CT For Treatment Planning Research Articles

The effect of coronary artery calcifications and radiotherapy on the risk of coronary artery disease in high-risk breast cancer patients in the DBCG RT-Nation cohort.

Radiotherapy improves outcomes for breast cancer. However, prior studies have correlated the risk of coronary artery disease (CAD) to the mean heart dose (MHD), mean dose to the left anterior descending artery (LAD_mean) and the left ventricle V5Gy (LV5). Other studies showed an increased risk of CAD for patients with pronounced coronary artery calcification (CAC) at the time of radiotherapy. This cohort study included 3355 high-risk breast cancer patients treated in Western Denmark (2008-2016). We analysed CT scans, treatment plans, and dose distributions. CAC was measured using the Agatston score (AS). We examined the dose-response relationship between MHD, LV5 and LAD_mean and CAD, and the effect of CAC presence at radiotherapy. Secondary analysis assessed overall survival. Of 3355 patients, 45 (1.2 %) developed CAD during follow-up. AS was a strong predictor of CAD risk with a hazard ratio of 9.51(CI95:5.16-17.53) for AS ≥ 100 versus AS < 100 and a 6.7 % difference in absolute cumulative CAD risk at ten years (7.7 % vs 1 %). For AS < 100 (97 % of patients) CAD risk increased with MHD, hazard ratio 1.25 (CI95:1.01-1.56) per Gy. ForAS ≥ 100, CAD risk was driven by CAC rather than radiation dose. CAC was associated with poorer overall survival. Median MHD for the whole cohort was 1.25 Gy (IQR:1.01-1.56) CONCLUSION: AS from planning CT-scans predicted CAD risk and overall survival in breast cancer patients receiving radiotherapy. The MHD remained the strongest predictor in patients with low CAC. For patients with high CAC, the high baseline risk from CAC was a stronger risk factor than the dose-related risk.

Evaluation and comparison of synthetic computed tomography algorithms with 3T MRI for prostate radiotherapy: AI-based versus bulk density method.

Synthetic computed tomography (sCT)-algorithms, which generate computed tomography images from magnetic resonance imaging data, are becoming part of the clinical radiotherapy workflow. The aim of this retrospective study was to evaluate and compare commercial bulk-density-method (BM)-based and AI (artificial intelligence)-based-algorithms using 3T magnetic resonance imaging (MRI) with patient data as part of the local clinical commissioning process. 44 prostate radiotherapy patients were subjected to MRI and treatment planning CT (TPCT) scans. From the MRI images, sCT images with two different sCT algorithms were generated. The sCT images were evaluated by visual inspection of artifacts. Both sCT methods were compared to TPCT, with Dice similarity score(DSC) of bone and body contours, DVH parameters for CTV, bladder and rectum, and gamma-analysis. Accuracy for treatment alignment using sCT images was also tested. Various limits were used to define whether the differences between sCT methods to TPCT were clinically relevant (DVH parameters<2%, gamma-analysis passing rates 90%, 95%, and 98%, and the DSC 0.98 for body and 0.7 for bone). Our results show that, differences in CTV-dose coverage values were<2% in most of the patients with both sCT algorithms when compared to reference dose coverage. While AI-sCT had mean dose coverage difference<0,5% and BM-sCT<1%. Gamma-analysis showed that the AI-sCT mean passing rates were 95.4%, 98.6%, and 99.4% with 1mm1%, 2mm2%, and 3mm3% criteria, respectively. Similarly for BM-sCT the mean passing rates were 93.4%, 98.2%, and 99.2%. For the treatment alignment accuracy, the mean difference in magnitude of the translational shifts was 1.43mm for BM-sCT and 1.57mm for AI-sCT. Even though AI-sCT showed statistically better correspondence to TPCT, the differences were not clinically relevant with any of the limits. Visual evaluation showed artifacts in the AI-sCT especially in the bowel area and fiducial markers were not generated with either of the sCT algorithms. In conclusion, sCT-algorithms were clinically usable on prostate treatments using 3T MR-only workflow. While AI-sCT showed better correspondence to TPCT than BM-sCT, it generated characteristic artifacts. As sCT algorithms perform well, we still recommend testing the sCT-algorithms with retrospective analyses from patient data prior to implementing sCT into the routine workflow to better understand the specific limitations and capabilities of these algorithms.

The TROG 15.01 stereotactic prostate adaptive radiotherapy utilizing kilovoltage intrafraction monitoring (SPARK) clinical trial database.

