Automatic catheter digitization in breast brachytherapy
Notice bibliographique
Résumé
BACKGROUND: High dose rate (HDR) brachytherapy requires clinicians to digitize catheters manually. This process is time-consuming, complex, and depends heavily on clinical experience-especially in breast cancer cases, where catheters may be inserted at varying angles and orientations due to an irregular anatomy. PURPOSE: This study is the first to automate catheter digitization specifically for breast HDR brachytherapy, emphasizing the unique challenges associated with this treatment site. It also introduces a pipeline that automatically digitizes catheters, generates dwell positions, and calculates the delivered dose for new breast cancer patients. METHODS: Treatment data from 117 breast cancer patients treated with HDR brachytherapy were used. Pseudo-contours for the catheters were created from the treatment digitization points and divided into three classes: catheter body, catheter head, and catheter tip. An nnU-Net pipeline was trained to segment the pseudo-contours on treatment planning computed tomography images of 88 patients (training and validation). Then, pseudo-contours were digitized by separating the catheters into connected components. Predicted catheters with an unusual volume were flagged for manual review. A custom algorithm was designed to report and separate connected components containing colliding catheters. Finally, a spline was fitted to every separated catheter, and the tip was identified on the spline using the tip contour prediction. Dwell positions were placed from the created tip at a regular step size extracted from the DICOM plan file. Distance from each dwell position used during the clinical treatment to the fitted spline (shaft distance) was computed, as well as the distance from the treatment tip to the one identified by our pipeline. Dwell times from the clinical plan were assigned to the nearest generated dwell positions. TG-43 dose in water was computed analytically, and the absorbed dose in the medium was predicted using a published AI-based dose prediction model. Dosimetric comparison between the clinically delivered plan dose and the created automated plan dose was evaluated regarding dosimetric indices percent error. RESULTS: values in all the organs at risk, the average error remained below 5%. The pipeline execution time, including auto-contouring, digitization, and dose to medium prediction, averages 118 s, ranging from 63 to 294 s. The pipeline successfully flagged all cases where digitization was not performed correctly. CONCLUSIONS: Our pipeline is the first to automate the digitization of catheters for breast brachytherapy, as well as the first to generate dwell positions and predict corresponding AI-based absorbed dose to medium based on automatically digitized catheters. The automatically digitized catheters are in excellent agreement with the manually digitized ones while more accurately reflecting their true anatomical shape.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».