Dose to organs at risk for total body irradiation: Single-institution data using the modulated arc total body irradiation technique.

BACKGROUND: Organs at risk (OAR) dose reporting for total body irradiation (TBI) patients is limited, and standardly reported only as mean doses to the lungs and kidneys. Consequently, dose received and effects on other OAR remain unexplored. To remedy this gap, this study reports dose data on an extensive list of OAR for patients treated at a single institution using the modulated arc total body irradiation (MATBI) technique. METHOD: An audit was undertaken of all patients treated with MATBI between January 2015 and March 2021 who had completed their course of treatment. OAR were contoured on MATBI patient treatment plans, with 12 Gy in six fraction prescription. OAR dose statistics and dose volume histogram data are reported for the whole body, lungs, kidneys, bones, brain, lens, heart, liver and bowel bag. RESULTS: The OAR dose data for 29 patients are reported. Mean dose results are body 11.77 Gy, lungs 9.86 Gy, kidneys 11.84 Gy, bones 12.03 Gy, brain 12.12 Gy, right lens 12.31 Gy, left lens 12.64 Gy, heart 11.07 Gy, liver 11.81 Gy and bowel bag 12.06 Gy. Dose statistics at 1-Gy intervals of V6-V13 for lungs and V10-V13 for kidneys are also included. CONCLUSION: This is the first time an extensive list of OAR data has been reported for any TBI technique. Due to the paucity of reporting, this information could be used by centres implementing the MATBI technique, in addition to aiding comparison between TBI techniques, with the potential for greater understanding of the relationship between dose volume data and toxicity.

Dosimetric impact of contour editing on CT and MRI deep-learning autosegmentation for brain OARs.

PURPOSE: To establish the clinical applicability of deep-learning organ-at-risk autocontouring models (DL-AC) for brain radiotherapy. The dosimetric impact of contour editing, prior to model training, on performance was evaluated for both CT and MRI-based models. The correlation between geometric and dosimetric measures was also investigated to establish whether dosimetric assessment is required for clinical validation. METHOD: CT and MRI-based deep learning autosegmentation models were trained using edited and unedited clinical contours. Autosegmentations were dosimetrically compared to gold standard contours for a test cohort. D1%, D5%, D50%, and maximum dose were used as clinically relevant dosimetric measures. The statistical significance of dosimetric differences between the gold standard and autocontours was established using paired Student's t-tests. Clinically significant cases were identified via dosimetric headroom to the OAR tolerance. Pearson's Correlations were used to investigate the relationship between geometric measures and absolute percentage dose changes for each autosegmentation model. RESULTS: Except for the right orbit, when delineated using MRI models, the dosimetric statistical analysis revealed no superior model in terms of the dosimetric accuracy between the CT DL-AC models or between the MRI DL-AC for any investigated brain OARs. The number of patients where the clinical significance threshold was exceeded was higher for the optic chiasm D1% than other OARs, for all autosegmentation models. A weak correlation was consistently observed between the outcomes of dosimetric and geometric evaluations. CONCLUSIONS: Editing contours before training the DL-AC model had no significant impact on dosimetry. The geometric test metrics were inadequate to estimate the impact of contour inaccuracies on dose. Accordingly, dosimetric analysis is needed to evaluate the clinical applicability of DL-AC models in the brain.

Less Irradiation to Lymphocyte-Related Organs Reduced the Risk of G4 Lymphopenia in Esophageal Cancer: Re-Analysis of Prospective Trials.

BACKGROUND: This study aimed to explore the relationship between irradiation of lymphocyte-related organs at risk (LOARs) and lymphopenia during definitive concurrent chemoradiotherapy (dCCRT) for esophageal squamous cell carcinoma (ESCC). MATERIALS AND METHODS: Cases of ESCC patients who received dCCRT from 2 prospective clinical trials were identified. To find its correlation with survival outcomes, grades of absolute lymphocyte counts (ALCs) nadir during radiotherapy were recorded following COX analysis. Associations of lymphocytes at nadir and dosimetric parameters including relative volumes of spleen and bone marrow receiving 0.5, 1, 2, 3, 5, 10, 20, 30, and 50Gy (V0.5, V1, V2, V3, V5, V10, V20, V30, and V50), and effective dose to circulating immune cells (EDIC) were examined by logistic risk regression analysis. The cutoffs of dosimetric parameters were determined by the receiver operating characteristic curve (ROC). RESULTS: A total of 556 patients were included. The incidences of grades 0, 1, 2, 3, and 4 (G4) lymphopenia during dCCRT were 0.2%, 0.5%, 9.7%, 59.7%, and 29.8%, respectively. Their median overall survival (OS) and progression-free survival (PFS) time were 50.2 and 24.3 months, respectively; the incidence of local recurrence and distant metastasis were 36.6% and 31.8%, respectively. Patients once suffering from G4 nadir during radiotherapy had unfavorable OS (HR, 1.28; P = .044) and a higher incidence of distant metastasis (HR, 1.52; P = .013). Furthermore, patients with EDIC ≤8.3Gy plus spleen V0.5 ≤11.1% and bone marrow V10 ≤33.2% were strongly associated with lower risk of G4 nadir (OR, 0.41; P = .004), better OS (HR, 0.71; P = .011) and lower risk of distant metastasis (HR, 0.56; P = .002). CONCLUSIONS: Smaller relative volumes of spleen V0.5 and bone marrow V10 plus lower EDIC were jointly prone to reduce the incidence of G4 nadir during definitive concurrent chemoradiotherapy. This modified therapeutic strategy could be a significant prognostic factor for survival outcomes in ESCC.

