Quantitative comparison of automatic and manual IMRT optimization for prostate cancer: the benefits of DVH prediction

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.

Automated Contouring and Planning in Radiation Therapy: What Is 'Clinically Acceptable'?

Developers and users of artificial-intelligence-based tools for automatic contouring and treatment planning in radiotherapy are expected to assess clinical acceptability of these tools. However, what is 'clinical acceptability'? Quantitative and qualitative approaches have been used to assess this ill-defined concept, all of which have advantages and disadvantages or limitations. The approach chosen may depend on the goal of the study as well as on available resources. In this paper, we discuss various aspects of 'clinical acceptability' and how they can move us toward a standard for defining clinical acceptability of new autocontouring and planning tools.

Automatic treatment planning for cervical cancer radiation therapy using direct three-dimensional patient anatomy match

Purpose

Current knowledge‐based planning methods for radiation therapy mainly use low‐dimensional features extracted from contoured structures to identify geometrically similar patients. Here, we propose a knowledge‐based treatment planning method where the anatomical similarity is quantified by the rigid registration of the three‐dimensional (3D) planning target volume (PTV) and organs at risks (OARs) between an incoming patient and database patients.

Methods

A database that contains PTV and OARs contours from 81 cervical cancer radiation therapy patients was established. To identify the anatomically similar patients, the PTV of the new patient was registered to each PTV in the database and the Dice similarity coefficients were calculated for the PTV, rectum, and bladder between the new patient and database patients. Then the top 20 patients in the PTV match and top 3 patients in the subsequent bladder or rectum match were selected. The best dose–volume histogram parameters from the top three patients were applied as the dose constraints to the automatic plan optimization. A fast Fourier transform algorithm was developed to accelerate the 3D PTV registration process run through the database. The entire treatment planning process was automated using in‐house customized Pinnacle scripts. The automatic plans were generated for 20 patients using leave‐one‐out scheme and were evaluated against the corresponding clinical plans.

Results

The automatic plans significantly reduced rectum and bladder V50Gy by 11.79% ± 5.2% (p < 0.01) and 2.85% ± 3.16% (p < 0.01), respectively. The dose parameters achieved for the PTV and other OARs were comparable to those in the clinical plans. The entire planning process, including both dose prediction and inverse optimization, costs about 6 min.

Conclusions

The direct 3D contour match method utilizes the full spatial information of the PTV and OARs of interest and provides an intuitive measurement for patient plan anatomy similarity. The proposed automatic planning method can generate plans with better quality and higher efficiency.

A review of the effects of tobacco smoking on the treatment of prostate cancer

Background:

Prostate cancer is the most commonly diagnosed malignancy and the third leading cause of death among Canadian men. The standard treatment modalities for prostate cancer include prostatectomy, radiation therapy, hormonal therapy and chemotherapy or any combination depending on the stage of the tumour. However, several studies have reported that tobacco smoking at the time of diagnosis and during treatment can potentially impact treatment efficacy, outcome and patients quality of life after treatment.

Materials and methods:

This narrative literature review elucidates the impacts of tobacco smoking on prostate cancer progression, treatment efficacy, including its effects on prostatectomy, radiation therapy and chemotherapy, risk of cancer recurrence and mortality and quality of life after treatment. Furthermore, we discuss the importance of integrating a smoking cessation programme into the treatment regimen for prostate cancer patients in order to yield more favourable treatment outcomes, reduce risk of recurrence and mortality and increase the quality of life after treatment for prostate cancer patients.

Conclusions:

Smoking cessation is one of the most important interventions to prevent cancer and it is also essential after the diagnosis of prostate cancer to improve clinical outcomes. All prostate cancer patients should be advised to quit tobacco use since it can potentially improve treatment response rates and survival, as well as reduce the risk of developing treatment complications and potentially improve the quality of life after treatment. There are several benefits to smoking cessation and it should become an important component of the cancer care continuum in all oncology programmes, starting from prevention of cancer through diagnosis, treatment, survivorship and palliative care. Evidence-based smoking cessation intervention should be sustainably integrated into any comprehensive cancer programme, and the information should be targeted to the specific benefits of cessation in cancer patients.

Dosimetric evaluation of whole-pelvis radiation therapy of prostate cancers: clinical experience

Background:

The standard treatment modalities for prostate cancer include surgery, chemotherapy, hormonal therapy and radiation therapy or any combination depending on the stage of the tumour. Radiation therapy is a common and effective treatment modality for low-intermediate-risk patients with localised prostate cancer, to treat the intact prostate and seminal vesicles or prostate bed post prostatectomy. However, for high-risk patients with lymph node involvement, treatment with radiation will usually include treatment of the whole pelvis to cover the prostate and seminal vesicles or prostate bed and the pelvic lymph nodes followed by a boost delivery dose to the prostate and seminal vesicles or prostate bed.

