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Enregistrement W2325561875 · doi:10.1097/01.cot.0000352156.57627.1f

RCC: Nomogram Found as Prognosis-Predicting with Targeted Theraphy

2009· article· en· W2325561875 sur OpenAlexaboutno aff
Charlene Laino

Notice bibliographique

RevueOncology Times · 2009
Typearticle
Langueen
DomaineMedicine
ThématiqueRadiomics and Machine Learning in Medical Imaging
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésNomogramMedicineOncologyRenal cell carcinomaInternal medicineTargeted therapyCancerClinical Oncology

Résumé

récupéré en direct d'OpenAlex

ORLANDO, FL—A six-factor nomogram can stratify patients with metastatic renal cell carcinoma into poor, intermediate, and favorable prognostic groups in the era of targeted therapy, researchers reported here at the Genitourinary Cancers Symposium. The prognostic factors are Karnofsky performance status, time between diagnosis and treatment, hemoglobin levels, calcium levels, neutrophil count, and platelet count, said Daniel Y.C. Heng, MD, Assistant Professor of Medical Oncology at the Tom Baker Cancer Center of the University of Calgary in Alberta.FigureSpeaking at his poster session at the meeting—which is cosponsored by the American Society of Clinical Oncology, American Society for Radiation Oncology, and Society of Urologic Oncology—Dr. Heng said, “Prognostic factors are important for patient counseling and risk-directed treatment. This model can be used to stratify patients in clinical trials and in clinical practice in the era of VEGF-targeted therapy.” Old Models Outdated Currently, Memorial Sloan-Kettering Cancer Center criteria are widely used to stratify patients with metastatic renal cell carcinoma into prognostic groups, but the model was derived from studies in the immunotherapy era, he explained. “Now that overall survival is significantly prolonged with use of targeted therapy, the criteria needed to be reassessed.” Cleveland Clinic researchers developed a five-factor nomogram to stratify patients treated with targeted therapy, but the model was based on a small sample size and use of a specific agent, he said. “We needed a large robust study to identify prognostic factors and to update survival data.” To develop the new nomogram, Dr. Heng and his colleagues obtained data on consecutive series of patients treated at seven cancer centers in the US and Canada. The records of 645 patients with metastatic renal cell carcinoma of any histology treated with sunitinib, sorafenib, or bevacizumab were analyzed. Prior use of immunotherapy was allowed. Demographic, clinical, laboratory, and outcome data were collected for each patient using uniform data collection software. The patients' median age at initiation of targeted therapy was 60, and 73% were male. The median time from diagnosis to initiation of targeted therapy was 1.4 years, and the median Karnofsky performance status was 80.Figure: DANIEL Y.C. HENG, MD: “The widely used Memorial Sloan-Kettering Cancer Center model was derived from studies in the immunotherapy era. Since overall survival is significantly prolonged with the use of targeted therapy, the criteria needed to be reassessed.”Sixty-seven percent had been treated with first-line targeted therapy; the rest received targeted drugs as second-line treatment. Sixty-one percent were treated with sunitinib, 31% with sorafenib, and 8% with bevacizumab The primary outcome was overall survival time. Prognostic Factors In univariable analysis, 10 factors demonstrated prognostic significance. Neither the use of prior immunotherapy nor the type of targeted therapy proved to be significant variables predicting overall survival times, Dr. Heng noted. In multivariable analysis, six independent predictors of poor overall survival remained: Karnofsky performance score of less than 80, a diagnosis-to-treatment interval of less than one year, and the presence of anemia, hypercalcemia, neutrophilia, and thrombocytosis. The median overall survival time for the entire cohort was 22 months. This compares with only 12 months in the era of immunotherapy, Dr. Heng said. During a follow-up period of 50 months, patients who had none of the poor prognostic factors did not reach the median overall survival time. Such patients should therefore be considered to have a favorable prognosis, he said. The median overall survival time of patients with one or two factors was 27 months; they therefore should be considered as having an intermediate prognosis, Dr. Heng said. If a patient had three to six factors, he or she had a poor prognosis, with a median overall survival time from 8.8 months. Dr. Heng said that the poor prognostic factors in the new nomogram are similar to those in the Sloan-Kettering model, “with the addition of neutrophilia and thrombocytosis. Validation of the prognostic model with external datasets is ongoing, he said. Nicholas J. Vogelzang, MD, Associate Center Director for Clinical Research and Head of the Genitourinary Cancer Program at the Nevada Cancer Institute, who led a poster walk for the session, said, “This model is a way to examine who will benefit from VEGF-targeted therapy. Until now, there have been minimal data for the VEGF era.”

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,615
Score d'incertitude au seuil0,489

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,008
Tête enseignante GPT0,307
Écart entre enseignants0,299 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2009
Routes d'admission1
Résumé présentoui

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