arXiv subjects

Ali Ajdari

Publications and source records attributed to Ali Ajdari.

5 recordsLinked to original sources

arXiv2026

Bi-level Multi-criteria Optimization for Risk-informed Radiotherapy

In radiation therapy (RT) treatment planning, multi-criteria optimization (MCO) supports efficient plan selection but is usually solved for population-based dosimetric criteria and ignores patient-specific biological risk, potentially compromising outcomes in high-risk patients. We propose risk-guided MCO, a one-shot method that embeds a clinical risk model into conventional MCO, enabling interactive navigation between dosimetric and biological endpoints. The proposed algorithm uses a special order relation to fuse the classical MCO sandwiching algorithm with bi-level optimization, restricting the Pareto set to plans that achieve improvement in the secondary risk objective for user-defined, acceptable loss in primary clinical objectives. Thus, risk-guided MCO generates risk-optimized counterparts of clinical plans in a single run rather than by sequential or lexicographic planning. To assess the performance, we retrospectively analyzed 19 lung cancer patients treated with RT. The endpoint was the risk of grade 2+ radiation pneumonitis (RP), modeled using bootstrapped stepwise logistics regression with interaction terms, including baseline lung function, smoking history, and dosimetric factors. The risk-guided plans yielded a mean reduction of 8.0% in total lung V20 and 9.5% in right lung V5, translating into an average RP risk reduction of 7.7% (range=0.3%-20.1%), with small changes in target coverage (mean -1.2 D98[%] for CTV) and modest increase in heart dose (mean +1.74 Gy). This study presents the first proof-of-concept for integrating biological risk models directly within multi-criteria RT planning, enabling an interactive balance between established population-wide dose protocols and individualized outcome prediction. Our results demonstrate that the risk-informed MCO can reduce the risk of RP while maintaining target coverage.

physics.med-ph↗

arXiv2025

Ising energy model for the stochastic prediction of tumor islets

A major challenge in diagnosing and treating cancer is the infiltrative growth of tumors into surrounding tissues. This microscopic spread of the disease is invisible on most diagnostic imaging modalities and can often only be detected histologically in biopsies. The purpose of this paper is to develop a physically based model of tumor spread that captures the histologically observed behavior in terms of seeding small tumor islets in prostate cancer. The model is based on three elementary events: a tumor cell can move, duplicate, or die. The propensity of each event is given by an Ising-like Hamiltonian that captures correlations between neighboring cells. The model parameters were fitted to clinical data obtained from surgical specimens taken from 23 prostate cancer patients. The results demonstrate that this straightforward physical model effectively describes the distribution of the size and the number of tumor islets in prostate cancer. The simulated tumor islets exhibit a regular, approximately spherical shape, correctly mimicking the shapes observed in histology. This is due to the Ising interaction term between neighboring cells acting as a surface tension that gives rise to regularly shaped islets. The model addresses the important clinical need of calculating the probability of tumor involvement in specific sub-volumes of the prostate, which is required for radiation treatment planning and other applications.

physics.med-ph↗

arXiv2025

A K-adaptability Approach to Proton Radiation Therapy Robust Treatment Planning

Uncertainties such as setup and range errors can significantly compromise proton therapy. A discrete uncertainty set is often constructed to represent different uncertainty scenarios. A min-max robust optimization approach is then utilized to optimize the worst-case performance of a radiation therapy plan against the uncertainty set. However, the min-max approach can be too conservative as a single plan has to account for the entire uncertainty set. K-adaptability is a novel approach to robust optimization which covers the uncertainty set with multiple (K) solutions, reducing the conservativeness. Solving K-adaptability to optimality is known to be computationally intractable. To that end, we developed a novel and efficient K-adaptability heuristic that iteratively clusters the scenarios based on plan-scenario performance for the proton radiation therapy planning problem. Compared to the conventional robust solution, the developed K-adaptability heuristic increased the worst-case CTV Dmin dose up to 4.52 Gy on average across five head and neck patients. The developed heuristic also demonstrated its superiority in objective value and time-efficiency compared to the competing methods we tested.

math.OC↗

arXiv2021

Robust fractionation in cancer radiotherapy

In cancer radiotherapy, the standard formulation of the optimal fractionation problem based on the linear-quadratic dose-response model is a non-convex quadratically constrained quadratic program (QCQP). An optimal solution for this QCQP can be derived by solving a two-variable linear program. Feasibility of this solution, however, crucially depends on the so-called alphaover-beta ratios for the organs-at-risk, whose true values are unknown. Consequently, the dosing schedule presumed optimal, in fact, may not even be feasible in practice. We address this by proposing a robust counterpart of the nominal formulation. We show that a robust solution can be derived by solving a small number of two-variable linear programs, each with a small number of constraints. We quantify the price of robustness, and compare the incidence and extent of infeasibility of the nominal and robust solutions via numerical experiments.

math.OC↗

arXiv2021

Adjustable robust treatment-length optimization in radiation therapy

Traditionally, optimization of radiation therapy (RT) treatment plans has been done before the initiation of RT course, using population-wide estimates for patients' response to therapy. However, recent technological advancements have enabled monitoring individual patient response during the RT course, in the form of biomarkers. Although biomarker data remains subject to substantial uncertainties, information extracted from this data may allow the RT plan to be adapted in a biologically informative way. We present a mathematical framework that optimally adapts the treatment-length of an RT plan based on the acquired mid-treatment biomarker information, while accounting for the inexact nature of this information. We formulate the adaptive treatment-length optimization problem as a 2-stage problem, wherein the information about the model parameters gathered during the first stage influences the decisions in the second stage. Using Adjustable Robust Optimization (ARO) techniques we derive explicit optimal decision rules for the stage-2 decisions and solve the optimization problem. The problem allows for multiple worst-case optimal solutions. To discriminate between these, we introduce the concept of Pareto Adjustable Robustly Optimal (PARO) solutions. In numerical experiments using lung cancer patient data, the ARO method is benchmarked against several other static and adaptive methods. In case of exact biomarker information, there is sufficient space to adapt, and numerical results show that taking into account both robustness and adaptability is not necessary. In case of inexact biomarker information, accounting for adaptability and inexactness of biomarker information is particularly beneficial when robustness (w.r.t. organ-at-risk (OAR) constraint violations) is of high importance. If minor OAR violations are allowed, a nominal folding horizon approach is a good performing alternative.

physics.med-ph↗