Background and purposeFor the first time, delivered dose to the rectum has been calculated and accumulated throughout the course of prostate radiotherapy using megavoltage computed tomography (MVCT) image guidance scans. Dosimetric parameters were linked with toxicity to test the hypothesis that delivered dose is a stronger predictor of toxicity than planned dose.Material and methodsDose–surface maps (DSMs) of the rectal wall were automatically generated from daily MVCT scans for 109 patients within the VoxTox research programme. Accumulated-DSMs, representing total delivered dose, and planned-DSMs, from planning CT data, were parametrised using Equivalent Uniform Dose (EUD) and ‘DSM dose-width’, the lateral dimension of an ellipse fitted to a discrete isodose cluster. Associations with 6 toxicity endpoints were assessed using receiver operator characteristic curve analysis.ResultsFor rectal bleeding, the area under the curve (AUC) was greater for accumulated dose than planned dose for DSM dose-widths up to 70 Gy. Accumulated 65 Gy DSM dose-width produced the strongest spatial correlation (AUC 0.664), while accumulated EUD generated the largest AUC overall (0.682). For proctitis, accumulated EUD was the only reportable predictor (AUC 0.673). Accumulated EUD was systematically lower than planned EUD.ConclusionsDosimetric parameters extracted from accumulated DSMs have demonstrated stronger correlations with rectal bleeding and proctitis, than planned DSMs.

Highlights The patterns of practice for adaptive radiotherapy were evaluated for 177 centres. Over half performed ad-hoc adaption but less than a third used specific protocols. CBCT was the main imaging modality in general but MR was used for daily replanning. 2/3 centres wished to implement ART; 40% of them had plans to do it within 2 years. The main barriers were human/material resources and technical limitations.

There is considerable variation in the level of toxicity patients experience for a given dose of radiotherapy, which is associated with differences in underlying individual normal tissue radiosensitivity. A number of syndromes have a large effect on clinical radiosensitivity, but these are rare. Among non-syndromic patients, variation is less extreme, but equivalent to a ±20% variation in dose. Thus, if individual normal tissue radiosensitivity could be measured, it should be possible to optimise schedules for individual patients. Early investigations of in vitro cellular radiosensitivity supported a link with tissue response, but individual studies were equivocal. A lymphocyte apoptosis assay has potential, and is currently under prospective validation. The investigation of underlying genetic variation also has potential. Although early candidate gene studies were inconclusive, more recent genome-wide association studies are revealing definite associations between genotype and toxicity and highlighting the potential for future genetic testing. Genetic testing and individualised dose prescriptions could reduce toxicity in radiosensitive patients, and permit isotoxic dose escalation to increase local control in radioresistant individuals. The approach could improve outcomes for half the patients requiring radical radiotherapy. As a number of patient- and treatment-related factors also affect the risk of toxicity for a given dose, genetic testing data will need to be incorporated into models that combine patient, treatment and genetic data.

Differences between delivered and planned spinal cord D are small in patients treated with daily IG. Even patients experiencing substantial weight loss or anatomical change during treatment do not require adaptive replanning for spinal cord safety.

IMPORTANCE Personalized radiotherapy planning depends on high-quality delineation of target tumors and surrounding organs at risk (OARs). This process puts additional time burdens on oncologists and introduces variability among both experts and institutions. OBJECTIVE To explore clinically acceptable autocontouring solutions that can be integrated into existing workflows and used in different domains of radiotherapy. DESIGN, SETTING, AND PARTICIPANTS This quality improvement study used a multicenter imaging data set comprising 519 pelvic and 242 head and neck computed tomography (CT) scans from 8 distinct clinical sites and patients diagnosed either with prostate or head and neck cancer. The scans were acquired as part of treatment dose planning from patients who received intensitymodulated radiation therapy between October 2013 and February 2020. Fifteen different OARs were manually annotated by expert readers and radiation oncologists. The models were trained on a subset of the data set to automatically delineate OARs and evaluated on both internal and external data sets. Data analysis was conducted October 2019 to September 2020. MAIN OUTCOMES AND MEASURES The autocontouring solution was evaluated on external data sets, and its accuracy was quantified with volumetric agreement and surface distance measures. Models were benchmarked against expert annotations in an interobserver variability (IOV) study. Clinical utility was evaluated by measuring time spent on manual corrections and annotations from scratch. RESULTS A total of 519 participants' (519 [100%] men; 390 [75%] aged 62-75 years) pelvic CT images and 242 participants' (184 [76%] men; 194 [80%] aged 50-73 years) head and neck CT images were included. The models achieved levels of clinical accuracy within the bounds of expert IOV for 13 of 15 structures (eg, left femur, κ = 0.982; brainstem, κ = 0.806) and performed consistently well across both external and internal data sets (eg, mean [SD] Dice score for left femur, internal vs external data sets: 98.52% [0.50] vs 98.04% [1.02]; P = .04). The correction time of autogenerated contours on 10 head and neck and 10 prostate scans was measured as a mean of 4.98 (95% CI, 4.44-5.52) min/scan and 3.40 (95% CI, 1.60-5.20) min/scan, respectively, to ensure clinically accepted accuracy, whereas contouring from scratch on the same scans was observed to be 73.25 (95% CI, 68.68-77.82) min/scan and 86.75 (95% CI, 75.21-92.29) min/scan, respectively, accounting for a 93% reduction in time. CONCLUSIONS AND RELEVANCE In this study, the models achieved levels of clinical accuracy within expert IOV while reducing manual contouring time and performing consistently well across previously unseen heterogeneous data sets. With the availability of open-source libraries and reliable (continued) Key Points Question Can machine learning models achieve clinically acceptable accuracy in image segmentation tasks in radiotherapy planning and reduce overall contouring time? Findings This quality improvement study was conducted on a set o...

