Clinical impact of DVH uncertainties.

Dose-volume histograms (DVH), along with dose and volume metrics, are central to radiotherapy planning. As such, errors have the potential to significantly impact the selection of appropriate treatment plans. Dose distributions that pass tests in one TPS may fail the same tests when transferred to another, even if using identical structures and dose grid information. This work shows the design and implementation of methods for assessing the accuracy of dose and volume computations performed by treatment planning systems (TPS), and other analytical tools. We demonstrate examples where differences in calculations between systems can change the assessment of a plan's clinical acceptability. Our work also provides a more detailed DVH analysis of single targets than earlier published studies. This is relevant for SRS plans and small structure dose assessments. Very small structures are a particular problem because of their coarse digital representation, and the impact of this is thoroughly examined. Reference DVH curves were derived mathematically, based on Gaussian dose distributions centered on spherical structures. The structures and dose distributions were generated synthetically, and imported into RayStation, MasterPlan, and ProKnow. Corresponding DVHs were analytically derived and taken as ground truth references, for comparison with the commercial DVH calculations. Two commonly used dose metrics PCI and MGI were used to determine the limit of calculation accuracy for small structures. In addition, to measure the DVH differences between a larger range of commercial DVH calculators, the D95 metric from a set of real clinical plans was compared across both the 3 DVH calculators under test, and across a further six TPSs from other hospitals. We show that even slight deviations between the results of DVH calculators can lead to plan check failures, and we illustrate this with the commonly used D95 planning metric. We present clinical data across eight planning systems that highlight instances where plan checks would pass in one software and fail in another due to DVH calculation differences. For the smallest volumes tested, errors of up to 20% were observed in the DVHs. RayStation was tested down to a 3 mm radius sphere (≈0.1 cc) and this showed close to 10% error, reducing to 1% for 10 mm radius (≈4.0 cc) and 0.1% for 20 mm radius (≈33 cc). In clinical plans, the variation in D95 was up to 9% for the smallest volumes, and typically around 2% in the range 0.5 cc-20 cc, and 1% in 20 cc-70 cc, falling to <0.1% for large volumes. Paddick Conformity Index (PCI) and Modified Gradient Index (MGI) are commonly used plan quality indicators for very small volumes. For volumes ≈0.1 cc we observed errors of up to 40% in PCI, and up to 75% in MGI. Our study extends the range of tested DVH calculators in published work, and shows their performance over a wider range of volume sizes. We provide quantitative evidence of the critical need to test the accuracy of DVH calculators in the TPS before clinical use. This work is particularly relevant for both stereotactic plan evaluation and for assessment of small volume doses in published dose constraint recommendations. We demonstrate that significant errors can occur in DVHs for volumes less than 1 cc, even if the volumes themselves are calculated accurately. Even for large structures, deviations between the outputs of DVH calculators can lead to indicated or reported plan check failures if they do not include appropriate tolerances. We urge caution in the use of DVH metrics for these very small volumes and recommend that appropriate DVH uncertainty tolerances are set in organ dose constraints when using them to evaluate clinical plans.

Comprehensive 3DCRT Hypofractionated Radiotherapy Schedule for Localized Prostate Adenocarcinoma in the Era of IMRT: Dosimetric and Endoscopic Analysis.

Background: Moderate hypofractionated radiotherapy (MHRT) has emerged as the preferred treatment modality for localized prostate cancer based on randomized controlled studies regarding efficacy and toxicity using contemporary radiotherapy techniques. In the setting of MHRT, available data on dosimetric parameters and late rectal toxicity are limited. Aim: To present the effects of MHRT on late rectal toxicity while conducting an extensive dosimetric analysis in conjunction with rectoscopy results. Methods: This is a prospective study including patients with intermediate-risk prostate adenocarcinoma. All patients were treated with MHRT 44 Gy in 16 fractions to the seminal vesicles and to the prostate, followed by a sequential boost to the prostate alone of 16.5 Gy in 6 fractions delivered with three-dimensional conformal radiation therapy (3DCRT). Acute and late toxicity were assessed. Endoscopy was performed at baseline, every 3 months post-therapy for the first year, and every 6 months for the year after. The Vienna Rectoscopy Score (VRS) was used to assess rectal mucosal injury related to radiotherapy. Dosimetric analysis for the rectum, rectal wall, and its subsegments (upper, mid, and low 1/3) was performed. Results: Between September 2015 and December 2019, 20 patients enrolled. Grade 1 late gastrointestinal toxicity occurred in 10% of the patients, whereas 5% had a grade ≥2. Twelve months post radiotherapy: 4 (20%) patients had VRS 1; 2 (10%) patients had VRS 2; 1(5%) patient had VRS 3. 24 months post radiotherapy, VRS 1 was observed in 4 patients (20%) and VRS 2 in 3 (15%) patients. The dosimetric analysis demonstrated noticeable variations between the rectum, rectal wall, and rectal wall subsegments. The dosimetric analysis of the rectum, rectal wall, and its mid and low segments with respect to rectoscopy findings showed that the higher dose endpoints V52.17Gy and V56.52Gy are associated with rectal mucosal injury. Conclusions: A thorough delineation of the rectal wall and its subsegments, together with the dosimetric analysis of these structures, may reduce late rectal toxicity. Dosimetric parameters such as V52.17Gy and V56.52Gy were identified to have a significant impact on rectal mucosal injury; additional dose endpoint validation and its relation to late GI toxicity is needed.

