Ongoing clinical trials designed to explore the use of extracranial stereotactic radiosurgery (ESR) for different tumour sites use large doses per fraction (15, 20, 30 Gy or even larger). The question of whether the linear-quadratic (LQ) model is appropriate to describe radiation response for such large fraction doses has been raised and has not been answered definitively. It has been proposed that mechanism-based models, such as the lethal-potentially lethal (LPL) model, could be more appropriate for such large fraction/acute doses. However, such models are not well characterized with clinical data and they are generally not easy to use. The purpose of this work is to modify the LQ model to more accurately describe radiation response for high fraction/acute doses. A new parameter is introduced in the modified LQ (MLQ) model. The new parameter introduced is characterized based both on in vitro cell survival data of several human tumour cell lines and in vivo animal iso-effect curves. The MLQ model produces a better fit to the iso-effect data than the LQ model. For a high single dose irradiation, the prediction of the MLQ is consistent with that from the LPL model. Unlike the LPL model, the MLQ model retains the simplicity of the LQ model and uses the well-characterized alpha and beta parameters. This work indicates that the standard LQ model can lead to erroneous results when used to calculate iso-effects with large fraction doses, such as those used for ESR. We present a solution to this problem.

Radiation therapy (RT) is an essential component of effective cancer care and is used across nearly all cancer types. The delivery of RT is becoming more precise through rapid advances in both computing and imaging. The direct integration of magnetic resonance imag-ing (MRI) with linear accelerators represents an exciting development with the potential todramatically impact cancer research and treatment. These impacts extend beyond improved imaging and dose deposition. Real-time MRI-guided RT is actively transforming the work flows and capabilities of virtually every aspect of RT. It has the opportunity to change entirely the delivery methods and response assessments of numerous malignancies. This review intends to approach the topic of MRI-based RT guidance from a vendor neutral and international perspective. It also aims to provide an *

Purpose To analyze pooled clinical data using different radiobiological models and to understand the relationship between biologically effective dose (BED) and tumor control probability (TCP) for stereotactic body radiotherapy (SBRT) of early-stage non-small cell lung cancer (NSCLC). Method and Materials The clinical data of 1-, 2-, 3-, and 5-year actuarial or Kaplan-Meier TCP from 46 selected studies were collected for SBRT of NSCLC in the literature. The TCP data were separated for Stage T1 and T2 tumors if possible, otherwise collected for combined stages. BED was calculated at isocenters using six radiobiological models. For each model, the independent model parameters were determined from a fit to the TCP data using the least chi-square (χ2) method with either one set of parameters regardless of tumor stages or two sets for T1 and T2 tumors separately. Results The fits to the clinic data yield consistent results of large α/β ratios of about 20 Gy for all models investigated. The regrowth model that accounts for the tumor repopulation and heterogeneity leads to a better fit to the data, compared to other 5 models where the fits were indistinguishable between the models. The models based on the fitting parameters predict that the T2 tumors require about additional 1 Gy physical dose at isocenters per fraction (≤5 fractions) to achieve the optimal TCP when compared to the T1 tumors. Conclusion This systematic analysis of a large set of published clinical data using different radiobiological models shows that local TCP for SBRT of early-stage NSCLC has strong dependence on BED with large α/β ratios of about 20 Gy. The six models predict that a BED (calculated with α/β of 20) of 90 Gy is sufficient to achieve TCP ≥ 95%. Among the models considered, the regrowth model leads to a better fit to the clinical data.

Compensator-based proton therapy of lung cancer using an un-gated treatment while allowing the patient to breathe freely requires a compensator design that ensures tumor coverage throughout respiration. Our investigation had two purposes: one is to investigate the dosimetric impact when a composite compensator correction is applied, or is not, and the other one is to evaluate the significance of using different respiratory phases as the reference computed tomography (CT) for treatment planning dose calculations. A 4D-CT-based phantom study and a real patient treatment planning study were performed. A 3D MIP dataset generated over all phases of the acquired 4D-CT scans was adopted to design the field-specific composite aperture and compensator. In the phantom study, the MIP-based compensator design plan named plan D was compared to the other three plans, in which average intensity projection (AIP) images in conjunction with the composite target volume contour copied from the MIP images were used. Relative electron densities within the target envelope were assigned either to original values from the AIP image dataset (plan A) or to predetermined values, 0.8 (plan B) and 0.9 (plan C). In the patient study, the dosimetric impact of a compensator design based on the MIP images (plan ITV(MIP)) was compared to designs based on end-of-inhale (EOI) (plan ITV(EOI)) and middle-of-exhale (MOE) CT images (plan ITV(MOE)). The dose distributions were recalculated for each phase. Throughout the ten phases, it shows that D(GTV)(min) changed slightly from 86% to 89% (SD = 0.9%) of prescribed dose (PD) in the MIP plan, while varying greatly from 10% to 79% (SD = 26.7%) in plan A, 17% to 73% (SD = 22.5%) in plan B and 53% to 73% (SD = 6.8%) in plan C. The same trend was observed for D(GTV)(mean) and V95 with less amplitude. In the MIP-based plan ITV(MIP), D(GTV)(mean) was almost identically equal to 95% in each phase (SD = 0.5%). The patient study verified that the MIP approach increased the minimum value of D99 of the clinical target volume (CTV) by 58.8% compared to plan ITV(EOI) and 12.9% compared to plan ITV(MOE). Minimum values of D99 were 37.60%, 83.50% and 96.40% for plan ITV(EOI), plan ITV(MOE) and plan ITV(MIP), respectively. Standard deviations of D99 were significantly decreased (SD = 0.5%) in the MIP plan as compared to plan ITV(EOI) (SD = 18.9%) or plan ITV(MOE) (SD = 4.0%). These studies demonstrate that the use of MIP images to design the patient-specific composite compensators provide superior and consistent tumor coverage throughout the entire respiratory cycle whilst maintaining a low average normal lung dose. The additional benefit of the MIP-based design approach is that the dose calculation can be implemented on any single phase as long as it uses the aperture and compensator optimized from the MIP images. This also reduces the requirement for contouring on all breathing phases down to just one.

