Planning Target Volume Research Articles

Setup errors analysis in iterative kV CBCT: A clinical study of cervical cancer treated with Volumetric Modulated Arc Therapy.

This study aims to analyze setup errors in pelvic Volumetric Modulated Arc Therapy (VMAT) for patients with non-surgical primary cervical cancer, utilizing the onboard iterative kV cone beam CT (iCBCT) imaging system on the Varian Halcyon 2.0 ring gantry structure accelerator to enhance radiotherapy precision. We selected 132 cervical cancer patients who underwent VMAT with daily iCBCT imaging guidance. Before each treatment session, a registration method based on the bony structure was employed to acquire iCBCT images with the corresponding planning CT images. Following verification and adjustment of image registration results along the three axes (but not rotational), setup errors in the lateral (X-axis), longitudinal (Y-axis), and vertical (Z-axis) directions were recorded for each patient. Subsequently, we analyzed 3642 iCBCT image setup errors. The mean setup errors for the X, Y, and Z axes were 4.50±3.79mm, 6.08±6.30mm, and 1.48±2.23mm, respectively. Before correction with iCBCT, setup margins based on the Van Herk formula for the X, Y, and Z axes were 6.28, 12.52, and 3.26mm, respectively. In individuals aged 60 years and older, setup errors in the X and Y axes were significantly larger than those in the younger group (p<0.05). Additionally, there is no significant linear correlation between setup errors and treatment fraction numbers. Data analysis underscores the importance of precise Y-axis setup for cervical cancer patients undergoing VMAT. Radiotherapy centers without daily iCBCT should appropriately extend the planning target volume (PTV) along the Y-axis for cervical cancer patients receiving pelvic VMAT. Elderly patients exhibit significantly larger setup errors compared to younger counterparts. In conclusion, iCBCT-guided radiotherapy is recommended for cervical cancer patients undergoing VMAT to improve setup precision.

Dosimetric evaluation and gradient analysis of various MLC leaf-width effects in external beam radiation therapy: TrueBeam Vs Halcyon

IntroductionThis study investigates dosimetric influence and gradient-analysis of leaf-width of various Multi-leaf-collimators (MLC) from Truebeam and Halcyon linear accelerators. MethodsThe leaf-width effects of Millennium120 and HD120MLC from Truebeam and SX1 and SX2 from Halcyon were studied using virtual phantom in Eclipse16.1.2 TPS. Target structures consist of bore and wave cylinders of equally spaced projected Planning target volume (PTV) of diameters ranging from 1-to-5 cm and 20 cm length. Treatment plans for all PTVs, and four different MLCs configurations from both machines were created utilizing 6MVFFF beam to deliver dose of 50Gy/25# using IMRT and VMAT. Plans were evaluated using plan quality indices including dose conformity, homogeneity, gradient radius, and Monitor-unit (MU). Also, dose gradients were analyzed by estimating distinct integral volume VD% and paired-t-tests were performed to evaluate statistical differences. ResultsAll the plan satisfied the minimum criteria of D95 %≥prescription dose and V107 %≤2cc. Mean conformity and homogeneity indices for SX1MLC(CI = 0.680,HI = 0.022) were found significantly higher and lower than SX2(CI = 0.746,HI = 0.016), Millennium120 (CI = 0.739,HI = 0.012), and HD120MLC(CI = 0.745,HI = 0.017), respectively. However, CI and HI for SX2, Millennium120, and HD120MLC found comparable. Gradient radius enclosing 50 % of isodose observed maximum and minimum for SX1 and HD120MLC, respectively. Plan MUs for Truebeam MLCs were found approximately 25 % higher than Halcyon MLCs. Dose distribution generated using SX2 and Millennium120 found comparable, however p ≤ 0.05 ascertained substantial differences among SX1, Millennium120, and HD120MLC. ConclusionMLC leaf-width influences intensity-modulation and significantly alters dosimetric outcome depending on the magnitude of the leaf-width. HD120MLC does not show much significant advantages over SX2 and Millennium120 except gradient control. Implications for practiceHalcyon SX1MLC produces least effective plan compared to all other MLC types. However, SX2 and Millennium120 can produce plans of comparable quality except plan MUs difference.

On the trail of CBCT-guided adaptive rectal boost radiotherapy, does daily delineation require a radiation oncologist?

