Dosimetric analysis of six whole-breast irradiation techniques in supine and prone positions.

In breast cancer radiation therapy, minimizing radiation-related risks and toxicity is vital for improving life expectancy. Tailoring radiotherapy techniques and treatment positions can reduce radiation doses to normal organs and mitigate treatment-related toxicity. This study entailed a dosimetric comparison of six different external beam whole-breast irradiation techniques in both supine and prone positions. We selected fourteen breast cancer patients, generating six treatment plans in both positions per patient. We assessed target coverage and organs at risk (OAR) doses to evaluate the impact of treatment techniques and positions. Excess absolute risk was calculated to estimate potential secondary cancer risk in the contralateral breast, ipsilateral lung, and contralateral lung. Additionally, we analyzed the distance between the target volume and OARs (heart and ipsilateral lung) while considering the treatment position. The results indicate that prone positioning lowers lung exposure in X-ray radiotherapy. However, particle beam therapies (PBTs) significantly reduce the dose to the heart and ipsilateral lung regardless of the patient's position. Notably, negligible differences were observed between arc-delivery and static-delivery PBTs in terms of target conformity and OAR sparing. This study provides critical dosimetric evidence to facilitate informed decision-making regarding treatment techniques and positions.

Radiomics-based hybrid model for predicting radiation pneumonitis: A systematic review and meta-analysis.

PURPOSE: This study reviewed and meta-analyzed evidence on radiomics-based hybrid models for predicting radiation pneumonitis (RP). These models are crucial for improving thoracic radiotherapy plans and mitigating RP, a common complication of thoracic radiotherapy. We examined and compared the RP prediction models developed in these studies with the radiomics features employed in RP models. METHODS: We systematically searched Google Scholar, Embase, PubMed, and MEDLINE for studies published up to April 19, 2024. Sixteen studies met the inclusion criteria. We compared the RP prediction models developed in these studies and the radiomics features employed. RESULTS: Radiomics, as a single-factor evaluation, achieved an area under the receiver operating characteristic curve (AUROC) of 0.73, accuracy of 0.69, sensitivity of 0.64, and specificity of 0.74. Dosiomics achieved an AUROC of 0.70. Clinical and dosimetric factors showed lower performance, with AUROCs of 0.59 and 0.58. Combining clinical and radiomic factors yielded an AUROC of 0.78, while combining dosiomic and radiomics factors produced an AUROC of 0.81. Triple combinations, including clinical, dosimetric, and radiomics factors, achieved an AUROC of 0.81. The study identifies key radiomics features, such as the Gray Level Co-occurrence Matrix (GLCM) and Gray Level Size Zone Matrix (GLSZM), which enhance the predictive accuracy of RP models. CONCLUSIONS: Radiomics-based hybrid models are highly effective in predicting RP. These models, combining traditional predictive factors with radiomic features, particularly GLCM and GLSZM, offer a clinically feasible approach for identifying patients at higher RP risk. This approach enhances clinical outcomes and improves patient quality of life. PROTOCOL REGISTRATION: The protocol of this study was registered on PROSPERO (CRD42023426565).

Dose-toxicity surface histogram-based prediction of radiation dermatitis severity and shape.

Objective.This study aimed to develop a new approach to predict radiation dermatitis (RD) by using the skin dose distribution in the actual area of RD occurrence to determine the predictive dose by grade.Approach.Twenty-three patients with head and neck cancer treated with volumetric modulated arc therapy were prospectively and retrospectively enrolled. A framework was developed to segment the RD occurrence area in skin photography by matching the skin surface image obtained using a 3D camera with the skin dose distribution. RD predictive doses were generated using the dose-toxicity surface histogram (DTH) calculated from the skin dose distribution within the segmented RD regions classified by severity. We then evaluated whether the developed DTH-based framework could visually predict RD grades and their occurrence areas and shapes according to severity.Main results.The developed framework successfully generated the DTH for three different RD severities: faint erythema (grade 1), dry desquamation (grade 2), and moist desquamation (grade 3); 48 DTHs were obtained from 23 patients: 23, 22, and 3 DTHs for grades 1, 2, and 3, respectively. The RD predictive doses determined using DTHs were 28.9 Gy, 38.1 Gy, and 54.3 Gy for grades 1, 2, and 3, respectively. The estimated RD occurrence area visualized by the DTH-based RD predictive dose showed acceptable agreement for all grades compared with the actual RD region in the patient. The predicted RD grade was accurate, except in two patients.Significance. The developed DTH-based framework can classify and determine RD predictive doses according to severity and visually predict the occurrence area and shape of different RD severities. The proposed approach can be used to predict the severity and shape of potential RD in patients and thus aid physicians in decision making.

