Evaluation of Automatic Segmentation Model With Dosimetric Metrics for Radiotherapy of Esophageal Cancer

Zhu, Ji; Chen, Xinyuan; Yang, Baiyu; Bi, Nan; Zhang, Tao; Men, Kuo; Dai, Jianrong

doi:10.3389/fonc.2020.564737

Cited by 24 publications

(32 citation statements)

References 21 publications

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“…2 and 3). This is in line with results found in other studies for different treatment sites such as head & neck and breast cancer [22,23,26]. To our knowledge, no studies have been published that include multiple types of structure sets, or analyzes both intra-observer variability together with dose difference on a reference contour set.…”

Section: Discussionsupporting

confidence: 91%

“…Lustberg et al showed significant differences between manual and user-adjusted contours of the heart, mediastinum and esophagus, even though all of them were accepted for clinical use by the same observer [12]. Multiple studies on inter-observer contour variability have been performed for several treatment sites [20][21][22][23][24][25][26]. To the best of our knowledge, no studies have been done that analyzed the influence of autosegmentation methods and intra-/inter-observer variability on the treatment plan outcome for lung cancer.…”

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confidence: 99%

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The impact of organ-at-risk contour variations on automatically generated treatment plans for NSCLC

Vaassen

Hazelaar

Canters

et al. 2021

Radiotherapy and Oncology

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Background and purpose: Quality of automatic contouring is generally assessed by comparison with manual delineations, but the effect of contour differences on the resulting dose distribution remains unknown. This study evaluated dosimetric differences between treatment plans optimized using various organ-at-risk (OAR) contouring methods. Materials and methods: OARs of twenty lung cancer patients were manually and automatically contoured, after which user-adjustments were made. For each contour set, an automated treatment plan was generated. The dosimetric effect of intra-observer contour variation and the influence of contour variations on treatment plan evaluation and generation were studied using dose-volume histogram (DVH)parameters for thoracic OARs. Results: Dosimetric effect of intra-observer contour variability was highest for Heart D max (3.4 ± 6.8 Gy) and lowest for Lungs-GTV D mean (0.3 ± 0.4 Gy). The effect of contour variation on treatment plan evaluation was highest for Heart D max (6.0 ± 13.4 Gy) and Esophagus D max (8.7 ± 17.2 Gy). Dose differences for the various treatment plans, evaluated on the reference (manual) contour, were on average below 1 Gy/1%. For Heart D mean , higher dose differences were found for overlap with PTV (median 0.2 Gy, 95% 1.7 Gy) vs. no PTV overlap (median 0 Gy, 95% 0.5 Gy). For D max -parameters, largest dose difference was found between 0-1 cm distance to PTV (median 1.5 Gy, 95% 4.7 Gy). Conclusion: Dose differences arising from automatic contour variations were of the same magnitude or lower than intra-observer contour variability. For Heart D mean , we recommend delineation errors to be corrected when the heart overlaps with the PTV. For D max -parameters, we recommend checking contours if the distance is close to PTV (<5 cm). For the lungs, only obvious large errors need to be adjusted.

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Section: Discussionsupporting

confidence: 91%

mentioning

confidence: 99%

The impact of organ-at-risk contour variations on automatically generated treatment plans for NSCLC

Vaassen

Hazelaar

Canters

et al. 2021

Radiotherapy and Oncology

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“…According to the International Commission on Radiation Units and Measurements (ICRU) 83 report, 21 these dosimetric endpoints (D98%, D mean , and D2%) represent the minimum, mean, and maximum doses received by the target. The CI value of target volume is defined according to the following equation, 12 , 21…”

Section: Methodsmentioning

confidence: 99%

“… 9 Recent research has suggested that despite the fact that these geometric indices can be easily calculated, they do not consider the clinical effect and may lack clinical relevance. 10 - 12 Furthermore, different geometric indices have different properties, but different automatic segmentation studies were not based on a uniform guide or rules to select geometric indices to evaluate the contour results. 13 - 15 Subject to the assumption of a reference contour, the method used for the clinical assessment of the accuracy of radiotherapy (RT) contours involves the determination and prediction of the deviation of its dosimetric indices based on the dose distribution of the radiation treatment plan.…”

