Implant time and process efficiency for CT-guided high-dose-rate brachytherapy for cervical cancer

Neural network dose prediction for cervical brachytherapy: Overcoming data scarcity for applicator-specific models

BACKGROUND

3D neural network dose predictions are useful for automating brachytherapy (BT) treatment planning for cervical cancer. Cervical BT can be delivered with numerous applicators, which necessitates developing models that generalize to multiple applicator types. The variability and scarcity of data for any given applicator type poses challenges for deep learning.

PURPOSE

The goal of this work was to compare three methods of neural network training-a single model trained on all applicator data, fine-tuning the combined model to each applicator, and individual (IDV) applicator models-to determine the optimal method for dose prediction.

METHODS

Models were produced for four applicator types-tandem-and-ovoid (T&O), T&O with 1-7 needles (T&ON), tandem-and-ring (T&R) and T&R with 1-4 needles (T&RN). First, the combined model was trained on 859 treatment plans from 266 cervical cancer patients treated from 2010 onwards. The train/validation/test split was 70%/16%/14%, with approximately 49%/10%/19%/22% T&O/T&ON/T&R/T&RN in each dataset. Inputs included four channels for anatomical masks (high-risk clinical target volume [HRCTV], bladder, rectum, and sigmoid), a mask indicating dwell position locations, and applicator channels for each applicator component. Applicator channels were created by mapping the 3D dose for a single dwell position to each dwell position and summing over each applicator component with uniform dwell time weighting. A 3D Cascade U-Net, which consists of two U-Nets in sequence, and mean squared error loss function were used. The combined model was then fine-tuned to produce four applicator-specific models by freezing the first U-Net and encoding layers of the second and resuming training on applicator-specific data. Finally, four IDV models were trained using only data from each applicator type. Performance of these three model types was compared using the following metrics for the test set: mean error (ME, representing model bias) and mean absolute error (MAE) over all dose voxels and ME of clinical metrics (HRCTV D90% and D2cc of bladder, rectum, and sigmoid), averaged over all patients. A positive ME indicates the clinical dose was higher than predicted. 3D global gamma analysis with the prescription dose as reference value was performed. Dice similarity coefficients (DSC) were computed for each isodose volume.

RESULTS

Fine-tuned and combined models showed better performance than IDV applicator training. Fine-tuning resulted in modest improvements in about half the metrics, compared to the combined model, while the remainder were mostly unchanged. Fine-tuned MAE = 3.98%/2.69%/5.36%/3.80% for T&O/T&R/T&ON/T&RN, and ME over all voxels = -0.08%/-0.89%/-0.59%/1.42%. ME D2cc were bladder = -0.77%/1.00%/-0.66%/-1.53%, rectum = 1.11%/-0.22%/-0.29%/-3.37%, sigmoid = -0.47%/-0.06%/-2.37%/-1.40%, and ME D90 = 2.6%/-4.4%/4.8%/0.0%. Gamma pass rates (3%/3 mm) were 86%/91%/83%/89%. Mean DSCs were 0.92%/0.92%/0.88%/0.91% for isodoses ≤ 150% of prescription.

CONCLUSIONS

3D BT dose was accurately predicted for all applicator types, as indicated by the low MAE and MEs, high gamma scores and high DSCs. Training on all treatment data overcomes challenges with data scarcity in each applicator type, resulting in superior performance than can be achieved by training on IDV applicators alone. This could presumably be explained by the fact that the larger, more diverse dataset allows the neural network to learn underlying trends and characteristics in dose that are common to all treatment applicators. Accurate, applicator-specific dose predictions could enable automated, knowledge-based planning for any cervical brachytherapy treatment.

Predicting treatment plan approval probability for high-dose-rate brachytherapy of cervical cancer using adversarial deep learning

Objective.Predicting the probability of having the plan approved by the physician is important for automatic treatment planning. Driven by the mathematical foundation of deep learning that can use a deep neural network to represent functions accurately and flexibly, we developed a deep-learning framework that learns the probability of plan approval for cervical cancer high-dose-rate brachytherapy (HDRBT).Approach.The system consisted of a dose prediction network (DPN) and a plan-approval probability network (PPN). DPN predicts organs at risk (OAR)D2ccand CTVD90%of the current fraction from the patient's current anatomy and prescription dose of HDRBT. PPN outputs the probability of a given plan being acceptable to the physician based on the patients anatomy and the total dose combining HDRBT and external beam radiotherapy sessions. Training of the networks was achieved by first training them separately for a good initialization, and then jointly via an adversarial process. We collected approved treatment plans of 248 treatment fractions from 63 patients. Among them, 216 plans from 54 patients were employed in a four-fold cross validation study, and the remaining 32 plans from other 9 patients were saved for independent testing.Main results.DPN predicted equivalent dose of 2 Gy for bladder, rectum, sigmoidD2ccand CTVD90%with a relative error of 11.51% ± 6.92%, 8.23% ± 5.75%, 7.12% ± 6.00%, and 10.16% ± 10.42%, respectively. In a task that differentiates clinically approved plans and disapproved plans generated by perturbing doses in ground truth approved plans by 20%, PPN achieved accuracy, sensitivity, specificity, and area under the curve 0.70, 0.74, 0.65, and 0.74.Significance.We demonstrated the feasibility of developing a novel deep-learning framework that predicts a probability of plan approval for HDRBT of cervical cancer, which is an essential component in automatic treatment planning.

Transitioning India to advanced image based adaptive brachytherapy: a national impact analysis of upgrading National Cancer Grid cervix cancer guidelines

Background

High-dose-rate image guided brachytherapy (IGBT) for cervical cancer leads to improved local control and reduced toxicity and is a critical component of treatment. However, transition to IGBT requires capacity upscaling. An institutional activity mapping and national impact analysis of such a transition were undertaken to understand feasibility.

Methods

Between September 2020 and March 2021, activity mapping was conducted in a high-volume centre that triaged cervical cancer patients for brachytherapy into four workflows; A: two-dimensional (2D) X-Ray point A-based intracavitary brachytherapy, B: CT point A-based intracavitary brachytherapy, C: MRI/CT-volume based intracavitary brachytherapy, D: MRI/CT volume-based intracavitary +/- interstitial brachytherapy. Clinical process time mapping was performed, and case scenarios for transition were modelled at the institutional and national levels based on available incidence and infrastructure levels. Treatment capacity changes were calculated, and potential strategies for workflow reorganisation were proposed.