The US National Institutes of Health state that Sharing of clinical trial data has great potential to accelerate scientific progress and ultimately improve public health by generating better evidence on the safety and effectiveness of therapies for patients (https://www.ncbi.nlm.nih.gov/books/NBK285999/ accessed 2024-01-24.). Aligned with this initiative, the Trial Management Committee of the Trans-Tasman Radiation Oncology Group (TROG) 15.01 Stereotactic Prostate Adaptive Radiotherapy utilizing Kilovoltage intrafraction monitoring (KIM) (SPARK) clinical trial supported the public sharing of the clinical trial data. The data originate from the TROG 15.01 SPARK clinical trial. The SPARK trial was a phase II prospective multi-institutional clinical trial (NCT02397317). The aim of the SPARK clinical trial was to measure the geometric and dosimetric cancer targeting accuracy achieved with a real-time image-guided radiotherapy technology named KIM for 48 prostate cancer patients treated in 5 treatment sessions. During treatment, real-time tumor translational and rotational motion were determined from x-ray images using the KIM technology. A dose reconstruction method was used to evaluate the dose delivered to the target and organs-at-risk. Patient-reported outcomes and toxicity data were monitored up to 2 years after the completion of the treatment. The dataset contains planning CT images, treatment plans, structure sets, planned and motion-included dose-volume histograms, intrafraction kilovoltage, and megavoltage projection images, tumor translational and rotational motion determined by KIM, tumor motion ground truth data, the linear accelerator trajectory traces, and patient treatment outcomes. The dataset is publicly hosted by the University of Sydney eScholarship Repository at https://doi.org/10.25910/qg5d-6058. The 3.6Tb dataset, with approximately 1 million patient images, could be used for a variety of applications, including the development of real-time image-guided methods, adaptation strategies, tumor, and normal tissue control modeling, and prostate-specific antigen kinetics.

Role of the FDG PET CT Scan in Pretreatment Evaluation of Oral Carcinomas.

Pre-treatment role of FDG PET CT scan to evaluate- the extent of the primary lesion, nodal staging and distant metastasis in oral carcinoma in various TNM stages. Additionally, our study investigated the Correlation between SUVmax values on FDG-PET CT scans and histopathological proven positive nodes in patients undergoing surgery. In this study, all suspected cases of oral carcinoma in adults who visited the ENT clinic were examined and evaluated using various methods, including clinical examination, cytology, histopathology, and imaging tests like CECT scans, ultrasound, and CEMRI. Based on the results of these evaluations, the patients were staged. The patients were then given FDG-PET CT scans (done within 2weeks of CECT/MRI scans), for restaging and treatment plans, such as surgery, chemotherapy, radiotherapy, or a combination of these methods, were developed. After surgery, the patients were restaged based on the histopathology report, and the results of the FDG-PET CT scans were compared to the histopathological findings and the histopathological positive nodes were compared to the SUV max value on the FDG PET CT scan and were correlated. In our study, the mean age of patients was found to be 50years with a male: female ratio of 3.5:1. Maximum tumors presented at the buccal mucosa region. 92% of patients gave a history of tobacco addiction, and 18% were smokers. As per initial biopsy reports, maximum (62%) tumors were detected to be WDSCC, 2% were MDSCC and 6% were PDSCC. All 50 patients had normal findings on the chest x-ray and USG abdomen. 41 patients underwent CECT scans and 9 patients got CEMRI scans done. Staging after FDG PET CT scan was compared with that of radiological staging. It was found that in T staging there was upstaging in 36% of cases and downstaging in 14% of cases following the FDG PET CT scan. Likewise, N staging showed upstaging in 36% of cases and downstaging in 16% of cases after the FDG PET CT scan. In M staging, there was upstaging in 10% of cases after the FDG PET CT scan. In our study 15 cases got operated on. All 8 (53.3%) cases of postoperative histopathological positive lymph nodes had SUV max values greater than their reference value (> 2.5), and all negative histopathological lymph nodes had a low SUV max (< 2.5) value in the PET CT scan. The FDG PET CT scan is a highly effective tool for accurately diagnosing, staging, and predicting the outcome of oral cancers at all stages of the disease. The SUV max value (> 2.5) of the PET CT scan is positively correlated with the likelihood of nodal metastasis, as higher values were found to correspond with histopathological evidence of positive neck nodes.

A potential usefulness of ultra-high-resolution computed tomography in quality assurance of remote after-loading system for cervical cancer.

Intracavitary brachytherapy with a remote after-loading system (RALS) is performed as a part of radical radiation therapy in cervical cancer. The radiation source is delivered directly through an applicator placed inside the uterus or vagina. Thorough quality control is important to prevent accidents that can lead to serious irradiation error, and an applicator check is one such quality control measure. We experienced a clinical situation in which a small volume of water was observed in the lumen of a post-sterilized applicator on treatment-planning CT. Although the submersion test was negative and no air bubbles emerged from the applicator, ultra-high-resolution computed tomography (U-HRCT) showed a linear crack reaching the inside of the applicator. This abnormality was not identified on treatment-planning CT, which has lower spatial resolution than U-HRCT. In addition, no linear cracks were seen on U-HRCT images of eight other applicators considered to be free from damage. U-HRCT may have superior potential to detect applicator damage and could be useful for quality assurance of the RALS procedure.