Materials and Methods:

We retrospectively analysed the treatment plans for 179 prostate cancer patients treated at the cancer centre with the volumetric-modulated arc therapy (VMAT) technique via RapidArc using 6 MV photon beam. Patients were either treated with a total prescription dose of 78 Gy in 39 fractions for patients with intact prostate or 66 Gy in 33 fractions for post prostatectomy patients.

Results:

There were 114 (64%) patients treated with 78 Gy/39 and 65 (36%) treated with 66 Gy/34. The mean homogeneity index (HI), conformity index (CI) and uniformity index (UI) for the PTV-primary of patients treated with 78 Gy are 0.06 ± 0.01, 1.04 ± 0.01 and 0.99 ± 0.01, respectively, and the corresponding mean values for patients treated with 66 Gy are 0.06 ± 0.02, 1.05 ± 0.01 and 0.99 ± 0.01, respectively. The mean PTV-primary V95%, V100% and V105% are 99.5 ± 0.5%, 78.8 ± 12.2% and 0.1 ± 0.5%, respectively, for patients treated with 78 Gy and 99.3 ± 0.9%, 78.1 ± 10.6% and 0.1 ± 0.4%, respectively, for patients treated with 66 Gy. The rectal V50Gy, V65Gy, V66.6Gy, V70Gy, V75Gy and V80Gy are 26.8 ± 9.1%, 14.2 ± 5.3%, 13.1 ± 5.0%, 10.8 ± 4.3%, 6.9 ± 3.1% and 0.1 ± 0.1%, respectively, for patients treated with 78 Gy and 33.7 ± 8.4%, 14.1 ± 4.5%, 6.7 ± 4.5%, 0.0 ± 0.2%, 0.0% and 0.0%, respectively, for patients treated with 66 Gy.

Conclusion:

The use of VMAT technique for radiation therapy of high-risk prostate cancer patients is an efficient and reliable method for achieving superior dose conformity, uniformity and homogeneity to the PTV and minimal doses to the organs at risk. Results from this study provide the basis for the development and implementation of consistent treatment criteria in radiotherapy programs, have the potential to establish an evaluation process to define a consistent, standardised and transparent treatment path for all patients that reduces significant variations in the acceptability of treatment plans and potentially improve patient standard of care.

Evaluation of complexity and deliverability of prostate cancer treatment plans designed with a knowledge-based VMAT planning technique

PURPOSE

Knowledge-based planning (KBP) techniques have been reported to improve plan quality, efficiency, and consistency in radiation therapy. However, plan complexity and deliverability have not been addressed previously for treatment plans guided by an established in-house KBP system. The purpose of this work was to assess dosimetric, mechanical, and delivery properties of plans designed with a common KBP method for prostate cases treated via volumetric modulated arc therapy (VMAT).

METHODS

Thirty-one prostate patients previously treated with VMAT were replanned with an in-house KBP method based on the overlap volume histogram. VMAT plan complexities of the KBP plans and the reference clinical plans were quantified via monitor units, modulation complexity scores, the edge metric, and average leaf motion per degree of gantry rotation. Each set of plans was delivered to the same diode array and agreement between computed and measured dose distributions was evaluated using the gamma index. Varying percent dose-difference (1-3%) and distance-to-agreement (1 mm to 3 mm) thresholds were assessed for gamma analyses.

RESULTS

Knowledge-based planning (KBP) plans achieved average reductions of 6.4 Gy (P < 0.001) and 8.2 Gy (P < 0.001) in mean bladder and rectum dose compared to reference plans, while maintaining clinically acceptable target dose. However, KBP plans were significantly more complex than reference plans in each evaluated metric (P < 0.001). KBP plans also showed significant reductions (P < 0.05) in gamma passing rates at each evaluated criterion compared to reference plans.

CONCLUSIONS

While KBP plans had significantly reduced bladder and rectum dose, they were significantly more complex and had significantly worse quality assurance outcomes than reference plans. These results suggest caution should be taken when implementing an in-house KBP technique.

Knowledge-based planning for intensity-modulated radiation therapy: A review of data-driven approaches

Purpose

Intensity‐Modulated Radiation Therapy (IMRT), including its variations (including IMRT, Volumetric Arc Therapy (VMAT), and Tomotherapy), is a widely used and critically important technology for cancer treatment. It is a knowledge‐intensive technology due not only to its own technical complexity, but also to the inherently conflicting nature of maximizing tumor control while minimizing normal organ damage. As IMRT experience and especially the carefully designed clinical plan data are accumulated during the past two decades, a new set of methods commonly termed knowledge‐based planning (KBP) have been developed that aim to improve the quality and efficiency of IMRT planning by learning from the database of past clinical plans. Some of this development has led to commercial products recently that allowed the investigation of KBP in numerous clinical applications. In this literature review, we will attempt to present a summary of published methods of knowledge‐based approaches in IMRT and recent clinical validation results.