Improving CBCT image quality to the CT level using RegGAN in esophageal cancer adaptive radiotherapy.

OBJECTIVE: This study aimed to improve the image quality and CT Hounsfield unit accuracy of daily cone-beam computed tomography (CBCT) using registration generative adversarial networks (RegGAN) and apply synthetic CT (sCT) images to dose calculations in radiotherapy. METHODS: The CBCT/planning CT images of 150 esophageal cancer patients undergoing radiotherapy were used for training (120 patients) and testing (30 patients). An unsupervised deep-learning method, the 2.5D RegGAN model with an adaptively trained registration network, was proposed, through which sCT images were generated. The quality of deep-learning-generated sCT images was quantitatively compared to the reference deformed CT (dCT) image using mean absolute error (MAE), root mean square error (RMSE) of Hounsfield units (HU), and peak signal-to-noise ratio (PSNR). The dose calculation accuracy was further evaluated for esophageal cancer radiotherapy plans, and the same plans were calculated on dCT, CBCT, and sCT images. RESULTS: The quality of sCT images produced by RegGAN was significantly improved compared to the original CBCT images. ReGAN achieved image quality in the testing patients with MAE sCT vs. CBCT: 43.7 ± 4.8 vs. 80.1 ± 9.1; RMSE sCT vs. CBCT: 67.2 ± 12.4 vs. 124.2 ± 21.8; and PSNR sCT vs. CBCT: 27.9 ± 5.6 vs. 21.3 ± 4.2. The sCT images generated by the RegGAN model showed superior accuracy on dose calculation, with higher gamma passing rates (93.3 ± 4.4, 90.4 ± 5.2, and 84.3 ± 6.6) compared to original CBCT images (89.6 ± 5.7, 85.7 ± 6.9, and 72.5 ± 12.5) under the criteria of 3 mm/3%, 2 mm/2%, and 1 mm/1%, respectively. CONCLUSION: The proposed deep-learning RegGAN model seems promising for generation of high-quality sCT images from stand-alone thoracic CBCT images in an efficient way and thus has the potential to support CBCT-based esophageal cancer adaptive radiotherapy.

Systematic risk analysis of radiation pneumonitis in breast cancer: role of cotreatment with chemo-, endocrine, and targeted therapy.

PURPOSE: A major complication of sequential and concomitant chemoradiation in breast cancer treatment is interstitial pneumonitis induced by radiation therapy (RT), systemic therapy, or a combination of both. Dose and volume of co-irradiated lung tissue directly correlate with the risk of radiation pneumonitis. Especially in case of combined treatment, it is often unclear which of the used therapeutic agents promote pneumonitis. METHODS: This was a prospective monocentric study including 396 breast cancer patients. A systematic analysis of single and combined therapeutic measures was performed in order to identify treatment-related factors enhancing the risk of pneumonitis post RT. RESULTS: Overall incidence of pneumonitis of any grade was 38%; 28% were asymptomatic (grade 1) and 10% were symptomatic (> grade 1). Pneumonitis > grade 2 did not occur. Beside age, smoking status, and mean lung dose, the combined treatment with goserelin and tamoxifen significantly enhanced the risk of pneumonitis in a supra-additive pattern (odds ratio [OR] 4.38), whereas each agent alone or combined with other drugs only nonsignificantly contributed to a higher pneumonitis incidence post RT (OR 1.52 and OR 1.16, respectively). None of the other systemic treatments, including taxanes, increased radiation pneumonitis risk in sequential chemoradiation. CONCLUSION: Common treatment schedules in sequential chemoradiation following breast-conserving surgery only moderately increase lung toxicity, mainly as an asymptomatic complication, or to a minor extent, as transient pneumonitis ≤ grade 2. However, combined treatment with tamoxifen and the LHRH analog goserelin significantly increased the risk of pneumonitis in breast cancer patients after chemoradiation. Thus, closer surveillance of involved patients is advisable.