IntroductionDose-escalation radiotherapy for rectal tumours is increasingly considered as a non-operative approach, with online-adaptive radiotherapy (oART) supporting this approach by correcting inter-fraction tumour position errors. However, using cone-beam computed tomography (CBCT)-guided oART requires daily target volume delineation by different operators, leading to inter-operator delineation variability and potential dosimetric issues. This study aims to compare and quantify the inter-operator and inter-professional delineation variability of the rectal boost volume on CBCT, including volumes by an automatically delineated oART treatment planning system. Materials and methodsA rectal boost volume, defined as the primary tumour extended to the entire adjacent rectal wall, was delineated on 10 CBCTs from 5 patients by 15 operators: 4 expert radiation oncologists (ROs), 4 radiation therapists (RTTs) and 7 non-expert ROs. These contours were compared between the different professional groups. A comparison to the average volume of the group (ROs, RTTs, or non-expert ROs) with the lowest delineation variability was also performed for each individual volume including the volume automatically generated by an oART treatment planning system. ResultsDelineation variability was the highest in the superior (range: 2.3–6.0 mm), and inferior (2.3–12.4 mm) directions, compared to the left (0.2–4.4 mm), right (0.3–2.0 mm), anterior (0.1–2.9 mm), and posterior (0.5–4.0 mm) directions. Non-expert ROs, RTTs, and automatic oART volume showed similar ranges of delineation errors when compared to the expert ROs’ volume, which was chosen as reference volume since this professional group showed the lowest variability. DiscussionExpert ROs showed consistent results. Other professional groups exhibit similar variability, comparable to the automatic oART volume. Therefore, RTTs could safely perform the rectal boost delineation without non-expert ROs supervision in the absence of expert ROs during CBCT-based oART. Moreover, these findings provide quantitative data to compute accurate margins for the rectal boost planning target volume in a CBCT-guided oART workflow.

HYBRID PLANNING A SIMPLE TECHNIQUE FOR CRANIOSPINAL IRRADIATION (CSI)

Purpose: A simple planning technique for craniospinal irradiation using Eclipse treatment planning system. Material and methods: In RT treatment planning, base plan optimization feature is used in sequential RT planning by anticipating the dose delivered to organ at risk (OAR) &amp; planning target volume (PTV) in the base plan. In hybrid planning technique, the whole PTV is divided into two parts, Brain PTV &amp; Spine PTV. Spine PTV is overlapped minimum 2-3 cm over the Brain PTV at their junction while creating structures for RT planning. Brain PTV is planned with conventional RT technique &amp; the Spine PTV planned with rapid arc technique (VMAT). In the plan optimization process of spine PTV, first base plan is selected then the optimization parameter is set accordingly as per total prescribed dose of 36 Gy for both the PTVs. The base plan is the dose calculated plan which is incorporated in the rapid arc plan optimization hence the pre-existed doses of Brain PTV &amp; OAR help to understand the plannerfor achieving desired planning objectives during Spine PTV, RT plan optimization. Finally for the dose calculation of Spine PTV RT plan, planner has to copy fields of Brain PTV &amp; paste it in Spine PTV plan and need to calculate dose for these copied fields. The final plan contains a dose calculated of Brain PTV fields and Spine PTV fields termed as hybrid plan. Result: Reduction in the total number of monitor units is observed for the hybrid plan compared to single VMAT plan, which is statistically significant (p&lt;0.05). Conclusion: Present study introduced the hybrid planning technique which can exploit benefits of conventional and modern techniques.

Modeling and prediction of set‑up errors in breast cancer image‑guided radiotherapy using the Gaussian mixture model.

The aim of the present study was to develop a prediction model for set-up error distribution in breast cancer image-guided radiotherapy (IGRT) using a Gaussian mixture model (GMM). To achieve this, the image-guided set-up errors data of 80 patients with breast cancer were selected, and the GMM was used to develop the set-up errors distribution prediction model. The predicted error center points, covariance and probability were calculated and compared with the planning target volume (PTV) margin formula. A total of 1,200 sets of set-up errors in IGRT for breast cancer were collected. The results of the Gaussian model parameters showed that the set-up errors were mainly in the direction of µ1-µ4 center points. All the raw errors in the lateral, longitudinal and vertical directions were -6.30-4.60, -5.40-1.47 and -2.70-1.70 mm, respectively. According to the probability of each center, the set-up error was most likely to shift in the µ1 direction, reaching 0.53. The set-up errors of the other three centers, µ2, µ3 and µ4, were 0.11, 0.34 and 0.12, respectively. According to the covariance parameters of the GMM, the maximum statistical standard deviation of the set-up errors reached 29.06. In conclusion, the results of the present study demonstrated that the GMM can be used to quantitatively describe and predict the distribution of set-up errors in IGRT for breast cancer, and these findings could be useful as a reference for set-up error control and tumor PTV expansion in breast cancer radiotherapy without routine, daily IGRT.

