prostate cancer: results of a consecutive case series.

External beam radiotherapy (EBRT) is a standard of care in the treatment of prostate cancer. Hypofractionation is a valid option either radiobiologically and logistically in this context. Image-guidance procedures are strongly needed to provide ballistic precision to radiation delivery. The Clarity platform allows for the acquisition of three-dimensional ultrasound scans (3D-US) to perform image-guided radiotherapy. We treated a consecutive series of intermediate-risk prostate cancer patients (according to NCCN stratification) with a hypofractionated schedule (70.2 Gy/26 fractions at 2.7 Gy/daily to the prostate gland excluding the seminal vesicles at 62.1 Gy) under 3D-US guidance with the Clarity platform. The 3-year biochemical-relapse-free survival, distant-metastases-free, cancer-specific and overall survival were 98.6% (CI: 91.1-99.6%), 98.6% (CI: 91.1-99.6%), 97.5% (CI: 94.5-99.1%), and 94.3% (CI: 90.4-96.7%), respectively. Maximum detected acute GU toxicity was G0 in 22 patients (29.7%), G1 in 30 (22.7%), G2 in 19 (25.6%), G3 in 3 (4%). Maximum detected acute GI toxicity at the end of EBRT was G0 in 42 patients (56.8%), G1 in 22 (29.7%), G2 in 9 (12.1%), G3 in 1 (1.4%). The 3-year actuarial rates of ≥ G2 late toxicities were 6.1% for genito-urinary and 8.9% for gastrointestinal. The whole image-guidance workflow resulted in being robust and reliable. EBRT delivered employing a hypofractionated schedule under 3D-US-based image guidance proved to be a safe and effective treatment approach with consistent biochemical control and a mild toxicity profile. Hence, it has been transferred into daily clinical practice in our Department.

Which characteristics are associated with changes in medication status for lower urinary tract symptoms among patients with prostate cancer receiving external beam radiotherapy?

Background: We clarified the predictive factors for changes in the status of medications for lower urinary tract symptoms (LUTS) 2 years after local radiotherapy for nonmetastatic prostate cancer. Materials and methods: We retrospectively included patients who underwent local external radiotherapy for nonmetastatic prostate cancer in 8 institutions between April 2001 and March 2016. Patients were divided into the medication and no-medication group based on the use of drugs for LUTS before radiotherapy. We defined improvement of LUTS as when the patient did not require medication for LUTS at 24 months after radiotherapy in the medication group and as deterioration when medication was required in the no-medication group. Logistic regression analysis was used to evaluate predictive factors for changes in medication status. Results: Altogether, 505 patients were divided into a no-medication group (n = 352) and a medication group (n = 153). The number of patients with deterioration and improvement in LUTS was 49 (14%) and 36 (23%), respectively. In the multivariate analysis, the predictive variables for deterioration were the International Prostate Symptom Score (≥8; odds ratio [OR], 2.21; p = 0.014) and the biopsy Gleason score (≤3 + 4 = 7; OR, 2.430; p = 0.008) in the no-medication group, whereas those for improvement were age (<75 years old; OR, 5.81; p = 0.002), the quality of life score (<3; OR, 3.15; p = 0.028), and a positive biopsy core rate (≥50%; OR, 2.530; p = 0.027) in the medication group. Conclusions: These predictive factors for changes in the status of medications for LUTS at 2 years after external radiotherapy may help determine the definitive therapy for nonmetastatic prostate cancer.

Patient-specific voxel-level dose prescription for prostate cancer radiotherapy considering tumor cell density and grade distribution.

