Risk of Cerebrovascular Events Among Childhood and Adolescent Patients Receiving Cranial Radiation Therapy: A Pediatric Normal Tissue Effects in the Clinic Normal Tissue Outcomes Comprehensive Review

PURPOSE

Radiation-induced cerebrovascular toxicity is a well-documented sequelae that can be both life-altering and potentially fatal. We performed a meta-analysis of the relevant literature to create practical models for predicting the risk of cerebral vasculopathy after cranial irradiation.

METHODS AND MATERIALS

A literature search was performed for studies reporting pediatric radiation therapy (RT) associated cerebral vasculopathy. When available, we used individual patient RT doses delivered to the Circle of Willis (CW) or optic chiasm (as a surrogate), as reported or digitized from original publications, to formulate a dose-response. A logistic fit and a Normal Tissue Complication Probability (NTCP) model was developed to predict future risk of cerebrovascular toxicity and stroke, respectively. This NTCP risk was assessed as a function of prescribed dose.

RESULTS

The search identified 766 abstracts, 5 of which were used for modeling. We identified 101 of 3989 pediatric patients who experienced at least one cerebrovascular toxicity: transient ischemic attack, stroke, moyamoya, or arteriopathy. For a range of shorter follow-ups, as specified in the original publications (approximate attained ages of 17 years), our logistic fit model predicted the incidence of any cerebrovascular toxicity as a function of dose to the CW, or surrogate structure: 0.2% at 30 Gy, 1.3% at 45 Gy, and 4.4% at 54 Gy. At an attained age of 35 years, our NTCP model predicted a stroke incidence of 0.9% to 1.3%, 1.8% to 2.7%, and 2.8% to 4.1%, respectively at prescribed doses of 30 Gy, 45 Gy, and 54 Gy (compared with a baseline risk of 0.2%-0.3%). At an attained age of 45 years, the predicted incidence of stroke was 2.1% to 4.2%, 4.5% to 8.6%, and 6.7% to 13.0%, respectively at prescribed doses of 30 Gy, 45 Gy, and 54 Gy (compared with a baseline risk of 0.5%-1.0%).

CONCLUSIONS

Risk of cerebrovascular toxicity continues to increase with longer follow-up. NTCP stroke predictions are very sensitive to model variables (baseline stroke risk and proportional stroke hazard), both of which found in the literature may be systematically erring on minimization of true risk. We hope this information will assist practitioners in counseling, screening, surveilling, and facilitating risk reduction of RT-related cerebrovascular late effects in this highly sensitive population.

Proton Arc Therapy vs Interstitial HDR Brachytherapy in Gynecologic Cancer with Parametrial/pelvic Side Wall Extension

Purpose

To investigate whether volumetric-modulated proton arc therapy (VPAT) plans generate comparable doses to organs at risk (OARs) compared with interstitial high–dose-rate (iHDR) brachytherapy for patients with gynecologic cancer with disease extension to parametrial/pelvic side wall, who are not eligible for the aggressive procedure.

Materials and Methods

VPAT delivers proton arc beams by modulated energies at the beam nozzle while maintaining the same incident energy to the gantry during the arc rotation. Plans of 10 patients previously treated with iHDR brachytherapy for high-risk clinical treatment volumes (HRCTV; 31.8–110.6 cm³; lateral dimensions, 4.2–5.6 cm) were selected and compared with VPAT plans. VPAT plans for each patient were designed using a 152- to 245-MeV range of energy-modulated proton beams.

Results

HRCTV coverage of the VPAT plans was comparable to that of the iHDR plans, with V150% showing no statistical differences. On average, the V100% and V90% of VPAT plans were higher than those of the iHDR plans, 95.0% vs 91.9% (P = .02) and 98.6% vs 97.5% (P = .02), respectively. D100 was also 17% higher for the VPAT plans (P = .03). On average, the D_2cm³ of bladder, rectum, and small bowels in the VPAT plans were considerably lower than those in iHDR plans (by 17.4%, 35.2%, and 65.6%, respectively; P < .05 for all OARs).

