Publications by authors named "Dong Joo Rhee"

15 Publications

  • Page 1 of 1

Clinical implementation of automated treatment planning for whole-brain radiotherapy.

J Appl Clin Med Phys 2021 Jul 10. Epub 2021 Jul 10.

Department of Radiation Oncology, Hoag Hospital, Newport Beach, CA, USA.

The purpose of the study was to develop and clinically deploy an automated, deep learning-based approach to treatment planning for whole-brain radiotherapy (WBRT). We collected CT images and radiotherapy treatment plans to automate a beam aperture definition from 520 patients who received WBRT. These patients were split into training (n = 312), cross-validation (n = 104), and test (n = 104) sets which were used to train and evaluate a deep learning model. The DeepLabV3+ architecture was trained to automatically define the beam apertures on lateral-opposed fields using digitally reconstructed radiographs (DRRs). For the beam aperture evaluation, 1st quantitative analysis was completed using a test set before clinical deployment and 2nd quantitative analysis was conducted 90 days after clinical deployment. The mean surface distance and the Hausdorff distances were compared in the anterior-inferior edge between the clinically used and the predicted fields. Clinically used plans and deep-learning generated plans were evaluated by various dose-volume histogram metrics of brain, cribriform plate, and lens. The 1st quantitative analysis showed that the average mean surface distance and Hausdorff distance were 7.1 mm (±3.8 mm) and 11.2 mm (±5.2 mm), respectively, in the anterior-inferior edge of the field. The retrospective dosimetric comparison showed that brain dose coverage (D99%, D95%, D1%) of the automatically generated plans was 29.7, 30.3, and 32.5 Gy, respectively, and the average dose of both lenses was up to 19.0% lower when compared to the clinically used plans. Following the clinical deployment, the 2nd quantitative analysis showed that the average mean surface distance and Hausdorff distance between the predicted and clinically used fields were 2.6 mm (±3.2 mm) and 4.5 mm (±5.6 mm), respectively. In conclusion, the automated patient-specific treatment planning solution for WBRT was implemented in our clinic. The predicted fields appeared consistent with clinically used fields and the predicted plans were dosimetrically comparable.
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http://dx.doi.org/10.1002/acm2.13350DOI Listing
July 2021

Technical Note: Dose prediction for head and neck radiotherapy using a three-dimensional dense dilated U-net architecture.

Med Phys 2021 Jun 22. Epub 2021 Jun 22.

Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

Purpose: Radiation therapy treatment planning is a time-consuming and iterative manual process. Consequently, plan quality varies greatly between and within institutions. Artificial intelligence shows great promise in improving plan quality and reducing planning times. This technical note describes our participation in the American Association of Physicists in Medicine Open Knowledge-Based Planning Challenge (OpenKBP), a competition to accurately predict radiation therapy dose distributions.

Methods: A three-dimensional (3D) densely connected U-Net with dilated convolutions was developed to predict 3D dose distributions given contoured CT images of head and neck patients as input. While traditional augmentation techniques such as rotations and translations were explored, it was found that training on random patches alone resulted in the greatest model performance. A custom-weighted mean squared error loss function was employed. Finally, an ensemble of best-performing networks was used to generate the final challenge predictions.

Results: Our team (SuperPod) placed second in the dose stream of the OpenKBP challenge. The average mean absolute difference between the predicted and clinical dose distributions of the testing dataset was 2.56 Gy. On average, the predicted normalized target DVH metrics were within 3% of the clinical plans, and the predicted organ at risk DVH metrics were within 2 Gy of the clinical plans.

Conclusions: The developed 3D dense dilated U-Net architecture can accurately predict 3D radiotherapy dose distributions and can be used as part of a fully automated radiation therapy planning pipeline.
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http://dx.doi.org/10.1002/mp.14827DOI Listing
June 2021

Development and dosimetric assessment of an automatic dental artifact classification tool to guide artifact management techniques in a fully automated treatment planning workflow.

Comput Med Imaging Graph 2021 Jun 26;90:101907. Epub 2021 Mar 26.

The University of Texas MD Anderson Cancer Center Graduate School of Biomedical Sciences, Houston, TX, USA; The University of Texas MD Anderson Cancer Center, Department of Radiation Physics, Houston, TX, USA.

