Clinical adequacy assessment of autocontours for prostate IMRT with meaningful endpoints

Probabilistic evaluation of plan quality for time-dependent anatomical deformations in head and neck cancer patients

PURPOSE

In addition to patient set-up uncertainties, anatomical deformations, e.g., weight loss, lead to time-dependent differences between the planned and delivered dose in a radiotherapy course that currently cannot easily be predicted. The aim of this study was to create time-varying prediction models to describe both the average and residual anatomical deformations.

METHODS

Weekly population-based principal component analysis models were generated from on-treatment cone-beam CT scans (CBCTs) of 30 head and neck cancer patients, with additional data of 35 patients used as a validation cohort. We simulated treatment courses accounting for a) anatomical deformations, b) set-up uncertainties and c) a combination of both. The dosimetric effects of the simulated deformations were compared to a direct dose accumulation based on deformable registration of the CBCT data.

RESULTS

Set-up uncertainties were seen to have a larger effect on the organ at risk (OAR) doses than anatomical deformations for all OARs except the larynx and the primary CTV. Distributions from simulation results were in good agreement with those of the accumulated dose.

CONCLUSIONS

We present a novel method of modelling time-varying organ deformations in head and neck cancer. The effect on the OAR doses from these deformations are smaller than the effect of set-up uncertainties for most OARs. These models can, for instance, be used to predict which patients could benefit from adaptive radiotherapy, prior to commencing treatment.

Dosimetric comparison of automatically propagated prostate contours with manually drawn contours in MRI-guided radiotherapy: A step towards a contouring free workflow?

Background

The prostate demonstrates inter- and intra- fractional changes and thus adaptive radiotherapy would be required to ensure optimal coverage. Daily adaptive radiotherapy for MRI-guided radiotherapy can be both time and resource intensive when structure delineation is completed manually. Contours can be auto-generated on the MR-Linac via a deformable image registration (DIR) based mapping process from the reference image. This study evaluates the performance of automatically generated target structure contours against manually delineated contours by radiation oncologists for prostate radiotherapy on the Elekta Unity MR-Linac.

Methods

Plans were generated from prostate contours propagated by DIR and rigid image registration (RIR) for forty fractions from ten patients. A two-dose level SIB (simultaneous integrated boost) IMRT plan is used to treat localised prostate cancer; 6000 cGy to the prostate and 4860 cGy to the seminal vesicles. The dose coverage of the PTV 6000 and PTV 4860 created from the manually drawn target structures was evaluated with each plan. If the dose objectives were met, the plan was considered successful in covering the gold standard (clinician-delineated) volume.

Results

The mandatory PTV 6000 dose objective (D98% > 5580 cGy) was met in 81 % of DIR plans and 45 % of RIR plans. The SV were mapped by DIR only and for all the plans, the PTV 4860 dose objective met the optimal target (D98% > 4617 cGy). The plans created by RIR led to under-coverage of the clinician-delineated prostate, predominantly at the apex or the bladder-prostate interface.

Conclusion

Plans created from DIR propagation of prostate contours outperform those created from RIR propagation. In approximately 1 in 5 DIR plans, dosimetric coverage of the gold standard PTV was not clinically acceptable. Thus, at our institution, we use a combination of DIR propagation of contours alongside manual editing of contours where deemed necessary for online treatments.

Impact of random outliers in auto-segmented targets on radiotherapy treatment plans for glioblastoma

AIMS

To save time and have more consistent contours, fully automatic segmentation of targets and organs at risk (OAR) is a valuable asset in radiotherapy. Though current deep learning (DL) based models are on par with manual contouring, they are not perfect and typical errors, as false positives, occur frequently and unpredictably. While it is possible to solve this for OARs, it is far from straightforward for target structures. In order to tackle this problem, in this study, we analyzed the occurrence and the possible dose effects of automated delineation outliers.

METHODS

First, a set of controlled experiments on synthetically generated outliers on the CT of a glioblastoma (GBM) patient was performed. We analyzed the dosimetric impact on outliers with different location, shape, absolute size and relative size to the main target, resulting in 61 simulated scenarios. Second, multiple segmentation models where trained on a U-Net network based on 80 training sets consisting of GBM cases with annotated gross tumor volume (GTV) and edema structures. On 20 test cases, 5 different trained models and a majority voting method were used to predict the GTV and edema. The amount of outliers on the predictions were determined, as well as their size and distance from the actual target.

