Dose-volume Histogram Parameters Research Articles

Interpretable Machine Learning Models for Severe Esophagitis Prediction in LA-NSCLC Patients Treated with Chemoradiation Therapy

Radiation esophagitis is a common adverse event that may occur during chemoradiotherapy (CRT) that can adversely affect survival. This study aimed to develop interpretable machine learning (ML) models to predict grade 3 and higher radiation esophagitis in patients receiving definitive CRT therapy for locally advanced non-small cell lung cancer (LA-NSCLC). A total of 335 patients with LA-NSCLC who received definitive concurrent CRT at a single institution from 2017 to 2021 were retrospectively identified. Patients with esophagitis were identified and graded according to CTCAE v5.0. For each patient, 31 clinical features and 1093 dose-volume histogram (DVH) parameters from 19 structures were collected. The data was then randomly split into training (n = 233) and testing (n = 102) datasets. Feature selection was performed on the training dataset using the minimum redundancy maximum relevance algorithm to find a set of relevant features while controlling for the redundancy within the selected features, which were then followed by the Boruta algorithm to remove unimportant features and make the ML model more accurate. Synthetic minority oversampling technique was used to handle class-imbalanced datasets by generating synthetic samples for the minority class. Four variants of the Generalized Additive Model (GAM), including Explainable Boosting Machine (EBM), neural GAM (NODE-GAM), eXtreme Gradient Boosting (XGB)-GAM, and Spline, were built with selected features. The models' performance in predicting esophagitis was evaluated using the area under the receiver operating characteristic curve (AUC) in the test dataset. Shape plots were used to interpret the models' output and explain the selected features' contribution to the prediction. NODE-GAM yielded the highest performance (F1 score = 0.57, accuracy = 0.8, and AUC = 0.837), followed by EBM (F1 score = 0.43, accuracy = 0.8, and AUC = 0.7), Spline (F1 score = 0.42, accuracy = 0.74, and AUC = 0.737), and XGB-GAM (F1 score = 0.42, accuracy = 0.76, and AUC = 0.71). Selected features included D95%[Gy], D90%[Gy], D65%[Gy] and V40Gy [%] for the esophagus, V10Gy [%] for the pulmonary artery, and the distance from GTVn to the ascending aorta. The analysis of the selected features indicated that an increased radiation dose delivered to the esophagus and a shorter distance between the ascending aorta and GTVn were associated with a higher risk of developing esophagitis. Our study demonstrates the feasibility of developing interpretable ML models to predict esophagitis in patients with LA-NSCLC patients treated with CRT. NODE-GAM provided the best accuracy while providing insights into the driving dosimetric factors that could be used to guide optimal RT planning.

Identification and Dosimetric Analysis of Axillary Substructures Associated with Breast Cancer Related Lymphedema

It has been hypothesized that substructures in the axillary region may be involved in the development of upper limb lymphedema in breast cancer patients. We aimed to compare dosimetric parameters of different substructures to identify risk regions and dose indicators related to the development of lymphedema. A total of 486 patients with breast cancer treated with axillary node dissection (ALND) and regional node irradiation (RNI) between 2017 and 2020 were analyzed. Ten axillary substructures were retrospectively contoured, and dose-volume histogram (DVH) parameters (maximum dose [Dmax], Dmean, Dmin, V5 Gy, V10 Gy, V15 Gy, V20 Gy, V25 Gy, V30 Gy, V35 Gy, V40 Gy, V45 Gy and V50 Gy) were evaluated. EQD2 using α/β = 3 was applied for hypofractionation. The cumulative incidence of lymphedema was 32.5% during the median follow-up of 25 month (IQR 15-35). Higher BMI (≥21.77 kg/m2, hazard ratio [HR]1.6, 95% CI 1.1-2.3, p = 0.019) and higher number of dissected lymph nodes (≥13, HR 1.8, 95% CI 1.1-3.0, p = 0.026) were baseline risk factors associated with breast cancer related lymphedema (BCRL). A cohort of 196 patients was obtained by 1:1 propensity score matching based on the above two factors (154 treated with 50 Gy/25 fractions [Fx] and 42 with 40.05 Gy/15Fx). The optimal metric was axillary-lateral thoracic vessel juncture (ALTJ) Dmean ≥36.84 Gy (HR 3.7, 95% CI 1.6-8.6, p = 0.002). Other significant risk factors are Medial of Axillary Nodes level I(ALN-I-M) Dmin <9.19 Gy (HR 2.3, 95% CI 1.4-3.7, p = 0.001) and Posterior of Axillary Nodes level II(ALN-II-P) Dmax <52.75 Gy (HR 2.0, 95% CI 1.2-3.5, p = 0.015). A nomogram incorporated the above three parameters was created to predict the risk of lymphedema with reasonable accuracy confirmed by both self-training (area under curve [AUC], 0.76; 95% CI, 0.67-0.84) and internal validation (AUC 0.66; 95% CI, 0.50-0.80). A mean dose of 36.84 Gy of ALTJ may be a suggested dose limit for patients indicated RNI after ALND, especially when patients with higher BMI and higher dissected nodes. Ideally, avoidance of ALTJ may be realized without compromising dose coverage to high-risk nodal region. Confirmation of this finding in future prospective studies is needed.