Purpose: We conducted our study to develop a tool capable of automatically detecting dental artifacts in a CT scan on a slice-by-slice basis and to assess the dosimetric impact of implementing the tool into the Radiation Planning Assistant (RPA), a web-based platform designed to fully automate the radiation therapy treatment planning process.

Methods: We developed an automatic dental artifact identification tool and assessed the dosimetric impact of its use in the RPA. Three users manually annotated 83,676 head-and-neck (HN) CT slices (549 patients). Majority-voting was applied to the individual annotations to determine the presence or absence of dental artifacts. The patients were divided into train, cross-validation, and test data sets (ratio: 3:1:1, respectively). A random subset of images without dental artifacts was used to balance classes (1:1) in the training data set. The Inception-V3 deep learning model was trained with the binary cross-entropy loss function. With use of this model, we automatically identified artifacts on 15 RPA HN plans on a slice-by-slice basis and investigated three dental artifact management methods applied before and after volumetric modulated arc therapy (VMAT) plan optimization. The resulting dose distributions and target coverage were quantified.

Results: Per-slice accuracy, sensitivity, and specificity were 99 %, 91 %, and 99 %, respectively. The model identified all patients with artifacts. Small dosimetric differences in total plan dose were observed between the various density-override methods (±1 Gy). For the pre- and post-optimized plans, 90 % and 99 %, respectively, of dose comparisons resulted in normal structure dose differences of ±1 Gy. Differences in the volume of structures receiving 95 % of the prescribed dose (V95[%]) were ≤0.25 % for 100 % of plans.

Conclusion: The dosimetric impact of applying dental artifact management before and after artifact plan optimization was minor. Our results suggest that not accounting for dental artifacts in the current RPA workflow (where only post-optimization dental artifact management is possible) may result in minor dosimetric differences. If RPA users choose to override CT densities as a solution to managing dental artifacts, our results suggest segmenting the volume of the artifact and overriding its density to water is a safe option.
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http://dx.doi.org/10.1016/j.compmedimag.2021.101907DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8180493PMC
June 2021

Impact of slice thickness, pixel size, and CT dose on the performance of automatic contouring algorithms.

J Appl Clin Med Phys 2021 May 29;22(5):168-174. Epub 2021 Mar 29.

Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Purpose: To investigate the impact of computed tomography (CT) image acquisition and reconstruction parameters, including slice thickness, pixel size, and dose, on automatic contouring algorithms.

Methods: Eleven scans from patients with head-and-neck cancer were reconstructed with varying slice thicknesses and pixel sizes. CT dose was varied by adding noise using low-dose simulation software. The impact of these imaging parameters on two in-house auto-contouring algorithms, one convolutional neural network (CNN)-based and one multiatlas-based system (MACS) was investigated for 183 reconstructed scans. For each algorithm, auto-contours for organs-at-risk were compared with auto-contours from scans with 3 mm slice thickness, 0.977 mm pixel size, and 100% CT dose using Dice similarity coefficient (DSC), Hausdorff distance (HD), and mean surface distance (MSD).

Results: Increasing the slice thickness from baseline value of 3 mm gave a progressive reduction in DSC and an increase in HD and MSD on average for all structures. Reducing the CT dose only had a relatively minimal effect on DSC and HD. The rate of change with respect to dose for both auto-contouring methods is approximately 0. Changes in pixel size had a small effect on DSC and HD for CNN-based auto-contouring with differences in DSC being within 0.07. Small structures had larger deviations from the baseline values than large structures for DSC. The relative differences in HD and MSD between the large and small structures were small.

Conclusions: Auto-contours can deviate substantially with changes in CT acquisition and reconstruction parameters, especially slice thickness and pixel size. The CNN was less sensitive to changes in pixel size, and dose levels than the MACS. The results contraindicated more restrictive values for the parameters should be used than a typical imaging protocol for head-and-neck.
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http://dx.doi.org/10.1002/acm2.13207DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8130223PMC
May 2021

Training deep-learning segmentation models from severely limited data.

Med Phys 2021 Apr 19;48(4):1697-1706. Epub 2021 Feb 19.

Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Purpose: To enable generation of high-quality deep learning segmentation models from severely limited contoured cases (e.g., ~10 cases).

Methods: Thirty head and neck computed tomography (CT) scans with well-defined contours were deformably registered to 200 CT scans of the same anatomic site without contours. Acquired deformation vector fields were used to train a principal component analysis (PCA) model for each of the 30 contoured CT scans by capturing the mean deformation and most prominent variations. Each PCA model can produce an infinite number of synthetic CT scans and corresponding contours by applying random deformations. We used 300, 600, 1000, and 2000 synthetic CT scans and contours generated from one PCA model to train V-Net, a 3D convolutional neural network architecture, to segment parotid and submandibular glands. We repeated the training using same numbers of training cases generated from 7, 10, 20, and 30 PCA models, with the data distributed evenly between each PCA model. Performance of the segmentation models was evaluated with Dice similarity coefficients between auto-generated contours and physician-drawn contours on 162 test CT scans for parotid glands and another 21 test CT scans for submandibular glands.

Results: Dice values varied with the number of synthetic CT scans and the number of PCA models used to train the network. By using 2000 synthetic CT scans generated from 10 PCA models, we achieved Dice values of 82.8% ± 6.8% for right parotid, 82.0% ± 6.9% for left parotid, and 74.2% ± 6.8% for submandibular glands. These results are comparable with those obtained from state-of-the-art auto-contouring approaches, including a deep learning network trained from more than 1000 contoured patients and a multi-atlas algorithm from 12 well-contoured atlases. Improvement was marginal when >10 PCA models or >2000 synthetic CT scans were used.

Conclusions: We demonstrated an effective data augmentation approach to train high-quality deep learning segmentation models from a limited number of well-contoured patient cases.
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http://dx.doi.org/10.1002/mp.14728DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8058262PMC
April 2021

Evaluation of a multiview architecture for automatic vertebral labeling of palliative radiotherapy simulation CT images.

Med Phys 2020 Nov 15;47(11):5592-5608. Epub 2020 Sep 15.

Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.

Purpose: The purpose of this work was to evaluate the performance of X-Net, a multiview deep learning architecture, to automatically label vertebral levels (S2-C1) in palliative radiotherapy simulation CT scans.

Methods: For each patient CT scan, our automated approach 1) segmented spinal canal using a convolutional-neural network (CNN), 2) formed sagittal and coronal intensity projection pairs, 3) labeled vertebral levels with X-Net, and 4) detected irregular intervertebral spacing using an analytic methodology. The spinal canal CNN was trained via fivefold cross validation using 1,966 simulation CT scans and evaluated on 330 CT scans. After labeling vertebral levels (S2-C1) in 897 palliative radiotherapy simulation CT scans, a volume of interest surrounding the spinal canal in each patient's CT scan was converted into sagittal and coronal intensity projection image pairs. Then, intensity projection image pairs were augmented and used to train X-Net to automatically label vertebral levels using fivefold cross validation (n = 803). Prior to testing upon the final test set (n = 94), CT scans of patients with anatomical abnormalities, surgical implants, or other atypical features from the final test set were placed in an outlier group (n = 20), whereas those without these features were placed in a normative group (n = 74). The performance of X-Net, X-Net Ensemble, and another leading vertebral labeling architecture (Btrfly Net) was evaluated on both groups using identification rate, localization error, and other metrics. The performance of our approach was also evaluated on the MICCAI 2014 test dataset (n = 60). Finally, a method to detect irregular intervertebral spacing was created based on the rate of change in spacing between predicted vertebral body locations and was also evaluated using the final test set. Receiver operating characteristic analysis was used to investigate the performance of the method to detect irregular intervertebral spacing.