Clinical feasibility of deep learning-based synthetic CT images from T2-weighted MR images for cervical cancer patients compared to MRCAT.

This work aims to investigate the clinical feasibility of deep learning-based synthetic CT images for cervix cancer, comparing them to MR for calculating attenuation (MRCAT). Patient cohort with 50 pairs of T2-weighted MR and CT images from cervical cancer patients was split into 40 for training and 10 for testing phases. We conducted deformable image registration and Nyul intensity normalization for MR images to maximize the similarity between MR and CT images as a preprocessing step. The processed images were plugged into a deep learning model, generative adversarial network. To prove clinical feasibility, we assessed the accuracy of synthetic CT images in image similarity using structural similarity (SSIM) and mean-absolute-error (MAE) and dosimetry similarity using gamma passing rate (GPR). Dose calculation was performed on the true and synthetic CT images with a commercial Monte Carlo algorithm. Synthetic CT images generated by deep learning outperformed MRCAT images in image similarity by 1.5% in SSIM, and 18.5 HU in MAE. In dosimetry, the DL-based synthetic CT images achieved 98.71% and 96.39% in the GPR at 1% and 1 mm criterion with 10% and 60% cut-off values of the prescription dose, which were 0.9% and 5.1% greater GPRs over MRCAT images.

Evaluation of the deliverability of dynamic conformal arc therapy (DCAT) by gantry wobble and its influence on dose.

We aimed to investigate the deliverability of dynamic conformal arc therapy (DCAT) by gantry wobble owing to the intrinsic inter-segment break of the Elekta linear accelerator (LINAC) and its adverse influence on the dose to the patient. The deliverability of DCAT was evaluated according to the plan parameters, which affect the gantry rotation speed and resultant positional inaccuracies; the deliverability according to the number of control points and dose rates was investigated by using treatment machine log files and dosimetry devices, respectively. A non-negligible degradation in DCAT deliverability due to gantry wobble was observed in both the treatment machine log files and dosimetry devices. The resulting dose-delivery error occurred below a certain number of control points or above a certain dose rate. Dose simulations in the patient domain showed a similar impact on deteriorated deliverability. For targets located primarily in the isocenter, the dose differences were negligible, whereas for organs at risk located mainly off-isocenter, the dose differences were significant up to - 8.77%. To ensure safe and accurate radiotherapy, optimal plan parameters should be selected, and gantry angle-specific validations should be conducted before treatment.

Normal tissue complication probability models of hypothyroidism after radiotherapy for breast cancer.

Purpose: We aimed to develop Lyman-Kutcher-Burman (LKB) and multivariable normal tissue complication probability (NTCP) models to predict the risk of radiation-induced hypothyroidism (RIHT) in breast cancer patients. Materials and methods: A total of 1,063 breast cancer patients who underwent whole breast irradiation between 2009 and 2016 were analyzed. Individual dose-volume histograms were used to generate LKB and multivariable logistic regression models. LKB model was fit using the thyroid radiation dose-volume parameters. A multivariable model was constructed to identify potential dosimetric and clinical parameters associated with RIHT. Internal validation was conducted using bootstrapping techniques, and model performance was evaluated using the area under the curve (AUC) and Hosmer-Lemeshow (HL) goodness-of-fit test. Results: RIHT developed in 4 % of patients with a median follow-up of 77.7 months. LKB and multivariable NTCP models exhibited significant agreement between the predicted and observed results (HL P values > 0.05). The multivariable NTCP model outperformed the LKB model in predicting RIHT (AUC 0.62 vs. 0.54). In the multivariable model, systemic therapy, age, and percentage of thyroid volume receiving ≥ 10 Gy (V10) were significant prognostic factors for RIHT. The cumulative incidence of RIHT was significantly higher in patients who exceeded the cut-off values for all three risk predictors (systemic therapy, age ≥ 40 years, and thyroid V10 ≥ 26 %, P < 0.005). Conclusions: Systemic therapy, age, and V10 of the thyroid were identified as strong risk factors for the development of RIHT. Our NTCP models provide valuable insights to clinicians for predicting and preventing hypothyroidism by identifying high-risk patients.