Methods

In March 2018, a literature search was conducted in the NIH Medline database using the PubMed interface to identify publications that describe methods and validations related to KBP in IMRT including variations such as VMAT and Tomotherapy. The search criteria were designed to have a broad scope to capture relevant results with high sensitivity. The authors filtered down the search results according to a predefined selection criteria by reviewing the titles and abstracts first and then by reviewing the full text. A few papers were added to the list based on the references of the reviewed papers. The final set of papers was reviewed and summarized here.

Results

The initial search yielded a total of 740 articles. A careful review of the titles, abstracts, and eventually the full text and then adding relevant articles from reviewing the references resulted in a final list of 73 articles published between 2011 and early 2018. These articles described methods for developing knowledge models for predicting such parameters as dosimetric and dose‐volume points, voxel‐level doses, and objective function weights that improve or automate IMRT planning for various cancer sites, addressing different clinical and quality assurance needs, and using a variety of machine learning approaches. A number of articles reported carefully designed clinical studies that assessed the performance of KBP models in realistic clinical applications. Overwhelming majority of the studies demonstrated the benefits of KBP in achieving comparable and often improved quality of IMRT planning while reducing planning time and plan quality variation.

Conclusions

The number of KBP‐related studies has been steadily increasing since 2011 indicating a growing interest in applying this approach to clinical applications. Validation studies have generally shown KBP to produce plans with quality comparable to expert planners while reducing the time and efforts to generate plans. However, current studies are mostly retrospective and leverage relatively small datasets. Larger datasets collected through multi‐institutional collaboration will enable the development of more advanced models to further improve the performance of KBP in complex clinical cases. Prospective studies will be an important next step toward widespread adoption of this exciting technology.

Automation in intensity modulated radiotherapy treatment planning-a review of recent innovations

Radiotherapy treatment planning of complex radiotherapy techniques, such as intensity modulated radiotherapy and volumetric modulated arc therapy, is a resource-intensive process requiring a high level of treatment planner intervention to ensure high plan quality. This can lead to variability in the quality of treatment plans and the efficiency in which plans are produced, depending on the skills and experience of the operator and available planning time. Within the last few years, there has been significant progress in the research and development of intensity modulated radiotherapy treatment planning approaches with automation support, with most commercial manufacturers now offering some form of solution. There is a rapidly growing number of research articles published in the scientific literature on the topic. This paper critically reviews the body of publications up to April 2018. The review describes the different types of automation algorithms, including the advantages and current limitations. Also included is a discussion on the potential issues with routine clinical implementation of such software, and highlights areas for future research.

Outlier identification in radiation therapy knowledge-based planning: A study of pelvic cases

PURPOSE

The purpose of this study was to apply statistical metrics to identify outliers and to investigate the impact of outliers on knowledge-based planning in radiation therapy of pelvic cases. We also aimed to develop a systematic workflow for identifying and analyzing geometric and dosimetric outliers.

METHODS

Four groups (G1-G4) of pelvic plans were sampled in this study. These include the following three groups of clinical IMRT cases: G1 (37 prostate cases), G2 (37 prostate plus lymph node cases) and G3 (37 prostate bed cases). Cases in G4 were planned in accordance with dynamic-arc radiation therapy procedure and include 10 prostate cases in addition to those from G1. The workflow was separated into two parts: 1. identifying geometric outliers, assessing outlier impact, and outlier cleaning; 2. identifying dosimetric outliers, assessing outlier impact, and outlier cleaning. G2 and G3 were used to analyze the effects of geometric outliers (first experiment outlined below) while G1 and G4 were used to analyze the effects of dosimetric outliers (second experiment outlined below). A baseline model was trained by regarding all G2 cases as inliers. G3 cases were then individually added to the baseline model as geometric outliers. The impact on the model was assessed by comparing leverages of inliers (G2) and outliers (G3). A receiver-operating-characteristic (ROC) analysis was performed to determine the optimal threshold. The experiment was repeated by training the baseline model with all G3 cases as inliers and perturbing the model with G2 cases as outliers. A separate baseline model was trained with 32 G1 cases. Each G4 case (dosimetric outlier) was subsequently added to perturb the model. Predictions of dose-volume histograms (DVHs) were made using these perturbed models for the remaining 5 G1 cases. A Weighted Sum of Absolute Residuals (WSAR) was used to evaluate the impact of the dosimetric outliers.

RESULTS

The leverage of inliers and outliers was significantly different. The Area-Under-Curve (AUC) for differentiating G2 (outliers) from G3 (inliers) was 0.98 (threshold: 0.27) for the bladder and 0.81 (threshold: 0.11) for the rectum. For differentiating G3 (outlier) from G2 (inlier), the AUC (threshold) was 0.86 (0.11) for the bladder and 0.71 (0.11) for the rectum. Significant increase in WSAR was observed in the model with 3 dosimetric outliers for the bladder (P < 0.005 with Bonferroni correction), and in the model with only 1 dosimetric outlier for the rectum (P < 0.005).

CONCLUSIONS

We established a systematic workflow for identifying and analyzing geometric and dosimetric outliers, and investigated statistical metrics for outlier detection. Results validated the necessity for outlier detection and clean-up to enhance model quality in clinical practice.