Radiomics and Machine Learning in Anal Squamous Cell Carcinoma: A New Step for Personalized Medicine?
Bibliographic record
Abstract
Anal squamous cell carcinoma (ASCC) is an uncommon yet rising cancer worldwide. Definitive chemo-radiation (CRT) remains the best curative treatment option for non-metastatic cases in terms of local control, recurrence-free and progression-free survival. Still, despite overall good results, with 80% five-year survival, a subgroup of ASCC patients displays a high level of locoregional and/or metastatic recurrence rates, up to 35%, and may benefit from a more aggressive strategy. Beyond initial staging, there is no reliable marker to predict recurrence following CRT. Imaging, mostly positron emission tomography-computed tomography (PET-CT) and magnetic resonance imaging (MRI), bears an important role in the diagnosis and follow-up of ASCC. The routine use of radiomics may enhance the quality of information derived from these modalities. It is thought that including data derived from radiomics into the input flow of machine learning algorithms may improve the prediction of recurrence. Although some studies have shown glimmers of hope, more data is needed before offering practitioners tools to identify high-risk patients and enable extensive clinical application, especially regarding the matters of imaging normalization, radiomics process standardization and access to larger patient databases with external validation in order to allow results extrapolation. The aim of this review is to present a critical overview from this data.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".