Dosimetric predictors for genitourinary toxicity in MR-guided stereotactic body radiation therapy (SBRT): Substructure with fraction-wise analysis.

BACKGROUND: MR-guided radiation therapy (MRgRT) systems provide superior soft tissue contrast than x-ray based systems and can acquire real-time cine for treatment gating. These features allow treatment planning margins to be reduced, allowing for improved critical structure sparing and reduced treatment toxicity. Despite this improvement, genitourinary (GU) toxicity continues to affect many patients. PURPOSE: (1) To identify dosimetric predictors, potentially in combination with clinical parameters, of GU toxicity following SBRT by leveraging MRgRT to accurately monitor daily dose, beyond predicted dose calculated during planning. (2) Improve awareness of toxicity-sensitive bladder substructures, specifically the trigone and urethra. METHODS: Sixty-nine prostate cancer patients (NCT04384770 clinical trial) were treated on a ViewRay MRIdian MRgRT system, with 40 Gy prescribed to 95% of the PTV in over five fractions. Overall, 17 (24.6%) prostate patients reported acute grade 2 GU toxicity. The CTV, PTV, bladder, bladder wall, trigone, urethra, rectum, and rectal wall were contoured on the planning and daily treatment MRIs. Planning and daily treatment DVHs (0.1 Gy increments), organ doses (min, max, mean), and organ volumes were recorded. Daily dose was estimated by transferring the planning dose distributions to the daily MRI based on the daily setup alignment. Patients were partitioned into a training (55) and testing set (14). Dose features were pre-filtered using a t-test followed by maximum relevance minimum redundancy (MRMR) algorithm. Logistic regression was investigated with regularization to select dosimetric predictors. Specifically, two approaches: time-group least absolute shrinkage and selection (LASSO), and interactive grouped greedy algorithm (IGA) were investigated. Shared features across the planning and five treatment fractions were grouped to encourage consistency and stability. The conventional flat non-temporally grouped LASSO was also evaluated to provide a solid benchmark. After feature selection, a final logistic regression model was trained. Dosimetric regression models were compared to a clinical regression model with only clinical parameters (age, baseline IPSS, prostate gland size, ADT usage, etc.) and a hybrid model, combining the best performing dosimetric features with the clinical parameters, was evaluated. Final model performance was evaluated on the testing set using accuracy, sensitivity, and specificity determined by the optimal threshold of the training set. RESULTS: IGA had the best testing performance with an accuracy/sensitivity/specificity of 0.79/0.67/0.82, selecting 12 groups covering the bladder (V19.8 Gy, V20.5 Gy), bladder wall (19.7 Gy), trigone (15.9, 18.2, 43.3 Gy), urethra (V41.4 Gy, V41.7 Gy), CTV (V41.9 Gy), rectum (V8.5 Gy), and rectal wall (1.2, 44.1 Gy) dose features. Absolute bladder V19.8 Gy and V20.5 Gy were the most important features, followed by relative trigone 15.9  and 18.2 Gy. Inclusion of clinical parameters in the hybrid model with IGA did not significantly change regression performance. CONCLUSION: Overall, IGA feature selection resulted in the best GU toxicity prediction performance. This exploratory study demonstrated the feasibility of identification and analysis of dosimetric toxicity predictors with awareness to sensitive substructures and daily dose to potentially provide consistent and stable dosimetric metrics to guide treatment planning. Further patient accruement is warranted to further assess dosimetric predictor and perform validation.

Patient specific quality assurance of volumetric modulated arc therapy of synchronous bilateral breast cancer.

Synchronous bilateral breast cancers (SBBC) present a considerable issue in external beam radiotherapy because of large fields size and large target volumes. Mono-isocentric volumetric modulated arc therapy (VMAT) appears as an appropriate irradiation technique for these types of tumors. The aim of this study was to demonstrate the utility of a 3D DVH pretreatment quality assurance program in VMAT of SBBC cases. Twenty SBBC patients who underwent radiation therapy in our department were retrospectively enrolled in this study. Fifteen patients were treated exclusively to the mammary glands. Five patients benefited from a dose boost on the tumor bed (60Gy). Nine patients were irradiated on the supraclavicular nodes (50Gy). This dose was delivered in 25 fractions and integrated boost was used when appropriate. Depending on the complexity of the treatment plans; 2 or 4 arcs VMAT plans were used in a mono-isocentric technique. The patient specific quality assurance (PSQA) was evaluated using COMPASS measured data, COMPASS reconstructed (CR) and COMPASS computed (CC) dose compared to treatment planning system (TPS) dose. Clinical evaluation was based on DVH metrics for target volumes and organ at risks. The maximum average dose deviation between TPS, CC, and CR was below 3%. The paired t-test between TPS, CC, and CR shows a strong agreement (p < 0.001). The 3DVH dose distribution comparison between TPS and COMPASS were also performed with good gamma score for global analysis. COMPASS was successfully evaluated as a 3DVH pretreatment system for SBBC despite the large fields size and complex target volumes. It allows the verification of the plan in 3D patient anatomy and the evaluation of dose discrepancies.

