A non-rigid point matching method with local topology preservation for accurate bladder dose summation in high dose rate cervical brachytherapy

Deformation trajectory prediction using a neural network trained on finite element data-application to library of CTVs creation for cervical cancer

Purpose. We propose a neural network for fast prediction of realistic, time-parametrized deformations between pairs of input segmentations. The proposed method was used to generate a library of planning CTVs for cervical cancer radiotherapy.Methods.A 3D convolutional neural network (CNN) was introduced to predict a stationary velocity field given the distance maps of the cervix CTV in empty and full bladder anatomy. Diffeomorphic deformation trajectories between the two states were obtained by time integration. Intermediate deformation states were used to populate a library of cervix CTVs. The network was trained on cervix CTV deformations of 20 patients generated by finite element modeling (FEM). Validation was performed on FEM data of 9 healthy volunteers. Additionally, for these subjects, CTV deformations were observed in a series of repeat MR scans as the bladder filled from empty to full. Predicted and FEM libraries were compared, and benchmarked against the observed deformations. Finally, for an independent test set of 20 patients the predicted libraries were evaluated clinically, and compared to the current method.Results.The median Dice score over the validation subjects between the predicted and FEM libraries was >0.95 throughout the deformation, with a median 90 percentile surface distance of <3 mm. The ability to cover observed CTVs was similar for both the FEM-based and the proposed method, with residual offsets being about twice as large as the difference between the two methods. Clinical evaluation showed improved library properties over the method currently used in clinic.Conclusions.We proposed a CNN trained on FEM deformations, which predicts the deformation trajectory between two input states of the cervix CTV in one forward pass. We applied this to CTV library prediction for cervical cancer. The network is able to mimic FEM deformations well, while being much faster and simpler in use.

Single Application Multifractionated Image Guided Adaptive High-Dose-Rate Brachytherapy for Cervical Cancer: Dosimetric and Clinical Outcomes

PURPOSE

A prospective phase 2 study was conducted to evaluate the feasibility and safety of single-application multifractionated (SA-MF), high-dose-rate (HDR), image guided adaptive brachytherapy (IGABT) for cervical cancer.

METHODS AND MATERIALS

Patients (N = 41) with International Federation of Gynaecology and Obstetrics 2009 stage IIB-IVA disease recruited between 2017 and 2019 underwent SA-MF. After completion of external beam radiation therapy of 50 Gy in 25 fractions, patients received magnetic resonance IGABT. The IGABT protocol consisted of a single brachytherapy (BT) application and treatment with 3 fractions of HDR (9 Gy on day 1; 2 fractions of 7 Gy with a minimum 6-hour gap on day 2) after achieving planning aims of the high-risk clinical target volume (HRCTV) receiving >84 Gy EQD2 and 2 cm³ of the bladder and rectum/sigmoid receiving ≤85 Gy and <71 Gy, respectively. Interfraction variation was addressed by performing computed tomography planning and coregistration using a mutual information-based coordinate system on day 2 before the second fraction. Organ at risk contouring was done on computed tomography, and doses were re-evaluated and reoptimized if required.

RESULTS

Thirty-eight patients were treated as per the protocol. All patients underwent Intracavitary + Interstitial BT with needles (median, 4; range, 3-11). The mean ± standard deviation HRCTV volume was 41 ± 21 cm³ and HRCTV D90 dose was 87.2 ± 3.6Gy. The 0.1 cm³ and 2 cm³ to bladder, rectum, and sigmoid were -103.2 ± 10.6 Gy and -84.6 ± 6.8 Gy, 82.2 ± 9.5 Gy and -68.3 ± 5.7 Gy, and 83.5 ± 9.8 Gy and -69.5 ± 5.9 Gy, respectively. Six patients required reoptimization before the second fraction to meet planning aims. Mean overall treatment time was 47 ± 6 days. With a median follow up of 22 months (range, 2-37), 2-year local control and disease-free and overall survival were 90.1%, 85%, and 94.5%, respectively. So far 1 patient with grade II and 2 patients with grade III rectal toxicities have been reported.

CONCLUSION

Magnetic resonance IGABT with SA-MF BT was feasible in 95% of patients. The dosimetric parameters and clinical results achieved so far look promising.

Rectum Protection by Rectal Gel Injection in Cervical Cancer Brachytherapy: A Dosimetric Study via Deformable Surface Dose Accumulation and Machine-Learning-Based Discriminative Modeling

PURPOSE

This retrospective study aimed to evaluate the dosimetric effects of a rectal insertion of Kushen Ningjiao on rectal protection using deformable dose accumulation and machine learning-based discriminative modelling.