Conclusion

VPAT–generated plans were dosimetrically superior to those with HDR brachytherapy with interstitial needles for locally advanced gynecologic cancer with parametrial/pelvic side wall disease extension. Dosimetrically, VPAT provides a noninvasive alternative to iHDR brachytherapy with a superior dosimetric profile.

Combining Serial and Parallel Functionality in Functional Lung Avoidance Radiation Therapy

PURPOSE

Functional lung avoidance (FLA) radiation therapy (RT) aims to minimize post-RT pulmonary toxicity by preferentially avoiding dose to high-functioning lung (HFL) regions. A common limitation is that FLA approaches do not consider the conducting architecture for gas exchange. We previously proposed the functionally weighted airway sparing (FWAS) method to spare airways connected to HFL regions, showing that it is possible to substantially reduce risk of radiation-induced airway injury. Here, we compare the performance of FLA and FWAS and propose a novel method combining both approaches.

METHODS

We used breath-hold computed tomography (BHCT) and simulation 4-dimensional computed tomography (4DCT) from 12 lung stereotactic ablative radiation therapy patients. Four planning strategies were examined: (1) Conventional: no sparing other than clinical dose-volume constraints; (2) FLA: using a 4DCT-based ventilation map to delineate the HFL, plans were optimized to reduce mean dose and V_13.50 in HFL; (3) FWAS: we autosegemented 11 to 13 generations of individual airways from each patient's BHCT and assigned priorities based on the relative contribution of each airway to total ventilation. We used these priorities in the optimization along with airway dose constraints, estimated as a function of airway diameter and 5% probability of collapse; and (4) FLA + FWAS: we combined information from the 2 strategies. We prioritized clinical dose constraints for organs at risk and planning target volume in all plans. We performed the evaluation in terms of ventilation preservation accounting for radiation-induced damage to both lung parenchyma and airways.

RESULTS

We observed average ventilation preservation for FLA, FWAS, and FLA + FWAS as 3%, 8.5%, and 14.5% higher, respectively, than for Conventional plans for patients with ventilation preservation in Conventional plans <90%. Generalized estimated equations showed that all improvements were statistically significant (P ≤ .036). We observed no clinically relevant improvements in outcomes of the sparing techniques in patients with ventilation preservation in Conventional plans ≥90%.

CONCLUSIONS

These initial results suggest that it is crucial to consider the parallel and the serial nature of the lung to improve post-radiation therapy lung function and, consequently, quality of life for patients.

Outcome-based multiobjective optimization of lymphoma radiation therapy plans

At its core, radiation therapy (RT) requires balancing therapeutic effects against risk of adverse events in cancer survivors. The radiation oncologist weighs numerous disease and patient-level factors when considering the expected risk-benefit ratio of combined treatment modalities. As part of this, RT plan optimization software is used to find a clinically acceptable RT plan delivering a prescribed dose to the target volume while respecting pre-defined radiation dose-volume constraints for selected organs at risk. The obvious limitation to the current approach is that it is virtually impossible to ensure the selected treatment plan could not be bettered by an alternative plan providing improved disease control and/or reduced risk of adverse events in this individual. Outcome-based optimization refers to a strategy where all planning objectives are defined by modeled estimates of a specific outcome's probability. Noting that various adverse events and disease control are generally incommensurable, leads to the concept of a Pareto-optimal plan: a plan where no single objective can be improved without degrading one or more of the remaining objectives. Further benefits of outcome-based multiobjective optimization are that quantitative estimates of risks and benefit are obtained as are the effects of choosing a different trade-off between competing objectives. Furthermore, patient-level risk factors and combined treatment modalities may be integrated directly into plan optimization. Here, we present this approach in the clinical setting of multimodality therapy for malignant lymphoma, a malignancy with marked heterogeneity in biology, target localization, and patient characteristics. We discuss future research priorities including the potential of artificial intelligence.