Proton therapy (PT) combined with concurrent chemotherapy for locally advanced non-small cell lung cancer with negative driver genes.

PURPOSE: To discuss the optimal treatment modality for inoperable locally advanced Non-Small Cell Lung Cancer patients with poor physical status, impaired cardio-pulmonary function, and negative driver genes, and provide clinical evidence. MATERIALS AND METHODS: Retrospective analysis of 62 cases of locally advanced non-small cell lung cancer patients with negative driver genes treated at Tsukuba University Hospital(Japan) and Qingdao University Affiliated Hospital(China).The former received proton therapy with concurrent chemotherapy, referred to as the proton group, with 25 cases included; while the latter underwent X-ray therapy with concurrent chemoradiotherapy followed by 1 year of sequential immunomodulatory maintenance therapy, referred to as the X-ray group, with 37 cases included.The treatment response and adverse reactions were assessed using RECIST v1.1 criteria and CTCAE v3.0, and radiotherapy planning and evaluation of organs at risk were performed using the CB-CHOP method.All data were subjected to statistical analysis using GraphPad Prism v9.0, with a T-test using P < 0.05 considered statistically significant. RESULTS: (1)Target dose distribution: compared to the X-ray group, the proton group exhibited smaller CTV and field sizes, with a more pronounced bragg peak.(2)Organs at risk dose: When comparing the proton group to the X-ray group, lung doses (V5, V20, MLD) and heart doses (V40, Dmax) were lower, with statistical significance (P < 0.05), while spinal cord and esophagus doses showed no significant differences between the two groups (P > 0.05).(3)Treatment-related toxicities: The incidence of grade 3 or higher adverse events in the proton group and X-ray group was 28.6% and 4.2%, respectively, with a statistically significant difference (P < 0.05). In terms of the types of adverse events, the proton group primarily experienced esophagitis and pneumonia, while the X-ray group primarily experienced pneumonia, esophagitis, and myocarditis. Both groups did not experience radiation myelitis or esophagotracheal fistula.(4)Efficacy evaluation: The RR in the proton group and X-ray group was 68.1% and 70.2%, respectively (P > 0.05), and the DCR was 92.2% and 86.4%, respectively (P > 0.05), indicating no significant difference in short-term efficacy between the two treatment modalities.(5)Survival status: The PFS in the proton group and X-ray group was 31.6 ± 3.5 months (95% CI: 24.7 ~ 38.5) and 24.9 ± 1.55 months (95% CI: 21.9 ~ 27.9), respectively (P > 0.05), while the OS was 51.6 ± 4.62 months (95% CI: 42.5 ~ 60.7) and 33.1 ± 1.99 months (95% CI: 29.2 ~ 37.1), respectively (P < 0.05).According to the annual-specific analysis, the PFS rates for the first to third years in both groups were as follows: 100%, 56.1% and 32.5% for the proton group vs. 100%, 54.3% and 26.3% for the X-ray group. No statistical differences were observed at each time point (P > 0.05).The OS rates for the first to third years in both groups were as follows: 100%, 88.2%, 76.4% for the proton group vs. 100%, 91.4%, 46.3% for the X-ray group. There was no significant difference in the first to second years (P > 0.05), but the third year showed a significant difference (P < 0.05). Survival curve graphs also depicted a similar trend. CONCLUSION: There were no significant statistical differences observed between the two groups in terms of PFS and OS within the first two years. However, the proton group demonstrated a clear advantage over the X-ray group in terms of adverse reactions and OS in the third year. This suggests a more suitable treatment modality and clinical evidence for populations with frail health, compromised cardio-pulmonary function, post-COVID-19 sequelae, and underlying comorbidities.

Phosphorylated bush sophora root polysaccharides protect the liver in duck viral hepatitis by preserving mitochondrial function.