Application of error classification model using indices based on dose distribution for characteristics evaluation of multileaf collimator position errors.

This study aims to evaluate the specific characteristics of various multileaf collimator (MLC) position errors that are correlated with the indices using dose distribution. The dose distribution was investigated using the gamma, structural similarity, and dosiomics indices. Cases from the American Association of Physicists in Medicine Task Group 119 were planned, and systematic and random MLC position errors were simulated. The indices were obtained from distribution maps and statistically significant indices were selected. The final model was determined when all values of the area under the curve, accuracy, precision, sensitivity, and specificity were higher than 0.8 (p < 0.05). The dose-volume histogram (DVH) relative percentage difference between the error-free and error datasets was examined to investigate clinical relations. Seven multivariate predictive models were finalized. The common significant dosiomics indices (GLCM Energy and GLRLM_LRHGE) can characterize the MLC position error. In addition, the finalized logistic regression model for MLC position error prediction showed excellent performance with AUC > 0.9. Furthermore, the results of the DVH were related to dosiomics analysis in that it reflects the characteristics of the MLC position error. It was also shown that dosiomics analysis could provide important information on localized dose-distribution differences in addition to DVH information.

Development of Automated Delivery Quality Assurance Analysis Software for Helical Tomotherapy.

BACKGROUND: To develop a fully automated in-house gamma analysis software for the "Cheese" phantom-based delivery quality assurance (QA) of helical tomotherapy plans. METHODS: The developed in-house software was designed to automate several procedures, which need to be manually performed using commercial software packages. The region of interest for the analysis was automatically selected by cropping out film edges and thresholding dose values (>10% of the maximum dose). The film-measured dose was automatically aligned to the computed dose using an image registration algorithm. An optimal film scaling factor was determined to maximize the percentage of pixels passing gamma (gamma passing rate) between the measured and computed doses (3%/3 mm criteria). This gamma analysis was repeated by introducing setup uncertainties in the anterior-posterior direction. For 73 tomotherapy plans, the gamma analysis results using the developed software were compared to those analyzed by medical physicists using a commercial software package. RESULTS: The developed software successfully automated the gamma analysis for the tomotherapy delivery quality assurance. The gamma passing rate (GPR) calculated by the developed software was higher than that by the clinically used software by 3.0%, on average. While, for 1 of the 73 plans, the GPR by the manual gamma analysis was higher than 90% (pass/fail criteria), the gamma analysis using the developed software resulted in fail (GPR < 90%). CONCLUSIONS: The use of automated and standardized gamma analysis software can improve both the clinical efficiency and veracity of the analysis results. Furthermore, the gamma analyses with various film scaling factors and setup uncertainties will provide clinically useful information for further investigations.

Incorporating axillary-lateral thoracic vessel juncture dosimetric variables improves model for predicting lymphedema in patients with breast cancer: A validation analysis.

Background: A relationship between the axillary-lateral thoracic vessel juncture (ALTJ) dose and lymphedema rate has been reported in patients with breast cancer. The purpose of this study was to validate this relationship and explore whether incorporation of the ALTJ dose-distribution parameters improves the prediction model's accuracy. Methods: A total of 1,449 women with breast cancer who were treated with multimodal therapies from two institutions were analyzed. We categorized regional nodal irradiation (RNI) as limited RNI, which excluded level I/II, vs extensive RNI, which included level I/II. The ALTJ was delineated retrospectively, and dosimetric and clinical parameters were analyzed to determine the accuracy of predicting the development of lymphedema. Decision tree and random forest algorithms were used to construct the prediction models of the obtained dataset. We used Harrell's C-index to assess discrimination. Results: The median follow-up time was 77.3 months, and the 5-year lymphedema rate was 6.8 %. According to the decision tree analysis, the lowest lymphedema rate (5-year, 1.2 %) was observed in patients with ≤ six removed lymph nodes and ≤ 66 % ALTJ V35Gy. The highest lymphedema rate was observed in patients with > 15 removed lymph nodes and an ALTJ maximum dose (Dmax) of > 53 Gy (5-year, 71.4 %). Patients with > 15 removed lymph nodes and an ALTJ Dmax ≤ 53 Gy had the second highest rate (5-year, 21.5 %). All other patients had relatively minor differences, with a rate of 9.5 % at 5 years. Random forest analysis revealed that the model's C-index increased from 0.84 to 0.90 if dosimetric parameters were included instead of RNI (P <.001). Conclusion: The prognostic value of ALTJ for lymphedema was externally validated. The estimation of lymphedema risk based on individual dose-distribution parameters of the ALTJ seemed more reliable than that based on the conventional RNI field design.

