A genetic algorithm for variable selection in logistic regression analysis of radiotherapy treatment outcomes

Gayou, Olivier; Das, Shiva K.; Zhou, Shu; Marks, Lawrence B.; Parda, David S.; Miften, Moyed

doi:10.1118/1.3005974

Cited by 22 publications

(17 citation statements)

References 28 publications

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“…A better method is to account for the nondosimetric factors in the model itself. This has been done either by including both dosimetric and nondosimetric variables in a logistic regression model, 16,18 or by the use of a "dose modifying factor." 17,41 With the dose modifying approach, nondosimetric factors are included in, e.g., the LKB model by adjusting the effective T D 50 for different subgroups.…”

Section: B the Importance Of Including Patient-specific Factors Inmentioning

confidence: 99%

“…However, many studies have demonstrated the importance of considering nondosimetric factors such as health status, surgery, chemotherapy, comorbidities, base-line organ function, and smoking status. [11][12][13][14][15][16][17][18][19][20] When fitting NTCP models with the dose distribution as the only risk factor, any other parameters influencing the outcome of the treatment act as confounding factors. This means that the best fit parameters intrinsically incorporate averages over confounding factors, which results in shallower dose-response curves.…”

Section: Introductionmentioning

confidence: 99%

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The performance of normal‐tissue complication probability models in the presence of confounding factors

2015

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Purpose: This work explores different methods for accounting for patient‐specific factors in normal‐tissue complication probability (NTCP) modeling, and compares the performance of models using pseudoclinical datasets for “lung” and “rectum” complications. Methods: Datasets consisting of dose distributions and resulting normal‐tissue complications were simulated, letting varying levels of confounding factors (i.e., nondosimetric factors) influence the outcome. The simulated confounding factors were patient radiosensitivity and health status. Seven empirical NTCP models were fitted to each dataset; this is analogous to fitting alternative models to datasets from different populations, treated with the same technique. The performance of these models was compared using the area under the ROC curve (AUC) and the impact of confounding factors on the model performance was studied. The patient‐specific factors were then accounted for by (1) stratification and (2) two ways of modifying the traditional NTCP models to include these factors. Results: Confounding factors had a greater impact on model performance than the choice of model. All models performed similarly well on the rectum datasets (except the maximum dose model), while critical‐volume type models were slightly better than the mean dose‐, the Lyman–Kutcher–Burman‐, and the relative seriality models for lung. This difference was more apparent without confounding factors in the dataset. The two alternative functions including patient‐specific factors used in this work (one logistic and one cumulative normal function) were found to be equivalent, and more efficient than stratifying datasets according to patient‐specific factors and fitting models to subgroups individually. For datasets including confounding factors, the performance improved greatly when using models accounting for these; AUC increased from around 0.7 to close to unity. Conclusions: This work shows that identifying confounding factors, and developing methods to quantify them, is more important than the choice of NTCP model. Most dose–volume histogram (DVH)‐based NTCP models can be generalized to include confounding factors.

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Section: B the Importance Of Including Patient-specific Factors Inmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The performance of normal‐tissue complication probability models in the presence of confounding factors

2015

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“…GAs have been successfully applied to solve optimization problems, both for continuous (whether differentiable or not) and discrete functions. Variable selection for logistic regression model can be regarded as an optimization problem, and thus can be solved by GAs (7)(8)(9)(10). This article aims to provide a tutorial on how to implement GAs for variable selection.…”

Section: Introductionmentioning

confidence: 99%

Variable selection in Logistic regression model with genetic algorithm

Zhang¹,

Treviño²,

Hoseini³

et al. 2018

Ann. Transl. Med.

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Variable or feature selection is one of the most important steps in model specification. Especially in the case of medical-decision making, the direct use of a medical database, without a previous analysis and preprocessing step, is often counterproductive. In this way, the variable selection represents the method of choosing the most relevant attributes from the database in order to build a robust learning models and, thus, to improve the performance of the models used in the decision process. In biomedical research, the purpose of variable selection is to select clinically important and statistically significant variables, while excluding unrelated or noise variables. A variety of methods exist for variable selection, but none of them is without limitations. For example, the stepwise approach, which is highly used, adds the best variable in each cycle generally producing an acceptable set of variables. Nevertheless, it is limited by the fact that it commonly trapped in local optima. The best subset approach can systematically search the entire covariate pattern space, but the solution pool can be extremely large with tens to hundreds of variables, which is the case in nowadays clinical data. Genetic algorithms (GA) are heuristic optimization approaches and can be used for variable selection in multivariable regression models. This tutorial paper aims to provide a step-by-step approach to the use of GA in variable selection. The R code provided in the text can be extended and adapted to other data analysis needs.

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“…The prediction of radiotherapy treatment-related toxicity has been of significant interest. [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] Toxicity prediction involves relating the dosimetric factors to the toxicity in question in a model, potentially in combination with clinical information. For urinary symptoms, the availability of such models is limited compared to rectal symptoms following prostate radiotherapy.…”

Section: Introductionmentioning

confidence: 99%

“…12 In most instances, the studies suggest that the more contemporary strategies show promising results and perform better. Nevertheless, except for the series of studies on radiotherapy-induced pneumonitis, [7][8][9][10]13 the statistical-learning strategies were rarely extensively compared side-by-side. Furthermore, comparisons in the context of toxicity prediction were mostly performed using only one endpoint, probably chosen based on the clinical relevance.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

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A genetic algorithm for variable selection in logistic regression analysis of radiotherapy treatment outcomes

Cited by 22 publications

References 28 publications

The performance of normal‐tissue complication probability models in the presence of confounding factors

The performance of normal‐tissue complication probability models in the presence of confounding factors

Variable selection in Logistic regression model with genetic algorithm

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

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