VEGFC ameliorates salt-sensitive hypertension and hypertensive nephropathy by inhibiting NLRP3 inflammasome via activating VEGFR3-AMPK dependent autophagy pathway.

Salt-sensitivity hypertension (SSHTN) is an independent predictor for cardiovascular mortality. VEGFC has been reported to be a protective role in SSHTN and hypertensive kidney injury. However, the underlying mechanisms remain largely unclear. The current study aimed to explore the protective effects and mechanisms of VEGFC against SSHTN and hypertensive nephropathy. Here, we reported that VEGFC attenuated high blood pressure as well as protected against renal inflammation and fibrosis in SSHTN mice. Moreover, VEGFC suppressed the activation of renal NLRP3 inflammasome in SSHTN mice. In vitro, we found VEGFC inhibited NLRP3 inflammasome activation, meanwhile, upregulated autophagy in high-salt-induced macrophages, while these effects were reversed by an autophagy inhibitor 3MA. Furthermore, in vivo, 3MA pretreatment weakened the protective effects of VEGFC on SSHTN and hypertensive nephropathy. Mechanistically, VEGF receptor 3 (VEGFR3) kinase domain activated AMPK by promoting the phosphorylation at Thr183 via binding to AMPK, thus enhancing autophagy activity in the context of high-salt-induced macrophages. These findings indicated that VEGFC inhibited NLRP3 inflammasome activation by promoting VEGFR3-AMPK-dependent autophagy pathway in high-salt-induced macrophages, which provided a mechanistic basis for the therapeutic target in SSHTN and hypertensive kidney injury.

A novel AP/PA total body irradiation technique using abutting IMRT fields at extended SSD.

PURPOSE: To develop a Total Body Irradiation (TBI) technique using IMRT at extended SSD that can be performed in any size Linac room. METHODS: Patients studied were placed on a platform close to the floor, directly under the gantry with cranial-caudal axis parallel to the gantry rotation plane and at SSD ∼200 cm. Two abutting fields with the same external isocenter at gantry angles of ±21˚, collimator angle of 90˚, and field size of 25 × 40 cm2 are employed for both supine and prone positions. An iterative optimization algorithm was developed to generate a uniform dose at the patient mid-plane with adequate shielding to critical organs such as lungs and kidneys. The technique was validated in both phantom and patient CT images for treatment planning, and dose measurement and QA were performed in phantom. RESULTS: A uniform dose distribution in the mid-plane within ±5% of the prescription dose was reached after a few iterations. This was confirmed with ion-chamber measurements in phantom. The mean dose to lungs and kidneys can be adjusted according to clinical requirements and can be as low as ∼25% of the prescription dose. For a typical prescription dose of 200 cGy/fraction, the total MU was ∼2400/1200 for the superior/inferior field. The overall treatment time for both supine/prone positions was ∼54 min to meet the maximum absorbed dose rate criteria of 15 cGy/min. IMRT QA with portal dosimetry shows excellent agreement. CONCLUSIONS: We have developed a promising TBI technique using abutting IMRT fields at extended SSD. The patient is in a comfortable recumbent position with good reproducibility and less motion during treatment. An additional benefit of this technique is that full 3D dose distribution is available from the TPS with a DVH summary for organs of interest. The technique allows precise sparing of lungs and kidneys and can be executed in any linac room.

Long-term risk of carotid stenosis and cerebrovascular disease after radiation therapy for head and neck cancer.

BACKGROUND: Recipients of radiation therapy (RT) for head and neck cancer (HNC) are at significantly increased risk for carotid artery stenosis (CAS) and cerebrovascular disease (CVD). We sought to determine (1) cumulative incidences of CAS and CVD among HNC survivors after RT and (2) whether CAS is associated with a RT dose response effect. METHODS: This single-institution retrospective cohort study examined patients with nonmetastatic HNC who completed (chemo)RT from January 2000 through October 2020 and subsequently received carotid imaging surveillance ≤2 years following RT completion and, in the absence of CAS, every 3 years thereafter. Exclusion criteria included history of known CAS/CVD. Asymptomatic CAS was defined as ≥50% reduction of luminal diameter, symptomatic CAS as stroke or transient ischemic attack, and composite CAS as asymptomatic or symptomatic CAS. RESULTS: Of 628 patients undergoing curative intent RT for HNC, median follow-up was 4.8 years (interquartile range, 2.6-8.3), with 97 patients followed ≥10 years. Median age was 61 years and 69% of patients received concurrent chemotherapy and 28% were treated postoperatively. Actuarial 10-year incidences of asymptomatic, symptomatic, and composite CAS were 29.6% (95% CI, 23.9-35.5), 10.1% (95% CI, 7.0-13.9), and 27.2% (95% CI, 22.5-32.1), respectively. Multivariable Cox models significant association between asymptomatic CAS and absolute carotid artery volume receiving ≥10 Gy (per mL: hazard ratio, 1.09; 95% CI, 1.02-1.16). CONCLUSIONS: HNC survivors are at high risk for post-RT CAS. A dose response effect was observed for asymptomatic CAS at doses as low as 10 Gy. PLAIN LANGUAGE SUMMARY: Recipients of radiation therapy for head and neck cancer are at significantly increased risk for carotid artery stenosis and cerebrovascular disease. However, carotid artery screening is not routinely performed among head and neck survivors following radiation therapy. In this single-institution retrospective cohort study, patients with head and neck cancer were initially screened for carotid artery stenosis ≤2 years following radiation therapy completion, then every 3 years thereafter. The 10-year actuarial incidence of carotid artery stenosis was >25% and stroke/transient ischemic attack >10%. Multivariable analysis demonstrated significant associations between asymptomatic carotid artery stenosis and artery volumes receiving ≥10 Gy.