Virtual bronchoscopy-guided lung SAbR: dosimetric implications of using AAA versus Acuros XB to calculate dose in airways

In previous works, we showed that incorporating individual airways as organs-at-risk (OARs) in the treatment of lung stereotactic ablative radiotherapy (SAbR) patients potentially mitigates post-SAbR radiation injury. However, the performance of common clinical dose calculation algorithms in airways has not been thoroughly studied. Airways are of particular concern because their small size and the density differences they create have the potential to hinder dose calculation accuracy. To address this gap in knowledge, here we investigate dosimetric accuracy in airways of two commonly used dose calculation algorithms, the anisotropic analytical algorithm (AAA) and Acuros-XB (AXB), recreating clinical treatment plans on a cohort of four SAbR patients. A virtual bronchoscopy software was used to delineate 856 airways on a high-resolution breath-hold CT (BHCT) image acquired for each patient. The planning target volumes (PTVs) and standard thoracic OARs were contoured on an average CT (AVG) image over the breathing cycle. Conformal and intensity-modulated radiation therapy plans were recreated on the BHCT image and on the AVG image, for a total of four plan types per patient. Dose calculations were performed using AAA and AXB, and the differences in maximum and mean dose in each structure were calculated. The median differences in maximum dose among all airways were ≤0.3Gy in magnitude for all four plan types. With airways grouped by dose-to-structure or diameter, median dose differences were still ≤0.5Gy in magnitude, with no clear dependence on airway size. These results, along with our previous airway radiosensitivity works, suggest that dose differences between AAA and AXB correspond to an airway collapse variation ≤0.7% in magnitude. This variation in airway injury risk can be considered as not clinically relevant, and the use of either AAA or AXB is therefore appropriate when including patient airways as individual OARs so as to reduce risk of radiation-induced lung toxicity.

Functionally weighted airway sparing (FWAS): a functional avoidance method for preserving post-treatment ventilation in lung radiotherapy

Recent changes to the guidelines for screening and early diagnosis of lung cancer have increased the interest in preserving post-radiotherapy lung function. Current investigational approaches are based on spatially mapping functional regions and generating regional avoidance plans that preferentially spare highly ventilated/perfused lung. A potentially critical, yet overlooked, aspect of functional avoidance is radiation injury to peripheral airways, which serve as gas conduits to and from functional lung regions. Dose redistribution based solely on regional function may cause irreparable damage to the 'supply chain'. To address this deficiency, we propose the functionally weighted airway sparing (FWAS) method. FWAS (i) maps the bronchial pathways to each functional sub-lobar lung volume; (ii) assigns a weighting factor to each airway based on the relative contribution of the sub-volume to overall lung function; and (iii) creates a treatment plan that aims to preserve these functional pathways. To evaluate it, we used four cases from a retrospective cohort of SAbR patients treated for lung cancer. Each patient's airways were auto-segmented from a diagnostic-quality breath-hold CT using a research virtual bronchoscopy software. A ventilation map was generated from the planning 4DCT to map regional lung function. For each terminal airway, as resolved by the segmentation software, the total ventilation within the sub-lobar volume supported by that airway was estimated and used as a function-based weighting factor. Upstream airways were weighted based on the cumulative volumetric ventilation supported by corresponding downstream airways. Using a previously developed model for airway radiosensitivity, dose constraints were determined for each airway corresponding to a <5% probability of airway collapse. Airway dose constraints, ventilation scores, and clinical dose constraints were input to a swarm optimization-based inverse planning engine to create a 3D conformal SAbR plan (CRT). The FWAS plans were compared to the patients' prescribed CRT clinical plans and the inverse-optimized clinical plans. Depending on the size and location of the tumour, the FWAS plan showed superior preservation of ventilation due to airflow preservation through open pathways (i.e. cumulative ventilation score from the sub-lobar volumes of open pathways). Improvements ranged between 3% and 23%, when comparing to the prescribed clinical plans, and between 3% and 35%, when comparing to the inverse-optimized clinical plans. The three plans satisfied clinical requirements for PTV coverage and OAR dose constraints. These initial results suggest that by sparing pathways to high-functioning lung subregions it is possible to reduce post-SAbR loss of respiratory function.