Optimizing target and diaphragmatic configuration, and dosimetric benefits using continuous positive airway pressure in stereotactic ablative radiotherapy for lung tumors.

This study aimed to evaluate the impact of facilitating target delineation of continuous positive airway pressure (CPAP) in patients undergoing stereotactic ablative radiation therapy (SABR) for lung tumors by lung expansion and respiratory motion management. We performed a prospective single-institutional trial of patients who were diagnosed with either primary lung cancer or lung metastases and received SABR with a dose of 40 to 60 Gy in 4 fractions. Four-dimensional computed tomography simulations were conducted for each patient: once without CPAP and again with CPAP. Thirty-two patients with 39 tumors were analyzed, after the withdrawal of five patients due to discomfort. For 26 tumors separated from the diaphragm, CPAP significantly increased the superoinferior distance between the tumor and the diaphragm (5.96 cm vs. 8.06 cm; p < 0.001). For 13 tumors located adjacent to the diaphragm, CPAP decreased the overlap of planning target volume (PTV) with the diaphragm significantly (6.32 cm3 vs. 4.09 cm3; p = 0.002). PTV showed a significant reduction with CPAP (25.06 cm3 vs. 22.52 cm3, p = 0.017). In dosimetric analyses, CPAP expanded lung volume by 58.4% with a significant reduction in mean dose and V5 to V40. No more than grade 2 adverse events were reported. This trial demonstrated significant improvement of CPAP in target delineation uncertainties for lung SABR, with dosimetric benefits, a favorable safety profile and tolerability. Further investigation is warranted to explore the role of CPAP as a novel strategy for respiratory motion management.

Effect of using bolus on the efficiency of the 3DCRT of the breast cancer after mastectomy.

To assess the effect of using bolus on the efficiency of three-dimensional conformation radiation treatment of breast cancer post-mastectomy. The prospective, clinical study was conducted at Al-Amal Center for Radiation Therapy and Nuclear Medicine, Iraq, from November 2020 to April 2021, and comprised patients with synchronous bilateral breast cancer with regional lymph nodes who underwent mastectomy. The three-diensional conformation radiation treatment technique was used to evaluate the dose received by the planning target volume and the organs at risk. The plans were generated using a treatment planning system, the plans were then fed into a linear accelerator. The plans had one isocenter for both breasts to avoid the errors that may cause the overlapping of the radiation fields with and without the use of bolus. Data was analysed using SPSS 24. There were 23 female patients with mean age 49.28±8.79 years (range: 35-62 years). There were 7(30.4%) females residing in rural areas, while 16(69.6%) lived in urban areas. The use of bolus increased the radiation dose coverage percentage of the planning target volume of the right (p=0.0002) and left (p=0.0164) breasts, while the right (p=0.3977) and left (p=0.7940) supraclavicular nodes were not significantly covered by the radiotherapy dose. The use of bolus showed a strong significant decrease of the mean dose that was received by the left lung (p=0.00001). Also, the use of bolus caused significant reduction in the amount of the mean scattered dose that reached the heart (p=0.00001) and larynx (p=0.00004). The use of bolus provided a complete radiotherapy dose coverage for the right and left breasts, as well as an excellent protection for the left lung, heart and larynx.

Simultaneous integrated boost-intensity modulated radiation therapy (SIBIMRT) planning for pelvic tumours: dose coverage to target volume and normal tissue sparing.

To evaluate the better radiotherapy plan for pelvic tumours that may achieve a high target coverage dose and low normal tissue tolerance dose using simultaneous integrated boost-intensity-modulated radiation therapy technique. The analytical, cross-sectional, descriptive study was conducted from October 2020 to March 2021 at Al- Amal National Hospital for Cancer Treatment, Baghdad, Iraq, and comprised male pelvic cancer patients aged 50-80 years. Computed tomography scans were randomly selected. For each patient, 4 treatment plans (phases Ι and Π) were generated using the simultaneous integrated boost-intensity-modulated radiation therapy technique while keeping all parameters constant except the number of beams, which were 5, 7, 9 and 11. The optimal and safe dose was defined as the one to cover 95% planning target volume. Homogeneity and conformity indices were used to assess the uniformity of dose distribution in the target volume, and various treatment plans in the same patient were compared. Data was analysed using SPSS 24. There were 15 males with mean age 67.12±2.84 years. There was a significant variance in the target volume dose and organ at risk in the 4 different plans generated using the simultaneous integrated boost-intensitymodulated radiation therapy technique (p<0.05). The 9-beam treatment plan was the best for coverage of tumours and protection of healthy organs assessed through homogeneity and conformity indices (p<0.05). The 9-beam treatment plan showed significant tumour coverage, homogeneity and conformity values, and sparing dose for organs at risk.