Multiparametric-MRI-Based Radiomics Model for Differentiating Primary Central Nervous System Lymphoma From Glioblastoma: Development and Cross-Vendor Validation.

BACKGROUND: Preoperative differentiation of primary central nervous system lymphoma (PCNSL) from glioblastoma (GBM) is important to guide neurosurgical decision-making. PURPOSE: To validate the generalization ability of radiomics models based on multiparametric-MRI (MP-MRI) for differentiating PCNSL from GBM. STUDY TYPE: Retrospective. POPULATION: In all, 240 patients with GBM (n = 129) or PCNSL (n = 111). FIELD STRENGTH/SEQUENCE: 3.0T scanners (two vendors). Sequences: fluid-attenuation inversion recovery, diffusion-weighted imaging (DWI), and contrast-enhanced T1 -weighted imaging (CE-T1 WI). Apparent diffusion coefficients (ADCs) were derived from DWI. ASSESSMENT: Cross-vendor and mixed-vendor validation were conducted. In cross-vendor validation, the training set was 149 patients' data from vendor 1, and test set was 91 patients' data from vendor 2. In mixed-vendor validation, a training set was 80% of data from both vendors, and the test set remained at 20% of data. Single and multisequence radiomics models were built. The diagnoses by radiologists with 5, 10, and 20 years' experience were obtained. The integrated models were built combining the diagnoses by the best-performing radiomics model and each radiologist. Model performance was validated in the test set using area under the ROC curve (AUC). Histological results were used as the reference standard. STATISTICAL TESTS: DeLong test: differences between AUCs. U-test: differences of numerical variables. Fisher's exact test: differences of categorical variables. RESULTS: In cross-vendor and mixed-vendor validation, the combination of CE-T1 WI and ADC produced the best-performing radiomics model, with AUC of 0.943 vs. 0.935, P = 0.854. The integrated models had higher AUCs than radiologists, with 5 (0.975 vs. 0.891, P = 0.002 and 0.995 vs. 0.885, P = 0.007), 10 (0.975 vs. 0.913, P = 0.029 and 0.995 vs. 0.900, P = 0.030), and 20 (0.975 vs. 0.945, P = 0.179 and 0.995 vs. 0.923, P = 0.046) years' experiences. DATA CONCLUSION: Radiomics for differentiating PCNSL from GBM was generalizable. The model combining MP-MRI and radiologists' diagnoses had superior performance compared to the radiologists alone. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 2.

Deep Learning for Automatic Differential Diagnosis of Primary Central Nervous System Lymphoma and Glioblastoma: Multi-Parametric Magnetic Resonance Imaging Based Convolutional Neural Network Model.

BACKGROUND: Differential diagnosis of primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM) is useful to guide treatment strategies. PURPOSE: To investigate the use of a convolutional neural network (CNN) model for differentiation of PCNSL and GBM without tumor delineation. STUDY TYPE: Retrospective. POPULATION: A total of 289 patients with PCNSL (136) or GBM (153) were included, the average age of the cohort was 54 years, and there were 173 men and 116 women. FIELD STRENGTH/SEQUENCE: 3.0 T Axial contrast-enhanced T1 -weighted spin-echo inversion recovery sequence (CE-T1 WI), T2 -weighted fluid-attenuation inversion recovery sequence (FLAIR), and diffusion weighted imaging (DWI, b = 0 second/mm2 , 1000 seconds/mm2 ). ASSESSMENT: A single-parametric CNN model was built using CE-T1 WI, FLAIR, and the apparent diffusion coefficient (ADC) map derived from DWI, respectively. A decision-level fusion based multi-parametric CNN model (DF-CNN) was built by combining the predictions of single-parametric CNN models through logistic regression. An image-level fusion based multi-parametric CNN model (IF-CNN) was built using the integrated multi-parametric MR images. The radiomics models were developed. The diagnoses by three radiologists with 6 years (junior radiologist Y.Y.), 11 years (intermediate-level radiologist Y.T.), and 21 years (senior radiologist Y.L.) of experience were obtained. STATISTICAL ANALYSIS: The 5-fold cross validation was used for model evaluation. The Pearson's chi-squared test was used to compare the accuracies. U-test and Fisher's exact test were used to compare clinical characteristics. RESULTS: The CE-T1 WI, FLAIR, and ADC based single-parametric CNN model had accuracy of 0.884, 0.782, and 0.700, respectively. The DF-CNN model had an accuracy of 0.899 which was higher than the IF-CNN model (0.830, P = 0.021), but had no significant difference in accuracy compared to the radiomics model (0.865, P = 0.255), and the senior radiologist (0.906, P = 0.886). DATA CONCLUSION: A CNN model can differentiate PCNSL from GBM without tumor delineation, and comparable to the radiomics models and radiologists. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 2.