Dosimetric comparison of robust angles in carbon-ion radiation therapy for prostate cancer.

The objective of this study is to compare the plan robustness at various beam angles. Hence, the influence of the beam angles on robustness and linear energy transfer (LET) was evaluated in gantry-based carbon-ion radiation therapy (CIRT) for prostate cancer. 10 patients with prostate cancer were considered, and a total dose of 51.6 Gy (Relative biological effectiveness (RBE) was prescribed for the target volume in 12 fractions. Five beam field plans comprising two opposed fields with different angle pairs were characterized. Further, dose parameters were extracted, and the RBE-weighted dose and LET values for all angle pairs were compared. All plans considering the setup uncertainty satisfied the dose regimen. When a parallel beam pair was used for perturbed scenarios to take into account set-up uncertainty in the anterior direction, the LET clinical treatment volume (CTV) D 95% standard deviation was 1.5 times higher, and the standard deviation of RBE-weighted CTV D 95% was 7.9 times higher compared to an oblique pair. The oblique beam fields were superior in terms of dose sparing for the rectum compared to the dose distribution using two conventional lateral opposed fields for prostate cancer.

Development and Validation of a Normal Tissue Complication Probability Model for Lymphedema After Radiation Therapy in Breast Cancer.

PURPOSE: To develop and test a multivariable normal tissue complication probability (NTCP) model predicting lymphedema in patients with breast cancer receiving radiation therapy. METHODS AND MATERIALS: We retrospectively reviewed 1345 patients with breast cancer who received radiation therapy from 2 independent institutions. The patients were divided into a training cohort (institution A, n = 368, all treated with 3-dimensional conformal external beam radiation therapy [RT] with 2 Gy/fraction) and an external validation cohort (institution B, n = 977, treated either with 3-dimensional conformal external beam RT or with volumetric modulated RT and either with 1.8-2.0 Gy/fraction or with 2.67 Gy/fraction). Axillary-lateral thoracic vessel juncture (ALTJ) was delineated. The multivariable model was generated using dosimetric and clinical parameters. The performance of the model was comprehensively validated internally and externally. RESULTS: During a median follow-up of 78.7 months for the entire cohort, 97 patients (7.2%) developed lymphedema. The multivariable model that took into account the number of lymph nodes dissected, as well as the volume of the ALTJ receiving a dose ≥35 Gy equivalent doses in 2-Gy fractions (ALTJ V35), showed good agreement between predicted and observed results for both internal and external validation (Hosmer-Lemeshow P value > .05). The area under the receiver operating characteristic curve (AUC) and negative log-likelihood values for the multivariable NTCP model were 0.89 and 0.19 in internal validation and 0.83 and 0.19 in external validation. In addition, the multivariable model performance was acceptable for hypofractionated regimens (AUC 0.70) and volumetric modulated arc therapy (AUC 0.69). The number of lymph nodes dissected and ALTJ V35 were found to be the most important factors influencing lymphedema after radiation therapy. CONCLUSIONS: We first developed and validated the multivariable NTCP model for the lymphedema incidence in patients with breast cancer after radiation therapy. The multivariable NTCP model showed excellent performance and robustness in predicting lymphedema in both internal and completely independent external validations. The multivariable model for lymphedema prediction was robust and reliable for different treatment modalities and fractionation regimens.

Novel framework for determining TPS-calculated doses corresponding to detector locations using 3D camera inin vivosurface dosimetry.

