Abstract A67: Improving tissue allocation for research in pediatric solid tumors
Notice bibliographique
Résumé
Abstract Introduction: In pediatric cancer, there is an urgent need for research that can identify and validate new therapeutic modalities for pediatric cancers. Diagnostic biopsy samples remain the ideal tissue samples for research and must be collected from surplus biopsy material, which is often extremely limited. Here we describe our efforts to optimize tissue allocation for clinical care and research as a joint effort between Department of Pathology at Boston Children’s Hospital and the Pediatric Solid Tumor Program at the Dana-Farber/Boston Children’s Cancer and Blood Disorders Center. Methods: Oncologists, pathologists, surgeons, interventional radiologists, pathology technical staff, and clinical research coordinators participated in the workflow design. The group agreed to develop a formalized procedure to address these five steps: 1) patient identification and consent, 2) prioritization of research objectives, 3) advance communication of tissue requests to the pathology staff, 4) tissue preparation, and 5) tissue distribution. It was unanimously agreed that all tissue must flow through the pathology department. On or before the day of surgery, clinical research teams sent the pathologist a patient-specific electronic communication indicating research consent and detailing the prioritized disease-specific research requests, including the procedure planned, all tissue types and tissue volumes requested, and details of any special preparation needed, such as avoiding decalcification in bone tumors. The communication was optimized to be clear but brief, with the goal of minimizing the impact to the pathologist’s work load. Pathologists were surveyed about the change in process. Results: Over a five-year period (2013-2018), 662 pediatric DFCI/BCH solid tumor patients have consented to one or more trials that request FFPE, frozen, or fresh tissue. Tumor types represent a spectrum of cases, with many rare and singular diagnoses. Of 1,768 research tissue requests, 1,121 (63%) were fulfilled. Clinical study requests from resection specimens were the most likely to be fulfilled (95% of 390 requests fulfilled), while basic research requests from core biopsies were the least likely to be fulfilled (26% of 255 requests fulfilled). In an anonymous survey, 7 of 7 pathologists report that the process had improved since the introduction of the electronic communication. Conclusions: A collaborative and informed model for tissue allocation is successful in distributing tissue for clinical studies and basic research projects. Our workflows and policies have gained pathologist approval and streamlined our processes. As clinical and research programs evolve, a thoughtful tissue allocation process will facilitate ongoing research. Citation Format: R. Seth Pinches, Catherine Clinton, Abigail Ward, Stephanie C Meyer, Alyaa Al-Ibraheemi, Suzanne Forrest, Gianna R. Strand, Hillary Detert, Anne Piche-Schulman, Kristen Gil, Tamara Restrepo, Rosemarie Tavares-Proulx, Jeffrey Goldsmith, Raja Shaikh, Christopher Weldon, Sanda Alexandrescu, Allison F. O’Neill, Monica Hollowell, Marian H. Harris, Katherine A. Janeway, Brian D. Crompton, Alanna Church. Improving tissue allocation for research in pediatric solid tumors [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr A67.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,057 | 0,062 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,003 | 0,008 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,018 | 0,006 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».