Abstract A007: Radiomics of anaplastic Wilms tumors: Unraveling oncogenic immune dynamics
Bibliographic record
Abstract
Abstract This study investigates the intricate interplay between oncogenic pathways and the immune microenvironment in anaplastic Wilms tumors (AWTs) and explores its radiological implications. AWTs, characterized by complex genetic alterations including TP53 and CTNNB1 mutations, exhibit a unique immunophenotype with implications for tumor-immune dynamics. Leveraging comprehensive genomic profiling and immunological markers, we analyze how these interactions influence the radiological presentation of AWTs. We performed whole-exome and/or RNA sequencing on genomic DNA derived from 12 matched tumor-normal WTs and extended analysis in an independent dataset of 9 cases. Leveraging comprehensive genomic profiling and immunological markers, we analyzed how these interactions influence the radiological presentation of AWTs. The delicate equilibrium between pro-tumorigenic and anti-tumorigenic immune elements is examined, emphasizing the potential of advanced imaging modalities to capture evolving tumor-immune interactions. Immune infiltrates, such as lymphocytes and myeloid cells, are assessed for prognostic value and radiological correlates. The study aims to identify radiomic features that correlate with the immunological landscape, providing radiologists with tools for risk stratification and treatment response prediction. This research sheds light on the complex interplay between oncogenic signaling and the immune system in AWTs, offering radiologists insights to enhance diagnostic accuracy and therapeutic monitoring. Integration of genomic, immunological, and radiological data holds promise for developing tailored imaging strategies, contributing to a comprehensive understanding of AWT biology and facilitating personalized therapeutic interventions. Citation Format: Xiaoping Su, Ying Yuan, Gabriel Malouf. Radiomics of anaplastic Wilms tumors: Unraveling oncogenic immune dynamics [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Mechanisms of Cancer Immunity and Cancer-related Autoimmunity; 2025 Sep 24-27; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2025;13(9 Suppl):Abstract nr A007.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".