METHODS: The agreement indices (or pass rates) for global and local gamma evaluation, maximum allowed dose difference (MADD) and divide and conquer (D&C) techniques were calculated using a selection of acceptance criteria for 429 patient-specific pretreatment quality assurance measurements. Regression analysis was used to quantify the similarity of behavior of each technique, to determine whether possible variations in sensitivity might be present.
RESULTS: The results demonstrated that the behavior of D&C gamma analysis and MADD box analysis differs from any other dose comparison techniques, whereas MADD gamma analysis exhibits similar performance to the standard global gamma analysis. Local gamma analysis had the least variation in behavior with criteria selection. Agreement indices calculated for 2%/2 mm and 2%/3 mm, and 3%/2 mm and 3%/3 mm were correlated for most comparison techniques.
CONCLUSION: Radiation oncology treatment centers looking to compare between different dose comparison techniques, criteria or lower dose thresholds may apply the results of this study to estimate the expected change in calculated agreement indices and possible variation in sensitivity to delivery dose errors.
MATERIALS/METHODS: Multivariable models developed to predict atomised and generalised urinary symptoms, both acute and late, were considered for validation using a dataset representing 754 participants from the TROG 03.04-RADAR trial. Endpoints and features were harmonised to match the predictive models. The overall performance, calibration and discrimination were assessed.
RESULTS: 14 models from four publications were validated. The discrimination of the predictive models in an independent external validation cohort, measured using the area under the receiver operating characteristic (ROC) curve, ranged from 0.473 to 0.695, generally lower than in internal validation. 4 models had ROC >0.6. Shrinkage was required for all predictive models' coefficients ranging from -0.309 (prediction probability was inverse to observed proportion) to 0.823. Predictive models which include baseline symptoms as a feature produced the highest discrimination. Two models produced a predicted probability of 0 and 1 for all patients.
CONCLUSIONS: Predictive models vary in performance and transferability illustrating the need for improvements in model development and reporting. Several models showed reasonable potential but efforts should be increased to improve performance. Baseline symptoms should always be considered as potential features for predictive models.
METHODS AND MATERIALS: The bladder dose-surface maps of 754 participants from the TROG 03.04-RADAR trial were generated from the volumetric data by virtually cutting the bladder at the sagittal slice, intersecting the bladder center-of-mass through to the bladder posterior and projecting the dose information on a 2-dimensional plane. Pixelwise dose comparisons were performed between patients with and without symptoms (dysuria, hematuria, incontinence, and an International Prostate Symptom Score increase of ≥10 [ΔIPSS10]). The results with and without permutation-based multiple-comparison adjustments are reported. The pixelwise multivariate analysis findings (peak-event model for dysuria, hematuria, and ΔIPSS10; event-count model for incontinence), with adjustments for clinical factors, are also reported.
RESULTS: The associations of the spatially specific dose measures to urinary dysfunction were dependent on the presence of specific symptoms. The doses received by the anteroinferior and, to lesser extent, posterosuperior surface of the bladder had the strongest relationship with the incidence of dysuria, hematuria, and ΔIPSS10, both with and without adjustment for clinical factors. For the doses to the posteroinferior region corresponding to the area of the trigone, the only symptom with significance was incontinence.
CONCLUSIONS: A spatially variable response of the bladder surface to the dose was found for symptoms of urinary dysfunction. Limiting the dose extending anteriorly might help reduce the risk of urinary dysfunction.
METHOD: The model was formulated by integrating the Caputo fractional derivative with the previous cancer treatment model. Thereafter, the linear-quadratic with the repopulation model was coupled into the model to account for the cells' population decay due to radiation. The treatment process was then simulated with numerical variables, numerical parameters, and radiation parameters. The numerical parameters which included the proliferation coefficients of the cells, competition coefficients of the cells, and the perturbation constant of the normal cells were obtained from previous literature. The radiation and numerical parameters were obtained from reported clinical data of six patients treated with radiotherapy. The patients had tumor volumes of 24.1cm3, 17.4cm3, 28.4cm3, 18.8cm3, 30.6cm3, and 12.6cm3 with fractionated doses of 2 Gy for the first two patients and 1.8 Gy for the other four. The initial tumor volumes were used to obtain initial populations of cells after which the treatment process was simulated in MATLAB. Subsequently, a global sensitivity analysis was done to corroborate the model with clinical data. Finally, 96 radiation protocols were simulated by using the biologically effective dose formula. These protocols were used to obtain a regression equation connecting the value of the Caputo fractional derivative with the fractionated dose.
RESULTS: The final tumor volumes, from the results of the simulations, were 3.58cm3, 8.61cm3, 5.68cm3, 4.36cm3, 5.75cm3, and 6.12cm3, while those of the normal cells were 23.87cm3, 17.29cm3, 28.17cm3, 18.68cm3, 30.33cm3, and 12.55cm3. The sensitivity analysis showed that the most sensitive model factors were the value of the Caputo fractional derivative and the proliferation coefficient of the cancer cells. Lastly, the obtained regression equation accounted for 99.14% of the prediction.
CONCLUSION: The model can simulate a cancer treatment process and predict the results of other radiation protocols.