Study on the Dynamic Changes of Myocardial Injury Markers in Radiotherapy for Esophageal Carcinoma and Its Correlation with Cardiac Dosimetry

It was hypothesized that radiotherapy for esophageal cancer could cause radiation-induced heart damage. To investigate the dynamic changes of myocardial enzyme, high-sensitive troponin T (hs-TnT), N-terminal pro-brain natriuretic peptide precursors (PRO-BNP) and left ventricular ejection fraction (LVEF) during radiotherapy and six months after radiotherapy for middle and lower thoracic squamous cell carcinoma, and to analyze the correlation between these indicators and dose-volume histogram (DVH) parameters of the heart. A total of 35 patients with thoracic esophageal squamous cell carcinoma who underwent radical concurrent chemoradiotherapy were enrolled in the study. Radiation therapy was performed for up to 6 weeks. All patients received Intensity modulated radiation therapy (IMRT). Total radiation dose was from 50.4Gy to 60 Gy in each patient with a dose of 1.8-2.0 Gy per fraction. Blood samples to determine creatine kinase (CK), creatine kinase isoenzyme MB (CK-MB), lactic dehydrogenase (LDH), alpha-hydroxybutyric dehydrogenase (α-HBDH), hs-TnT, PRO-BNP and LVEF were measured before radiotherapy, during (10th -20th fraction), at the end of radiotherapy, 1, 3, 6 months after radiotherapy. The dynamic changes of the above indexes were analyzed. The correlation between the above indexes and the mean heart dose (Dmean), V5-V60 (dose-volume histograms data were recorded in discrete 5Gy dose levels) of the heart in the course of radiotherapy was also analyzed. The Serum hs-TNT and LVEF show an upward trend during radiotherapy, at the end of radiotherapy and 6 months after radiotherapy for esophageal cancer. The hs-TnT of patients before, during, at the end of radiotherapy and 1, 3, and 6 months after radiotherapy were 7.2pg/ml, 9.1pg/ml, 9.1pg/ml, 9.0pg/ml, 9.4pg/ml, and 8.1pg/ml, respectively (p<0.05). The LVEF were 63.7%, 62.4%, 62.0%, 62.5%, 62.2%, 61.9% respectively (p<0.05). The pro-BNP showed an upward trend during radiotherapy and gradually returned to normal after radiotherapy. The CK and CK-MB showed a downward trend during radiotherapy and 1 month after radiotherapy, and gradually returned to normal 3 months after radiotherapy. Compared with the low dose group (the average dose of heart < 2000cGy), the high dose group (≥2000cGy) had a greater increase in hs-TNT, pro-BNP and LVEF, and a slower recovery time. There was no correlation between the changes of myocardial enzyme, hs-TnT, PRO-BNP, LVEF and the heart mean dose, V5-V60 during radiotherapy (p>0.05). Cardiac injury induced by concurrent chemoradiotherapy for middle and lower thoracic esophageal cancer can lead to the increase of serum hs-TNT and pro-BNP and the increase of LVEF in the early stage of treatment, and this phenomenon is more obvious in the high-dose group. The hs-TnT and PRO-BNP are sensitive parameters to reflect the heart damage in esophageal cancer radiotherapy, which may provide reference for the heart protection during radiotherapy.

