8 papers
Robust Multicenter CT Radiogenomics for Dual EGFR and KRAS Prediction in Lung Cancer with Stability-Aware Modeling and SHAP Interpretation
Somayeh Sadat Mehrnia, Fatemeh Razavi, Helia Abedini +3
Accurate identification of EGFR and KRAS mutations is essential for precision therapy in non-small cell lung cancer (NSCLC), but tissue genotyping is invasive and may not capture t…
Click, Predict, Trust: Clinician-in-the-Loop AI Segmentation for Lung Cancer CT-Based Prognosis within the Knowledge-to-Action Framework
Mohammad R. Salmanpour, Sonya Falahati, Amir Hossein Pouria +10
Lung cancer remains the leading cause of cancer mortality, with CT imaging central to screening, prognosis, and treatment. Manual segmentation is variable and time-intensive, while…
Semi-Supervised Radiomics for Glioblastoma IDH Mutation: Limited Labels, Data Sensitivity, and SHAP Interpretation
Amir Hossein Pouria, Shahram Taeb, Somayeh Sadat Mehrnia +4
Glioblastoma (GBM) is an aggressive brain tumor in which IDH mutation status is a key prognostic biomarker, but traditional testing requires invasive biopsies, emphasizing the need…
Enhancement Without Contrast: Stability-Aware Multicenter Machine Learning for Glioma MRI Imaging
Sajad Amiri, Shahram Taeb, Sara Gharibi +6
Gadolinium-based contrast agents (GBCAs) are central to glioma imaging but raise safety, cost, and accessibility concerns. Predicting contrast enhancement from non-contrast MRI usi…
Handcrafted vs. Deep Radiomics vs. Fusion vs. Deep Learning: A Comprehensive Review of Machine Learning -Based Cancer Outcome Prediction in PET and SPECT Imaging
Mohammad R. Salmanpour, Somayeh Sadat Mehrnia, Sajad Jabarzadeh Ghandilu +7
Machine learning (ML), including deep learning (DL) and radiomics-based methods, is increasingly used for cancer outcome prediction with PET and SPECT imaging. However, the compara…
Robust Semi-Supervised CT Radiomics for Lung Cancer Prognosis: Cost-Effective Learning with Limited Labels and SHAP Interpretation
Mohammad R. Salmanpour, Amir Hossein Pouria, Sonia Falahati +7
Background: CT imaging is vital for lung cancer management, offering detailed visualization for AI-based prognosis. However, supervised learning SL models require large labeled dat…
AllMetrics: A Unified Python Library for Standardized Metric Evaluation and Robust Data Validation in Machine Learning
Morteza Alizadeh, Mehrdad Oveisi, Sonya Falahati +6
Machine learning (ML) models rely heavily on consistent and accurate performance metrics to evaluate and compare their effectiveness. However, existing libraries often suffer from…
Pathobiological Dictionary Defining Pathomics and Texture Features: Addressing Understandable AI Issues in Personalized Liver Cancer; Dictionary Version LCP1.0
Mohammad R. Salmanpour, Seyed Mohammad Piri, Somayeh Sadat Mehrnia +5
Artificial intelligence (AI) holds strong potential for medical diagnostics, yet its clinical adoption is limited by a lack of interpretability and generalizability. This study int…