Investigation of effect of filter on the stand-up technique for total skin irradiation by Monte Carlo simulation.

PURPOSE: The aim of this study was to investigate dosimetric effects of scattering filter on the stand-up technique for total skin irradiation (TSI) with a single electron field by Monte Carlo (MC) simulation. METHODS: MC simulations were performed with BEAMnrc and DOSXYZnrc packages under EGSnrc environment. Scattering filter of a metal disc was mounted in the accessory slot. The filter materials (Cu, Fe, Au, Zn, Ag) were investigated, with thickness ranging from 0.05 to 0.55 mm, depending on material. The extended source to skin distance (SSD) ranging from 250 to 350 cm was studied. The following dosimetric quantities were evaluated: percent depth dose (PDD), profiles and output factor at depth of maximum, and composite dose distribution on a 30-cm diameter cylindrical phantom. They were compared with the standard dual beam technique used at our clinic. The effects on different patient sizes were also studied. RESULTS: No filter produced acceptable profile flatness (±10% within the central 160 cm) at 250 cm SSD. At 300 cm SSD, Au (0.1 mm), Ag (0.25 mm), Cu (0.5 mm) produced acceptable flatness while Zn (0.45 mm) required 325 cm SSD. For these four configurations, the dmax was 0.90-0.99 cm, similar to dual beam (0.97 cm); R50 was 1.85-1.91 cm, compared with dual beam of 2.06 cm; the output factor ranged from 0.025 to 0.029, lower than the dual beam (0.080). With the composite fields for four configurations, the dmax was 0.10 cm, compared with dual beam (0.16 cm). The surface dose was 97%, similar to dual beam (96%). B-factor was 3.3-3.4, compared with dual beam of 3.1. The maximum X-ray contamination was 3%, higher than dual beam (1%). CONCLUSIONS: The investigation suggests the TSI stand-up technique can be implemented using a single electron beam if a customized filter is used. More dosimetric measurements are needed to validate the MC results and clinical implementation.

Validation of the dosimetry of total skin irradiation techniques by Monte Carlo simulation.

PURPOSE: To validate the dose measurements for two total skin irradiation techniques with Monte Carlo simulation, providing more information on dose distributions, and guidance on further technique optimization. METHODS: Two total skin irradiation techniques (stand-up and lay-down) with different setup were simulated and validated. The Monte Carlo simulation was primarily performed within the EGSnrc environment. Parameters of jaws, MLCs, and a customized copper (Cu) filter were first tuned to match the profiles and output measured at source-to-skin distance (SSD) of 100 cm where the secondary source is defined. The secondary source was rotated to simulate gantry rotation. VirtuaLinac, a cloud-based Monte Carlo package, was used for Linac head simulation as a secondary validation. The following quantities were compared with measurements: for each field/direction at the treatment SSDs, the percent depth dose (PDD), the profiles at the depth of maximum, and the absolute dosimetric output; the composite dose distribution on cylindrical phantoms of 20 to 40 cm diameters. RESULTS: Cu filter broadened the FWHM of the electron beam by 44% and degraded the mean energy by 0.7 MeV. At SSD = 100 cm, MC calculated PDDs agreed with measured data within 2%/2 mm (except for the surface voxel) and lateral profiles agreed within 3%. At the treatment SSD, profiles and output factors of individual field matched within 4%; dmax and R80 of the simulated PDDs also matched with measurement within 2 mm. When all fields were combined on the cylindrical phantom, the dmax shifted toward the surface. For lay-down technique, the maximum x-ray contamination at the central axis was (MC: 2.2; Measurement: 2.1)% and reduced to 0.2% at 40 cm off the central axis. CONCLUSIONS: The Monte Carlo results in general agree well with the measurement, which provides support in our commissioning procedure, as well as the full three-dimensional dose distribution of the patient phantom.

A Single-Atom Nanozyme for Wound Disinfection Applications.

Single-atom catalysts (SACs), as homogeneous catalysts, have been widely explored for chemical catalysis. However, few studies focus on the applications of SACs in enzymatic catalysis. Herein, we report that a zinc-based zeolitic-imidazolate-framework (ZIF-8)-derived carbon nanomaterial containing atomically dispersed zinc atoms can serve as a highly efficient single-atom peroxidase mimic. To reveal its structure-activity relationship, the structural evolution of the single-atom nanozyme (SAzyme) was systematically investigated. Furthermore, the coordinatively unsaturated active zinc sites and catalytic mechanism of the SAzyme are disclosed using density functional theory (DFT) calculations. The SAzyme, with high therapeutic effect and biosafety, shows great promises for wound antibacterial applications.

