Predictive Modeling of Survival and Toxicity in Patients With Hepatocellular Carcinoma After Radiotherapy

Chamseddine, Ibrahim; Kim, Yejin; De, Brian; Naqa, Issam El; Duda, Dan G.; Wolfgang, John A.; Pursley, J.; Paganetti, Harald; Wo, Jennifer Y.; Hong, Theodore S.; Koay, Eugene J.; Grassberger, Clemens

doi:10.1200/cci.21.00169

Cited by 7 publications

(6 citation statements)

References 51 publications

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“…These models enable anticipation of outcomes, facilitate clinical decision-making, and enhance cancer prevention strategies [ 40 ]. In the realm of oncology, predictive models have been developed utilizing both linear and nonlinear algorithms to forecast survival, nonlocal failure, radiation-induced liver disease, and other outcomes in patients with specific conditions such as metastatic castration-resistant prostate cancer [ 41 ].…”

Section: Reviewmentioning

confidence: 99%

Revolutionizing Pulmonary Diagnostics: A Narrative Review of Artificial Intelligence Applications in Lung Imaging

Sindhu,

Jadhav,

Ghewade

et al. 2024

Cureus

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Artificial intelligence (AI) has emerged as a transformative force in healthcare, particularly in pulmonary diagnostics. This comprehensive review explores the impact of AI on revolutionizing lung imaging, focusing on its applications in detecting abnormalities, diagnosing pulmonary conditions, and predicting disease prognosis. We provide an overview of traditional pulmonary diagnostic methods and highlight the importance of accurate and efficient lung imaging for early intervention and improved patient outcomes. Through the lens of AI, we examine machine learning algorithms, deep learning techniques, and natural language processing for analyzing radiology reports. Case studies and examples showcase the successful implementation of AI in pulmonary diagnostics, alongside challenges faced and lessons learned. Finally, we discuss future directions, including integrating AI into clinical workflows, ethical considerations, and the need for further research and collaboration in this rapidly evolving field. This review underscores the transformative potential of AI in enhancing the accuracy, efficiency, and accessibility of pulmonary healthcare.

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Section: Reviewmentioning

confidence: 99%

Revolutionizing Pulmonary Diagnostics: A Narrative Review of Artificial Intelligence Applications in Lung Imaging

Sindhu,

Jadhav,

Ghewade

et al. 2024

Cureus

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“…Imaging techniques based on toxicity- or resistance-associated biomarkers could provide an alternative non-invasive, spatiotemporal, and objective assessment approach to implementing these criteria for assessing radiation-induced adverse effects. Meanwhile, advanced imaging processing tools, particularly machine learning and artificial intelligence, may play a crucial role in predicting treatment failure and RT-induced toxicities 158 .…”

Section: Biomarker-driven Molecular Imaging Probes In Radiotherapy Wo...mentioning

confidence: 99%

Biomarker-driven molecular imaging probes in radiotherapy

Li,

Gong,

Luo

2024

Theranostics

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Background: Biomarker-driven molecular imaging has emerged as an integral part of cancer precision radiotherapy. The use of molecular imaging probes, including nanoprobes, have been explored in radiotherapy imaging to precisely and noninvasively monitor spatiotemporal distribution of biomarkers, potentially revealing tumor-killing mechanisms and therapy-induced adverse effects during radiation treatment. Methods: We summarized literature reports from preclinical studies and clinical trials, which cover two main parts: 1) Clinically-investigated and emerging imaging biomarkers associated with radiotherapy, and 2) instrumental roles, functions, and activatable mechanisms of molecular imaging probes in the radiotherapy workflow. In addition, reflection and future perspectives are proposed. Results: Numerous imaging biomarkers have been continuously explored in decades, while few of them have been successfully validated for their correlation with radiotherapeutic outcomes and/or radiation-induced toxicities. Meanwhile, activatable molecular imaging probes towards the emerging biomarkers have exhibited to be promising in animal or small-scale human studies for precision radiotherapy. Conclusion: Biomarker-driven molecular imaging probes are essential for precision radiotherapy. Despite very inspiring preliminary results, validation of imaging biomarkers and rational design strategies of probes await robust and extensive investigations. Especially, the correlation between imaging biomarkers and radiotherapeutic outcomes/toxicities should be established through multi-center collaboration involving a large cohort of patients.

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“…In radiation oncology, the development of outcome prediction models offers an objective approach to personalized cancer treatment [1]. These models, based on machine learning (ML), provide a data-driven understanding of patient-specific responses to radiation therapy, thereby enabling clinicians to anticipate and mitigate potential toxicities more accurately [2][3][4]. This enhances the precision of therapeutic interventions and may significantly improve patient outcomes by preemptively addressing the likelihood of specific toxicities.…”

Section: Introductionmentioning

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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Predictive Modeling of Survival and Toxicity in Patients With Hepatocellular Carcinoma After Radiotherapy

Cited by 7 publications

References 51 publications

Revolutionizing Pulmonary Diagnostics: A Narrative Review of Artificial Intelligence Applications in Lung Imaging

Revolutionizing Pulmonary Diagnostics: A Narrative Review of Artificial Intelligence Applications in Lung Imaging

Biomarker-driven molecular imaging probes in radiotherapy

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

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