FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W1974133615> ?p ?o ?g. }

• W1974133615 endingPage "320" @default.
• W1974133615 startingPage "317" @default.
• W1974133615 abstract "Along with the improved treatment conformity achieved with the recently implemented radiotherapy (RT) planning and delivery approaches [[1]Rodrigues G. Alexander L. Gregory V. et al.Categorizing segmentation quality using a quantitative quality assurance algorithm.J Med Imaging Radiat Oncol. 2012; 56: 668-678Crossref PubMed Scopus (5) Google Scholar], there is growing awareness of the uncertainties connected to the definition and delineation of the RT targets as well as the organs at risk (ORs). To assure accurate reproducibility of the planned treatment in order to avoid ‘geographical miss’ of the target, it is mandatory to correctly identify the volumes to be irradiated and to appropriately manage these uncertainties in the definition of the gross target volume (GTV) as well as of standardized, disease-specific, nodal clinical target volumes (CTVs).Accurate segmentation of primarily the GTVs and CTVs, as well as the ORs, therefore represents the foundation for successful RT. International or institutional guidelines, contouring atlases, case libraries and numerous recommendations have thus recently been developed [2Young A.V. Wortham A. Wernick I. Evans A. Ennis R.D. Atlas-based segmentation improves consistency and decreases time required for contouring postoperative endometrial cancer nodal volumes.Int J Radiat Oncol Biol Phys. 2011; 79: 943-947Abstract Full Text Full Text PDF PubMed Scopus (53) Google Scholar, 3Anders L.C. Stieler F. Siebenlist K. Schäfer J. Lohr F. Wenz F. Performance of an atlas-based autosegmentation software for delineation of target volumes for radiotherapy of breast and anorectal cancer.Radiother Oncol. 2012; 102: 68-73Abstract Full Text Full Text PDF PubMed Scopus (66) Google Scholar, 4Teguh D.N. Levendag P.C. Voet P.W. et al.Clinical validation of atlas-based auto-segmentation of multiple target volumes and normal tissue (swallowing/mastication) structures in the head and neck.Int J Radiat Oncol Biol Phys. 2011; 81: 950-957Abstract Full Text Full Text PDF PubMed Scopus (133) Google Scholar, 5Gambacorta M.A. Valentini C. Dinapoli N. et al.Clinical validation of atlas-based auto-segmentation of pelvic volumes and normal tissue in rectal tumors using auto-segmentation computed system.Acta Oncol. 2013; 52: 1676-1681Crossref PubMed Scopus (37) Google Scholar, 6Mattiucci G.C. Boldrini L. Chiloiro G. et al.Automatic delineation for replanning in nasopharynx radiotherapy: what is the agreement among experts to be considered as benchmark?.Acta Oncol. 2013; 52: 1417-1422Crossref PubMed Scopus (37) Google Scholar, 7Zhu M. Bzdusek K. Brink C. et al.Multi-institutional quantitative evaluation and clinical validation of Smart Probabilistic Image Contouring Engine (SPICE) autosegmentation of target structures and normal tissues on computer tomography images in the head and neck, thorax, liver, and male pelvis areas.Int J Radiat Oncol Biol Phys. 2013; 87: 809-816Abstract Full Text Full Text PDF PubMed Scopus (25) Google Scholar, 8La Macchia M. Fellin F. Amichetti M. et al.Systematic evaluation of three different commercial software solutions for automatic segmentation for adaptive therapy in head-and-neck, prostate and pleural cancer.Radiat Oncol. 2012; 7: 160Crossref PubMed Scopus (116) Google Scholar, 9Eriksen J.G. Salembier C. Rivera S. et al.Four years with FALCON – an ESTRO educational project: Achievements and perspectives.Radiother Oncol. 2014; 112: 145-149Abstract Full Text Full Text PDF PubMed Scopus (39) Google Scholar, 10Grégoire V. Ang K. Budach W. et al.Delineation of the neck node levels for head and neck tumors: a 2013 update. DAHANCA, EORTC, HKNPCSG, NCIC CTG, NCRI, RTOG, TROG consensus guidelines.Radiother Oncol. 2014; 110: 172-181Abstract Full Text Full Text PDF PubMed Scopus (415) Google Scholar, 11Nijkamp J. de Haas-Kock D.F. Beukema J.C. et al.Target volume delineation variation in radiotherapy for early stage rectal cancer in the Netherlands.Radiother Oncol. 2012; 102: 14-21Abstract Full Text Full Text PDF PubMed Scopus (54) Google Scholar, 12Boersma L.J. Janssen T. Elkhuizen P.H. et al.Reducing interobserver variation of boost-CTV delineation in breast conserving radiation therapy using a pre-operative CT and delineation guidelines.Radiother Oncol. 