Section: Introductionmentioning

confidence: 99%

Clinically Oriented Target Contour Evaluation Using Geometric and Dosimetric Indices Based on Simple Geometric Transformations

Xian

Xiao

et al. 2021

Technol Cancer Res Treat

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Purpose: In radiotherapy, geometric indices are often used to evaluate the accuracy of contouring. However, the ability of geometric indices to identify the error of contouring results is limited primarily because they do not consider the clinical background. The purpose of this study is to investigate the relationship between geometric and clinical dosimetric indices. Methods: Four different types of targets were selected (C-shaped target, oropharyngeal cancer, metastatic spine cancer, and prostate cancer), and the translation, scaling, rotation, and sine function transformation were performed with the software Python to introduce systematic and random errors. The transformed contours were regarded as reference contours. Dosimetric indices were obtained from the original dose distribution of the radiotherapy plan. The correlations between geometric and dosimetric indices were quantified by linear regression. Results: The correlations between the geometric and dosimetric indices were inconsistent. For systematic errors, and with the exception of the sine function transformation (R2: 0.023-0.04, P > 0.05), the geometric transformations of the C-shaped target were correlated with the D98% and Dmean (R2: 0.689-0.988), 80% of which were P < 0.001. For the random errors, the correlations obtained by the all targets were R2 > 0.384, P < 0.05. The Wilcoxon signed-rank test was used to compare the spatial direction resolution capability of geometric indices in different directions of the C-shaped target (with systematic errors), and the results showed only the volumetric geometric indices with P < 0.05. Conclusions: Clinically, an assessment of the contour accuracy of the region-of-interest is not feasible based on geometric indices alone. Dosimetric indices should be added to the evaluations of the accuracy of the delineation results, which can be helpful for explaining the clinical dose response relationship of delineation more comprehensively and accurately.

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“…Interestingly, ML may also be used to propose risk factors for anastomotic leakage after esophagectomy [120]. Other attempts included using DL to identify optimal dosing of radiotherapy in GEA or defining the optimal target volume and organs at risk [121][122][123][124][125]. A dedicated analysis by Rahman et.…”

Section: Epidemiology Radiation Oncology and Blood Biomarkersmentioning

confidence: 99%

Machine Learning for Future Subtyping of the Tumor Microenvironment of Gastro-Esophageal Adenocarcinomas

Klein

Duda

2021

Cancers

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Tumor progression involves an intricate interplay between malignant cells and their surrounding tumor microenvironment (TME) at specific sites. The TME is dynamic and is composed of stromal, parenchymal, and immune cells, which mediate cancer progression and therapy resistance. Evidence from preclinical and clinical studies revealed that TME targeting and reprogramming can be a promising approach to achieve anti-tumor effects in several cancers, including in GEA. Thus, it is of great interest to use modern technology to understand the relevant components of programming the TME. Here, we discuss the approach of machine learning, which recently gained increasing interest recently because of its ability to measure tumor parameters at the cellular level, reveal global features of relevance, and generate prognostic models. In this review, we discuss the relevant stromal composition of the TME in GEAs and discuss how they could be integrated. We also review the current progress in the application of machine learning in different medical disciplines that are relevant for the management and study of GEA.

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Evaluation of Automatic Segmentation Model With Dosimetric Metrics for Radiotherapy of Esophageal Cancer

Cited by 24 publications

References 21 publications

The impact of organ-at-risk contour variations on automatically generated treatment plans for NSCLC

The impact of organ-at-risk contour variations on automatically generated treatment plans for NSCLC

Clinically Oriented Target Contour Evaluation Using Geometric and Dosimetric Indices Based on Simple Geometric Transformations

Machine Learning for Future Subtyping of the Tumor Microenvironment of Gastro-Esophageal Adenocarcinomas

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