Application of surface-guided radiation therapy in prostate cancer: comparative analysis of differences with skin marking-guided patient setup.

PURPOSE: Surface-guided radiation therapy is an image-guided method using optical surface imaging that has recently been adopted for patient setup and motion monitoring during treatment. We aimed to determine whether the surface guide setup is accurate and efficient compared to the skin-marking guide in prostate cancer treatment. MATERIALS AND METHODS: The skin-marking setup was performed, and vertical, longitudinal, and lateral couch values (labeled as "M") were recorded. Subsequently, the surface-guided setup was conducted, and couch values (labeled as "S") were recorded. After performing cone-beam computed tomography (CBCT), the final couch values was recorded (labeled as "C"), and the shift value was calculated (labeled as "Gap (M-S)," "Gap (M-C)," "Gap (S-C)") and then compared. Additionally, the setup times for the skin marking and surface guides were also compared. RESULTS: One hundred and twenty-five patients were analyzed, totaling 2,735 treatment fractions. Gap (M-S) showed minimal differences in the vertical, longitudinal, and lateral averages (-0.03 cm, 0.07 cm, and 0.06 cm, respectively). Gap (M-C) and Gap (S-C) exhibited a mean difference of 0.04 cm (p = 0.03) in the vertical direction, a mean difference of 0.35 cm (p = 0.52) in the longitudinal direction, and a mean difference of 0.11 cm (p = 0.91) in the lateral direction. There was no correlation between shift values and patient characteristics. The average setup time of the skin-marking guide was 6.72 minutes, and 7.53 minutes for the surface guide. CONCLUSION: There was no statistically significant difference between the surface and skin-marking guides regarding final CBCT shift values and no correlation between translational shift values and patient characteristics. We also observed minimal difference in setup time between the two methods. Therefore, the surface guide can be considered an accurate and time-efficient alternative to skin-marking guides.

Deep learning proton beam range estimation model for quality assurance based on two-dimensional scintillated light distributions in simulations.

BACKGROUND: Many studies have utilized optical camera systems with volumetric scintillators for quality assurances (QA) to estimate the proton beam range. However, previous analytically driven range estimation methods have the difficulty to derive the dose distributions from the scintillation images with quenching and optical effects. PURPOSE: In this study, a deep learning method utilized to QA was used to predict the beam range and spread-out Bragg peak (SOBP) for two-dimensional (2D) map conversion from the scintillation light distribution (LD) into the dose distribution in a water phantom. METHODS: The 2D residual U-net modeling for deep learning was used to predict the 2D water dose map from a 2D scintillation LD map. Monte Carlo simulations for dataset preparation were performed with varying monoenergetic proton beam energies, field sizes, and beam axis shifts. The LD was reconstructed using photons backpropagated from the aperture as a virtual lens. The SOBP samples were constructed based on monoenergetic dose distributions. The training set, including the validation set, consisted of 8659 image pairs of LD and water dose maps. After training, dose map prediction was performed using a 300 image pair test set generated under random conditions. The pairs of simulated and predicted dose maps were analyzed by Bragg peak fitting and gamma index maps to evaluate the model prediction. RESULT: The estimated beam range and SOBP width resolutions were 0.02 and 0.19 mm respectively for varying beam conditions, and the beam range and SOBP width deviations from the reference simulation result were less than 0.1 and 0.8 mm respectively. The simulated and predicted distributions showed good agreement in the gamma analysis, except for rare cases with failed gamma indices in the proximal and field-marginal regions. CONCLUSION: The deep learning conversion method using scintillation LDs in an optical camera system with a scintillator is feasible for estimating proton beam range and SOBP width with high accuracy.

A study of quantitative indicators for slice sorting in cine-mode 4DCT.

The uncertainties of four-dimensional computed tomography (4DCT), also called as residual motion artefacts (RMA), induced from irregular respiratory patterns can degrade the quality of overall radiotherapy. This study aims to quantify and reduce those uncertainties. A comparative study on quantitative indicators for RMA was performed, and based on this, we proposed a new 4DCT sorting method that is applicable without disrupting the current clinical workflow. In addition to the default phase sorting strategy, both additional amplitude information from external surrogates and the quantitative metric for RMA, investigated in this study, were introduced. The comparison of quantitative indicators and the performance of the proposed sorting method were evaluated via 10 cases of breath-hold (BH) CT and 30 cases of 4DCT. It was confirmed that N-RMSD (normalised root-mean-square-deviation) was best matched to the visual standards of our institute's regime, manual sorting method, and could accurately represent RMA. The performance of the proposed method to reduce 4DCT uncertainties was improved by about 18.8% in the averaged value of N-RMSD compared to the default phase sorting method. To the best of our knowledge, this is the first study that evaluates RMA indicators using both BHCT and 4DCT with visual-criteria-based manual sorting and proposes an improved 4DCT sorting strategy based on them.

Active small bowel sparing in intracavitary brachytherapy for cervical cancer.