RESULTS

We found that plans containing outliers result in an increased dose to healthy brain tissue. The extent of the dose effect is dependent on the relative size, location and the distance to the main targets and involved OARs. Generally, the larger the absolute outlier volume and the distance to the target the higher the potential dose effect. For 120 predicted GTV and edema structures, we found 1887 outliers. After construction of the planning treatment volume (PTV), 137 outliers remained with a mean distance to the target of 38.5 ± 5.0 mm and a mean size of 1010.8 ± 95.6 mm3. We also found that majority voting of DL results is capable to reduce outliers.

CONCLUSIONS

This study shows that there is a severe risk of false positive outliers in current DL predictions of target structures. Additionally, these errors will have an evident detrimental impact on the dose and therefore could affect treatment outcome.

Auto-segmentation for total marrow irradiation

Purpose

To evaluate the accuracy and efficiency of Artificial-Intelligence (AI) segmentation in Total Marrow Irradiation (TMI) including contours throughout the head and neck (H&N), thorax, abdomen, and pelvis.

Methods

An AI segmentation software was clinically introduced for total body contouring in TMI including 27 organs at risk (OARs) and 4 planning target volumes (PTVs). This work compares the clinically utilized contours to the AI-TMI contours for 21 patients. Structure and image dicom data was used to generate comparisons including volumetric, spatial, and dosimetric variations between the AI- and human-edited contour sets. Conventional volume and surface measures including the Sørensen-Dice coefficient (Dice) and the 95th% Hausdorff Distance (HD95) were used, and novel efficiency metrics were introduced. The clinical efficiency gains were estimated by the percentage of the AI-contour-surface within 1mm of the clinical contour surface. An unedited AI-contour has an efficiency gain=100%, an AI-contour with 70% of its surface<1mm from a clinical contour has an efficiency gain of 70%. The dosimetric deviations were estimated from the clinical dose distribution to compute the dose volume histogram (DVH) for all structures.

Results

A total of 467 contours were compared in the 21 patients. In PTVs, contour surfaces deviated by >1mm in 38.6% ± 23.1% of structures, an average efficiency gain of 61.4%. Deviations >5mm were detected in 12.0% ± 21.3% of the PTV contours. In OARs, deviations >1mm were detected in 24.4% ± 27.1% of the structure surfaces and >5mm in 7.2% ± 18.0%; an average clinical efficiency gain of 75.6%. In H&N OARs, efficiency gains ranged from 42% in optic chiasm to 100% in eyes (unedited in all cases). In thorax, average efficiency gains were >80% in spinal cord, heart, and both lungs. Efficiency gains ranged from 60-70% in spleen, stomach, rectum, and bowel and 75-84% in liver, kidney, and bladder. DVH differences exceeded 0.05 in 109/467 curves at any dose level. The most common 5%-DVH variations were in esophagus (86%), rectum (48%), and PTVs (22%).

Conclusions

AI auto-segmentation software offers a powerful solution for enhanced efficiency in TMI treatment planning. Whole body segmentation including PTVs and normal organs was successful based on spatial and dosimetric comparison.

Knowledge-based quality control of organ delineations in radiation therapy

PURPOSE

To reduce the likelihood of errors in organ delineations used for radiotherapy treatment planning, a knowledge-based quality control (KBQC) system, which discriminates between valid and anomalous delineations is developed.

METHOD AND MATERIALS

The KBQC is comprised of a group-wise inference system and anomaly detection modules trained using historical priors from 296 locally advanced lung and prostate cancer patient computational tomographies (CTs). The inference system discriminates different organs based on shape, relational, and intensity features. For a given delineated image set, the inference system solves a combinatorial optimization problem that results in an organ group whose relational features follow those of the training set considering the posterior probabilities obtained from support vector machine (SVM), discriminant subspace ensemble (DSE), and artificial neural network (ANN) classifiers. These classifiers are trained on nonrelational features with a 10-fold cross-validation scheme. The anomaly detection module is a bank of ANN autoencoders, each corresponding with an organ, trained on nonrelational features. A heuristic rule detects anomalous organs that exceed predefined organ-specific tolerances for the feature reconstruction error and the classifier's posterior probabilities. Independent data sets with anomalous delineations were used to test the overall performance of the KBQC system. The anomalous delineations were manually manipulated, computer-generated, or propagated based on a transformation obtained by imperfect registrations. Both peer-review-based scoring system and shape similarity coefficient (DSC) were used to label regions of interest (ROIs) as normal or anomalous in two independent test cohorts.