Quality assurance and other challenges in paediatric radiotherapy: Accurate delivery of craniospinal radiotherapy.

Cranio-spinal radiotherapy (CSI) is used to treat central nervous system malignancies in paediatric, adolescent/young adult (AYA), and adult patients. Its delivery in the paediatric/AYA population is particularly challenging across different age groups. This study aims to assess the setup variations and dosimetric impact of CSI in paediatric and AYA patients. This retrospective analysis included, 10 paediatric and AYA patients (aged 4-25) who underwent volumetric modulated arc therapy (VMAT) CSI between 2016 and 2022. Patient characteristics, diagnoses, prescribed CSI doses, and fractionation details were assessed. CT simulation and treatment planning followed standard protocols with setup errors were quantified by comparing daily treatment setup images with the planned position. The study evaluated the dosimetric impact on target volumes and organs at risk (OARs). The setup errors were identified, ranging from 0.5 to 6.2 mm in different directions, especially in the cranio-caudal direction. Despite these variations, there was minimal impact observed on the coverage of clinical target volumes (CTV) and doses to OARs (<1% relative change). Ensuring precise setup in paediatric and AYA patients undergoing CSI is essential to maintain adequate CTV coverage. Although occasional substantial setup variations occurred during treatment, they had a limited impact on CTV coverage and OAR doses when infrequent. Appropriate planning target volume (PTV) margins can effectively compensate for occasional shifts. However, systematic errors could compromise treatment quality if undetected. Regular off-line review of patient set-up trends is recommended.

Deep Texture Analysis—Enhancing CT Radiomics Features for Prediction of Head and Neck Cancer Treatment Outcomes: A Machine Learning Approach

(1) Background: Some cancer patients do not experience tumour shrinkage but are still at risk of experiencing unwanted treatment side effects. Radiomics refers to mining biomedical images to quantify textural characterization. When radiomics features are labelled with treatment response, retrospectively, they can train predictive machine learning (ML) models. (2) Methods: Radiomics features were determined from lymph node (LN) segmentations from treatment-planning CT scans of head and neck (H&amp;N) cancer patients. Binary treatment outcomes (complete response versus partial or no response) and radiomics features for n = 71 patients were used to train support vector machine (SVM) and k-nearest neighbour (k-NN) classifier models with 1–7 features. A deep texture analysis (DTA) methodology was proposed and evaluated for second- and third-layer radiomics features, and models were evaluated based on common metrics (sensitivity (%Sn), specificity (%Sp), accuracy (%Acc), precision (%Prec), and balanced accuracy (%Bal Acc)). (3) Results: Models created with both classifiers were found to be able to predict treatment response, and the results suggest that the inclusion of deeper layer features enhanced model performance. The best model was a seven-feature multivariable k-NN model trained using features from three layers deep of texture features with %Sn = 74%, %Sp = 68%, %Acc = 72%, %Prec = 81%, %Bal Acc = 71% and with an area under the curve (AUC) the receiver operating characteristic (ROC) of 0.700. (4) Conclusions: H&amp;N Cancer patient treatment-planning CT scans and LN segmentations contain phenotypic information regarding treatment response, and the proposed DTA methodology can improve model performance by enhancing feature sets and is worth consideration in future radiomics studies.

Voxel-Level Dosimetry for Combined Iodine 131 Radiopharmaceutical Therapy and External Beam Radiation Therapy Treatment Paradigms for Head and Neck Cancer

PurposeTargeted radiopharmaceutical therapy (RPT) in combination with external beam radiotherapy (EBRT) shows promise as a method to increase tumor control and mitigate potential high-grade toxicities associated with re-treatment for patients with recurrent head and neck cancer. This work establishes a patient-specific dosimetry framework that combines Monte Carlo based dosimetry from the two radiation modalities at the voxel level using deformable image registration (DIR) and radiobiological constructs for patients enrolled in a phase I clinical trial combining EBRT and RPT. MethodsSerial SPECT/CT patient scans performed at approximately 24, 48, 72, and 168 hours post-injection of 577.2 MBq/m2 (15.6 mCi/m2) iodine-131 containing RPT agent called XXX 131. Clinical EBRT treatment plans were created on a treatment planning CT (TPCT) using RayStation; SPECT/CT images were deformably registered to the TPCT using the Elastix DIR module in 3D Slicer and assessed by measuring mean activity concentrations and absorbed doses. Monte Carlo EBRT dosimetry was computed using EGSnrc. RPT dosimetry was conducted using a GEANT4 based RPT dosimetry platform named XXX. Radiobiological metrics (BED, EQD2) were utilized to combine the two radiation modalities. ResultsThe DIR method provided good agreement for the activity concentrations and calculated absorbed dose in the tumor volumes for the SPECT/CT and TPCT; the maximum mean absorbed dose difference was -11.2%. Based on the RPT absorbed dose calculations, two to four EBRT fractions were removed from patients’ EBRT treatments. From the combined treatment, the absorbed dose to target volumes ranged from 57.14 – 75.02 Gy. When including partial volume corrections (PVC), the mean EQD2 to the PTV from EBRT+RPT was -3.11% to 1.40% different compared to EBRT alone. ConclusionThis work demonstrated the clinical feasibility of performing combined EBRT+RPT dosimetry on TPCTs. Dosimetry guides treatment decisions for EBRT and this work provides a bridge for the same paradigm to be implemented within the rapidly emerging clinical RPT space.