Results: The spinal canal architecture yielded centroid coordinates spanning S2-C1 with submillimeter accuracy (mean ± standard deviation, 0.399 ± 0.299 mm; n = 330 patients) and was robust in the localization of spinal canal centroid to surgical implants and widespread metastases. Cross-validation testing of X-Net for vertebral labeling revealed that the deep learning model performance (F score, precision, and sensitivity) improved with CT scan length. The X-Net, X-Net Ensemble, and Btrfly Net mean identification rates and localization errors were 92.4% and 2.3 mm, 94.2% and 2.2 mm, and 90.5% and 3.4 mm, respectively, in the final test set and 96.7% and 2.2 mm, 96.9% and 2.0 mm, and 94.8% and 3.3 mm, respectively, within the normative group of the final test set. The X-Net Ensemble yielded the highest percentage of patients (94%) having all vertebral bodies identified correctly in the final test set when the three most inferior and superior vertebral bodies were excluded from the CT scan. The method used to detect labeling failures had 67% sensitivity and 95% specificity when combined with the X-Net Ensemble and flagged five of six patients with atypical vertebral counts (additional thoracic (T13), additional lumbar (L6) or only four lumbar vertebrae). Mean identification rate on the MICCAI 2014 dataset using an X-Net Ensemble was increased from 86.8% to 91.3% through the use of transfer learning and obtained state-of-the-art results for various regions of the spine.

Conclusions: We trained X-Net, our unique convolutional neural network, to automatically label vertebral levels from S2 to C1 on palliative radiotherapy CT images and found that an ensemble of X-Net models had high vertebral body identification rate (94.2%) and small localization errors (2.2 ± 1.8 mm). In addition, our transfer learning approach achieved state-of-the-art results on a well-known benchmark dataset with high identification rate (91.3%) and low localization error (3.3 mm ± 2.7 mm). When we pre-screened radiotherapy CT images for the presence of hardware, surgical implants, or other anatomic abnormalities prior to the use of X-Net, it labeled the spine correctly in more than 97% of patients and 94% of patients when scans were not prescreened. Automatically generated labels are robust to widespread vertebral metastases and surgical implants and our method to detect labeling failures based on neighborhood intervertebral spacing can reliably identify patients with an additional lumbar or thoracic vertebral body.
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http://dx.doi.org/10.1002/mp.14415DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7756475PMC
November 2020

The Emergence of Artificial Intelligence within Radiation Oncology Treatment Planning.

Oncology 2021 22;99(2):124-134. Epub 2020 Dec 22.

Department of Radiation Oncology and Radiation Therapy, Stanford University, Stanford, California, USA.

Background: The future of artificial intelligence (AI) heralds unprecedented change for the field of radiation oncology. Commercial vendors and academic institutions have created AI tools for radiation oncology, but such tools have not yet been widely adopted into clinical practice. In addition, numerous discussions have prompted careful thoughts about AI's impact upon the future landscape of radiation oncology: How can we preserve innovation, creativity, and patient safety? When will AI-based tools be widely adopted into the clinic? Will the need for clinical staff be reduced? How will these devices and tools be developed and regulated?

Summary: In this work, we examine how deep learning, a rapidly emerging subset of AI, fits into the broader historical context of advancements made in radiation oncology and medical physics. In addition, we examine a representative set of deep learning-based tools that are being made available for use in external beam radiotherapy treatment planning and how these deep learning-based tools and other AI-based tools will impact members of the radiation treatment planning team. Key Messages: Compared to past transformative innovations explored in this article, such as the Monte Carlo method or intensity-modulated radiotherapy, the development and adoption of deep learning-based tools is occurring at faster rates and promises to transform practices of the radiation treatment planning team. However, accessibility to these tools will be determined by each clinic's access to the internet, web-based solutions, or high-performance computing hardware. As seen by the trends exhibited by many technologies, high dependence on new technology can result in harm should the product fail in an unexpected manner, be misused by the operator, or if the mitigation to an expected failure is not adequate. Thus, the need for developers and researchers to rigorously validate deep learning-based tools, for users to understand how to operate tools appropriately, and for professional bodies to develop guidelines for their use and maintenance is essential. Given that members of the radiation treatment planning team perform many tasks that are automatable, the use of deep learning-based tools, in combination with other automated treatment planning tools, may refocus tasks performed by the treatment planning team and may potentially reduce resource-related burdens for clinics with limited resources.
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http://dx.doi.org/10.1159/000512172DOI Listing
February 2021

Automatic Segmentation Using Deep Learning to Enable Online Dose Optimization During Adaptive Radiation Therapy of Cervical Cancer.

