Assessing tumor centrality in lung stereotactic ablative body radiotherapy (SABR): the effects of variations in bronchial tree delineation and potential for automated methods

Geometric and dosimetric evaluation of deep learning based auto-segmentation for clinical target volume on breast cancer

BACKGROUND

Recently, target auto-segmentation techniques based on deep learning (DL) have shown promising results. However, inaccurate target delineation will directly affect the treatment planning dose distribution and the effect of subsequent radiotherapy work. Evaluation based on geometric metrics alone may not be sufficient for target delineation accuracy assessment. The purpose of this paper is to validate the performance of automatic segmentation with dosimetric metrics and try to construct new evaluation geometric metrics to comprehensively understand the dose-response relationship from the perspective of clinical application.

MATERIALS AND METHODS

A DL-based target segmentation model was developed by using 186 manual delineation modified radical mastectomy breast cancer cases. The resulting DL model were used to generate alternative target contours in a new set of 48 patients. The Auto-plan was reoptimized to ensure the same optimized parameters as the reference Manual-plan. To assess the dosimetric impact of target auto-segmentation, not only common geometric metrics but also new spatial parameters with distance and relative volume ( R V ${R}_V$ ) to target were used. Correlations were performed using Spearman's correlation between segmentation evaluation metrics and dosimetric changes.

RESULTS

Only strong (|R² | > 0.6, p < 0.01) or moderate (|R² | > 0.4, p < 0.01) Pearson correlation was established between the traditional geometric metric and three dosimetric evaluation indices to target (conformity index, homogeneity index, and mean dose). For organs at risk (OARs), inferior or no significant relationship was found between geometric parameters and dosimetric differences. Furthermore, we found that OARs dose distribution was affected by boundary error of target segmentation instead of distance and R V ${R}_V$ to target.

CONCLUSIONS

Current geometric metrics could reflect a certain degree of dose effect of target variation. To find target contour variations that do lead to OARs dosimetry changes, clinically oriented metrics that more accurately reflect how segmentation quality affects dosimetry should be constructed.

A Review of the Metrics Used to Assess Auto-Contouring Systems in Radiotherapy

Auto-contouring could revolutionise future planning of radiotherapy treatment. The lack of consensus on how to assess and validate auto-contouring systems currently limits clinical use. This review formally quantifies the assessment metrics used in studies published during one calendar year and assesses the need for standardised practice. A PubMed literature search was undertaken for papers evaluating radiotherapy auto-contouring published during 2021. Papers were assessed for types of metric and the methodology used to generate ground-truth comparators. Our PubMed search identified 212 studies, of which 117 met the criteria for clinical review. Geometric assessment metrics were used in 116 of 117 studies (99.1%). This includes the Dice Similarity Coefficient used in 113 (96.6%) studies. Clinically relevant metrics, such as qualitative, dosimetric and time-saving metrics, were less frequently used in 22 (18.8%), 27 (23.1%) and 18 (15.4%) of 117 studies, respectively. There was heterogeneity within each category of metric. Over 90 different names for geometric measures were used. Methods for qualitative assessment were different in all but two papers. Variation existed in the methods used to generate radiotherapy plans for dosimetric assessment. Consideration of editing time was only given in 11 (9.4%) papers. A single manual contour as a ground-truth comparator was used in 65 (55.6%) studies. Only 31 (26.5%) studies compared auto-contours to usual inter- and/or intra-observer variation. In conclusion, significant variation exists in how research papers currently assess the accuracy of automatically generated contours. Geometric measures are the most popular, however their clinical utility is unknown. There is heterogeneity in the methods used to perform clinical assessment. Considering the different stages of system implementation may provide a framework to decide the most appropriate metrics. This analysis supports the need for a consensus on the clinical implementation of auto-contouring.

Optimal and actual rates of Stereotactic Ablative Body Radiotherapy (SABR) utilisation for primary lung cancer in Australia

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Stereotactic Ablative Body Radiotherapy (SABR) plays a major role in the management of early-stage non-small cell lung cancer (NSCLC).

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An evidence-based model is developed to estimate optimal rates of lung SABR utilisation within the Australian population.

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Optimal utilisation rates are compared against actual utilisation rates to evaluate service provision.

Background and purpose

Radiotherapy utilisation rates considerably vary across different countries and service providers, highlighting the need to establish reliable benchmarks against which utilisation rates can be assessed. Here, optimal utilisation rates of Stereotactic Ablative Body Radiotherapy (SABR) for lung cancer are estimated and compared against actual utilisation rates to identify potential shortfalls in service provision.

Materials and Methods

An evidence-based optimal utilisation model was constructed after reviewing practice guidelines and identifying indications for lung SABR based on the best available evidence. The proportions of patients likely to develop each indication were obtained, whenever possible, from Australian population-based studies. Sensitivity analysis was performed to account for variations in epidemiological data. Practice pattern studies were reviewed to obtain actual utilisation rates.

Results

A total of 6% of all lung cancer patients were estimated to optimally require SABR at least once during the course of their illness (95% CI: 4–6%). Optimal utilisation rates were estimated to be 32% for stage I and 10% for stage II NSCLC. Actual utilisation rates for stage I NSCLC varied between 6 and 20%. For patients with inoperable stage I, 27–74% received SABR compared to the estimated optimal rate of 82%.

Conclusion

The estimated optimal SABR utilisation rates for lung cancer can serve as useful benchmarks to highlight gaps in service delivery and help plan for more adequate and efficient provision of care. The model can be easily modified to determine optimal utilisation rates in other populations or updated to reflect any changes in practice guidelines or epidemiological data.