Biological optimization for mediastinal lymphoma radiotherapy - a preliminary study

Purpose: In current radiotherapy (RT) planning and delivery, population-based dose-volume constraints are used to limit the risk of toxicity from incidental irradiation of organs at risks (OARs). However, weighing tradeoffs between target coverage and doses to OARs (or prioritizing different OARs) in a quantitative way for each patient is challenging. We introduce a novel RT planning approach for patients with mediastinal Hodgkin lymphoma (HL) that aims to maximize overall outcome for each patient by optimizing on tumor control and mortality from late effects simultaneously.Material and Methods: We retrospectively analyzed 34 HL patients treated with conformal RT (3DCRT). We used published data to model recurrence and radiation-induced mortality from coronary heart disease and secondary lung and breast cancers. Patient-specific doses to the heart, lung, breast, and target were incorporated in the models as well as age, sex, and cardiac risk factors (CRFs). A preliminary plan of candidate beams was created for each patient in a commercial treatment planning system. From these candidate beams, outcome-optimized (O-OPT) plans for each patient were created with an in-house optimization code that minimized the individual risk of recurrence and mortality from late effects. O-OPT plans were compared to VMAT plans and clinical 3DCRT plans.Results: O-OPT plans generally had the lowest risk, followed by the clinical 3DCRT plans, then the VMAT plans with the highest risk with median (maximum) total risk values of 4.9 (11.1), 5.1 (17.7), and 7.6 (20.3)%, respectively (no CRFs). Compared to clinical 3DCRT plans, O-OPT planning reduced the total risk by at least 1% for 9/34 cases assuming no CRFs and 11/34 cases assuming presence of CRFs.Conclusions: We developed an individualized, outcome-optimized planning technique for HL. Some of the resulting plans were substantially different from clinical plans. The results varied depending on how risk models were defined or prioritized.

The emergence of Q1 genotype of avian infectious bronchitis virus in Iran, 2019: the first report

BACKGROUND

Avian infectious bronchitis (IB) is an infectious viral disease of chickens. The effective protection of chickens against many different infectious bronchitis virus (IBV) variants is not achieved unless the circulating genotypes in the region are identified and the cross-protection of the potential of vaccines in use is assessed.

AIMS

In a monitoring program of IBVs, a new genotype was identified in the north of Iran, 2019. This work was conducted to isolate and characterize this new IBV genotype.

METHODS

Tracheal tissues were collected from chickens showing signs of respiratory involvement. Specimens were homogenized and inoculated to the allantoic fluid of embryonated specific pathogen-free (SPF) eggs. Infectious bronchitis virus was detected using real time-polymerase chain reaction (RT-PCR). The hypervariable region of the IBV S1 gene was amplified for sequencing.

RESULTS

Positive samples were phylogenetically analyzed, and both positive isolates were clustered with Q1 IBV strains.

CONCLUSION

This is the first report of the Q1 outbreak in Iran. More investigations are needed to find the role of Q1 IBV in the respiratory disease complex of chickens.

Accounting for respiratory motion in small serial structures during radiotherapy planning: proof of concept in virtual bronchoscopy-guided lung functional avoidance radiotherapy

Respiratory motion management techniques in radiotherapy (RT) planning are primarily focused on maintaining tumor target coverage. An inadequately addressed need is accounting for motion in dosimetric estimations in smaller serial structures. Accurate dose estimations in such structures are more sensitive to motion because respiration can cause them to move completely in or out of a high dose-gradient field. In this work, we study three motion management strategies (m1-m3) to find an accurate method to estimate the dosimetry in airways. To validate these methods, we generated a 'ground truth' digital breathing model based on a 4DCT scan from a lung stereotactic ablative radiotherapy (SAbR) patient. We simulated 225 breathing cycles with  ±10% perturbations in amplitude, respiratory period, and time per respiratory phase. A high-resolution breath-hold CT (BHCT) was also acquired and used with a research virtual bronchoscopy software to autosegment 239 airways. Contours for planning target volume (PTV) and organs at risk (OARs) were defined on the maximum intensity projection of the 4DCT (CT_MIP) and transferred to the average of the 10 4DCT phases (CT_AVG). To design the motion management methods, the RT plan was recreated using different images and structure definitions. Methods m1 and m2 recreated the plan using the CT_AVG image. In method m1, airways were deformed to the CT_AVG. In m2, airways were deformed to each of the 4DCT phases, and union structures were transferred onto the CT_AVG. In m3, the RT plan was recreated on each of the 10 phases, and the dose distribution from each phase was deformed to the BHCT and summed. Dose errors (mean [min, max]) in airways were: m1: 21% (0.001%, 93%); m2: 45% (0.1%, 179%); and m3: 4% (0.006%, 14%). Our work suggests that accurate dose estimation in moving small serial structures requires customized motion management techniques (like m3 in this work) rather than current clinical and investigational approaches.