Int J Radiat Oncol Biol Phys 2021 03 9;109(4):1096-1110. Epub 2020 Nov 9.

Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas; Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas.

Purpose: This study investigated deep learning models for automatic segmentation to support the development of daily online dose optimization strategies, eliminating the need for internal target volume expansions and thereby reducing toxicity events of intensity modulated radiation therapy for cervical cancer.

Methods And Materials: The cervix-uterus, vagina, parametrium, bladder, rectum, sigmoid, femoral heads, kidneys, spinal cord, and bowel bag were delineated on 408 computed tomography (CT) scans from patients treated at MD Anderson Cancer Center (n = 214), Polyclinique Bordeaux Nord Aquitaine (n = 30), and enrolled in a Medical Image Computing & Computer Assisted Intervention challenge (n = 3). The data were divided into 255 training, 61 validation, 62 internal test, and 30 external test CT scans. Two models were investigated: the 2-dimensional (2D) DeepLabV3+ (Google) and 3-dimensional (3D) Unet in RayStation (RaySearch Laboratories). Three intensity modulated radiation therapy plans were generated on each CT of the internal and external test sets using either the manual, 2D model, or 3D model segmentations. The dose constraints followed the External beam radiochemotherapy and MRI based adaptive BRAchytherapy in locally advanced CErvical cancer (EMBRACE) II protocol, with reduced margins of 5 and 3 mm for the target and nodal planning target volume. Geometric discrepancies between the manual and predicted contours were assessed using the Dice similarity coefficient (DSC), distance-to-agreement, and Hausdorff distance. Dosimetric discrepancies between the manual and model doses were assessed using clinical indices on the manual contours and the gamma index. Interobserver variability was assessed for the cervix-uterus, parametrium, and vagina for the definition of the primary clinical target volume (CTV) on the external test set.

Results: Average DSCs across all organs were 0.67 to 0.96, 0.71 to 0.97, and 0.42 to 0.92 for the 2D model and 0.66 to 0.96, 0.70 to 0.97, and 0.37 to 0.93 for the 3D model on the validation, internal, and external test sets. Average DSCs of the CTV were 0.88 and 0.81 for the 2D model and 0.87 and 0.82 for the 3D model on the internal and external test sets. Interobserver variability of the CTV corresponded to a mean (range) DSC of 0.85 (0.77-0.90) on the external test set. On the internal test set, the doses from the 2D and 3D model contours provided a CTV V42.75 Gy >98% for 98% and 91% of the CT scans, respectively. On the external test set, these percentages were increased to 100% and 93% for the 2D and 3D models, respectively.

Conclusions: The investigated models provided auto-segmentation of the cervix anatomy with similar performances on 2 institutional data sets and reasonable dosimetric accuracies using small planning target volume margins, paving the way to automatic online dose optimization for advanced adaptive radiation therapy strategies.
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http://dx.doi.org/10.1016/j.ijrobp.2020.10.038DOI Listing
March 2021

Generating High-Quality Lymph Node Clinical Target Volumes for Head and Neck Cancer Radiation Therapy Using a Fully Automated Deep Learning-Based Approach.

Int J Radiat Oncol Biol Phys 2021 03 14;109(3):801-812. Epub 2020 Oct 14.

Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas.

Purpose: To develop a deep learning model that generates consistent, high-quality lymph node clinical target volumes (CTV) contours for head and neck cancer (HNC) patients, as an integral part of a fully automated radiation treatment planning workflow.

Methods And Materials: Computed tomography (CT) scans from 71 HNC patients were retrospectively collected and split into training (n = 51), cross-validation (n = 10), and test (n = 10) data sets. All had target volume delineations covering lymph node levels Ia through V (Ia-V), Ib through V (Ib-V), II through IV (II-IV), and retropharyngeal (RP) nodes, which were previously approved by a radiation oncologist specializing in HNC. Volumes of interest (VOIs) about nodal levels were automatically identified using computer vision techniques. The VOI (cropped CT image) and approved contours were used to train a U-Net autosegmentation model. Each lymph node level was trained independently, with model parameters optimized by assessing performance on the cross-validation data set. Once optimal model parameters were identified, overlap and distance metrics were calculated between ground truth and autosegmentations on the test set. Lastly, this final model was used on 32 additional patient scans (not included in original 71 cases) and autosegmentations visually rated by 3 radiation oncologists as being "clinically acceptable without requiring edits," "requiring minor edits," or "requiring major edits."