2012; 103: 178-182Abstract Full Text Full Text PDF PubMed Scopus (48) Google Scholar]. Atlases and guidelines are widely recognized and contribute to reducing inter-observer variability, but they are static documents that also lack interactivity.To address this challenge, several commercial auto-contouring software solutions have recently been released, representing an opportunity for individualizing the existing atlases, automatically propagating clinically reliable contours to patients’ specific anatomies [[13]Sykes J. Reflections on the current status of commercial automated segmentation systems in clinical practice.J Med Radiat Sci. 2014; 61: 131-134Crossref Scopus (27) Google Scholar]. They also have potential for lowering the segmentation time, increasing the adherence to existing guidelines and, not the least, to reduce the inter-observer variability that still may be a major source of uncertainty in RT. The current issue presents several studies related to the use of such software in RT [14Jameson M.G. Kumar S. Vinod S.K. Metcalfe P.E. Holloway L.C. Correlation of contouring variation with modeled outcome for conformal non-small cell lung cancer radiotherapy.Radiother Oncol. 2014; 112: 332-336Abstract Full Text Full Text PDF Scopus (26) Google Scholar, 15Conson M. Cella L. Pacelli R. et al.Automated delineation of brain structures in patients undergoing radiotherapy for primary brain tumours: from atlas to dose-volume histograms.Radiother Oncol. 2014; 112: 326-331Abstract Full Text Full Text PDF Scopus (30) Google Scholar, 16Walker G.V. Awan M. Tao R. et al.Prospective Randomized Double-Blind Study of Atlas-Based Organ-at-Risk Autosegmentation-Assisted Radiation Treatment Planning in Head and Neck Cancer.Radiother Oncol. 2014; 112: 321-325Abstract Full Text Full Text PDF PubMed Scopus (76) Google Scholar].A basic requirement of the application of auto-segmentation software is the existence of a widely recognized and reliable definition of the volumes, according to the anatomical region, the histology as well as the stage of the disease. This can be described as software ontology [[17]Boldrini L Damiani A Valentini V. Principles and clinical applications of autocontouring software. FrancoAngeli, Milano2014Google Scholar], covering clinical, anatomical, pathological and imaging information. This ontology should be linked to benchmark performance values obtained through comparisons with the agreement between multiple observers/operators, or between manual delineations and auto-contouring. There are several metrics for the agreement between the contours, and establishing ranges indicating clinically acceptable agreement on the scales of these metrics plays an essential role in translating the obtained observations to everyday practice.In this paper we would like to share comments and criticisms on how evidence can be derived from the use of auto-segmentation software, addressing the key aspects that should be considered in future studies in this field: ontology definition, benchmark evaluation methods and performance evaluation tools. We also discuss the potential benefits that can be achieved with these tools.Ontology definitionThe term ontology refers to a form of dictionary where information is specified and organized in a well-defined semantic data collection model; a set of concepts within a domain, and also the relationships between those concepts. In the auto-contouring setting the ontology is therefore represented by the prior reference knowledge regarding GTV and CTV definition, in practise related to the existence of contouring guidelines and the adherence to these.The segmentation of the GTV relies on interpretation of multi-modal imaging data (e.g. CT, MRI, PET), often involving the use of specific image registration and segmentation tools [[18]Thariat J. Marcy P.Y. Lacout A. et al.Radiotherapy and radiology: joint efforts for modern radiation planning and practice.Diagn Interv Imaging. 2012; 93: 342-350Crossref PubMed Scopus (3) Google Scholar]. GTV segmentation is frequently performed in close consultation with other medical disciplines (e.g. radiologists, surgeons). Delineation of the nodal CTV reflects the lymphatic drainage of the disease site and its relapse risk, which are generally identified through retrospective pathological and/or surgical observations performed for the specific stage of disease. Prospective randomized studies of the effect of lymph node irradiation, such as in the RTOG 9413 study of prostate cancer with or without pelvic irradiation [[19]Roach III, M. DeSilvio M. Lawton C. et al.Phase III trial comparing whole-pelvic versus prostate-only radiotherapy and neoadjuvant versus adjuvant combined androgen suppression: Radiation Therapy Oncology Group 9413.J Clin Oncol. 