Inverse radiotherapy planning based on bioeffect modelling for locally advanced left-sided breast cancer

BACKGROUND AND PURPOSE

Treatment planning of radiotherapy (RT) for left-sided breast cancer is a challenging case. Several competing concerns are incorporated at present through protocol-defined dose-volume constraints, e.g. cardiac exposure and target coverage. Such constraints are limited by neglecting patient-specific risk factors (RFs). We propose an alternative RT planning method based solely on bioeffect models to minimize the estimated risks of breast cancer recurrence (BCR) and radiation-induced mortality endpoints considering patient-specific factors.

METHODS AND MATERIALS

Thirty-nine patients with left-sided breast cancer treated with comprehensive post-lumpectomy loco-regional conformal RT were included. An in-house particle swarm optimization (PSO) engine was used to choose fields from a large set of predefined fields and optimize monitor units to minimize the total risk of BCR and mortality caused by radiation-induced ischaemic heart disease (IHD), secondary lung cancer (SLC) and secondary breast cancer (SBC). Risk models included patient age, smoking status and cardiac risk and were developed using published multi-institutional data.

RESULTS

For the clinical plans the normal tissue complication probability, i.e. summed risk of IHD, SLC and SBC, was <3.7% and the risk of BCR was <6.1% for all patients. Median total decrease in mortality or recurrence achieved with individualized PSO plans was 0.4% (range, 0.06-2.0%)/0.5% (range, 0.11-2.2%) without/with risk factors.

CONCLUSIONS

Inverse RT plan optimization using bioeffect probability models allows individualization according to patient-specific risk factors. The modelled benefit when compared to clinical plans is, however, modest in most patients, demonstrating that current clinical plans are close to optimal. Larger gains may be achievable with morbidity endpoints rather than mortality.

Inverse-planned deliverable 4D-IMRT for lung SBRT

PURPOSE

We present a particle swarm optimization (PSO)-based technique to create deliverable four-dimensional (4D = 3D + time) intensity-modulated radiation therapy (IMRT) plans for lung stereotactic body radiotherapy (SBRT). The 4D planning concept uses respiratory motion as an additional degree of freedom to achieve further sparing of organs at risk (OARs). The 4D-IMRT plan involves the delivery of an order of magnitude more IMRT apertures (~15,000-20,000), with potentially large interaperture variations in the delivered fluence, compared to conventional (i.e., 3D) IMRT. In order to deliver the 4D plan in an efficient manner, we present an optimization-based aperture sequencing technique.

METHOD

A graphic processing unit (GPU)-enabled PSO-based inverse planning engine, developed and integrated with a research version of the Eclipse (Varian, Palo Alto, CA) treatment planning system (TPS), was employed to create 4D-IMRT plans as follows. Four-dimensional computed tomography scans (4DCTs) and beam configurations from clinical treatment plans of seven lung cancer patients were retrospectively collected, and in each case, the PSO engine iteratively adjusted aperture monitor unit (MU) weights for all beam apertures across all respiratory phases to optimize OAR dose sparing while maintaining planning target volume (PTV) coverage. We calculated the transition times from each aperture to all other apertures for each beam, taking into account the maximum leaf velocity of the multileaf collimator (MLC), and developed a mixed integer optimization technique for aperture sequencing. The goal of sequencing was to maximize delivery efficiency (i.e., minimize the time required to deliver the dose map) by accounting for leaf velocity, aperture MUs, and duration of each respiratory phase. The efficiency of the proposed delivery method was compared with that of a greedy algorithm which chose only from neighboring apertures for the subsequent steps in the sequence.

