Statistical-learning strategies generate only modestly performing predictive models for urinary symptoms following external beam radiotherapy of the prostate: A comparison of conventional and machine-learning methods

Yahya, Noorazrul; Ebert, Martin A.; Bulsara, Max; House, Michael J.; Kennedy, Angel; Joseph, David; Denham, James W.

doi:10.1118/1.4944738

Cited by 32 publications

(28 citation statements)

References 52 publications

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“…When looking at the AUC metric alone our results were comparable to values published in other SML based EBRT prediction studies. 13,17,18,27,28 Direct comparison of models is limited, however, because most other studies used toxicity as an endpoint rather than dose tolerance. The exception being Caine et al who also investigated protocol compliance but did not provide AUC or other performance metrics.…”

Section: Discussionmentioning

confidence: 99%

“…Each of these approaches vary in complexity but are commonly used in supervised classification problems such as this and have been published in previous EBRT studies with varying levels of success. 13,17,18 Typically, binary classification with SML models is based on a 50% probability threshold. However, many clinical decisions for a binary outcome are not made at this level.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

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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“…Many research scientists [89][90][91][92][93][94][95] have investigated the application of ML in radiotherapy treatment response and outcome predictions. Lee et al [89] studied utilizing of Bayesian network ensemble to predict radiation pneumonitis risk for NSCLC patients whom received curative 3D conformal radiotherapy.…”

Section: Treatment Outcomementioning

confidence: 99%

“…The study results indicated that random forest and elastic net logistic regression yield higher discriminative performance in (chemo) radiotherapy outcome and toxicity prediction than other studied classifiers. Yahya et al [93] explored multiple statistical-learning strategies for prediction of urinary symptoms following external beam radiotherapy of the prostate. The study results showed that logistic regression and multivariate adaptive regression splines (MARS) were most likely to be the best-performing strategy for the prediction of urinary symptoms.…”

Section: Treatment Outcomementioning

confidence: 99%

Radiation Oncology in the Era of Big Data and Machine Learning for Precision Medicine

Osman¹

2019

Artificial Intelligence - Applications in Medicine and Biology

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Machine learning (ML) applications in medicine represent an emerging field of research with the potential to revolutionize the field of radiation oncology, in particular. With the era of big data, the utilization of machine learning algorithms in radiation oncology research is growing fast with applications including patient diagnosis and staging of cancer, treatment simulation, treatment planning, treatment delivery, quality assurance, and treatment response and outcome predictions. In this chapter, we provide the interested reader with an overview of the ongoing advances and cutting-edge applications of state-of-the-art ML techniques in radiation oncology process from the radiotherapy workflow perspective, starting from patient's diagnosis to follow-up. We present with discussion the areas where ML has presently been used and also areas where ML could be applied to improve the efficiency (i.e., optimizing and automating the clinical processes) and quality (i.e., potentials for decision-making support toward a practical application of precision medicine in radiation therapy) of patient care.

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“…Urinary toxicity is a challenging issue, not only due to the variety of associated irritating or obstructive symptoms, but also owing to the limitations of dose descriptors and difficulties identifying the regions at risk responsible for those symptoms [4][5][6]. The bladder, for example, presents the largest inter-fraction shape variations, causing geometric and dose uncertainties that limit the possibility of accurately modeling the dose-volume response concerning GU toxicity [4,7,8,9 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-atlas-based segmentation of prostatic urethra from planning CT imaging to quantify dose distribution in prostate cancer radiotherapy

Acosta

Mylona

Dain

et al. 2017

Radiotherapy and Oncology

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Statistical-learning strategies generate only modestly performing predictive models for urinary symptoms following external beam radiotherapy of the prostate: A comparison of conventional and machine-learning methods

Cited by 32 publications

References 52 publications

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

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

Radiation Oncology in the Era of Big Data and Machine Learning for Precision Medicine

Multi-atlas-based segmentation of prostatic urethra from planning CT imaging to quantify dose distribution in prostate cancer radiotherapy

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