The recent advances in radiation delivery can improve tumour control probability (TCP) and reduce treatment-related toxicity. The use of intensity-modulated radiotherapy (IMRT) in particular can reduce normal tissue toxicity, an objective in its own right, and can allow safe dose escalation in selected cases. Ideally, IMRT should be combined with image guidance to verify the position of the target, since patients, target and organs at risk can move day to day. Daily image guidance scans can be used to identify the position of normal tissue structures and potentially to compute the daily delivered dose. Fundamentally, it is still the tolerance of the normal tissues that limits radiotherapy (RT) dose and therefore tumour control. However, the dose–response relationships for both tumour and normal tissues are relatively steep, meaning that small dose differences can translate into clinically relevant improvements. Differences exist between individuals in the severity of toxicity experienced for a given dose of RT. Some of this difference may be the result of differences between the planned dose and the accumulated dose (DA). However, some may be owing to intrinsic differences in radiosensitivity of the normal tissues between individuals. This field has been developing rapidly, with the demonstration of definite associations between genetic polymorphisms and variation in toxicity recently described. It might be possible to identify more resistant patients who would be suitable for dose escalation, as well as more sensitive patients for whom toxicity could be reduced or avoided. Daily differences in delivered dose have been investigated within the VoxTox research programme, using the rectum as an example organ at risk. In patients with prostate cancer receiving curative RT, considerable daily variation in rectal position and dose can be demonstrated, although the median position matches the planning scan well. Overall, in 10 patients, the mean difference between planned and accumulated rectal equivalent uniform doses was −2.7 Gy (5%), and a dose reduction was seen in 7 of the 10 cases. If dose escalation was performed to take rectal dose back to the planned level, this should increase the mean TCP (as biochemical progression-free survival) by 5%. Combining radiogenomics with individual estimates of DA might identify almost half of patients undergoing radical RT who might benefit from either dose escalation, suggesting improved tumour cure or reduced toxicity or both.

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Background and Purpose: Associations between dose and rectal toxicity in prostate radiotherapy are generally poorly understood. Evaluating spatial dose distributions to the rectal wall (RW) may lead to improvements in dose-toxicity modelling by incorporating geometric information, masked by dose-volume histograms. Furthermore, predictive power may be strengthened by incorporating the effects of interfraction motion into delivered dose calculations.Here we interrogate 3D dose distributions for patients with and without toxicity to identify rectal subregions at risk (SRR), and compare the discriminatory ability of planned and delivered dose. Material and Methods: Daily delivered dose to the rectum was calculated using image guidance scans, and accumulated at the voxel level using biomechanical finite element modelling. SRRs were statistically determined for rectal bleeding, proctitis, faecal incontinence and stool frequency from a training set (n = 139), and tested on a validation set (n = 47). Results: SRR patterns differed per endpoint. Analysing dose to SRRs improved discriminative ability with respect to the full RW for three of four endpoints. Training set AUC and OR analysis produced stronger toxicity associations from accumulated dose than planned dose. For rectal bleeding in particular, accumulated dose to the SRR (AUC 0.76) improved upon dose-toxicity associations derived from planned dose to the RW (AUC 0.63). However, validation results could not be considered significant. Conclusions: Voxel-level analysis of dose to the RW revealed SRRs associated with rectal toxicity, suggesting nonhomogeneous intra-organ radiosensitivity. Incorporating spatial features of accumulated delivered dose improved dose-toxicity associations. This may be an important tool for adaptive radiotherapy in the future.