Comparison of Early Outcomes of Stereotactic Accelerated Partial Breast Irradiation vs. Volumetric Modulated Arc Therapy-Based FAST-FORWARD Whole Breast Irradiation for Breast Cancer

Despite evidence supporting APBI from 8 published prospective randomized trials enrolling over 10,000 women, the uptake of APBI in clinical practice is surprisingly low. This is being exacerbated by a new, convenient, and safe shortened WBI schedule. Here, we report the dosimetric and early outcome analyses of the first >1000 patients treated at our institution since the first adoption of stereotactic APBI and the ultra-hypofractionated WBI regimen. From 2016 to 2022, 801 women with breast cancers in the suitable or cautionary categories according to the ASTRO APBI consensus panel guidelines, received 30 Gy in 5 fractions (92%) either using a robotic stereotactic radiation system (83%) or stereotactic volumetric-based arc therapy (VMAT, 17%). Between 2020 and 2022, 468 women, who were not candidates for APBI and not undergoing any regional irradiation received 26 Gy in 5 fractions using VMAT to the whole breast with the addition of cardiac sparing technique in left-sided breast cancer patients. Tumor bed boosts were delivered in 99% of FF-WBI patients. We evaluated dose-volume histogram parameters for target volumes and organs-at-risk and radiation-related toxicities during RT or within 6 months after the end of RT. Target volume coverage was acceptable in both groups, with mean 96% of the target volumes receiving 95% of the prescribed doses and 0 cm3 within target volumes exceeding 105% of the prescribed doses. S-APBI resulted in small, but statistically significant, reductions in the radiation dose delivered to the ipsilateral breast, contralateral breast, lungs, heart, and coronary artery compared with FF-WBI. Comparing WBI to APBI, the mean contralateral breast dose, ipsilateral lung V20 Gy, mean contralateral lung dose, and mean heart dose, were reduced by 89%, 78%, 73%, and 29%, respectively. With median follow-up periods of 32 months for s-APBI and 19 months for FF-WBI, acute toxicity was assessable in all patients. The risks of any grade acute toxicity were 21% for s-APBI and 25% for FF-WBI (p = .117). Among them, grade 2 rates were 1.3% in both groups and no severe toxicity has been reported. We found s-APBI and VMAT-based FF WBI were associated with favorable dosimetric and acute toxicity profiles. However, considering significantly less irradiated volume in the breast, lungs, and heart, APBI with advanced available technique options should be considered over any WBI-based approach for patients at low risk for local recurrence.

Uncertainty in magnetic resonance imaging-based prostate postimplant dosimetry: Results of a 10-person human observer study, and comparisons with automatic postimplant dosimetry

Uncertainties in postimplant quality assessment (QA) for low-dose-rate prostate brachytherapy (LDRPBT) are introduced at two steps: seed localization and contouring. We quantified how interobserver variability (IoV) introduced in both steps impacts dose-volume-histogram (DVH) parameters for MRI-based LDRPBT, and compared it with automatically derived DVH parameters. Twenty-five patients received MRI-based LDRPBT. Seven clinical observers contoured the prostate and four organs at risk, and 4 dosimetrists performed seed localization, on each MRI. Twenty-eight unique manual postimplant QAs were created for each patient from unique observer pairs. Reference QA and automatic QA were also performed for each patient. IoV of prostate, rectum, and external urinary sphincter (EUS) DVH parameters owing to seed localization and contouring was quantified with coefficients of variation. Automatically derived DVH parameters were compared with those of the reference plans. Coefficients of variation (CoVs) owing to contouring variability (CoVcontours) were significantly higher than those due to seed localization variability (CoVseeds) (median CoVcontours vs. median CoVseeds: prostate D90-15.12% vs. 0.65%, p < 0.001; prostate V100-5.36% vs. 0.37%, p < 0.001; rectum V100-79.23% vs. 8.69%, p < 0.001; EUS V200-107.74% vs. 21.18%, p < 0.001). CoVcontours were lower when the contouring observers were restricted to the 3 radiation oncologists, but were still markedly higher than CoVseeds. Median differences in prostate D90, prostate V100, rectum V100, and EUS V200 between automatically computed and reference dosimetry parameters were 3.16%, 1.63%, -0.00 mL, and -0.00 mL, respectively. Seed localization introduces substantially less variability in postimplant QA than does contouring for MRI-based LDRPBT. While automatic seed localization may potentially help improve workflow efficiency, it has limited potential for improving the consistency and quality of postimplant dosimetry.