2003; 21: 1904-1911Crossref PubMed Scopus (572) Google Scholar], still remains uncommon. Areas of nodal sub-volumes of different risks (both for lymphatic spread and/or relapse) are generally described by contouring atlases (with related text descriptions), defining their boundaries and anatomical relations. Together with the interpretation of the TNM stage of the disease [[20]Sobin L. Wittekind C. Gospodarowicz M.K. TNM classification of malignant tumours.7th ed. Wiley-Blackwell, New York2009Google Scholar] the evaluation of nodal involvement will dictate which sub-volumes should be included in the nodal CTV [2Young A.V. Wortham A. Wernick I. Evans A. Ennis R.D. Atlas-based segmentation improves consistency and decreases time required for contouring postoperative endometrial cancer nodal volumes.Int J Radiat Oncol Biol Phys. 2011; 79: 943-947Abstract Full Text Full Text PDF PubMed Scopus (53) Google Scholar, 3Anders L.C. Stieler F. Siebenlist K. Schäfer J. Lohr F. Wenz F. Performance of an atlas-based autosegmentation software for delineation of target volumes for radiotherapy of breast and anorectal cancer.Radiother Oncol. 2012; 102: 68-73Abstract Full Text Full Text PDF PubMed Scopus (66) Google Scholar, 5Gambacorta M.A. Valentini C. Dinapoli N. et al.Clinical validation of atlas-based auto-segmentation of pelvic volumes and normal tissue in rectal tumors using auto-segmentation computed system.Acta Oncol. 2013; 52: 1676-1681Crossref PubMed Scopus (37) Google Scholar]. Reliable definition and segmentation of the normal tissues has also considerable dose/volume and clinical consequences, in particular for dose-limiting ORs [[21]Thor M. Bentzen L. Elstrøm U.V. Petersen J.B. Muren L.P. Dose/volume-based evaluation of the accuracy of deformable image registration for the rectum and bladder.Acta Oncol. 2013; 52: 1411-1416Crossref PubMed Scopus (28) Google Scholar].For all main RT tumour sites there have been several ontologies (i.e. delineation guidelines) suggested, yet there are seldom any criteria to define which represents the most accurate and clinically reliable standard for each considered anatomical district. A first attempt of a hierarchical organization of these proposals was the endorsement of the proposed contours by national and international scientific societies but, having prostate as an example, at least eight atlases with the endorsement of four different organisations can be counted today, demonstrating a substantial lack of harmony [22Poortmans P. Bossi A. Vandeputte K. et al.Guidelines for target volume definition in post-operative radiotherapy for prostate cancer, on behalf of the EORTC Radiation Oncology Group.Radiother Oncol. 2007; 84: 121-127Abstract Full Text Full Text PDF PubMed Scopus (228) Google Scholar, 23Wiltshire K.L. Brock K.K. Haider M.A. et al.Anatomic boundaries of the clinical target volume (prostate bed) after radical prostatectomy.Int J Radiat Oncol Biol Phys. 2007; 69: 1090-1099Abstract Full Text Full Text PDF PubMed Scopus (120) Google Scholar, 24Sidhom M.A. Kneebone A.B. Lehman M. et al.Post-prostatectomy radiation therapy: consensus guidelines of the Australian and New Zealand Radiation Oncology Genito-Urinary Group.Radiother Oncol. 2008; 88: 10-19Abstract Full Text Full Text PDF PubMed Scopus (126) Google Scholar, 25Lawton C.A. Michalski J. El Naqa I. et al.RTOG GU radiation oncology specialists reach consensus on pelvic lymph node volumes for high-risk prostate cancer.Int J Radiat Oncol Biol Phys. 2009; 74: 383-387Abstract Full Text Full Text PDF PubMed Scopus (298) Google Scholar, 26Michalski J.M. Lawton C. El Naqa I. et al.Development of RTOG consensus guidelines for the definition of the clinical target volume for postoperative conformal radiation therapy for prostate cancer.Int J Radiat Oncol Biol Phys. 2010; 76: 361-368Abstract Full Text Full Text PDF PubMed Scopus (262) Google Scholar, 27Velker V.M. Rodrigues G.B. Dinniwell R. Hwee J. Louie A.V. Creation of RTOG compliant patient CT-atlases for automated atlas based contouring of local regional breast and high-risk prostate cancers.Radiat Oncol. 