Clinical Target Volume Research Articles

RADT-06. ENCOURAGING OUTCOMES AFTER CRANIO-SPINAL IRRADIATION (CSI) WITH MODERN IMAGE GUIDED INTENSITY MODULATED PROTON THERAPY (IMPT) (IMPT-CSI) IN CHILDREN AND YOUNG ADULTS (AYA)- AN INSTITUTION EXPERIENCE

Abstract BACKGROUND Outcomes of IMPT-CSI in children and adolescent-young adults (AYA) treated at our institute over a period of 5 years was analyzed MATERIALS AND METHODS 92 children/AYA were treated by IMPT-CSI. PTV definition of the spine for skeletally immature patients was modified to only the vertebral body without isotropic margins. All plans were multifield optimized for 5 mm robustness (cranio-caudal) & 2.5% range uncertainty. The treatment set-up was confirmed by both orthogonal radiograph and CBCT. Toxicity was graded as per RTOG criteria. RESULTS Median CSI dose of 35GyE in 21 fractions was given to cohort with 74% males, median age of 7 years (2-39 years), consisting of Medulloblastoma (58%), other Embryonal tumours (20%), recurrent Ependymoma (10%), GCT (10%) and others (2%) with 18% being reirradiation and under sedation in 25%. Concurrent chemotherapy was given in 40%. The median V98% for clinical target volumes and V95% for PTVs of the brain, spine and craniospinal were >97%. Isodose 95% covered the cribriform plate completely and optic nerves keeping Dmax to the lens <3.9 GyRBE, critical OARs (mean of the cohort- Cochlea Dmax 28GyE, midline mucosa 23.2GyE, Esophagus 23.2GyE and gonads (testes and ovaries- 0GyE and 0.4GyE). The online set-up verification showed translational and rotational deviation within 2 mm and 0.5 in 94% and 97% of the cases. Radiation dermatitis was Grade 2-40%; Gr3-10%. Neutropenia was Grade 3 in 30%; few needed G-CSF. Less than 10% developed >grade 3 significant weight loss/ anorexia/ mucositis. At median F/U 20 months (6–60m), 12 patients have progressed with PFS of 78% & OS of 87%, best in GCT and worst in Ependymoma (recurrent), 100% vs. 55% (p<0.014) CONCLUSION Outcomes of IMPT-CSI in children and young adults, showed very encouraging dosimetry, toxicity profile and long-term survival.

The current state and future perspectives of radiotherapy for cervical cancer.

Radiotherapy is an effective treatment method for cervical cancer and is typically administered as external beam radiotherapy followed by intracavitary brachytherapy. In Japan, center shielding is used in external beam radiotherapy to shorten treatment time and reduce the doses delivered to the rectum or bladder. However, it has several challenges, such as uncertainties in calculating the cumulative dose. Recently, external beam radiotherapy has been increasingly performed with intensity-modulated radiotherapy, which reduces doses to the rectum or bladder without center shielding. In highly conformal radiotherapy, uncertainties in treatment delivery, such as inter-fractional anatomical structure movements, affect treatment outcomes; therefore, image-guided radiotherapy is essential for appropriate and safe performance. Regarding intracavitary brachytherapy, the use of magnetic resonance imaging-based image-guided adaptive brachytherapy is becoming increasingly widespread because it allows dose escalation to the tumor and accurately evaluates the dose delivered to the surrounding normal organs. According to current evidence, a minimal dose of D90% of the high-risk clinical target volume is significantly relevant to local control. Further improvements in target coverage have been achieved with combined interstitial and intracavity brachytherapy for massive tumors with extensive parametrical involvement. Introducing artificial intelligence will enable faster and more accurate generation of brachytherapy plans. Charged-particle therapies have biological and dosimetric advantages, and current evidence has proven their effectiveness and safety in cervical cancer treatment. Recently, radiotherapy-related technologies have advanced dramatically. This review provides an overview of technological innovations and future perspectives in radiotherapy for cervical cancer.

Deep learning promoted target volumes delineation of total marrow and total lymphoid irradiation for accelerated radiotherapy: A multi-institutional study

Background and purposeOne of the current roadblocks to the widespread use of Total Marrow Irradiation (TMI) and Total Marrow and Lymphoid Irradiation (TMLI) is the challenging difficulties in tumor target contouring workflow. This study aims to develop a hybrid neural network model that promotes accurate, automatic, and rapid segmentation of multi-class clinical target volumes. Materials and methodsPatients who underwent TMI and TMLI from January 2018 to May 2022 were included. Two independent oncologists manually contoured eight target volumes for patients on CT images. A novel Dual-Encoder Alignment Network (DEA-Net) was developed and trained using 46 patients from one internal institution and independently evaluated on a total of 39 internal and external patients. Performance was evaluated on accuracy metrics and delineation time. ResultsThe DEA-Net achieved a mean dice similarity coefficient of 90.1 % ± 1.8 % for internal testing dataset (23 patients) and 91.1 % ± 2.5 % for external testing dataset (16 patients). The 95 % Hausdorff distance and average symmetric surface distance were 2.04 ± 0.62 mm and 0.57 ± 0.11 mm for internal testing dataset, and 2.17 ± 0.68 mm, and 0.57 ± 0.20 mm for external testing dataset, respectively, outperforming most of existing state-of-the-art methods. In addition, the automatic segmentation workflow reduced delineation time by 98 % compared to the conventional manual contouring process (mean 173 ± 29 s vs. 12168 ± 1690 s; P < 0.001). Ablation study validate the effectiveness of hybrid structures. ConclusionThe proposed deep learning framework achieved comparable or superior target volume delineation accuracy, significantly accelerating the radiotherapy planning process.