Hippocampal avoidance in whole brain radiotherapy and prophylactic cranial irradiation: a systematic review and meta-analysis.

PURPOSE: We systematically reviewed the current landscape of hippocampal-avoidance radiotherapy, focusing specifically on rates of hippocampal tumor recurrence and changes in neurocognitive function. METHODS: PubMed was queried for studies involving hippocampal-avoidance radiation therapy and results were screened using PRISMA guidelines. Results were analyzed for median overall survival, progression-free survival, hippocampal relapse rates, and neurocognitive function testing. RESULTS: Of 3709 search results, 19 articles were included and a total of 1611 patients analyzed. Of these studies, 7 were randomized controlled trials, 4 prospective cohort studies, and 8 retrospective cohort studies. All studies evaluated hippocampal-avoidance whole brain radiation treatment (WBRT) and/or prophylactic cranial irradiation (PCI) in patients with brain metastases. Hippocampal relapse rates were low (overall effect size = 0.04; 95% confidence interval [0.03, 0.05]) and there was no significant difference in risk of relapse between the five studies that compared HA-WBRT/HA-PCI and WBRT/PCI groups (risk difference = 0.01; 95% confidence interval [- 0.02, 0.03]; p = 0.63). 11 out of 19 studies included neurocognitive function testing. Significant differences were reported in overall cognitive function and memory and verbal learning 3-24 months post-RT. Differences in executive function were reported by one study, Brown et al., at 4 months. No studies reported differences in verbal fluency, visual learning, concentration, processing speed, and psychomotor speed at any timepoint. CONCLUSION: Current studies in HA-WBRT/HA-PCI showed low hippocampal relapse or metastasis rates. Significant differences in neurocognitive testing were most prominent in overall cognitive function, memory, and verbal learning. Studies were hampered by loss to follow-up.

Consistency in contouring of organs at risk by artificial intelligence vs oncologists in head and neck cancer patients.

BACKGROUND: In the Danish Head and Neck Cancer Group (DAHANCA) 35 trial, patients are selected for proton treatment based on simulated reductions of Normal Tissue Complication Probability (NTCP) for proton compared to photon treatment at the referring departments. After inclusion in the trial, immobilization, scanning, contouring and planning are repeated at the national proton centre. The new contours could result in reduced expected NTCP gain of the proton plan, resulting in a loss of validity in the selection process. The present study evaluates if contour consistency can be improved by having access to AI (Artificial Intelligence) based contours. MATERIALS AND METHODS: The 63 patients in the DAHANCA 35 pilot trial had a CT from the local DAHANCA centre and one from the proton centre. A nationally validated convolutional neural network, based on nnU-Net, was used to contour OARs on both scans for each patient. Using deformable image registration, local AI and oncologist contours were transferred to the proton centre scans for comparison. Consistency was calculated with the Dice Similarity Coefficient (DSC) and Mean Surface Distance (MSD), comparing contours from AI to AI and oncologist to oncologist, respectively. Two NTCP models were applied to calculate NTCP for xerostomia and dysphagia. RESULTS: The AI contours showed significantly better consistency than the contours by oncologists. The median and interquartile range of DSC was 0.85 [0.78 - 0.90] and 0.68 [0.51 - 0.80] for AI and oncologist contours, respectively. The median and interquartile range of MSD was 0.9 mm [0.7 - 1.1] mm and 1.9 mm [1.5 - 2.6] mm for AI and oncologist contours, respectively. There was no significant difference in ΔNTCP. CONCLUSIONS: The study showed that OAR contours made by the AI algorithm were more consistent than those made by oncologists. No significant impact on the ΔNTCP calculations could be discerned.

Extensive clinical testing of Deep Learning Segmentation models for thorax and breast cancer radiotherapy planning.

BACKGROUND: The performance of deep learning segmentation (DLS) models for automatic organ extraction from CT images in the thorax and breast regions was investigated. Furthermore, the readiness and feasibility of integrating DLS into clinical practice were addressed by measuring the potential time savings and dosimetric impact. MATERIAL AND METHODS: Thirty patients referred to radiotherapy for breast cancer were prospectively included. A total of 23 clinically relevant left- and right-sided organs were contoured manually on CT images according to ESTRO guidelines. Next, auto-segmentation was executed, and the geometric agreement between the auto-segmented and manually contoured organs was qualitatively assessed applying a scale in the range [0-not acceptable, 3-no corrections]. A quantitative validation was carried out by calculating Dice coefficients (DSC) and the 95% percentile of Hausdorff distances (HD95). The dosimetric impact of optimizing the treatment plans on the uncorrected DLS contours, was investigated from a dose coverage analysis using DVH values of the manually delineated contours as references. RESULTS: The qualitative analysis showed that 93% of the DLS generated OAR contours did not need corrections, except for the heart where 67% of the contours needed corrections. The majority of DLS generated CTVs needed corrections, whereas a minority were deemed not acceptable. Still, using the DLS-model for CTV and heart delineation is on average 14 minutes faster. An average DSC=0.91 and H95=9.8 mm were found for the left and right breasts, respectively. Likewise, and average DSC in the range [0.66, 0.76]mm and HD95 in the range [7.04, 12.05]mm were found for the lymph nodes. CONCLUSION: The validation showed that the DLS generated OAR contours can be used clinically. Corrections were required to most of the DLS generated CTVs, and therefore warrants more attention before possibly implementing the DLS models clinically.

