Validation of mathematical models for the prediction of organs-at-risk dosimetric metrics in high-dose-rate gynecologic interstitial brachytherapy

Impact of bowel space contouring variability on radiation dose and volume assessments in treatment planning for gynaecologic cancers

INTRODUCTION

Correlations between radiation dose/volume measures and small bowel (SB) toxicity are inconsistent in the medical literature. We assessed the impact of inter-provider variation in bowel bag contouring technique on estimates of radiation dose received by the SB during pelvic radiotherapy.

METHODS

Ten radiation oncologists contoured rectum, bladder and bowel bags on treatment planning computed tomography (CT) scans of two patients receiving adjuvant radiation for endometrial cancer. A radiation plan was generated for each patient and used to determine the radiation dose/volume for each organ. Kappa statistics were applied to assess the inter-provider contouring agreement, and Levene test evaluated the homogeneity of variance for radiation dose/volume metrics, including the V45Gy (cm3 ).

RESULTS

The bowel bag showed greater variation in radiation dose/volume estimates compared to the bladder and rectum. The V45Gy ranged from 163 to 384 cm3 for data set A and 109 to 409 cm3 for dataset B. Kappa values were 0.82/0.83, 0.92/0.92 and 0.94/0.86 for the bowel bag, rectum, and bladder on data sets A/B, demonstrating lower inter-provider agreement for bowel bag compared with bladder and rectum.

CONCLUSION

Inter-provider contouring variability is more significant for the bowel bag than the rectum and bladder, with an associated greater variability in dose and volume estimates during radiation planning.

Impact of bowel space contouring variability on radiation dose and volume assessments in treatment planning for gynaecologic cancers

Introduction

Correlations between radiation dose/volume measures and small bowel (SB) toxicity are inconsistent in the medical literature. We assessed the impact of inter‐provider variation in bowel bag contouring technique on estimates of radiation dose received by the SB during pelvic radiotherapy.

Methods

Ten radiation oncologists contoured rectum, bladder and bowel bags on treatment planning computed tomography (CT) scans of two patients receiving adjuvant radiation for endometrial cancer. A radiation plan was generated for each patient and used to determine the radiation dose/volume for each organ. Kappa statistics were applied to assess the inter‐provider contouring agreement, and Levene test evaluated the homogeneity of variance for radiation dose/volume metrics, including the V45Gy (cm3).

Results

The bowel bag showed greater variation in radiation dose/volume estimates compared to the bladder and rectum. The V45Gy ranged from 163 to 384 cm3 for data set A and 109 to 409 cm3 for dataset B. Kappa values were 0.82/0.83, 0.92/0.92 and 0.94/0.86 for the bowel bag, rectum, and bladder on data sets A/B, demonstrating lower inter‐provider agreement for bowel bag compared with bladder and rectum.

Conclusion

Inter‐provider contouring variability is more significant for the bowel bag than the rectum and bladder, with an associated greater variability in dose and volume estimates during radiation planning.

Dose prediction of organs at risk in patients with cervical cancer receiving brachytherapy using needle insertion based on a neural network method

OBJECTIVE

A neural network method was employed to establish a dose prediction model for organs at risk (OAR) in patients with cervical cancer receiving brachytherapy using needle insertion.

METHODS

A total of 218 CT-based needle-insertion brachytherapy fraction plans for loco-regionally advanced cervical cancer treatment were analyzed in 59 patients. The sub-organ of OAR was automatically generated by self-written MATLAB, and the volume of the sub-organ was read. Correlations between D2cm³ of each OAR and volume of each sub-organ-as well as high-risk clinical target volume for bladder, rectum, and sigmoid colon-were analyzed. We then established a neural network predictive model of D2cm³ of OAR using the matrix laboratory neural net. Of these plans, 70% were selected as the training set, 15% as the validation set, and 15% as the test set. The regression R value and mean squared error were subsequently used to evaluate the predictive model.

RESULTS

The D2cm³/D90 of each OAR was related to volume of each respective sub-organ. The R values for bladder, rectum, and sigmoid colon in the training set for the predictive model were 0.80513, 0.93421, and 0.95978, respectively. The ∆D2cm³/D90 for bladder, rectum, and sigmoid colon in all sets was 0.052 ± 0.044, 0.040 ± 0.032, and 0.041 ± 0.037, respectively. The MSE for bladder, rectum, and sigmoid colon in the training set for the predictive model was 4.779 × 10^-3, 1.967 × 10^-3 and 1.574 × 10^-3, respectively.

CONCLUSION

The neural network method based on a dose-prediction model of OAR in brachytherapy using needle insertion was simple and reliable. In addition, it only addressed volumes of sub-organs to predict the dose of OAR, which we believe is worthy of further promotion and application.

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.

Support Vector Machine Model Predicts Dose for Organs at Risk in High-Dose Rate Brachytherapy of Cervical Cancer

Introduction

This study aimed to establish a support vector machine (SVM) model to predict the dose for organs at risk (OARs) in intracavitary brachytherapy planning for cervical cancer with tandem and ovoid treatments.

Methods

Fifty patients with loco-regionally advanced cervical cancer treated with 200 CT-based tandem and ovoid brachytherapy plans were included. The brachytherapy plans were randomly divided into the training (N = 160) and verification groups (N = 40). The bladder, rectum, sigmoid colon, and small intestine were divided into sub-OARs. The SVM model was established using MATLAB software based on the sub-OAR volume to predict the bladder, rectum, sigmoid colon, and small intestine D 2 c m 3 . Model performance was quantified by mean squared error (MSE) and δ ( δ = | D 2 c m 3 / D prescription ( actual ) - D 2 c m 3 / D prescription ( predicted ) | ) . The goodness of fit of the model was quantified by the coefficient of determination (R²). The accuracy and validity of the SVM model were verified using the validation group.

Results

The D 2 c m 3 value of the bladder, rectum, sigmoid colon, and small intestine correlated with the volume of the corresponding sub-OARs in the training group. The mean squared error (MSE) in the SVM model training group was <0.05; the R² of each OAR was >0.9. There was no significant difference between the D 2 c m 3 -predicted and actual values in the validation group (all P > 0.05): bladder δ = 0.024 ± 0.022, rectum δ = 0.026 ± 0.014, sigmoid colon δ = 0.035 ± 0.023, and small intestine δ = 0.032 ± 0.025.

Conclusion

The SVM model established in this study can effectively predict the D 2 c m 3 for the bladder, rectum, sigmoid colon, and small intestine in cervical cancer brachytherapy.

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.