The partial adaptation strategy for online-adaptive proton therapy: A proof of concept study in head and neck cancer patients.

The accuracy of intensity-modulated proton therapy (IMPT) is greatly affected by anatomy variations that might occur during the treatment course. Online plan adaptations have been proposed as a solution to intervene promptly during a treatment session once the anatomy changes are detected. The implementation of online-adaptive proton therapy (OAPT) is still hindered by time-consuming tasks in the workflow. The study introduces the novel concept of partial adaptation and aims at investigating its feasibility as a potential solution to parallelize tasks during an OAPT workflow for saving valuable in-room time. The proof-of-principle simulation study includes datasets from six head and neck cancer (HNC) patients, each consisting of one planning CT (pCT) and three contoured control CTs (cCTs). Robust 3-field normo-fractionated initial IMPT plans were generated on the pCTs with a standardized field configuration, delivering 66 Gy and 54Gy to the high-risk and low-risk clinical target volume (CTVHigh and CTVLow), respectively.For each cCT, a dose-mimicking-based partial adaptation was applied: two fields were adapted on the current anatomy taking into account the background dose of the first non-adapted field supposedly delivered in the meantime. Fraction doses on the cCTs resulting from partially adapted plans with different first (non-adapted) field assignments were compared against those from non-adapted and fully adapted plans regarding target coverage and organs at risk (OARs) sparing. The robustness of partially adapted plans was also evaluated. Partially adapted plans showed comparable results to fully adapted plans and were superior to non-adapted plans for both target coverage and OAR sparing. Target coverage degradation in the non-adapted plans (median D98%: 95.9% and 97.5% for CTVLow and CTVHigh, respectively) was recovered by both partial (98.0% and 98.5%) and full adaptation (98.2% and 98.7%) in comparison to the initial plans (98.7% and 98.8%). The initial hotspot dose for the CTVHigh (median D2%: 101.8%) increased in the non-adapted plans (102.9%) and was recovered by the adaptive strategies (partial: 102.5%, full: 101.9%). The near-maximum dose (D0.01cc) to brainstem and spinal cord was within clinical constraints for all investigated dose distributions, but clearly increased for no adaptation and improved in the (both partially and fully) adapted plans with respect to the non-adapted ones. The parotids' median doses (D50) were mainly patient-specific depending on the proximity to the target region, but anyway lower for the partially and fully adapted plans compared to the non-adapted ones. OAR sparing was furthermore improved for the partially adapted plans in comparison to full adaptation. Robustness of the target dose metrics was preserved in all evaluated scenarios. For OAPT of HNC patients, partial adaptation is able to generate plans of superior conformity to non-adapted plans and of comparable conformity as fully adapted plans, while having the potential to speed up the online-adaptive workflows. Thus, partial adaptation represents an intermediate approach until fast online adaptation workflows become available. Furthermore, it can be applied in workflows where online treatment verification stops the delivery and triggers an online adaptation for the remaining fraction.

Magnetic resonance image-guided adaptive radiotherapy enables safe CTV-to-PTV margin reduction in prostate cancer: a cine MRI motion study.

We aimed to establish if stereotactic body radiotherapy to the prostate can be delivered safely using reduced clinical target volume (CTV) to planning target volume (PTV) margins on the 1.5T MR-Linac (MRL) (Elekta, Stockholm, Sweden), in the absence of gating. Cine images taken in 3 orthogonal planes during the delivery of prostate SBRT with 36.25 Gray (Gy) in 5 fractions on the MRL were analysed. Using the data from 20 patients, the percentage of radiotherapy (RT) delivery time where the prostate position moved beyond 1, 2, 3, 4 and 5 mm in the left-right (LR), superior-inferior (SI), anterior-posterior (AP) and any direction was calculated. The prostate moved less than 3 mm in any direction for 90% of the monitoring period in 95% of patients. On a per-fraction basis, 93% of fractions displayed motion in all directions within 3 mm for 90% of the fraction delivery time. Recurring motion patterns were observed showing that the prostate moved with shallow drift (most common), transient excursions and persistent excursions during treatment. A 3 mm CTV-PTV margin is safe to use for the treatment of 5 fraction prostate SBRT on the MRL, without gating. In the context of gating this work suggests that treatment time will not be extensively lengthened when an appropriate gating window is applied.