Findings

Eighty-four patients were included in the study. The total time taken for the workflows A, B, C, and D were 176 min (57-208), 224 min (74-260), 267 min (101-302), and 348 min (232-383), respectively. The transition from workflow A to D through sequential steps led to 35%, 49%, and 64% loss of treatment capacity in the index institution. Solutions such as 10-hour or 12-hour overlapping shifts increased treatment capacity by 25% and 50% and performing single implants and delivering multiple fractions increased capacity by 100%. Twenty-three Indian states and Union Territories are predicted to be able to transition to advanced workflows. For four Indian states, it may be detrimental considering the current infrastructure level, and eight Indian states lacked brachytherapy access. Further financial investment is required in the latter 12 states for transition to advanced workflows.

Interpretation

Our study demonstrates that unplanned transition to IGBT can lead to treatment capacity loss and increase in waiting lists to access treatment. The proposed solutions of workflow reorganisation, using strategies such as single brachytherapy applicator implant and delivering multiple treatment fractions can improve access to treatment for women with cervix cancer in resource-strained and high patient-volume settings. We recommend state-wise solutions for the upscale from conventional 2D workflows to IGBT, subject to the availability of skilled personnel, infrastructure and training. Financial investments may be needed in some states to achieve this goal.

Funding

International Atomic Energy Agency (IAEA) supported the salary of VH through project E33042 that focussed on implementation strategies of image guided brachytherapy.

A randomized patient education trial investigating treatment-related distress and satisfaction with the use of an at-home gynecologic brachytherapy educational video

BACKGROUND

Physician explanation of gynecologic brachytherapy can be overwhelming or induce patient anxiety, and may be time-constrained given clinical limitations. We report the first randomized trial of an educational video intervention in gynecologic brachytherapy on patient-reported outcomes.

METHODS

Between February 2020 and January 2022, 80 gynecologic cancer patients prescribed brachytherapy were randomly assigned to either standard informed consent (Arm A) or a supplemental 16 min brachytherapy educational video (https://vimeo.com/403385455/d0716e3cc8) via the internet (Arm B). Primary outcome was treatment-related distress (National Comprehensive Cancer Network (NCCN) distress scale scored 0 (no distress) to 10 (maximum distress)). Secondary outcome was patient satisfaction (summated Likert-scale scored 11-55). Surveys were administered at baseline, after first treatment, and prior to brachytherapy completion.

RESULTS

All patients completed the prescribed brachytherapy. In Arm B, 19/40 (48%) patients and 10/40 (25%) patients' family/friends viewed the video. For patients that completed all surveys (Arm A n=29, Arm B n=28), there was no difference between arms in the sociodemographic, clinical, or treatment variables. Distress scores were low at baseline (Arm A median 4, Arm B median 4, p=0.65) and there was no detectable change in distress between arms on surveys 1 and 2 (β 0.36, p=0.67) or surveys 1 and 3 (β -1.02, p=0.29) in multivariable analysis. Satisfaction scores were high at baseline (Arm A median 54, Arm B median 54.5, p=0.64) and there was no detectable change in satisfaction between arms on surveys 1 and 2 (β 0.22, p=0.93) or surveys 1 and 3 (β 0.63, p=0.85) in multivariable analysis.

CONCLUSIONS

Among patients randomized to an educational video tool for gynecologic brachytherapy, approximately 50% of the cohort and 25% of the cohort's family/friends used the video. Overall, patients had low distress scores and high satisfaction scores with no significant differences between the standard and video intervention arms. Further work is needed to understand factors contributing to gynecologic brachytherapy anxiety.

TRIAL REGISTRATION NUMBER

NCT04363957.

Automated treatment planning framework for brachytherapy of cervical cancer using 3D dose predictions

Objective. To lay the foundation for automated knowledge-based brachytherapy treatment planning using 3D dose estimations, we describe an optimization framework to convert brachytherapy dose distributions directly into dwell times (DTs).Approach. A dose rate kernelḋ(r,θ,φ)was produced by exporting 3D dose for one dwell position from the treatment planning system and normalizing by DT. By translating and rotating this kernel to each dwell position, scaling by DT and summing over all dwell positions, dose was computed (Dcalc). We used a Python-coded COBYLA optimizer to iteratively determine the DTs that minimize the mean squared error betweenDcalcand reference doseDref, computed using voxels withDref80%-120% of prescription. As validation of the optimization, we showed that the optimizer replicates clinical plans whenDref= clinical dose in 40 patients treated with tandem-and-ovoid (T&O) or tandem-and-ring (T&R) and 0-3 needles. Then we demonstrated automated planning in 10 T&O usingDref= dose predicted from a convolutional neural network developed in past work. Validation and automated plans were compared to clinical plans using mean absolute differences (MAD=1N∑n=1Nabsxn-xn') over all voxels (xn= Dose,N= #voxels) and DTs (xn= DT,N= #dwell positions), mean differences (MD) in organD2ccand high-risk CTV D90 over all patients (where positive indicates higher clinical dose), and mean Dice similarity coefficients (DSC) for 100% isodose contours.Main results. Validation plans agreed well with clinical plans (MADdose= 1.1%, MADDT= 4 s or 0.8% of total plan time,D2ccMD = -0.2% to 0.2% and D90 MD = -0.6%, DSC = 0.99). For automated plans, MADdose= 6.5% and MADDT= 10.3 s (2.1%). The slightly higher clinical metrics in automated plans (D2ccMD = -3.8% to 1.3% and D90 MD = -5.1%) were due to higher neural network dose predictions. The overall shape of the automated dose distributions were similar to clinical doses (DSC = 0.91).Significance. Automated planning with 3D dose predictions could provide significant time savings and standardize treatment planning across practitioners, regardless of experience.

