Search

Background: Predicting models of the gamma passing rate (GPR) have been studied to substitute the measurement‐based gamma analysis. Since these studies used data from different radiotherapy systems comprising TPS, linear accelerator, and detector array, it has been difficult to compare the performances of the predicting models among institutions with different radiotherapy systems. Purpose: We aimed to develop unbiased scoring methods to evaluate the performance of the models predicting the GPR, by introducing both best and worst limits for the performance of the GPR prediction. Methods: Two hundred head‐and‐neck VMAT plans were used to develop a framework. The GPRs were measured using the ArcCHECK device. The predicted GPR [p] was generated using a deep learning‐based model [pDL]. The predicting model was evaluated using four metrics: standard deviation (SD) [σ], Pearson's correlation coefficient (CC) [r], mean squared error (MSE) [s], and mean absolute error (MAE) [a]. The best limit [σm${\sigma _m}$, rm${r_m}$, sm${s_m}$, and am${a_m}$] was estimated by measuring the SD of measured GPR [m] by shifting the device along the longitudinal direction to measure different sampling points. Mimicked best and worst p's [pbest and pworst] were generated from pDL. The worst limit was defined such that m and p have no correlation [CC ∼ 0]. The worst limit [σMix, rMix, sMix, and aMix] was generated using the event‐mixing (EM) technique originally introduced in high‐energy physics experiments. The range of σ, r, s, and a was defined to be [σm,σMix]$[ {{\sigma _m},{\sigma _{{\mathrm{Mix}}}}} ]$, [0,rm]$[ {0,{r_m}} ]$, [sm,sMix]$[ {{s_m},{s_{{\mathrm{Mix}}}}} ]$, and [am,aMix]$[ {{a_m},{a_{{\mathrm{Mix}}}}} ]$. The achievement score (AS) independently based on σ, r, s, and a were calculated for pDL, pbest and pworst. The probability that p fails the gamma analysis (alert frequency; AF) was estimated as a function of σd${\sigma _d}$ values within the [σm${\sigma _m}$, σMix] range for the 3%/2 mm data with a 95% criterion. Results: SDs of the best limit were well reproduced by σm=0.531100−m${\sigma _m} = \;0.531\sqrt {100 - m} $. The EM technique successfully generated the (m,p)$({m,p})$ pairs with no correlation. The AS using four metrics showed good agreement. This agreement indicates successful definitions of both best and worst limits, consistent definitions of the AS, and successful generations of mixed events. The AF for the DL‐based model with the 3%/2 mm tolerance was 31.5% and 63.0% with CL's 99% and 99.9%, respectively. Conclusion: We developed the AS to evaluate the predicting model of the GPR in an unbiased manner by excluding the effects of the precision of the radiotherapy system and the spreading of the GPR. The best and worst limits of the GPR prediction were successfully generated using the measured precision of the GPR and the EM technique, respectively. The AS and σp${\sigma _p}$ are expected to enable objective evaluation of the predicting model and setting exact achievement goal of precision for the predicted GPR. [ABSTRACT FROM AUTHOR]

Research is presented into the rates of solvolysis of tertiary alkyl chlorides with different steric requirement and experimental gas-phase stabilities.

Purpose: Intensity‐modulated radiation therapy (IMRT) utilizes many small fields for producing a uniform dose distribution. Therefore, there are many field junctions in the target region, and resulting dose uncertainties are accumulated. However, such accumulation of the dose uncertainty has not been implemented in the current practice of IMRT dose verification. The purpose of this study is to develop a method to predict the gamma passing rate (GPR) using a dose uncertainty accumulation model. Methods: Thirty‐three intensity‐modulated (IM) beams for head‐and‐neck cases with step‐and‐shoot techniques were used in this study. The treatment plan was created using the XiO treatment planning system (TPS). The IM beam was produced by the ONCOR Impression Plus linear accelerator. MapCHECK was used to measure the dose distribution. The distribution of a dose uncertainty potential (DUP) was generated by in‐house software that accumulated field shapes weighted by a segmental monitor unit, followed by Gaussian folding. The width of the Gaussian was determined from the width of the lateral penumbra. The dose difference between the calculated and measured doses was compared with the estimated DUP at each point. The GPR of each beam was predicted for 2%/2‐mm, 3%/2‐mm, and 3%/3‐mm tolerances by its own DUP histogram and a GPR‐vs‐DUP correlation of other beams using the leave‐one‐out cross‐validation method. The predicted GPR was compared with the measured GPR to evaluate the performance of this prediction method. The criteria for the predicted GPR corresponding to a measured GPR ≥ 90% were estimated to examine the feasibility of estimating the measured GPR by this GPR prediction method. Results: The DUP was confirmed to have proportionality to the standard deviation (SD) of the dose difference. The SDs of the difference between the measured and predicted GPRs were 3.1, 1.7, and 1.4% for 2%/2‐mm, 3%/2‐mm, and 3%/3‐mm tolerances, respectively. The criteria of the predicted GPR corresponding to the measured GPR ≥ 90% were 94.1 and 95.0% with confidence levels of 99 and 99.9%, respectively. Conclusion: In this study, we confirmed the good proportionality between the dose difference and the estimated DUP. The results showed a feasibility to predict the dose difference from DUP as estimated by a DUP accumulation model. The predicted GPR developed in this study showed good accuracy for planar dose distributions of head and neck IMRT. The prediction method developed in this study is considered to be feasible as a substitute for the current practice of measurement‐based verification of the dose distribution with gamma analysis. [ABSTRACT FROM AUTHOR]