Using weighted power mean for equivalent square estimation.

PURPOSE: Equivalent Square (ES) enables the calculation of many radiation quantities for rectangular treatment fields, based only on measurements from square fields. While it is widely applied in radiotherapy, its accuracy, especially for extremely elongated fields, still leaves room for improvement. In this study, we introduce a novel explicit ES formula based on Weighted Power Mean (WPM) function and compare its performance with the Sterling formula and Vadash/Bjärngard's formula. METHODS: The proposed WPM formula is ESWPMa,b=waα+1-wbα1/α for a rectangular photon field with sides a and b. The formula performance was evaluated by three methods: standard deviation of model fitting residual error, maximum relative model prediction error, and model's Akaike Information Criterion (AIC). Testing datasets included the ES table from British Journal of Radiology (BJR), photon output factors (Scp ) from the Varian TrueBeam Representative Beam Data (Med Phys. 2012;39:6981-7018), and published Scp data for Varian TrueBeam Edge (J Appl Clin Med Phys. 2015;16:125-148). RESULTS: For the BJR dataset, the best-fit parameter value α = -1.25 achieved a 20% reduction in standard deviation in ES estimation residual error compared with the two established formulae. For the two Varian datasets, employing WPM reduced the maximum relative error from 3.5% (Sterling) or 2% (Vadash/Bjärngard) to 0.7% for open field sizes ranging from 3 cm to 40 cm, and the reduction was even more prominent for 1 cm field sizes on Edge (J Appl Clin Med Phys. 2015;16:125-148). The AIC value of the WPM formula was consistently lower than its counterparts from the traditional formulae on photon output factors, most prominent on very elongated small fields. CONCLUSION: The WPM formula outperformed the traditional formulae on three testing datasets. With increasing utilization of very elongated, small rectangular fields in modern radiotherapy, improved photon output factor estimation is expected by adopting the WPM formula in treatment planning and secondary MU check.

An end-to-end deep convolutional neural network-based dose engine for parotid gland cancer seed implant brachytherapy.

BACKGROUND: Seed implant brachytherapy (SIBT) is a promising treatment modality for parotid gland cancers (PGCs). However, the current clinical standard dose calculation method based on the American Association of Physicists in Medicine (AAPM) Task Group 43 (TG-43) Report oversimplifies patient anatomy as a homogeneous water phantom medium, leading to significant dose calculation errors due to heterogeneity surrounding the parotid gland. Monte Carlo Simulation (MCS) can yield accurate dose distributions but the long computation time hinders its wide application in clinical practice. PURPOSE: This paper aims to develop an end-to-end deep convolutional neural network-based dose engine (DCNN-DE) to achieve fast and accurate dose calculation for PGC SIBT. METHODS: A DCNN model was trained using the patient's CT images and TG-43-based dose maps as inputs, with the corresponding MCS-based dose maps as the ground truth. The DCNN model was enhanced based on our previously proposed model by incorporating attention gates (AGs) and large kernel convolutions. Training and evaluation of the model were performed using a dataset comprising 188 PGC I-125 SIBT patient cases, and its transferability was tested on an additional 16 non-PGC head and neck cancers (HNCs) I-125 SIBT patient cases. Comparison studies were conducted to validate the superiority of the enhanced model over the original one and compare their overall performance. RESULTS: On the PGC testing dataset, the DCNN-DE demonstrated the ability to generate accurate dose maps, with percentage absolute errors (PAEs) of 0.67% ± 0.47% for clinical target volume (CTV) D90 and 1.04% ± 1.33% for skin D0.1cc. The comparison studies revealed that incorporating AGs and large kernel convolutions resulted in 8.2% (p < 0.001) and 3.1% (p < 0.001) accuracy improvement, respectively, as measured by dose mean absolute error. On the non-PGC HNC dataset, the DCNN-DE exhibited good transferability, achieving a CTV D90 PAE of 1.88% ± 1.73%. The DCNN-DE can generate a dose map in less than 10 ms. CONCLUSIONS: We have developed and validated an end-to-end DCNN-DE for PGC SIBT. The proposed DCNN-DE enables fast and accurate dose calculation, making it suitable for application in the plan optimization and evaluation process of PGC SIBT.

Automatic planning for head and neck seed implant brachytherapy based on deep convolutional neural network dose engine.