Purpose. To address the shortcomings of current procedures for evaluating the measured-to-planned dose agreement inin vivodosimetry (IVD), this study aimed to develop an accurate and efficient novel framework to identify the detector location placed on a patient's skin surface using a 3D camera and determine the planned dose at the same anatomical position corresponding to the detector location.Methods. Breast cancer treatment was simulated using an anthropomorphic adult female phantom (ATOM 702D; CIRS, Norfolk, VA, USA). An optically stimulated luminescent dosimeter was used for surface dose measurements (MyOSLchip, RadPro International GmbH, Germany) at six IVD points. Three-dimensional surface imaging (3DSI) of the phantom with the detector was performed in the treatment position using a 3D camera. The developed framework, iSMART, was designed to import 3DSI and treatment planning data for determining the position of the IVD detectors in the 3D treatment planning DICOM image. The clinical usefulness of iSMART was evaluated in terms of accuracy and efficiency, for comparison with the results obtained using cone-beam computed tomography (CBCT) image guidance.Results. The relative dose difference between the planned doses determined using iSMART and CBCT images displayed similar accuracies (within approximately ±2.0%) at all detector locations. The relative dose differences between the planned and measured doses at the six detector locations ranged from -4.8% to 3.1% for the CBCT images and -3.5% to 2.1% for iSMART. The total time required to read the planned doses at six detector locations averaged at 8.1 and 0.8 min for the CBCT images and iSMART, respectively.Conclusions. The proposed framework can improve the robustness of IVD analyses and aid in accurate and efficient evaluations of the measured-to-planned dose agreement.

Metal artifact reduction in kV CT images throughout two-step sequential deep convolutional neural networks by combining multi-modal imaging (MARTIAN).

This work attempted to construct a new metal artifact reduction (MAR) framework in kilo-voltage (kV) computed tomography (CT) images by combining (1) deep learning and (2) multi-modal imaging, defined as MARTIAN (Metal Artifact Reduction throughout Two-step sequentIAl deep convolutional neural Networks). Most CNNs under supervised learning require artifact-free images to artifact-contaminated images for artifact correction. Mega-voltage (MV) CT is insensitive to metal artifacts, unlike kV CT due to different physical characteristics, which can facilitate the generation of artifact-free synthetic kV CT images throughout the first network (Network 1). The pairs of true kV CT and artifact-free kV CT images after post-processing constructed a subsequent network (Network 2) to conduct the actual MAR process. The proposed framework was implemented by GAN from 90 scans for head-and-neck and brain radiotherapy and validated with 10 independent cases against commercial MAR software. The artifact-free kV CT images following Network 1 and post-processing led to structural similarity (SSIM) of 0.997, and mean-absolute-error (MAE) of 10.2 HU, relative to true kV CT. Network 2 in charge of actual MAR successfully suppressed metal artifacts, relative to commercial MAR, while retaining the detailed imaging information, yielding the SSIM of 0.995 against 0.997 from the commercial MAR.

Synthetic contrast-enhanced computed tomography generation using a deep convolutional neural network for cardiac substructure delineation in breast cancer radiation therapy: a feasibility study.

BACKGROUND: Adjuvant radiation therapy improves the overall survival and loco-regional control in patients with breast cancer. However, radiation-induced heart disease, which occurs after treatment from incidental radiation exposure to the cardiac organ, is an emerging challenge. This study aimed to generate synthetic contrast-enhanced computed tomography (SCECT) from non-contrast CT (NCT) using deep learning (DL) and investigate its role in contouring cardiac substructures. We also aimed to determine its applicability for a retrospective study on the substructure volume-dose relationship for predicting radiation-induced heart disease. METHODS: We prepared NCT-CECT cardiac scan pairs of 59 patients. Of these, 35, 4, and 20 pairs were used for training, validation, and testing, respectively. We adopted conditional generative adversarial network as a framework to generate SCECT. SCECT was validated in the following three stages: (1) The similarity between SCECT and CECT was evaluated; (2) Manual contouring was performed on SCECT and CECT with sufficient intervals and based on this, the geometric similarity of cardiac substructures was measured between them; (3) The treatment plan was quantitatively analyzed based on the contours of SCECT and CECT. RESULTS: While the mean values (± standard deviation) of the mean absolute error, peak signal-to-noise ratio, and structural similarity index measure between SCECT and CECT were 20.66 ± 5.29, 21.57 ± 1.85, and 0.77 ± 0.06, those were 23.95 ± 6.98, 20.67 ± 2.34, and 0.76 ± 0.07 between NCT and CECT, respectively. The Dice similarity coefficients and mean surface distance between the contours of SCECT and CECT were 0.81 ± 0.06 and 2.44 ± 0.72, respectively. The dosimetry analysis displayed error rates of 0.13 ± 0.27 Gy and 0.71 ± 1.34% for the mean heart dose and V5Gy, respectively. CONCLUSION: Our findings displayed the feasibility of SCECT generation from NCT and its potential for cardiac substructure delineation in patients who underwent breast radiation therapy.

