Changes in patient-reported outcomes (PROs) and tumor markers (TMs) to predict treatment response and survival outcomes in patients with metastatic gastrointestinal (GI) cancer.
Bibliographic record
Abstract
6560 Background: PROs assessing quality of life (QOL) and symptoms at a single timepoint frequently correlate with clinical outcomes in patients with cancer, yet efforts to understand how longitudinal changes in PROs can predict for treatment outcomes are lacking. In practice, oncologists often use changes in serum TMs (CEA and CA19-9) to monitor patients with GI cancer, and thus we sought to examine associations of 1-month changes in PROs and TMs with treatment response and survival outcomes among patients with advanced GI cancer. Methods: We prospectively enrolled patients with metastatic GI cancer prior to initiating chemotherapy at Massachusetts General Hospital from 5/2019-12/2020. At baseline (start of treatment) and 1-month later, we collected PROs (QOL [Functional Assessment of Cancer Therapy General {FACT-G}], physical symptoms [Edmonton Symptom Assessment System {ESAS}], and psychological symptoms [Patient Health Questionnaire-4 {PHQ-4}]) and TMs. We used regression models to examine associations of 1-month changes in PROs and TMs with treatment response (clinical benefit [defined as decreased or stable tumor burden] or progressive disease at the time of first scan) and survival outcomes (progression-free survival [PFS] and overall survival [OS]), adjusted for baseline values of each respective variable. Results: We enrolled 159 of 191 patients approached (83.2% enrollment); 134 had 1-month follow-up data (median age = 64 years [range: 28 to 84 years], 64.2% male, 46.3% pancreaticobiliary cancer). For treatment response, 63.4% had clinical benefit and 36.6% had progressive disease at the time of first scan (mean time to first scan = 2.01 months). Changes in PROs (ESAS-Total: OR = 0.97, p = 0.022; ESAS-Physical: OR = 0.96, p = 0.027; PHQ-4 depression: OR = 0.67, p = 0.014; FACT-G: OR = 1.07, p = 0.001), but not TMs (CEA: OR = 1.00, p = 0.836 and CA19-9: OR = 1.00, p = 0.796), were associated with clinical benefit at the time of first scan. Changes in ESAS-Total (HR = 1.03, p = 0.004), ESAS-Physical (HR = 1.03, p = 0.021), PHQ-4 depression (HR = 1.22, p = 0.042), FACT-G (HR = 0.97, p = 0.003), and CEA (HR = 1.00, p = 0.001) were predictors of PFS. Changes in ESAS-Total (HR = 1.03, p = 0.006) and ESAS-Physical (HR = 1.04, p = 0.015) were predictors of OS, but 1-month changes in TMs (CEA: HR = 1.00, p = 0.377 and CA19-9: HR = 1.00, p = 0.367) did not significantly predict for OS. Conclusions: We found that 1-month changes in PROs can predict for treatment response and survival outcomes in patients with advanced GI cancers. Notably, 1-month changes in CEA only correlated with PFS, while changes in CA19-9 did not significantly predict treatment response or survival outcomes. These findings highlight the potential for early changes in PROs to predict treatment outcomes while underscoring the need to monitor and address PROs in patients with advanced cancer.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".