RESULTS

4D-IMRT-optimized plans achieved PTV coverage comparable to clinical plans while improving OAR sparing by an average of 39.7% for D max heart, 20.5% for D max esophagus, 25.6% for D max spinal cord, and 2.1% for V 13 lung (with D max standing for maximum dose and V 13 standing for volume receiving ≥ 13 Gy). Our mixed integer optimization-based aperture sequencing enabled the delivery to be performed in fewer cycles compared to the greedy method. This reduction was 89 ± 79 cycles corresponding to an improvement of 15.94 ± 8.01%, when considering respiratory cycle duration of 4 s, and 55 ± 33 cycles corresponding to an improvement of 15.14 ± 4.45%, when considering respiratory cycle duration of 6 s.

CONCLUSION

PSO-based 4D-IMRT represents an attractive technique to further improve OAR sparing in lung SBRT. Efficient delivery of a large number of sparse apertures (control points) introduces a challenge in 4D-IMRT treatment planning and delivery. Through judicious optimization of the aperture sequence across all phases, such delivery can be performed on a clinically feasible time scale.

Virtual Bronchoscopy-Guided Treatment Planning to Map and Mitigate Radiation-Induced Airway Injury in Lung SAbR

PURPOSE

Radiation injury to the bronchial tree is an important yet poorly understood potential side effect in lung stereotactic ablative radiation therapy (SAbR). We investigate the integration of virtual bronchoscopy in radiation therapy planning to quantify dosage to individual airways. We develop a risk model of airway collapse and develop treatment plans that reduce the risk of radiation-induced airway injury.

METHODS AND MATERIALS

Pre- and post-SAbR diagnostic-quality computerized tomography (CT) scans were retrospectively collected from 26 lung cancer patients. From each scan, the bronchial tree was segmented using a virtual bronchoscopy system and registered deformably to the planning CT. Univariate and stepwise multivariate Cox regressions were performed, examining factors such as age, comorbidities, smoking pack years, airway diameter, and maximum point dosage (D_max). Logistic regression was utilized to formulate a risk function of segmental collapse based on D_max and diameter. The risk function was incorporated into the objective function along with clinical dosage volume constraints for planning target volume (PTV) and organs at risk (OARs).

RESULTS

Univariate analysis showed that segmental diameter (P = .014) and D_max (P = .007) were significantly correlated with airway segment collapse. Multivariate stepwise Cox regression showed that diameter (P = .015), D_max (P < .0001), and pack/years of smoking (P = .02) were significant independent factors associated with collapse. Risk management-based plans enabled significant dosage reduction to individual airway segments while fulfilling clinical dosimetric objectives.

CONCLUSION

To our knowledge, this is the first systematic investigation of functional avoidance in lung SAbR based on mapping and minimizing doses to individual bronchial segments. Our early results show that it is possible to substantially lower airway dosage. Such dosage reduction may potentially reduce the risk of radiation-induced airway injury, while satisfying clinically prescribed dosimetric objectives.

Multi-GPU configuration of 4D intensity modulated radiation therapy inverse planning using global optimization

We report on the design, implementation and characterization of a multi-graphic processing unit (GPU) computational platform for higher-order optimization in radiotherapy treatment planning. In collaboration with a commercial vendor (Varian Medical Systems, Palo Alto, CA), a research prototype GPU-enabled Eclipse (V13.6) workstation was configured. The hardware consisted of dual 8-core Xeon processors, 256 GB RAM and four NVIDIA Tesla K80 general purpose GPUs. We demonstrate the utility of this platform for large radiotherapy optimization problems through the development and characterization of a parallelized particle swarm optimization (PSO) four dimensional (4D) intensity modulated radiation therapy (IMRT) technique. The PSO engine was coupled to the Eclipse treatment planning system via a vendor-provided scripting interface. Specific challenges addressed in this implementation were (i) data management and (ii) non-uniform memory access (NUMA). For the former, we alternated between parameters over which the computation process was parallelized. For the latter, we reduced the amount of data required to be transferred over the NUMA bridge. The datasets examined in this study were approximately 300 GB in size, including 4D computed tomography images, anatomical structure contours and dose deposition matrices. For evaluation, we created a 4D-IMRT treatment plan for one lung cancer patient and analyzed computation speed while varying several parameters (number of respiratory phases, GPUs, PSO particles, and data matrix sizes). The optimized 4D-IMRT plan enhanced sparing of organs at risk by an average reduction of [Formula: see text] in maximum dose, compared to the clinical optimized IMRT plan, where the internal target volume was used. We validated our computation time analyses in two additional cases. The computation speed in our implementation did not monotonically increase with the number of GPUs. The optimal number of GPUs (five, in our study) is directly related to the hardware specifications. The optimization process took 35 min using 50 PSO particles, 25 iterations and 5 GPUs.