Deep learning algorithm performance in contouring head and neck organs at risk: a systematic review and single-arm meta-analysis.

PURPOSE: The contouring of organs at risk (OARs) in head and neck cancer radiation treatment planning is a crucial, yet repetitive and time-consuming process. Recent studies have applied deep learning (DL) algorithms to automatically contour head and neck OARs. This study aims to conduct a systematic review and meta-analysis to summarize and analyze the performance of DL algorithms in contouring head and neck OARs. The objective is to assess the advantages and limitations of DL algorithms in contour planning of head and neck OARs. METHODS: This study conducted a literature search of Pubmed, Embase and Cochrane Library databases, to include studies related to DL contouring head and neck OARs, and the dice similarity coefficient (DSC) of four categories of OARs from the results of each study are selected as effect sizes for meta-analysis. Furthermore, this study conducted a subgroup analysis of OARs characterized by image modality and image type. RESULTS: 149 articles were retrieved, and 22 studies were included in the meta-analysis after excluding duplicate literature, primary screening, and re-screening. The combined effect sizes of DSC for brainstem, spinal cord, mandible, left eye, right eye, left optic nerve, right optic nerve, optic chiasm, left parotid, right parotid, left submandibular, and right submandibular are 0.87, 0.83, 0.92, 0.90, 0.90, 0.71, 0.74, 0.62, 0.85, 0.85, 0.82, and 0.82, respectively. For subgroup analysis, the combined effect sizes for segmentation of the brainstem, mandible, left optic nerve, and left parotid gland using CT and MRI images are 0.86/0.92, 0.92/0.90, 0.71/0.73, and 0.84/0.87, respectively. Pooled effect sizes using 2D and 3D images of the brainstem, mandible, left optic nerve, and left parotid gland for contouring are 0.88/0.87, 0.92/0.92, 0.75/0.71 and 0.87/0.85. CONCLUSIONS: The use of automated contouring technology based on DL algorithms is an essential tool for contouring head and neck OARs, achieving high accuracy, reducing the workload of clinical radiation oncologists, and providing individualized, standardized, and refined treatment plans for implementing "precision radiotherapy". Improving DL performance requires the construction of high-quality data sets and enhancing algorithm optimization and innovation.

Deep learning-based classification of organs at risk and delineation guideline in pelvic cancer radiation therapy.

Deep learning (DL) models for radiation therapy (RT) image segmentation require accurately annotated training data. Multiple organ delineation guidelines exist; however, information on the used guideline is not provided with the delineation. Extraction of training data with coherent guidelines can therefore be challenging. We present a supervised classification method for pelvis structure delineations where bowel cavity, femoral heads, bladder, and rectum data, with two guidelines, were classified. The impact on DL-based segmentation quality using mixed guideline training data was also demonstrated. Bowel cavity was manually delineated on CT images for anal cancer patients (n = 170) according to guidelines Devisetty and RTOG. The DL segmentation quality from using training data with coherent or mixed guidelines was investigated. A supervised 3D squeeze-and-excite SENet-154 model was trained to classify two bowel cavity delineation guidelines. In addition, a pelvis CT dataset with manual delineations from prostate cancer patients (n = 1854) was used where data with an alternative guideline for femoral heads, rectum, and bladder were generated using commercial software. The model was evaluated on internal (n = 200) and external test data (n = 99). By using mixed, compared to coherent, delineation guideline training data mean DICE score decreased 3% units, mean Hausdorff distance (95%) increased 5 mm and mean surface distance (MSD) increased 1 mm. The classification of bowel cavity test data achieved 99.8% unweighted classification accuracy, 99.9% macro average precision, 97.2% macro average recall, and 98.5% macro average F1. Corresponding metrics for the pelvis internal test data were all 99% or above and for the external pelvis test data they were 96.3%, 96.6%, 93.3%, and 94.6%. Impaired segmentation performance was observed for training data with mixed guidelines. The DL delineation classification models achieved excellent results on internal and external test data. This can facilitate automated guideline-specific data extraction while avoiding the need for consistent and correct structure labels.

