Predicting recurrence in osteosarcoma via a quantitative histological image classifier derived from tumour nuclear morphological features
Bibliographic record
Abstract
Abstract Recurrence is the key factor affecting the prognosis of osteosarcoma. Currently, there is a lack of clinically useful tools to predict osteosarcoma recurrence. The application of pathological images for artificial intelligence‐assisted accurate prediction of tumour outcomes is increasing. Thus, the present study constructed a quantitative histological image classifier with tumour nuclear features to predict osteosarcoma outcomes using haematoxylin and eosin (H&E)‐stained whole‐slide images (WSIs) from 150 osteosarcoma patients. We first segmented eight distinct tissues in osteosarcoma H&E‐stained WSIs, with an average accuracy of 90.63% on the testing set. The tumour areas were automatically and accurately acquired, facilitating the tumour cell nuclear feature extraction process. Based on six selected tumour nuclear features, we developed an osteosarcoma histological image classifier (OSHIC) to predict the recurrence and survival of osteosarcoma following standard treatment. The quantitative OSHIC derived from tumour nuclear features independently predicted the recurrence and survival of osteosarcoma patients, thereby contributing to precision oncology. Moreover, we developed a fully automated workflow to extract quantitative image features, evaluate the diagnostic values of feature sets and build classifiers to predict osteosarcoma outcomes. Thus, the present study provides a novel tool for predicting osteosarcoma outcomes, which has a broad application prospect in clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".