2013; 25: 8-188Google Scholar, 28Gay H.A. Barthold H.J. O’Meara E. et al.Pelvic normal tissue contouring guidelines for radiation therapy: a Radiation Therapy Oncology Group consensus panel atlas.Int J Radiat Oncol Biol Phys. 2012; 83: e353-62Abstract Full Text Full Text PDF PubMed Scopus (326) Google Scholar, 29Martinez Monge R. Fernandes P.S. Gupta N. Gahbauer R. Cross-sectional nodal atlas: a tool for the definition of clinical target volumes in three-dimensional radiation therapy planning.Radiology. 1999; 211: 815-828Crossref PubMed Scopus (105) Google Scholar].Dealing with auto-contouring software, it is necessary to understand how the auto-segmentation system propagates the chosen ontology on the single case, e.g. by the adoption of a commonly agreed atlas delineation by the institution’s organ oriented expert team, or through the creation of an atlas through selection of cases uploaded into a library system available in an atlas-based contouring system [17Boldrini L Damiani A Valentini V. Principles and clinical applications of autocontouring software. FrancoAngeli, Milano2014Google Scholar, 30Ramus L. Thariat J. Marcy P.Y. et al.Outils de contourage, utilisation et construction d’atlas anatomiques: exemples des cancers de la tête et du cou – automatic segmentation using atlases in head and neck cancers: methodology.Cancer Radiother. 2010; 14: 206-212Crossref PubMed Scopus (11) Google Scholar].Performance evaluation toolsThe performance of auto-segmentation software is usually evaluated with respect to contour similarity indices as well as potential time/workload savings. So far a number of approaches have been used to quantify the amount of similarity between the proposed contours and others. Analysis and comparison among the five main methods (area of intersection, Dice similarity index, Jaccard index, Conformation number, Hausdorff distance) found that they are not effective in distinguishing random from systematic errors, or to separate false positives from false negatives [17Boldrini L Damiani A Valentini V. Principles and clinical applications of autocontouring software. FrancoAngeli, Milano2014Google Scholar, 31Hanna G.G. Hounsell A.R. O’Sullivan J.M. Geometrical analysis of radiotherapy target volume delineation: a systematic review of reported comparison methods.Clin Oncol (R Coll Radiol). 2010; 22: 515-525Abstract Full Text Full Text PDF PubMed Scopus (84) Google Scholar, 32Fotina I. Lütgendorf-Caucig C. Stock M. Pötter R. Georg D. Critical discussion of evaluation parameters for inter-observer variability in target definition for radiation therapy.Strahlenther Onkol. 2012; 188: 160-167Crossref PubMed Scopus (98) Google Scholar, 33Dice L.R. Measures of the amount of ecologic association between species.Ecology. 1945; 26: 297-302Crossref Google Scholar, 34Jaccard P. Étude comparative de la distribution florale dans une portion des Alpes et des Jura.Bull Soc Vaud Sci Nat. 1901; 37: 547-579Google Scholar]. They also operate “out of context”, without any consideration to the potential effects of a contouring error. What the measures do quantify is how close a given contour is to an ‘ideal’ contour. This is suitable for a first level analysis and is useful as a comparison tool, especially if a combination of a dimensionless index and a measure such as the Hausdorff distance is used; this is even more appropriate if the attempted contour is sufficiently close to its reference.The Dice index is linear with respect to the intersection, while the other conformation indices are essentially non-linear, showing instead a tendency to amplify the effect of a similar degree of discrepancy when the intersection is larger. This does not pose any problem (the monotonicity is assured), however, the non-uniformity should have an adequate justification from a clinical and/or educational point of view.A combination of conformation scores, metric elements and clinical risk assessment could lead to a new class of indices, which could prove useful at first in an educational environment evaluating a “test” contour against a reference created by a domain expert.Closely related to the benchmark contouring definition is the measurement of the contouring time spent, to quantify the time saved with the auto-segmentation approach. It is important to measure the time spent during all components of the process, and to include the time spent in