Results: When comparing ground truths to autosegmentations on the test data set, median Dice Similarity Coefficients were 0.90, 0.90, 0.89, and 0.81, and median mean surface distance values were 1.0 mm, 1.0 mm, 1.1 mm, and 1.3 mm for node levels Ia-V, Ib-V, II-IV, and RP nodes, respectively. Qualitative scoring varied among physicians. Overall, 99% of autosegmented target volumes were either scored as being clinically acceptable or requiring minor edits (ie, stylistic recommendations, <2 minutes).

Conclusions: We developed a fully automated artificial intelligence approach to autodelineate nodal CTVs for patients with intact HNC. Most autosegmentations were found to be clinically acceptable after qualitative review when considering recommended stylistic edits. This promising work automatically delineates nodal CTVs in a robust and consistent manner; this approach can be implemented in ongoing efforts for fully automated radiation treatment planning.
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http://dx.doi.org/10.1016/j.ijrobp.2020.10.005DOI Listing
March 2021

Automatic contouring system for cervical cancer using convolutional neural networks.

Med Phys 2020 Nov 9;47(11):5648-5658. Epub 2020 Oct 9.

Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Purpose: To develop a tool for the automatic contouring of clinical treatment volumes (CTVs) and normal tissues for radiotherapy treatment planning in cervical cancer patients.

Methods: An auto-contouring tool based on convolutional neural networks (CNN) was developed to delineate three cervical CTVs and 11 normal structures (seven OARs, four bony structures) in cervical cancer treatment for use with the Radiation Planning Assistant, a web-based automatic plan generation system. A total of 2254 retrospective clinical computed tomography (CT) scans from a single cancer center and 210 CT scans from a segmentation challenge were used to train and validate the CNN-based auto-contouring tool. The accuracy of the tool was evaluated by calculating the Sørensen-dice similarity coefficient (DSC) and mean surface and Hausdorff distances between the automatically generated contours and physician-drawn contours on 140 internal CT scans. A radiation oncologist scored the automatically generated contours on 30 external CT scans from three South African hospitals.

Results: The average DSC, mean surface distance, and Hausdorff distance of our CNN-based tool were 0.86/0.19 cm/2.02 cm for the primary CTV, 0.81/0.21 cm/2.09 cm for the nodal CTV, 0.76/0.27 cm/2.00 cm for the PAN CTV, 0.89/0.11 cm/1.07 cm for the bladder, 0.81/0.18 cm/1.66 cm for the rectum, 0.90/0.06 cm/0.65 cm for the spinal cord, 0.94/0.06 cm/0.60 cm for the left femur, 0.93/0.07 cm/0.66 cm for the right femur, 0.94/0.08 cm/0.76 cm for the left kidney, 0.95/0.07 cm/0.84 cm for the right kidney, 0.93/0.05 cm/1.06 cm for the pelvic bone, 0.91/0.07 cm/1.25 cm for the sacrum, 0.91/0.07 cm/0.53 cm for the L4 vertebral body, and 0.90/0.08 cm/0.68 cm for the L5 vertebral bodies. On average, 80% of the CTVs, 97% of the organ at risk, and 98% of the bony structure contours in the external test dataset were clinically acceptable based on physician review.

Conclusions: Our CNN-based auto-contouring tool performed well on both internal and external datasets and had a high rate of clinical acceptability.
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http://dx.doi.org/10.1002/mp.14467DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7756586PMC
November 2020

Automated Radiation Treatment Planning for Cervical Cancer.

Semin Radiat Oncol 2020 10;30(4):340-347

Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX.