Development and validation of a scatter-corrected CBCT image-guided method for cervical cancer brachytherapy

Background and purpose

Multiple patient transfers have a nonnegligible impact on the accuracy of dose delivery for cervical cancer brachytherapy. We consider using on-site cone-beam CT (CBCT) to resolve this problem. However, CBCT clinical applications are limited due to inadequate image quality. This paper implements a scatter correction method using planning CT (pCT) prior to obtaining high-quality CBCT images and evaluates the dose calculation accuracy of CBCT-guided brachytherapy for cervical cancer.

Materials and methods

The CBCT of a self-developed female pelvis phantom and five patients was first corrected using empirical uniform scatter correction in the projection domain and further corrected in the image domain. In both phantom and patient studies, the CBCT image quality before and after scatter correction was evaluated with registered pCT (rCT). Model-based dose calculation was performed using the commercial package Acuros®BV. The dose distributions of rCT-based plans and corrected CBCT-based plans in the phantom and patients were compared using 3D local gamma analysis. A statistical analysis of the differences in dosimetric parameters of five patients was also performed.

Results

In both phantom and patient studies, the HU error of selected ROIs was reduced to less than 15 HU. Using the dose distribution of the rCT-based plan as the baseline, the γ pass rate (2%, 2 mm) of the corrected CBCT-based plan in phantom and patients all exceeded 98% and 93%, respectively, with the threshold dose set to 3, 6, 9, and 12 Gy. The average percentage deviation (APD) of D90 of HRCTV and D2cc of OARs was less than 1% between rCT-based and corrected CBCT-based plans.

Conclusion

Scatter correction using a pCT prior can effectively improve the CBCT image quality and CBCT-based cervical brachytherapy dose calculation accuracy, indicating promising prospects in both simplified brachytherapy processes and accurate brachytherapy dose delivery.

A deep learning-based self-adapting ensemble method for segmentation in gynecological brachytherapy

Purpose

Fast and accurate outlining of the organs at risk (OARs) and high-risk clinical tumor volume (HRCTV) is especially important in high-dose-rate brachytherapy due to the highly time-intensive online treatment planning process and the high dose gradient around the HRCTV. This study aims to apply a self-configured ensemble method for fast and reproducible auto-segmentation of OARs and HRCTVs in gynecological cancer.

Materials and methods

We applied nnU-Net (no new U-Net), an automatically adapted deep convolutional neural network based on U-Net, to segment the bladder, rectum and HRCTV on CT images in gynecological cancer. In nnU-Net, three architectures, including 2D U-Net, 3D U-Net and 3D-Cascade U-Net, were trained and finally ensembled. 207 cases were randomly chosen for training, and 30 for testing. Quantitative evaluation used well-established image segmentation metrics, including dice similarity coefficient (DSC), 95% Hausdorff distance (HD95%), and average surface distance (ASD). Qualitative analysis of automated segmentation results was performed visually by two radiation oncologists. The dosimetric evaluation was performed by comparing the dose-volume parameters of both predicted segmentation and human contouring.

Results

nnU-Net obtained high qualitative and quantitative segmentation accuracy on the test dataset and performed better than previously reported methods in bladder and rectum segmentation. In quantitative evaluation, 3D-Cascade achieved the best performance in the bladder (DSC: 0.936 ± 0.051, HD95%: 3.503 ± 1.956, ASD: 0.944 ± 0.503), rectum (DSC: 0.831 ± 0.074, HD95%: 7.579 ± 5.857, ASD: 3.6 ± 3.485), and HRCTV (DSC: 0.836 ± 0.07, HD95%: 7.42 ± 5.023, ASD: 2.094 ± 1.311). According to the qualitative evaluation, over 76% of the test data set had no or minor visually detectable errors in segmentation.

Conclusion

This work showed nnU-Net’s superiority in segmenting OARs and HRCTV in gynecological brachytherapy cases in our center, among which 3D-Cascade shows the highest accuracy in segmentation across different applicators and patient anatomy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13014-022-02121-3.

Improved survival in cervical cancer patients receiving care at National Cancer Institute-designated cancer centers

Background

Locally advanced cervical cancer (CC) remains lethal in the United States. We investigate the effect of receiving care at an National Cancer Institute–designated cancer center (NCICC) on survival.

Methods

Data for women diagnosed with CC from 2004 to 2016 who received radiation treatment were extracted from the California Cancer Registry (n = 4250). Cox proportional hazards regression models assessed whether (1) receiving care at NCICCs was associated with risk of CC‐specific death, (2) this association remained after multivariable adjustment for age, race/ethnicity, and insurance status, and (3) this association was explained by receipt of guideline‐concordant treatment.

Results

Median age was 50 years (interquartile range [IQR] 41–61 years), with median follow‐up of 2.7 years (IQR 1.3–6.0 years). One‐third of patients were seen at an NCICC, and 29% died of CC. The hazard of CC‐specific death was reduced by 20% for those receiving care at NCICCs compared with patients receiving care elsewhere (HR = .80; 95% CI, 0.70–0.90). Adjustment for guideline‐concordant treatment and other covariates minimally attenuated the association to 0.83 (95% CI, 0.74–0.95), suggesting that the survival advantage associated with care at NCICCs may not be due to receipt of guideline‐concordant treatment.

Conclusions

This study demonstrates survival benefit for patients receiving care at NCICCs compared with those receiving care elsewhere that is not explained by differences in guideline‐concordant care. Structural, organizational, or provider characteristics and differences in patients receiving care at centers with and without NCI designation could explain observed associations. Further understanding of these factors will promote equality across oncology care facilities and survival equity for patients with CC.

This study demonstrates survival benefit for patients receiving care for cervical cancer at National Cancer Institute–designated cancer centers that is not explained by receipt of guideline‐concordant treatment. Further understanding of these factors will promote equality across oncology care facilities resulting in survival equity for patients with cervical cancer.

Knowledge-based three-dimensional dose prediction for tandem-and-ovoid brachytherapy

PURPOSE

The purpose of this work was to develop a knowledge-based dose prediction system using a convolution neural network (CNN) for cervical brachytherapy treatments with a tandem-and-ovoid applicator.