Generation of synthetic CT from CBCT using deep learning approaches for head and neck cancer patients

Purpose. To create a synthetic CT (sCT) from daily CBCT using either deep residual U-Net (DRUnet), or conditional generative adversarial network (cGAN) for adaptive radiotherapy planning (ART). Methods. First fraction CBCT and planning CT (pCT) were collected from 93 Head and Neck patients who underwent external beam radiotherapy. The dataset was divided into training, validation, and test sets of 58, 10 and 25 patients respectively. Three methods were used to generate sCT, 1. Nonlocal means patch based method was modified to include multiscale patches defining the multiscale patch based method (MPBM), 2. An encoder decoder 2D Unet with imbricated deep residual units was implemented, 3. DRUnet was integrated to the generator part of cGAN whereas a convolutional PatchGAN classifier was used as the discriminator. The accuracy of sCT was evaluated geometrically using Mean Absolute Error (MAE). Clinical Volumetric Modulated Arc Therapy (VMAT) plans were copied from pCT to registered CBCT and sCT and dosimetric analysis was performed by comparing Dose Volume Histogram (DVH) parameters of planning target volumes (PTVs) and organs at risk (OARs). Furthermore, 3D Gamma analysis (2%/2mm, global) between the dose on the sCT or CBCT and that on the pCT was performed. Results. The average MAE calculated between pCT and CBCT was 180.82 ± 27.37HU. Overall, all approaches significantly reduced the uncertainties in CBCT. Deep learning approaches outperformed patch-based methods with MAE = 67.88 ± 8.39HU (DRUnet) and MAE = 72.52 ± 8.43HU (cGAN) compared to MAE = 90.69 ± 14.3HU (MPBM). The percentages of DVH metric deviations were below 0.55% for PTVs and 1.17% for OARs using DRUnet. The average Gamma pass rate was 99.45 ± 1.86% for sCT generated using DRUnet. Conclusion. DL approaches outperformed MPBM. Specifically, DRUnet could be used for the generation of sCT with accurate intensities and realistic description of patient anatomy. This could be beneficial for CBCT based ART.

Clinical benefit of range uncertainty reduction in proton treatment planning based on dual-energy CT for neuro-oncological patients.

Several studies have shown that dual-energy CT (DECT) can lead to improved accuracy for proton range estimation. This study investigated the clinical benefit of reduced range uncertainty, enabled by DECT, in robust optimisation for neuro-oncological patients. DECT scans for 27 neuro-oncological patients were included. Commercial software was applied to create stopping-power ratio (SPR) maps based on the DECT scan. Two plans were robustly optimised on the SPR map, keeping the beam and plan settings identical to the clinical plan. One plan was robustly optimised and evaluated with a range uncertainty of 3% (as used clinically; denoted 3%-plan); the second plan applied a range uncertainty of 2% (2%-plan). Both plans were clinical acceptable and optimal. The dose-volume histogram parameters were compared between the two plans. Two experienced neuro-radiation oncologists determined the relevant dose difference for each organ-at-risk (OAR). Moreover, the OAR toxicity levels were assessed. For 24 patients, a dose reduction >0.5/1 Gy (relevant dose difference depending on the OAR) was seen in one or more OARs for the 2%-plan; e.g. for brainstem D0.03cc in 10 patients, and hippocampus D40% in 6 patients. Furthermore, 12 patients had a reduction in toxicity level for one or two OARs, showing a clear benefit for the patient. Robust optimisation with reduced range uncertainty allows for reduction of OAR toxicity, providing a rationale for clinical implementation. Based on these results, we have clinically introduced DECT-based proton treatment planning for neuro-oncological patients, accompanied with a reduced range uncertainty of 2%. This study shows the clinical benefit of range uncertainty reduction from 3% to 2% in robustly optimised proton plans. A dose reduction to one or more OARs was seen for 89% of the patients, and 44% of the patients had an expected toxicity level decrease.