The radiation treatment-planning process includes contouring, planning, and reviewing the final plan, and each component requires substantial time and effort from multiple experts. Automation of treatment planning can save time and reduce the cost of radiation treatment, and potentially provides more consistent and better quality plans. With the recent breakthroughs in computer hardware and artificial intelligence technology, automation methods for radiation treatment planning have achieved a clinically acceptable level of performance in general. At the same time, the automation process should be developed and evaluated independently for different disease sites and treatment techniques as they are unique from each other. In this article, we will discuss the current status of automated radiation treatment planning for cervical cancer for simple and complex plans and corresponding automated quality assurance methods. Furthermore, we will introduce Radiation Planning Assistant, a web-based system designed to fully automate treatment planning for cervical cancer and other treatment sites.
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http://dx.doi.org/10.1016/j.semradonc.2020.05.006DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7446764PMC
October 2020

Automatic detection of contouring errors using convolutional neural networks.

Med Phys 2019 Nov 26;46(11):5086-5097. Epub 2019 Sep 26.

Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.

Purpose: To develop a head and neck normal structures autocontouring tool that could be used to automatically detect the errors in autocontours from a clinically validated autocontouring tool.

Methods: An autocontouring tool based on convolutional neural networks (CNN) was developed for 16 normal structures of the head and neck and tested to identify the contour errors from a clinically validated multiatlas-based autocontouring system (MACS). The computed tomography (CT) scans and clinical contours from 3495 patients were semiautomatically curated and used to train and validate the CNN-based autocontouring tool. The final accuracy of the tool was evaluated by calculating the Sørensen-Dice similarity coefficients (DSC) and Hausdorff distances between the automatically generated contours and physician-drawn contours on 174 internal and 24 external CT scans. Lastly, the CNN-based tool was evaluated on 60 patients' CT scans to investigate the possibility to detect contouring failures. The contouring failures on these patients were classified as either minor or major errors. The criteria to detect contouring errors were determined by analyzing the DSC between the CNN- and MACS-based contours under two independent scenarios: (a) contours with minor errors are clinically acceptable and (b) contours with minor errors are clinically unacceptable.

Results: The average DSC and Hausdorff distance of our CNN-based tool was 98.4%/1.23 cm for brain, 89.1%/0.42 cm for eyes, 86.8%/1.28 cm for mandible, 86.4%/0.88 cm for brainstem, 83.4%/0.71 cm for spinal cord, 82.7%/1.37 cm for parotids, 80.7%/1.08 cm for esophagus, 71.7%/0.39 cm for lenses, 68.6%/0.72 for optic nerves, 66.4%/0.46 cm for cochleas, and 40.7%/0.96 cm for optic chiasm. With the error detection tool, the proportions of the clinically unacceptable MACS contours that were correctly detected were 0.99/0.80 on average except for the optic chiasm, when contours with minor errors are clinically acceptable/unacceptable, respectively. The proportions of the clinically acceptable MACS contours that were correctly detected were 0.81/0.60 on average except for the optic chiasm, when contours with minor errors are clinically acceptable/unacceptable, respectively.

Conclusion: Our CNN-based autocontouring tool performed well on both the publically available and the internal datasets. Furthermore, our results show that CNN-based algorithms are able to identify ill-defined contours from a clinically validated and used multiatlas-based autocontouring tool. Therefore, our CNN-based tool can effectively perform automatic verification of MACS contours.
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http://dx.doi.org/10.1002/mp.13814DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6842055PMC
November 2019

Auto-delineation of oropharyngeal clinical target volumes using 3D convolutional neural networks.

Phys Med Biol 2018 11 7;63(21):215026. Epub 2018 Nov 7.

Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America. Author to whom any correspondence should be addressed.