In order to ascertain the mechanism underlying the therapeutic efficacy of Bush sophora root polysaccharides (BSRPS) and phosphorylated Bush sophora root polysaccharides (pBSRPS) in the treatment of in duck viral hepatitis (DVH), an investigation was conducted to assess the protective impact of BSRPS and pBSRPS against duck hepatitis A virus type 1 (DHAV-1) induced mitochondrial dysfunction both in vivo and vitro. The BSRPS underwent modification through the utilization of the sodium trimetaphosphate - sodium tripolyphosphate method, and was subsequently characterized though Fourier infrared spectroscopy and scanning electron microscopy. Following this, the degree of mitochondrial oxidative damage and dysfunction was described through the use of fluorescence probes and various antioxidative enzyme assay kits. Furthermore, the utilization of transmission electron microscopy facilitated the observation of alterations in the mitochondrial ultrastructure within the liver tissue. Our findings demonstrated that both BSRPS and pBSRPS effectively mitigated mitochondrial oxidative stress and conserved mitochondrial functionality, as evidenced by heightened antioxidant enzyme activity, augmented ATP production, and stabilized mitochondrial membrane potential. Meanwhile, the histological and biochemical examinations revealed that the administration of BSRPS and pBSRPS resulted in a reduction of focal necrosis and infiltration of inflammatory cells, thereby mitigating liver injury. Additionally, both BSRPS and pBSRPS exhibited the ability to maintain liver mitochondrial membrane integrity and enhance the survival rate of ducklings infected with DHAV-1. Notably, pBSRPS demonstrated superior performance in all aspects of mitochondrial function compared to BSRPS. The findings indicated that maintaining mitochondrial homeostasis is a crucial factor in DHAV-1 infections, and the administration of BSRPS and pBSRPS may mitigate mitochondrial dysfunction and safeguard liver function.

Espadon, an R package for automation, exploitation and processing of DICOM files in medical physics and clinical research.

INTRODUCTION: One of the main issues in the field of clinical research is to enhance clinical databases with information from imaging (CT, MR, PET-scan), contouring (RTstruct), or produced by TPS such as dose distribution (RTdose) or treatment plans (RTplan). To perform these analyses automatically, we propose the new open-source package "espadon", developed in R environment. This package also opens up numerous perspectives for TPS-independant calculation, automation and processing of DICOM data. RESULTS: The espadon package converts DICOM objects into espadon objects. Several tools have been developed to manipulate these objects and extract the desired information. In addition to decode DICOM files and pseudonomize them, the great advantage of espadon is that it presents the links between patient data (images, structures, treatment plans) in a didactic way, respecting the dates of the examinations. It can visualize volumes or structures in 2D or 3D, resample volumes, segment them, and change geometric frames of reference. It integrates dose-volume histogram functions on a selection, with Monte Carlo calculations of random shifts of contours. It offers the automatic calculation of several usual radiotherapy indices, as well as the calculation of Gamma and Chi indices. CONCLUSIONS: Espadon is a toolkit designed to be easily used by radiotherapists, medical physicists or students. Espadon's functions are implemented in an R script, and allow the automatic extraction or calculation of data from DICOM files, which can be used for statistical modelling or machine-learning in the R environment. This package is available on the Comprehensive R Archive Network (CRAN) repository.

Artificial intelligence to predict outcomes of head and neck radiotherapy.

Head and neck radiotherapy induces important toxicity, and its efficacy and tolerance vary widely across patients. Advancements in radiotherapy delivery techniques, along with the increased quality and frequency of image guidance, offer a unique opportunity to individualize radiotherapy based on imaging biomarkers, with the aim of improving radiation efficacy while reducing its toxicity. Various artificial intelligence models integrating clinical data and radiomics have shown encouraging results for toxicity and cancer control outcomes prediction in head and neck cancer radiotherapy. Clinical implementation of these models could lead to individualized risk-based therapeutic decision making, but the reliability of the current studies is limited. Understanding, validating and expanding these models to larger multi-institutional data sets and testing them in the context of clinical trials is needed to ensure safe clinical implementation. This review summarizes the current state of the art of machine learning models for prediction of head and neck cancer radiotherapy outcomes.

Machine learning for normal tissue complication probability prediction: Predictive power with versatility and easy implementation.

Background and purpose: A popular Normal tissue Complication (NTCP) model deployed to predict radiotherapy (RT) toxicity is the Lyman-Burman Kutcher (LKB) model of tissue complication. Despite the LKB model's popularity, it can suffer from numerical instability and considers only the generalized mean dose (GMD) to an organ. Machine learning (ML) algorithms can potentially offer superior predictive power of the LKB model, and with fewer drawbacks. Here we examine the numerical characteristics and predictive power of the LKB model and compare these with those of ML. Materials and methods: Both an LKB model and ML models were used to predict G2 Xerostomia on patients following RT for head and neck cancer, using the dose volume histogram of parotid glands as the input feature. Model speed, convergence characteristics and predictive power was evaluated on an independent training set. Results: We found that only global optimization algorithms could guarantee a convergent and predictive LKB model. At the same time our results showed that ML models remained unconditionally convergent and predictive, while staying robust to gradient descent optimization. ML models outperform LKB in Brier score and accuracy but compare to LKB in ROC-AUC. Conclusion: We have demonstrated that ML models can quantify NTCP better than or as well as LKB models, even for a toxicity that the LKB model is particularly well suited to predict. ML models can offer this performance while offering fundamental advantages in model convergence, speed, and flexibility, and so could offer an alternative to the LKB model that could potentially be used in clinical RT planning decisions.

Clinical Validation and Treatment Plan Evaluation Based on Autodelineation of the Clinical Target Volume for Prostate Cancer Radiotherapy.