Pencil beam kernel-based dose calculations on CT data for a mixed neutron-gamma fission field applying tissue correction factors.

Objective.For fast neutron therapy with mixed neutron and gamma radiation at the fission neutron therapy facility MEDAPP at the research reactor FRM II in Garching, no clinical dose calculation software was available in the past. Here, we present a customized solution for research purposes to overcome this lack of three-dimensional dose calculation.Approach.The applied dose calculation method is based on two sets of decomposed pencil beam kernels for neutron and gamma radiation. The decomposition was performed using measured output factors and simulated depth dose curves and beam profiles in water as reference medium. While measurements were performed by applying the two-chamber dosimetry method, simulated data was generated using the Monte Carlo code MCNP. For the calculation of neutron dose deposition on CT data, tissue-specific correction factors were generated for soft tissue, bone, and lung tissue for the MEDAPP neutron spectrum. The pencil beam calculations were evaluated with reference to Monte Carlo calculations regarding accuracy and time efficiency.Main results.In water, dose distributions calculated using the pencil beam approach reproduced the input from Monte Carlo simulations. For heterogeneous media, an assessment of the tissue-specific correction factors with reference to Monte Carlo simulations for different tissue configurations showed promising results. Especially for scenarios where no lung tissue is present, the dose calculation could be highly improved by the applied correction method.Significance.With the presented approach, time-efficient dose calculations on CT data and treatment plan evaluations for research purposes are now available for MEDAPP.

Dose distribution prediction for head-and-neck cancer radiotherapy using a generative adversarial network: influence of input data.

A three-dimensional deep generative adversarial network (GAN) was used to predict dose distributions for locally advanced head and neck cancer radiotherapy. Given the labor- and time-intensive nature of manual planning target volume (PTV) and organ-at-risk (OAR) segmentation, we investigated whether dose distributions could be predicted without the need for fully segmented datasets. GANs were trained/validated/tested using 320/30/35 previously segmented CT datasets and treatment plans. The following input combinations were used to train and test the models: CT-scan only (C); CT+PTVboost/elective (CP); CT+PTVs+OARs+body structure (CPOB); PTVs+OARs+body structure (POB); PTVs+body structure (PB). Mean absolute errors (MAEs) for the predicted dose distribution and mean doses to individual OARs (individual salivary glands, individual swallowing structures) were analyzed. For the five models listed, MAEs were 7.3 Gy, 3.5 Gy, 3.4 Gy, 3.4 Gy, and 3.5 Gy, respectively, without significant differences among CP-CPOB, CP-POB, CP-PB, among CPOB-POB. Dose volume histograms showed that all four models that included PTV contours predicted dose distributions that had a high level of agreement with clinical treatment plans. The best model CPOB and the worst model PB (except model C) predicted mean dose to within ±3 Gy of the clinical dose, for 82.6%/88.6%/82.9% and 71.4%/67.1%/72.2% of all OARs, parotid glands (PG), and submandibular glands (SMG), respectively. The R2 values (0.17/0.96/0.97/0.95/0.95) of OAR mean doses for each model also indicated that except for model C, the predictions correlated highly with the clinical dose distributions. Interestingly model C could reasonably predict the dose in eight patients, but on average, it performed inadequately. We demonstrated the influence of the CT scan, and PTV and OAR contours on dose prediction. Model CP was not statistically different from model CPOB and represents the minimum data statistically required to adequately predict the clinical dose distribution in a group of patients.