Simultaneous integrated boost intensity-modulated radiotherapy (SIB-IMRT) combined with nimotuzumab for locally advanced esophageal squamous cell carcinoma (ESCC): A phase II clinical trial

ObjectiveTo evaluate the feasibility, safety and efficacy of concurrent simultaneous integrated boost intensity-modulated radiotherapy (SIB-IMRT) combined with nimotuzumab in the treatment of locally advanced esophageal squamous cell cancer (ESCC).MethodsEligible patients were histologically proven to have locally advanced ESCC, and were unable to tolerate or refuse concurrent chemoradiotherapy (CCRT). Enrolled patients underwent concurrent SIB-IMRT in combination with nimotuzumab. SIB-IMRT: For the planning target volume of clinical target volume (PTV-C), the prescription dose was 50.4 Gy/28fractions, 1.8 Gy/fraction, 5fractions/week, concurrently, the planning target volume of gross tumor (PTV-G) undergone an integrated boost therapy, with a prescription dose of 63 Gy/28fractions, 2.25 Gy/fraction, 5 fractions/week. Nimotuzumab was administered concurrently with radiotherapy, 200 mg/time, on D1, 8, 15, 22, 29, and 36, with a total accumulation of 1200 mg through intravenous infusion. The primary endpoint of the study was the safety and efficacy of the combined treatment regimen, and the secondary endpoints were 1-year, 2-year, and 3-year local control and survival outcomes.Results(1) From December 2018 to August 2021, 35 patients with stage II-IVA ESCC were enrolled and 34 patients completed the full course of radiotherapy and the intravenous infusion of full-dose nimotuzumab. The overall completion rate of the protocol was 97.1%. (2) No grade 4–5 adverse events occurred in the entire group. The most common treatment-related toxicity was acute radiation esophagitis, with a total incidence of 68.6% (24/35). The incidence of grade 2 and 3 acute esophagitis was 25.7% (9/35) and 17.1% (6/35), respectively. The incidence of acute radiation pneumonitis was 8.6% (3/35), including one case each of Grades 1, 2, and 3 pneumonitis. Adverse events in other systems included decreased blood cells, hypoalbuminemia, electrolyte disturbances, and skin rash. Among these patients, five experienced grade 3 electrolyte disturbances during the treatment period (three with grade 3 hyponatremia and two with grade 3 hypokalemia). (3) Efficacy: The overall CR rate was 22.8%, PR rate was 71.4%, ORR rate was 94.2%, and DCR rate was 97.1%.(4) Local control and survival: The 1-, 2-, and 3-year local control (LC) rate, progression-free survival(PFS) rate, and overall survival(OS) rate for the entire group were 85.5%, 75.4%, and 64.9%; 65.7%, 54.1%, and 49.6%; and 77.1%, 62.9%, and 54.5%, respectively.ConclusionsThe combination of SIB-IMRT and nimotuzumab for locally advanced esophageal cancer demonstrated good feasibility, safety and efficacy. It offered potential benefits in local control and survival. Acute radiation esophagitis was the primary treatment-related toxicity, which is clinically manageable. This comprehensive treatment approach is worthy of further clinical exploration (ChiCTR1900027936).

Attention 3D U-NET for dose distribution prediction of high-dose-rate brachytherapy of cervical cancer: Direction modulated brachytherapy tandem applicator.

Direction Modulated Brachytherapy (DMBT) enables conformal dose distributions. However, clinicians may face challenges in creating viable treatment plans within a fast-paced clinical setting, especially for a novel technology like DMBT, where cumulative clinical experience is limited. Deep learning-based dose prediction methods have emerged as effective tools for enhancing efficiency. To develop a voxel-wise dose prediction model using an attention-gating mechanism and a 3D UNET for cervical cancer high-dose-rate (HDR) brachytherapy treatment planning with DMBT six-groove tandems with ovoids or ring applicators. A multi-institutional cohort of 122 retrospective clinical HDR brachytherapy plans treated to a prescription dose in the range of 4.8-7.0Gy/fraction was used. A DMBT tandem model was constructed and incorporated onto a research version of BrachyVision Treatment Planning System (BV-TPS) as a 3D solid model applicator and retrospectively re-planned all cases by seasoned experts. Those plans were randomly divided into 64:16:20 as training, validating, and testing cohorts, respectively. Data augmentation was applied to the training and validation sets to increase the size by a factor of 4. An attention-gated 3D UNET architecture model was developed to predict full 3D dose distributions based on high-risk clinical target volume (CTVHR) and organs at risk (OARs) contour information. The model was trained using the mean absolute error loss function, Adam optimization algorithm, a learning rate of 0.001, 250 epochs, and a batch size of eight. In addition, a baseline UNET model was trained similarly for comparison. The model performance was evaluated on the testing dataset by analyzing the outcomes in terms of mean dose values and derived dose-volume-histogram indices from 3D dose distributions and comparing the generated dose distributions against the ground-truth dose distributions using dose statistics and clinically meaningful dosimetric indices. The proposed attention-gated 3D UNET model showed competitive accuracy in predicting 3D dose distributions that closely resemble the ground-truth dose distributions. The average values of the mean absolute errors were 1.82±29.09Gy (vs. 6.41±20.16Gy for a baseline UNET) in CTVHR, 0.89±1.25Gy (vs. 0.94±3.96Gy for a baseline UNET) in the bladder, 0.33±0.67Gy (vs. 0.53±1.66Gy for a baseline UNET) in the rectum, and 0.55±1.57Gy (vs. 0.76±2.89Gy for a baseline UNET) in the sigmoid. The results showed that the mean absolute error (MAE) for the bladder, rectum, and sigmoid were 0.22±1.22Gy (3.62%) (p=0.015), 0.21±1.06Gy (2.20%) (p=0.172), and -0.03±0.54Gy (1.13%) (p=0.774), respectively. The MAE for D90, V100%, and V150% of the CTVHR were 0.46±2.44Gy (8.14%) (p=0.018), 0.57±11.25% (5.23%) (p=0.283), and -0.43±19.36% (4.62%) (p=0.190), respectively. The proposed model needs less than 5 s to predict a full 3D dose distribution of 64×64×64 voxels for any new patient plan, thus making it sufficient for near real-time applications and aiding with decision-making in the clinic. Attention gated 3D-UNET model demonstrated a capability in predicting voxel-wise dose prediction, in comparison to 3D UNET, for DMBT intracavitary brachytherapy planning. The proposed model could be used to obtain dose distributions for near real-time decision-making before DMBT planning and quality assurance. This will guide future automated planning, making the workflow more efficient and clinically viable.

