Implementing Remote Radiotherapy Planning to Increase Patient Flow at a Johannesburg Academic Hospital, South Africa: Protocol for a Prospective Feasibility Study
Notice bibliographique
Résumé
BACKGROUND: Access to timely radiotherapy in resource-constrained environments, particularly low- and middle-income countries (LMIC), is hampered by infrastructure constraints, workforce shortages, and a rising cancer burden. Remote radiotherapy planning (treatment planning as a service [TPaaS]) has the potential to enhance workflow efficiency, reduce wait times, and expand access to treatment. However, its integration and feasibility in LMIC public health systems remain underexplored. OBJECTIVE: This study evaluates the feasibility and initial effectiveness of remote radiotherapy planning using the Varian Eclipse system integrated with Elekta Versa HD linear accelerators (LINACs) at the busiest public hospital in South Africa. The primary goal is to determine whether remote planning can maintain plan quality while enhancing efficiency and minimizing treatment delays. METHODS: A prospective, single-site, pilot study is being conducted at Charlotte Maxeke Johannesburg Academic Hospital (CMJAH) in 2 phases. Phase 1 (feasibility) encompasses system commissioning, including beam modeling, computed tomography (CT)-to-electron density calibration, multileaf collimator (MLC) optimization, and dose calculations using the anisotropic analytical algorithm. System performance is validated through gamma index analysis (≥95% pass at 3%/3 mm). Interoperability and workflow readiness are assessed using simulated clinical scenarios and time integration steps. Phase 2 (effectiveness/impact) evaluates operational outcomes in 100 screened adult patients (≥18 years) with cervical, breast, prostate, head and neck, or rectal cancers requiring curative radiotherapy. Patients are grouped by cancer type (25 per group). Time to treatment, plan quality, and system efficiency will be compared with historical in-person planning data. Key workflow metrics include dates of first consultation, CT simulation, planning initiation, plan approval, quality assurance, and treatment start and completion. RESULTS: The study commenced enrollment in November 2023, with completion anticipated by mid-2025. As of July 2024, approximately 44 patients were screened and are anticipated to complete the remote planning. Initial findings show successful MLC transmission and dosimetric leaf gap optimization through iterative testing. Gamma pass rates exceeded 90% on both clinical and test servers, demonstrating initial accuracy. Results, including planning timelines, quality assurance outcomes, and system performance, will be available following comprehensive analysis in the third quarter of 2025. Preliminary findings indicate effective integration of remote planning in a resource-constrained public health sector setting. CONCLUSIONS: This study shows that remote radiotherapy planning is feasible and might improve cancer treatment in LMIC. The integration of commercially available systems, such as TPaaS, was successfully achieved without compromising dosimetric quality and ensured workflow continuity. Remote planning could serve as an effective tool to reduce treatment delays and enhance resource utilization in oncology units facing high demand. These findings offer valuable insights into technical integration, quality planning, and workflow outcomes, which can inform the future implementation of effective strategies in resource-constrained settings. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/60131.
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 | 0,034 | 0,026 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,043 | 0,009 |
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 ».