METHODS

A 3D U-NET CNN was utilized to make voxel-wise dose predictions based on organ-at-risk (OAR), high-risk clinical target volume (HRCTV), and possible source location geometry. The model comprised 395 previously treated cases: training (273), validation (61), test (61). To assess voxel prediction accuracy, we evaluated dose differences in all cohorts across the dose range of 20-130% of prescription, mean(SD) and standard deviation (σ), as well as isodose dice similarity coefficients for clinical and/or predicted dose distributions. We examined discrete Dose-Volume Histogram(DVH) metrics utilized for brachytherapy plan quality assessment (HRCTV D90%, and bladder and/or rectum and/or sigmoid D2cc) with ΔD_x=D_x,actual-D_x,predicted Pearson correlation coefficient, standard deviation, and mean further quantifying model performance.

RESULTS

Ranges of voxel-wise dose difference accuracy (δD¯±σ) for 20-130% dose interval in training (test) sets ranged from [-0.5% ± 2.0% to +2.0% ± 14.0%] ([-0.1% ± 4.0% to +4.0% ± 26.0%]) in all voxels, [-1.7% ± 5.1% to -3.5% ± 12.8%] ([-2.9% ± 4.8% to -2.6% ± 18.9%]) in HRCTV, [-0.02% ± 2.40% to +3.2% ± 12.0%] ([-2.5% ± 3.6% to +0.8% ± 12.7%]) in bladder, [-0.7% ± 2.4% to +15.5% ± 11.0%] ([-0.9% ± 3.2% to +27.8% ± 11.6%]) in rectum, and [-0.7% ± 2.3% to +10.7% ± 15.0%] ([-0.4% ± 3.0% to +18.4% ± 11.4%]) in sigmoid. Isodose dice similarity coefficients ranged from [0.96,0.91] for training and [0.94,0.87] for test cohorts. Relative DVH metric prediction in the training (test) set were HRCTV ΔD¯₉₀±σ_ΔD=-0.19 ± 0.55Gy(-0.09 ± 0.67 Gy), bladder ΔD¯_2cc±σ_ΔD= -0.06 ± 0.54Gy(-0.17 ± 0.67 Gy), rectum ΔD¯_2cc±σ_ΔD= -0.03 ± 0.36Gy(-0.04 ± 0.46 Gy), and sigmoid ΔD¯_2cc±σ_ΔD= -0.01 ± 0.34Gy(0.00 ± 0.44 Gy).

CONCLUSIONS

A 3D knowledge-based dose predictions provide voxel-level and DVH metric estimates that could be used for treatment plan quality control and data-driven plan guidance.

Phase I Trial of Stereotactic MRI-Guided Online Adaptive Radiation Therapy (SMART) for the Treatment of Oligometastatic Ovarian Cancer

PURPOSE

Stereotactic body radiation therapy is increasingly used to treat a variety of oligometastatic histologies, but few data exist for ovarian cancer. Ablative stereotactic body radiation therapy dosing is challenging in sites like the abdomen, pelvis, and central thorax due to proximity and motion of organs at risk. A novel radiation delivery method, stereotactic magnetic-resonance-guided online-adaptive radiation therapy (SMART), may improve the therapeutic index of stereotactic body radiation therapy through enhanced soft-tissue visualization, real-time nonionizing imaging, and ability to adapt to the anatomy-of-the-day, with the goal of producing systemic-therapy-free intervals. This phase I trial assessed feasibility, safety, and dosimetric advantage of SMART to treat ovarian oligometastases.

METHODS AND MATERIALS

Ten patients with recurrent oligometastatic ovarian cancer underwent SMART for oligometastasis ablation. Initial plans prescribed 35 Gy/5 fractions with goal 95% planning target volume coverage by 95% of prescription, with dose escalation permitted, subject to strict organ-at-risk dose constraints. Daily adaptive planning was used to protect organs-at-risk and/or increase target dose. Feasibility (successful delivery of >80% of fractions in the first on-table attempt) and safety of this approach was evaluated, in addition to efficacy, survival metrics, quality-of-life, prospective timing and dosimetric outcomes.

RESULTS

Ten women with seventeen ovarian oligometastases were treated with SMART, and 100% of treatment fractions were successfully delivered. Online adaptive plans were selected at time of treatment for 58% of fractions, due to initial plan violation of organs-at-risk constraints (84% of adapted fractions) or observed opportunity for planning target volume dose escalation (16% of adapted fractions), with a median on-table time of 64 minutes. A single Grade ≥3 acute (within 6 months of SMART) treatment-related toxicity (duodenal ulcer) was observed. Local control at 3 months was 94%; median progression-free survival was 10.9 months. Median Kaplan-Meier estimated systemic-therapy-free survival after radiation completion was 11.5 months, with concomitant quality-of-life improvements.

CONCLUSIONS

SMART is feasible and safe for high-dose radiation therapy ablation of ovarian oligometastases of the abdomen, pelvis, and central thorax with minimal toxicity, high rates of local control, and prolonged systemic-therapy-free survival translating into improved quality-of-life.

Non-anesthetist-administered moderate sedation with midazolam and fentanyl for outpatient MRI-aided hybrid intracavitary and interstitial brachytherapy in cervix cancer: a single-institution experience

Purpose

The aim of the study was to determine the feasibility of interstitial brachytherapy under non-anesthetist-administered moderate sedation, to identify factors influencing the insertion, and the total procedural time.

Material and methods

A total of 47 insertions with hybrid intracavitary and interstitial applicators were performed in 23 patients from March 2017 to March 2020. Moderate sedation was achieved with intravenous midazolam and fentanyl administered by non-anesthetist. Insertion time and procedural time was recorded. Univariate and multivariate analysis were performed to evaluate the impact of different factors on insertion and procedural time.

Results

A total of 238 needles (range, 2-8 per insertion) were implanted, with an average insertion depth of 30 mm (range, 20-40 mm). The mean doses for midazolam and fentanyl were 3 mg (standard deviation [SD] = 1) and 53.3 mcg (SD = 23.9) per insertion, respectively. The median insertion time was 30 minutes (interquartile range [IQR] = 22-40), and the median total procedural time was 4.3 hours (IQR = 3.6-5.2). First time insertion, insertions performed before 2019, and higher midazolam dose were associated with significantly longer insertion time, whereas longer insertion time, MRI-based planning, and insertions performed before 2019 were associated with significantly longer total procedural time.