Uncertainty estimation using a 3D probabilistic U-Net for segmentation with small radiotherapy clinical trial datasets

Background and objectivesBio-medical image segmentation models typically attempt to predict one segmentation that resembles a ground-truth structure as closely as possible. However, as medical images are not perfect representations of anatomy, obtaining this ground truth is not possible. A surrogate commonly used is to have multiple expert observers define the same structure for a dataset. When multiple observers define the same structure on the same image there can be significant differences depending on the structure, image quality/modality and the region being defined. It is often desirable to estimate this type of aleatoric uncertainty in a segmentation model to help understand the region in which the true structure is likely to be positioned. Furthermore, obtaining these datasets is resource intensive so training such models using limited data may be required. With a small dataset size, differing patient anatomy is likely not well represented causing epistemic uncertainty which should also be estimated so it can be determined for which cases the model is effective or not. MethodsWe use a 3D probabilistic U-Net to train a model from which several segmentations can be sampled to estimate the range of uncertainty seen between multiple observers. To ensure that regions where observers disagree most are emphasised in model training, we expand the Generalised Evidence Lower Bound (ELBO) with a Constrained Optimisation (GECO) loss function with an additional contour loss term to give attention to this region. Ensemble and Monte-Carlo dropout (MCDO) uncertainty quantification methods are used during inference to estimate model confidence on an unseen case. We apply our methodology to two radiotherapy clinical trial datasets, a gastric cancer trial (TOPGEAR, TROG 08.08) and a post-prostatectomy prostate cancer trial (RAVES, TROG 08.03). Each dataset contains only 10 cases each for model development to segment the clinical target volume (CTV) which was defined by multiple observers on each case. An additional 50 cases are available as a hold-out dataset for each trial which had only one observer define the CTV structure on each case. Up to 50 samples were generated using the probabilistic model for each case in the hold-out dataset. To assess performance, each manually defined structure was matched to the closest matching sampled segmentation based on commonly used metrics. ResultsThe TOPGEAR CTV model achieved a Dice Similarity Coefficient (DSC) and Surface DSC (sDSC) of 0.7 and 0.43 respectively with the RAVES model achieving 0.75 and 0.71 respectively. Segmentation quality across cases in the hold-out datasets was variable however both the ensemble and MCDO uncertainty estimation approaches were able to accurately estimate model confidence with a p-value < 0.001 for both TOPGEAR and RAVES when comparing the DSC using the Pearson correlation coefficient. ConclusionsWe demonstrated that training auto-segmentation models which can estimate aleatoric and epistemic uncertainty using limited datasets is possible. Having the model estimate prediction confidence is important to understand for which unseen cases a model is likely to be useful.

Impact of joint peer review with a specialist radiologist in head and neck cancer radiotherapy planning.

e18059 Background: Head and neck oncology is heavily dependent on interpretation of radiology. This raises the question whether input from radiology should be formally scheduled as part of the radiotherapy contouring processes. Our aim was to assess the impact of this practice through qualitative and quantitative analysis. Methods: H &amp; N patients treated with radiotherapy between May-2022 and August-2023 were reviewed by a specialist HN radiologist. Incidence of changes in T, N, M and TNM-stage, change as defined by The RCR guidance: 'major' (change in gross tumour volume and/or high-dose clinical target volume, dose/fractionation) or 'minor' (change in intermediate or elective dose clinical target volumes or organs at risk), was recorded. We evaluated whether any of these changes were time dependent (Time interval between MDT to RT Peer Review). Other clinical variables: age, gender, primary diagnosis, tumour site, significant incidental findings, post-operative pTNM, post-operative disease status in adjuvant setting on planning imaging (NIRADS), need for additional imaging. We also recorded number of scans reviewed per patient, types of imaging studies reviewed, time taken for review and number of radiologist PA’s required. Retrospective analysis of a prospective database was performed. Results: 410 patients (252-definitive, 137-adjuvant, 21-palliative) were reviewed of which 51.5%-oropharynx followed by 26.8%-Larynx &amp; Hypopharynx. Mean age was 66.14 yrs (SD 11.46) and M:F was 4:1. Baseline and Therapy planning imaging was reviewed for 100% patients of which baseline included: MR (91.7%), CT (96.1%), US (34.1%), PETCT (31%) and therapy planning (62%-CECT, 19%-MR&amp;CT, 15%-CT, 4%-PETCT). In all patients (n=410), T/N/M/TNM-stage percentage upstage was 27/36.8/8.3/42.2% and in definitive cohort (n=252), it was 41.7/56.7/7.1/59.5%. Reviewing RT-planning Imaging in adjuvant cohort (n=137), 76.6% were NIRADS-1, 11.7% NIRADS-3/4/loco-regional disease and 11.7% distant metastasis. T/N/M/TNM-stage upstage was time dependent and the mean time interval between MDT to RT was higher in upstaged patients (p &lt;0.001). Overall percentage change in treatment plan: no/minor/major/palliative was 40.5/33.9/17.3/8.3% and in definitive cohort 30.2/42.1/20.6/7.1%. Change in plan was not dependent on mean time interval between MDT to RT. Average 10 patients (4 imaging studies/patient) were reviewed by radiologist in 1PA (time to review and peer review meeting time). Conclusions: Routine head and neck radiologist input in radiotherapy peer review is feasible and resulted in a number of major and minor changes to treatment. Inputs not only eased the contouring for oncologists but also lead to updating of current disease stage just prior to treatment. Overall, our results suggest that this is a practice changing step in the radiotherapy workflow which has a direct impact on patient management.