the definition of the atlas cases and in making the right atlas case choice (manual or automatic).Benchmark evaluation methodsSo far only a few studies have described benchmark values in various anatomical regions, often discussing these in relation to the reduction in segmentation time [2Young A.V. Wortham A. Wernick I. Evans A. Ennis R.D. Atlas-based segmentation improves consistency and decreases time required for contouring postoperative endometrial cancer nodal volumes.Int J Radiat Oncol Biol Phys. 2011; 79: 943-947Abstract Full Text Full Text PDF PubMed Scopus (53) Google Scholar, 3Anders L.C. Stieler F. Siebenlist K. Schäfer J. Lohr F. Wenz F. Performance of an atlas-based autosegmentation software for delineation of target volumes for radiotherapy of breast and anorectal cancer.Radiother Oncol. 2012; 102: 68-73Abstract Full Text Full Text PDF PubMed Scopus (66) Google Scholar, 4Teguh D.N. Levendag P.C. Voet P.W. et al.Clinical validation of atlas-based auto-segmentation of multiple target volumes and normal tissue (swallowing/mastication) structures in the head and neck.Int J Radiat Oncol Biol Phys. 2011; 81: 950-957Abstract Full Text Full Text PDF PubMed Scopus (133) Google Scholar, 5Gambacorta M.A. Valentini C. Dinapoli N. et al.Clinical validation of atlas-based auto-segmentation of pelvic volumes and normal tissue in rectal tumors using auto-segmentation computed system.Acta Oncol. 2013; 52: 1676-1681Crossref PubMed Scopus (37) Google Scholar, 6Mattiucci G.C. Boldrini L. Chiloiro G. et al.Automatic delineation for replanning in nasopharynx radiotherapy: what is the agreement among experts to be considered as benchmark?.Acta Oncol. 2013; 52: 1417-1422Crossref PubMed Scopus (37) Google Scholar, 7Zhu M. Bzdusek K. Brink C. et al.Multi-institutional quantitative evaluation and clinical validation of Smart Probabilistic Image Contouring Engine (SPICE) autosegmentation of target structures and normal tissues on computer tomography images in the head and neck, thorax, liver, and male pelvis areas.Int J Radiat Oncol Biol Phys. 2013; 87: 809-816Abstract Full Text Full Text PDF PubMed Scopus (25) Google Scholar, 8La Macchia M. Fellin F. Amichetti M. et al.Systematic evaluation of three different commercial software solutions for automatic segmentation for adaptive therapy in head-and-neck, prostate and pleural cancer.Radiat Oncol. 2012; 7: 160Crossref PubMed Scopus (116) Google Scholar, 17Boldrini L Damiani A Valentini V. Principles and clinical applications of autocontouring software. FrancoAngeli, Milano2014Google Scholar]. These studies usually compare the performance of auto-segmentation tools versus a reference segmentation delineated by one of more human observers. In such settings, the agreement between multiple human segmentations represents the benchmark value the auto-contoured structures should be compared with.The auto-segmentation studies have reported Dice similarity index values well below unity in all considered anatomical regions, ranging from 0.67 to 0.79 in the anorectal region to 0.86–0.91 in the breast [[3]Anders L.C. Stieler F. Siebenlist K. Schäfer J. Lohr F. Wenz F. Performance of an atlas-based autosegmentation software for delineation of target volumes for radiotherapy of breast and anorectal cancer.Radiother Oncol. 2012; 102: 68-73Abstract Full Text Full Text PDF PubMed Scopus (66) Google Scholar]. These values should be evaluated relative to comparisons between contours drawn manually by experts, where the existing variability shows similar values [6Mattiucci G.C. Boldrini L. Chiloiro G. et al.Automatic delineation for replanning in nasopharynx radiotherapy: what is the agreement among experts to be considered as benchmark?.Acta Oncol. 2013; 52: 1417-1422Crossref PubMed Scopus (37) Google Scholar, 17Boldrini L Damiani A Valentini V. Principles and clinical applications of autocontouring software. FrancoAngeli, Milano2014Google Scholar, 32Fotina I. Lütgendorf-Caucig C. Stock M. Pötter R. Georg D. Critical discussion of evaluation parameters for inter-observer variability in target definition for radiation therapy.Strahlenther Onkol. 