Review of breast screening: Toward clinical realization of microwave imaging

Microwave imaging (MI) technology has come a long way to introduce a noninvasive, inexpensive, fast, convenient, and safe screening tool for clinical breast monitoring. However, there is a niche between the existing understanding of MI by engineers versus clinicians. Our manuscript targets that niche and highlights the state of the art in MI technology compared to the existing breast cancer detection modalities (mammography, ultrasound, molecular imaging, and magnetic resonance). The significance of our review article is in consolidation of up-to-date breast clinician views with the practical needs and engineering challenges of a novel breast screening modality. We summarize breast tissue abnormalities and highlight the benefits as well as potential drawbacks of the MI as a cancer detection methodology. Our goal is to present an article that MI researchers as well as practitioners in the field can use to assess the viability of the MI technology as a competing or complementary modality to the existing means of breast cancer screening.

Inversed-Planned Respiratory Phase Gating in Lung Conformal Radiation Therapy

PURPOSE

To assess whether the optimal gating window for each beam during lung radiation therapy with respiratory gating will be dependent on a variety of patient-specific factors, such as tumor size and location and the extent of relative tumor and organ motion.

METHODS AND MATERIALS

To create optimal gating treatment plans, we started from an optimized clinical plan, created a plan per respiratory phase using the same beam arrangements, and used an inverse planning optimization approach to determine the optimal gating window for each beam and optimal beam weights (ie, monitor units). Two pieces of information were used for optimization: (1) the state of the anatomy at each phase, extracted from 4-dimensional computed tomography scans; and (2) the time spent in each state, estimated from a 2-minute monitoring of the patient's breathing motion. We retrospectively studied 15 lung cancer patients clinically treated by hypofractionated conformal radiation therapy, for whom 45 to 60 Gy was administered over 3 to 15 fractions using 7 to 13 beams. Mean gross tumor volume and respiratory-induced tumor motion were 82.5 cm³ and 1.0 cm, respectively.

RESULTS

Although patients spent most of their respiratory cycle in end-exhalation (EE), our optimal gating plans used EE for only 34% of the beams. Using optimal gating, maximum and mean doses to the esophagus, heart, and spinal cord were reduced by an average of 15% to 26%, and the beam-on times were reduced by an average of 23% compared with equivalent single-phase EE gated plans (P<.034, paired 2-tailed t test).

CONCLUSIONS

We introduce a personalized respiratory-gating technique in which inverse planning optimization is used to determine patient- and beam-specific gating phases toward enhancing dosimetric quality of radiation therapy treatment plans.

Inverse 4D conformal planning for lung SBRT using particle swarm optimization

A critical aspect of highly potent regimens such as lung stereotactic body radiation therapy (SBRT) is to avoid collateral toxicity while achieving planning target volume (PTV) coverage. In this work, we describe four dimensional conformal radiotherapy using a highly parallelizable swarm intelligence-based stochastic optimization technique. Conventional lung CRT-SBRT uses a 4DCT to create an internal target volume and then, using forward-planning, generates a 3D conformal plan. In contrast, we investigate an inverse-planning strategy that uses 4DCT data to create a 4D conformal plan, which is optimized across the three spatial dimensions (3D) as well as time, as represented by the respiratory phase. The key idea is to use respiratory motion as an additional degree of freedom. We iteratively adjust fluence weights for all beam apertures across all respiratory phases considering OAR sparing, PTV coverage and delivery efficiency. To demonstrate proof-of-concept, five non-small-cell lung cancer SBRT patients were retrospectively studied. The 4D optimized plans achieved PTV coverage comparable to the corresponding clinically delivered plans while showing significantly superior OAR sparing ranging from 26% to 83% for D max heart, 10%-41% for D max esophagus, 31%-68% for D max spinal cord and 7%-32% for V 13 lung.

