Validation of the integrated prediction model algorithm for outcome of cytoreduction in advanced ovarian cancer.
Bibliographic record
Abstract
5546 Background: In advanced ovarian cancer, the decision for primary cytoreductive surgery(PCS) or neoadjuvant chemotherapy(NACT) remains a challenge and may impact survival. We previously developed the integrated prediction model(IPM) using a 4-step algorithm of unresectable stage IVb, patient factors, surgical resectability and surgical complexity to predict outcome of optimal cytoreduction in advanced epithelial ovarian cancer(AEOC) and triage patients to NACT or PCS. The objective of the current study was to validate this model on a retrospective historical cohort of patients. Methods: This is a retrospective cohort study of 107 patients with AEOC treated at the Princess Margaret Cancer Centre between January 2017 and September 2018 undergoing PCS or NACT. All diagnostic imaging was retrospectively reviewed to assign surgical resectability score (SRS) for sites of disease and the surgical complexity score (SCS) for procedures anticipated to be required to achieve optimal cytoreduction based on pre-operative imaging. Those scores were modified from previously validated tools. Patient factors (PF) included age, ECOG and albumin. We previously developed an IPM algorithm to achieve outcome of optimal cytoreduction and determined cut-offs using the Youden index J. Triaging patients to PCS without stage IVb unresectable disease, PF≤ 2, SRS≤5 and SCS≤ 9 led to an 85% specificity and 75% accuracy for outcome of optimal cytoreduction < 1 cm. The current validation study was performed reporting sensitivity, specificity, negative (NPV) and positive predictive value (PPV) on an external cohort. Results: Among 107 patients, 61 had PCS and 46 had NACT followed by ICS. Patients treated with NACT were significantly older (63.5 vs 61 years, p = 0.037), more likely stage IV (52% vs 18%, p < 0.001), had a higher proportion of ECOG > 1 (30% vs 11%, 0.045), a lower pre-operative album (37 vs 40, p < 0.001) and higher CA-125 (970 vs 227.5, p < 0.001) compared to PCS. They also had higher PF (2 vs 0, p = 0.013), SRS (4 vs 1, p < 0.001) and SCS (8 vs 5, p = < 0.001). There was no significant difference in outcome of cytoreduction; the optimal cytoreduction rate was 85% vs 87%, p = 0.12 between PCS and ICS patients. In this validation cohort, triaging patients without unresectable stage IVb disease, PF≤ 2, SRS≤ 5 and SCS≤ 9 to PCS had a sensitivity of 91% to correctly identify patients who will have optimal cytoreduction of < 1 cm at PCS and a specificity of 81%. The PPV was 83%, NPV was 90% and accuracy was 86%. Application of the IPM would have prevented 5 suboptimal patients and correctly triaged them to NACT. Conclusions: We validated a triage algorithm integrating patient factors, surgical complexity and surgical resectability for patients with AEOC to achieve optimal cytoreduction at PCS with high sensitivity and specificity. This may therefore be used in a clinical setting to decide between PCS and NACT.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".