MATERIALS/METHODS: A total of 10 patients with synchronous bilateral breast cancer who were treated with VMAT at our institution were retrospectively analyzed. Clinical target volume (CTV) included chest wall and regional nodes (supraclavicular fossa and internal mammary chain) and prescription dose was 40.05 Gy in 15 daily fractions. HT and IMPT plans were generated for each patient. Dose-volume statistics, including planning target volume (PTV) coverage and dose to OAR: lungs, heart, thyroid, spinal cord, brachial plexus and esophagus, were compared between modalities using a paired T-test.
RESULTS: Mean age of patients was 61 years (43-84). Majority of the patients (80%) were ER+ PR+ and HER2-. 40% of patients underwent breast reconstruction following surgery. All 3 techniques provided adequate target volume distribution and OAR sparing. Compared to VMAT and HT plans, IMPT had better heart and lung sparing effects, resulting in lower mean and V25 Gy heart dose; mean, V20 Gy and V5 Gy lung dose (p<0.0001). There was no significant difference in VMAT and HT plans for mean heart and lung dose. VMAT plans showed significantly lower V25 Gy heart dose on average (p = 0.04). V5 Gy lung dose was slightly lower in HT compared to VMAT plans, approaching statistical significance (p = 0.08). PTV coverage was adequate for all 3 techniques. All techniques fulfilled cord, esophagus, thyroid and brachial plexus constraints.
CONCLUSION: IMPT plans showed significantly better OAR sparing compared to photon techniques. All 3 techniques met OAR constraints, and resulted in adequate target volume coverage. As IMPT is significantly more costly than VMAT or HT techniques, appropriate patient selection is important to deliver treatment in the most resource-effective manner for patients who would derive the most benefit, for example those with young age or existing heart or lung comorbidities.
METHODS: ARCHERY is a non-randomised prospective study to evaluate the quality and economic impact of AI-based automated radiotherapy treatment planning for cervical, head and neck, and prostate cancers, which are endemic in LMICs, and for which radiotherapy is the primary curative treatment modality. The sample size of 990 patients (330 for each cancer type) has been calculated based on an estimated 95% treatment plan acceptability rate. Time and cost savings will be analysed as secondary outcome measures using the time-driven activity-based costing model. The 48-month study will take place in six public sector cancer hospitals in India (n=2), Jordan (n=1), Malaysia (n=1) and South Africa (n=2) to support implementation of the software in LMICs.
ETHICS AND DISSEMINATION: The study has received ethical approval from University College London (UCL) and each of the six study sites. If the study objectives are met, the AI-based software will be offered as a not-for-profit web service to public sector state hospitals in LMICs to support expansion of high quality radiotherapy capacity, improving access to and affordability of this key modality of cancer cure and control. Public and policy engagement plans will involve patients as key partners.
METHODS: A 3-step framework was proposed, consisting of: (1) 3D LV model reconstruction from motion-corrected 4D cine-MRI; (2) Registration of 2D LGE-MRI with 4D cine-MRI; (3) LV contour extraction from the intersection of LGE slices with the LV model. The framework was evaluated against cardiac MRI data from 27 patients scanned within 6 months after acute myocardial infarction. We compared the use of local Pearson's correlation (LPC) and normalized mutual information (NMI) as similarity measures for the registration. The use of 2 and 6 long-axis (LA) cine-MRI scans was also compared. The accuracy of the framework was evaluated using manual segmentation, and the interobserver variability of the scar volume derived from the segmented LV was determined using Bland-Altman analysis.
RESULTS: LPC outperformed NMI as a similarity measure for the proposed framework using 6 LA scans, with Hausdorrf distance (HD) of 1.19 ± 0.53 mm versus 1.51 ± 2.01 mm (endocardial) and 1.21 ± 0.48 mm versus 1.46 ± 1.78 mm (epicardial), respectively. Segmentation using 2 LA scans was comparable to 6 LA scans with a HD of 1.23 ± 0.70 mm (endocardial) and 1.25 ± 0.74 mm (epicardial). The framework yielded a lower interobserver variability in scar volumes compared with manual segmentation.
CONCLUSION: The framework showed high accuracy and robustness in delineating LV in LGE-MRI and allowed for bidirectional mapping of information between LGE- and cine-MRI scans, crucial in personalized model studies for treatment planning.
METHODS AND MATERIALS: This retrospective study used data from 5 consecutive patients with NPC who were treated with bolus for large neck nodes using IMRT from November 2011-January 2012 in our institute. All these patients were treated radically with IMRT according to our institution's protocol. Re-planning with IMRT without bolus for these patients with exactly the same target volumes were done for comparison. Comparison of the plans was done by comparing the V70 of PTV70-N, V66.5 of PTV70-N, V65.1 of PTV70-N and the surface dose of the PTV70-N.
RESULTS: The mean size of the largest diameter of the enlarged lymph nodes for the 5 patients was 3.9 cm. The mean distance of the GTV-N to the skin surface was 0.6 cm. The mean V70 of PTV70-N for the 5 patients showed an absolute advantage of 10.8% (92.4% vs. 81.6%) for the plan with bolus while the V66.5 of PTV70-N had an advantage of 8.1% (97.0% vs. 88.9%). The mean V65.1 also had an advantage of 7.1% (97.6% vs. 90.5%). The mean surface dose for the PTV70-N was also much higher at 61.1 Gy for the plans with bolus compared to only 23.5 Gy for the plans without bolus.
CONCLUSION: Neck node bolus technique should be strongly considered in the treatment of NPC with enlarged lymph nodes treated with IMRT. It yields a superior dosimetry compared to non-bolus plans with acceptable skin toxicity.