Educative Impact of Automatic Delineation Applied to Head and Neck Cancer Patients on Radiation Oncology Residents.

To evaluate the educational impact on radiation oncology residents in training when introducing an automatic segmentation software in head and neck cancer patients regarding organs at risk (OARs) and prophylactic cervical lymph node level (LNL) volumes. Two cases treated by exclusive intensity-modulated radiotherapy were delineated by an expert radiation oncologist and were considered as reference. Then, these cases were delineated by residents divided into two groups: group 1 (control group), experienced residents delineating manually, group 2 (experimental group), young residents on their first rotation trained with automatic delineation, delineating manually first (M -) and then after using the automatic system (M +). The delineation accuracy was assessed using the Overlap Volume (OV). Regarding the OARs, mean OV was 0.62 (SD = 0.05) for group 1, 0.56 (SD = 0.04) for group 2 M - , and 0.61 (SD = 0.03) for group 2 M + . Mean OV was higher in group 1 compared to group 2 M - (p = 0.01). There was no OV difference between group 1 and group 2 M + (p = 0.67). Mean OV was higher in the group 2 M + compared to group 2 M - (p < 0.003). Regarding LNL, mean OV was 0.53 (SD = 0.06) in group 1, 0.54 (SD = 0.03) in group 2 M - , and 0.58 (SD = 0.04) in group 2 M + . Mean OV was higher in group 2 M + for 11 of the 12 analysed structures compared to group 2 M - (p = 0.016). Prior use of the automatic delineation software reduced the average contouring time per case by 34 to 40%. Prior use of atlas-based automatic segmentation reduces the delineation duration, and provides reliable OARs and LNL delineations.

Stereotactic body radiation therapy to the spine: contouring the cauda equina instead of the spinal cord is more practical as the organ at risk.

Background: Stereotactic body radiotherapy (SBRT) is recognized as a curative treatment for oligometastasis. The spinal cord becomes the cauda equina at the lumbar level, and the nerves are located dorsally. Recently, a consensus has been reached that the cauda equina should be contoured as an organ at risk (OAR). Here, we examined the separate contouring benefits for the spinal canal versus the cauda equina only as the OAR. Materials and methods: A medical physicist designed a simulation plan for 10 patients with isolated lumbar metastasis. The OAR was set with three contours: the whole spinal canal, cauda equina only, and cauda equina with bilateral nerve roots. The prescribed dose for the planning target volume (PTV) was 30 Gy/3 fx. Results: For the constrained QAR doses, D90 and D95 were statistically significant due to the different OAR contouring. The maximum dose (Dmax) was increased to the spinal canal when the cauda equina max was set to ≤ 20 Gy, but dose hotspots were observed in most cases in the medullary area. The Dmax and PTV coverage were negatively correlated for the cauda equina and the spinal canal if Dmax was set to ≤ 20 Gy for both. Conclusions: A portion of the spinal fluid is also included when the spinal canal is set as the OAR. Thus, the PTV coverage rate will be poor if the tumor is in contact with the spinal canal. However, the PTV coverage rate increases if only the cauda equina is set as the OAR.

Stereotactic body radiotherapy of lymph node metastases under MR-guidance: First clinical results and patient-reported outcomes.

OBJECTIVE: Stereotactic body radiotherapy (SBRT) is a noninvasive treatment option for lymph node metastases (LNM). Magnetic resonance (MR)-guidance offers superior tissue contrast and enables treatment of targets in close vicinity to radiosensitive organs at risk (OAR). However, literature on MR-guided SBRT of LNM is scarce with no report on outcome parameters. MATERIALS AND METHODS: We report a subgroup analysis of a prospective observational study comprising patients with LNM. Patients received MR-guided SBRT at our MRIdian Linac (ViewRay Inc., Mountain View, CA, USA) between January 2019 and February 2020. Local control (LC), progression-free survival (PFS) and overall survival (OS) analysis were performed using the Kaplan-Meier method with log rank test to test for significance (p < 0.05). Our patient-reported outcome questionnaire was utilized to evaluate patients' perspective. The CTCAE (Common Terminology Criteria for Adverse Events) v. 5.0 was used to describe toxicity. RESULTS: Twenty-nine patients (72.4% with prostate cancer; 51.7% with no distant metastases) received MR-guided SBRT for in total 39 LNM. Median dose was 27 Gy in three fractions, prescribed to the 80% isodose. At 1­year, estimated LC, PFS and OS were 92.6, 67.4 and 100.0%. Compared to baseline, six patients (20.7%) developed new grade I toxicities (mainly fatigue). One grade II toxicity occurred (fatigue), with no adverse event grade ≥III. Overall treatment experience was rated particularly positive, while the technically required low room temperature still represents the greatest obstacle in the pursuit of the ideal patient acceptance. CONCLUSION: MR-guided SBRT of LNM was demonstrated to be a well-accepted treatment modality with excellent preliminary results. Future studies should evaluate the clinical superiority to conventional SBRT.