2012; 188: 160-167Crossref PubMed Scopus (98) Google Scholar, 35Chao K.S. Bhide S. Chen H. et al.Reduce in variation and improve efficiency of target volume delineation by a computer-assisted system using a deformable image registration approach.Int J Radiat Oncol Biol Phys. 2007; 68: 1512-1521Abstract Full Text Full Text PDF PubMed Scopus (108) Google Scholar].Struggling to obtain a complete overlap between two manually contoured structures could therefore be deceptive and can lead to dead-end streets in evaluating inter-observer studies, as the existence of a certain degree of variability will remain unavoidable in daily clinical practice.This translates into the necessity of building up a reliable “gold structure set” which will represent the unique benchmark of the study and will be the referral contour to which all other contours should be compared with. There are several strategies that can be followed when defining these benchmark structures: a commonly agreed delineation by two or more operators with the same expertise; a gold contour coming from the experience of one highly skilled physician or from a team of experts of the chosen anatomical district. Each of these strategies has its own pros and cons: trusting a single operator for the gold contour delineation is quick and easy but lacks the fundamental independent cross check of the structures with other operators, while enrolling a team of experts represents a heavier effort but it could offer a more reliable segmentation.DiscussionThe issues covered in the three previous sections can be summarized into a set of recommendations (Table 1) that should be considered in future studies of auto-segmentation issues.Table 1Recommendations for studies of auto-contouring software in radiotherapy.DomainRecommendationsOntology•A reliable ontology must always be described in details, specifying:–the chosen delineation guidelines–the applied selection/filtering criteria–anatomical and pathological information about the atlas patients selection (for example TNM, tumour site, patient’s gender, BMI)•Describe in the patients’ library presentation the chosen structures, target or ORs and clearly report them in the paper•Define and describe the adopted propagation method, underlining its pro and consEvaluation•Choose a time measurement unit for all the phases of the research that could allow a swift, friendly, comprehension by the reader, avoiding unnecessary calculations.•Report the reasons of the choice of the similarity indices describing their characteristics, nature and mathematical aspects•Consider the different indices categories, bearing in mind that the use of only one kind of index would heavily limit the descriptive and statistical power of the study•Describe the existing contouring variability from different points of view, using of a combination of multiple different toolsBenchmark•Describe how the gold contour has been achieved, specifying if:–realized defining a mean contour among those segmented by the single components of the contouring group–delineated only once with the common agreement of all operators–result of a mutual independent check by the involved operators–result of a test between the delineations proposed by the various operators and a master contour•Illustrate the degree of expertise of the different operators involved in the benchmark definition describing contouring reference group composition, technical skills and scientific background•Avoid implicit assumptions of a common expertise, knowledge, attitude and imaging interpretation skills among the reference group members•Report specific imaging techniques chosen for delineation purposes (when used) Open table in a new tab The benefits expected from the use of auto-contouring software are several, and cover aspects related to clinical impact, the resulting dose distributions as well as education. The correct definition of RT targets and ORs has direct clinical consequences, with respect to both disease control and toxicity. In particular the contouring of nodal CTV sub-volumes is critical, and auto-contouring software can be very useful for this purpose. This is also related to the increasing importance of correlating CTV sub-volumes (and critical choices in this setting) with (imaging) data of local control and survival [[36]Due A.K. Vogelius I.R. Aznar M.C. et al.Methods for estimating the site of origin of locoregional recurrence in head and neck squamous cell carcinoma.Strahlenther Onkol. 2012; 188: 671-676Crossref PubMed Scopus (31) Google Scholar]. Such studies will provide essential information on which structures to include in the auto-contouring atlases and templates. This information is also key input to individualized and adaptive approaches to prediction databases and models [37Lambin P. van Stiphout R.G. Starmans M.H. et al.Predicting outcomes in radiation oncology-multifactorial decision support systems.Nat Rev Clin Oncol. 