Probabilistic classification of tumour habitats in soft tissue sarcoma
Bibliographic record
Abstract
The purpose of this work is to propose a method to characterize tumour heterogeneity on MRI, using probabilistic classification based on a reference tissue. The method uses maps of the apparent diffusion coefficient (ADC), T2 relaxation, and a calculated map representing high‐b‐value diffusion‐weighted MRI (denoted simDWI) to identify up to five habitats (i.e. sub‐regions) of tumours. In this classification method, the parameter values (ADC, T2, and simDWI) from each tumour voxel are compared against the corresponding parameter probability distributions in a reference tissue. The probability that a tumour voxel belongs to a specific habitat is the joint probability for all parameters. The classification can be visualized using a custom colour scheme. The proposed method was applied to data from seven patients with biopsy‐confirmed soft tissue sarcoma, at three time‐points over the course of pre‐operative radiotherapy. Fast‐spin‐echo images with two different echo times and diffusion MRI with three b‐values were obtained and used as inputs to the method. Imaging findings were compared with pathology reports from pre‐radiotherapy biopsy and post‐surgical resection. Regions of hypercellularity, high‐T2 proteinaceous fluid, necrosis, collagenous stroma, and fibrosis were identified within soft tissue sarcoma. The classifications were qualitatively consistent with pathological observations. The percentage of necrosis on imaging correlated strongly with necrosis estimated from FDG‐PET before radiotherapy (R2 = 0.97) and after radiotherapy (R2 = 0.96). The probabilistic classification method identifies realistic habitats and reflects the complex microenvironment of tumours, as demonstrated in soft tissue sarcoma.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".