arXiv subjects

Hania Al-Hallaq

Publications and source records attributed to Hania Al-Hallaq.

3 recordsLinked to original sources

arXiv2026

Circulating Lymphocytes Preservation in Lung Cancer Stereotactic Body Radiation Therapy with Ultra-Fast Proton Delivery Using Modularized Pin Ridge Filters

Purpose: Radiation-induced lymphopenia is an increasingly recognized toxicity in lung radiotherapy and has been linked to radiation exposure to circulating lymphocytes (CL). In intensity-modulated proton therapy (IMPT), prolonged pencil beam scanning (PBS) delivery may increase CL dose. We recently developed a patient-specific pin ridge filter (pRF) framework that enables ultra-fast proton delivery with a single beam energy. This study evaluated whether pRF-based lung stereotactic body radiotherapy (SBRT) plans delivered at conventional (pRFCONV) and FLASH dose rates (pRFFLASH) improve immune sparing using time-resolved blood dose accumulation and CL survival modeling. Methods: pRF plans were created for 10 lung SBRT patients previously treated with IMPT. PBS delivery simulations modeled spot delivery, scanning, and energy switching. Blood dose-volume histograms (bDVHs) were calculated with the hematological dose framework. CL survival fractions (SF) were estimated from bDVHs with saturation and linear-quadratic models derived from in-vitro survival data for CD4/CD8 CL. Results: Compared with IMPT, pRFCONV/pRFFLASH plans reduced delivery time (mean reductions: 85.3/99.9%) and irradiated blood volume per fraction (mean reductions: 52.9/81.3%). pRFCONV/pRFFLASH plans reduced blood V5cGy by 26.4/39.4%, and V50cGy by 4.5/6.9%, respectively. pRF plans improved modeled CL survival across all models and subpopulations. Unstimulated CD4/CD8 CL had the largest SF differences, for which mean saturation-model SF improved by 7.9/8.6% for pRFCONV (p=0.03/0.02) and 9.6/10.4% for pRFFLASH (p=0.02/0.01), respectively. Conclusion: pRF plans improved modeled CL survival by significantly shortening delivery time and reducing irradiation of circulating blood. Our findings suggest that pRF's ultra-fast delivery may provide a practical strategy for immune sparing in proton lung SBRT.

physics.med-ph↗

arXiv2025

Clinically Interpretable Survival Risk Stratification in Head and Neck Cancer Using Bayesian Networks and Markov Blankets

Purpose: To identify a clinically interpretable subset of survival-relevant features in HN cancer using Bayesian Network (BN) and evaluate its prognostic and causal utility. Methods and Materials: We used the RADCURE dataset, consisting of 3,346 patients with H&N cancer treated with definitive (chemo)radiotherapy. A probabilistic BN was constructed to model dependencies among clinical, anatomical, and treatment variables. The Markov Blanket (MB) of two-year survival (SVy2) was extracted and used to train a logistic regression model. After excluding incomplete cases, a temporal split yielded a train/test (2,174/820) dataset using 2007 as the cutoff year. Model performance was assessed using area under the ROC curve (AUC), C-index, and Kaplan-Meier (KM) survival stratification. Model fit was further evaluated using a log-likelihood ratio (LLR) test. Causal inference was performed using do-calculus interventions on MB variables. Results: The MB of SVy2 included 6 clinically relevant features: ECOG performance status, T-stage, HPV status, disease site, the primary gross tumor volume (GTVp), and treatment modality. The model achieved an AUC of 0.65 and C-index of 0.78 on the test dataset, significantly stratifying patients into high- and low-risk groups (log-rank p < 0.01). Model fit was further supported by a log-likelihood ratio of 70.32 (p < 0.01). Subgroup analyses revealed strong performance in HPV-negative (AUC = 0.69, C-index = 0.76), T4 (AUC = 0.69, C-index = 0.80), and large-GTV (AUC = 0.67, C-index = 0.75) cohorts, each showing significant KM separation. Causal analysis further supported the positive survival impact of ECOG 0, HPV-positive status, and chemoradiation. Conclusions: A compact, MB-derived BN model can robustly stratify survival risk in HN cancer. The model enables explainable prognostication and supports individualized decision-making across key clinical subgroups.

physics.med-ph↗

arXiv2024

Photon-Counting CT in Cancer Radiotherapy: Technological Advances and Clinical Benefits

Photon-counting computed tomography (PCCT) marks a significant advancement over conventional energy-integrating detector (EID) CT systems. This review highlights PCCT's superior spatial and contrast resolution, reduced radiation dose, and multi-energy imaging capabilities, which address key challenges in radiotherapy, such as accurate tumor delineation, precise dose calculation, and treatment response monitoring. PCCT's improved anatomical clarity enhances tumor targeting while minimizing damage to surrounding healthy tissues. Additionally, metal artifact reduction (MAR) and quantitative imaging capabilities optimize workflows, enabling adaptive radiotherapy and radiomics-driven personalized treatment. Emerging clinical applications in brachytherapy and radiopharmaceutical therapy (RPT) show promising outcomes, although challenges like high costs and limited software integration remain. With advancements in artificial intelligence (AI) and dedicated radiotherapy packages, PCCT is poised to transform precision, safety, and efficacy in cancer radiotherapy, marking it as a pivotal technology for future clinical practice.

physics.med-ph↗