Dosimetric and workflow impact of synthetic-MRI use in prostate high-dose-rate brachytherapy

Target and organ delineation during prostate high-dose-rate (HDR) brachytherapy treatment planning can be improved by acquiring both a postimplant CT and MRI. However, this leads to a longer treatment delivery workflow and may introduce uncertainties due to anatomical motion between scans. We investigated the dosimetric and workflow impact of MRI synthesized from CT for prostate HDR brachytherapy. Seventy-eight CT and T2-weighted MRI datasets from patients treated with prostate HDR brachytherapy at our institution were retrospectively collected to train and validate our deep-learning-based image-synthesis method. Synthetic MRI was assessed against real MRI using the dice similarity coefficient (DSC) between prostate contours drawn using both image sets. The DSC between the same observer's synthetic and real MRI prostate contours was compared with the DSC between two different observers' real MRI prostate contours. New treatment plans were generated targeting the synthetic MRI-defined prostate and compared with the clinically delivered plans using target coverage and dose to critical organs. Variability between the same observer's prostate contours from synthetic and real MRI was not significantly different from the variability between different observer's prostate contours on real MRI. Synthetic MRI-planned target coverage was not significantly different from that of the clinically delivered plans. There were no increases above organ institutional dose constraints in the synthetic MRI plans. We developed and validated a method for synthesizing MRI from CT for prostate HDR brachytherapy treatment planning. Synthetic MRI use may lead to a workflow advantage and removal of CT-to-MRI registration uncertainty without loss of information needed for target delineation and treatment planning.

New Imaging Techniques.

The chapter discusses the advancement of new imaging techniques, the role of imaging in CCA diagnosis, anatomical and morphological classification, ultrasound screening of CCA, ultrasound findings of MF-CCA, PI-CCA, ID-CCA, the use of CT in CCA diagnosis, staging and treatment planning, CT volumetry and estimation of future liver remnant, post-treatment follow-up and surveillance, MRI imaging, Positron Emission Tomography (PET)/CT, limitations to contrast studies and resolution, internal receivers for CCA imaging, and in vitro imaging of CCA.

Implementation of Data-Driven Timelines and Visual Guidance Tools to Improve Radiation Oncology Task Completion: “Plan Readiness”

<h3>Purpose/Objective(s)</h3> The time between physician treatment plan approval and patient treatment initiation is often too short in our current Radiation Oncology practice. This reflects inconsistencies in the timeframe of task completion between CT simulation and treatment plan approval, the first and last steps in a complex, multi-step planning workflow in Radiation Oncology. This poses challenges in terms of the QA and plan checks needed prior to treatment initiation, with potential implications for patient safety. Our goal was to implement a sustainable and clear departmental process that would increase the percentage of plans approved by the attending Radiation Oncologist 1 full business day prior to treatment start for all non-urgent patients. Secondary goals included a reduction in last-minute patient delays and improvement in team member engagement and satisfaction. <h3>Materials/Methods</h3> We established realistic timelines for each task from simulation to treatment initiation, while setting a cutoff time for implementing patient appointment delay when expectations were not met. Treatment site, technique, and complexity-specific timelines were built by our team based on 4 major factors: 1) Clinical Urgency, 2) Task Complexity, 3) Evaluation of "task completion" data from a large cohort of previously treated patients, and 4) Interviews with all team members involved in the treatment planning process. These timelines were generated in a visual task management tool, and were linked as serial or parallel to keep the entire team on schedule for each patient. Prior to implementation, appropriate departmental meetings were held to discuss these changes, with extensive team member feedback utilized to generate the new policy. Timelines were generated that ranged from 5 to 12 working days, with clear goals and guidelines for each involved team member regarding their task(s). Our overarching goal, termed "Plan Readiness", was to achieve an electronically approved treatment plan 1 full working day prior to treatment start for > 90% of our patients. Appropriate exclusions were made for emergencies and special/new procedures in the department. <h3>Results</h3> For the 5 months prior to implementation, our baseline data demonstrated that 71% of plans were electronically approved in our treatment planning system greater than 24 hours prior to treatment start. After implementation of our new "Plan Readiness" process in March 2021, we have been able to stay above 90% pass rate, with a cumulative average of 94.6% over an ongoing 10-month timeframe. <h3>Conclusion</h3> The design and execution of data-driven timelines and visual guidance tools, combined with a team-based approach of communication and open feedback, has resulted in preliminary favorable outcomes in achieving timely "Plan Readiness" in our relatively large, hospital-based Department of Radiation Oncology. Future directions include the use of analytics software and automated data collection to perform more robust assessments.