OBJECTIVE: To propose and evaluate an active method for sparing the small bowel in the treatment field of cervical cancer brachytherapy by prone position procedure. METHODS: The prone position procedure consists of five steps: making bladder empty, prone-positioning a patient on belly board, making the small bowel move to abdomen, filling the bladder with Foley catheter and finally turning the patient into the supine position. The proposed method was applied for the treatment of seven cervical cancer patients. Its effectiveness was evaluated and a correlation between the patient characteristics and the volumetric dose reduction of small bowel was also investigated. Brachytherapy treatment plans were built before and after the proposed method, and their dose-volume histograms were compared for targets and organs-at-risk. In this comparison, all plans were normalized to satisfy the same D90% for high-risk clinical target volume. RESULTS: For the enrolled patients, the average dose of small bowel was significantly reduced from 75.2 ± 4.9 Gy before to 60.2 ± 4.0 Gy after the prone position procedure, while minor dosimetric changes were observed in rectum, sigmoid and bladder. The linear correlation to body mass index, thickness and width of abdominopelvic cavity and bladder volume were 76.2, 69.7, 28.8 and -36.3%, respectively. CONCLUSIONS: The application of prone position procedure could effectively lower the volumetric dose of the small bowel. The dose reduction in the small bowel had a strong correlation with the patient's obesity and abdominal thickness. This means the patients for whom the proposed method would be beneficial can be judiciously selected for safe brachytherapy.

Integration of rotatable tandem applicator to conventional ovoid applicator toward complete framework of intensity modulated brachytherapy (IMBT) for cervical cancer.

A new tandem applicator with tungsten shield for Ir-192 radiation source used in intra-cavitary brachytherapy (ICBT) enabled intensity modulated brachytherapy (IMBT) in cervical cancer treatment through fluence-modulation by rotating shield. Our previous work employed group-wise and element-wise sparsity constraints for plan optimization of tandem applicator to minimizes the number of activated angles and source dwell points for delivery efficiency. It, however, did not incorporate the ovoid applicators into the optimizing process, which is generally used to prevent cancer recurrence. To integrate ovoid applicators to the new tandem applicator, this work proposed a comprehensive framework that modifies 1) dose deposition matrix for inverse planning, and 2) plan optimizing algorithm. The dose deposition matrix was newly formulated by the Monte-Carlo simulated dose distribution for 10 positions of ovoid applicators, followed by combining those with tandem-associated dose deposition matrix. The plan optimizing algorithm decomposed entire elements into tandem and ovoid applicators, which were governed by different constraints adaptive to specified plan objectives. The integrated framework was compared against conventional ICBT, and IMBT with tandem only for three patients with asymmetric dose distributions. Integrated IMBT framework resulted in the most optimal plans. Including fluence-modulation by rotating-shield outperformed conventional ICBT in dose sparing to critical organs. Adopting ovoid applicators to the optimization yielded more conformal dose distribution around inferior, laterally expanded region of target volume. The resulting plans reduced D5cc and D2cc by 30.9% and 27.8% for critical organs over conventional ICBT, and by 20.6% and 21.5% for target volume over IMBT with tandem only.

Feasibility of automated planning for whole-brain radiation therapy using deep learning.

PURPOSE: The purpose of this study was to develop automated planning for whole-brain radiation therapy (WBRT) using a U-net-based deep-learning model for predicting the multileaf collimator (MLC) shape bypassing the contouring processes. METHODS: A dataset of 55 cases, including 40 training sets, five validation sets, and 10 test sets, was used to predict the static MLC shape. The digitally reconstructed radiograph (DRR) reconstructed from planning CT images as an input layer and the MLC shape as an output layer are connected one-to-one via the U-net modeling. The Dice similarity coefficient (DSC) was used as the loss function in the training and ninefold cross-validation. Dose-volume-histogram (DVH) curves were constructed for assessing the automatic MLC shaping performance. Deep-learning (DL) and manually optimized (MO) approaches were compared based on the DVH curves and dose distributions. RESULTS: The ninefold cross-validation ensemble test results were consistent with DSC values of 94.6 ± 0.4 and 94.7 ± 0.9 in training and validation learnings, respectively. The dose coverages of 95% target volume were (98.0 ± 0.7)% and (98.3 ± 0.8)%, and the maximum doses for the lens as critical organ-at-risk were 2.9 Gy and 3.9 Gy for DL and MO, respectively. The DL technique shows the consistent results in terms of the DVH parameter except for MLC shaping prediction for dose saving of small organs such as lens. CONCLUSIONS: Comparable with the MO plan result, the WBRT plan quality obtained using the DL approach is clinically acceptable. Moreover, the DL approach enables WBRT auto-planning without the time-consuming manual MLC shaping and target contouring.

Plan optimization with L0-norm and group sparsity constraints for a new rotational, intensity-modulated brachytherapy for cervical cancer.