Role of the gel spacer in safely delivering whole pelvic radiation therapy without central shielding in computed tomography-based image-guided adaptive brachytherapy for uterine cervical cancer patients

BackgroundTo protect the rectum and bladder from high dose exposure, the Japanese guidelines for managing uterine cervical carcinoma recommend pelvic irradiation with central shielding (CS). Conversely, the European Society for Radiotherapy and Oncology (ESTRO) and the American Brachytherapy Society (ABS) guidelines recommend delivering ≥ 85 Gy to high-risk clinical target volume D90 (CTVHR D90%). In this study, we investigated whether a gel spacer can enable the safe delivery of the ESTRO/ABS-recommended doses to the target while observing dose constraints for the OARs without using CS in external beam radiation therapy (EBRT). Materials and MethodsTwenty patients who received definitive radiation therapy without CS and were treated by brachytherapy with a gel spacer between April 2017 and May 2022 were retrospectively reviewed. The cumulative doses of EBRT and brachytherapy expressed in the form of the equivalent dose in 2 Gy fractions (EQD2) were calculated. Treatment outcomes and incidence of adverse events were also examined. ResultsThe median cumulative CTVHR D90%, rectum D2cm3, and bladder D2cm3 were 86.6 Gy (range, 69.8–103.7), 62.9 Gy (range, 55.3–72.5), and 72.0 Gy (range, 62.9–84.1), respectively. The median follow-up was 35.9 months (range, 10.9–70.3). The 2-year local control rate was 95% (95% CI: [69–99%]). There were no CTCAE ≥Grade 3 late gastrointestinal or genitourinary adverse events. ConclusionsThe use of gel spacer can enable ESTRO/ABS-recommended dose constraints even without using CS in EBRT, with favorable outcomes and low adverse event rates.

Uncertainty estimation- and attention-based semi-supervised models for automatically delineate clinical target volume in CBCT images of breast cancer

ObjectivesAccurate segmentation of the clinical target volume (CTV) of CBCT images can observe the changes of CTV during patients' radiotherapy, and lay a foundation for the subsequent implementation of adaptive radiotherapy (ART). However, segmentation is challenging due to the poor quality of CBCT images and difficulty in obtaining target volumes. An uncertainty estimation- and attention-based semi-supervised model called residual convolutional block attention-uncertainty aware mean teacher (RCBA-UAMT) was proposed to delineate the CTV in cone-beam computed tomography (CBCT) images of breast cancer automatically.MethodsA total of 60 patients who undergone radiotherapy after breast-conserving surgery were enrolled in this study, which involved 60 planning CTs and 380 CBCTs. RCBA-UAMT was proposed by integrating residual and attention modules in the backbone network 3D UNet. The attention module can adjust channel and spatial weights of the extracted image features. The proposed design can train the model and segment CBCT images with a small amount of labeled data (5%, 10%, and 20%) and a large amount of unlabeled data. Four types of evaluation metrics, namely, dice similarity coefficient (DSC), Jaccard, average surface distance (ASD), and 95% Hausdorff distance (95HD), are used to assess the model segmentation performance quantitatively.ResultsThe proposed method achieved average DSC, Jaccard, 95HD, and ASD of 82%, 70%, 8.93, and 1.49 mm for CTV delineation on CBCT images of breast cancer, respectively. Compared with the three classical methods of mean teacher, uncertainty-aware mean-teacher and uncertainty rectified pyramid consistency, DSC and Jaccard increased by 7.89–9.33% and 14.75–16.67%, respectively, while 95HD and ASD decreased by 33.16–67.81% and 36.05–75.57%, respectively. The comparative experiment results of the labeled data with different proportions (5%, 10% and 20%) showed significant differences in the DSC, Jaccard, and 95HD evaluation indexes in the labeled data with 5% versus 10% and 5% versus 20%. Moreover, no significant differences were observed in the labeled data with 10% versus 20% among all evaluation indexes. Therefore, we can use only 10% labeled data to achieve the experimental objective.ConclusionsUsing the proposed RCBA-UAMT, the CTV of breast cancer CBCT images can be delineated reliably with a small amount of labeled data. These delineated images can be used to observe the changes in CTV and lay the foundation for the follow-up implementation of ART.

Long-term follow-up of protective effects on salivary and swallowing structures and improvement of late xerostomia and dysphagia by level IIb optimisation in clinical target volume of nasopharyngeal carcinoma.