BACKGROUND: In prostate radiation therapy, recent studies have indicated a benefit in increasing the dose to intraprostatic lesions (IPL) compared with standard whole gland radiation therapy. Such approaches typically aim to deliver a target dose to the IPL(s) with no deliberate effort to modulate the dose within the IPL. Prostate cancers demonstrate intra-tumor heterogeneity and hence it is hypothesized that further gains in the optimal delivery of radiation therapy can be achieved through modulation of the dose distribution within the tumor. To account for tumor heterogeneity, biologically targeted radiation therapy (BiRT) aims to utilize a voxel-wise approach to IPL dose prescription by incorporating knowledge of the spatial distribution of tumor characteristics. PURPOSE: The aim of this study was to develop a workflow for generating voxel-wise optimal dose prescriptions that maximize patient tumor control probability (TCP), and evaluate the feasibility and benefits of applying this workflow on a cohort of 62 prostate cancer patients. METHOD: The source data for this proof-of-concept study included high resolution histology images annotated with tumor location and grade. Image processing techniques were used to compute voxel-level cell density distribution maps. An absolute tumor cell distribution was calculated via linearly scaling according to published estimated tumor cell numbers. For the IPLs of each patient, optimal dose prescriptions were obtained via three alternative methods for redistribution of IPL boost doses according to maximization of TCP. The radiosensitivity uncertainties were considered using a truncated log-normally distributed linear radiosensitivity parameter ( α k ${\alpha }_k$ ) and compared with Gleason pattern (GP) dependent radiosensitivity parameters that were derived based on previously published methods. An ensemble machine learning method was implemented to identify patient-specific features that predict the TCP improvement resulting from dose redistribution relative to a uniform dose distribution. RESULTS: The Gleason pattern-dependent radiosensitivity parameters were calculated for 20 published prostate cancer α / ß ${{\alpha}}/{{\beta}}$ ratios. Optimal voxel-level dose prescriptions were generated for all 62 PCa patients. For all dose redistribution scenarios, the optimal dose distribution always shows a higher (or equivalent) TCP level than the uniform dose distribution. The applied random forest regressor could predict patient-specific TCP improvement with low root mean square error (≤1.5%) by using total tumor number, volume of IPLs and the standard deviation of tumor cell number among all voxels. CONCLUSION: Biologically-optimized redistribution of a boost dose can yield TCP improvement relative to a uniform-boost dose distribution. Patient-specific tumor characteristics can be used to predict the likelihood of benefit from a redistribution approach for the individual patient.

Deep learning-based segmentation of prostatic urethra on computed tomography scans for treatment planning.

Background and purpose: The intraprostatic urethra is an organ at risk in prostate cancer radiotherapy, but its segmentation in computed tomography (CT) is challenging. This work sought to: i) propose an automatic pipeline for intraprostatic urethra segmentation in CT, ii) analyze the dose to the urethra, iii) compare the predictions to magnetic resonance (MR) contours. Materials and methods: First, we trained Deep Learning networks to segment the rectum, bladder, prostate, and seminal vesicles. Then, the proposed Deep Learning Urethra Segmentation model was trained with the bladder and prostate distance transforms and 44 labeled CT with visible catheters. The evaluation was performed on 11 datasets, calculating centerline distance (CLD) and percentage of centerline within 3.5 and 5 mm. We applied this method to a dataset of 32 patients treated with intensity-modulated radiation therapy (IMRT) to quantify the urethral dose. Finally, we compared predicted intraprostatic urethra contours to manual delineations in MR for 15 patients without catheter. Results: A mean CLD of 1.6 ± 0.8 mm for the whole urethra and 1.7 ± 1.4, 1.5 ± 0.9, and 1.7 ± 0.9 mm for the top, middle, and bottom thirds were obtained in CT. On average, 94% and 97% of the segmented centerlines were within a 3.5 mm and 5 mm radius, respectively. In IMRT, the urethra received a higher dose than the overall prostate. We also found a slight deviation between the predicted and manual MR delineations. Conclusion: A fully-automatic segmentation pipeline was validated to delineate the intraprostatic urethra in CT images.

Clinical Validation and Treatment Plan Evaluation Based on Autodelineation of the Clinical Target Volume for Prostate Cancer Radiotherapy.