BACKGROUND: Seed implant brachytherapy (SIBT) is an effective treatment modality for head and neck (H&N) cancers; however, current clinical planning requires manual setting of needle paths and utilizes inaccurate dose calculation algorithms. PURPOSE: This study aims to develop an accurate and efficient deep convolutional neural network dose engine (DCNN-DE) and an automatic SIBT planning method for H&N SIBT. METHODS: A cohort of 25 H&N patients who received SIBT was utilized to develop and validate the methods. The DCNN-DE was developed based on 3D-unet model. It takes single seed dose distribution from a modified TG-43 method, the CT image and a novel inter-seed shadow map (ISSM) as inputs, and predicts the dose map of accuracy close to the one from Monte Carlo simulations (MCS). The ISSM was proposed to better handle inter-seed attenuation. The accuracy and efficacy of the DCNN-DE were validated by comparing with other methods taking MCS dose as reference. For SIBT planning, a novel strategy inspired by clinical practice was proposed to automatically generate parallel or non-parallel potential needle paths that avoid puncturing bone and critical organs. A heuristic-based optimization method was developed to optimize the seed positions to meet clinical prescription requirements. The proposed planning method was validated by re-planning the 25 cases and comparing with clinical plans. RESULTS: The absolute percentage error in the TG-43 calculation for CTV V100 and D90 was reduced from 5.4% and 13.2% to 0.4% and 1.1% with DCNN-DE, an accuracy improvement of 93% and 92%, respectively. The proposed planning method could automatically obtain a plan in 2.5 ± 1.5 min. The generated plans were judged clinically acceptable with dose distribution comparable with those of the clinical plans. CONCLUSIONS: The proposed method can generate clinically acceptable plans quickly with high accuracy in dose evaluation, and thus has a high potential for clinical use in SIBT.

Commissioning and dosimetric characteristics of TrueBeam system: composite data of three TrueBeam machines.

PURPOSE: A TrueBeam linear accelerator (TB-LINAC) is designed to deliver traditionally flattened and flattening-filter-free (FFF) beams. Although it has been widely adopted in many clinics for patient treatment, limited information is available related to commissioning of this type of machine. In this work, commissioning data of three units were measured, and multiunit comparison was presented to provide valuable insights and reliable evaluations on the characteristics of the new treatment system. METHODS: The TB-LINAC is equipped with newly designed waveguide, carousel assembly, monitoring control, and integrated imaging systems. Each machine in this study has 4, 6, 8, 10, 15 MV flattened photon beams, and 6 MV and 10 MV FFF photon beams as well as 6, 9, 12, 16, 20, and 22 MeV electron beams. Dosimetric characteristics of the three new TB-LINAC treatment units are systematically measured for commissioning. High-resolution diode detectors and ion chambers were used to measure dosimetric data for a range of field sizes from 10 × 10 to 400 × 400 mm(2). The composite dosimetric data of the three units are presented in this work. The commissioning of intensity modulated radiotherapy (IMRT), volumetric modulated arc therapy (VMAT), image-guided radiation therapy, and gating systems are also illustrated. Critical considerations of P(ion) of FFF photon beams and small field dosimetric measurements were investigated. RESULTS: The authors found all PDDs and profiles matched well among the three machines. Beam data were quantitatively compared and combined through average to yield composite beam data. The discrepancies among the machines were quantified using standard deviation (SD). The mean SD of the PDDs among the three units is 0.12%, and the mean SD of the profiles is 0.40% for 10 MV FFF open fields. The variations of P(ion) of the chamber CC13 is 1.2 ± 0.1% under 6 MV FFF and 2.0 ± 0.5% under 10 MV FFF from dmax to the 18 cm-off-axis point at 35 cm depth under 40 × 40 cm(2). The mean penumbra of crossplane flattened photon beams at collimator angle of 0° is measured from 5.88 ± 0.09 to 5.99 ± 0.13 mm from 4 to 15 MV at 10 cm depth of 100 × 100 mm(2). The mean penumbra of crossplane beams at collimator angle of 0° is measured as 3.70 ± 0.21 and 4.83 ± 0.04 mm for 6 MV FFF and 10 MV FFF, respectively, at 10 cm depth with a field size of 5 × 5 cm(2). The end-to-end test procedures of both IMRT and VMAT were performed for various energy modes. The mean ion chamber measurements of three units showed less than 2% between measurement and calculation; the mean MultiCube ICA measurements demonstrated over 90% pixels passing gamma analysis (3%, 3 mm, 5% threshold). The imaging dosimetric data of KV planar imaging and CBCT demonstrated improved consistency with vendor specifications and dose reduction for certain imaging protocols. The gated output verification showed a discrepancy of 0.05% or less between gating radiation delivery and nongating radiation delivery. CONCLUSIONS: The commissioning data indicated good consistency among the three TB-LINAC units. The commissioning data provided us valuable insights and reliable evaluations on the characteristics of the new treatment system. The systematically measured data might be useful for future reference.

A "rolling average" multiple adaptive planning method to compensate for target volume changes in image-guided radiotherapy of prostate cancer.