Performance Evaluation of Deformable Image Registration Algorithms Using Computed Tomography of Multiple Lung Metastases.

Purpose: Various deformable image registration (DIR) methods have been used to evaluate organ deformations in 4-dimensional computed tomography (4D CT) images scanned during the respiratory motions of a patient. This study assesses the performance of 10 DIR algorithms using 4D CT images of 5 patients with fiducial markers (FMs) implanted during the postoperative radiosurgery of multiple lung metastases. Methods: To evaluate DIR algorithms, 4D CT images of 5 patients were used, and ground-truths of FMs and tumors were generated by physicians based on their medical expertise. The positions of FMs and tumors in each 4D CT phase image were determined using 10 DIR algorithms, and the deformed results were compared with ground-truth data. Results: The target registration errors (TREs) between the FM positions estimated by optical flow algorithms and the ground-truth ranged from 1.82 ± 1.05 to 1.98 ± 1.17 mm, which is within the uncertainty of the ground-truth position. Two algorithm groups, namely, optical flow and demons, were used to estimate tumor positions with TREs ranging from 1.29 ± 1.21 to 1.78 ± 1.75 mm. With respect to the deformed position for tumors, for the 2 DIR algorithm groups, the maximum differences of the deformed positions for gross tumor volume tracking were approximately 4.55 to 7.55 times higher than the mean differences. Errors caused by the aforementioned difference in the Hounsfield unit values were also observed. Conclusions: We quantitatively evaluated 10 DIR algorithms using 4D CT images of 5 patients and compared the results with ground-truth data. The optical flow algorithms showed reasonable FM-tracking results in patient 4D CT images. The iterative optical flow method delivered the best performance in this study. With respect to the tumor volume, the optical flow and demons algorithms delivered the best performance.

A pilot study of a novel method to visualize three-dimensional dose distribution on skin surface images to evaluate radiation dermatitis.

Predicting the radiation dose‒toxicity relationship is important for local tumor control and patients' quality of life. We developed a first intuitive evaluation system that directly matches the three-dimensional (3D) dose distribution with the skin surface image of patients with radiation dermatitis (RD) to predict RD in patients undergoing radiotherapy. Using an RGB-D camera, 82 3D skin surface images (3DSSIs) were acquired from 19 patients who underwent radiotherapy. 3DSSI data acquired included 3D skin surface shape and optical imaging of the area where RD occurs. Surface registration between 3D skin dose (3DSD) and 3DSSI is performed using the iterative closest point algorithm, then reconstructed as a two-dimensional color image. The developed system successfully matched 3DSSI and 3DSD, and visualized the planned dose distribution onto the patient's RD image. The dose distribution pattern was consistent with the occurrence pattern of RD. This new approach facilitated the evaluation of the direct correlation between skin-dose distribution and RD and, therefore, provides a potential to predict the probability of RD and thereby decrease RD severity by enabling informed treatment decision making by physicians. However, the results need to be interpreted with caution due to the small sample size.

Development of a Margin Determination Framework for Tumor-Tracking Radiation Therapy With Intraoperatively Implanted Fiducial Markers.

PURPOSE: To develop an internal target volume (ITV) margin determination framework (or decision-supporting framework) for treating multiple lung metastases using CyberKnife Synchrony with intraoperatively implanted fiducial markers (IIFMs). The feasibility of using non-ideally implanted fiducial markers (a limited number and/or far from a target) for tracking-based lung stereotactic ablative radiotherapy (SABR) was investigated. METHODS: In the developed margin determination framework, an optimal set of IIFMs was determined to minimize a tracking uncertainty-specific ITV (ITVtracking) margin (margin required to cover target-to-marker motion discrepancy), i.e., minimize the motion discrepancies between gross tumor volume (GTV) and the selected set of fiducial markers (FMs). The developed margin determination framework was evaluated in 17 patients with lung metastases. To automatically calculate the respiratory motions of the FMs, a template matching-based FM tracking algorithm was developed, and GTV motion was manually measured. Furthermore, during-treatment motions of the selected FMs were analyzed using log files and compared with those calculated using 4D CTs. RESULTS: For 41 of 42 lesions in 17 patients (97.6%), an optimal set of the IIFMs was successfully determined, requiring an ITVtracking margin less than 5 mm. The template matching-based FM tracking algorithm calculated the FM motions with a sub-millimeter accuracy compared with the manual measurements. The patient respiratory motions during treatment were, on average, significantly smaller than those measured at simulation for the patient cohort considered. CONCLUSION: Use of the developed margin determination framework employing CyberKnife Synchrony with a limited number of IIFMs is feasible for lung SABR.