The aim of this work is to build a framework that comprehends inverse planning procedure and plan optimization algorithm tailored to a novel directional beam intensity-modulated brachytherapy (IMBT) of cervical cancer using a rotatable, single-channel radiation shield. Inverse planning is required for finding optimal beam emitting direction, source dwell position and dwell time, which begin with creating a kernel matrix for each structure based on Monte-Carlo simulated dose distribution in the rotatable shield. For efficient beam delivery and less transit dose, the number of source dwell positions and angles needs to be minimized. It can be solved by L0-norm regularization for fewest possible dwell points, and by group sparsity constraint in L2,p-norm (0≤p<1) besides L0-norm for fewest active applicator rotating angles. The dose distributions from our proposed algorithms were compared to those of conventional tandem-based intracavitary brachytherapy (ICR) plans for six cervical cancer patients. The algorithmic performance was evaluated in delivery efficiency and plan quality relative to the unconstrained algorithm. The proposed framework yielded substantially enhanced plan quality over the conventional ICR plans. The L0-norm and (group sparsity+L0-norm) constrained algorithms reduced the number of source dwell points by 60 and 70% and saved 5 and 8 rotational angles on average (7 and 11 angles for highly modulated cases), relative to the unconstrained algorithm, respectively. Though both algorithms reduced the optimal source dwell positions and angles, the group sparsity constrained optimization with L0-norm was more effective than the L0-norm constraint only, mainly because of considering physical constraints of the new IMBT applicator. With much fewer dwell points compared to the unconstrained, the proposed algorithms led to statistically similar plan quality in dose volume histograms and iso-dose lines. It also demonstrated that the plan optimized by rotating the applicator resulted in much better plan quality than that of conventional applicator-based plans.

Comparative clinical evaluation of atlas and deep-learning-based auto-segmentation of organ structures in liver cancer.

BACKGROUND: Accurate and standardized descriptions of organs at risk (OARs) are essential in radiation therapy for treatment planning and evaluation. Traditionally, physicians have contoured patient images manually, which, is time-consuming and subject to inter-observer variability. This study aims to a) investigate whether customized, deep-learning-based auto-segmentation could overcome the limitations of manual contouring and b) compare its performance against a typical, atlas-based auto-segmentation method organ structures in liver cancer. METHODS: On-contrast computer tomography image sets of 70 liver cancer patients were used, and four OARs (heart, liver, kidney, and stomach) were manually delineated by three experienced physicians as reference structures. Atlas and deep learning auto-segmentations were respectively performed with MIM Maestro 6.5 (MIM Software Inc., Cleveland, OH) and, with a deep convolution neural network (DCNN). The Hausdorff distance (HD) and, dice similarity coefficient (DSC), volume overlap error (VOE), and relative volume difference (RVD) were used to quantitatively evaluate the four different methods in the case of the reference set of the four OAR structures. RESULTS: The atlas-based method yielded the following average DSC and standard deviation values (SD) for the heart, liver, right kidney, left kidney, and stomach: 0.92 ± 0.04 (DSC ± SD), 0.93 ± 0.02, 0.86 ± 0.07, 0.85 ± 0.11, and 0.60 ± 0.13 respectively. The deep-learning-based method yielded corresponding values for the OARs of 0.94 ± 0.01, 0.93 ± 0.01, 0.88 ± 0.03, 0.86 ± 0.03, and 0.73 ± 0.09. The segmentation results show that the deep learning framework is superior to the atlas-based framwork except in the case of the liver. Specifically, in the case of the stomach, the DSC, VOE, and RVD showed a maximum difference of 21.67, 25.11, 28.80% respectively. CONCLUSIONS: In this study, we demonstrated that a deep learning framework could be used more effectively and efficiently compared to atlas-based auto-segmentation for most OARs in human liver cancer. Extended use of the deep-learning-based framework is anticipated for auto-segmentations of other body sites.

Two-dimensional in vivo rectal dosimetry during high-dose-rate brachytherapy for cervical cancer: a phantom study.

BACKGROUND: The aim of the present study was to verify the dosimetric accuracy of two-dimensional (2D) in vivo rectal dosimetry using an endorectal balloon (ERB) with unfoldable EBT3 films for high-dose-rate (HDR) brachytherapy for cervical cancer. The clinical applicability of the technique was discussed. MATERIAL AND METHODS: ERB inflation makes the EBT3 films unrolled, whereas its deflation makes them rolled. Patient-specific quality assurance (pQA) tests were performed in 20 patient plans using an Ir-192 remote afterloading system and a water-filled cervical phantom with the ERB. The dose distributions measured in ERBs were compared with those of the treatment plans. RESULTS: The absolute dose profiles measured by the ERBs were in good agreement with those of treatment plans. The global gamma passing rates were 96-100% and 91-100% over 20 pQAs under the criteria of 3%/3 mm and 3%/2 mm, respectively, with a 30% low-dose threshold. Dose-volume histograms of the rectal wall were obtained from the measured dose distributions and showed small volume differences less than 2% on average from the patients' plans over the entire dose interval. The positioning error of the applicator set was detectable with high sensitivity of 12% dose area variation per mm. Additionally, the clinical applicability of the ERB was evaluated in volunteers, and none of them felt any pain when the ERB was inserted or removed. CONCLUSIONS: The 2D in vivo rectal dosimetry using the ERB with EBT3 films was effective and might be clinically applicable for HDR brachytherapy for cervical and prostate cancers to monitor treatment accuracy and consistency as well as to predict rectal toxicity.