The accuracy of cone beam computed tomography (CBCT) technique as an image-guided radiation therapy (IGRT) technique.

To measure the setup error of the patient's positioning using cone-beam computed tomography during radiation therapy treatment fractions by finding systematic, random errors and the planned target volume errors. The observational, longitudinal cohort study was conducted at the Al-Warith International Cancer Institute, Karbala, Iraq, from January to May 2022, and comprised patients with head and neck cancer who underwent radiation therapy. The oncologist delineated and the medical physicist planned. Then the medical physicist modified the positioning system using the cone beam computed tomography option workstation. The vertical value was taken in anteroposterior site, longitudinal in superoinferior, and lateral in e mediolateral. The SPSS 25 were used to analyse data. Of the 31 patients, 17(54.8%) were females and 14(45.2%) were males. The overall mean age was 48.3 ± 10.22 (range: 4-77 years), and 22(70.96%) patients had been treated previously with chemotherapy. The lateral shifting inaccuracy 2.501mm was above the limit, whereas the vertical shifting 1.164mm was within acceptable limits (±2mm). The longitudinal shifting had the smallest displacement 0.436mm. Random error displayed longitudinal moving 1.965mm, lateral shifting 0.623mm and vertical shifting 0.276mm. The planned target volume margins were too wide in longitudinal shifting 3.333mm. Vertical shifting 0.481mm was greater than lateral 1.092mm, but both were within limits (±2mm). Radiation-induced errors in normal tissues must be reduced by reducing planned target volume margins, especially for longitudinal and lateral directions.

Fractionated Stereotactic Intensity-Modulated Radiotherapy for Large Brain Metastases: Comprehensive Analyses of Dose–Volume Predictors of Radiation-Induced Brain Necrosis

Background: The objective was to explore dosimetric predictors of brain necrosis (BN) in fractionated stereotactic radiotherapy (SRT). Methods: After excluding collinearities carefully, multivariate logistic models were developed for comprehensive analyses of dosimetric predictors in patients who received first-line fractionated SRT for brain metastases (BMs). The normal brain volume receiving an xx Gy biological dose in 2 Gy fractions (VxxEQD2) was calculated from the retrieved dose–volume parameters. Results: Thirty Gy/3 fractions (fr) SRT was delivered to 34 patients with 75 BMs (median target volume, 3.2 cc), 35 Gy/5 fr to 30 patients with 57 BMs (6.4 cc), 37.5 Gy/5 fr to 28 patients with 47 BMs (20.2 cc), and 40 Gy/10 fr to 20 patients with 37 BMs (24.3 cc), according to protocols, depending on the total target volume (p &lt; 0.001). After excluding the three-fraction groups, the incidence of symptomatic BN was significantly higher in patients with a larger V50EQD2 (adjusted odds ratio: 1.07, p &lt; 0.02), V55EQD2 (1.08, p &lt; 0.01), or V60EQD2 (1.09, p &lt; 0.01) in the remaining five- and ten-fraction groups. The incidence of BN was also significantly higher in cases with V55EQD2 &gt; 30 cc or V60EQD2 &gt; 20 cc (p &lt; 0.05). These doses correspond to 28 or 30 Gy/5 fr and 37 or 40 Gy/10 fr, respectively. Conclusions: In five- or ten-fraction SRT, larger V55EQD2 or V60EQD2 are BN risk predictors. These biologically high doses may affect BN incidence. Thus, the planning target volume margin should be minimized as much as possible.

Dosimetric Evaluation of Hippocampus Sparing Intensity Modulated Radiation Therapy in Patients With Stage T1-T2 and Stage T3-T4 Nasopharyngeal Carcinoma

