arXiv subjects

Arman Gorji

Publications and source records attributed to Arman Gorji.

5 recordsLinked to original sources

arXiv2025

Towards Interpretable AI in Personalized Medicine: A Radiological-Biological Radiomics Dictionary Connecting Semantic Lung-RADS and imaging Radiomics Features; Dictionary LC 1.0

Lung cancer remains the leading cause of cancer-related mortality worldwide, with survival strongly dependent on early detection. Standard-dose computed tomography (CT) screening using the Lung Imaging Reporting and Data System (Lung-RADS) standardizes pulmonary nodule assessment but is limited by inter-reader variability and reliance on qualitative descriptors, while radiomics offers quantitative biomarkers that often lack clinical interpretability. To bridge this gap, we propose a radiological-biological dictionary that aligns radiomic features (RFs) with Lung-RADS semantic categories. A clinically informed dictionary translating ten Lung-RADS descriptors into radiomic proxies was developed through literature curation and validated by eight expert reviewers. As a proof of concept, imaging and clinical data from 977 patients across 12 collections in The Cancer Imaging Archive (TCIA) were analyzed; following preprocessing and manual segmentation, 110 RFs per nodule were extracted using PyRadiomics in compliance with the Image Biomarker Standardization Initiative (IBSI). A semi-supervised learning framework incorporating 499 labeled and 478 unlabeled cases was applied to improve generalizability, evaluating seven feature selection methods and ten interpretable classifiers. The optimal pipeline (ANOVA feature selection with a support vector machine) achieved a mean validation accuracy of 0.79. SHapley Additive exPlanations (SHAP) analysis identified key RFs corresponding to Lung-RADS semantics such as attenuation, margin irregularity, and spiculation, supporting the validity of the proposed mapping. Overall, this dictionary provides an interpretable framework linking radiomics and Lung-RADS semantics, advancing explainable artificial intelligence for CT-based lung cancer screening.

physics.med-ph↗

arXiv2025

Click, Predict, Trust: Clinician-in-the-Loop AI Segmentation for Lung Cancer CT-Based Prognosis within the Knowledge-to-Action Framework

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 deep learning (DL) offers automation but faces barriers to clinical adoption. Guided by the Knowledge-to-Action framework, this study develops a clinician-in-the-loop DL pipeline to enhance reproducibility, prognostic accuracy, and clinical trust. Multi-center CT data from 999 patients across 12 public datasets were analyzed using five DL models (3D Attention U-Net, ResUNet, VNet, ReconNet, SAM-Med3D), benchmarked against expert contours on whole and click-point cropped images. Segmentation reproducibility was assessed using 497 PySERA-extracted radiomic features via Spearman correlation, ICC, Wilcoxon tests, and MANOVA, while prognostic modeling compared supervised (SL) and semi-supervised learning (SSL) across 38 dimensionality reduction strategies and 24 classifiers. Six physicians qualitatively evaluated masks across seven domains, including clinical meaningfulness, boundary quality, prognostic value, trust, and workflow integration. VNet achieved the best performance (Dice = 0.83, IoU = 0.71), radiomic stability (mean correlation = 0.76, ICC = 0.65), and predictive accuracy under SSL (accuracy = 0.88, F1 = 0.83). SSL consistently outperformed SL across models. Radiologists favored VNet for peritumoral representation and smoother boundaries, preferring AI-generated initial masks for refinement rather than replacement. These results demonstrate that integrating VNet with SSL yields accurate, reproducible, and clinically trusted CT-based lung cancer prognosis, highlighting a feasible path toward physician-centered AI translation.

cs.CV↗

arXiv2025

Radiological and Biological Dictionary of Radiomics Features: Addressing Understandable AI Issues in Personalized Breast Cancer; Dictionary Version BM1.0

Radiomics-based AI models show promise for breast cancer diagnosis but often lack interpretability, limiting clinical adoption. This study addresses the gap between radiomic features (RF) and the standardized BI-RADS lexicon by proposing a dual-dictionary framework. First, a Clinically-Informed Feature Interpretation Dictionary (CIFID) was created by mapping 56 RFs to BI-RADS descriptors (shape, margin, internal enhancement) through literature and expert review. The framework was applied to classify triple-negative breast cancer (TNBC) versus non-TNBC using dynamic contrast-enhanced MRI from a multi-institutional cohort of 1,549 patients. We trained 27 machine learning classifiers with 27 feature selection methods. SHapley Additive exPlanations (SHAP) were used to interpret predictions and generate a complementary Data-Driven Feature Interpretation Dictionary (DDFID) for 52 additional RFs. The best model, combining Variance Inflation Factor (VIF) selection with Extra Trees Classifier, achieved an average cross-validation accuracy of 0.83. Key predictive RFs aligned with clinical knowledge: higher Sphericity (round/oval shape) and lower Busyness (more homogeneous enhancement) were associated with TNBC. The framework confirmed known imaging biomarkers and uncovered novel, interpretable associations. This dual-dictionary approach (BM1.0) enhances AI model transparency and supports the integration of RFs into routine breast cancer diagnosis and personalized care.

