Predicting teratoma histology in postchemotherapy residual lesions of non-seminoma testicular cancer (NSTC) patients using integrated CT radiomics and circulating MicroRNAs modelling.
Bibliographic record
Abstract
5035 Background: Chemotherapy is the primary treatment for metastatic NSTC, but patients often have residual masses afterward. Accurate non-invasive models are needed to predict the histology of these masses, guiding treatment and reserving surgery for those with teratoma. This study aims to enhance predictive accuracy by integrating CT-driven radiomics features with miRNAs 371 and 375 (miR371-375) to distinguish between teratoma and non-teratoma histologies in post-chemotherapy residual masses. Methods: We retrospectively reviewed 52 patients with teratoma (n=56), fibrosis/necrosis (n=34), vGCT (n=11), and seminoma (n=10) lesions, divided into training (N=78) and test (N=33) cohorts with equal class distribution. Lesions included lymph nodes (n=68 retroperitoneum, n=11 mediastinum, n=4 pelvic, n=4 neck), lung (n=21), and brain (n=3) with a median size of 1.6 cm (Q1-Q3 interval=1.2-2.73 cm). Using 3D Slicer version 5.6.1, metastatic masses >1 cm (short axis) were segmented and radiomics features were extracted from venous phase CT images. Plasma miR371 and miR375 levels were measured by RT-PCR before resection. Four machine learning models evaluated the predictive value of radiomics alone (R-only) and combined with miR371/miR375 levels for teratoma histology, and the best performer, Cat Boosting (CB) method, is reported. Results: The analysis of datasets revealed a consistent pattern of superior performance in training sets compared to test sets across all metrics. The CB model R+371+375 dataset demonstrated the most robust overall performance, with the highest AUC values (0.96 [95% CI 0.88-1.0] for training, 0.83 [95% CI 0.68-0.98] for test) and a well-balanced sensitivity (0.71) and specificity (0.76) in the test set for predicting teratoma histology. R+375 followed closely with an AUC of 0.82 (95% CI 0.66-0.97). Conclusions: Combining miR 371 and 375 with CT-driven radiomics features improves the accuracy of classifying teratoma histology in metastatic NSTCs. This method can help characterize teratoma in residual metastatic disease, aiding treatment decisions and minimizing under or over-treatment risks. Further refinement, including the integration of clinical features, will be reported.
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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 | 0.000 | 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".