RefineNet-based 2D and 3D automatic segmentations for clinical target volume and organs at risks for patients with cervical cancer in postoperative radiotherapy.

PURPOSE: An accurate and reliable target volume delineation is critical for the safe and successful radiotherapy. The purpose of this study is to develop new 2D and 3D automatic segmentation models based on RefineNet for clinical target volume (CTV) and organs at risk (OARs) for postoperative cervical cancer based on computed tomography (CT) images. METHODS: A 2D RefineNet and 3D RefineNetPlus3D were adapted and built to automatically segment CTVs and OARs on a total of 44 222 CT slices of 313 patients with stage I-III cervical cancer. Fully convolutional networks (FCNs), U-Net, context encoder network (CE-Net), UNet3D, and ResUNet3D were also trained and tested with randomly divided training and validation sets, respectively. The performances of these automatic segmentation models were evaluated by Dice similarity coefficient (DSC), Jaccard similarity coefficient, and average symmetric surface distance when comparing them with manual segmentations with the test data. RESULTS: The DSC for RefineNet, FCN, U-Net, CE-Net, UNet3D, ResUNet3D, and RefineNet3D were 0.82, 0.80, 0.82, 0.81, 0.80, 0.81, and 0.82 with a mean contouring time of 3.2, 3.4, 8.2, 3.9, 9.8, 11.4, and 6.4 s, respectively. The generated RefineNetPlus3D demonstrated a good performance in the automatic segmentation of bladder, small intestine, rectum, right and left femoral heads with a DSC of 0.97, 0.95, 091, 0.98, and 0.98, respectively, with a mean computation time of 6.6 s. CONCLUSIONS: The newly adapted RefineNet and developed RefineNetPlus3D were promising automatic segmentation models with accurate and clinically acceptable CTV and OARs for cervical cancer patients in postoperative radiotherapy.

Association between tumor morphology and dosimetric parameters of organs at risk after intensity-modulated radiotherapy in esophagus cancer.

PURPOSE: We explored the effects of geometrical topological properties of tumors such as tumor length and "axial cross-sectional area (ACSA)" of tumors (planning target volume [PTV] volume /PTV length) on the dosimetric parameters of organs at risk (lung and heart) in patients with esophagus cancer (EPC) treated by way of intensity-modulated radiation therapy (IMRT), so as to provide a guideline for the dosimetric limitation for organs at risk in IMRT treatment. METHODS: A retrospective analysis was done on 103 cases of patients with EPC who were treated by IMRT from November 2010 to August 2019, in which PTV-G stood for the externally expanded planning target volume (PTV) of the gross tumor volume (GTV) and PTV-C for the externally expanded volume of the clinical target volume (CTV). A linear regression model was employed to analyze the several pairs of correlation: the 1st one between the relative length of tumors (PTV length/lung length) and pulmonary dose-volume parameters, the 2nd one between ACSA of tumors and pulmonary dose-volume parameters, the 3rd one between PTV length and the dosimetric parameters of the heart, and the last one between ACSA of tumors and the dosimetric parameters of the heart. RESULTS: (i) There was a strong positive correlation between the relative length of tumors (PTV length/lung length) and V5 (p < 0.001, r = 0.73), and V10 (p < 0.001, r = 0.66) of the lung. There was a moderate positive correlation between the relative length of tumors and V30 (p < 0.001, r = 0.44) of the lung, and a weak positive correlation between the relative length of tumors and V20 (p < 0.001, r = 0.39) of the lung. (ii) There was a strong positive correlation between ACSA of tumors (PTV volume/PTV length) and V30 (p < 0.001, r = 0.67) of the lung, a moderate positive correlation between ACSA of tumors and V20 (p <0.001, r = 0.51) of the lung, and a weak positive correlation between ACSA of tumors and V10 (p = 0.019, r = 0.23) of the lung, yet there was not an obvious correlation between ACSA of tumors and V5 p > 0.05) of the lung. (iii) There was a moderate positive correlation between PTV length and V40 (p < 0.001, r = 0.58), and Dmean (p < 0.001, r = 0.52) of the heart, yet there was no obvious correlation between ACSA of tumors and Dmean and V40 of the heart (p > 0.05). CONCLUSIONS: (i) Compared with the high-dose region of the lung, the relative length of tumors (PTV length/lung length) has a greater impact on the low-dose region of the lung. The linear regression equation of scatter plot showed that when the relative length of tumors increased by 0.1, the lung dose-volume parameters of V5 , V10 , V20 , and V30 increased by approximately 5.37%, 3.59%, 1.05%, and 1.08%, respectively. When PTV length increased by 1 cm, Dmean and V40 of the heart increased by approximately 153.6 cGy and 2.03%, respectively. (ii) Compared with the low-dose region of the lung, the value of ACSA of tumors (PTV volume/PTV length) has a greater impact on the high-dose region of the lung. However, the value of ACSA of tumors has no significant effect on the dosimetric parameters of the heart (Dmean and V40 ). The linear regression equation of scatter plot showed that when ACSA of tumors increased by 10 cm2 , the lung dose-volume parameters of V10 , V20, and V30 increased by approximately 3.11%, 3.37%, and 4.01%, respectively.

