Random Forest Modeling of Acute Toxicity in Anal Cancer: Effects of Peritoneal Cavity Contouring Approaches on Model Performance

Salazar, Ramon M.; Duryea, Jack D.; Leone, Alexandra O.; Nair, Saurabh S.; Mumme, Raymond P.; De, Brian; Corrigan, Kelsey L.; Rooney, Michael K.; Das, Prajnan; Holliday, Emma B.; Court, Laurence E.; Niedzielski, Joshua S.

doi:10.1016/j.ijrobp.2023.08.042

Cited by 2 publications

(2 citation statements)

References 28 publications

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“…One dataset involved 246 SCCA (squamous cell carcinoma of the anus) patients with different grades of acute GIT (Suppl. Table S1) [17]. The second dataset consisted of 232 NSCLC (non-small cell lung cancer) patients, where the outcome of interest was RP (Suppl.…”

Section: Patient Characteristicsmentioning

confidence: 99%

Performance comparison of ten state-of-the-art machine learning algorithms for outcome prediction modeling of radiation-induced toxicity

Salazar,

Nair,

Leone

et al. 2024

Preprint

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Purpose: To evaluate the efficacy of prominent machine learning algorithms in predicting normal tissue complication probability utilizing clinical data obtained from two distinct disease sites, and to create a software tool that facilitates the automatic determination of the optimal algorithm to model any given labeled dataset. Methods and Materials: We obtained 3 sets of radiation toxicity data (478 patients) from our clinic, gastrointestinal toxicity (GIT), radiation pneumonitis (RP), and radiation esophagitis (RE). These data comprised clinicopathological and dosimetric information for patients diagnosed with non-small cell lung cancer and anal squamous cell carcinoma. Each dataset was modeled using ten commonly employed machine learning algorithms (elastic net, LASSO, random forest, regression forest, support vector machine, XGBoost, k-nearest-neighbors, neural network, Bayesian-LASSO, and Bayesian neural network) by randomly dividing the dataset into a training and test set. The training set was used to create and tune the model, and the test set served to assess it by calculating performance metrics. This process was repeated 100 times by each algorithm for each dataset. Figures were generated to visually compare the performance of the algorithms. A graphical user interface was developed to automate this whole process. Results: LASSO achieved the highest area under the precision-recall curve (AUPRC) (0.807±0.067) for RE, random forest for GIT (0.726±0.096), and the neural network for RP (0.878±0.060). Area-under-the-curve was 0.754±0.069, 0.889±0.043, and 0.905±0.045, respectively. The graphical user interface was used to compare all algorithms for each dataset automatically. When averaging AUPRC across all toxicities, Bayesian-LASSO was the best model. Conclusion: Our results show that there is no best algorithm for all datasets. Therefore, it is important to compare multiple algorithms when training an outcome prediction model on a new dataset. The graphical user interface created for this study automatically compares the performance of these ten algorithms for any dataset.

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Section: Patient Characteristicsmentioning

confidence: 99%

Performance comparison of ten state-of-the-art machine learning algorithms for outcome prediction modeling of radiation-induced toxicity

Salazar,

Nair,

Leone

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

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“…This type of cross-validation is a common method used to approximate the generalizability of the modeling process. [30][31][32] Each iteration of cross-validation creates a unique model, with the overall process creating 100 unique models for a given model type. This results in a paired comparison of individual models, where statistical significance of performance metrics can be derived from the distribution of models for each model type.…”

Section: Model Buildingmentioning

confidence: 99%

Multi-region Multiomic Random Forest Toxicity Modeling of Radiation Pneumonitis

Nair,

Salazar,

Leone

et al. 2024

Preprint

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PurposeRadiation pneumonitis (RP) is a major dose-limiting toxicity resulting from non-small-cell lung cancer (NSCLC) radiotherapy. Multiomic features (radiomics and dosiomics) could provide additional predictive information as compared to traditionally used clinical and dose-volume histogram (DVH) parameters. We aimed to investigate the utility of multiomic features to improve RP toxicity models.MethodsOut of 329 NSCLC patients considered, 85 patients (25.84%) were found to have toxicity ≥ grade 2 RP per CTCAE v5.0. A total of 422 radiomic and dosiomic features were extracted. Four toxicity prediction model types were created using clinical factors together with respective features from one of the following groups: (a) DVH (base model), (b) whole lung radiomics and dosiomics (WL-RD), (c) multi-region radiomics and dosiomics (MR - RD) and (d) multi-region DVH, radiomics and dosiomics (MR-DVHRD). Toxicity models were created using a random forest classifier with a Monte Carlo cross-validation approach of 100 iterations, and a training/test split of 80%/20%, respectively. Model predictive performance was evaluated by area under the receiver operating characteristic curve (AUC) and area under the precision-recall curve (AUPRC).ResultsThe AUC and AUPRC values (mean ± standard deviation) for the 4 model types were 0.81±0.04/0.70±0.06 (base model), 0.82±0.05/0.73±0.08 (WL-RD, p<0.05), 0.83±0.06/0.75±0.08 (MR–RD, p<0.05), and 0.82±0.05/0.72±0.08 (MR-DVHRD, p<0.05), respectively, wherein a paired test compared the performance metrics of omic models with the base model built on each iteration of cross0020validation.ConclusionsAll multiomic model types outperformed the base DVH model. MR-RD model had the best performance among all model types.

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Random Forest Modeling of Acute Toxicity in Anal Cancer: Effects of Peritoneal Cavity Contouring Approaches on Model Performance

Cited by 2 publications

References 28 publications

Performance comparison of ten state-of-the-art machine learning algorithms for outcome prediction modeling of radiation-induced toxicity

Performance comparison of ten state-of-the-art machine learning algorithms for outcome prediction modeling of radiation-induced toxicity

Multi-region Multiomic Random Forest Toxicity Modeling of Radiation Pneumonitis

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