Forcing lateral electron disequilibrium to spare lung tissue: a novel technique for stereotactic body radiation therapy of lung cancer
Notice bibliographique
Résumé
Stereotactic body radiation therapy (SBRT) has quickly become a preferred treatment option for early-stage lung cancer patients who are ineligible for surgery. This technique uses tightly conformed megavoltage (MV) x-ray beams to irradiate a tumour with ablative doses in only a few treatment fractions. Small high energy x-ray fields can cause lateral electron disequilibrium (LED) to occur within low density media, which can reduce tumour dose. These dose effects may be challenging to predict using analytic dose calculation algorithms, especially at higher beam energies. As a result, previous authors have suggested using low energy photons (<10 MV) and larger fields (>5 × 5 cm(2)) for lung cancer patients to avoid the negative dosimetric effects of LED. In this work, we propose a new form of SBRT, described as LED-optimized SBRT (LED-SBRT), which utilizes radiotherapy (RT) parameters designed to cause LED to advantage. It will be shown that LED-SBRT creates enhanced dose gradients at the tumour/lung interface, which can be used to manipulate tumour dose, and/or normal lung dose. To demonstrate the potential benefits of LED-SBRT, the DOSXYZnrc (National Research Council of Canada, Ottawa, ON) Monte Carlo (MC) software was used to calculate dose within a cylindrical phantom and a typical lung patient. 6 MV or 18 MV x-ray fields were focused onto a small tumour volume (diameter ∼1 cm). For the phantom, square fields of 1 × 1 cm(2), 3 × 3 cm(2), or 5 × 5 cm(2) were applied. However, in the patient, 3 × 1 cm(2), 3 × 2 cm(2), 3 × 2.5 cm(2), or 3 × 3 cm(2) field sizes were used in simulations to assure target coverage in the superior-inferior direction. To mimic a 180° SBRT arc in the (symmetric) phantom, a single beam profile was calculated, rotated, and beams were summed at 1° segments to accumulate an arc dose distribution. For the patient, a 360° arc was modelled with 36 equally weighted (and spaced) fields focused on the tumour centre. A planning target volume (PTV) was generated by considering the extent of tumour motion over the patient's breathing cycle and set-up uncertainties. All patient dose results were normalized such that at least 95% of the PTV received at least 54 Gy (i.e. D95 = 54 Gy). Further, we introduce 'LED maps' as a novel clinical tool to compare the magnitude of LED resulting from the various SBRT arc plans. Results from the phantom simulation suggest that the best lung sparing occurred for RT parameters that cause severe LED. For equal tumour dose coverage, normal lung dose (2 cm outside the target region) was reduced from 92% to 23%, comparing results between the 18 MV (5 × 5 cm(2)) and 18 MV (1 × 1 cm(2)) arc simulations. In addition to reduced lung dose for the 18 MV (1 × 1 cm(2)) arc, maximal tumour dose increased beyond 125%. Thus, LED can create steep dose gradients to spare normal lung, while increasing tumour dose levels (if desired). In the patient simulation, a LED-optimized arc plan was designed using either 18 MV (3 × 1 cm(2)) or 6 MV (3 × 3cm(2)) beams. Both plans met the D95 dose coverage requirement for the target. However, the LED-optimized plan increased the maximum, mean, and minimum dose within the PTV by as much as 80 Gy, 11 Gy, and 3 Gy, respectively. Despite increased tumour dose levels, the 18 MV (3 × 1 cm(2)) arc plan improved or maintained the V20, V5, and mean lung dose metrics compared to the 6 MV (3 × 3 cm(2)) simulation. We conclude that LED-SBRT has the potential to increase dose gradients, and dose levels within a small lung tumour. The magnitude of tumour dose increase or lung sparing can be optimized through manipulation of RT parameters (e.g. beam energy and field size).
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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 | 0,000 | 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 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 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 ».