Development and Performance Evaluation of Wearable Respiratory Self-Training System Using Patch Type Magnetic Sensor.

PURPOSE: Respiratory training system that can be used by patients themselves was developed with a micro-electro-mechanical-system (MEMS)-based patch-type magnetic sensor. We conducted a basic function test and clinical usability evaluation to determine the system's clinical applicability. METHODS: The system is designed with a sensor attached to the patient's chest and a magnet on the back to monitor the patient's respiration by measuring changes in magnetic intensity related to respiratory movements of the thoracic surface. The system comprises a MEMS-based patch-type magnetic sensor capable of wireless communication and being applied to measurement magnets and mobile applications. System performance was evaluated by the level of systemic noise, the precision of the sensor in various breathing patterns, how measurement signals change for varying distances, or the presence or absence of material between the sensor and the magnet. Various breathing patterns were created using the QUASAR respiratory motion phantom; the data obtained were analyzed using the fitting and peak value analysis methods. RESULTS: The sensor had a noise ratio of <0.54% of the signal; the average errors in signal amplitude and period for breathing patterns were 78.87 um and 72 ms, respectively. The signal could be measured consistently when the sensor-magnet distance was 10-25 cm. The signal difference was 1.89% for the presence or absence of a material, indicating that its influence on the measurement signal is relatively small. CONCLUSION: The potential of our MEMS-based patch-type wearable respiratory self-training system was confirmed via basic function tests and clinical usability evaluations. We believe that the training system could provide thorough respiratory training for patients after a clinical trial with actual patients confirming its clinical efficacy and usability.

Risk of Hypothyroidism in Women After Radiation Therapy for Breast Cancer.

PURPOSE: To study the hypothyroidism risk after adjuvant radiation therapy (RT) and the association of different RT targets with hypothyroidism risk. METHODS: We studied 4073 women treated with adjuvant RT for breast cancer from 2007 to 2016. The primary endpoint was hypothyroidism development after RT. Patients were divided and analyzed into 3 groups: whole breast (WB)-alone (n = 2468), regional node irradiation (RNI)-Lv.4 (n = 215; cranial border at the subclavian artery, according to the European Society for Radiotherapy and Oncology consensus guideline), and RNI-supraclavicular lymph node (SCL) (n = 1390; cranial border at the cricoid cartilage). In general, RNI-Lv.4 was used in the patients with high-risk pN0 and pN1 breast cancer. In auxiliary analysis, the mean thyroid dose was estimated in each group (total n = 600, 200 from each group). All the doses were converted to the equivalent dose in 2 Gy fractions (EQD2) with α/ß ratios of 3. RESULTS: The median follow-up duration was 84 months (WB-alone, 84 months; RNI-Lv.4, 44 months; RNI-SCL, 91 months). The 3-year hypothyroidism incidence rate differed significantly between the RNI-SCL and WB-alone groups (2.2% vs 0.8%; Bonferroni corrected P [Pc] < .001) but not between the RNI-Lv.4 and WB-alone groups (0.9% vs 0.8%; Pc > .05). The Cox model revealed an adjusted hazard ratio of 2.25 (95% CI, 1.49-3.38) for RNI-SCL vs WB-alone, 1.69 (95% CI, 1.12-2.56) for adjuvant systemic therapies, and 2.07 (95% CI, 1.07-3.99) for age <60 years. In the subgroup analysis, the hypothyroidism risk became more prominent in patients aged <60 years. The mean exposure doses to the thyroid were 0.23 versus 1.93 versus 7.89 Gy (EQD2) for the WB-alone versus RNI-Lv.4 versus RNI-SCL groups (P < .001). No statistically different locoregional recurrence rates were seen between groups (5-year rate: <3%). CONCLUSIONS: The risk of hypothyroidism increases after RNI-SCL for breast cancer but not after RNI-Lv 4. These data support routine contouring of the thyroid in the RNI setting, and future studies are required to develop optimal dose-volume constraints.