PURPOSE: Clinical target volumes (CTVs) and organs at risk (OARs) could be autocontoured to save workload. This study aimed to assess a convolutional neural network for automatic and accurate CTV and OARs in prostate cancer, while comparing possible treatment plans based on autocontouring CTV to clinical treatment plans. METHODS: Computer tomography (CT) scans of 217 patients with locally advanced prostate cancer treated at our hospital were retrospectively collected and analyzed from January 2013 to January 2019. A deep learning-based method, CUNet, was used to delineate CTV and OARs. A training set of 195 CT scans and a test set of 28 CT scans were randomly chosen from the dataset. The mean Dice similarity coefficient (DSC), 95th percentile Hausdorff distance (95HD), and subjective evaluation were used to evaluate the performance of this strategy. Predetermined evaluation criteria were used to grade treatment plans, and percentage errors for clinical doses to the planned target volume (PTV) and OARs were calculated. RESULTS: The mean DSC and 95HD values of the defined CTVs were (0.84 ± 0.05) and (5.04 ± 2.15) mm, respectively. The average delineation time was < 15 s for each patient's CT scan. The overall positive rates for clinicians A and B were 53.15% versus 46.85%, and 54.05% versus 45.95%, respectively (P > .05) when CTV outlines from CUNet were blindly chosen and compared with the ground truth (GT). Furthermore, 8 test patients were randomly chosen to design the predicted plan based on the autocontouring CTVs and OARs, demonstrating acceptable agreement with the clinical plan: average absolute dose differences in mean value of D2, D50, D98, Dmax, and Dmean for PTV were within 0.74%, and average absolute volume differences in mean value of V45 and V50 for OARs were within 3.4%. CONCLUSION: Our results revealed that the CTVs and OARs for prostate cancer defined by CUNet were close to the GT. CUNet could halve the time spent by radiation oncologists in contouring, demonstrating the potential of the novel autocontouring method.

A novel tool to evaluate and quantify radiation pneumonitis: A retrospective analysis of correlation of dosimetric parameters with volume of pneumonia patch.

Introduction: In lung cancer, radiation-induced lung injury (RILI) or radiation pneumonitis (RP) are major concerns after radiotherapy. We investigated the correlation between volumes of RP lesions and their RP grades after radiotherapy. Methods and materials: We retrospectively collected data from patients with non-small lung cancer that received curative doses to the thorax without undergoing chest radiotherapy before this treatment course. The post-treatment computed tomography (CT) image was used to register to the planning CT to evaluate the correlation between dosimetric parameters and volume of pneumonia patch by using deformable image registration. Results: From January 1, 2019, to December 30, 2020, 71 patients with non-small cell lung cancer with 169 sets of CT images met our criteria for evaluation. In all patient groups, we found the RPv max and RP grade max to be significant (p<0.001). Some parameters that were related to the dose-volume histogram (DVH) and RP were lung Vx (x=1-66 Gy, percentage of lung volume received ≥x Gy), and mean lung dose. Comparing these parameters of the DVH with RP grade max showed that the mean lung dose and lung V1-V31 were significantly correlated. The cut-off point for the occurrence of symptoms in all patient groups, the RPv max value, was 4.79%, while the area under the curve was 0.779. In the groups with grades 1 and 2 RP, the dose curve of 26 Gy covered ≥80% of RP lesions in >80% of patients. Patients who had radiotherapy in combination with chemotherapy had significantly shorter locoregional progression-free survival (p=0.049) than patients who received radiation therapy in combination with target therapy. Patients with RPv max >4.79% demonstrated better OS (p=0.082). Conclusion: The percentage of RP lesion volume to total lung volume is a good indicator for quantifying RP. RP lesions can be projected onto the original radiation therapy plan using coverage of the 26 Gy isodose line to determine whether the lesion is RILI.

Prognostic value of 18F-FDG PET/CT-based radiomics combining dosiomics and dose volume histogram for head and neck cancer.

OBJECTIVES: By comparing the prognostic performance of 18F-FDG PET/CT-based radiomics combining dose features [Includes Dosiomics feature and the dose volume histogram (DVH) features] with that of conventional radiomics in head and neck cancer (HNC), multidimensional prognostic models were constructed to investigate the overall survival (OS) in HNC. MATERIALS AND METHODS: A total of 220 cases from four centres based on the Cancer Imaging Archive public dataset were used in this study, 2260 radiomics features and 1116 dosiomics features and 8 DVH features were extracted for each case, and classified into seven different models of PET, CT, Dose, PET+CT, PET+Dose, CT+Dose and PET+CT+Dose. Features were selected by univariate Cox and Spearman correlation coefficients, and the selected features were brought into the least absolute shrinkage and selection operator (LASSO)-Cox model. A nomogram was constructed to visually analyse the prognostic impact of the incorporated dose features. C-index and Kaplan-Meier curves (log-rank analysis) were used to evaluate and compare these models. RESULTS: The cases from the four centres were divided into three different training and validation sets according to the hospitals. The PET+CT+Dose model had C-indexes of 0.873 (95% CI 0.812-0.934), 0.759 (95% CI 0.663-0.855) and 0.835 (95% CI 0.745-0.925) in the validation set respectively, outperforming the rest models overall. The PET+CT+Dose model did well in classifying patients into high- and low-risk groups under all three different sets of experiments (p < 0.05). CONCLUSION: Multidimensional model of radiomics features combining dosiomics features and DVH features showed high prognostic performance for predicting OS in patients with HNC.

