Variations in target volume position between and during treatment fractions can lead to measurable differences in the dose distribution delivered to each patient. Current methods to estimate the ongoing cumulative delivered dose distribution make idealized assumptions about individual patient motion based on average motions observed in a population of patients. In the delivery of intensity modulated radiation therapy (IMRT) with a multi-leaf collimator (MLC), errors are introduced in both the implementation and delivery processes. In addition, target motion and MLC motion can lead to dosimetric errors from interplay effects. All of these effects may be of clinical importance. Here we present a method to compute delivered dose distributions for each treatment beam and fraction, which explicitly incorporates synchronized real-time patient motion data and real-time fluence and machine configuration data. This synchronized dynamic dose reconstruction method properly accounts for the two primary classes of errors that arise from delivering IMRT with an MLC: (a) Interplay errors between target volume motion and MLC motion, and (b) Implementation errors, such as dropped segments, dose over/under shoot, faulty leaf motors, tongue-and-groove effect, rounded leaf ends, and communications delays. These reconstructed dose fractions can then be combined to produce high-quality determinations of the dose distribution actually received to date, from which individualized adaptive treatment strategies can be determined.

Collimator

Cumulative dose

10.1118/1.2388157

Cite

Citations (38)

Initial Experience with Radiosurgery Delivered Using a Linear Accelerator with Multileaf Collimation and a Computer Control System Paper #764

Neurosurgery (1996)

Donald A. Ross James M. Balter Daniel L. McShan Paul Archer K Forster

Ross, Donald A.; Balter, J. M.; McShan, D. L.; Archer, P.; Forster, K. M.; Fraass, D. A.; Sandler, H. M.; Ten Haken, R. K.; Martel, M. K.

Collimated light

10.1227/00006123-199609000-00114

Cite

Citations (0)

The prediction of radiation‐induced liver dysfunction using a local dose and regional venous perfusion model

Medical Physics (2007)

Yue Cao Joel F. Platt Isaac R. Francis James M. Balter Charlie C. Pan

We have shown that high dose conformal radiation combined with chemotherapy appears to prolong the survival of patients with unresectable intrahepatic cancers. The ability to safely deliver higher doses is primarily limited by the development of radiation‐induced liver disease, characterized by venous occlusion. In this study, we investigated whether portal venous perfusion measured prior to the end of radiation therapy (RT) together with dose could predict liver venous perfusion dysfunction after treatment. Ten patients with unresectable intrahepatic cancer participated in an IRB‐approved computer tomography (CT) perfusion study. Hepatic arterial and portal vein perfusion distributions were estimated by using dynamic contrast enhanced CT and the single compartmental model. Scans were obtained at four time points: prior to treatment, after 15 and 30 fractions of treatments, and one month following the completion of RT. Multivariant linear regression was used to determine covariances among the first three time point measurements plus dose for prediction of the post RT measurement. The reduction in the regional venous perfusion one month following RT was predicted by the local accumulated dose and the change in the regional venous perfusion after fractions . Each Gy produced an approximately 1.2% of reduction in the venous perfusion. This local dose and venous perfusion model has the potential to predict individual sensitivity to radiation. This is the first step toward developing a method to deliver higher and potentially more curative radiation doses to the patients who can safely receive these higher doses.

Liver perfusion

10.1118/1.2431081

Cite

Citations (37)

Radiation Therapy Outcomes Models in the Era of Radiomics and Radiogenomics: Uncertainties and Validation

International Journal of Radiation Oncology*Biology*Physics (2018)

Issam El Naqa Gaurav Pandey Hugo J.W.L. Aerts Jen‐Tzung Chien Christian Nicolaj Andreassen

Overfitting

Radiogenomics

Radiation oncology

10.1016/j.ijrobp.2018.08.022

Cite