A review of interventions to reduce inter‐observer variability in volume delineation in radiation oncology

Vinod, Shalini; Min, Myo; Jameson, Michael; Holloway, Lois

doi:10.1111/1754-9485.12462

Cited by 142 publications

(150 citation statements)

References 98 publications

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“…CT-based delineation of the LC is prone to IOV, even among experienced radiation oncologists 7,10,11,31-34. In various tumour sites, MRI has been shown to reduce contouring uncertainties when compared with CT 35-41.…”

Section: Discussionmentioning

confidence: 99%

“…However, there are many studies that failed to demonstrate improved contouring with the use of MRI for various tumour sites 43-47. As far as breast cancer is concerned, several authors investigated the impact of adding MRI to CT for delineation of lumpectomy cavity with negative or inconclusive outcome 11,16,20-23. Den Hartogh et al .…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, inter-observer concordance depended directly on the ability to visualize lumpectomy cavity, which was superior on MRI. Of note, in all of the reports which failed to show benefit of MRI, clips were placed at the edges of LC 11,16,20-23. In a study by Giezen et al ., four observers (2 radiologists and 2 radiation oncologists) obtained a mean CVS of 2.8 +/− 1.7 for MRI and 2.9 +/− 1.7 for CT.…”

Section: Discussionmentioning

confidence: 99%

“…For the individual patient, geographical miss of the target volume leads to increased chance of relapse, while unnecessary irradiation of normal tissues increases the probability of side effects. Respecting the common contouring guidelines accompanied by adequate training and high quality imaging are the most important strategies to reduce contouring variation 7-11…”

Section: Introductionmentioning

confidence: 99%

“…Due to its poor ability for soft tissue depiction, placement of surgical clips or markers at the edges of LC is recommended to improve tumour bed delineation 12-16. But reliability of inserted markers as a surrogate for tumour bed is a matter of debate9,16-18 and omission of their placement in some patients poses a special challenge to the radiation oncologist during CTV LC delineation.19 The role of MRI for contouring in breast cancer radiotherapy is controversial11,20-23 and the evidence to support its use in patients without markers in the tumour bed is scarce 24,25. In our present study, we aimed to (1) quantify the IOV and (2) assess the accuracy of CT- and MRI-based CTV LC contouring in patients without clips in the LC.…”

Section: Introductionmentioning

confidence: 99%

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MRI reduces variation of contouring for boost clinical target volume in breast cancer patients without surgical clips in the tumour bed

Al-Hammadi

Caparrotti

Divakar

et al. 2017

Radiology and Oncology

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BackgroundOmitting the placement of clips inside tumour bed during breast cancer surgery poses a challenge for delineation of lumpectomy cavity clinical target volume (CTVLC). We aimed to quantify inter-observer variation and accuracy for CT- and MRI-based segmentation of CTVLC in patients without clips.Patients and methodsCT- and MRI-simulator images of 12 breast cancer patients, treated by breast conserving surgery and radiotherapy, were included in this study. Five radiation oncologists recorded the cavity visualization score (CVS) and delineated CTVLC on both modalities. Expert-consensus (EC) contours were delineated by a senior radiation oncologist, respecting opinions of all observers. Inter-observer volumetric variation and generalized conformity index (CIgen) were calculated. Deviations from EC contour were quantified by the accuracy index (AI) and inter-delineation distances (IDD).ResultsMean CVS was 3.88 +/− 0.99 and 3.05 +/− 1.07 for MRI and CT, respectively (p = 0.001). Mean volumes of CTVLC were similar: 154 +/− 26 cm3 on CT and 152 +/− 19 cm3 on MRI. Mean CIgen and AI were superior for MRI when compared with CT (CIgen: 0.74 +/− 0.07 vs. 0.67 +/− 0.12, p = 0.007; AI: 0.81 +/− 0.04 vs. 0.76 +/− 0.07; p = 0.004). CIgen and AI increased with increasing CVS. Mean IDD was 3 mm +/− 1.5 mm and 3.6 mm +/− 2.3 mm for MRI and CT, respectively (p = 0.017).ConclusionsWhen compared with CT, MRI improved visualization of post-lumpectomy changes, reduced interobserver variation and improved the accuracy of CTVLC contouring in patients without clips in the tumour bed. Further studies with bigger sample sizes are needed to confirm our findings.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

MRI reduces variation of contouring for boost clinical target volume in breast cancer patients without surgical clips in the tumour bed

