Interpretable Machine Learning for Choosing Radiation Dose-volume Constraints on Cardio-pulmonary Substructures Associated with Overall Survival in NRG Oncology RTOG 0617

Lee, Sang Ho; Geng, H.; Arnold, Jacinta; Caruana, Richard A.; Fan, Yong; Rosen, Mark; Apte, Aditya; Deasy, Joseph O.; Bradley, Jeffrey D.; Xiao, Ying

doi:10.1016/j.ijrobp.2023.06.009

Cited by 6 publications

(1 citation statement)

References 49 publications

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“…These databases, created at the level of a care center or even at a national level [17], exploitable by artificial intelligence-based tools [21], present significant potential for improving patient care. Similar to various medical domains, this will have the potential to predict treatment toxicity [22,23], propose personalized treatments [24], provide decision support tools [25][26][27], establish new dose reference framework [28], and much more.…”

Section: Discussionmentioning

confidence: 99%

Enhancing Patient Care in Radiotherapy: Proof-of-Concept of a Monitoring Tool

Beldjoudi,

Eugène,

Grégoire

et al. 2024

Technologies

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Introduction: A monitoring tool, named Oncology Data Management (ODM), was developed in radiotherapy to generate structured information based on data contained in an Oncology Information System (OIS). This study presents the proof-of-concept of the ODM tool and highlights its applications to enhance patient care in radiotherapy. Material & Methods: ODM is a sophisticated SQL query which extracts specific features from the Mosaiq OIS (Elekta, UK) database into an independent structured database. Data from 2016 to 2022 was extracted to enable monitoring of treatment units and evaluation of the quality of patient care. Results: A total of 25,259 treatments were extracted. Treatment machine monitoring revealed a daily 11-treatement difference between two units. ODM showed that the unit with fewer daily treatments performed more complex treatments on diverse locations. In 2019, the implementation of ODM led to the definition of quality indicators and in organizational changes that improved the quality of care. As consequences, for palliative treatments, there was an improvement in the proportion of treatments prepared within 7 calendar days between the scanner and the first treatment session (29.1% before 2020, 40.4% in 2020 and 46.4% after 2020). The study of fractionation in breast treatments exhibited decreased prescription variability after 2019, with distinct patient age categories. Bi-fractionation once a week for larynx prescriptions of 35 × 2.0 Gy achieved an overall treatment duration of 47.0 ± 3.0 calendar days in 2022. Conclusions: ODM enables data extraction from the OIS and provides quantitative tools for improving organization of a department and the quality of patient care in radiotherapy.

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Section: Discussionmentioning

confidence: 99%

Enhancing Patient Care in Radiotherapy: Proof-of-Concept of a Monitoring Tool

Beldjoudi,

Eugène,

Grégoire

et al. 2024

Technologies

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A review of evaluation approaches for explainable AI with applications in cardiology

Salih,

Galazzo,

Gkontra

et al. 2024

Artif Intell Rev

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Explainable artificial intelligence (XAI) elucidates the decision-making process of complex AI models and is important in building trust in model predictions. XAI explanations themselves require evaluation as to accuracy and reasonableness and in the context of use of the underlying AI model. This review details the evaluation of XAI in cardiac AI applications and has found that, of the studies examined, 37% evaluated XAI quality using literature results, 11% used clinicians as domain-experts, 11% used proxies or statistical analysis, with the remaining 43% not assessing the XAI used at all. We aim to inspire additional studies within healthcare, urging researchers not only to apply XAI methods but to systematically assess the resulting explanations, as a step towards developing trustworthy and safe models.

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Risk of clinically significant cardiovascular disease associated with postoperative radiotherapy in non-small cell lung cancer patients receiving surgical resection followed by adjuvant chemotherapy: A Korean nationwide cohort study

Kwon,

Kim

2024

Radiotherapy and Oncology

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Interpretable Machine Learning for Choosing Radiation Dose-volume Constraints on Cardio-pulmonary Substructures Associated with Overall Survival in NRG Oncology RTOG 0617

Cited by 6 publications

References 49 publications

Enhancing Patient Care in Radiotherapy: Proof-of-Concept of a Monitoring Tool

Enhancing Patient Care in Radiotherapy: Proof-of-Concept of a Monitoring Tool

A review of evaluation approaches for explainable AI with applications in cardiology

Risk of clinically significant cardiovascular disease associated with postoperative radiotherapy in non-small cell lung cancer patients receiving surgical resection followed by adjuvant chemotherapy: A Korean nationwide cohort study

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