PURPOSE: Clinical target volumes (CTVs) and organs at risk (OARs) could be autocontoured to save workload. This study aimed to assess a convolutional neural network for automatic and accurate CTV and OARs in prostate cancer, while comparing possible treatment plans based on autocontouring CTV to clinical treatment plans. METHODS: Computer tomography (CT) scans of 217 patients with locally advanced prostate cancer treated at our hospital were retrospectively collected and analyzed from January 2013 to January 2019. A deep learning-based method, CUNet, was used to delineate CTV and OARs. A training set of 195 CT scans and a test set of 28 CT scans were randomly chosen from the dataset. The mean Dice similarity coefficient (DSC), 95th percentile Hausdorff distance (95HD), and subjective evaluation were used to evaluate the performance of this strategy. Predetermined evaluation criteria were used to grade treatment plans, and percentage errors for clinical doses to the planned target volume (PTV) and OARs were calculated. RESULTS: The mean DSC and 95HD values of the defined CTVs were (0.84 ± 0.05) and (5.04 ± 2.15) mm, respectively. The average delineation time was < 15 s for each patient's CT scan. The overall positive rates for clinicians A and B were 53.15% versus 46.85%, and 54.05% versus 45.95%, respectively (P > .05) when CTV outlines from CUNet were blindly chosen and compared with the ground truth (GT). Furthermore, 8 test patients were randomly chosen to design the predicted plan based on the autocontouring CTVs and OARs, demonstrating acceptable agreement with the clinical plan: average absolute dose differences in mean value of D2, D50, D98, Dmax, and Dmean for PTV were within 0.74%, and average absolute volume differences in mean value of V45 and V50 for OARs were within 3.4%. CONCLUSION: Our results revealed that the CTVs and OARs for prostate cancer defined by CUNet were close to the GT. CUNet could halve the time spent by radiation oncologists in contouring, demonstrating the potential of the novel autocontouring method.

Evaluation of hyaluronic acid gel dissolution with hyaluronidase in an in-vitro prostate cancer model.

AIM: To determine a dose response relationship of disintegration of a hyaluronic acid (HA) and hyaluronidase (HAS) used in prostate cancer radiotherapy. MATERIALS AND METHODS: Five in-vitro models are applicated with 3 ml (ml) HA. For dissolution varying doses of HAS were used: 6 ml, 3 ml, 1.5 ml, and 0 ml. One ml contains 150 International Units (IU). Each HAS was added with saline till the complementary amount of 6 ml. One phantom was solely implanted with a HA 3 ml acting as a control. Length, width and height were measured on different time points: 1st day 4 times, 2nd day 3 times, third day 2 times, and then once daily during one week, with a final measurement 2 weeks after implantation. The experiments were performed in duplicate to exclude variations and confirm the results. RESULTS: The fastest dissolution was observed with the highest concentration of HAS, already observed at the first time point 2 h after implantation, with volume decrease of 50% on the second day, and less than 1 ml residue (33%) on day 4. The 2 other concentrations of HAS also showed a volume decrease, with less than 2 ml (66%) on day 4. All the applied quantities of HAS are observed with a residue of less than 1 ml after 7 days. After 14 days the control phantom and the saline filled one remains on steady state volume (3 ml). CONCLUSIONS: A dose response was observed by HAS injection: highest volumes of HAS dissolute most swiftly. Using a ratio of HA:HAS of 1:2 results in a decrease to half of initial volume within 24 h. This is of special interest when used in clinical practice following erroneous positioning, and dissolution is urgently needed.

Acute Side-effects of Different Radiotherapy Treatment Schedules in Early Prostate Cancer.