Multi-center dosimetric predictions to improve plan quality for brachytherapy for cervical cancer treatment.

BACKGROUND AND PURPOSE: Image-guided adaptive brachytherapy (IGABT) is an important modality in the cervical cancer treatment, and plan quality is sensitive to time pressure in the workflow. Patient anatomy-based quality-assurance (QA) with overlap volume histograms (OVHs) has been demonstrated to detect suboptimal plans (outliers). This analysis quantifies the possible improvement of plans detected as outliers, and investigates its suitability as a clinical QA tool in a multi-center setting. MATERIALS AND METHODS: In previous work OVH-based models were investigated for the use of QA. In this work a total of 160 plans of 68 patients treated in accordance with the current state-of-the-art IGABT protocol from Erasmus MC (EMC) were analyzed, with a model based on 120 plans (60 patients) from UMC Utrecht (UMCU). Machine-learning models were trained to define QA thresholds, and to predict dose D2cm3 to bladder, rectum, sigmoid and small bowel with the help of OVHs of the EMC cohort. Plans out of set thresholds (outliers) were investigated and retrospectively replanned based on predicted D2cm3 values. RESULTS: Analysis of replanned plans demonstrated a median improvement of 0.62 Gy for all Organs At Risk (OARs) combined and an improvement for 96 % of all replanned plans. Outlier status was resolved for 36 % of the replanned plans. The majority of the plans that could not be replanned were reported having implantation complications or insufficient coverage due to tumor geometry. CONCLUSION: OVH-based QA models can detect suboptimal plans, including both unproblematic BT applications and suboptimal planning circumstances in general. OVH-based QA models demonstrate potential for clinical use in terms of performance and user-friendliness, and could be used for knowledge transfer between institutes. Further research is necessary to differentiate between (sub)optimal planning circumstances.

Effects of lymphopenia on survival in proton therapy with chemotherapy for non-small cell lung cancer.

Lymphocytes play an important role in the cancer immune system. In the present study, we aimed to evaluate the associations of lymphopenia during proton beam therapy (PBT) and concurrent chemotherapy with clinical outcomes and to determine whether lung or bone is more influential on lymphopenia during PBT. Data from 41 patients with stage III non-small cell lung cancer (NSCLC) who received PBT of 74 GyE with concurrent chemotherapy between 2007 and 2017 were reviewed retrospectively. The correlation between dosimetry parameters obtained from dose-volume histograms of the bone and lung and lymphopenia during PBT were analyzed. Minimum absolute lymphocyte count (ALCmin) and maximum neutrophil/lymphocyte ratio (NLRmax) were used as indicators of lymphopenia. Bone V5-20 and lung V5-50 were significantly correlated with the ALCmin and NLRmax during PBT. Multivariable analysis showed that the NLRmax, but not the ALCmin, was associated with overall survival (OS), progression-free survival (PFS) and distant metastasis-free survival (DMFS). The 3-year rates of OS, PFS and DMFS of patients with a low (≤ 6.3) versus high (> 6.3) NLRmax were 73.9% vs 44.4% (P = 0.042), 26.1% vs 5.6% (P = 0.022) and 39.1% vs 5.6% (P < 0.001), respectively. Lung V20 was significantly associated with DMFS on multivariable analyses (hazard ratio: 1.094, P = 0.008), whereas bone V5 had no impact on survival outcomes. We concluded that the NLRmax was a better prognostic indicator than the ALCmin, and the lung dose had more influence than the bone dose on the main survival outcomes in stage III NSCLC patients treated with PBT combined with concurrent chemotherapy.

Evaluation of Lung and Liver Tumor Dose Coverage Treated With the CyberKnife Synchrony System With Consideration of Measured Tracking Errors.

BACKGROUND/AIM: Lung and liver tumor dose coverage was evaluated for the CyberKnife synchrony respiratory tracking system (SRTS) with consideration of the motion tracking accuracy measured for motion patterns of individual patients. PATIENTS AND METHODS: Seven treatment plans of six cases treated with the SRTS were evaluated. The motion phantom was moved with the motion data derived from the treatment log files. A laser emitted from the linac head to the moving phantom block was recorded with a webcam, and the tracking accuracy was evaluated. The dose volume histogram (DVH) of planning target volume (PTV) and gross tumor volume (GTV) were calculated by a pencil beam algorithm with shifting the beams with Gaussian random numbers mimicking the measured tracking errors. RESULTS: The tracking errors measured with the motion phantom in the lateral direction were within ±2 mm for 90% of beam-on time. The tracking errors in the longitudinal direction were within ±3.0 mm and ±1.1 mm for 90% and 50% of beam-on time, respectively. Although one case showed a decrease in the dose covering 95% of PTV (D95%) by 1.8%, the change in the dose covering 99% of GTV (D99%) was within 1%. CONCLUSION: This study evaluated the motion tracking errors of the SRTS by a motion phantom moved with the patients' respiration signal, and the impact of the tracking errors on the target coverage was calculated. Even for respiratory patterns with large maximum tracking errors, sufficient GTV coverage is achievable if the beam is accurately delivered for high percentage of beam-on time.