RESULTS

The accuracy of the classifiers was ≥ $\ge$ 99.8%, and the minimum per-class F1-scores were 0.99, 0.99, and 0.98 for SVM, DSE, and ANN, respectively. The group-wise inference system reduced the miss-classification likelihood for the test data set with anomalous delineations compared to each individual classifier and a fused classifier that used the average posterior probability of all classifiers. For 15 independent locally advanced lung patients, the system detected > $>$ 79% of the anomalous ROIs. For 1320 auto-segmented abdominopelvic organs, the anomaly detection system identified anomalous delineations, which also had low Dice similarity coefficient values with respect to manually delineated organs in the training data set.

CONCLUSION

The KBQC system detected anomalous delineations with superior accuracy compared to classification methods that judge only based on posterior probabilities.

Dose escalation in the definite target volume

PURPOSE

To introduce the definite target volume (DTV) and evaluate dosimetric consequences of boosting dose to this region of high clinical target volume (CTV)- and low organs at risk (OAR)-probability.

METHODS

This work defines the DTV via occupancy probability and via contraction of the CTV by margin M less any planning risk volume (PRV) volumes. The equivalence to within varying occupancy probability of the two methods is established for spherical target volumes. We estimate a margin for four radiation treatment sites based on modern images guided radiation therapy-literature utilizing repeat volumetric imaging. Based on margins and patient-specific DTV targets, the ability to dose escalate the DTV including the effects of spatial uncertainty was evaluated. We simulate delivery assuming violation of the underlying spatial uncertainty of 130%.

RESULTS

Contracting the planning target volume (PTV) by M and excluding PRV volumes, the DTV ranged from 7.3 to 93.6 cc. In a brain treatment, DTV-Dmax increased to 66.8 Gy (145% of prescription isodose); in advanced lung DTV-Dmax increased to 122.2 Gy (204% of prescription isodose), in a pancreatic case DTV-Dmax was boosted up to 87.3 Gy (173% or prescription isodose), and in retroperitoneal sarcoma to 74.6 Gy (249% of prescription isodose). The high point doses were not associated with increased dose to OARs, even when considering the effects of spatial uncertainty. Simulated delivery at 130% of assumed spatial uncertainties revealed DTV-based planning can result in minor increases in OAR Dmean/Dmax of 2.7 ± 2.1 Gy/1.8 ± 2.2 Gy with duodenum Dmax > 110% of prescription isodose in the pancreatic case. These dose increases were consistent with simulation of clinical, homogenous PTV-dose distributions.

CONCLUSION

We have proposed and tested a method to deliver extremely high doses to subvolumes of target volumes in multiple treatment sites by defining a new target volume, the DTV. Based on simulated delivery, the method does not result in significant increases in dose to OARs if spatial uncertainty can be estimated.

Clinical Evaluation of Deep Learning and Atlas-Based Auto-Contouring of Bladder and Rectum for Prostate Radiation Therapy

PURPOSE

Auto-contouring may reduce workload, interobserver variation, and time associated with manual contouring of organs at risk. Manual contouring remains the standard due in part to uncertainty around the time and workload savings after accounting for the review and editing of auto-contours. This preliminary study compares a standard manual contouring workflow with 2 auto-contouring workflows (atlas and deep learning) for contouring the bladder and rectum in patients with prostate cancer.

METHODS AND MATERIALS

Three contouring workflows were defined based on the initial contour-generation method including manual (MAN), atlas-based auto-contour (ATLAS), and deep-learning auto-contour (DEEP). For each workflow, initial contour generation was retrospectively performed on 15 patients with prostate cancer. Then, radiation oncologists (ROs) edited each contour while blinded to the manner in which the initial contour was generated. Workflows were compared by time (both in initial contour generation and in RO editing), contour similarity, and dosimetric evaluation.

