Evaluation of artificial intelligence-based treatment planning and delineation algorithms for external radiotherapy
Notice bibliographique
Résumé
External radiotherapy aims to treat cancer cells using ionizing radiation. The challenge is to irradiate target volumes while sparing healthy organs. These treatments rely on delineating regions of interest and treatment planning, carried out through Treatment Planning Systems (TPS). These processes are manually realized, demand precision and time. Their quality and execution time depend on the operator. In this context, automation solutions emerge, promising time efficiency, practices uniformization, and maintaining or enhancing treatment quality. This thesis aims to evaluate and clinically implement 2 artificial intelligence (AI) algorithms, one for automatic segmentation and the other for treatment planning. These algorithms are available in RayStation TPS (RaySearch Medical Laboratories AB, Stockholm, Sweden). The first chapter introduces the clinical environment for implementing automation techniques. The second chapter concerns the automatic segmentation algorithm. It is quantitatively and qualitatively evaluated against reference structures validated by physician. Initially, the AI is compared to 2 other algorithms within RS: one based on multi-atlases, and another on statistical models. Subsequently, AI validation is performed for 24 different organs within the thorax, abdomen, head, and neck. Lastly, this AI is compared to a second AI available in another automatic segmentation software called Limbus (Limbus AI Inc., Regina, SK, Canada). The third chapter focuses on treatment planning automation using AI. First, we describe the construction of three databases (DBs) provided to RaySearch, enabling the creation of AI models tailored to our clinical practices for pelvic and whole-brain treatments. This AI is compared to manually generated plans and a multi-criteria optimization (MCO) algorithm, demonstrating AI's clinical utility. Models are adjusted using a cohort of 100 patients, validating acceptability and deliverability of automatically generated plans for pelvic locations. However, these plans are improvable and can be enhanced manually by operators. We present, later in this chapter, a user experience with clinical use of AI for the first 79 automatically generated and manually improved treatment plans. The chapter's end focuses on the value of training AI with a database generated by MCO plans. AI evaluation, compared to manually generated and MCO plans, reveals that AI-generated plans do not match MCO plan quality but enhance manual plans. In the final chapter, we propose a guide for implementing automatic methods clinically. This ranges from defining expectations to ethical AI use, utilizing on-site developed Python scripts, development, validation methods, and limitations encountered for each automation type. In summary, the algorithms assessed in this thesis, notably the AIs, save time, standardize practices, and aid treatment planning quality enhancement. However, their implementation demands substantial preliminary effort, and their utilization should be combined with human expertise to ensure treatment quality.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».