MATERIALS AND METHODS

Sixty-two patients with cervical cancer enrolled in a clinical trial, who received a Kushen Ningjiao injection of 20 g into their rectum for rectal protection via high-dose rate brachytherapy (HDR-BT, 6 Gy/f), were studied. The cumulative equivalent 2-Gy fractional rectal surface dose was deformably summed using an in-house-developed topography-preserved point-matching deformable image registration method. The cumulative three-dimensional (3D) dose was flattened and mapped to a two-dimensional (2D) plane to obtain the rectal surface dose map (RSDM). For analysis, the rectal dose (RD) was further subdivided as follows: whole, anterior, and posterior 3D-RD and 2D-RSDM. The dose-volume parameters (DVPs) were extracted from the 3D-RD, while the dose geometric parameters (DGPs) and textures were extracted from the 2D-RSDM. These features were fed into 192 classification models (built with 8 classifiers and 24 feature selection methods) for discriminating the dose distributions between pre-Kushen Ningjiao and pro-Kushen Ningjiao.

RESULTS

The rectal insertion of Kushen Ningjiao dialated the rectum in the ambilateral direction, with the rectal column increased from pre-KN 15 cm3 to post-KN 18 cm3 (P < 0.001). The characteristics of DGPs accounted for the largest portions of the top-ranked features. The top-ranked dosimetric features extracted from the posterior rectum were more reliable indicators of the dosimetric effects/changes introduced by the rectal insertion of Kushen Ningjiao. A significant dosimetric impact was found on the dose-volume parameters D1.0cc-D2.5cc extracted on the posterior rectal wall.

CONCLUSIONS

The rectal insertion of Kushen Ningjiao incurs significant dosimetric changes on the posterior rectal wall. Whether this effect is eventually translated into clinical gains requires further long-term follow-up and more clinical data for confirmation.

Investigating rectal toxicity associated dosimetric features with deformable accumulated rectal surface dose maps for cervical cancer radiotherapy

BACKGROUND

Better knowledge of the dose-toxicity relationship is essential for safe dose escalation to improve local control in cervical cancer radiotherapy. The conventional dose-toxicity model is based on the dose volume histogram, which is the parameter lacking spatial dose information. To overcome this limit, we explore a comprehensive rectal dose-toxicity model based on both dose volume histogram and dose map features for accurate radiation toxicity prediction.

METHODS

Forty-two cervical cancer patients treated with combined external beam radiotherapy (EBRT) and brachytherapy (BT) were retrospectively studied, including 12 with Grade ≥ 2 rectum toxicity and 30 patients with Grade 0-1 toxicity (non-toxicity patients). The cumulative equivalent 2-Gy rectal surface dose was deformably summed using the deformation vector fields obtained through a recent developed local topology preserved non-rigid point matching algorithm. The cumulative three-dimensional (3D) dose was flattened and mapped to a two-dimensional (2D) plane to obtain the rectum surface dose map (RSDM). The dose volume parameters (DVPs) were calculated from the 3D rectum surface, while the texture features and the dose geometric parameters (DGPs) were extracted from the 2D RSDM. Representative features further computed from DVPs, textures and DGPs by principle component analysis (PCA) and statistical analysis were respectively fed into a support vector machine equipped with a sequential feature selection procedure. The predictive powers of the representative features were compared with the GEC-ESTRO dosimetric parameters D_0.1/1/2cm³.

RESULTS

Satisfactory predictive accuracy of sensitivity 74.75 and 84.75%, specificity 72.67 and 79.87%, and area under the receiver operating characteristic curve (AUC) 0.82 and 0.91 were respectively achieved by the PCA features and statistical significant features, which were superior to the D_0.1/1/2cm³ (AUC 0.71). The relative area in dose levels of 64Gy, 67Gy, 68Gy, 87Gy, 88Gy and 89Gy, perimeters in dose levels of 89Gy, as well as two texture features were ranked as the important factors that were closely correlated with rectal toxicity.

CONCLUSIONS

Our extensive experimental results have demonstrated the feasibility of the proposed scheme. A future large patient cohort study is still needed for model validation.

Internal Motion Estimation by Internal-external Motion Modeling for Lung Cancer Radiotherapy

The aim of this study is to develop an internal-external correlation model for internal motion estimation for lung cancer radiotherapy. Deformation vector fields that characterize the internal-external motion are obtained by respectively registering the internal organ meshes and external surface meshes from the 4DCT images via a recently developed local topology preserved non-rigid point matching algorithm. A composite matrix is constructed by combing the estimated internal phasic DVFs with external phasic and directional DVFs. Principle component analysis is then applied to the composite matrix to extract principal motion characteristics, and generate model parameters to correlate the internal-external motion. The proposed model is evaluated on a 4D NURBS-based cardiac-torso (NCAT) synthetic phantom and 4DCT images from five lung cancer patients. For tumor tracking, the center of mass errors of the tracked tumor are 0.8(±0.5)mm/0.8(±0.4)mm for synthetic data, and 1.3(±1.0)mm/1.2(±1.2)mm for patient data in the intra-fraction/inter-fraction tracking, respectively. For lung tracking, the percent errors of the tracked contours are 0.06(±0.02)/0.07(±0.03) for synthetic data, and 0.06(±0.02)/0.06(±0.02) for patient data in the intra-fraction/inter-fraction tracking, respectively. The extensive validations have demonstrated the effectiveness and reliability of the proposed model in motion tracking for both the tumor and the lung in lung cancer radiotherapy.

