To propose a method of intensity-modulated radiotherapy (IMRT) planning that generates achievable dose-volume histogram (DVH) objectives using a database containing geometric and dosimetric ...information of previous patients.
The overlap volume histogram (OVH) is used to compare the spatial relationships between the organs at risk and targets of a new patient with those of previous patients in a database. From the OVH analysis, the DVH objectives of the new patient were generated from the database and used as the initial planning goals. In a retrospective OVH-assisted planning demonstration, 15 patients were randomly selected from a database containing clinical plans (CPs) of 91 previous head-and-neck patients treated by a three-level IMRT-simultaneous integrated boost technique. OVH-assisted plans (OPs) were planned in a leave-one-out manner by a planner who had no knowledge of CPs. Thus, DVH objectives of an OP were generated from a subdatabase containing the information of the other 90 patients. Those DVH objectives were then used as the initial planning goals in IMRT optimization. Planning efficiency was evaluated by the number of clicks of the "Start Optimization" button in the course of planning. Although the Pinnacle(3) treatment planning system allows planners to interactively adjust the DVH parameters during optimization, planners in our institution have never used this function in planning.
The average clicks required for completing the CP and OP was 27.6 and 1.9, respectively (p <.00001); three OPs were finished within a single click. Ten more patient's cord + 4 mm reached the sparing goal D(0.1cc) <44 Gy (p <.0001), where D(0.1cc) represents the dose corresponding to 0.1 cc. For planning target volume uniformity, conformity, and other organ at risk sparing, the OPs were at least comparable with the CPs. Additionally, the averages of D(0.1cc) to the cord + 4 mm decreased by 6.9 Gy (p <.0001); averages of D(0.1cc) to the brainstem decreased by 7.7 Gy (p <.005). The averages of V(30 Gy) to the contralateral parotid decreased by 8.7% (p <.0001), where V(30 Gy) represents the percentage volume corresponding to 30 Gy.
The method heralds the possibility of automated IMRT planning.
To analyze baseline CT/MR-based image features of salivary glands to predict radiation-induced xerostomia 3-months after head-and-neck cancer (HNC) radiotherapy.
A retrospective analysis was ...performed on 266 HNC patients who were treated using radiotherapy at our institution between 2009 and 2018. CT and T1 post-contrast MR images along with NCI-CTCAE xerostomia grade (3-month follow-up) were prospectively collected at our institution. CT and MR images were registered on which parotid/submandibular glands were contoured. Image features were extracted for ipsilateral/contralateral parotid and submandibular glands relative to the location of the primary tumor. Dose-volume-histogram (DVH) parameters were also acquired. Features were pre-selected based on Spearman correlation before modelling by examining the correlation with xerostomia (p < 0.05). A shrinkage regression analysis of the pre-selected features was performed using LASSO. The internal validity of the variable selection was estimated by repeating the entire variable selection procedure using a leave-one-out-cross-validation. The most frequently selected variables were considered in the final model. A generalized linear regression with repeated ten-fold cross-validation was developed to predict radiation-induced xerostomia at 3-months after radiotherapy. This model was tested in an independent dataset (n = 50) of patients who were treated at the same institution in 2017-2018. We compared the prediction performances under eight conditions (DVH-only, CT-only, MR-only, CT + MR, DVH + CT, DVH + CT + MR, Clinical+CT + MR, and Clinical+DVH + CT + MR) using the area under the receiver operating characteristic curve (ROC-AUC).
Among extracted features, 7 CT, 5 MR, and 2 DVH features were selected. The internal cohort (n = 216) ROC-AUC values for DVH, CT, MR, and Clinical+DVH + CT + MR features were 0.73 ± 0.01, 0.69 ± 0.01, 0.70 ± 0.01, and 0.79 ± 0.01, respectively. The validation cohort (n = 50) ROC-AUC values for DVH, CT, MR, and Clinical+DVH + CT + MR features were 0.63, 0.57, 0.66, and 0.68, respectively. The DVH-ROC was not significantly different than the CT-ROC (p = 0.8) or MR-ROC (p = 0.4). However, the CT + MR-ROC was significantly different than the CT-ROC (p = 0.03), but not the Clinical+DVH + CT + MR model (p = 0.5).
Our results suggest that baseline CT and MR image features may reflect baseline salivary gland function and potential risk for radiation injury. The integration of baseline image features into prediction models has the potential to improve xerostomia risk stratification with the ultimate goal of truly personalized HNC radiotherapy.
In radiotherapy for prostate cancer, the rectum is the major dose-limiting structure. Physically separating the rectum from the prostate (e.g., by injecting a spacer) can reduce the rectal radiation ...dose. Despite pilot clinical studies, no careful analysis has been done of the risks, benefits, and dosimetric effects of this practice.