A dosimetric comparative study following RTOG and ESTRO contouring guidelines for breast radiation therapy

PurposeTo compare the dosimetric parameters considering the Radiation Therapy Oncology Group (RTOG) and European Society for Radiotherapy and Oncology (ESTRO) guidelines for breast cancer radiotherapy. Two radiotherapy techniques, intensity-modulated radiation therapy (IMRT) and volumetric-modulated arc therapy (VMAT), were considered. Patients and methodsTwenty-eight patients with left-sided medially-located TanyN2M0 tumors were contoured based on RTOG and ESTRO guidelines. 9-field IMRT, 10-field IMRT, 11-field IMRT, and VMAT treatment plans were applied as radiotherapy (RT) techniques for both contouring sets. The dosimetric parameters of the RT plans were extracted and compared. ResultsComparing dose-volume histogram (DVH) parameters, equivalent uniform dose (EUD), and normal tissue complication probability (NTCP) of OARs across the contouring guidelines and considering each RT technique showed that the only significant differences were higher Dmax, Dmean, V30, and V45, EUD, and NTCP of the thyroid in all treatment modalities when the RTOG guideline had been adopted. Using the VMAT technique, PTV's EUD and the tumor control probability (TCP) were considerably higher when the ESTRO guideline was adopted. Moreover, the conformity index (CI) of VMAT plans was significantly higher when the ESTRO guideline was used. ConclusionUnless having higher doses to thyroid when the RTOG guideline was adopted, the doses to other organs-at-risk (OAR) were similar between the two considering guidelines. Moreover, except for higher EUD, TCP, and CI for VMAT when the ESTRO guideline was used, no other significant differences were obtained between dosimetric parameters of target volumes considering the RT techniques and contouring guidelines.

A qualitative study of improving megavoltage computed tomography image quality and maintaining dose accuracy using cycleGAN-based image synthesis.

Due to inconsistent positioning, tumor shrinking, and weight loss during fractionated treatment, the initial plan was no longer appropriate after a few fractional treatments, and the patient will require adaptive helical tomotherapy (HT) to overcome the issue. Patients are scanned with megavoltage computed tomography (MVCT) before each fractional treatment, which is utilized for patient setup and provides information for dose reconstruction. However, the low contrast and high noise of MVCT make it challenging to delineate treatment targets and organs at risk (OAR). This study developed a deep-learning-based approach to generate high-quality synthetic kilovoltage computed tomography (skVCT) from MVCT and meet clinical dose requirements. Data from 41 head and neck cancer patients were collected; 25 (2995 slices) were used for training, and 16 (1898 slices) for testing. A cycle generative adversarial network (cycleGAN) based on attention gate and residual blocks was used to generate MVCT-based skVCT. For the 16 patients, kVCT-based plans were transferred to skVCT images and electron density profile-corrected MVCT images to recalculate the dose. The quantitative indices and clinically relevant dosimetric metrics, including the mean absolute error (MAE), structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), gamma passing rates, and dose-volume-histogram (DVH) parameters (Dmax , Dmean , Dmin ), were used to assess the skVCT images. The MAE, PSNR, and SSIM of MVCT were 109.6±12.3 HU, 27.5±1.1dB, and 91.9%±1.7%, respectively, while those of skVCT were 60.6±9.0 HU, 34.0±1.9dB, and 96.5%±1.1%. The image quality and contrast were enhanced, and the noise was reduced. The gamma passing rates improved from 98.31%±1.11% to 99.71%±0.20% (2mm/2%) and 99.77%±0.18% to 99.98%±0.02% (3mm/3%). No significant differences (p>0.05) were observed in DVH parameters between kVCT and skVCT. With training on a small data set (2995 slices), the model successfully generated skVCT with improved image quality, and the dose calculation accuracy was similar to that of MVCT. MVCT-based skVCT can increase treatment accuracy and offer the possibility of implementing adaptive radiotherapy.

