Bibliographic record
Abstract
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 Old Models Outdated Currently, Memorial Sloan-Kettering Cancer Center criteria are widely used to stratify patients with metastatic renal cell carcinoma into prognostic groups,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.”
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".