Conclusions

Outpatient interstitial brachytherapy with non-anesthetist-administered sedation is achievable and well-tolerated. This method may significantly lessen the burden on hospital resources and has the potential to be cost-effective.

Benefit of replanning in MR-guided online adaptive radiation therapy in the treatment of liver metastasis

PURPOSE

To assess the effects of daily adaptive MR-guided replanning in stereotactic body radiation therapy (SBRT) of liver metastases based on a patient individual longitudinal dosimetric analysis.

METHODS

Fifteen patients assigned to SBRT for oligometastatic liver metastases underwent daily MR-guided target localization and on-table treatment plan re-optimization. Gross tumor volume (GTV) and organs at risk (OARs) were adapted to the anatomy-of-the-day. A reoptimized plan (RP) and a rigidly shifted baseline plan (sBP) without re-optimization were generated for each fraction. After extraction of DVH parameters for GTV, planning target volume (PTV), and OARs (stomach, duodenum, bowel, liver, heart) plans were compared on a per-patient basis.

RESULTS

Median pre-treatment GTV and PTV were 14.9 cc (interquartile range (IQR): 7.7-32.9) and 62.7 cc (IQR: 42.4-105.5) respectively. SBRT with RP improved PTV coverage (V100%) for 47/75 of the fractions and reduced doses to the most proximal OARs (D1cc, Dmean) in 33/75 fractions compared to sBP. RP significantly improved PTV coverage (V100%) for metastases within close proximity to an OAR by 4.0% (≤ 0.2 cm distance from the edge of the PTV to the edge of the OAR; n = 7; p = 0.01), but only by 0.2% for metastases farther away from OAR (> 2 cm distance; n = 7; p = 0.37). No acute grade 3 treatment-related toxicities were observed.

CONCLUSIONS

MR-guided online replanning SBRT improved target coverage and OAR sparing for liver metastases with a distance from the edge of the PTV to the nearest luminal OAR < 2 cm. Only marginal improvements in target coverage were observed for target distant to critical OARs, indicating that these patients do not benefit from daily adaptive replanning.

Semi-automatic sigmoid colon segmentation in CT for radiation therapy treatment planning via an iterative 2.5-D deep learning approach

Automatic sigmoid colon segmentation in CT for radiotherapy treatment planning is challenging due to complex organ shape, close distances to other organs, and large variations in size, shape, and filling status. The patient bowel is often not evacuated, and CT contrast enhancement is not used, which further increase problem difficulty. Deep learning (DL) has demonstrated its power in many segmentation problems. However, standard 2-D approaches cannot handle the sigmoid segmentation problem due to incomplete geometry information and 3-D approaches often encounters the challenge of a limited training data size. Motivated by human's behavior that segments the sigmoid slice by slice while considering connectivity between adjacent slices, we proposed an iterative 2.5-D DL approach to solve this problem. We constructed a network that took an axial CT slice, the sigmoid mask in this slice, and an adjacent CT slice to segment as input and output the predicted mask on the adjacent slice. We also considered other organ masks as prior information. We trained the iterative network with 50 patient cases using five-fold cross validation. The trained network was repeatedly applied to generate masks slice by slice. The method achieved average Dice similarity coefficients of 0.82 0.06 and 0.88 0.02 in 10 test cases without and with using prior information.

A knowledge-based organ dose prediction tool for brachytherapy treatment planning of patients with cervical cancer

PURPOSE

The purpose of this study is to explore knowledge-based organ-at-risk dose estimation for intracavitary brachytherapy planning for cervical cancer. Using established external-beam knowledge-based dose-volume histogram (DVH) estimation methods, we sought to predict bladder, rectum, and sigmoid D_2cc for tandem and ovoid treatments.

METHODS AND MATERIALS

A total of 136 patients with loco-regionally advanced cervical cancer treated with 456 (356:100 training:validation ratio) CT-based tandem and ovoid brachytherapy fractions were analyzed. Single fraction prescription doses were 5.5-8 Gy with dose criteria for the high-risk clinical target volume, bladder, rectum, and sigmoid. DVH estimations were obtained by subdividing training set organs-at-risk into high-risk clinical target volume boundary distance subvolumes and computing cohort-averaged differential DVHs. Full DVH estimation was then performed on the training and validation sets. Model performance was quantified by ΔD_2cc = D_2cc(actual)-D_2cc(predicted) (mean and standard deviation). ΔD_2cc between training and validation sets were compared with a Student's t test (p < 0.01 significant). Categorical variables (physician, fraction-number, total fractions, and case complexity) that might explain model variance were examined using an analysis of variance test (Bonferroni-corrected p < 0.01 threshold).

RESULTS

Training set deviations were bladder ΔD_2cc = -0.04 ± 0.61 Gy, rectum ΔD_2cc = 0.02 ± 0.57 Gy, and sigmoid ΔD_2cc = -0.05 ± 0.52 Gy. Model predictions on validation set did not statistically differ: bladder ΔD_2cc = -0.02 ± 0.46 Gy (p = 0.80), rectum ΔD_2cc = -0.007 ± 0.47 Gy (p = 0.53), and sigmoid ΔD_2cc = -0.07 ± 0.47 Gy (p = 0.70). The only significant categorical variable was the attending physician for bladder and rectum ΔD_2cc. CONCLUSION: A simple boundary distance-driven knowledge-based DVH estimation exhibited promising results in predicting critical brachytherapy dose metrics. Future work will examine the utility of these predictions for quality control and automated brachytherapy planning.

