A rapid review of influential factors and appraised solutions on organ delineation uncertainties reduction in radiotherapy

Contouring aid tools in radiotherapy. Smoothing: the false friend

OBJECTIVE

Contouring accuracy is critical in modern radiotherapy. Several tools are available to assist clinicians in this task. This study aims to evaluate the performance of the smoothing tool in the ARIA system to obtain more consistent volumes.

METHODS

Eleven different geometric shapes were delineated in ARIA v15.6 (Sphere, Cube, Square Prism, Six-Pointed Star Prism, Arrow Prism, And Cylinder and the respective volumes at 45° of axis deviation (_45)) in 1, 3, 5, 7, and 10 cm side or diameter each. Post-processing drawing tools to smooth those first-generated volumes were applied in different options (2D-ALL vs 3D) and grades (1, 3, 5, 10, 15, and 20). These volumetric transformations were analyzed by comparing different parameters: volume changes, center of mass, and DICE similarity coefficient index. Then we studied how smoothing affected two different volumes in a head and neck cancer patient: a single rounded node and the volume delineating cervical nodal areas.

RESULTS

No changes in data were found between 2D-ALL or 3D smoothing. Minimum deviations were found (range from 0 to 0.45 cm) in the center of mass. Volumes and the DICE index decreased as the degree of smoothing increased. Some discrepancies were found, especially in figures with cleft and spikes that behave differently. In the clinical case, smoothing should be applied only once throughout the target delineation process, preferably in the largest volume (PTV) to minimize errors.

CONCLUSION

Smoothing is a good tool to reduce artifacts due to the manual delineation of radiotherapy volumes. The resulting volumes must be always carefully reviewed.

Analyzing the Relationship between Dose and Geometric Agreement Metrics for Auto-Contouring in Head and Neck Normal Tissues

This study aimed to determine the relationship between geometric and dosimetric agreement metrics in head and neck (H&N) cancer radiotherapy plans. A total 287 plans were retrospectively analyzed, comparing auto-contoured and clinically used contours using a Dice similarity coefficient (DSC), surface DSC (sDSC), and Hausdorff distance (HD). Organs-at-risk (OARs) with ≥200 cGy dose differences from the clinical contour in terms of Dmax (D0.01cc) and Dmean were further examined against proximity to the planning target volume (PTV). A secondary set of 91 plans from multiple institutions validated these findings. For 4995 contour pairs across 19 OARs, 90% had a DSC, sDSC, and HD of at least 0.75, 0.86, and less than 7.65 mm, respectively. Dosimetrically, the absolute difference between the two contour sets was <200 cGy for 95% of OARs in terms of Dmax and 96% in terms of Dmean. In total, 97% of OARs exhibiting significant dose differences between the clinically edited contour and auto-contour were within 2.5 cm PTV regardless of geometric agreement. There was an approximately linear trend between geometric agreement and identifying at least 200 cGy dose differences, with higher geometric agreement corresponding to a lower fraction of cases being identified. Analysis of the secondary dataset validated these findings. Geometric indices are approximate indicators of contour quality and identify contours exhibiting significant dosimetric discordance. For a small subset of OARs within 2.5 cm of the PTV, geometric agreement metrics can be misleading in terms of contour quality.

Deep learning aided oropharyngeal cancer segmentation with adaptive thresholding for predicted tumor probability in FDG PET and CT images

Objective. Tumor segmentation is a fundamental step for radiotherapy treatment planning. To define an accurate segmentation of the primary tumor (GTVp) of oropharyngeal cancer patients (OPC) each image volume is explored slice-by-slice from different orientations on different image modalities. However, the manual fixed boundary of segmentation neglects the spatial uncertainty known to occur in tumor delineation. This study proposes a novel deep learning-based method that generates probability maps which capture the model uncertainty in the segmentation task.Approach. We included 138 OPC patients treated with (chemo)radiation in our institute. Sequences of 3 consecutive 2D slices of concatenated FDG-PET/CT images and GTVp contours were used as input. Our framework exploits inter and intra-slice context using attention mechanisms and bi-directional long short term memory (Bi-LSTM). Each slice resulted in three predictions that were averaged. A 3-fold cross validation was performed on sequences extracted from the axial, sagittal, and coronal plane. 3D volumes were reconstructed and single- and multi-view ensembling were performed to obtain final results. The output is a tumor probability map determined by averaging multiple predictions.Main Results. Model performance was assessed on 25 patients at different probability thresholds. Predictions were the closest to the GTVp at a threshold of 0.9 (mean surface DSC of 0.81, median HD₉₅of 3.906 mm).Significance. The promising results of the proposed method show that is it possible to offer the probability maps to radiation oncologists to guide them in a in a slice-by-slice adaptive GTVp segmentation.

Failure modes in stereotactic radiosurgery. A narrative review

OBJECTIVES

Stereotactic radiosurgery (SRS) refers to an advanced radiotherapy technique that requires a high level of precision and accuracy and a flawless workflow. Failures within the SRS process can lead to serious consequences due to high doses delivered per treatment. This narrative review aimed to identify the riskiest failure modes (FMs) and the stages at which they occur in the SRS process, as well as the strategies applied to mitigate the risks. It was based on the analysis of published failure mode and effects analysis (FMEA) data.

KEY FINDINGS

From the literature search in PubMed and Scopus, 7 articles met the eligibility criteria for inclusion in the qualitative synthesis. In total, 9 radiotherapy departments conducted FMEA in the SRS process. 4 of them were community hospitals and 5 were academic centers. Overall, 54 high-risk FMs were identified with treatment planning (FMs: 18), treatment delivery (FMs: 12), consultation and patient registration (FMs: 10) being the riskiest stages. 10 FMs were stereotactic specific, while the remaining 44 could be met in any radiotherapy technique. Failures associated with contouring, medical records review, target reirradiation, and patient positioning were mostly outlined. Risk mitigation strategies included timeouts, double-checks, checklists, training and changes in the working practice.

CONCLUSION

Our review demonstrated that crucial FMs can occur in all SRS stages. Although generalisations were challenging, the FMs analysis provided a significant source of information about potential high risks and continuous improvement strategies that can be applied both in the SRS and other radiotherapy processes.

IMPLICATIONS FOR PRACTICE

The results of this research will assist radiotherapy facilities in proactive risk management studies and will allow radiotherapy professionals to reflect on their practice and learn from others' experiences.