Deep Learning-Based Segmentation of Head and Neck Organs-at-Risk with Clinical Partially Labeled Data.

Radiotherapy is one of the main treatments for localized head and neck (HN) cancer. To design a personalized treatment with reduced radio-induced toxicity, accurate delineation of organs at risk (OAR) is a crucial step. Manual delineation is time- and labor-consuming, as well as observer-dependent. Deep learning (DL) based segmentation has proven to overcome some of these limitations, but requires large databases of homogeneously contoured image sets for robust training. However, these are not easily obtained from the standard clinical protocols as the OARs delineated may vary depending on the patient's tumor site and specific treatment plan. This results in incomplete or partially labeled data. This paper presents a solution to train a robust DL-based automated segmentation tool exploiting a clinical partially labeled dataset. We propose a two-step workflow for OAR segmentation: first, we developed longitudinal OAR-specific 3D segmentation models for pseudo-contour generation, completing the missing contours for some patients; with all OAR available, we trained a multi-class 3D convolutional neural network (nnU-Net) for final OAR segmentation. Results obtained in 44 independent datasets showed superior performance of the proposed methodology for the segmentation of fifteen OARs, with an average Dice score coefficient and surface Dice similarity coefficient of 80.59% and 88.74%. We demonstrated that the model can be straightforwardly integrated into the clinical workflow for standard and adaptive radiotherapy.

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OBJECTIVES: The automatic delineation of organs at risk (OARs) can help doctors make radiotherapy plans efficiently and accurately, and effectively improve the accuracy of radiotherapy and the therapeutic effect. Therefore, this study aims to propose an automatic delineation method for OARs in cervical cancer scenarios of both after-loading and external irradiation. At the same time, the similarity of OARs structure between different scenes is used to improve the segmentation accuracy of OARs in difficult segmentations. METHODS: Our ensemble model adopted the strategy of ensemble learning. The model obtained from the pre-training based on the after-loading and external irradiation was introduced into the integrated model as a feature extraction module. The data in different scenes were trained alternately, and the personalized features of the OARs within the model and the common features of the OARs between scenes were introduced. Computer tomography (CT) images for 84 cases of after-loading and 46 cases of external irradiation were collected as the train data set. Five-fold cross-validation was adopted to split training sets and test sets. The five-fold average dice similarity coefficient (DSC) served as the figure-of-merit in evaluating the segmentation model. RESULTS: The DSCs of the OARs (the rectum and bladder in the after-loading images and the bladder in the external irradiation images) were higher than 0.7. Compared with using an independent residual U-net (convolutional networks for biomedical image segmentation) model [residual U-net (Res-Unet)] delineate OARs, the proposed model can effectively improve the segmentation performance of difficult OARs (the sigmoid in the after-loading CT images and the rectum in the external irradiation images), and the DSCs were increased by more than 3%. CONCLUSIONS: Comparing to the dedicated models, our ensemble model achieves the comparable result in segmentation of OARs for different treatment options in cervical cancer radiotherapy, which may be shorten time for doctors to sketch OARs and improve doctor's work efficiency.

Dynamic conformal arc radiotherapy for locally advanced lung cancer: a comparison with static-beam conformal radiotherapy.

