Simultaneous Truth and Performance Level Estimation Method for Evaluation of Target Contouring in Radiosurgery

Sandström, Helena; Toma-Daşu, Iuliana; Chung, Caroline; Gårding, Jonas; Jokura, Hidefumi; Daşu, Alexandru

doi:10.21873/anticanres.14774

Cited by 3 publications

(2 citation statements)

References 17 publications

(32 reference statements)

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“…Dice Similarity Coefficient (DSC), 15 Hausdorff distance (HDD), 16 mean distance to agreement (mDTA), volume comparisons, and visual inspections were the metrics employed to evaluate C Auto . Provided that only three potential reference contours were generated per OAR, and the accuracy of consensus‐driven methods to merge contours, such as STAPLE, is highly dependent on the number of structures included, 17 any of the human delineated OAR(s) scored as clinically usable was used as the ground truth for the comparisons. It should be noted that while only the OARs in C Auto not scored as perfect were rectified, the metrics indicated above were performed on all of them.…”

Section: Methodsmentioning

confidence: 99%

Feasibility evaluation of novel AI‐based deep‐learning contouring algorithm for radiotherapy

Maduro Bustos,

Sarkar,

Doyle

et al. 2023

J Applied Clin Med Phys

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PurposeTo evaluate the clinical feasibility of the Siemens Healthineers AI‐Rad Companion Organs RT VA30A (Organs‐RT) auto‐contouring algorithm for organs at risk (OARs) of the pelvis, thorax, and head and neck (H&N).MethodsComputed tomography (CT) datasets from 30 patients (10 pelvis, 10 thorax, and 10 H&N) were collected. Four sets of OARs were generated on each scan, one set by Organs‐RT and the others by three experienced users independently. A physician (expert) then evaluated each contour by assigning a score from the following scale: 1‐Must Redo, 2‐Major Edits, 3‐Minor Edits, 4‐Clinically usable. Using the highest‐scored OAR from the human users as a reference, the contours generated by Organs‐RT were evaluated via Dice Similarity Coefficient (DSC), Hausdorff Distance (HDD), Mean Distance to Agreement (mDTA), Volume comparison, and visual inspection. Additionally, each human user recorded the time to delineate each structure set and time‐saving efficiency was measured.ResultsThe average DSC obtained for the pelvic OARs ranged between (0.81 ± 0.06)Rectum and (0.94 ± 0.03)Bladder. (0.75 ± 0.09)Esophagus to for the thoracic OARs and (0.66 ± 0.07)Lips to (0.83 ± 0.04)Brainstem for the H&N. The average HDD in cm for the pelvis cohort ranged between (0.95 ± 0.35)Bladder to (3.62 ± 2.50)Rectum, (0.42 ± 0.06)SpinalCord to (2.09 ± 2.00)Esophagus for the thoracic set and to (1.50 ± 0.50)Mandible for the H&N region. The time‐saving efficiency was 67% for H&N, 83% for pelvis, and 84% for thorax. 72.5%, 82%, and 50% of the pelvis, thorax, and H&N OARs were scored as clinically usable by the expert, respectively.ConclusionsThe highest agreement registered between OARs generated by Organs‐RT and their respective references was for the bladder, heart, lungs, and femoral heads, with an overall DSC≥0.92. The poorest agreement was for the rectum, esophagus, and lips, with an overall DSC⩽0.81. Nonetheless, Organs‐RT serves as a reliable auto‐contouring tool by minimizing overall contouring time and increasing time‐saving efficiency in radiotherapy treatment planning.

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Section: Methodsmentioning

confidence: 99%

Feasibility evaluation of novel AI‐based deep‐learning contouring algorithm for radiotherapy

Maduro Bustos,

Sarkar,

Doyle

et al. 2023

J Applied Clin Med Phys

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“…The STAPLE algorithm has been evaluated in a human paper that found it to be a valuable estimation tool with increasing robustness with increasing input contours. The study eval-uated numbers of contours up to 12 28 and the algorithm has been relied on in a number of human studies for establishment of consensus guidelines. [29][30][31][32][33] The dose protocol and organ-at-risk metrics used for this study reflect what is used at this institution, and other protocols and metrics are used elsewhere.…”

Section: Ctmentioning

confidence: 99%

Use of CT and MR imaging in radiation therapy planning of imaging‐diagnosed canine intracranial meningioma achieves better tumor coverage than CT alone

Walther,

Warfield,

Akbarzadeh

et al. 2023

Vet Radiology Ultrasound

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The aim of this retrospective, secondary analysis study was to quantify the dosimetric impact of the lack of interobserver agreement on gross tumor volume (GTV) delineation for canine meningioma. This study used a previously reported population of 13 dogs with GTVs contoured on CT alone and on registered CT‐MR by 18 radiation oncologists. The “true” GTV was generated for each dog using a simultaneous truth and performance‐level estimation algorithm, and “true” brain was defined as the whole brain minus true GTV. Treatment plans were generated for each dog and observer combination, using criteria applied to the observer's GTV and brain contours. Plans were then categorized as a pass (met all planning criteria for true GTV and true brain) or fail. A mixed‐effects linear regression was performed to examine differences in metrics between CT and CT‐MR plans and mixed‐effects logistic regression was performed to examine differences in percentages of pass/fail between CT and CT‐MRI plans. The mean percent coverage of true GTV by prescribed dose was higher for CT‐MR plans than for CT plans (mean difference 5.9%; 95% CI, 3.7–8.0; P < 0.001). There was no difference in the mean volume of true brain receiving ≥24 Gy and in maximum true brain dose between CT plans and CT‐MR plans (P ≥ 0.198). CT‐MR plans were significantly more likely to pass the criteria for true GTV and true brain than CT plans (OR 1.75; 95% CI, 1.02–3.01; P = 0.044). This study demonstrated significant dosimetric impact when GTV contouring was performed on CT alone compared with CT‐MR.

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Impact of a contouring atlas on radiographer inter-observer variation in male pelvis radiotherapy

Clough,

Chuter,

Hales

et al. 2024

Journal of Medical Imaging and Radiation Sciences

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Simultaneous Truth and Performance Level Estimation Method for Evaluation of Target Contouring in Radiosurgery

Cited by 3 publications

References 17 publications

Feasibility evaluation of novel AI‐based deep‐learning contouring algorithm for radiotherapy

Feasibility evaluation of novel AI‐based deep‐learning contouring algorithm for radiotherapy

Use of CT and MR imaging in radiation therapy planning of imaging‐diagnosed canine intracranial meningioma achieves better tumor coverage than CT alone

Impact of a contouring atlas on radiographer inter-observer variation in male pelvis radiotherapy

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