Improving Access to Proton Treatment in Canada Via a Proton Plan Consultation Service: A Use Case Example

Aim: To provide an overview of the Proton Plan Consultation Service offered at our cancer centre and to demonstrate how that service triggered a reversal in the decision that would have denied a patient access to a treatment not currently offered in Canada. Process: The Proton Consultation Service is a multidisciplinary team comprised of a Radiation Oncologist, two Medical Physicists and two Medical Dosimertists. A referring physician can request a proton plan by completing the online referral form and sending the patients planning CT and photon treatment plan including targets and clinical goals. A 14 yo male with an orbital rhabdomyosarcoma was referred to the Proton Plan Consultation Service from a cancer centre in Montreal. The patient had recently been denied referral to a U.S. Proton Centre for treatment. A proton plan was generated by the consultation service and exported back to the Montreal referring Radiation Oncologist. After reviewing this comparative proton plan against the original photon plan an approval committee for the Quebec government reversed their initial decision and supported sending the child to a U.S. Proton Centre for treatment. Benefits/Challenges: Proton plans allow for reduced normal tissue dose to surrounding structures when compared to photon plans. To highlight with a few examples from this case, the pituitary mean dose was reduced from 1137 cGy in the photon plan to 73 cGy in the proton plan. Brainstem max dose to 0.1 cc was reduced from 1086 cGy to 3 cGy. Finally, right hippocampus max dose to 0.1cc was reduced from 803 cGy to 25 cGy. As radiotherapy can cause neurocognition changes, these dose reductions for normal brain structures may have long-term benefits for this child. The challenges encountered are based around educating the dosimetry staff around best practices for proton planning and expediting the proton plan for the referring centre so the patient experiences limited delay in start of treatment. Impact/Outcomes: This use case demonstrates how the Proton Consultation Service can be used to ensure Canadians have appropriate access to proton treatment. It also serves as an education tool for oncology staff across Canada, highlighting the benefits of proton treatment as a valuable tool for the treatment of cancer in difficult sites.

Onboard cone-beam CT-based replan evaluation for head and neck proton therapy.

PurposeQuality assurance computed tomography (QACT) is the current clinical practice in proton therapy to evaluate the needs for replan. QACT could falsely indicate replan because of setup issues that would be solved on the treatment machine. Deforming the treatment planning CT (TPCT) to the pretreatment CBCT may eliminate this issue. We investigated the performance of replan evaluation based on deformed TPCT (TPCTdir) for proton head and neck (H&N) therapy.Methods and materialsTwenty‐eight H&N datasets along with pretreatment CBCT and QACT were used to validate the method. The changes in body volume were analyzed between the no‐replan and replan groups. The dose on the TPCTdir, the deformed QACT (QACTdir), and the QACT were calculated by applying the clinical plans to these image sets. Dosimetric parameters’ changes, including ΔD95, ΔDmean, and ΔD1 for the clinical target volumes (CTVs) were calculated. Receiver operating characteristic curves for replan evaluation based on ΔD95 on QACT and TPCTdir were calculated, using ΔD95 on QACTdir as the reference. A threshold for replan based on ΔD95 on TPCTdir is proposed. The specificities for the proposed method were calculated.ResultsThe changes in the body contour were 95.8 ± 83.8 cc versus 305.0 ± 235.0 cc (p < 0.01) for the no‐replan and replan groups, respectively. The ΔD95, ΔDmean, and ΔD1 are all comparable for all the evaluations. The differences between TPCTdir and QACTdir evaluations were 0.30% ± 0.86%, 0.00 ± 0.22 Gy, and −0.17 ± 0.61 Gy for CTV ΔD95, ΔDmean, and ΔD1, respectively. The corresponding differences between the QACT and QACTdir were 0.12% ± 1.1%, 0.02 ± 0.32 Gy, and −0.01 ± 0.71 Gy. CTV ΔD95 > 2.6% in TPCTdir was chosen as the threshold to trigger QACT/replan. The corresponding specificity was 94% and 98% for the clinical practice and the proposed method, respectively.ConclusionsThe replan evaluation based on TPCTdir provides better specificity than that based on the QACT.

Brachytherapy dose changes: comparing in-room and out-room image-guided brachytherapy. A randomized study.

PurposeImage-based brachytherapy, involving an image machine and a brachytherapy unit in the same room (in-room brachytherapy [IRBT]), limits patient movements; however, this technique may not be feasible in high workload centers. In this study, we compared changes in the dose and volume of organs at risk (OARs) with out-room brachytherapy (ORBT) technique, in which patients musted be transferred to a waiting room and then transferred back for brachytherapy delivery.Material and methodsThis was a randomized prospective study comparing changes in D2cc doses and volume of OARs during IRBT and ORBT. Patients underwent CT for treatment planning (CT1) installed in brachytherapy loading room, and another CT immediately before brachytherapy (CT2) during each fraction. While patients remained on CT table after CT1 during treatment planning and delivery in IRBT arm, they were transferred out to waiting room and back to CT table in ORBT arm. CT2 was analyzed with CT1 to evaluate any changes in volumes and doses.ResultsA total of 294 fractions of brachytherapy were considered. The findings indicated no significant differences in the mean D2cc changes (Gy) (CT2 minus CT1) to the bladder, rectum, and sigmoid between IRBT and ORBT (mean ±SD: –0.07 ±0.36 vs. –0.01 ±0.39, p = 0.1426; –0.15 ±0.32 vs. –0.14 ±0.29, p = 0.8898; –0.17 ±0.38 vs. –0.19 ±0.31, p = 0.5221, respectively). Moreover, significant correlations were observed between D2cc changes and volume changes to each of OARs, p < 0.001.ConclusionsIRBT does not result in differences in dose changes between planning and pre-treatment imaging when compared with ORBT. Consequently, ORBT can be considered for routine practice in high workload centers. Correlations in volume change and dose change to OARs were also observed.