PurposeTo compare the hippocampus (HPC) dose reduced by HPC-sparing IMRT plans between nasopharyngeal carcinoma (NPC) patients of stages T1-T2 and T3-T4, and to investigate the correlation between the dose of the hippocampus and the volume of PTVnx70 (the planning target volume of the primary tumor in the nasopharynx received 70 Gy). Methods and MaterialsFifty-eight NPC patients were retrospectively evaluated. HPC non-sparing IMRT or sparing IMRT for each patient was designed according to the protocol for NPC. Dose-volume histogram (DVH) was used to evaluate the IMRT plans for each patient. The difference values of HPC parameters (e.g., Dmin[NS] - Dmin[S]) between HPC sparing and non-sparing plans in stage T1-T2 Group and stage T3-T4 Group were compared. The correlations between the dose of the hippocampus and the volume of PTVnx70 were analyzed. ResultsThere was no significance between HPC-sparing and non-sparing IMRT plans. Compared with the HPC non-sparing plans, the HPC sparing plans significantly decreased both dosimetric and volumetric parameters for the HPC (P < 0.05), except for Dmin, D98%, and V5. The medians of Dmedian[NS] - Dmedian[S], Dmean[NS] - Dmean[S], D40%[NS] - D40%[S], V30[NS] - V30[S], V40[NS] - V40[S] and V50[NS] - V50[S] in the T1-T2 Group were significantly lower than in the T3-T4 Group (P < 0.05), respectively. Both dosimetric and volumetric parameters for the HPC were positively correlated with the volume of PTVnx70 in HPC-sparing and non-sparing plans (P < 0.05). The volume of PTVnx70 was positively correlated with Dmedian[NS] - Dmedian[S], Dmean[NS] - Dmean[S], D40%[NS] - D40%[S], V40[NS] - V40[S] and V50[NS] - V50[S] (P < 0.05). ConclusionsHPC-sparing IMRT plans may play a more significant role in decreasing Dmedian, Dmean, D40%, and V30 ∼ V50 of HPC in NPC patients with stages T3-T4 than those in stages T1-T2. PTVnx70 volume of NPC patients are positively correlated with all dosimetric and volumetric parameters of HPC and the reduction of specific dosage parameters by HPC-sparing IMRT plans.

Development of brain metastases in patients managed with non-curative thoracic radiotherapy for stage II/III non-small cell lung cancer

BackgroundThis retrospective study analyzed the incidence of subsequent brain metastases after palliative radiotherapy or chemoradiation in patients with stage II/III non-small cell lung cancer (NSCLC). Risk factors for brain metastases development and survival after diagnosis were evaluated.MethodsDifferent baseline parameters including but not limited to age, stage and target volume size were assessed. Outcomes were abstracted from electronic health records. Uni- and multivariate tests were performed.ResultsThe study included 102 patients and found an actuarial risk of brain metastases of 15% (standard error ± 4) at one year and 20% (± 5) at two years. The maximum time interval was 15 months from start of radiation treatment. A non-significant survival difference was observed (median 12 months without versus 8.3 months with brain metastases, p = 0.21). Incidence was higher in patients with N2/3 stage, larger planning target volume size, and younger age (univariately significant factors). Trends were seen for stage III and adenocarcinoma histology. The multivariate analysis confirmed age as the most important risk factor.ConclusionThe risk of brain metastases development was comparable to that reported in studies of curative chemoradiation. All events occurred within 15 months of follow-up, suggesting that long-term surveillance imaging may not be warranted. Patients younger than 60 years had a very high risk of brain metastases development.

Patient, tumour, and dosimetric factors influencing survival in non-small cell lung cancer patients treated with stereotactic ablative body radiotherapy.

We aimed to analyse clinical outcomes of peripheral, early-stage non-small cell lung cancer (NSCLC) patients treated with stereotactic ablative body radiotherapy (SABR), and evaluate potential patient, tumour, and dosimetric variables influencing survival. Data were collected retrospectively from patients treated between September 2012 and December 2016 and followed up until January 2021. Patient demographics, tumour characteristics, SABR dosimetric parameters, and survival data were collected from electronic patient medical records. Descriptive statistics were performed, and SPSS software was used for survival analysis. Eighty-nine patients were included of whom 49.5% were male and 50.5% female. Median age was 74 years. 98.8% of patients had T1-2 tumours and 89.9% underwent 55 Gy in 5 fractions. Median overall survival time was 58.7 months. On uni- and multi-variate analysis, neither patient nor tumour variables showed association with overall survival. However, planning target volume (PTV) and minimum dose to PTV correlated with overall survival. There was a signal for association between mean lung dose and overall survival on multivariate analysis. Our long-term results show SABR is an effective treatment for peripheral, early-stage NSCLC with excellent overall survival, comparable to other series. Our study found only the PTV and minimum dose to PTV had an impact on overall survival, which demonstrates the importance of generating optimal SABR plans. Our work identified lung SABR dosimetric parameters that correlate with survival, which illustrates the importance of producing optimal lung SABR plans.

Large-language-model empowered 3D dose prediction for intensity-modulated radiotherapy.