Radiotherapy Planning Using an Improved Search Strategy in Particle Swarm Optimization

OBJECTIVE

Evolutionary stochastic global optimization algorithms are widely used in large-scale, nonconvex problems. However, enhancing the search efficiency and repeatability of these techniques often requires well-customized approaches. This study investigates one such approach.

METHODS

We use particle swarm optimization (PSO) algorithm to solve a 4D radiation therapy (RT) inverse planning problem, where the key idea is to use respiratory motion as an additional degree of freedom in lung cancer RT. The primary goal is to administer a lethal dose to the tumor target while sparing surrounding healthy tissue. Our optimization iteratively adjusts radiation fluence-weights for all beam apertures across all respiratory phases. We implement three PSO-based approaches: conventionally used unconstrained, hard-constrained, and our proposed virtual search. As proof of concept, five lung cancer patient cases are optimized over ten runs using each PSO approach. For comparison, a dynamically penalized likelihood (DPL) algorithm-a popular RT optimization technique is also implemented and used.

RESULTS

The proposed technique significantly improves the robustness to random initialization while requiring fewer iteration cycles to converge across all cases. DPL manages to find the global optimum in 2 out of 5 RT cases over significantly more iterations.

CONCLUSION

The proposed virtual search approach boosts the swarm search efficiency, and consequently, improves the optimization convergence rate and robustness for PSO.

SIGNIFICANCE

RT planning is a large-scale, nonconvex optimization problem, where finding optimal solutions in a clinically practical time is critical. Our proposed approach can potentially improve the optimization efficiency in similar time-sensitive problems.

A novel discrete particle swarm optimization algorithm for estimating dielectric constants of tissue

Global optimization algorithms basically create a set of solutions, classify them, and then search for the best answer, iteratively. In this paper, a new discrete particle swarm optimization algorithm is proposed to estimate the permittivity arrangements of lossy multilayer structures, which represent body tissue models. Microwave imaging (AMI) is the modality in which the proposed algorithm is used for reconstructing the image. The main objective of this article is to depict the flexibility of PSO-based methods in handling complex problems expeditiously and successfully. Our new algorithm improves the estimation time by 85% as compared to our previous proposed one. Here, the impact of various parameters, namely, the AMI frequency, the immersion medium, the number of agents, the smoothing coefficient, and the maximum velocity, on the estimation performance are studied in terms of the maximum estimation error. It is demonstrated that by choosing the parameters correctly, one can achieve estimation results with a maximum error less that 10% in only 0.1 minute.

Permittivity estimation for breast cancer detection using particle swarm optimization algorithm

In this paper, particle swarm optimization (PSO) algorithm is used to estimate the permittivities of the tissue layers at microwave frequency band. According to the literature, microwave radiometry (MWR) is potentially a promising cancer detection technique. In addition, breast cancer is an appropriate candidate of MWR due to the breast's exclusive physiology. Several algorithms have been evaluated for analyzing the measurement data and solving the inverse scattering problem in MWR, and different levels of accuracy have been reported. In this paper, the potential of PSO in solving this problem is demonstrated at 1-2.25 GHz. Two distinct algorithms are developed for the two considered scenarios. In the first scenario, we assume no a priori knowledge of the tissue under the test, whereas, in the second scenario, a priori knowledge is assumed. It is noteworthy that, there are only a few research articles studying PSO for permittivity estimation. However, since these studies underestimate the loss encountered by the test samples, the methods are not valid for body tissue case. Here, measurement-based loss coefficients, reported in the existing literature, are included in the calculations. It is shown that the algorithm converges relatively fast, and, distinguishes between different tissues with an acceptable accuracy.