Background: This study investigated whether the dose distribution of lung cancer can be improved by dynamic arc conformal radiotherapy (dynamic CRT) compared with static multiple-beam radiotherapy (static CRT). Materials and methods: A dummy study of static CRT and dynamic CRT was performed, designed to meet the predetermined dose constraints. A dose of 60 Gy in 30 fractions was administered using two dose prescription methods: dose prescribed to the isocenter (IC prescription), and dose prescribed to > 50% of the planning target volume (D50 prescription). Dose-volume parameters were compared between the plans. Results: Among 20 patients with locally advanced lung cancer, dose conformity was significantly better with dynamic CRT than static CRT (median conformity index: 1.3 vs. 2.2; p < 0.01). As for the lung dose, compared with static CRT, dynamic CRT did not increase the percentage lung volume receiving ≥ 20 Gy (18.9% vs. 19.3%, p = 0.09). The maximum spinal cord dose was significantly reduced by dynamic CRT (static vs. dynamic CRT: 44.1 vs. 25.2 Gy, p < 0.001). With the change from IC to D50 prescription, the 95% isodose volume increased by 18.3 cc in static CRT and by 4.1 cc in dynamic CRT, while doses to the lung and spinal cord remained within the acceptable ranges. Conclusion: The dynamic CRT technique showed better target coverage and lower doses to the spinal cord in exchange for increased low-dose lung area, compared with static CRT. Dynamic CRT with D50 prescription instead of prescription to the isocenter has excellent dose distribution profiles without compromising doses to organs at risk for lung cancer at favorable locations.

Evaluation of automatic VMAT plans in locally advanced nasopharyngeal carcinoma.

OBJECTIVE: This study aimed to evaluate the quality of locally advanced nasopharyngeal carcinoma (NPC) radiotherapy plans generated by the automated planning module of a commercial treatment planning system (TPS). METHODS: Data of 30 patients with locally advanced NPC were retrospectively investigated. For each patient, volumetric modulated arc therapy (VMAT) plans with double arcs were generated manually by experienced physicists and automatically in the Pinnacle3 Auto-Planning module (Philips Medical Systems, Fitchburg, WI, USA). The anatomic distance between the second clinical target volume (CTV2) and the pons of the brainstem, and the T category of disease were factored into the evaluation. Dosimetric verification was evaluated in terms of gamma pass rate. Target coverage, sparing of organs at risk (OARs), and monitor units were evaluated and compared between the manual and automatic VMAT plans. RESULTS: Not all treatment plans fully met the dose objectives for planning target volumes (PTVs) and OARs, particularly in T4 patients. Overall, automatic VMAT provides a comparable or superior plan quality to manual VMAT in most cases. In stratified analysis, plan quality is mainly independent on T category but is also affected by anatomic distance. If the anatomic distance is less than 5 mm, the automatic VMAT plan quality is equal or even inferior to manual VMAT performed by experienced physicists. Conversely, if the anatomic distance is greater than 5 mm, the automatic VMAT plan quality is superior to manual VMAT. Gamma pass rates for quality assurance are similar between manual and automatic VMAT plans for the former case, but significantly higher in automatic VMAT for the latter. CONCLUSION: The selection of manual versus automatic VMAT planning in locally advanced NPC should be made individually based on the anatomic distance, rather than blindly and habitually, since automatic VMAT is not good enough to completely replace manual VMAT.

Improving interinstitutional and intertechnology consistency of pulmonary SBRT by dose prescription to the mean internal target volume dose.

PURPOSE: Dose, fractionation, normalization and the dose profile inside the target volume vary substantially in pulmonary stereotactic body radiotherapy (SBRT) between different institutions and SBRT technologies. Published planning studies have shown large variations of the mean dose in planning target volume (PTV) and gross tumor volume (GTV) or internal target volume (ITV) when dose prescription is performed to the PTV covering isodose. This planning study investigated whether dose prescription to the mean dose of the ITV improves consistency in pulmonary SBRT dose distributions. MATERIALS AND METHODS: This was a multi-institutional planning study by the German Society of Radiation Oncology (DEGRO) working group Radiosurgery and Stereotactic Radiotherapy. CT images and structures of ITV, PTV and all relevant organs at risk (OAR) for two patients with early stage non-small cell lung cancer (NSCLC) were distributed to all participating institutions. Each institute created a treatment plan with the technique commonly used in the institute for lung SBRT. The specified dose fractionation was 3â€¯× 21.5 Gy normalized to the mean ITV dose. Additional dose objectives for target volumes and OAR were provided. RESULTS: In all, 52 plans from 25 institutions were included in this analysis: 8 robotic radiosurgery (RRS), 34 intensity-modulated (MOD), and 10 3D-conformal (3D) radiation therapy plans. The distribution of the mean dose in the PTV did not differ significantly between the two patients (median 56.9 Gy vs 56.6 Gy). There was only a small difference between the techniques, with RRS having the lowest mean PTV dose with a median of 55.9 Gy followed by MOD plans with 56.7 Gy and 3D plans with 57.4 Gy having the highest. For the different organs at risk no significant difference between the techniques could be found. CONCLUSIONS: This planning study pointed out that multiparameter dose prescription including normalization on the mean ITV dose in combination with detailed objectives for the PTV and ITV achieve consistent dose distributions for peripheral lung tumors in combination with an ITV concept between different delivery techniques and across institutions.