This study aimed to assess the long-term effect of level IIb clinical target volume (CTV) optimisation on survival, xerostomia, and dysphagia in patients with nasopharyngeal carcinoma (NPC). Clinical data of 415 patients with NPC treated with intensity-modulated radiotherapy between December 2014 and October 2018 were retrospectively analysed. The patients were categorised into modified and comparison groups. Late xerostomia and dysphagia were evaluated using Radiation Therapy Oncology Group/European Organisation for Research and Treatment of Cancer scoring. Survival analysis was performed using the Kaplan-Meier method. Differences in late toxicity and dose parameters between both groups were compared. Prognostic factors for survival and late toxicity were assessed using regression analyses. Patients in the modified group developed late xerostomia and dysphagia less frequently than those in the comparison group did (P < 0.001). The mean dose (Dmean) and V26 of parotid glands; Dmean and V39 of submandibular glands; and Dmean of sublingual glands, oral cavity, larynx, and superior, middle, and lower pharyngeal constrictor muscles were lower in the modified group than those in the comparison group (all P < 0.001). Both groups had no significant differences in overall, local recurrence-free, distant metastasis-free, or progression-free survival. The Dmean of the parotid and sublingual glands was a risk factor for xerostomia. The Dmean of the parotid and sublingual glands and middle pharyngeal constrictor muscle was a risk factor for dysphagia. Level IIb optimisation in NPC patients who meet certain criteria specially the exclusion of positive retropharyngeal nodes treated with intensity-modulated radiotherapy has the potential to better protect the salivary and swallowing structures, decreasing the development of late radiation-induced xerostomia and dysphagia while maintaining long-term survival.

Preliminary results of adjuvant image-guided vaginal brachytherapy alone for early stage endometrial carcinoma

ObjectiveThis retrospective study evaluated the preliminary outcomes of image-guided vaginal brachytherapy (IG-VBT) in the adjuvant treatment of high intermediate risk endometrial cancer. Materials and MethodsData were collected from 48 patients who underwent adjuvant IG-VBT between 2019 and 2022 at the Division of Radiation Oncology, Department of Radiology, Faculty of Medicine, Chiang Mai University. The vaginal cuff clinical target volume (CTV-VC) is composed of a 4-mm-thick band around vaginal cylinder at the upper 3 cm of the vaginal cuff. A total dose of 21 Gy in three fractions was delivered to the CTV-VC, and the dose to the bladder and rectum were evaluated. Treatment details, patient characteristics, and outcomes were analyzed. Descriptive statistics were used for analysis, and Kaplan-Meier method was employed for survival analysis. ResultsThe mean age was 62 years, with mainly endometrioid carcinoma pathology (96 %). All patients were at stage I, with 87.5 % receiving complete surgical staging. Mean total treatment time was 10 days with mean D90 of CTV-VC was 29.7 Gy, and D2cc of bladder, rectum, and sigmoid were 24.6 Gy, 21.0 Gy, and 7.7 Gy, respectively. At a median follow-up of 37 months, 3-year local control, disease-free survival, and overall survival rates were 100 %, 100 %, and 97.9 %. respectively. Two patients (4.2 %) experienced grade 1–2 gastrointestinal toxicity, while no genitourinary toxicity or serious adverse events were observed. ConclusionsThe preliminary results of IG-VBT in endometrial cancer demonstrated favorable outcomes in terms of vaginal control and toxicity. Further studies with larger cohorts and longer follow-up durations are warranted.

Stereotactic arrhythmia radioablation for refractory ventricular tachycardia. A retrospective analysis and optimisation of the planning phase

Abstract Introduction Stereotactic arrhythmia radioablation (STAR) is used as a rescue treatment for refractory ventricular tachycardia (VT). However, the planning phase to accurately delineate the clinical target volume (CTV) still suffers from limitations due to the incompatibility of data formats between 3D electro-anatomical mapping systems (EAM) used for VT ablation and STAR planning software. The concordance between the CTVs as defined by the planning software (CTVSTAR) and the CTV based on EAM data (CTVEAM) remains unknown. Purpose Within the STOPSTORM European consortium, we compared the concordance between CTVSTAR and CTVEAM in our series of 13 patients (pts) treated by STAR for refractory VT. We also evaluated need for redo ablation of VT after STAR. Method Using the software Slicer3D, two protocols were retrospectively compared: Protocol 1 comprised the manual contouring of the CTVSTAR based on 4D-CT SCAN drawn side-by-side using EAM (Carto3, Biosense Webster) screenshots and diagnostic reports. Protocol 2 comprised semiautomatic segmentation of CTVEAM based on 4D-CT SCAN using imported EAMs annotated with landmarks for VT substrate delimitation. The delineation of CTVEAM included 3 steps: (1) semiautomatic segmentation of ventricular myocardium (VM), the blood-pool of the concerned ventricle and at least 2 additional cardiac cavities; (2) 3D-alignment of EAM with the segmented cavities; and (3), 3D-delineation of CTVSTAR identified as the transmural section of VM segment surrounding the EAM’s VT substrate delimited by STAR tags (Figure 1). Volume overlap (Dice coefficient, 100% meaning complete overlap) and volume absolute difference (VAD, 0% meaning similar volumes) between CTVs were compared. The following cumulative averages were computed over time: redo ablation (RedoA) and CTV. Results Figure 1 illustrates the last treated patient (#13) in whom the delineated volumes, CTVSTAR (red) and CTVEAM (blue), displayed good matching (Dice 72% and VAD 4. Figure 2 shows the comparison between CTVSTAR and CTVEAM for the 13 pts (65±7 yo) treated with STAR, resulting in a mean Dice of 47±17% and mean VAD of 40±51%. Figure 3 shows the cumulative RedoA and the cumulative averaged CTVSTAR and CTVEAM over time. Interestingly, the need for redo ablation decreased with increasing CTVs. Conclusion Our study demonstrates the efficacy of STAR treatment for refractory VT. Our confidence in STAR led to higher CTVs and lower RedoA over time. The high variability of CTV delineation using two different protocols suggests that STAR planning phase can be optimised by appointing intraprocedural anatomical landmarks to the VT substrate.