RESULTS

Mean durations for initial contour generation were 10.9 min, 1.4 min, and 1.2 min for MAN, DEEP, and ATLAS, respectively. Initial DEEP contours were more geometrically similar to initial MAN contours. Mean durations of the RO editing steps for MAN, DEEP, and ATLAS contours were 4.1 min, 4.7 min, and 10.2 min, respectively. The geometric extent of RO edits was consistently larger for ATLAS contours compared with MAN and DEEP. No differences in clinically relevant dose-volume metrics were observed between workflows.

CONCLUSION

Auto-contouring software affords time savings for initial contour generation; however, it is important to also quantify workload changes at the RO editing step. Using deep-learning auto-contouring for bladder and rectum contour generation reduced contouring time without negatively affecting RO editing times, contour geometry, or clinically relevant dose-volume metrics. This work contributes to growing evidence that deep-learning methods are a clinically viable solution for organ-at-risk contouring in radiation therapy.

Quantifying the dosimetric impact of organ-at-risk delineation variability in head and neck radiation therapy in the context of patient setup uncertainty

The purpose of this study was to quantify the potential dosimetric impact of delineation variability (DV) in head and neck radiation therapy (RT) when inherent patient setup variability (SV) is also considered. The impact of DV was assessed by generating plans with multiple structure sets, cross-evaluating them, including SV, across sets, and determining P _PQM: the probability of achieving organ-specific plan quality metrics (PQM). DV was incorporated by: (1) using multiple organ at risk (OAR) structure sets delineated by independent manual observers; and (2) randomly perturbing manually generated OARs to generate alternatives with varying levels of uncertainty (low, medium, and high DV). For each structure set, independent VMAT plans were auto-generated to meet clinical PQMs. Each plan was cross-evaluated using OARs from multiple structure sets with simulated SV including per-fraction random (σ _s) and per-treatment-course systematic (Σ_s) setup errors. The dosimetric impact of DV was assessed by examining P _PQM with and without SV/DV. Clinically significant differences were defined by those that exceeded differences caused by a  +2% output variation. Without including SV, simulated DV at the medium level reduced P _PQM by an average of 5.5% for all OARs with D _max PQMs. This reduction decreased to 2.8% for SV  =  2 mm and 2.4% for SV  =  4 mm (the average P _PQM reduction due to 2% output errors was 2.7%). For OARs with D _mean PQMs, the average P _PQM reduction was 0.9% for SV  =  0 and  ⩽0.1% for SV  ⩾  2 mm. The effect of DV was larger for OARs that directly abutted a target volume than for those that did not. These trends were also observed with real DV from multi-observer delineations. The dosimetric impact of DV appeared to decrease when random and systematic SV was considered. Sensitivity to DV was affected by OAR objective type (i.e. D _mean versus D _max objectives) as well as distance from the target volume.

Quality assurance tool for organ at risk delineation in radiation therapy using a parametric statistical approach

PURPOSE

To develop a quality assurance (QA) tool that identifies inaccurate organ at risk (OAR) delineations.

METHODS

The QA tool computed volumetric features from prior OAR delineation data from 73 thoracic patients to construct a reference database. All volumetric features of the OAR delineation are computed in three-dimensional space. Volumetric features of a new OAR are compared with respect to those in the reference database to discern delineation outliers. A multicriteria outlier detection system warns users of specific delineation outliers based on combinations of deviant features. Fifteen independent experimental sets including automatic, propagated, and clinically approved manual delineation sets were used for verification. The verification OARs included manipulations to mimic common errors. Three experts reviewed the experimental sets to identify and classify errors, first without; and then 1 week after with the QA tool.

RESULTS

In the cohort of manual delineations with manual manipulations, the QA tool detected 94% of the mimicked errors. Overall, it detected 37% of the minor and 85% of the major errors. The QA tool improved reviewer error detection sensitivity from 61% to 68% for minor errors (P = 0.17), and from 78% to 87% for major errors (P = 0.02).

CONCLUSIONS

The QA tool assists users to detect potential delineation errors. QA tool integration into clinical procedures may reduce the frequency of inaccurate OAR delineation, and potentially improve safety and quality of radiation treatment planning.