BACKGROUND: Optimal radiation therapy (RT) fractionation in early prostate cancer in elderly patients is controversial. We compared acute toxicities of fractionation schedules: 78/2 Gy, 60/3 Gy and 36.25/7.25 Gy, in this single-centre study. We also evaluated the effect of the rectal immobilization system Rectafix on quality of life (QoL). PATIENTS AND METHODS: Seventy-three patients with one or two intermediate prostate cancer risk factors according to National Comprehensive Cancer Network criteria were recruited. Twenty-one patients were treated with 78/2 Gy and 60/3 Gy, and 31 patients with 36.25/7.25 Gy. Their QoL data were assessed with regard to genitourinary, gastrointestinal and sexual wellbeing at the beginning and end of RT and at 3 months after treatment. Rectafix was used in the 78/2 Gy and 60/3 Gy groups. RESULTS: There were no statistically significant QoL differences in between the treatment groups 3 months after RT. The 78/2 Gy group had significantly increased bowel movements between baseline and 3 months after RT (p=0.036). At 3 months after RT, this group also had significantly more erectile dysfunction than the 60/3 Gy group (p=0.025). At the end of RT, the 78/2 Gy group had more symptoms than the 36.25/7.25 Gy group. Rectafix did not reduce acute toxicities in the 78/2 Gy or 60/3 Gy groups. CONCLUSION: Treatment with the 78/2 Gy schedule is no longer to be recommended due to its increased acute toxicity compared to treatments of 60/3 Gy and 36.25/7.25 Gy. The shortest schedule of 36.25 Gy in five fractions seems to be a convenient treatment option with tolerable acute toxicity.

A value-based approach to prostate cancer image-guidance in a regional radiation therapy centre: a cost-minimisation analysis.

Background and objectives: Usual practice for the insertion of prostate fiducial markers involves at least one week delay between insertion and simulation. An evidence-based practice change was implemented whereby fiducial marker insertion occurred on the same day as radiotherapy simulation. The aim of this study was to quantify the health service costs and clinical outcomes associated with this practice change. Methods: A cost-minimisation analysis was undertaken from the perspective of the local health service. A retrospective chart audit was conducted to collect data on 149 patients in the pre-implementation cohort and 138 patients in the post-implementation cohort. Associated costs with insertion and simulation were calculated and compared across the two cohorts; this included subsided travel costs for rural and remote patients. Fiducial marker positions on planning CT and first treatment CBCT were measured for all patients as the surrogate clinical outcome measure for oedema. Results: The health service saved an average of AU$ 361 (CI $311 - $412) per patient after the practice change. There was no significant difference in fiducial marker position pre- and post- implementation (p < 0.05). Conclusion: The practice change to perform insertion and radiotherapy simulation on the same day resulted in substantial savings to the health system, without compromising clinical outcomes. The decrease in number of required patient attendances is of real consequence to rural and remote populations. The practice change increases both the value and accessibility of best-practice health care to those most at risk of missing out.

MRI only in a patient with prostate cancer with bilateral metal hip prostheses: case study.

OBJECTIVE: To outline a practical method of performing prostate cancer radiotherapy in patients with bilateral metal hip prostheses with the standard resources available in a modern general hospital. The proposed workflow is based exclusively on magnetic resonance imaging (MRI) to avoid computed tomography (CT) artifacts. CASE DESCRIPTION: This study concerns a 73-year-old man with bilateral hip prostheses with an elevated risk prostate cancer. Magnetic resonance images with assigned electron densities were used for planning purposes, generating a synthetic CT (sCT). Imaging acquisition was performed with an optimized Dixon sequence on a 1.5T MRI scanner. The images were contoured by autosegmentation software, based on an MRI database of 20 patients. The sCT was generated assigning averaged electron densities to each contour. Two volumetric modulated arc therapy plans, a complete arc and a partial one, where the beam entrances through the prostheses were avoided for about 50° on both sides, were compared. The feasibility of matching daily cone beam CT (CBCT) with MRI reference images was also tested by visual evaluations of different radiation oncologists. CONCLUSIONS: The use of magnetic resonance images improved accuracy in targets and organs at risk (OARs) contouring. The complete arc plan was chosen because of 10% lower mean and maximum doses to prostheses with the same planning target volume coverage and OAR sparing. The image quality of the match between performed CBCTs and MRI was considered acceptable. The proposed method seems promising to improve radiotherapy treatments for this complex category of patients.

Evaluation of a urology specialist therapeutic radiographer implemented radiotherapy pathway for prostate cancer patients.

