Explainable Artificial Intelligence to Identify Dosimetric Predictors of Toxicity in Patients with Locally Advanced Non-Small Cell Lung Cancer: A Secondary Analysis of RTOG 0617

Ladbury, Colton; Li, Richard; Danesharasteh, Anseh; Ertem, Zeynep; Tam, Anamarie; Liu, Jason; Hao, Claire; Li, Rose; McGee, Heather M.; Sampath, Sagus; Williams, Terence M.; Glaser, Scott; Khasawneh, Mohammad T.; Liao, Zhongxing; Lee, Percy; Ryckman, Jeff; Shaikh, P.; Amini, Arya

doi:10.1016/j.ijrobp.2023.06.019

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…By providing interpretable and accurate toxicity predictions, our models could enhance the decision-making process in radiation therapy. The use of Shapley values adds a layer of interpretability that is vital for clinical acceptance and application [30]. Clinicians can leverage these insights to understand the key factors influencing toxicity risk, allowing for more informed and personalized patient care strategies [31].…”

Section: Discussionmentioning

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: Discussionmentioning

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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“…Motivated by the establishment of various diagnostic signatures based on REOs to aid clinical HCC diagnosis decision, we designed robust and powerful predictors in this work. The developed predictors hybridized several algorithms, i.e., REOs, mRMR 21 , MRMD 22 , support vector machine (SVM) 23 , 24 , k-nearest neighbor (KNN) 24 , decision tree (DT) 25 , 26 , logistic regression (LR) 26 , extreme gradient boosting (XGBoost) 24 , logistic model trees (LMT) 27 , adaptive boosting M1 (AdaBoostM1) 28 and naïve bayes (NB) 29 . The REOs method was used for feature construction, mRMR and MRMD were used for feature ranking and selection, 2902 secreted genes (genes encoding secreted proteins) collected public database were used for feature filtering, and SVM, KNN, DT, LR, XGBoost, LMT, AdaBoostM1 and NB algorithms were used for classification purposes.…”

Section: Introductionmentioning

confidence: 99%

Development of machine learning-based predictors for early diagnosis of hepatocellular carcinoma

Zhang,

Huang,

Liu

et al. 2024

Sci Rep

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Hepatocellular carcinoma (HCC) remains a formidable malignancy that significantly impacts human health, and the early diagnosis of HCC holds paramount importance. Therefore, it is imperative to develop an efficacious signature for the early diagnosis of HCC. In this study, we aimed to develop early HCC predictors (eHCC-pred) using machine learning-based methods and compare their performance with existing methods. The enhancements and advancements of eHCC-pred encompassed the following: (i) utilization of a substantial number of samples, including an increased representation of cirrhosis tissues without HCC (CwoHCC) samples for model training and augmented numbers of HCC and CwoHCC samples for model validation; (ii) incorporation of two feature selection methods, namely minimum redundancy maximum relevance and maximum relevance maximum distance, along with the inclusion of eight machine learning-based methods; (iii) improvement in the accuracy of early HCC identification, elevating it from 78.15 to 97% using identical independent datasets; and (iv) establishment of a user-friendly web server. The eHCC-pred is freely accessible at http://www.dulab.com.cn/eHCC-pred/. Our approach, eHCC-pred, is anticipated to be robustly employed at the individual level for facilitating early HCC diagnosis in clinical practice, surpassing currently available state-of-the-art techniques.

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PROshot: Regional Nodal Irradiation, Prophylactic Radiation for Bone Metastases, Adaptive Therapy for Hodgkin Lymphoma, Immunoscore, and Heart Dosimetry

Dover,

Dulaney

2024

Practical Radiation Oncology

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Explainable Artificial Intelligence to Identify Dosimetric Predictors of Toxicity in Patients with Locally Advanced Non-Small Cell Lung Cancer: A Secondary Analysis of RTOG 0617

Cited by 3 publications

References 23 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

Development of machine learning-based predictors for early diagnosis of hepatocellular carcinoma

PROshot: Regional Nodal Irradiation, Prophylactic Radiation for Bone Metastases, Adaptive Therapy for Hodgkin Lymphoma, Immunoscore, and Heart Dosimetry

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