Accurate clinical target volume (CTV) delineation is essential to ensure proper tumor coverage in radiation therapy. This is a particularly difficult task for head-and-neck cancer patients where detailed knowledge of the pathways of microscopic tumor spread is necessary. This paper proposes a solution to auto-segment these volumes in oropharyngeal cancer patients using a two-channel 3D U-Net architecture. The first channel feeds the network with the patient's CT image providing anatomical context, whereas the second channel provides the network with tumor location and morphological information. Radiation therapy simulation computer tomography scans and their corresponding manually delineated CTV and gross tumor volume (GTV) delineations from 285 oropharyngeal patients previously treated at MD Anderson Cancer Center were used in this study. CTV and GTV delineations underwent rigorous group peer-review prior to the start of treatment delivery. The convolutional network's parameters were fine-tuned using a training set of 210 patients using 3-fold cross-validation. During hyper-parameter selection, we use a score based on the overlap (dice similarity coefficient (DSC)) and missed volumes (false negative dice (FND)) to minimize any possible under-treatment. Three auto-delineated models were created to estimate tight, moderate, and wide CTV margin delineations. Predictions on our test set (75 patients) resulted in auto-delineations with high overlap and close surface distance agreement (DSC  >  0.75 on 96% of cases for tight and moderate auto-delineation models and 97% of cases having mean surface distance  ⩽  5.0 mm) to the ground-truth. We found that applying a 5 mm uniform margin expansion to the auto-delineated CTVs would cover at least 90% of the physician CTV volumes for a large majority of patients; however, determination of appropriate margin expansions for auto-delineated CTVs merits further investigation.
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http://dx.doi.org/10.1088/1361-6560/aae8a9DOI Listing
November 2018

A snapshot of medical physics practice patterns.

J Appl Clin Med Phys 2018 Nov 1;19(6):306-315. Epub 2018 Oct 1.

Morton Plant Mease Health System, Clearwater, FL, USA.

A large number of surveys have been sent to the medical physics community addressing many clinical topics for which the medical physicist is, or may be, responsible. Each survey provides an insight into clinical practice relevant to the medical physics community. The goal of this study was to create a summary of these surveys giving a snapshot of clinical practice patterns. Surveys used in this study were created using SurveyMonkey and distributed between February 6, 2013 and January 2, 2018 via the MEDPHYS and MEDDOS listserv groups. The format of the surveys included questions that were multiple choice and free response. Surveys were included in this analysis if they met the following criteria: more than 20 responses, relevant to radiation therapy physics practice, not single-vendor specific, and formatted as multiple-choice questions (i.e., not exclusively free-text responses). Although the results of free response questions were not explicitly reported, they were carefully reviewed, and the responses were considered in the discussion of each topic. Two-hundred and fifty-two surveys were available, of which 139 passed the inclusion criteria. The mean number of questions per survey was 4. The mean number of respondents per survey was 63. Summaries were made for the following topics: simulation, treatment planning, electron treatments, linac commissioning and quality assurance, setup and treatment verification, IMRT and VMAT treatments, SRS/SBRT, breast treatments, prostate treatments, brachytherapy, TBI, facial lesion treatments, clinical workflow, and after-hours/emergent treatments. We have provided a coherent overview of medical physics practice according to surveys conducted over the last 5 yr, which will be instructive for medical physicists.
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http://dx.doi.org/10.1002/acm2.12464DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6236839PMC
November 2018

Anterior or posterior obliquity of the lag screw in the lateral view--does it affect the sliding characteristics on unstable trochanteric fractures?

Injury 2007 Jul 30;38(7):785-91. Epub 2007 May 30.

Department of Orthopaedic Surgery, Yonsei University, College of Medicine, Seoul, Republic of Korea.

We investigated the sliding pattern characteristics of proximal fragment in 16 proximal femur models of 31-A2.1 type trochanteric fractures fixed with dynamic hip screw system under load. In eight models, the lag screw was inserted with an anterior-to-posterior vector within the neck and the head of femur (group 1). The other eight had a posterior-to-anterior vector (group 2). A force of 500 N (30 cycles) was applied to the fracture fragment-plate complex using the Instron 6022. No significant difference was noted in the sliding distance between the two groups (p=0.17). However, the mean angle of rotation of the proximal fragment was twice as great in group 1 (16+/-3.7 degrees ) than that in group 2 (8.1+/-1.5 degrees ) (p<0.001). In group 1, early eccentric contact in the anterior aspect of the intertrochanteric area caused internal rotation of the proximal fragment. Breakage of the anterior cortex of the lateral wall was noted in five out of the eight cases in group 1 and no case in group 2 (p=0.026). Fracture of the anterior cortices significantly threatened the integrity of the lateral wall in group 1.
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http://dx.doi.org/10.1016/j.injury.2007.03.010DOI Listing
July 2007
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