INTRODUCTION: The role of the Urology Specialist Therapeutic Radiographer (USTR) was introduced to support a busy NHS uro-oncology practice. Key objectives were to improve patient preparedness for and experience of radiotherapy, focussed on prostate cancer. Pre-radiotherapy information seminars were developed, and on-treatment patient review managed by the USTRs. To evaluate the revamped patient pathway and direct further improvements, a patient experience survey was designed. METHODS: An 18-point patient questionnaire was produced. The questionnaire captured patient experience and preparedness; pre, during and at completion of treatment. The patient population comprised men receiving radiotherapy for primary prostate cancer within one UK Trust. RESULTS: Two-hundred and fifty-one responses were received. Seventy-three percent of patients felt completely prepared for radiotherapy, higher in those who attended a seminar (77%) compared to those who did not (61%). Eighty-nine and eighty-six percent of respondents were completely satisfied with verbal and written information received prior to commencing radiotherapy respectively. Seventy-three percent of responders would have found additional resources helpful. With respect to on-treatment clinics; eighty-five percent were seen on time or within 20 minutes, eighty-three percent felt fully involved in decisions regarding their care and ninety-one percent reported complete satisfaction with the knowledge of the health care professional reviewing them. The follow-up process was completely understood by eighty-eight percent and overall patient experience rated excellent by eighty-five percent of responders. CONCLUSION: The revamped pathway implemented by USTRs has achieved high levels of satisfaction at all stages of the prostate patient's radiotherapy. By diversifying the format of information giving, the USTRs hope to further meet the information needs of patients. IMPLICATIONS FOR PRACTICE: Validation of a prostate cancer radiotherapy pathway which employs USTRs and utilises a patient preparation seminar. This model could support the introduction of Specialist Therapeutic Radiographers in other Trusts and treatment sites.

A deep learning-based framework for segmenting invisible clinical target volumes with estimated uncertainties for post-operative prostate cancer radiotherapy.

In post-operative radiotherapy for prostate cancer, precisely contouring the clinical target volume (CTV) to be irradiated is challenging, because the cancerous prostate gland has been surgically removed, so the CTV encompasses the microscopic spread of tumor cells, which cannot be visualized in clinical images like computed tomography or magnetic resonance imaging. In current clinical practice, physicians' segment CTVs manually based on their relationship with nearby organs and other clinical information, but this allows large inter-physician variability. Automating post-operative prostate CTV segmentation with traditional image segmentation methods has yielded suboptimal results. We propose using deep learning to accurately segment post-operative prostate CTVs. The model proposed is trained using labels that were clinically approved and used for patient treatment. To segment the CTV, we segment nearby organs first, then use their relationship with the CTV to assist CTV segmentation. To ease the encoding of distance-based features, which are important for learning both the CTV contours' overlap with the surrounding OARs and the distance from their borders, we add distance prediction as an auxiliary task to the CTV network. To make the DL model practical for clinical use, we use Monte Carlo dropout (MCDO) to estimate model uncertainty. Using MCDO, we estimate and visualize the 95% upper and lower confidence bounds for each prediction which informs the physicians of areas that might require correction. The model proposed achieves an average Dice similarity coefficient (DSC) of 0.87 on a holdout test dataset, much better than established methods, such as atlas-based methods (DSC<0.7). The predicted contours agree with physician contours better than medical resident contours do. A reader study showed that the clinical acceptability of the automatically segmented CTV contours is equal to that of approved clinical contours manually drawn by physicians. Our deep learning model can accurately segment CTVs with the help of surrounding organ masks. Because the DL framework can outperform residents, it can be implemented practically in a clinical workflow to generate initial CTV contours or to guide residents in generating these contours for physicians to review and revise. Providing physicians with the 95% confidence bounds could streamline the review process for an efficient clinical workflow as this would enable physicians to concentrate their inspecting and editing efforts on the large uncertain areas.

Ten Year Results of Extensive Nodal Radiotherapy and Moderately Hypofractionated Simultaneous Integrated Boost in Unfavorable Intermediate-, High-, and Very High-Risk Prostate Cancer.

