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Record W3118964091 · doi:10.1007/s00259-020-05163-5

International recommendations for personalised selective internal radiation therapy of primary and metastatic liver diseases with yttrium-90 resin microspheres

2021· article· en· W3118964091 on OpenAlexaff
Hugo Levillain, Oreste Bagni, Christophe M. Deroose, Arnaud Dieudonné, Silvano Gnesin, Oliver S. Großer, S. Cheenu Kappadath, Andrew S. Kennedy, Nima Kokabi, David Liu, David C. Madoff, Armeen Mahvash, Antonio Martínez-Cuesta, David Chee Eng Ng, Philipp M. Paprottka, Cinzia Pettinato, Macarena Rodríguez‐Fraile, Riad Salem, Bruno Sangro, Lidia Strigari, Daniel Y. Sze, Berlinda J. de Wit–van der Veen, Patrick Flamen

Bibliographic record

VenueEuropean Journal of Nuclear Medicine and Molecular Imaging · 2021
Typearticle
Languageen
FieldMedicine
TopicHepatocellular Carcinoma Treatment and Prognosis
Canadian institutionsVancouver General HospitalUniversity of British Columbia
FundersNational Center for Advancing Translational SciencesWake Forest School of MedicineGenentechSirtex MedicalIpsenEisaiRijksuniversiteit GroningenSylvester Comprehensive Cancer Center, University of Miami Health SystemsWestfälische Wilhelms-Universität MünsterUniversiteit LeidenBoston Scientific CorporationVanderbilt University Medical CenterGeorgetown UniversityVanderbilt UniversityExelixisEli Lilly and Company
KeywordsSelective internal radiation therapyMedicineDosimetryHepatocellular carcinomaMicrosphereNuclear medicineMedical physicsRadiation treatment planningRadiation therapyRadiologyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Purpose A multidisciplinary expert panel convened to formulate state-of-the-art recommendations for optimisation of selective internal radiation therapy (SIRT) with yttrium-90 ( 90 Y)-resin microspheres. Methods A steering committee of 23 international experts representing all participating specialties formulated recommendations for SIRT with 90 Y-resin microspheres activity prescription and post-treatment dosimetry, based on literature searches and the responses to a 61-question survey that was completed by 43 leading experts (including the steering committee members). The survey was validated by the steering committee and completed anonymously. In a face-to-face meeting, the results of the survey were presented and discussed. Recommendations were derived and level of agreement defined (strong agreement ≥ 80%, moderate agreement 50%–79%, no agreement ≤ 49%). Results Forty-seven recommendations were established, including guidance such as a multidisciplinary team should define treatment strategy and therapeutic intent (strong agreement); 3D imaging with CT and an angiography with cone-beam-CT, if available, and 99m Tc-MAA SPECT/CT are recommended for extrahepatic/intrahepatic deposition assessment, treatment field definition and calculation of the 90 Y-resin microspheres activity needed (moderate/strong agreement). A personalised approach, using dosimetry (partition model and/or voxel-based) is recommended for activity prescription, when either whole liver or selective, non-ablative or ablative SIRT is planned (strong agreement). A mean absorbed dose to non-tumoural liver of 40 Gy or less is considered safe (strong agreement). A minimum mean target-absorbed dose to tumour of 100–120 Gy is recommended for hepatocellular carcinoma, liver metastatic colorectal cancer and cholangiocarcinoma (moderate/strong agreement). Post-SIRT imaging for treatment verification with 90 Y-PET/CT is recommended (strong agreement). Post-SIRT dosimetry is also recommended (strong agreement). Conclusion Practitioners are encouraged to work towards adoption of these recommendations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.097
metaresearch head score (Gemma)0.083
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.097
Threshold uncertainty score0.515

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0970.083
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.004
Bibliometrics0.0040.003
Science and technology studies0.0010.002
Scholarly communication0.0040.002
Open science0.0060.005
Research integrity0.0090.008
Insufficient payload (model declined to judge)0.0030.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.040
GPT teacher head0.262
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations245
Published2021
Admission routes1
Has abstractyes

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