Treatment planning is currently a patient specific, time-consuming, and resource demanding task in radiotherapy. Dose-volume histogram (DVH) prediction plays a critical role in automating this process. The geometric relationship between DVHs in radiotherapy plans and organs-at-risk (OAR) and planning target volume (PTV) has been well established. This study explores the potential of deep learning models for predicting DVHs using images and subsequent human intervention facilitated by a large-language model (LLM) to enhance the planning quality. We propose a pipeline to convert unstructured images to a structured graph consisting of image-patch nodes and dose nodes. A novel Dose Graph Neural Network (DoseGNN) model is developed for predicting DVHs from the structured graph. The proposed DoseGNN is enhanced with the LLM to encode massive knowledge from prescriptions and interactive instructions from clinicians. In this study, we introduced an online human-AI collaboration (OHAC) system as a practical implementation of the concept proposed for the automation of intensity-modulated radiotherapy (IMRT) planning. The proposed DoseGNN model was compared to widely employed DL models used in radiotherapy, including Swin Transformer, 3D U-Net CNN, and vanilla MLP. For PTV, DoseGNN achieved the mean absolute error (MAE) of , , , and between true plans and predicted plans that were 64%, 53%, 64%, 61% of the best baseline model. For the worst case among OARs (left lung, right lung, chest wall, heart, spinal cord), DoseGNN achieved the mean absolute error of , , that were 85%, 91%, 80% of the best baseline model. Moreover, the LLM-empowered DoseGNN model facilitates seamless adjustment to treatment plans through interaction with clinicians using natural language. We developed DoseGNN, a novel deep learning model for predicting delivered radiation doses from medical images, enhanced by LLM to allow adjustment through seamless interaction with clinicians. The preliminary results confirm DoseGNN's superior accuracy in DVH prediction relative to typical DL methods, highlighting its potential to facilitate an online clinician-AI collaboration system for streamlined treatment planning automation.

Dose advantage of abdominal deep inspiratory breath-hold (aDIBH) in postoperative adjuvant radiotherapy for left breast cancer

PurposeWe explored the dosimetric efficacy of the abdominal deep inspiration breath hold (aDIBH) technique using an audio-guided device in patients with left breast cancer undergoing postoperative adjuvant radiotherapy compared to free breathing (FB).MethodsA total of 35 patients with early stage left breast cancer underwent two computed tomography simulation scans each with aDIBH and FB after breast-conserving surgery. Treatment planning was optimized using the Pinnacle3 9.10 planning system. The heart, left anterior descending coronary artery (LADCA), and left lung was defined as organs at risk (OARs). The dosimetric differences in the planning target volume (PTV) and OARs were compared between aDIBH and FB.ResultsCompared with FB, the heart moved farther caudally and away from the chest wall, and the volume of heart became smaller under aDIBH due to expansion of the lungs. The D mean of the heart, LADCA and left lung of aDIBH were respectively reduced by 332.79 ± 264.61 cGy (P < 0.001), 1290.37 ± 612.09 cGy (P < 0.047) and 69.94 ± 117.73 cGy (P < 0.001). The V20 and V30 of the OARs were also significantly reduced with statistical differences (P < 0.05). In addition, there was no significant difference in the dosimetric parameters of the PTV between the two groups (P > 0.05).ConclusionsImplementation of the aDIBH technique for postoperative radiotherapy after breast-conserving surgery of the left breast cancer could reduce irradiation of the heart dose, LADCA dose and left lung dose, without compromising target coverage.

Pseudo‐CT synthesis in adaptive radiotherapy based on a stacked coarse‐to‐fine model: Combing diffusion process and spatial‐frequency convolutions