AIMS: To report 10-year outcomes of WPRT and HD moderately hypofractionated SIB to the prostate in UIR, HR, and VHR PCa. METHODS: From 11/2005 to 12/2015, 224 UIR, HR, and VHR PCa patients underwent WPRT at 51.8 Gy/28 fractions and SIB at 74.2 Gy (EQD2 88 Gy) to the prostate. Androgen deprivation therapy (ADT) was prescribed in up to 86.2% of patients. RESULTS: Median follow-up was 96.3 months (IQR: 71-124.7). Median age was 75 years (IQR: 71.3-78.1). At last follow up, G3 GI-GU toxicity was 3.1% and 8%, respectively. Ten-year biochemical relapse-free survival (bRFS) was 79.8% (95% CI: 72.3-88.1%), disease-free survival (DFS) 87.8% (95% CI: 81.7-94.3%), overall survival (OS) 65.7% (95% CI: 58.2-74.1%), and prostate cancer-specific survival (PCSS) 94.9% (95% CI: 91.0-99.0%). Only two patients presented local relapse. At univariate analysis, VHR vs. UIR was found to be a significant risk factor for biochemical relapse (HR: 2.8, 95% CI: 1.17-6.67, p = 0.021). After model selection, only Gleason Score ≥ 8 emerged as a significant factor for biochemical relapse (HR = 2.3, 95% CI: 1.12-4.9, p = 0.023). Previous TURP (HR = 3.5, 95% CI: 1.62-7.54, p = 0.001) and acute toxicity ≥ G2 (HR = 3.1, 95% CI = 1.45-6.52, p = 0.003) were significant risk factors for GU toxicity ≥ G3. Hypertension was a significant factor for GI toxicity ≥ G3 (HR = 3.63, 95% CI: 1.06-12.46, p = 0.041). ADT (HR = 0.31, 95% CI: 0.12-0.8, p = 0.015) and iPsa (HR = 0.37, 95% CI: 0.16-0.83, p = 0.0164) played a protective role. CONCLUSIONS: WPRT and HD SIB to the prostate combined with long-term ADT, in HR PCa, determine good outcomes with acceptable toxicity.

Planning With Patient-Specific Rectal Sub-Region Constraints Decreases Probability of Toxicity in Prostate Cancer Radiotherapy.

Background: A rectal sub-region (SRR) has been previously identified by voxel-wise analysis in the inferior-anterior part of the rectum as highly predictive of rectal bleeding (RB) in prostate cancer radiotherapy. Translating the SRR to patient-specific radiotherapy planning is challenging as new constraints have to be defined. A recent geometry-based model proposed to optimize the planning by determining the achievable mean doses (AMDs) to the organs at risk (OARs), taking into account the overlap between the planning target volume (PTV) and OAR. The aim of this study was to quantify the SRR dose sparing by using the AMD model in the planning, while preserving the dose to the prostate. Material and Methods: Three-dimensional volumetric modulated arc therapy (VMAT) planning dose distributions for 60 patients were computed following four different strategies, delivering 78 Gy to the prostate, while meeting the genitourinary group dose constraints to the OAR: (i) a standard plan corresponding to the standard practice for rectum sparing (STDpl), (ii) a plan adding constraints to SRR (SRRpl), (iii) a plan using the AMD model applied to the rectum only (AMD_RECTpl), and (iv) a final plan using the AMD model applied to both the rectum and the SRR (AMD_RECT_SRRpl). After PTV dose normalization, plans were compared with regard to dose distributions, quality, and estimated risk of RB using a normal tissue complication probability model. Results: AMD_RECT_SRRpl showed the largest SRR dose sparing, with significant mean dose reductions of 7.7, 3, and 2.3 Gy, with respect to the STDpl, SRRpl, and AMD_RECTpl, respectively. AMD_RECT_SRRpl also decreased the mean rectal dose by 3.6 Gy relative to STDpl and by 3.3 Gy relative to SRRpl. The absolute risk of grade ≥1 RB decreased from 22.8% using STDpl planning to 17.6% using AMD_RECT_SRRpl considering SRR volume. AMD_RECT_SRRpl plans, however, showed slightly less dose homogeneity and significant increase of the number of monitor units, compared to the three other strategies. Conclusion: Compared to a standard prostate planning, applying dose constraints to a patient-specific SRR by using the achievable mean dose model decreased the mean dose by 7.7 Gy to the SRR and may decrease the relative risk of RB by 22%.