Deep-learning assisted automatic digitization of interstitial needles in 3D CT image based high dose-rate brachytherapy of gynecological cancer

Digitization of interstitial needles is a complicated and tedious process for the treatment planning of 3D CT image based interstitial high dose-rate brachytherapy (HDRBT) of gynecological cancer. We developed a deep-learning assisted auto-digitization method for interstitial needles. The digitization method consisted of two steps. The first step used a deep neural network with a U-net structure to segment all needles from CT images. The second step simultaneously clustered the segmented voxels into different needle groups and generated the needle central trajectories by solving an optimization problem. We evaluated the effectiveness of the developed method in ten interstitial HDRBT patient cases that were not used in the training of the U-net. Average number of needles per case was 20.7. For the segmentation step, average Dice similarity coefficient between automatic and manual segmentation was 0.93. For the digitization step, Hausdorff distance between needle trajectories determined by our method and manually by qualified medical physicists was ~0.71 mm on average and mean difference of tip positions was ~0.63 mm, which were considered acceptable for HDRBT treatment planning. It took ~5 min to complete the digitization process of an interstitial HDRBT case. The achieved accuracy and efficiency made our method clinically attractive.

Automatic tandem and ring reconstruction using MRI for cervical cancer brachytherapy

PURPOSE

The MRI-guided cervical cancer brachytherapy provides unparalleled soft-tissue contrast for target and normal tissue contouring, but eliminates the ability to use conventional metallic fiducials for radiation source path reconstruction as required for treatment planning. Instead, the source path is reconstructed by manually aligning a library model to the signal void produced by the applicator, which takes time intraoperatively and precludes fully automated treatment planning. The purpose of this study is to present and validate an algorithm to automatically reconstruct tandem and ring applicators using MRI for cervical cancer brachytherapy treatment planning.

METHODS

Applicators were reconstructed using T2-weighted MR images acquired at 1.5 T from 33 brachytherapy fractions including 10 patients using a model-to-image registration algorithm. The algorithm involves (a) image filtering and maximum intensity projection to highlight the applicator, (b) ring center identification using the circular Hough transform, and (c) three-dimensional surface model registration, optimized by maximizing the image intensity gradient normal to the model surface. Two independent observers manually reconstructed all applicators, enabling the calculation of interobserver variability and establishing a ground truth. Algorithm variability was calculated by comparing algorithm results to each individual observer, and algorithm accuracy was calculated by comparing algorithm results to the ground truth. The algorithm variability and accuracy were compared to the interobserver variability using paired t-tests.

RESULTS

Mean ± SD interobserver variability was 0.83 ± 0.31 mm and 0.78 ± 0.29 mm for the ring and tandem, respectively. The algorithm had mean ± SD variability and accuracy of 0.72 ± 0.32 mm (P = 0.02) and 0.60 ± 0.24 mm (P = 0.0005) for the ring, and 0.70 ± 0.29 mm (P = 0.11) and 0.58 ± 0.24 mm (P = 0.004) for the tandem, respectively.

CONCLUSIONS

The algorithm variability and accuracy were within the interobserver variability measured in this study. The algorithm accuracy and mean execution time of 10.0 s are sufficient for clinical tandem and ring reconstruction, and are a step toward fully automated tandem and ring brachytherapy treatment planning.

Workflow and efficiency in MRI-based high-dose-rate brachytherapy for cervical cancer in a high-volume brachytherapy center

PURPOSE

We report the clinical workflow and time required for MRI-based image-guided brachytherapy (MR-IGBT) of cervical cancer patients in a high-volume brachytherapy center with 10 years of experiences to provide a practical guideline for implementing MR-IGBT into clinical use.

METHODS AND MATERIALS

We recorded the time and workflow of each procedure step within the 40 consecutive ring and tandem applicator fractions of MR-IGBT by our multidisciplinary team. We divided the entire procedure into four sections based on where the procedure was performed: (1) applicator insertion under sedation, (2) MR imaging, (3) planning, and (4) treatment delivery. In addition, we compared the current procedure time to the initial procedure time when first implementing MR-IGBT in 2007-2008 via a retrospective review.

RESULTS

Mean total procedure time was 149.3 min (SD 17.9, ranges 112-178). The multidisciplinary team included an anesthesia team, radiologist, radiation oncologist, nurses, radiation therapists, MRI technicians, dosimetrists, and physicists. The mean procedure time and ranges for each section (min) were as follows: (1) 56.2 (28.0-103.0), (2) 31.0 (19.0-70.0), (3) 44.3 (21.0-104.0), and (4) 17.8 (9.0-34.0). Under current setting, the combined mean procedure time for MR imaging and planning was 63.2 min. In comparison, the same procedure took 137.7 min in 2007-2008 period, which was significantly longer than the current workflow (p < 0.001).

CONCLUSIONS

A skilled and dedicated multidisciplinary team is required for an efficient clinical workflow and delivery of MR-IGBT. Over the years, we have improved efficiency with clinical experience and continuous efforts in staff education.

Underutilization of brachytherapy and disparities in survival for patients with cervical cancer in California

PURPOSE

The treatment for locally advanced cervical cancer is external beam radiation (EBRT), concurrent chemotherapy, and brachytherapy (BT). We investigated demographic and socioeconomic factors that influence trends in BT utilization and disparities in survival.

METHODS

Using the California Cancer Registry, cervical cancer patients FIGO IB2-IVA from 2004 to 2014 were identified. We collected tumor, demographic and socioeconomic (SES) factors. We used multivariable logistic regression analysis to determine predictors of use of BT. Using Cox proportional hazards, we examined the impact of BT vs EBRT boost on cause specific (CSS) and overall survival (OS).

RESULTS

We identified 4783 patients with FIGO stage 11% IB2; 32% II, 54% III, 3% IVA. Nearly half (45%) of patients were treated with BT, 18% were treated with a EBRT boost, and 37% had no boost. Stage II and III were more likely to be treated with BT (p = 0.002 and p = 0.0168) vs Stage IB2. As patients aged, the use of BT decreased. Using multivariate analysis, BT impacted CCS (HR 1.16, p = 0.0330) and OS (HR 1.14, p = 0.0333). Worse CSS was observed for black patients (p = 0.0002), low SES (p = 0.0263), stage III and IVA (p < 0.0001. Black patients, low and middle SES had worse OS, (p = 0.0003).

CONCLUSIONS

The utilization of BT in locally advanced cervical cancer was low at 45%, with a decrease in CSS and OS. Black patients and those in low SES had worse CSS. As we strive for outcome improvement in cervical cancer, we need to target increasing access and disparities for quality and value.