LINAC-Based STereotactic Arrhythmia Radioablation (STAR) for paroxysmal atrial fibrillation in elderly: a prospective phase II trial

Abstract Background and aims Stereotactic arrhythmia radioablation (STAR) is a novel therapeutic approach for cardiac arrhythmias. The aim of this trial is to investigate the feasibility of STAR for the treatment of paroxysmal atrial fibrillation (AF) in elderly patients. Method inclusion criteria were: age &amp;gt; 70 years, symptomatic AF, antiarrhythmic drugs failure or intolerance. All patients underwent to 4D cardiac Computed Tomography simulation. The clinical target volume was identified in the area around pulmonary veins (PV). STAR was performed with a total dose of 25 Gy (single fraction) delivered in 3 minutes. Results Twenty patients were enrolled and 18 underwent STAR. One patient withdrew informed consent before treatment and 1 patient was excluded due to unfavorable esophagus position. With a median follow-up (FU) of 16 months (range 12-23), no acute toxicity more than grade 3 was reported. Five patients had a grade 1 esophagitis 24 hours after STAR; eight patients had an asymptomatic grade 1 pericardial effusion, one patient had a torsade de pointes treated effectively by electrical cardioversion and subsequent cardiac ICD implantation. Most patients had a significant reduction in AF episodes. Five patients, due to arrhythmias recurrences after STAR, performed electrophysiological study documenting successful PV isolation. Finally, a significant improvement of quality of life was documented (48 ± 15 at enrollment vs 75 ± 15 at 12 months FU; p &amp;lt; 0.001). Conclusion The present phase II trial demonstrated the feasibility of STAR in paroxysmal AF elderly patients and its potential role in increasing the quality of life. Surely, more robust data are needed about safety and efficacy.

Study of late toxicity biomarkers of locally advanced head and neck cancer patients treated with radiotherapy plus cisplatin or cetuximab points to the relevance of skin macrophages (TOX-TTCC-2015-01).

Radiotherapy (RT) with concomitant cisplatin (CRT) or cetuximab (ERT) are accepted treatment options for locally advanced squamous cell carcinoma of the head and neck (LA-SCCHN). Long-term adverse events (AEs) have a vast impact on patients' quality of life. This study explored tissue biomarkers which could help predict late toxicity. Single-institution prospective study including patients aged ≥ 18 with histologically confirmed newly diagnosed LA-SCCHN treated with RT and either concomitant cisplatin q3w or weekly cetuximab, according to institutional protocols. All patients underwent pre- and post-treatment skin biopsies of neck regions included in the clinical target volume. Angiogenesis, macrophages, and extracellular matrix (ECM) markers were evaluated by immunohistochemistry (IHC). From April 15, 2016, to December 11, 2017; 31 patients were evaluated [CRT = 12 (38.7%) and ERT = 19 (61.3%)]. 27 patients (87%) had received induction chemotherapy. All patients finished RT as planned. IHC expression of vasculature (CD34) and collagen (Masson's Trichrome) did not differ significantly between and within CRT and ERT arms. Conversely, an increased CD68 and CD163 macrophage infiltration expression was observed after treatment, without significant impact of treatment modality. Patients with higher late toxicity showed lower expression of macrophage markers in pre-treatment samples compared with those with lower late toxicity, with statistically significant differences for CD68. Angiogenesis and ECM biomarkers did not differ significantly between CRT and ERT. Macrophage markers increased after both treatments and deserve further investigation as predictors of late toxicity in LA-SCCHN patients. [Protocol code: TOX-TTCC-2015-01/Spanish registry of clinical studies (REec): 2015-003012-21/Date of registration: 27/01/2016].

Acute hematologic toxicity prediction using dosimetric and radiomics features in patients with cervical cancer: does the treatment regimen matter?