[Prediction of rectal toxicity of radiotherapy for prostate cancer based on multi-modality feature and multi-classifiers].

OBJECTIVE: To evaluate rectal toxicity of radiotherapy for prostate cancer using a novel predictive model based on multi-modality and multi-classifier fusion. METHODS: We retrospectively collected the clinical data from 44 prostate cancer patients receiving external beam radiation (EBRT), including the treatment data, clinical parameters, planning CT data and the treatment plans. The clinical parameter features and dosimetric features were extracted as two different modality features, and a subset of features was selected to train the 5 base classifiers (SVM, Decision Tree, K-nearest-neighbor, Random forests and XGBoost). To establish the multi-modality and multi-classifier fusion model, a multi-criteria decision-making based weight assignment algorithm was used to assign weights for each base classifier under the same modality. A repeat 5-fold cross-validation and the 4 indexes including the area under ROC curve (AUC), accuracy, sensitivity and specificity were used to evaluate the proposed model. In addition, the proposed model was compared quantitatively with different feature selection methods, different weight allocation algorithms, the model based on single mode single classifier, and two integrated models using other fusion methods. RESULTS: Repeated (5 times) 5-fold cross validation of the proposed model showed an accuracy of 0.78 for distinguishing toxicity from non-toxicity with an AUC of 0.83, a specificity of 0.79 and a sensitivity of 0.76. CONCLUSIONS: Compared with the models based on a single mode or a single classifier and other fusion models, the proposed model can more accurately predict rectal toxicity of radiotherapy for prostate cancer.

Independent component analysis for rectal bleeding prediction following prostate cancer radiotherapy.

BACKGROUND AND PURPOSE: To evaluate the benefit of independent component analysis (ICA)-based models for predicting rectal bleeding (RB) following prostate cancer radiotherapy. MATERIALS AND METHODS: A total of 593 irradiated prostate cancer patients were prospectively analyzed for Grade ≥2 RB. ICA was used to extract two informative subspaces (presenting RB or not) from the rectal DVHs, enabling a set of new pICA parameters to be estimated. These DVH-based parameters, along with others from the principal component analysis (PCA) and functional PCA, were compared to "standard" features (patient/treatment characteristics and DVH bins) using the Cox proportional hazards model for RB prediction. The whole cohort was divided into: (i) training (N = 339) for ICA-based subspace identification and Cox regression model identification and (ii) validation (N = 254) for RB prediction capability evaluation using the C-index and the area under the receiving operating curve (AUC), by comparing predicted and observed toxicity probabilities. RESULTS: In the training cohort, multivariate Cox analysis retained pICA and PC as significant parameters of RB with 0.65 C-index. For the validation cohort, the C-index increased from 0.64 when pICA was not included in the Cox model to 0.78 when including pICA parameters. When pICA was not included, the AUC for 3-, 5-, and 8-year RB prediction were 0.68, 0.66, and 0.64, respectively. When included, the AUC increased to 0.83, 0.80, and 0.78, respectively. CONCLUSION: Among the many various extracted or calculated features, ICA parameters improved RB prediction following prostate cancer radiotherapy.

Feasibility of anatomical feature points for the estimation of prostate locations in the Bayesian delineation frameworks for prostate cancer radiotherapy.