Using cadaveric specimens, 20 mL of a hydrogel was injected between the prostate and rectum using a transperineal approach. Imaging was performed before and after spacer placement, and the cadavers were subsequently dissected. Ten intensity-modulated radiotherapy plans were generated (five before and five after separation), allowing for characterization of the rectal dose reduction. To quantify the amount of prostate-rectum separation needed for effective rectal dose reduction, simulations were performed using nine clinically generated intensity-modulated radiotherapy plans.
In the cadaveric studies, an average of 12.5 mm of prostate-rectum separation was generated with the 20-mL hydrogel injections (the seminal vesicles were also separated from the rectum). The average rectal volume receiving 70 Gy decreased from 19.9% to 4.5% (p < .05). In the simulation studies, a prostate-rectum separation of 10 mm was sufficient to reduce the mean rectal volume receiving 70 Gy by 83.1% (p <.05). No additional reduction in the average rectal volume receiving 70 Gy was noted after 15 mm of separation. In addition, spacer placement allowed for increased planning target volume margins without exceeding the rectal dose tolerance.
Prostate-rectum spacers can allow for reduced rectal toxicity rates, treatment intensification, and/or reduced dependence on complex planning and treatment delivery techniques.
To prospectively determine whether overlap volume histogram (OVH)-driven, automated simultaneous integrated boosted (SIB)-intensity-modulated radiation therapy (IMRT) treatment planning for ...head-and-neck cancer can be implemented in clinics.
A prospective study was designed to compare fully automated plans (APs) created by an OVH-driven, automated planning application with clinical plans (CPs) created by dosimetrists in a 3-dose-level (70 Gy, 63 Gy, and 58.1 Gy), head-and-neck SIB-IMRT planning. Because primary organ sparing (cord, brain, brainstem, mandible, and optic nerve/chiasm) always received the highest priority in clinical planning, the study aimed to show the noninferiority of APs with respect to PTV coverage and secondary organ sparing (parotid, brachial plexus, esophagus, larynx, inner ear, and oral mucosa). The sample size was determined a priori by a superiority hypothesis test that had 85% power to detect a 4% dose decrease in secondary organ sparing with a 2-sided alpha level of 0.05. A generalized estimating equation (GEE) regression model was used for statistical comparison.
Forty consecutive patients were accrued from July to December 2010. GEE analysis indicated that in APs, overall average dose to the secondary organs was reduced by 1.16 (95% CI = 0.09-2.33) with P=.04, overall average PTV coverage was increased by 0.26% (95% CI = 0.06-0.47) with P=.02 and overall average dose to the primary organs was reduced by 1.14 Gy (95% CI = 0.45-1.8) with P=.004. A physician determined that all APs could be delivered to patients, and APs were clinically superior in 27 of 40 cases.
The application can be implemented in clinics as a fast, reliable, and consistent way of generating plans that need only minor adjustments to meet specific clinical needs.
The application of big data to the quality assurance of radiation therapy is multifaceted. Big data can be used to detect anomalies and suboptimal quality metrics through both statistical means and ...more advanced machine learning and artificial intelligence. The application of these methods to clinical practice is discussed through examples of guideline adherence, contour integrity, treatment delivery mechanics, and treatment plan quality. The ultimate goal is to apply big data methods to direct measures of patient outcomes for care quality. The era of big data and machine learning is maturing and the implementation for quality assurance promises to improve the quality of care for patients.
Existing definitions of high-risk prostate cancer consist of men who experience significant heterogeneity in outcomes. As such, criteria that identify a subpopulation of National Comprehensive Cancer ...Network (NCCN) high-risk prostate cancer patients who are at very high risk (VHR) for poor survival outcomes following prostatectomy were recently developed at our institution and include the presence of any of the following disease characteristics: multiple NCCN high-risk factors, primary Gleason pattern 5 disease and/or ≥5 biopsy cores with Gleason sums of 8 to 10. Whether these criteria also apply to men undergoing definitive radiation is unclear, as is the optimal treatment regimen in these patients.
All men consecutively treated with definitive radiation by a single provider from 1993 to 2006 and who fulfilled criteria for NCCN high-risk disease were identified (n=288), including 99 patients (34%) with VHR disease. Multivariate-adjusted competing risk regression models were constructed to assess associations between the VHR definition and biochemical failure (BF), distant metastasis (DM), and prostate cancer-specific mortality (PCSM). Multivariate-adjusted Cox regression analysis assessed the association of the VHR definition with overall mortality (OM). Cumulative incidences of failure endpoints were compared between VHR men and other NCCN high-risk men.
Men with VHR disease compared to other NCCN high-risk men experienced a higher 10-year incidence of BF (54.0% vs 35.4%, respectively, P<.001), DM (34.9% vs 13.4%, respectively, P<.001), PCSM (18.5% vs 5.9%, respectively, P<.001), and OM (36.4% vs 27.0%, respectively, P=.04). VHR men with a detectable prostate-specific antigen (PSA) concentration at the end of radiation (EOR) remained at high risk of 10-year PCSM compared to VHR men with an undetectable EOR PSA (31.0% vs 13.7%, respectively, P=.05).