2013; 10: 27-40Crossref PubMed Scopus (280) Google Scholar, 38Meldolesi E. van Soest J. Dinapoli N. et al.An umbrella protocol for standardized data collection (SDC) in rectal cancer: a prospective uniform naming and procedure convention to support personalized medicine.Radiother Oncol. 2014; : S0167-8140Google Scholar].Auto-contouring software has clearly a potential to play an important role in future RT, based on sound clinical evidence and linking survival to volume choice and their automatic propagation. However, building up clinical evidence is time consuming (not the least within a prospective setting), and there are considerable future challenges in constructing reliable predictive models [39Whitfield G.A. Price P. Price G.J. Moore C.J. Automated delineation of radiotherapy volumes: are we going in the right direction?.Br J Radiol. 2013; 1021: 20110718Crossref Scopus (35) Google Scholar, 40Hapdey S. Edet-Sanson A. Gouel P. et al.Delineation of small mobile tumours with FDG-PET/CT in comparison to pathology in breast cancer patients.Radiother Oncol. 2014; 112: 407-412Abstract Full Text Full Text PDF Scopus (5) Google Scholar, 41Brouwer C.L. Steenbakkers R.J. Gort E. et al.Differences in delineation guidelines for head and neck cancer result in inconsistent reported dose and corresponding NTCP.Radiother Oncol. 2014; 111: 148-152Abstract Full Text Full Text PDF PubMed Scopus (25) Google Scholar]. Quantifying the potential clinical benefits of auto-contouring in a retrospective way could be a first step in this direction. In any case it should be underlined that present auto-contouring tools are merely a supplementary instrument that should be used together with careful manual editing of the proposed structures.Besides the simple geometrical comparison, it is useful to explore the relation between auto-segmentation and dose/volume parameters relating to target dose coverage and/or normal tissue irradiation. Voet et al. [[42]Voet P.W. Dirkx M.L. Teguh D.N. Hoogeman M.S. Levendag P.C. Heijmen B.J. Does atlas-based autosegmentation of neck levels require subsequent manual contour editing to avoid risk of severe target underdosage? A dosimetric analysis.Radiother Oncol. 2011; 98: 373-377Abstract Full Text Full Text PDF PubMed Scopus (74) Google Scholar] recently observed that moderate geometrical differences in the PTV may have a large dosimetric impact on the target. Specifically, they observed reductions in D99 up to 11 Gy even for Dice coefficients higher than 0.8 and mean contour distances lower than 1 mm between the autocontoured volumes and the manual referral ones. In a paper in this issue, Jameson and colleagues showed how target delineation uncertainties can also be transformed into tumour control probability predictions [[14]Jameson M.G. Kumar S. Vinod S.K. Metcalfe P.E. Holloway L.C. Correlation of contouring variation with modeled outcome for conformal non-small cell lung cancer radiotherapy.Radiother Oncol. 2014; 112: 332-336Abstract Full Text Full Text PDF Scopus (26) Google Scholar]. Also in this issue, Conson et al. reported on the dose/volume effects of the use of auto-segmentation for normal tissues in the brain [[15]Conson M. Cella L. P" @default.
• W1974133615 created "2016-06-24" @default.
• W1974133615 creator A5008208200 @default.
• W1974133615 creator A5014143201 @default.
• W1974133615 creator A5057694715 @default.
• W1974133615 creator A5078958139 @default.
• W1974133615 date "2014-09-01" @default.
• W1974133615 modified "2023-10-16" @default.
• W1974133615 title "Recommendations on how to establish evidence from auto-segmentation software in radiotherapy" @default.
• W1974133615 cites W140435101 @default.
• W1974133615 cites W1965408806 @default.
• W1974133615 cites W1976449749 @default.
• W1974133615 cites W1982441646 @default.
• W1974133615 cites W1987869189 @default.
• W1974133615 cites W1988499416 @default.
• W1974133615 cites W1999705754 @default.
• W1974133615 cites W2006698358 @default.
• W1974133615 cites W2006918239 @default.
• W1974133615 cites W2007912355 @default.
• W1974133615 cites W2008634645 @default.
• W1974133615 cites W2009938361 @default.
• W1974133615 cites W2012952352 @default.
• W1974133615 cites W2016939578 @default.