This study aimed to investigate the feasibility of anatomical feature points for the estimation of prostate locations in the Bayesian delineation frameworks for prostate cancer radiotherapy. The relationships between the reference centroids of prostate regions (CPRs) (prostate locations) and anatomical feature points were explored, and the most feasible anatomical feature points were selected based on the smallest location estimation errors of CPRs and the largest Dice's similarity coefficient (DSC) between the reference and extracted prostates. The reference CPRs were calculated according to reference prostate contours determined by radiation oncologists. Five anatomical feature points were manually determined on a prostate, bladder, and rectum in a sagittal plane of a planning computed tomography image for each case. The CPRs were estimated using three machine learning architectures [artificial neural network, random forest, and support vector machine (SVM)], which learned the relationships between the reference CPRs and anatomical feature points. The CPRs were applied for placing a prostate probabilistic atlas at the coordinates and extracting prostate regions using a Bayesian delineation framework. The average estimation errors without and with SVM using three feature points, which indicated the best performance, were 5.6 ± 3.7 mm and 1.8 ± 1.0 mm, respectively (the smallest error) (p < 0.001). The average DSCs without and with SVM using the three feature points were 0.69 ± 0.13 and 0.82 ± 0.055, respectively (the highest DSC) (p < 0.001). The anatomical feature points may be feasible for the estimation of prostate locations, which can be applied to the general Bayesian delineation frameworks for prostate cancer radiotherapy.

Predictive Factors of Late-onset Rectal Mucosal Changes After Radiotherapy of Prostate Cancer.

BACKGROUND/AIM: The Vienna Rectoscopy Score (VRS; from 0, absence of rectal mucosal changes, to 5) assessed 1 year after radiotherapy is a surrogate end-point of late rectal toxicity. The aim of this study was to investigate the association between treatment-related factors and 1-year VRS. PATIENTS AND METHODS: We performed a retrospective analysis of prospectively collected data. Patients with prostate adenocarcinoma treated with definitive or postoperative radiotherapy (RT) underwent endoscopy 1 year after RT. Relationships between VRS of 2 or more and treatment parameters were investigated by univariate and multivariate logistic analyses. RESULTS: One hundred and ninety-five patients (mean age=69 years; range=43-81 years) were considered eligible for the study. At univariate analysis, patients treated with hypofractionation plus radiosurgery boost (p<0.001) and an equivalent dose in 2 Gy per fraction (EQD2) (α/ß=3) ≥75 Gy (p<0.001) was associated with a significantly higher incidence of VRS ≥2 after 1 year of follow-up. At multivariate analysis, radiosurgery boost was an independent risk factor for developing rectal mucosal lesions (VRS ≥2), yielding an odds ratio (OR) of 4.14 (95% confidence interval (CI)=1.2-13.8), while pelvic surgery was inversely associated with VRS ≥2 (OR=0.39; 95% CI=0.17-0.94). CONCLUSION: Hypofractionation followed by radiosurgery boost significantly increased the risk of developing late-onset rectal mucosal changes. Therefore, special care and preventative treatment strategies are needed when using radiosurgery boost after hypofractionated RT.

Population model of bladder motion and deformation based on dominant eigenmodes and mixed-effects models in prostate cancer radiotherapy.

In radiotherapy for prostate cancer irradiation of neighboring organs at risk may lead to undesirable side-effects. Given this setting, the bladder presents the largest inter-fraction shape variations hampering the computation of the actual delivered dose vs. planned dose. This paper proposes a population model, based on longitudinal data, able to estimate the probability of bladder presence during treatment, using only the planning computed tomography (CT) scan as input information. As in previously-proposed principal component analysis (PCA) population-based models, we have used the data to obtain the dominant eigenmodes that describe bladder geometric variations between fractions. However, we have used a longitudinal analysis along each mode in order to properly characterize patient's variance from the total population variance. We have proposed is a mixed-effects (ME) model in order to separate intra- and inter-patient variability, in an effort to control confounding cohort effects. Other than using PCA, bladder shapes are represented by using spherical harmonics (SPHARM) that additionally enables data compression without information lost. Based on training data from repeated CT scans, the ME model was thus implemented following dimensionality reduction by means of SPHARM and PCA. We have evaluated the model in a leave-one-out cross validation framework on the training data but also using independent data. Probability maps (PMs) were thus generated with several draws from the learnt model as predicted regions where the bladder will likely move and deform. These PMs were compared with the actual regions using metrics based on mutual information distance and misestimated voxels. The prediction was also compared with two previous population PCA-based models. The proposed model was able to reduce the uncertainties in the estimation of the probable region of bladder motion and deformation. This model can thus be used for tailoring radiotherapy treatments.