The accuracy of helium ion CT based particle therapy range prediction: an experimental study comparing different particle and x-ray CT modalities

This work provides a quantitative assessment of helium ion CT (HeCT) for particle therapy treatment planning. For the first time, HeCT based range prediction accuracy in a heterogeneous tissue phantom is presented and compared to single-energy x-ray CT (SECT), dual-energy x-ray CT (DECT) and proton CT (pCT). HeCT and pCT scans were acquired using the US pCT collaboration prototype particle CT scanner at the Heidelberg Ion-Beam Therapy Center. SECT and DECT scans were done with a Siemens Somatom Definition Flash and converted to RSP. A Catphan CTP404 module was used to study the RSP accuracy of HeCT. A custom phantom of 20 cm diameter containing several tissue equivalent plastic cubes was used to assess the spatial resolution of HeCT and compare it to DECT. A clinically realistic heterogeneous tissue phantom was constructed using cranial slices from a pig head placed inside a cylindrical phantom (ø150 mm). A proton beam (84.67 mm range) depth-dose measurement was acquired using a stack of GafchromicTM EBT-XD films in a central dosimetry insert in the phantom. CT scans of the phantom were acquired with each modality, and proton depth-dose estimates were simulated based on the reconstructions. The RSP accuracy of HeCT for the plastic phantom was found to be 0.3 ± 0.1%. The spatial resolution for HeCT of the cube phantom was 5.9 ± 0.4 lp cm−1 for central, and 7.6 ± 0.8 lp cm−1 for peripheral cubes, comparable to DECT spatial resolution (7.7 ± 0.3 lp cm−1 and 7.4 ± 0.2 lp cm−1, respectively). For the pig head, HeCT, SECT, DECT and pCT predicted range accuracy was 0.25%, −1.40%, −0.45% and 0.39%, respectively. In this study, HeCT acquired with a prototype system showed potential for particle therapy treatment planning, offering RSP accuracy, spatial resolution, and range prediction accuracy comparable to that achieved with a commercial DECT scanner. Still, technical improvements of HeCT are needed to enable clinical implementation.

Chinese Expert Consensus on Iodine125 Seed Implantation for Recurrent Cervical Cancer in 2021.

The treatment modality for recurrent cervical cancer (rCC) is limited, and the prognosis of these patients is poor. Seed implantation could be an important component of rCC management in the context of dose boost or salvage therapy after surgery or radiotherapy, which is characterized by a minimally invasive, high local dose, and rapidly does fall, sparing normal tissue. For patients with good performance status and lateral pelvic wall recurrence with an available puncture path, seed implantation was recommended, as well as for selected central pelvic recurrence and extra-pelvic recurrence. The combination of brachytherapy treatment planning system and CT guidance was needed, and three-dimensional printing templates could greatly improve the accuracy, efficiency, and quality of seed implantation to achieve a potential ablative effect and provide an efficient treatment for rCC. However, the recommendations of seed implantation were mainly based on retrospective articles and lack high-quality evidence, and multicenter prospective randomized studies are needed. In this consensus on iodine125 seed implantation for rCC, indication selection, technical process and requirements, dosimetry criteria, radiation protection, combined systemic therapy, and outcomes of seed implantation for rCC are discussed.

Significant Incidental Findings Unrelated to the Primary Cancer on CT Simulation Treatment Planning Scans

