Variability of clinical target volume delineation for rectal cancer patients planned for neoadjuvant radiotherapy with the aid of the platform Anatom-e

Franco, Pierfrancesco; Arcadipane, Francesca; Trino, Elisabetta; Gallio, E.; Martini, Stefania; Iorio, Giuseppe Carlo; Piva, Cristina; Moretto, Francesco; Redda, Maria Grazia Ruo; Verna, Roberta; Tseroni, Vassilikì; Bona, Cristina; Pozzi, G.; Fiandra, C.; Ragona, Riccardo; Bertetto, Oscar; Ricardi, Umberto

doi:10.1016/j.ctro.2018.06.002

Cited by 23 publications

(16 citation statements)

References 28 publications

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“…A is the investigator (automatic) delineation, and B is the GT (manual) delineation. The MDA indicates the average distance of outline points of the automatic contouring volume to the outline of reference manual delineation perfect overlap volume (15). The lower the values (mm) of MDA, the higher the correspondence between the automatic and manual contouring volumes (15).…”

Section: Dsc(a B) =mentioning

confidence: 99%

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

et al. 2020

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Results: Average DSC values were greater than 0.92 except for the spinal cord (0.82), and average MDA values were <0.90 mm except for the heart (1.74 mm). Eleven of the 20 dosimetric metrics of the OARs were not significant (P > 0.05). Although there were significant differences (P < 0.05) for the spinal cord (D2%), left lung (V10, V20, V30, and mean dose), and bilateral lung (V10, V20, V30, and mean dose), their absolute differences were small and acceptable for the clinic. The maximum dosimetric metrics differences of OARs between manual and automatic delineations were D2% = 0.35 Gy for the spinal cord and V30 = 0.4% for the bilateral lung, which were within the clinical criteria in this study. Conclusion: Dosimetric metrics were proposed to evaluate the automatic delineation in radiotherapy planning of esophageal cancer. Consequently, the automatic delineation could substitute the manual delineation for esophageal cancer radiotherapy planning based on the dosimetric evaluation in this study.

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Section: Dsc(a B) =mentioning

confidence: 99%

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

et al. 2020

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“…Internationally, there are several examples of strategies to reduce contour variability of target volumes, most of them being focused on clinical target volume (CTV) standardization for one particular treatment site in a specific study protocol [19] , [20] , [21] , [22] . For instance, the Belgian initiative PROCARE [21] took a national approach to improve outcome in rectal cancer patients between 2003 and 2014, including the creation of a central review platform to reduce variability for CTV delineation.…”

Section: Discussionmentioning

confidence: 99%

Initial experience with introducing national guidelines for CT- and MRI-based delineation of organs at risk in radiotherapy

Olsson

Nyholm

Wieslander

et al. 2019

Physics and Imaging in Radiation Oncology

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A fundamental problem in radiotherapy is the variation of organ at risk (OAR) volumes. Here we present our initial experience in engaging a large Radiation Oncology (RO) community to agree on national guidelines for OAR delineations. Our project builds on associated standardization initiatives and invites professionals from all radiotherapy departments nationwide. Presently, one guideline (rectum) has successfully been agreed on by a majority vote. Reaching out to all relevant parties in a timely manner and motivating funding agencies to support the work represented early challenges. Population-based data and a scalable methodological approach are major strengths of the proposed strategy.

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“…When building the dataset and training the DL models, the purity of the data is critical, known as "garbage-in, garbageout." In the target and OAR structure data, interobserver variability exists and must be recognized and handled [44][45][46][47][48].…”

Section: Target and Organs-at-risk Segmentationmentioning

confidence: 99%

Deep Learning in Radiation Oncology

Cheon

Kim

2020

Progress in Medical Physics

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Deep learning (DL) is a subset of machine learning and artificial intelligence that has a deep neural network with a structure similar to the human neural system and has been trained using big data. DL narrows the gap between data acquisition and meaningful interpretation without explicit programming. It has so far outperformed most classification and regression methods and can automatically learn data representations for specific tasks. The application areas of DL in radiation oncology include classification, semantic segmentation, object detection, image translation and generation, and image captioning. This article tries to understand what is the potential role of DL and what can be more achieved by utilizing it in radiation oncology. With the advances in DL, various studies contributing to the development of radiation oncology were investigated comprehensively. In this article, the radiation treatment process was divided into six consecutive stages as follows: patient assessment, simulation, target and organs-at-risk segmentation, treatment planning, quality assurance, and beam delivery in terms of workflow. Studies using DL were classified and organized according to each radiation treatment process. State-of-the-art studies were identified, and the clinical utilities of those researches were examined. The DL model could provide faster and more accurate solutions to problems faced by oncologists. While the effect of a data-driven approach on improving the quality of care for cancer patients is evidently clear, implementing these methods will require cultural changes at both the professional and institutional levels. We believe this paper will serve as a guide for both clinicians and medical physicists on issues that need to be addressed in time.

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Variability of clinical target volume delineation for rectal cancer patients planned for neoadjuvant radiotherapy with the aid of the platform Anatom-e

Cited by 23 publications

References 28 publications

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

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

Initial experience with introducing national guidelines for CT- and MRI-based delineation of organs at risk in radiotherapy

Deep Learning in Radiation Oncology

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