feat(publications): add Simpler Radiomics Can Be Enough: Identifying the Minimal Feature Complexity Required for Accurate Prediction

src/content/publications/simpler-radiomics-can-be-enough-identifying-the-minimal-feature-complexity-required-for-accurate-prediction-2026.md

@@ -0,0 +1,28 @@
+## Date
+
+2026-08-07
+
+## Authors
+
+- [Mahdi Loutfi](/team/mahdi-loutfi)
+- [Martin Vallières](/team/martin-vallieres)
+
+## Summary
+
+**Purpose**: Clinical translation of radiomics is hindered by the high-dimensional feature sets and lack of accessible tools for clinicians. MEDiml, an open-source platform designed to help democratize the development of radiomics models by identifying the simplest predictive features through both a code-based and graphical interface.
+
+**Methods**: MEDiml was evaluated using 89,714 features from five oncological datasets (n=2,104). Tasks included histology subtype prediction for non-small cell lung cancer (NSCLC, MRI); renal cell carcinoma (RCC, MRI); and RCC (contrast-enhanced CT); IDH1 mutation prediction for low-grade glioma (LGG, MRI); and grade prediction for meningioma (MRI). For modeling, features are categorized by complexity (morphological, intensity, texture, linear/nonlinear filters) for XGBoost training. The pipeline cleans invariants and filters collinearity, retaining only features highly correlated with clinical endpoints.
+
+**Results**: We released the MEDiml software with detailed documentation (mediml.app). Evaluation demonstrated that maximum predictive performance does not always require high-complexity features. Optimal levels were: morphological for LGG-IDH1 and Meningioma-grading; intensity in NSCLC and RCC -CECT; and texture for RCC-MRI. In the RCC-CECT cohort, optimizing the re-segmentation range, a parameter for excluding non-target voxels affecting intensity features, improved performance from an AUC of 0.82 to 0.86.
+
+**Conclusion**: MEDiml successfully identifies the "optimal complexity level" for specific clinical outcomes, demonstrating that simpler models can often match or exceed the performance of high-dimensional sets. By providing an interactive, user-friendly interface and a strategy for feature minimization, MEDiml lowers the barrier to entry for clinicians, providing a scalable pathway for the clinical integration of radiomic biomarkers.
+
+## Links
+
+- [Primary link](NA)
+
+## BibTeX
+
+```bibtex
+
+```

src/data/publications.json

@@ -20,6 +20,16 @@
         "type": "Journal Papers",
         "date": "2026-02-25"
       },
+      {
+        "title": "Simpler Radiomics Can Be Enough: Identifying the Minimal Feature Complexity Required for Accurate Prediction",
+        "slug": "simpler-radiomics-can-be-enough-identifying-the-minimal-feature-complexity-required-for-accurate-prediction-2026",
+        "contributors": ["Mahdi Loutfi", "Martin Vallières"],
+        "journal": "Lady Davis Institute (LDI) Conference (Poster Presentation)",
+        "link": "NA",
+        "markdown": "publications/simpler-radiomics-can-be-enough-identifying-the-minimal-feature-complexity-required-for-accurate-prediction-2026.md",
+        "type": "Presentations",
+        "date": "2026-08-07"
+      },
       {
         "title": "Kaféfak Presentation - First Publication Award",
         "slug": "kafefak-presentation-first-publication-award-2026",