AbstractBackgroundCone beam computed tomography (CBCT) provides critical anatomical information for adaptive radiotherapy (ART), especially for tumors in the pelvic region that undergo significant deformation. However, CBCT suffers from inaccurate Hounsfield Unit (HU) values and lower soft tissue contrast. These issues affect the accuracy of pelvic treatment plans and implementation of the treatment, hence requiring correction.PurposeA novel stacked coarse‐to‐fine model combining Denoising Diffusion Probabilistic Model (DDPM) and spatial‐frequency domain convolution modules is proposed to enhance the imaging quality of CBCT images.MethodsThe enhancement of low‐quality CBCT images is divided into two stages. In the coarse stage, the improved DDPM with U‐ConvNeXt architecture is used to complete the denoising task of CBCT images. In the fine stage, the deep convolutional network model jointly constructed by fast Fourier and dilated convolution modules is used to further enhance the image quality in local details and global imaging. Finally, the accurate pseudo‐CT (pCT) images consistent with the size of the original data are obtained. Two hundred fifty paired CBCT‐CT images from cervical and rectal cancer, combined with 200 public dataset cases, were used collectively for training, validation, and testing.ResultsTo evaluate the anatomical consistency between pCT and real CT, we have used the mean(std) of structure similarity index measure (SSIM), peak signal to noise ratio (PSNR), and normalized cross‐correlation (NCC). The numerical results for the above three metrics comparing the pCT synthesized by the proposed model against real CT for cervical cancer cases were 87.14% (2.91%), 34.02 dB (1.35 dB), and 88.01% (1.82%), respectively. For rectal cancer cases, the corresponding results were 86.06% (2.70%), 33.50 dB (1.41 dB), and 87.44% (1.95%). The paired t‐test analysis between the proposed model and the comparative models (ResUnet, CycleGAN, DDPM, and DDIM) for these metrics revealed statistically significant differences (p &lt; 0.05). The visual results also showed that the anatomical structures between the real CT and the pCT synthesized by the proposed model were closer. For the dosimetric verification, mean absolute error of dosimetry (MAEdoes) values for the maximum dose (Dmax), the minimum dose (Dmin), and the mean dose (Dmean) in the planning target volume (PTV) were analyzed, with results presented as mean (lower quartile, upper quartile). The experimental results show that the values of the above three dosimetry indexes (Dmin, Dmax, and Dmean) for the pCT images synthesized by the proposed model were 0.90% (0.48%, 1.29%), 0.82% (0.47%, 1.17%), and 0.57% (0.44%, 0.67%). Compared with 10 cases of the original CBCT image by Mann–Whitney test (p &lt; 0.05), it also proved that pCT can significantly improve the accuracy of HU values for the dose calculation.ConclusionThe pCT synthesized by the proposed model outperforms the comparative models in numerical accuracy and visualization, promising for ART of pelvic cancers.

Deep learning-based prediction of the dose-volume histograms for volumetric modulated arc therapy of left-sided breast cancer.

The advancements in artificial intelligence and computational power have made deep learning an attractive tool for radiotherapy treatment planning. Deep learning has the potential to significantly simplify the trial-and-error process involved in inverse planning required by modern treatment techniques such as volumetric modulated arc therapy (VMAT). In this study, we explore the ability of deep learning to predict organ-at-risk (OAR) dose-volume histograms (DVHs) of left-sided breast cancer patients undergoing VMAT treatment based solely on their anatomical characteristics. The predicted DVHs could be used to derive patient-specific dose constraints and dose objectives, streamlining the treatment planning process, standardizing the quality of the plans, and personalizing the treatment planning. This study aimed to develop a deep learning-based framework for the prediction of organ-specific dose-volume histograms (DVH) based on structures delineated for left-sided breast cancer treatment. We used a dataset of 249 left-sided breast cancer patients treated with tangential VMAT fields. We extracted delineated structures and dose distributions for each patient and derived slice-by-slice DVHs for planning target volume (PTV) and organs-at-risk. The patients were divided into training (70%, n=174), validation (10%, n=24), and test (20%, n=51) sets. Collected data were used to train a deep learning model for the prediction of the DVHs based on the delineated structures. The developed deep learning model comprised a modified DenseNet architecture followed by a recurrent neural network. In the independent test set (n=51), the point-wise differences in the slice-by-slice DVHs between the clinical and predicted DVHs were small; the mean squared errors were 3.53, 1.58, 2.28, 3.37, and 1.44 [×10-4] for PTV, heart, ipsilateral lung, contralateral lung, and contralateral breast, respectively. With the derived cumulative DVHs, the mean absolute difference±standard deviation of mean doses between the clinical and the predicted DVH were 0.08± 0.04Gy, 0.24± 0.22Gy, 0.73± 0.46Gy, 0.07± 0.06Gy, and 0.14± 0.14Gy for PTV, heart, ipsilateral lung, contralateral lung, and contralateral breast, respectively. The deep learning-based approach enabled automatic and reliable prediction of the DVH based on delineated structures. The predicted DVHs could potentially serve as patient-specific clinical goals used to aid treatment planning and avoid suboptimal plans or to derive optimization objectives and constraints for automated treatment planning.

Implementing stereotactic arrhythmia radioablation with STOPSTORM.eu consortium support: intermediate results of aprospective Israeli single-institutional trial.