NCCN high-risk prostate cancer patients who meet VHR criteria experience distinctly worse outcomes following definitive radiation and long-term androgen deprivation therapy, particularly if an EOR PSA is detectable. Optimal use of local therapies for VHR patients should be explored further, as should novel agents.
To develop consensus contouring guidelines for postoperative stereotactic body radiation therapy (SBRT) for spinal metastases.
Ten spine SBRT specialists representing 10 international centers ...independently contoured the clinical target volume (CTV), planning target volume (PTV), spinal cord, and spinal cord planning organ at risk volume (PRV) for 10 representative clinical scenarios in postoperative spine SBRT for metastatic solid tumor malignancies. Contours were imported into the Computational Environment for Radiotherapy Research. Agreement between physicians was calculated with an expectation minimization algorithm using simultaneous truth and performance level estimation with κ statistics. Target volume definition guidelines were established by finding optimized confidence level consensus contours using histogram agreement analyses.
Nine expert radiation oncologists and 1 neurosurgeon completed contours for all 10 cases. The mean sensitivity and specificity were 0.79 (range, 0.71-0.89) and 0.94 (range, 0.90-0.99) for the CTV and 0.79 (range, 0.70-0.95) and 0.92 (range, 0.87-0.99) for the PTV), respectively. Mean κ agreement, which demonstrates the probability that contours agree by chance alone, was 0.58 (range, 0.43-0.70) for CTV and 0.58 (range, 0.37-0.76) for PTV (P<.001 for all cases). Optimized consensus contours were established for all patients with 80% confidence interval. Recommendations for CTV include treatment of the entire preoperative extent of bony and epidural disease, plus immediately adjacent bony anatomic compartments at risk of microscopic disease extension. In particular, a "donut-shaped" CTV was consistently applied in cases of preoperative circumferential epidural extension, regardless of extent of residual epidural extension. Otherwise more conformal anatomic-based CTVs were determined and described. Spinal instrumentation was consistently excluded from the CTV.
We provide consensus contouring guidelines for common scenarios in postoperative SBRT for spinal metastases. These consensus guidelines are subject to clinical validation.
This Vision 20/20 paper considers what computational advances are likely to be implemented in clinical radiation oncology in the coming years and how the adoption of these changes might alter the ...practice of radiotherapy. Four main areas of likely advancement are explored: cloud computing, aggregate data analyses, parallel computation, and automation. As these developments promise both new opportunities and new risks to clinicians and patients alike, the potential benefits are weighed against the hazards associated with each advance, with special considerations regarding patient safety under new computational platforms and methodologies. While the concerns of patient safety are legitimate, the authors contend that progress toward next-generation clinical informatics systems will bring about extremely valuable developments in quality improvement initiatives, clinical efficiency, outcomes analyses, data sharing, and adaptive radiotherapy.
To determine whether a machine learning approach optimizes survival estimation for patients with symptomatic bone metastases (SBM), we developed the Bone Metastases Ensemble Trees for Survival ...(BMETS) to predict survival using 27 prognostic covariates. To establish its relative clinical utility, we compared BMETS with 2 simpler Cox regression models used in this setting.
For 492 bone sites in 397 patients evaluated for palliative radiation therapy (RT) for SBM from January 2007 to January 2013, data for 27 clinical variables were collected. These covariates and the primary outcome of time from consultation to death were used to build BMETS using random survival forests. We then performed Cox regressions as per 2 validated models: Chow's 3-item (C-3) and Westhoff's 2-item (W-2) tools. Model performance was assessed using cross-validation procedures and measured by time-dependent area under the curve (tAUC) for all 3 models. For temporal validation, a separate data set comprised of 104 bone sites treated in 85 patients in 2018 was used to estimate tAUC from BMETS.
Median survival was 6.4 months. Variable importance was greatest for performance status, blood cell counts, recent systemic therapy type, and receipt of concurrent nonbone palliative RT. tAUC at 3, 6, and 12 months was 0.83, 0.81, and 0.81, respectively, suggesting excellent discrimination of BMETS across postconsultation time points. BMETS outperformed simpler models at each time, with respective tAUC at each time of 0.78, 0.76, and 0.74 for the C-3 model and 0.80, 0.78, and 0.77 for the W-2 model. For the temporal validation set, respective tAUC was similarly high at 0.86, 0.82, and 0.78.
For patients with SBM, BMETS improved survival predictions versus simpler traditional models. Model performance was maintained when applied to a temporal validation set. To facilitate clinical use, we developed a web platform for data entry and display of BMETS-predicted survival probabilities.
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