• W1974133615 cites W2021220421 @default.
• W1974133615 cites W2022945234 @default.
• W1974133615 cites W2027509401 @default.
• W1974133615 cites W2027944909 @default.
• W1974133615 cites W2036220888 @default.
• W1974133615 cites W2065493387 @default.
• W1974133615 cites W2066208236 @default.
• W1974133615 cites W2066517105 @default.
• W1974133615 cites W2081582681 @default.
• W1974133615 cites W2082425609 @default.
• W1974133615 cites W2091266620 @default.
• W1974133615 cites W2097580797 @default.
• W1974133615 cites W2103683686 @default.
• W1974133615 cites W2108057069 @default.
• W1974133615 cites W2111784792 @default.
• W1974133615 cites W2114329266 @default.
• W1974133615 cites W2114585202 @default.
• W1974133615 cites W2128086327 @default.
• W1974133615 cites W2128625986 @default.
• W1974133615 cites W2146087749 @default.
• W1974133615 cites W2148754315 @default.
• W1974133615 cites W2153303342 @default.
• W1974133615 cites W2168746203 @default.
• W1974133615 cites W79471600 @default.
• W1974133615 doi "https://doi.org/10.1016/j.radonc.2014.09.014" @default.
• W1974133615 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/25315862" @default.
• W1974133615 hasPublicationYear "2014" @default.
• W1974133615 type Work @default.
• W1974133615 sameAs 1974133615 @default.
• W1974133615 citedByCount "46" @default.
• W1974133615 countsByYear W19741336152015 @default.
• W1974133615 countsByYear W19741336152016 @default.
• W1974133615 countsByYear W19741336152017 @default.
• W1974133615 countsByYear W19741336152018 @default.
• W1974133615 countsByYear W19741336152019 @default.
• W1974133615 countsByYear W19741336152020 @default.
• W1974133615 countsByYear W19741336152021 @default.
• W1974133615 countsByYear W19741336152022 @default.
• W1974133615 countsByYear W19741336152023 @default.
• W1974133615 crossrefType "journal-article" @default.
• W1974133615 hasAuthorship W1974133615A5008208200 @default.
• W1974133615 hasAuthorship W1974133615A5014143201 @default.
• W1974133615 hasAuthorship W1974133615A5057694715 @default.
• W1974133615 hasAuthorship W1974133615A5078958139 @default.
• W1974133615 hasBestOaLocation W19741336151 @default.
• W1974133615 hasConcept C126322002 @default.
• W1974133615 hasConcept C154945302 @default.
• W1974133615 hasConcept C19527891 @default.
• W1974133615 hasConcept C199360897 @default.
• W1974133615 hasConcept C2777904410 @default.
• W1974133615 hasConcept C41008148 @default.
• W1974133615 hasConcept C509974204 @default.
• W1974133615 hasConcept C71924100 @default.
• W1974133615 hasConcept C89600930 @default.
• W1974133615 hasConceptScore W1974133615C126322002 @default.
• W1974133615 hasConceptScore W1974133615C154945302 @default.
• W1974133615 hasConceptScore W1974133615C19527891 @default.
• W1974133615 hasConceptScore W1974133615C199360897 @default.
• W1974133615 hasConceptScore W1974133615C2777904410 @default.
• W1974133615 hasConceptScore W1974133615C41008148 @default.
• W1974133615 hasConceptScore W1974133615C509974204 @default.
• W1974133615 hasConceptScore W1974133615C71924100 @default.
• W1974133615 hasConceptScore W1974133615C89600930 @default.
• W1974133615 hasIssue "3" @default.
• W1974133615 hasLocation W19741336151 @default.
• W1974133615 hasLocation W19741336152 @default.
• W1974133615 hasLocation W19741336153 @default.
• W1974133615 hasLocation W19741336154 @default.
• W1974133615 hasOpenAccess W1974133615 @default.
• W1974133615 hasPrimaryLocation W19741336151 @default.
• W1974133615 hasRelatedWork W1596801655 @default.
• W1974133615 hasRelatedWork W2130043461 @default.
• W1974133615 hasRelatedWork W2350741829 @default.
• W1974133615 hasRelatedWork W2358668433 @default.
• W1974133615 hasRelatedWork W2376932109 @default.

• next