physics.comp-ph↗

arXiv2025

Censor-Aware Semi-Supervised Survival Time Prediction in Lung Cancer Using Clinical and Radiomics Features

Objectives: Lung cancer poses a significant global health challenge, necessitating improved prognostic methods for personalized treatment. This study introduces a censor-aware semi-supervised learning (SSL) framework that integrates clinical and imaging data, addressing biases in traditional models handling censored data. Methods: We analyzed clinical, PET and CT data from 199 lung cancer patients from public and local data respositories, focusing on overall survival (OS) time as the primary outcome. Handcrafted (HRF) and Deep Radiomics features (DRF) were extracted after preprocessing using ViSERA software and were combined with clinical features (CF). Feature dimensions were optimized using Principal Component Analysis (PCA), followed by the application of supervised learning (SL) and SSL. SSL incorporated pseudo-labeling of censored data to improve performance. Seven regressors and three hazard ratio survival analysis (HRSA) algorithms were optimized using five-fold cross-validation, grid search and external test bootstrapping. Results: For PET HRFs, SSL reduced the mean absolute error (MAE) by 26.5%, achieving 1.55 years with PCA+decision tree regression, compared to SL's 2.11 years with PCA+KNNR (p<0.05). Combining HRFs (CT_HRF) and DRFs from CT images using SSL+PCA+KNNR achieved an MAE of 2.08 years, outperforming SL's 2.26 years by 7.96% (p<0.05). In HRSA, CT_HRF applied to PCA+Component Wise Gradient Boosting Survival Analysis achieved an external c-index of 0.65, effectively differentiating high- and low-risk groups. Conclusions: We demonstrated that the SSL strategy significantly outperforms SL across PET, CT, and CF. As such, censor-aware SSL applied to HRFs from PET images significantly improved survival prediction performance by 26.5% compared to the SL approach.

physics.med-ph↗

arXiv2024

Enhanced Lung Cancer Survival Prediction using Semi-Supervised Pseudo-Labeling and Learning from Diverse PET/CT Datasets

Objective: This study explores a semi-supervised learning (SSL), pseudo-labeled strategy using diverse datasets to enhance lung cancer (LCa) survival predictions, analyzing Handcrafted and Deep Radiomic Features (HRF/DRF) from PET/CT scans with Hybrid Machine Learning Systems (HMLS). Methods: We collected 199 LCa patients with both PET & CT images, obtained from The Cancer Imaging Archive (TCIA) and our local database, alongside 408 head&neck cancer (HNCa) PET/CT images from TCIA. We extracted 215 HRFs and 1024 DRFs by PySERA and a 3D-Autoencoder, respectively, within the ViSERA software, from segmented primary tumors. The supervised strategy (SL) employed a HMLSs: PCA connected with 4 classifiers on both HRF and DRFs. SSL strategy expanded the datasets by adding 408 pseudo-labeled HNCa cases (labeled by Random Forest algorithm) to 199 LCa cases, using the same HMLSs techniques. Furthermore, Principal Component Analysis (PCA) linked with 4 survival prediction algorithms were utilized in survival hazard ratio analysis. Results: SSL strategy outperformed SL method (p-value<0.05), achieving an average accuracy of 0.85 with DRFs from PET and PCA+ Multi-Layer Perceptron (MLP), compared to 0.65 for SL strategy using DRFs from CT and PCA+ K-Nearest Neighbor (KNN). Additionally, PCA linked with Component-wise Gradient Boosting Survival Analysis on both HRFs and DRFs, as extracted from CT, had an average c-index of 0.80 with a Log Rank p-value<<0.001, confirmed by external testing. Conclusions: Shifting from HRFs and SL to DRFs and SSL strategies, particularly in contexts with limited data points, enabling CT or PET alone to significantly achieve high predictive performance.

cs.CV↗