Evidence map›Paper›PMID 28188486›Full record

ArticleInternational journal of computer assisted radiology and surgery2017

Validation of a method for retroperitoneal tumor segmentation.

Cristina Suárez-Mejías, José A Pérez-Carrasco, Carmen Serrano, José L López-Guerra, Tomás Gómez-Cía, Carlos L Parra-Calderón, Begoña Acha

Abstract readValidation Study
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In one paragraph

Article in International journal of computer assisted radiology and surgery, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Cristina Suárez-MejíasTechnological Innovation Group, Virgen del Rocío University Hospital, Avda Manuel Siurot, s/n, 41013, Sevilla, Spain.
José A Pérez-CarrascoSignal Theory and Communications Department, University of Seville, Camino de los Descubrimientos, s/n, 41092, Sevilla, Spain. jcarrasco79@gmail.com.
Carmen SerranoSignal Theory and Communications Department, University of Seville, Camino de los Descubrimientos, s/n, 41092, Sevilla, Spain.
José L López-GuerraOncology Unit, Virgen del Rocío University Hospital, Avda Manuel Siurot, s/n, 41013, Sevilla, Spain.
Tomás Gómez-CíaSurgery Unit, Virgen del Rocío University Hospital, Avda Manuel Siurot, s/n, 41013, Sevilla, Spain.
Carlos L Parra-CalderónTechnological Innovation Group, Virgen del Rocío University Hospital, Avda Manuel Siurot, s/n, 41013, Sevilla, Spain.
Begoña AchaSignal Theory and Communications Department, University of Seville, Camino de los Descubrimientos, s/n, 41092, Sevilla, Spain.

Funding

Agencia de Innovación y Desarrollo de Andalucía P11-TIC-7727Instituto de Salud Carlos III (ES) PT13/0006/0036
6 · The paper itself

Abstract

purposeIn 2005, an application for surgical planning called AYRA[Formula: see text] was designed and validated by different surgeons and engineers at the Virgen del Rocío University Hospital, Seville (Spain). However, the segmentation methods included in AYRA and in other surgical planning applications are not able to segment accurately tumors that appear in soft tissue. The aims of this paper are to offer an exhaustive validation of an accurate semiautomatic segmentation tool to delimitate retroperitoneal tumors from CT images and to aid physicians in planning both radiotherapy doses and surgery.

methodsA panel of 6 experts manually segmented 11 cases of tumors, and the segmentation results were compared exhaustively with: the results provided by a surgical planning tool (AYRA), the segmentations obtained using a radiotherapy treatment planning system (Pinnacle[Formula: see text]), the segmentation results obtained by a group of experts in the delimitation of retroperitoneal tumors and the segmentation results using the algorithm under validation.

results11 cases of retroperitoneal tumors were tested. The proposed algorithm provided accurate results regarding the segmentation of the tumor. Moreover, the algorithm requires minimal computational time-an average of 90.5% less than that required when manually contouring the same tumor.

conclusionA method developed for the semiautomatic selection of retroperitoneal tumor has been validated in depth. AYRA, as well as other surgical and radiotherapy planning tools, could be greatly improved by including this algorithm.

Indexed as

AlgorithmsAdolescentAdultHumansImaging, Three-DimensionalMaleRadiotherapy Planning, Computer-AssistedRetroperitoneal NeoplasmsTomography, X-Ray ComputedYoung AdultContinuous convex relaxationCTRadiotherapy planningRetroperitoneal tumorSurgical planning

Identifiers

PMID28188486

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.