Acute hematologic toxicity (HT) is a prevalent adverse tissue reaction observed in cervical cancer patients undergoing chemoradiotherapy (CRT), which may lead to various negative effects such as compromised therapeutic efficacy and prolonged treatment duration. Accurate prediction of HT occurrence prior to CRT remains challenging. A discovery dataset comprising 478 continuous cervical cancer patients (140 HT patients) and a validation dataset consisting of 205 patients (52 HT patients) were retrospectively enrolled. Both datasets were categorized into the CRT group and radiotherapy (RT)-alone group based on the treatment regimen, i.e., whether chemotherapy was administered within the focused RT duration. Radiomics features were derived by contouring three regions of interest (ROIs)-bone marrow (BM), femoral head (FH), and clinical target volume (CTV)-on the treatment planning CT images before RT. A comprehensive model combining the radiomics features as well as the demographic, clinical, and dosimetric features was constructed to classify patients exhibiting acute HT symptoms in the CRT group, RT group, and combination group. Furthermore, the time-to-event analysis of the discriminative ROI was performed on all patients with acute HT to understand the HT temporal progression. Among three ROIs, BM exhibited the best performance in classifying acute HT, which was verified across all patient groups in both discovery and validation datasets. Among different patient groups in the discovery dataset, acute HT was more precisely predicted in the CRT group [area under the curve (AUC) = 0.779, 95% CI: 0.657-0.874] than that in the RT-alone (AUC = 0.686, 95% CI: 0.529-0.817) or combination group (AUC = 0.748, 95% CI: 0.655-0.827). The predictive results in the validation dataset similarly coincided with those in the discovery dataset: CRT group (AUC = 0.802, 95% CI: 0.669-0.914), RT-alone group (AUC = 0.737, 95% CI: 0.612-0.862), and combination group (AUC = 0.793, 95% CI: 0.713-0.874). In addition, distinct feature sets were adopted for different patient groups. Moreover, the predicted HT risk of BM was also indicative of the HT temporal progression. HT prediction in cervical patients is dependent on both the treatment regimen and ROI selection, and BM is closely related to the occurrence and progression of HT, especially for CRT patients.

Penalty weight tuning in high dose rate brachytherapy using multi-objective Bayesian optimization

Objective. Treatment plan optimization in high dose rate brachytherapy often requires manual fine-tuning of penalty weights for each objective, which can be time-consuming and dependent on the planner's experience. To automate this process, this study used a multi-criteria approach called multi-objective Bayesian optimization with q-noisy expected hypervolume improvement as its acquisition function (MOBO-qNEHVI). Approach. The treatment plans of 13 prostate cancer patients were retrospectively imported to a research treatment planning system, RapidBrachyMTPS, where fast mixed integer optimization (FMIO) performs dwell time optimization given a set of penalty weights to deliver 15 Gy to the target volume. MOBO-qNEHVI was used to find patient-specific Pareto optimal penalty weight vectors that yield clinically acceptable dose volume histogram metrics. The relationship between the number of MOBO-qNEHVI iterations and the number of clinically acceptable plans per patient (acceptance rate) was investigated. The performance time was obtained for various parameter configurations. Main results. MOBO-qNEHVI found clinically acceptable treatment plans for all patients. With increasing the number of MOBO-qNEHVI iterations, the acceptance rate grew logarithmically while the performance time grew exponentially. Fixing the penalty weight of the tumour volume to maximum value, adding the target dose as a parameter, initiating MOBO-qNEHVI with 25 parallel sampling of FMIO, and running 6 MOBO-qNEHVI iterations found solutions that delivered 15 Gy to the hottest 95% of the clinical target volume while respecting the dose constraints to the organs at risk. The average acceptance rate for each patient was 89.74% ± 8.11%, and performance time was 66.6 ± 12.6 s. The initiation took 22.47 ± 7.57 s, and each iteration took 7.35 ± 2.45 s to find one Pareto solution.Significance. MOBO-qNEHVI combined with FMIO can automatically explore the trade-offs between treatment plan objectives in a patient specific manner within a minute. This approach can reduce the dependency of plan quality on planner’s experience and reduce dose to the organs at risk.

LINAC–BASED STEREOTACTIC ARRHYTHMIA RADIOABLATION (STAR) FOR PAROXYSMAL ATRIAL FIBRILLATION IN ELDERLY: A PROSPECTIVE PHASE II TRIAL

Abstract Background and Aims The aim of this trial is to investigate the feasibility of radiotherapy for the treatment of paroxysmal atrial fibrillation (AF) in elderly patients. Method inclusion criteria were: age &amp;gt; 70 years, symptomatic AF, antiarrhythmic drugs failure or intolerance. All patients underwent to 4D cardiac Computed Tomography simulation. The clinical target volume was identified in the area around pulmonary veins (PV). STAR was performed with a total dose of 25 Gy (single fraction) delivered in 3 minutes. Results Twenty patients were enrolled and 18 underwent STAR. One patient withdrew informed consent before treatment and 1 patient was excluded due to unfavorable esophagus position. With a median follow–up (FU) of 16 months (range 12–23), no acute toxicity more than grade 3 was reported. Five patients had a grade 1 esophagitis 24 hours after STAR; eight patients had an asymptomatic grade 1 pericardial effusion, one patient had a torsade de pointes treated effectively by electrical cardioversion and subsequent cardiac ICD implantation. Most patients had a significant reduction in AF episodes. Five patients, due to arrhythmias recurrences after STAR, performed electrophysiological study documenting successful PV isolation. Finally, a significant improvement of quality of life was documented (48 ± 15 at enrollment vs 75 ± 15 at 12 months FU; p &amp;lt; 0.001). Conclusion The present phase II trial demonstrated the feasibility of STAR in paroxysmal AF elderly patients and its potential role in increasing the quality of life. Surely, more robust data are needed about safety and efficacy.

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

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