[Rectal toxicity prediction based on accurate rectal surface dose summation for cervical cancer radiotherapy]

OBJECTIVE

To propose arectal toxicity prediction method based on deformable surface dose accumulation.

METHODS

The clinical data were collected retrospectively from 42patients receiving radiotherapy for cervical cancer. With the first fraction as the reference, the other fractions of rectum surface were registered to the reference fraction to obtain the deformation vector fields (DVFs), which were used to deform and sum the fractional rectal doses to yield the cumulative rectal dose. The cumulative rectal dose was flattened via 3D-2D mapping to generate a 2D rectum surface dose map. Two dosimetric features, namely DVPs and DGPs were extracted. Logistic regression embedded with sequential forward feature selection was used as the prediction model. The predictive performance was evaluated in terms of the accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve (AUC).

RESULTS

Significant improvements for rectum surface DIR were achieved. The best predictive results were achieved by using both DVPs and DGPs as the features with a sensitivity of 79.5%, a specificity of 81.3% and an AUC of 0.88.

CONCLUSION

The proposed method is feasible for predicting clinical rectal toxicity in patients undergoing radiotherapy for cervical cancer.

Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: a feasibility study

Better understanding of the dose-toxicity relationship is critical for safe dose escalation to improve local control in late-stage cervical cancer radiotherapy. In this study, we introduced a convolutional neural network (CNN) model to analyze rectum dose distribution and predict rectum toxicity. Forty-two cervical cancer patients treated with combined external beam radiotherapy (EBRT) and brachytherapy (BT) were retrospectively collected, including twelve toxicity patients and thirty non-toxicity patients. We adopted a transfer learning strategy to overcome the limited patient data issue. A 16-layers CNN developed by the visual geometry group (VGG-16) of the University of Oxford was pre-trained on a large-scale natural image database, ImageNet, and fine-tuned with patient rectum surface dose maps (RSDMs), which were accumulated EBRT  +  BT doses on the unfolded rectum surface. We used the adaptive synthetic sampling approach and the data augmentation method to address the two challenges, data imbalance and data scarcity. The gradient-weighted class activation maps (Grad-CAM) were also generated to highlight the discriminative regions on the RSDM along with the prediction model. We compare different CNN coefficients fine-tuning strategies, and compare the predictive performance using the traditional dose volume parameters, e.g. D _0.1/1/2cc, and the texture features extracted from the RSDM. Satisfactory prediction performance was achieved with the proposed scheme, and we found that the mean Grad-CAM over the toxicity patient group has geometric consistence of distribution with the statistical analysis result, which indicates possible rectum toxicity location. The evaluation results have demonstrated the feasibility of building a CNN-based rectum dose-toxicity prediction model with transfer learning for cervical cancer radiotherapy.

Bladder accumulated dose in image-guided high-dose-rate brachytherapy for locally advanced cervical cancer and its relation to urinary toxicity

The purpose of this study was to estimate locally accumulated dose to the bladder in multi-fraction high-dose-date (HDR) image-guided intracavitary brachytherapy (IG-ICBT) for cervical cancer, and study the locally-accumulated dose parameters as predictors of late urinary toxicity. A retrospective study of 60 cervical cancer patients who received five HDR IG-ICBT sessions was performed. The bladder outer and inner surfaces were segmented for all sessions and a bladder-wall contour point-set was created in MATLAB. The bladder-wall point-sets for each patient were registered using a deformable point-set registration toolbox called coherent point drift (CPD), and the fraction doses were accumulated. Various dosimetric and volumetric parameters were calculated using the registered doses, including [Formula: see text] (minimum dose to the most exposed n-cm³ volume of bladder wall), r V _{n Gy} (wall volume receiving at least m Gy), and [Formula: see text] (minimum equivalent biologically weighted dose to the most exposed n-cm³ of bladder wall), where n  =  1/2/5/10 and m  =  3/5/10. Minimum dose to contiguous 1 and 2 cm³ hot-spot volumes was also calculated. The unregistered dose volume histogram (DVH)-summed equivalent of [Formula: see text] and [Formula: see text] parameters (i.e. [Formula: see text] and [Formula: see text]) were determined for comparison. Late urinary toxicity was assessed using the LENT-SOMA scale, with toxicity Grade 0-1 categorized as Controls and Grade 2-4 as Cases. A two-sample t-test was used to identify the differences between the means of Control and Case groups for all parameters. A binomial logistic regression was also performed between the registered dose parameters and toxicity grouping. Seventeen patients were in the Case and 43 patients in the Control group. Contiguous values were on average 16 and 18% smaller than parameters for 1 and 2 cm³ volumes, respectively. Contiguous values were on average 26 and 27% smaller than parameters. The only statistically significant finding for Case versus Control based on both methods of analysis was observed for r V_{3 Gy} (p  =  0.01). DVH-summed parameters based on unregistered structure volumes overestimated the bladder dose in our patients, particularly when contiguous high dose volumes were considered. The bladder-wall volume receiving at least 3 Gy of accumulated dose may be a parameter of interest in further investigations of Grade 2+  urinary toxicity.