Phase I trial of stereotactic MR-guided online adaptive radiation therapy (SMART) for the treatment of oligometastatic or unresectable primary malignancies of the abdomen

PURPOSE/OBJECTIVES

SBRT is used to treat oligometastatic or unresectable primary abdominal malignancies, although ablative dose delivery is limited by proximity of organs-at-risk (OAR). Stereotactic, magnetic resonance (MR)-guided online-adaptive radiotherapy (SMART) may improve SBRT's therapeutic ratio. This prospective Phase I trial assessed feasibility and potential advantages of SMART to treat abdominal malignancies.

MATERIALS/METHODS

Twenty patients with oligometastatic or unresectable primary liver (n = 10) and non-liver (n = 10) abdominal malignancies underwent SMART. Initial plans prescribed 50 Gy/5 fractions (BED 100 Gy) with goal 95% PTV coverage by 95% of prescription, subject to hard OAR constraints. Daily real-time online-adaptive plans were created as needed, based on daily setup MR-image-set tumor/OAR "anatomy-of-the-day" to preserve hard OAR constraints, escalate PTV dose, or both. Treatment times, patient outcomes, and dosimetric comparisons between initial and adaptive plans were prospectively recorded.

RESULTS

Online adaptive plans were created at time of treatment for 81/97 fractions, due to initial plan violation of OAR constraints (61/97) or observed opportunity for PTV dose escalation (20/97). Plan adaptation increased PTV coverage in 64/97 fractions. Zero Grade ≥ 3 acute (<6 months) treatment-related toxicities were observed.

DISCUSSION

SMART is clinically deliverable and safe, allowing PTV dose escalation and/or simultaneous OAR sparing compared to non-adaptive abdominal SBRT.

From Computed Tomography-Guided to Magnetic Resonance Imaging-Guided Intracavitary Brachytherapy for Cervical Cancer: What Do the Key Stakeholders Have to Say about the Transition?

BACKGROUND

International brachytherapy consortiums are advocating for the incorporation of magnetic resonance imaging (MRI) into the cervical brachytherapy process as a standard-of-care. Although some evaluations have been performed to quantify the effect on procedural time, little is known about the views and experiences of key stakeholders during the transition from computed tomography to MR-guided brachytherapy. This qualitative research project explored insights from key stakeholders related to a change in the gynaecologic brachytherapy process.

METHODS AND MATERIALS

Semi-structured interviews were designed using Lean Methodology principles and all key members in the gynaecologic brachytherapy team were approached for participation: radiation oncologists, medical physicists, radiation therapists, the lead MR technologist, and the ward nurse manager. Interviews were recorded and transcribed, and analysis was performed to identify themes from the data.

RESULTS

Ten of 12 (83% participation rate) key members of the team were interviewed. Four themes emerged from the data: challenges to efficiency, staff availability, patient history and disease characteristics, and team communication. The stakeholders expressed that the challenges during this transition were procedural inefficiency (sharing of the MRI scanner and increased procedure length because of increased complexity in contouring and planning), and staff availability (radiation oncologist and transportation staff). The clinical team identified the value of communicating patient history and disease characteristics ahead of the brachytherapy procedure day and also using an inclusive mode of communication during the procedure was beneficial.

CONCLUSIONS

This research provides nuanced insights into process and practice changes that occur when one imaging technology is simply swapped for another, emphasizing how intertwined and complex brachytherapy procedures can be. It emphasizes that not all challenges to efficiency are considered Lean Wastes, and that seemingly simple procedural changes can result in unanticipated differences in staff availability, communication pathways, and knowledge requirements.

Evaluation of Delivery Costs for External Beam Radiation Therapy and Brachytherapy for Locally Advanced Cervical Cancer Using Time-Driven Activity-Based Costing

PURPOSE

To evaluate the delivery costs, using time-driven activity-based costing, and reimbursement for definitive radiation therapy for locally advanced cervical cancer.

METHODS AND MATERIALS

Process maps were created to represent each step of the radiation treatment process and included personnel, equipment, and consumable supplies used to deliver care. Personnel were interviewed to estimate time involved to deliver care. Salary data, equipment purchasing information, and facilities costs were also obtained. We defined the capacity cost rate (CCR) for each resource and then calculated the total cost of patient care according to CCR and time for each resource. Costs were compared with 2016 Medicare reimbursement and relative value units (RVUs).

RESULTS

The total cost of radiation therapy for cervical cancer was $12,861.68, with personnel costs constituting 49.8%. Brachytherapy cost $8610.68 (66.9% of total) and consumed 423 minutes of attending radiation oncologist time (80.0% of total). External beam radiation therapy cost $4055.01 (31.5% of total). Personnel costs were higher for brachytherapy than for the sum of simulation and external beam radiation therapy delivery ($4798.73 vs $1404.72). A full radiation therapy course provides radiation oncologists 149.77 RVUs with intensity modulated radiation therapy or 135.90 RVUs with 3-dimensional conformal radiation therapy, with total reimbursement of $23,321.71 and $16,071.90, respectively. Attending time per RVU is approximately 4-fold higher for brachytherapy (5.68 minutes) than 3-dimensional conformal radiation therapy (1.63 minutes) or intensity modulated radiation therapy (1.32 minutes).

CONCLUSIONS

Time-driven activity-based costing was used to calculate the total cost of definitive radiation therapy for cervical cancer, revealing that brachytherapy delivery and personnel resources constituted the majority of costs. However, current reimbursement policy does not reflect the increased attending physician effort and delivery costs of brachytherapy. We hypothesize that the significant discrepancy between treatment costs and physician effort versus reimbursement may be a potential driver of reported national trends toward poor compliance with brachytherapy, and we suggest re-evaluation of payment policies to incentivize quality care.

American Brachytherapy Task Group Report: A pooled analysis of clinical outcomes for high-dose-rate brachytherapy for cervical cancer

PURPOSE

Advanced imaging used in combination with brachytherapy (BT) has revolutionized the treatment of patients with cervical cancer. We present a comprehensive review of the literature for definitive radiation with high-dose-rate (HDR) BT. In addition, we investigate potential outcome improvement with image-based brachytherapy (IBBT) compared to studies using traditional Point A dosing. This review extensively investigates acute and late toxicities.

