Nomograms for predicting progression-free survival and overall survival after surgery and concurrent chemoradiotherapy for glioblastoma: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Glioblastoma (GBM) is the most common malignant brain tumor in adults. The prognosis of GBM patients is poor. Even with active standard treatment, the median overall survival is only 14.6 months. It is therefore critical to ascertain recurrence and search for factors that influence the prognosis of GBM. This study aimed to screen the variables related to the progression-free survival (PFS) and overall survival (OS) of GBM patients undergoing surgery and concurrent chemoradiotherapy, as well as propose a nomogram for individual risk prediction based on preoperative imaging parameters and clinicopathological variables readily available in clinical practice. METHODS: We retrospectively analyzed 114 consecutive patients with GBM who underwent surgery and concurrent chemoradiotherapy at the Second Affiliated Hospital, Zhejiang University School of Medicine from January 1st, 2015, to June 1st, 2018. Twenty-four preoperative magnetic resonance imaging (MRI) parameters were extracted manually from the Picture Archiving and Communication System (PACS). Clinicopathological factors were extracted from the electronic medical record system (EMRS). Least absolute shrinkage and selection operator (LASSO) regression and Cox regression were used for feature selection and model prediction, respectively. The models were presented using nomograms, which were applied to identify the risk of recurrence and survival according to the score. The performance of the nomograms to predict PFS and OS was tested with C-statistics, calibration plots, and Kaplan-Meier curves. RESULTS: The results revealed that sex, Karnofsky performance score (KPS), O6-methylglucamine-DNA methyltransferase (MGMT) protein expression, number of adjuvant chemotherapy cycles with temozolomide (TMZ), and the MRI signature effectively predicted PFS; and sex, KPS, extent of surgery, number of TMZ cycles, and MRI signature effectively predicted OS. The nomogram revealed good discriminative ability (C-statistics: 0.81 for PFS and 0.79 for OS). In the nomogram of PFS, patients with a score greater than 122 were considered to have a high risk of recurrence. In the nomogram of OS, the cutoff score were 115 and 145, and then patients were classified as low, medium, and high risk. CONCLUSIONS: In conclusion, our nomograms can effectively predict the risk of recurrence and survival of GBM patients and thus can be a good guide for clinical practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".