Ventricular tachycardia (VT) is alife-threatening arrhythmia originating from the heart's ventricles. Traditional treatments include antiarrhythmic medications, implantable cardioverter-defibrillators (ICDs), and catheter ablation. Stereotactic body radiation therapy (SBRT) targeting the arrhythmogenic focus in the left ventricle-stereotactic arrhythmia radioablation (STAR)-is an emerging treatment and may offer apotential solution for patients with refractory VT. We designed an interventional prospective clinical trial in Israel aligned with the STOPSTORM.eu consortium's benchmarks, recommendations, and directives to assess the safety and efficacy of STAR in patients with refractory VT. Our phaseI/II single-institutional trial was approved by the Ministry of Health of Israel for 10patients, initially assessing safety in the first 3patients. We included patients with ICDs experiencing symptomatic monomorphic VT after an inadequate response to previous therapies. The primary endpoints were treatment-related serious adverse events and areduction in VT burden as assessed by ICD interrogation. Secondary outcomes included areduction in antiarrhythmic medications and changes in quality of life. From August 2023 to August 2024, 3patients underwent STAR treatment. The prescription dose was asingle fraction of 25 Gy. Planning target volumes were 47.8, 49.7, and 91.8 cc, and treatment was successfully delivered with no grade3 or higher adverse events reported. Over afollow-up period of 12months for the first patient and 8months for the second one, no VT events were recorded after treatment. The third patient died from progressive heart failure 3months after treatment. Left ventricular ejection fraction remained stable, and no significant radiation-induced inflammatory changes were noted. The initial results of this trial suggest that STAR can reduce VT episodes in patients with refractory VT without severe adverse effects. The study highlights the importance of international collaboration and standardization in pioneering new treatments. Further follow-up and additional patient data will be necessary to confirm these findings and evaluate long-term outcomes, including potential adjustments to antiarrhythmic medication regimens.

Nested CNN architecture for three-dimensional dose distribution prediction in tomotherapy for prostate cancer.

The hypothesis of changing network layers to increase the accuracy of dose distribution prediction, instead of expanding their dimensions, which requires complex calculations, has been considered in our study. Atotal of 137 prostate cancer patients treated with the tomotherapy technique were categorized as 80% training and validating as well as 20% testing for the nested UNet and UNet architectures. Mean absolute error (MAE) was used to measure the dosimetry indices of dose-volume histograms (DVHs), and geometry indices, including the structural similarity index measure (SSIM), dice similarity coefficient (DSC), and Jaccard similarity coefficient (JSC), were used to evaluate the isodose volume (IV) similarity prediction. To verify a statistically significant difference, the two-way statistical Wilcoxon test was used at alevel of 0.05 (p < 0.05). Use of anested UNet architecture reduced the predicted dose MAE in DVH indices. The MAE for planning target volume (PTV), bladder, rectum, and right and left femur were D98% = 1.11 ± 0.90; D98% = 2.27 ± 2.85, Dmean = 0.84 ± 0.62; D98% = 1.47 ± 12.02, Dmean = 0.77 ± 1.59; D2% = 0.65 ± 0.70, Dmean = 0.96 ± 2.82; and D2% = 1.18 ± 6.65, Dmean = 0.44 ± 1.13, respectively. Additionally, the greatest geometric similarity was observed in the mean SSIM for UNet and nested UNet (0.91 vs.0.94, respectively). The nested UNet network can be considered asuitable network due to its ability to improve the accuracy of dose distribution prediction compared to the UNet network in an acceptable time.

Exploring the impact of network depth on 3D U-Net-based dose prediction for cervical cancer radiotherapy.

The 3D U-Net deep neural network structure is widely employed for dose prediction in radiotherapy. However, the attention to the network depth and its impact on the accuracy and robustness of dose prediction remains inadequate. 92 cervical cancer patients who underwent Volumetric Modulated Arc Therapy (VMAT) are geometrically augmented to investigate the effects of network depth on dose prediction by training and testing three different 3D U-Net structures with depths of 3, 4, and 5. For planning target volume (PTV), the differences between predicted and true values of D98, D99, and Homogeneity were statistically 1.00 ± 0.23, 0.32 ± 0.72, and -0.02 ± 0.02 for the model with a depth of 5, respectively. Compared to the other two models, these parameters were also better. For most of the organs at risk, the mean and maximum differences between the predicted values and the true values for the model with a depth of 5 were better than for the other two models. The results reveal that the network model with a depth of 5 exhibits superior performance, albeit at the expense of the longest training time and maximum computational memory in the three models. A small server with two NVIDIA GeForce RTX 3090 GPUs with 24 G of memory was employed for this training. For the 3D U-Net model with a depth of more than 5 cannot be supported due to insufficient training memory, the 3D U-Net neural network with a depth of 5 is the commonly used and optimal choice for small servers.