Practical issues regarding angular and energy response in in vivo intraoperative electron radiotherapy dosimetry

AimTo estimate angular response deviation of MOSFETs in the realm of intraoperative electron radiotherapy (IOERT), review their energy dependence, and propose unambiguous names for detector rotations.BackgroundMOSFETs have been used in IOERT. Movement of the detector, namely rotations, can spoil results.Materials and methodsWe propose yaw, pitch, and roll to name the three possible rotations in space, as these unequivocally name aircraft rotations. Reinforced mobile MOSFETs (model TN-502RDM-H) and an Elekta Precise linear accelerator were used. Two detectors were placed in air for the angular response study and the whole set of five detectors was calibrated as usual to evaluate energy dependence.ResultsThe maximum readout was obtained with a roll of 90° and 4[[ce:hsp sp="0.25"/]]MeV. With regard to pitch movement, a substantial drop in readout was achieved at 90°. Significant overresponse was measured at 315° with 4[[ce:hsp sp="0.25"/]]MeV and at 45° with 15[[ce:hsp sp="0.25"/]]MeV. Energy response is not different for the following groups of energies: 4, 6, and 9[[ce:hsp sp="0.25"/]]MeV; and 12[[ce:hsp sp="0.25"/]]MeV, 15[[ce:hsp sp="0.25"/]]MeV, and 18[[ce:hsp sp="0.25"/]]MeV.ConclusionsOur proposal to name MOSFET rotations solves the problem of defining sensor orientations. Angular response could explain lower than expected results when the tip of the detector is lifted due to inadvertent movements. MOSFETs energy response is independent of several energies and differs by a maximum of 3.4% when dependent. This can limit dosimetry errors and makes it possible to calibrate the detectors only once for each group of energies, which saves time and optimizes lifespan of MOSFETs

Failure mode and effect analysis oriented to risk-reduction interventions in intraoperative electron radiation therapy: The specific impact of patient transportation, automation, and treatment planning availability

Background and purpose: Industrial companies use failure mode and effect analysis (FMEA) to improve quality. Our objective was to describe an FMEA and subsequent interventions for an automated intraoperative electron radiotherapy (IOERT) procedure with computed tomography simulation, pre-planning, and a fixed conventional linear accelerator. Material and methods: A process map, an FMEA, and a fault tree analysis are reported. The equipment considered was the radiance treatment planning system (TPS), the Elekta Precise linac, and TN-502RDM-H metal–oxide-semiconductor-field-effect transistor in vivo dosimeters. Computerized order-entry and treatment-automation were also analyzed. Results: Fifty-seven potential modes and effects were identified and classified into ‘treatment cancellation’ and ‘delivering an unintended dose’. They were graded from ‘inconvenience’ or ‘suboptimal treatment’ to ‘total cancellation’ or ‘potentially wrong’ or ‘very wrong administered dose’, although these latter effects were never experienced. Risk priority numbers (RPNs) ranged from 3 to 324 and totaled 4804. After interventions such as double checking, interlocking, automation, and structural changes the final total RPN was reduced to 1320. Conclusions: FMEA is crucial for prioritizing risk-reduction interventions. In a semi-surgical procedure like IOERT double checking has the potential to reduce risk and improve quality. Interlocks and automation should also be implemented to increase the safety of the procedure

Failure mode and effect analysis oriented to risk-reduction interventions in intraoperative electron radiation therapy: The specific impact of patient transportation, automation, and treatment planning availability

Background and purpose: Industrial companies use failure mode and effect analysis (FMEA) to improve quality. Our objective was to describe an FMEA and subsequent interventions for an automated intraoperative electron radiotherapy (IOERT) procedure with computed tomography simulation, pre-planning, and a fixed conventional linear accelerator. Material and methods: A process map, an FMEA, and a fault tree analysis are reported. The equipment considered was the radiance treatment planning system (TPS), the Elekta Precise linac, and TN-502RDM-H metal–oxide-semiconductor-field-effect transistor in vivo dosimeters. Computerized order-entry and treatment-automation were also analyzed. Results: Fifty-seven potential modes and effects were identified and classified into ‘treatment cancellation’ and ‘delivering an unintended dose’. They were graded from ‘inconvenience’ or ‘suboptimal treatment’ to ‘total cancellation’ or ‘potentially wrong’ or ‘very wrong administered dose’, although these latter effects were never experienced. Risk priority numbers (RPNs) ranged from 3 to 324 and totaled 4804. After interventions such as double checking, interlocking, automation, and structural changes the final total RPN was reduced to 1320. Conclusions: FMEA is crucial for prioritizing risk-reduction interventions. In a semi-surgical procedure like IOERT double checking has the potential to reduce risk and improve quality. Interlocks and automation should also be implemented to increase the safety of the procedure