Al-Hammadi

Caparrotti

Divakar

et al. 2017

Radiology and Oncology

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Voxel‐based supervised machine learning of peripheral zone prostate cancer using noncontrast multiparametric MRI

Gholizadeh

Simpson

Ramadan

et al. 2020

J Applied Clin Med Phys

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Purpose The aim of this study was to develop and assess the performance of supervised machine learning technique to classify magnetic resonance imaging (MRI) voxels as cancerous or noncancerous using noncontrast multiparametric MRI (mp‐MRI), comprised of T2‐weighted imaging (T2WI), diffusion‐weighted imaging (DWI), and advanced diffusion tensor imaging (DTI) parameters. Materials and methods In this work, 191 radiomic features were extracted from mp‐MRI from prostate cancer patients. A comprehensive set of support vector machine (SVM) models for T2WI and mp‐MRI (T2WI + DWI, T2WI + DTI, and T2WI + DWI + DTI) were developed based on novel Bayesian parameters optimization method and validated using leave‐one‐patient‐out approach to eliminate any possible overfitting. The diagnostic performance of each model was evaluated using the area under the receiver operating characteristic curve (AUROC). The average sensitivity, specificity, and accuracy of the models were evaluated using the test data set and the corresponding binary maps generated. Finally, the SVM plus sigmoid function of the models with the highest performance were used to produce cancer probability maps. Results The T2WI + DWI + DTI models using the optimal feature subset achieved the best performance in prostate cancer detection, with the average AUROC , sensitivity, specificity, and accuracy of 0.93 ± 0.03, 0.85 ± 0.05, 0.82 ± 0.07, and 0.83 ± 0.04, respectively. The average diagnostic performance of T2WI + DTI models was slightly higher than T2WI + DWI models (+3.52%) using the optimal radiomic features. Conclusions Combination of noncontrast mp‐MRI (T2WI, DWI, and DTI) features with the framework of a supervised classification technique and Bayesian optimization method are able to differentiate cancer from noncancer voxels with high accuracy and without administration of contrast agent. The addition of cancer probability maps provides additional functionality for image interpretation, lesion heterogeneity evaluation, and treatment management.

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Methodological approach to create an atlas using a commercial auto‐contouring software

Casati

Piffer

Calusi

et al. 2020

J Applied Clin Med Phys

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The aim of this work was to establish a methodological approach for creation and optimization of an atlas for auto-contouring, using the commercial software MIM MAESTRO (MIM Software Inc. Cleveland OH). Methods: A computed tomography (CT) male pelvis atlas was created and optimized to evaluate how different tools and options impact on the accuracy of automatic segmentation. Pelvic lymph nodes (PLN), rectum, bladder, and femurs of 55 subjects were reviewed for consistency by a senior consultant radiation oncologist with 15 yr of experience. Several atlas and workflow options were tuned to optimize the accuracy of auto-contours. The deformable image registration (DIR), the finalization method, the k number of atlas best matching subjects, and several postprocessing options were studied. To test our atlas performances, automatic and reference manual contours of 20 test subjects were statistically compared based on dice similarity coefficient (DSC) and mean distance to agreement (MDA) indices. The effect of field of view (FOV) reduction on auto-contouring time was also investigated. Results: With the optimized atlas and workflow, DSC and MDA median values of bladder, rectum, PLN, and femurs were 0.91 and 1.6 mm, 0.85 and 1.6 mm, 0.85 and 1.8 mm, and 0.96 and 0.5 mm, respectively. Auto-contouring time was more than halved by strictly cropping the FOV of the subject to be contoured to the pelvic region. Conclusion: A statistically significant improvement of auto-contours accuracy was obtained using our atlas and optimized workflow instead of the MIM Software pelvic atlas.

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A review of interventions to reduce inter‐observer variability in volume delineation in radiation oncology

Abstract: Inter-observer variability in volume delineation can be reduced with the use of guidelines, provision of autocontours and teaching. The use of multimodality imaging is useful in certain tumour sites.

Cited by 142 publications

References 98 publications

MRI reduces variation of contouring for boost clinical target volume in breast cancer patients without surgical clips in the tumour bed

MRI reduces variation of contouring for boost clinical target volume in breast cancer patients without surgical clips in the tumour bed

Voxel‐based supervised machine learning of peripheral zone prostate cancer using noncontrast multiparametric MRI

Methodological approach to create an atlas using a commercial auto‐contouring software

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