Using feasibility dose-volume histograms to reduce intercampus plan quality variability for head-and-neck cancer.

The purpose of this work is to objectively assess variability of intercampus plan quality for head-and-neck (HN) cancer and to test utility of a priori feasibility dose-volume histograms (FDVHs) as planning dose goals. In this study, 109 plans treated from 2017 to 2019 were selected, with 52 from the main campus and 57 from various regional centers. For each patient, the planning computed tomography images and contours were imported into a commercial program to generate FDVHs with a feasibility value (f-value) ranging from 0.0 to 0.5. For 10 selected organs-at-risk (OARs), we used the Dice similarity coefficient (DSC) to quantify the overlaps between FDVH and clinically achieved DVH of each OAR and determined the f-value associated with the maximum DSC (labeled as f-max). Subsequently, 10 HN plans from the regional centers were replanned with planning dose goals guided by FDVHs. The clinical and feasibility-guided auto-planning (FgAP) plans were evaluated using our institutional criteria. Among plans from the main campus and regional centers, the median f-max values were statistically significantly different (p < 0.05) for all OARs except for the left parotid (p = 0.622), oral cavity (p = 0.057), and mandible (p = 0.237). For the 10 FgAP plans, the median values of f-max were 0.21, compared to 0.37 from the clinical plans. With comparable dose coverage to the tumor volumes, the significant differences (p < 0.05) in the median f-max and corresponding dose reduction (shown in parenthesis) for the spinal cord, larynx, supraglottis, trachea, and esophagus were 0.27 (8.5 Gy), 0.3 (7.6 Gy), 0.19 (5.9 Gy), 0.19 (8.9 Gy), and 0.12 (4.0 Gy), respectively. In conclusion, the FDVH prediction is an objective quality assurance tool to evaluate the intercampus plan variability. This tool can also provide guideline in planning dose goals to further improve plan quality.

Knowledge-based DVH estimation and optimization for breast VMAT plans with and without avoidance sectors.

BACKGROUND: To analyze RapidPlan knowledge-based models for DVH estimation of organs at risk from breast cancer VMAT plans presenting arc sectors en-face to the breast with zero dose rate, feature imposed during the optimization phase (avoidance sectors AS). METHODS: CT datasets of twenty left breast patients in deep-inspiration breath-hold were selected. Two VMAT plans, PartArc and AvoidArc, were manually generated with double arcs from ~ 300 to ~ 160°, with the second having an AS en-face to the breast to avoid contralateral breast and lung direct irradiation. Two RapidPlan models were generated from the two plan sets. The two models were evaluated in a closed loop to assess the model performance on plans where the AS were selected or not in the optimization. RESULTS: The PartArc plans model estimated DVHs comparable with the original plans. The AvoidArc plans model estimated a DVH pattern with two steps for the contralateral structures when the plan does not contain the AS selected in the optimization phase. This feature produced mean doses of the contralateral breast, averaged over all patients, of 0.4 ± 0.1 Gy, 0.6 ± 0.2 Gy, and 1.1 ± 0.2 Gy for the AvoidArc plan, AvoidArc model estimation, RapidPlan generated plan, respectively. The same figures for the contralateral lung were 0.3 ± 0.1 Gy, 1.6 ± 0.6 Gy, and 1.2 ± 0.5 Gy. The reason was found in the possible incorrect information extracted from the model training plans due to the lack of knowledge about the AS. Conversely, in the case of plans with AS set in the optimization generated with the same AvoidArc model, the estimated and resulting DVHs were comparable. Whenever the AvoidArc model was used to generate DVH estimation for a plan with AS, while the optimization was made on the plan without the AS, the optimizer evidentiated the limitation of a minimum dose rate of 0.2 MU/°, resulting in an increased dose to the contralateral structures respect to the estimation. CONCLUSIONS: The RapidPlan models for breast planning with VMAT can properly estimate organ at risk DVH. Attention has to be paid to the plan selection and usage for model training in the presence of avoidance sectors.

Determining the Occurrence of Hypothyroidism Following Treatment With Radiation Therapy in Head and Neck Carcinoma Patients and the Associated Role of Risk Factors and Dose-Volume Histograms: A Prospective Study.