METHODS AND MATERIALS

This study reviews the literature from 2000 to 2015 with an emphasis on modern approaches including concurrent chemotherapy (chemoRT), radiation, and HDR BT and IBBT. Descriptive statistics and pelvic control (PC), disease-free survival (DFS), and overall survival (OS) outcomes were calculated using weighted means to report pooled analysis of outcomes.

RESULTS

Literature search yielded 16 prospective, 51 retrospective studies that reported survival outcomes, and 13 retrospective studies that focused on acute and late toxicity outcomes regardless of applicator type. There are 57 studies that report Point A dose specification with 33 having chemoRT, and 10 studies that use IBBT, 8 with chemoRT. Patients receiving radiation and chemoRT with HDR BT in the prospective studies, with >24 months followup, rates of PC were: for RT: 73%, SD: 11; CRT: 82%, SD: 8; DFS-RT: 55%, SD: 10; CRT: 65%, SD: 7; OS-RT: 66%, SD: 7; CRT: 70%, SD: 11. In the retrospective studies, the PC rates (weighted means) for the radiation and chemoradiation outcomes are 75% vs. 80%, and for DFS, the values were 55% vs. 63%, respectively. Comparing patients receiving chemoRT and IBBT to traditional Point A dose specification, there is a significant improvement in PC (p < 0.01) and DFS (p < 0.01) with IBBT. The range of genitourinary late toxicity reported for radiation was Grade 3: 1-6% and for chemoRT 2-20%. The range of late gastrointestinal toxicity for radiation was Grade 3: 4-11% and for chemoRT, 1-11%. For the late gynecologic toxicity, only 1 of the 16 prospective trials report a Grade 1-2 of 17% for radiation and 9% for chemoRT effects.

CONCLUSIONS

We present concise outcomes of PC, DFS, OS, and toxicity for cervical cancer patients treated with chemoradiation and HDR BT. Our data suggest an improvement in outcomes with the use of IBBT compared with traditional Point A dose prescriptions. In conclusion, HDR BT is a safe, effective modality when combined with IBBT.

MR-guided brachytherapy for cervical cancer: Quantifying process waste and identifying opportunities for system performance improvement

PURPOSE

The aim of this study was to evaluate the current cervical cancer magnetic resonance-guided brachytherapy (MRgBT) process in the study institution and seek opportunities to improve efficiency and optimize quality of care.

METHODS

Eight procedures were observed where the time, location, and activities performed by health care professionals were recorded using Lean method principles. Wastes, as defined within Lean methodology, were identified during the observation. Workflow was illustrated by process maps. Milestone activities were identified for final time analysis. Finally, the research team developed solutions by eliminating wastes in the workflow.

RESULTS

The mean procedure time ± standard deviation was 8.2 ± 0.41 hours, including 1.9 ± 0.18 hours spent on Lean wastes (84% waiting, 16% transportation). The most inefficient high-level activity was MRI with 1.1 hours spent on waiting and transportation. The parallel processing solution yields a 2.3 hours' time saving in each case. The optimal solution recommends an integrated MRgBT suite and dedicated human resource, yields a 4.1 hours' time saving (50% increase in efficiency) in each case.

CONCLUSION

This was the first study to quantify the performance of an MRgBT process. This study can serve as a template for other brachytherapy process improvements.

Parallelized patient-specific quality assurance for high-dose-rate image-guided brachytherapy in an integrated computed tomography-on-rails brachytherapy suite

PURPOSE

To describe a parallelized patient-specific quality assurance (QA) program designed to ensure safety and quality in image-guided high-dose-rate brachytherapy in an integrated computed tomography (CT)-on-rails brachytherapy suite.

MATERIALS AND METHODS

A patient-specific QA program has been modified for the image-guided brachytherapy (IGBT) program in an integrated CT-on-rails brachytherapy suite. In the modification of the QA procedures of Task Group-59, the additional patient-specific QA procedures are included to improve rapid IGBT workflow with applicator placement, imaging, planning, treatment, and applicator removal taking place in one room.

RESULTS

The IGBT workflow is partitioned into two groups of tasks that can be performed in parallel by two or more staff members. One of the unique components of our implemented workflow is that groups work together to perform QA steps in parallel and in series during treatment planning and contouring. Coordinating efforts in this systematic way enable rapid and safe brachytherapy treatment while incorporating 3-dimensional anatomic variations between treatment days.

CONCLUSIONS

Implementation of these patient-specific QA procedures in an integrated CT-on-rails brachytherapy suite ensures confidence that a rapid workflow IGBT program can be implemented without sacrificing patient safety or quality and deliver highly-conformal dose to target volumes. These patient-specific QA components may be adapted to other IGBT environments that seek to provide rapid workflow while ensuring quality.

Redesign of process map to increase efficiency: Reducing procedure time in cervical cancer brachytherapy

PURPOSE

To increase intraprocedural efficiency in the use of clinical resources and to decrease planning time for cervical cancer brachytherapy treatments through redesign of the procedure's process map.

METHODS AND MATERIALS

A multidisciplinary team identified all tasks and associated resources involved in cervical cancer brachytherapy in our institution and arranged them in a process map. A redesign of the treatment planning component of the process map was conducted with the goal of minimizing planning time. Planning time was measured on 20 consecutive insertions, of which 10 were performed with standard procedures and 10 with the redesigned process map, and results were compared. Statistical significance (p < 0.05) was measured with a two-tailed t test.

RESULTS

Twelve tasks involved in cervical cancer brachytherapy treatments were identified. The process map showed that in standard procedures, the treatment planning tasks were performed sequentially. The process map was redesigned to specify that contouring and some planning tasks are performed concomitantly. Some quality assurance tasks were reorganized to minimize adverse effects of a possible error on procedure time. Test dry runs followed by live implementation confirmed the applicability of the new process map to clinical conditions. A 29% reduction in planning time (p < 0.01) was observed with the introduction of the redesigned process map.

CONCLUSIONS

A process map for cervical cancer brachytherapy was generated. The treatment planning component of the process map was redesigned, resulting in a 29% decrease in planning time and a streamlining of the quality assurance process.