For prostate cancer radiotherapy, the interfractional organ motion can have several forms: changes in position, shape, and volume. The interfractional motion can be managed through either online or offline image guidance (IG). The position changes are commonly corrected through online IG by correcting couch position at each treatment fraction, while the shape and volume changes, or target deformation, can be compensated by margins in offline adaptive planning. In this study, we proposed and evaluated a rolling-average (RA) adaptive replanning method to account for the target volume variations. A total of 448 repeated helical computed tomography (HCT) scans from 28 patients were included in the study. Both low-risk patients (LRP, CTV = prostate) and intermediate-risk patients (IRP, CTV = prostate + seminal vesicles) were simulated. The benefit of RA strategy was evaluated geometrically and compared with the standard online IG-only method and a single replanning adaptive hybrid strategy. A new geometric index, cumulative index of target volume (CITV), was used for the evaluation. Two extreme scenarios of target volume changes, Type Ascending and Descending, were simulated by sorting the CTV volumes of actual patient data in order to have a better evaluation of the methods. Modest target volume variations were observed in our patient group. The prostate volume change was -0.14 ± 0.11 cc/day (or -0.30% ± 0.26% per day). It is found that RA is superior to the online IG and hybrid techniques. However, the magnitude of improvement depends on how significantly and rapidly the target volume changes. On the issue of planning complexity, the hybrid is more complex than online IG only, requiring one offline replanning, and RA is significantly more complex, with multiple replanning. In clinical implementation of RA, the effectiveness and efficiency should be balanced. The effectiveness is dependent on the patient population. For low-risk patients, RA is beneficial if there is significant time trend in target volume during the treatment course of radiotherapy. The optimal number of fractions necessary for the internal target volume (ITV) construction is 2 for LRP and 3 for IRP for RA strategy.

Does radiation therapy need more than two photon energies from Linac?

Purpose: Modern Linacs are equipped with multiple photon energies for radiation therapy, and proper energy is chosen for each case based on tumor characteristics and patient anatomy. The aim of this study is to investigate whether it is necessary to have more than two photons energies. Methods: The principle of photon energy synthesis is presented. It is shown that a photon beam of any intermediate energy (Esyn) can be synthesized from a linear combination of a low energy (Elow) and a high energy (Ehigh). The principle is validated on a wide range of scenarios: different intermediate photon energies on the same Linac; between Linacs from the same manufacturer or different manufacturers; open and wedge beams; and extensive photon energies available from published reference data. In addition, 3D dose distributions in water phantom are compared using Gamma analysis. The method is further demonstrated in clinical cases of various tumor sites and multiple treatment modalities. Experimental measurements are performed for IMRT plans and they are analyzed using the standard clinical protocol. Results: The synthesis coefficients vary with energy and field size. The root mean square error (RMSE) is within 1.1% for open and wedge fields. Excellent agreement was observed for British Journal of Radiology (BJR) data with an average RMSE of 0.11%. The 3D Gamma analysis shows a good match for all field sizes in the water phantom and all treatment modalities for the five clinical cases. The minimum gamma passing rate of 95.7% was achieved at 1%/1mm criteria for two measured dose distributions of IMRT plans. Conclusion: A Linac with two photon energies is capable of producing dosimetrically equivalent plans of any energy in-between through the photon energy synthesis, supporting the notion that there is no need to equip more than two photon energies on each Linac. This can significantly reduce the cost of equipment for radiation therapy.

Purposeful irradiation of the epidural space to enhance local control without compromising cord sparing in spine radiosurgery†.

Purpose: The epidural space is a frequent site of cancer recurrence after spine stereotactic radiosurgery (SSRS). This may be due to microscopic disease in the epidural space which is underdosed to obey strict spinal cord dose constraints. We hypothesized that the epidural space could be purposefully irradiated to prescription dose levels, potentially reducing the risk of recurrence in the epidural space without increasing toxicity. Methods and materials: SSRS clinical treatment plans with spinal cord contours, spinal planning target volumes (PTVspine), and delivered dose distributions were retrospectively identified. An epidural space PTV (PTVepidural) was contoured to avoid the spinal cord and focus on regions near the PTVspine. Clinical plan constraints included PTVspine constraints (D95% and D5%, based on prescription dose) and spinal cord constraints (Dmax < 1300 cGy, D10% < 1000 cGy). Plans were revised with three prescriptions of 1800, 2000 and 2400 cGy in two sets, with one set of revisions (supplemented plans) designed to additionally target the PTVepidural by optimizing PTVepidural D95% in addition to meeting every clinical plan constraint. Clinical and revised plans were compared according to their PTVepidural DVH distributions, and D95% distributions. Results: Seventeen SSRS plans meeting the above criteria were identified. Supplemented plans had higher doses to the epidural low-dose regions at all prescription levels. Epidural PTV D95% values for the supplemented plans were all statistically significantly different from the values of the base plans (p < 10-4). The epidural PTV D95% increases depended on the initial prescription, increasing from 11.52 to 16.90 Gy, 12.23 to 18.85 Gy, and 13.87 to 19.54 Gy for target prescriptions of 1800, 2000 and 2400 cGy, respectively. Conclusions: Purposefully targeting the epidural space in SSRS may increase control in the epidural space without significantly increasing the risk of spinal cord toxicity. A clinical trial of this approach should be considered.