Background Head and neck carcinomas are one of the most common malignancies in developing countries including India. Most patients are treated with radiotherapy. Although post-radiotherapy hypothyroidism is a known complication, data regarding its incidence and factors influencing it are scarce. This study aimed to determine the incidence of post-radiotherapy hypothyroidism in head and neck carcinoma patients treated with radiotherapy and the factors influencing it. Methodology Patients with head and neck carcinomas treated with radiotherapy as one of the modalities were included in this study. Thyroid function tests were done, and quality of life questionnaires were completed before treatment and during follow-up. Dose-volume histogram (DVH), demographic data, and disease-related parameters were compared. Results Out of the 95 patients screened, 14 were found to be hypothyroid prior to the commencement of radiotherapy and were excluded. With a median follow-up duration of 34 weeks, 29.6% developed hypothyroidism, with 19% developing it in the first year. On univariate and multivariate analysis of the DVH of the thyroid gland, volume receiving 50 Gy (V50), dose received to 50% volume (D50), and the mean dose (more than 50 Gy) were found to be significantly associated with hypothyroidism. Conclusions Hypothyroidism is a significant comorbid factor in Indian patients with head and neck carcinomas. The incidence of post-radiotherapy hypothyroidism is significant and occurs early compared to the western population leading to significant deterioration in the quality of life. Parameters such as the volume of the thyroid gland, V50, D50, and mean dose to the thyroid gland influence the incidence of hypothyroidism. The use of appropriate constraints can significantly prevent radiotherapy-induced hypothyroidism.

Dosimetric and radiobiological evaluation of treatment plan for cervical cancer high-dose-rate intracavitary brachytherapy.

Purpose: The purpose of the study was to consider and calculate dosimetric parameters during treatment planning to improve radiobiological outcomes for cervical cancer patients treated with high-dose-rate (HDR) intracavitary brachytherapy (ICBT). Material and methods: In the present study, dose volume histograms (DVH) of 30 cervical cancer patients treated with HDR brachytherapy using computer tomography (CT)-based planning were analyzed. High-risk clinical target volume (HR-CTV) was contoured as the main target volume for all the patients, with an assumption that there was no presence of gross tumor at the time of brachytherapy. Values of target coverage volumes (100%, 150%, and 200%) were obtained from DVH, which was used to calculate different quality indices (QIs), including coverage index (CI), dose homogeneity index (DHI), overdose volume index (ODI), and dose non-uniformity ratio (DNR). Values of these QIs were further used to calculate tumor control probability (TCP). Statistical correlation between all QIs with TCP was established. Also, normal tissue complication probabilities for bladder (NTCP_B) and rectum (NTCP_R) were calculated. Results: The mean values of the various calculated parameters, including CI, DHI, ODI, DNR, TCP, NTCP_B and NTCP_R were 0.92 ±0.07, 0.26 ±0.10, 0.50 ±0.10, 0.74 ±0.10, 0.92 ±0.07, 0.08 ±0.25, and 0.36 ±0.27, respectively. Pearson's product moment correlation coefficient between CI, DHI, ODI, and DNR with regards to TCP was +0.85, -0.85, +0.84, and +0.85, respectively. Conclusions: The correlation between dosimetric and radiobiological parameters was found statistically significant, which shows the influence of dosimetric parameters on the radiobiological outcome. Therefore, these parameters should be considered during the treatment planning to improve the radiobiological outcome.

Single fraction of HDR brachytherapy for prostate cancer: Results of the SiFEPI phase II prospective trial.

Purpose: To report the results of the Single Fraction Early Prostate Irradiation (SiFEPI) phase 2 prospective trial. Materials/Methods: The SiFEPI trial (NCT02104362) evaluated a single fraction of high-dose rate brachytherapy (HDB) for low- (LR) and favorable-intermediate (FIR) risk prostate cancers. After rectal spacer placement, a single fraction of 20 Gy was delivered to the prostate. Oncological outcome (biochemical (bRFS) and local (lRFS) relapses, disease-free (DFS) and overall (OS) survivals and toxicity (acute/late genito-urinary (GU), gastro-intestinal (GI) and sexual (S) toxicities were investigated. Results: From 03/2014 to 10/2017, 35 pts were enrolled, of whom 33 were evaluable. With a median age of 66 y [46-79], 25 (76 %) and 8 (24 %) pts were LR and FIR respectively. With a MFU of 72.8 months [64-86], 6y-bRFS, lRFS and mRFS were 62 % [45-85], 61 % [44-85] and 93 % [85-100] respectively while 6y-DFS, CSS and OS were 54 % [37-77], 100 % and 89 % [77-100] respectively. Late GU, GI and S toxicities were observed in 11 pts (33 %;18G1), 4 pts (12 %;4G1) and 7 pts (21 %;1G1,5G2,1G3) respectively. Biochemical relapse (BR) was observed in 11 pts (33 %;7LR,4FIR) with a median time interval between HDB and BR of 51 months [24-69]. Nine of these pts (82 %) presented a histologically proven isolated local recurrence. Conclusions: Long-term results of the SiFEPI trial show that a single fraction of 20 Gy leads to sub-optimal biochemical control for LR/FIR prostate cancers. The late GU and GI toxicity profile is encouraging, leading to consideration of HDB as a safe irradiation technique.