Development of a virtual spacer to support the decision for the placement of an implantable rectum spacer for prostate cancer radiotherapy: Comparison of dose, toxicity and cost-effectiveness

Wijk, Yvonka van; Vanneste, Ben; Walsh, Seán; Meer, Skadi van der; Ramaekers, Bram; Elmpt, Wouter van; Pinkawa, Michael

doi:10.1016/j.radonc.2017.07.026

Cited by 24 publications

(26 citation statements)

References 28 publications

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“…Twenty-five patients are on the lower end of what is appropriate to draw firm conclusions and is a limiting factor of this study. However, the patient sample size is similar to previous in silico published studies [ 15 , 43 , 44 , 45 ]. Additionally, Kolmogorov–Smirnov tests at the 1% confidence level revealed the TCP plan and NTCP plan data to be normally distributed for each modality; this supports the assumption that our patient cohort is representative of the overall population.…”

Section: Discussionsupporting

confidence: 60%

Towards a Clinical Decision Support System for External Beam Radiation Oncology Prostate Cancer Patients: Proton vs. Photon Radiotherapy? A Radiobiological Study of Robustness and Stability

Walsh

Roelofs

Kueß

et al. 2018

Cancers

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We present a methodology which can be utilized to select proton or photon radiotherapy in prostate cancer patients. Four state-of-the-art competing treatment modalities were compared (by way of an in silico trial) for a cohort of 25 prostate cancer patients, with and without correction strategies for prostate displacements. Metrics measured from clinical image guidance systems were used. Three correction strategies were investigated; no-correction, extended-no-action-limit, and online-correction. Clinical efficacy was estimated via radiobiological models incorporating robustness (how probable a given treatment plan was delivered) and stability (the consistency between the probable best and worst delivered treatments at the 95% confidence limit). The results obtained at the cohort level enabled the determination of a threshold for likely clinical benefit at the individual level. Depending on the imaging system and correction strategy; 24%, 32% and 44% of patients were identified as suitable candidates for proton therapy. For the constraints of this study: Intensity-modulated proton therapy with online-correction was on average the most effective modality. Irrespective of the imaging system, each treatment modality is similar in terms of robustness, with and without the correction strategies. Conversely, there is substantial variation in stability between the treatment modalities, which is greatly reduced by correction strategies. This study provides a ‘proof-of-concept’ methodology to enable the prospective identification of individual patients that will most likely (above a certain threshold) benefit from proton therapy.

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Section: Discussionsupporting

confidence: 60%

Towards a Clinical Decision Support System for External Beam Radiation Oncology Prostate Cancer Patients: Proton vs. Photon Radiotherapy? A Radiobiological Study of Robustness and Stability

Walsh

Roelofs

Kueß

et al. 2018

Cancers

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“…In order to use the isotoxic model to predict the improvement of the IRS in a specified patient, CT images of a patient are needed before the placement of the IRS. To this end, a previously developed V-IRS was used [15]. This V-IRS uses a model IRS derived from 7 delineations of a RBI and uses image deformation to insert the model into a CT image of a patient without an IRS, thus creating a virtual image of the patient with an IRS.…”

Section: Virtual Spacermentioning

confidence: 99%

“…This method is applied to compare the possible dose escalation on patients with plans available before and after the placement of an IRS. This model is combined with a so-called 'virtual IRS' (V-IRS) [15]. This method uses image deformation to obtain CT images of patients with a predicted IRS position without having to place one (watch the animation: https://www.youtube.…”

Section: Introductionmentioning

confidence: 99%

Development of an isotoxic decision support system integrating genetic markers of toxicity for the implantation of a rectum spacer

et al. 2018

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“…One approach to this problem by Van Wijk and colleagues investigated the use of a virtual spacer created on a computed tomography (CT) image set. 9 A dosimetric plan was subsequently created and used with a published normal tissue complication probability model to determine likely toxicity outcomes. This work was then further extended through addition of biomarkers in the prediction model.…”

Section: Introductionmentioning

confidence: 99%

Comparison of statistical machine learning models for rectal protocol compliance in prostate external beam radiation therapy

et al. 2020

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Purpose: Limiting the dose to the rectum can be one of the most challenging aspects of creating a dosimetric external beam radiation therapy (EBRT) plan for prostate cancer treatment. Rectal sparing devices such as hydrogel spacers offer the prospect of increased space between the prostate and rectum, causing reduced rectal dose and potentially reduced injury. This study sought to help identify patients at higher risk of developing rectal injury based on estimated rectal dosimetry compliance prior to the EBRT simulation and planning procedure. Three statistical machine learning methods were compared for their ability to predict rectal dose outcomes with varied classification thresholds applied. Methods: Prostate cancer patients treated with conventionally fractionated EBRT to a reference dose of 74-78 Gy were invited to participate in the study. The dose volume histogram data from each dosimetric plan was used to quantify planned rectal volume receiving 50%, 83% 96%, and 102% of the reference dose. Patients were classified into two groups for each of these dose levels: either meeting tolerance by having a rectal volume less than a clinically acceptable threshold for the dose level (Y) or violating the tolerance by having a rectal volume greater than the threshold for the dose level (N). Logistic regression, classification and regression tree, and random forest models were compared for their ability to discriminate between class outcomes. Performance metrics included area under the receiver operator characteristic curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value. Finally, three classification threshold levels were evaluated for their impact on model performance. Results: A total of 176 eligible participants were recruited. Variable importance differed between model methods. Area under the receiver operator characteristic curve performance varied greatly across the different rectal dose levels and between models. Logistic regression performed best at the 83% reference dose level with an AUC value of 0.844, while random forest demonstrated best discrimination at the 96% reference dose level with an AUC value of 0.733. In addition to the standard classification probability threshold of 50%, the clinically representative threshold of 10%, and the best threshold from each AUC plot was applied to compare metrics. This showed that using a 50% threshold and the best threshold from the AUC plots yields similar results. Conversely, applying the more conservative clinical threshold of 10% maximized the sensitivity at V83_RD and V96_RD for all model types. Based on the combination of the metrics, logistic regression would be the recommendation for rectal protocol compliance prediction at the 83% reference dose level, and random forest for the 96% reference dose level, particularly when using the clinical probability threshold of 10%. Conclusions: This study demonstrated the efficacy of statistical machine learning models on rectal protocol compliance prediction for prostate cancer...

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Development of a virtual spacer to support the decision for the placement of an implantable rectum spacer for prostate cancer radiotherapy: Comparison of dose, toxicity and cost-effectiveness

Abstract: The V-IRS approach in combination with a toxicity prediction model and a cost-effectiveness analyses is a promising basis for a decision support tool for the implantation of either a hydrogel spacer or a rectum balloon implant.

Cited by 24 publications

References 28 publications

Towards a Clinical Decision Support System for External Beam Radiation Oncology Prostate Cancer Patients: Proton vs. Photon Radiotherapy? A Radiobiological Study of Robustness and Stability

Towards a Clinical Decision Support System for External Beam Radiation Oncology Prostate Cancer Patients: Proton vs. Photon Radiotherapy? A Radiobiological Study of Robustness and Stability

Development of an isotoxic decision support system integrating genetic markers of toxicity for the implantation of a rectum spacer

Comparison of statistical machine learning models for rectal protocol compliance in prostate external beam radiation therapy

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