Input feature design and its impact on the performance of deep learning models for predicting fluence maps in intensity-modulated radiation therapy.

Objective. Deep learning (DL) models for fluence map prediction (FMP) have great potential to reduce treatment planning time in intensity-modulated radiation therapy (IMRT) by avoiding the lengthy inverse optimization process. This study aims to improve the rigor of input feature design in a DL-FMP model by examining how different designs of input features influence model prediction performance.Approach. This study included 231 head-and-neck intensity-modulated radiation therapy patients. Three input feature designs were investigated. The first design (D1) assumed that information of all critical structures from all beam angles should be combined to predict fluence maps. The second design (D2) assumed that local anatomical information was sufficient for predicting radiation intensity of a beamlet at a respective beam angle. The third design (D3) assumed the need for both local anatomical information and inter-beam modulation to predict radiation intensity values of the beamlets that intersect at a voxel. For each input design, we tailored the DL model accordingly. All models were trained using the same set of ground truth plans (GT plans). The plans generated by DL models (DL plans) were analyzed using key dose-volume metrics. One-way ANOVA with multiple comparisons correction (Bonferroni method) was performed (significance level = 0.05).Main results. For PTV-related metrics, all DL plans had significantly higher maximum dose (p < 0.001), conformity index (p < 0.001), and heterogeneity index (p < 0.001) compared to GT plans, with D2 being the worst performer. Meanwhile, except for cord+5 mm (p < 0.001), DL plans of all designs resulted in OAR dose metrics that are comparable to those of GT plans.Significance. Local anatomical information contains most of the information that DL models need to predict fluence maps for clinically acceptable OAR sparing. Input features from beam angles are needed to achieve the best PTV coverage. These results provide valuable insights for further improvement of DL-FMP models and DL models in general.

Applying pytorch toolkit to plan optimization for circular cone based robotic radiotherapy.

BACKGROUND: Robotic linac is ideally suited to deliver hypo-fractionated radiotherapy due to its compact head and flexible positioning. The non-coplanar treatment space improves the delivery versatility but the complexity also leads to prolonged optimization and treatment time. METHODS: In this study, we attempted to use the deep learning (pytorch) framework for the plan optimization of circular cone based robotic radiotherapy. The optimization problem was topologized into a simple feedforward neural network, thus the treatment plan optimization was transformed into network training. With this transformation, the pytorch toolkit with high-efficiency automatic differentiation (AD) for gradient calculation was used as the optimization solver. To improve the treatment efficiency, plans with fewer nodes and beams were sought. The least absolute shrinkage and selection operator (lasso) and the group lasso were employed to address the "sparsity" issue. RESULTS: The AD-S (AD sparse) approach was validated on 6 brain and 6 liver cancer cases and the results were compared with the commercial MultiPlan (MLP) system. It was found that the AD-S plans achieved rapid dose fall-off and satisfactory sparing of organs at risk (OARs). Treatment efficiency was improved by the reduction in the number of nodes (28%) and beams (18%), and monitor unit (MU, 24%), respectively. The computational time was shortened to 47.3 s on average. CONCLUSIONS: In summary, this first attempt of applying deep learning framework to the robotic radiotherapy plan optimization is promising and has the potential to be used clinically.

Evaluations of an adaptive planning technique incorporating dose feedback in image-guided radiotherapy of prostate cancer.

PURPOSE: Online image guidance (IG) has been used to effectively correct the setup error and inter-fraction rigid organ motion for prostate cancer. However, planning margins are still necessary to account for uncertainties such as deformation and intra-fraction motion. The purpose of this study is to investigate the effectiveness of an adaptive planning technique incorporating offline dose feedback to manage inter-fraction motion and residuals from online correction. METHODS: Repeated helical CT scans from 28 patients were included in the study. The contours of prostate and organs-at-risk (OARs) were delineated on each CT, and online IG was simulated by matching center-of-mass of prostate between treatment CTs and planning CT. A seven beam intensity modulated radiation therapy (IMRT) plan was designed for each patient on planning CT for a total of 15 fractions. Dose distribution at each fraction was evaluated based on actual contours of the target and OARs from that fraction. Cumulative dose up to each fraction was calculated by tracking each voxel based on a deformable registration algorithm. The cumulative dose was compared with the dose from initial plan. If the deviation exceeded the pre-defined threshold, such as 2% of the D99 to the prostate, an adaptive planning technique called dose compensation was invoked, in which the cumulative dose distribution was fed back to the treatment planning system and the dose deficit was made up through boost radiation in future treatment fractions. The dose compensation was achieved by IMRT inverse planning. Two weekly compensation delivery strategies were simulated: one intended to deliver the boost dose in all future fractions (schedule A) and the other in the following week only (schedule B). The D99 to prostate and generalized equivalent uniform dose (gEUD) to rectal wall and bladder were computed and compared with those without the dose compensation. RESULTS: If only 2% underdose is allowed at the end of the treatment course, then 11 patients fail. If the same criteria is assessed at the end of each week (every five fractions), then 14 patients fail, with three patients failing the 1st or 2nd week but passing at the end. The average dose deficit from these 14 patients was 4.4%. They improved to 2% after the weekly compensation. Out of these 14 patients who needed dose compensation, ten passed the dose criterion after weekly dose compensation, three patients failed marginally, and one patient still failed the criterion significantly (10% deficit), representing 3.6% of the patient population. A more aggressive compensation frequency (every three fractions) could successfully reduce the dose deficit to the acceptable level for this patient. The average number of required dose compensation re-planning per patient was 0.82 (0.79) per patient for schedule A (B) delivery strategy. The doses to OARs were not significantly different from the online IG only plans without dose compensation. CONCLUSIONS: We have demonstrated the effectiveness of offline dose compensation technique in image-guided radiotherapy for prostate cancer. It can effectively account for residual uncertainties which cannot be corrected through online IG. Dose compensation allows further margin reduction and critical organs sparing.