<h3>Purpose/Objective(s)</h3> Incidental tumor findings on radiographic imaging are rare and often do not manifest symptoms such as a lump or associated pain. These tumors may represent major clinically relevant findings that need additional imaging and testing. Although some studies published metastatic disease of the primary cancer, we are not aware of publications in the U.S. focusing solely on major, significant findings, and unrelated to the primary disease during radiotherapy planning. In this paper, we introduce several cases where CT planning images revealed significant clinical findings unrelated to the primary cancer that required further investigation. All patients with these major findings were followed-up for diagnostic imaging, examination, and proper referral. <h3>Materials/Methods</h3> To simulate real-life situation when CT treatment planning is performed in a radiation oncology department, the radiation oncologist reviewed a total of 115 patients with CT treatment planning that was performed during the 2-year period of 2017-2018. These CTs were performed during routine radiotherapy planning for curative purposes: brain tumors (n = 10 or 9%), head and neck cancer (n = 15 or 13%), lung cancer (n = 30 or 26%), breast cancer (n = 35 or 30%), prostate cancer (n = 25 or 22%). <h3>Results</h3> We report incidental but significant findings on radiotherapy planning CT scans unrelated to the primary cancer. A total of 4 abnormal findings (3 neoplasms and 1 benign) were discovered from a total of 115 CT scans done for radiotherapy planning. The 3 neoplasms were: hepatocellular carcinoma, thyroid cancer, and large adrenal adenoma. The one benign finding was extensive eventration of the left hemi-diaphragm. <h3>Conclusion</h3> CT planning scans are routinely performed separately in the radiation oncology department for proper treatment mapping of cancer patients. These CT images are usually stored in the RO department server and not in radiology PACS system. At the present time, it is not known how many percent of radiation oncologists routinely check CT scans for incidental findings. The 4 case reports stated in this article demonstrate that although rare, there are malignancies/abnormalities unrelated to the primary cancer that could be missed in the CT planning process. We therefore recommend that radiation oncologists take charge of this issue and implement a method of proper interpretation of CT simulation images by either the radiation oncologists responsible for the case, or designated diagnostic radiologists (if such arrangement exists), to ensure that incidental findings are not missed. In any radiation oncology (RO) department, a routine QA (quality assurance) checklist often includes: consultation note, verification of pathology, consent form, verification of dosimetry, verification of physics report etc., we believe that proper interpretation of CT simulation images for incidental findings should be part of the QA checklist in a modern RO department

Cone Beam CT-Based Online Evaluation in Two Minutes Using a Commercial Treatment Planning Software for Proton Therapy

Daily online treatment evaluation is essential for proton therapy, which is sensitive to daily anatomy and setup uncertainties. We have developed a program based on a commercial treatment planning system (TPS) to streamline the online evaluation process using pre-treatment cone-beam CT (CBCT) images. Herein, we evaluate the speed and accuracy of the tool based on clinical proton plans.Data from 25 previously treated proton patients (7 HN, 11 lung, and 7 prostate) at our institution were evaluated. Corrected CBCT (cCBCT) images were generated using a research version of the TPS, which removes artifacts and calibrates HU for proton dose calculation. A quality assurance simulation CT (QACT) scanned on the same day as the CBCT is deformed to the cCBCT and used as a reference (dQACT). The cCBCT is deformed to the treatment planning CT (TPCT), and all contours on the initial TPCT were deformed to the cCBCT and then rigidly copied to the dQACT. Original clinical treatment plans were applied to both the cCBCT and dQACT, and the doses were calculated with the GPU-based Monte-Carlo dose calculation engine at 0.5% statistical uncertainty. The entire evaluation time (Eval_time) from importing CBCT image to completing final dose calculation for cCBCT was recorded. Plan parameters, including the number of beams (N_bm), proton spot number (N_sp), dose grid voxel numbers (N_voxel), were collected. Pearson correlation coefficient between the Eval_time and the N_bm, N_sp, and N_vosel were calculated. Dosimetric parameters were calculated for the cCBCT and compared to those calculated from the reference dQACT. Dosimetric parameters of interest included the following: for all, CTV D99, D95, and V100, and plan maximum dose (Dmax); for H&N cases, parotid mean dose (P_Dmean) and V30Gy (P_V30), and oral cavity mean dose (C_Dmean); and for H&N and lung cases, spinal cord max dose (SC_Dmax).Mean online Eval_time is 106.6 ± 24.9s for all patients, with HN as the fastest site 87.9 ± 17.5s for HN, 104.5 ± 15.9s for lung, 128.5 ± 28.0s for prostate, P < 0.01. The corresponding (N_bm, N_sp, N_voxel) were (3.9, 7781, 2.6E6) for HN, (3.3, 11080, 2.4E6) for lung, and (3.0, 10484, 3.8E6) for prostate. Eval_time was significantly correlated to N_sp (P = 0.02), but not to N_bm and N_voxel. The absolute differences for CTV D99, D95, V100, plan Dmax, HN P_Dmean, P_V30, C_Dmean, and SC_Dmax are 0.85 ± 0.60%, 0.39 ± 0.22%, 0.48 ± 0.41%, 0.46 ± 0.48%, 0.08 ± 0.05Gy, 0.05 ± 0.06%, 0.21 ± 0.20 Gy, and 0.57 ± 0.53Gy, respectively.Online evaluation can be achieved within 2 minutes for most clinical proton cases (80% in this study) with the commercial TPS. The speed can be further increased with reduced OAR mapping, fine-tuned deformable registration, optimized and reduced spot number in the initial plans, and upgraded computer server. This proposed method may optimize the efficiency and quality of care for proton radiotherapy.