We have previously developed an online adaptive replanning technique to rapidly adapt the original plan according to daily CT. This paper reports the quality assurance (QA) developments in its clinical implementation for prostate cancer patients. A series of pre-clinical validation tests were carried out to verify the overall accuracy and consistency of the online replanning procedure. These tests include (a) phantom measurements of 22 individual patient adaptive plans to verify their accuracy and deliverability and (b) efficiency and applicability of the online replanning process. A four-step QA procedure was established to ensure the safe and accurate delivery of an adaptive plan, including (1) offline phantom measurement of the original plan, (2) online independent monitor unit (MU) calculation for a redundancy check, (3) online verification of plan-data transfer using an in-house software and (4) offline validation of actually delivered beam parameters. The pre-clinical validations demonstrate that the newly implemented online replanning technique is dosimetrically accurate and practically efficient. The four-step QA procedure is capable of identifying possible errors in the process of online adaptive radiotherapy and to ensure the safe and accurate delivery of the adaptive plans. Based on the success of this work, the online replanning technique has been used in the clinic to correct for interfractional changes during the prostate radiation therapy.

Objective: To investigate the changes in CT number (CTN) in gross tumour volume (GTV) and organs at risk (OARs) during the course of radiation therapy (RT) for nasopharyngeal cancer (NPC). Methods: Daily megavoltage CT (MVCT) data collected from 30 patients with NPC treated with a prescription dose of 70 Gy in 30-33 fractions using helical tomotherapy were retrospectively analyzed. The contours of GTV and OARs on daily MVCTs were obtained by populating the planning contours from planning CT to daily MVCTs with manual editing, if necessary. The changes of GTV and OAR volumes and the histograms of CTN in the GTV and OARs during the course of RT delivery were analyzed. Results: Volumes of GTV and parotid glands were reduced during the course of radiation treatment, with an average shrinkage rate of 0.23% per day (range, 0.02-0.8%) and 1.2% per day (range, 0.2-2.3%), respectively. The mean CTN changes in GTV and ipsilateral and contralateral parotid glands were reduced by 52 6 35 HU, 18 6 20 HU and 17 6 22 HU, respectively. For GTV, the CTN and GTV volume decreases were found to be correlated with each other (p , 0.0001). No noticeable CTN change was found in the spinal cord and non-specified tissue irradiated with low doses. Conclusion: The CTN changes in GTV and parotids are measurable during the delivery of fractionated radiotherapy for NPC, were associated with the doses received (the number of fractions delivered) and were patient specific. Advances in knowledge: The CTN change during radiotherapy is dose dependent and is measurable for NPC.

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Purpose To reduce workload and inconsistencies in organ segmentation for radiation treatment planning, we developed and evaluated general and custom autosegmentation models on computed tomography (CT) for three major tumor sites using a well‐established deep convolutional neural network (DCNN). Methods Five CT‐based autosegmentation models for 42 organs at risk (OARs) in head and neck (HN), abdomen (ABD), and male pelvis (MP) were developed using a full three‐dimensional (3D) DCNN architecture. Two types of deep learning (DL) models were separately trained using either general diversified multi‐institutional datasets or custom well‐controlled single‐institution datasets. To improve segmentation accuracy, an adaptive spatial resolution approach for small and/or narrow OARs and a pseudo scan extension approach, when CT scan length is too short to cover entire organs, were implemented. The performance of the obtained models was evaluated based on accuracy and clinical applicability of the autosegmented contours using qualitative visual inspection and quantitative calculation of dice similarity coefficient (DSC), mean distance to agreement (MDA), and time efficiency. Results The five DL autosegmentation models developed for the three anatomical sites were found to have high accuracy (DSC ranging from 0.8 to 0.98) for 74% OARs and marginally acceptable for 26% OARs. The custom models performed slightly better than the general models, even with smaller custom datasets used for the custom model training. The organ‐based approaches improved autosegmentation accuracy for small or complex organs (e.g., eye lens, optic nerves, inner ears, and bowels). Compared with traditional manual contouring times, the autosegmentation times, including subsequent manual editing, if necessary, were substantially reduced by 88% for MP, 80% for HN, and 65% for ABD models. Conclusions The obtained autosegmentation models, incorporating organ‐based approaches, were found to be effective and accurate for most OARs in the male pelvis, head and neck, and abdomen. We have demonstrated that our multianatomical DL autosegmentation models are clinically useful for radiation treatment planning.