Introducing matrix sparsity with kernel truncation into dose calculations for fluence optimization.

Deep learning algorithms for radiation therapy treatment planning automation require large patient datasets and complex architectures that often take hundreds of hours to train. Some of these algorithms require constant dose updating (such as with reinforcement learning) and may take days. When these algorithms rely on commerical treatment planning systems to perform dose calculations, the data pipeline becomes the bottleneck of the entire algorithm's efficiency. Further, uniformly accurate distributions are not always needed for the training and approximations can be introduced to speed up the process without affecting the outcome. These approximations not only speed up the calculation process, but allow for custom algorithms to be written specifically for the purposes of use in AI/ML applications where the dose and fluence must be calculated a multitude of times for a multitude of different situations. Here we present and investigate the effect of introducing matrix sparsity through kernel truncation on the dose calculation for the purposes of fluence optimzation within these AI/ML algorithms. The basis for this algorithm relies on voxel discrimination in which numerous voxels are pruned from the computationally expensive part of the calculation. This results in a significant reduction in computation time and storage. Comparing our dose calculation against calculations in both a water phantom and patient anatomy in Eclipse without heterogenity corrections produced gamma index passing rates around 99% for individual and composite beams with uniform fluence and around 98% for beams with a modulated fluence. The resulting sparsity introduces a reduction in computational time and space proportional to the square of the sparsity tolerance with a potential decrease in cost greater than 10 times that of a dense calculation allowing not only for faster caluclations but for calculations that a dense algorithm could not perform on the same system.

Technical note: A fast and accurate analytical dose calculation algorithm for 125 I seed-loaded stent applications.

PURPOSE: The safety and clinical efficacy of 125 I seed-loaded stent for the treatment of portal vein tumor thrombosis (PVTT) have been shown. Accurate and fast dose calculation of the 125 I seeds with the presence of the stent is necessary for the plan optimization and evaluation. However, the dosimetric characteristics of the seed-loaded stents remain unclear and there is no fast dose calculation technique available. This paper aims to explore a fast and accurate analytical dose calculation method based on Monte Carlo (MC) dose calculation, which takes into account the effect of stent and tissue inhomogeneity. METHODS: A detailed model of the seed-loaded stent was developed using 3D modeling software and subsequently used in MC simulations to calculate the dose distribution around the stent. The dose perturbation caused by the presence of the stent was analyzed, and dose perturbation kernels (DPKs) were derived and stored for future use. Then, the dose calculation method from AAPM TG-43 was adapted by integrating the DPK and appropriate inhomogeneity correction factors (ICF) to calculate dose distributions analytically. To validate the proposed method, several comparisons were performed with other methods in water phantom and voxelized CT phantoms for three patients. RESULTS: The stent has a considerable dosimetric effect reducing the dose up to 47.2% for single-seed stent and 11.9%-16.1% for 16-seed stent. In a water phantom, dose distributions from MC simulations and TG-43-DP-ICF showed a good agreement with the relative error less than 3.3%. In voxelized CT phantoms, taking MC results as the reference, the relative errors of TG-43 method can be up to 33%, while those of TG-43-DP-ICF method were less than 5%. For a dose matrix with 256 × 256 × 46 grid (corresponding to a phantom of 17.2 × 17.2 × 11.5 cm3 ) for 16-seed-loaded stent, it only takes 17 s for TG-43-DP-ICF to compute, compared to 25 h for the full MC calculation. CONCLUSIONS: The combination of DPK and inhomogeneity corrections is an effective approach to handle both the presence of stent and tissue heterogeneity. Exhibiting good agreement with MC calculation and computational efficiency, the proposed TG-43-DP-ICF method is adequate for dose evaluation and optimization in seed-loaded stent implantation treatment planning.