Objective. While the biological effective dose (BED) has been used to estimate the damage to tumor cells in radiotherapy, BED does not consider intrafractional interruption (IFI) occurring during irradiation. We aim to develop a framework to evaluate the decrease in BED [ΔBED] and to create a plan compensating for the decrease by IFI. Approach. ΔBEDwas calculated using a model based on the microdosimetric kinetic model (MKM) for four brain tumor cases treated using a volumetric-modulated arc therapy. Four biologically compensated plans (BCPs) were created in the treatment planning system by a single-time optimization using a base plan consideringΔBEDcreated in in-house software and optimization objectives for the original clinically delivered plan to achieve a homogeneous BED distribution within the planning target volume (PTV). The BED-volume histogram was evaluated for non-compensated plan and BCP with different timepoint of interruption, a percentage of gantry rotation angle (GRA) before interruption in planned GRA,ηand duration of interruptionτ. Characteristics of the dose accumulation were analyzed for different collimator angle sets, Plan A (10°, 85°) and Plan B (45° and 315°), for the first case. Main Results. Hot spots in theΔBEDdistribution forη= 25%, 50%, and 75% were observed at superior-and-interior ends, central region, and peripheral region in PTV, respectively. These behaviors could be understood by characteristics of the MKM-based model producing maximumΔBEDat 50% of the dose accumulation.ΔBED50%ranged 4.5%-6.6%, 5.0%-7.3%, and 5.3%-7.7% forτ= 60, 90, and 120 min, respectively. Plan A showed fast dose accumulation at superior and inferior edges while slow on peripheries in the lateral dose profile. Plan B showed more homogeneous PD distributions than Plan A during irradiation. Significance. The developed framework successfully evaluated and compensated for the decreased BED distribution., (© 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

There is a paucity of studies reporting the use of automated contouring function on cone-beam computed tomography (CBCT) images acquired during image-guided radiotherapy (IGRT) with offline adaptive radiotherapy for locally advanced non-small cell lung cancer (NSCLC). This case report discusses the use of an automated contouring function on CBCT images acquired during IGRT to quantify the dose distribution variations associated with tumor shrinkage in a patient with large NSCLC. A 72-year-old woman with locally advanced squamous cell carcinoma of the lung (T4N1M0; tumor diameter: 14 cm) underwent chemoradiotherapy. Two offline adaptive radiotherapy sessions were performed, resulting in no evidence of recurrence or distant metastasis, as well as no side effects of Grade 2 or higher toxicity., Competing Interests: Human subjects: Consent for treatment and open access publication was obtained or waived by all participants in this study. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, Ohguri et al.)

Kim introduced the geometric weighting factor concept into the field mapping method, and estimated collimator scatter factors of rectangular fields by correcting collimator exchange effects. The source plane is present at a specific position in the field mapping method, and, accordingly, the geometric weighting factor is constant. In this study, we changed the position of the source plane based on the measurement results, and we estimated the collimator factors of rectangular fields using the field mapping method. A geometric weighting factor at which the measured collimator scatter factors optimally fitted a square collimator scatter factor was calculated in each field. Collimator scatter factors can be accurately calculated recursively by changing the geometric weighting factor, resulting in altering the position of the source plane, as in this method.