Development of a prediction model for head and neck volume reduction by clinical factors, dose-volume histogram parameters and radiomics in head and neck cancer†.

In external radiotherapy of head and neck (HN) cancers, the reduction of irradiation accuracy due to HN volume reduction often causes a problem. Adaptive radiotherapy (ART) can effectively solve this problem; however, its application to all cases is impractical because of cost and time. Therefore, finding priority cases is essential. This study aimed to predict patients with HN cancers are more likely to need ART based on a quantitative measure of large HN volume reduction and evaluate model accuracy. The study included 172 cases of patients with HN cancer who received external irradiation. The HN volume was calculated using cone-beam computed tomography (CT) for irradiation-guided radiotherapy for all treatment fractions and classified into two groups: cases with a large reduction in the HN volume and cases without a large reduction. Radiomic features were extracted from the primary gross tumor volume (GTV) and nodal GTV of the planning CT. To develop the prediction model, four feature selection methods and two machine-learning algorithms were tested. Predictive performance was evaluated by the area under the curve (AUC), accuracy, sensitivity and specificity. Predictive performance was the highest for the random forest, with an AUC of 0.662. Furthermore, its accuracy, sensitivity and specificity were 0.692, 0.700 and 0.813, respectively. Selected features included radiomic features of the primary GTV, human papillomavirus in oropharyngeal cancer and the implementation of chemotherapy; thus, these features might be related to HN volume change. Our model suggested the potential to predict ART requirements based on HN volume reduction .

Applying a novel two-step deep learning network to improve the automatic delineation of esophagus in non-small cell lung cancer radiotherapy.

To introduce a model for automatic segmentation of thoracic organs at risk (OARs), especially the esophagus, in non-small cell lung cancer radiotherapy, using a novel two-step deep learning network. A total of 59 lung cancer patients' CT images were enrolled, of which 39 patients were randomly selected as the training set, 8 patients as the validation set, and 12 patients as the testing set. The automatic segmentations of the six OARs including the esophagus were carried out. In addition, two sets of treatment plans were made on the basis of the manually delineated tumor and OARs (Plan1) as well as the manually delineated tumor and the automatically delineated OARs (Plan2). The Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and average surface distance (ASD) of the proposed model were compared with those of U-Net as a benchmark. Next, two groups of plans were also compared according to the dose-volume histogram parameters. The DSC, HD95, and ASD of the proposed model were better than those of U-Net, while the two groups of plans were almost the same. The highest mean DSC of the proposed method was 0.94 for the left lung, and the lowest HD95 and ASD were 3.78 and 1.16mm for the trachea, respectively. Moreover, the DSC reached 0.73 for the esophagus. The two-step segmentation method can accurately segment the OARs of lung cancer. The mean DSC of the esophagus realized preliminary clinical significance (>0.70). Choosing different deep learning networks based on different characteristics of organs offers a new option for automatic segmentation in radiotherapy.

Applicator-guided proton therapy versus multichannel brachytherapy for vaginal vault irradiation.

To dosimetrically compare applicator-guided intensity-modulated proton therapy(IMPT) and multichannel brachytherapy (MC-BRT) for vaginal vault irradiation(VVI) with special focus on dose to organs at risk (OARs) and normal tissues. Ten patients with uterine confined endometrial cancer who received adjuvant vaginal cuff brachytherapy were included in this study. For each patient an additional IMPT treatment plan was created using the same computed tomography dataset and contours segmented for MC-BRT plans. Clinical target volume (CTV) was defined as the proximal 3.5 cm of the vagina including the entire thickness of vaginal wall. Planning target volume for IMPT plans was generated from the CTV with an addition isotropic 3 mm margin. OARs included rectum, bladder, sigmoid, small bowel and femoral heads. The prescribed dose was 21 Gy in 3 fractions. For simplicity, all doses were expressed in Gy and a constant relative biological effectiveness of 1.1 was used for IMPT plans. Plan comparison was performed using dose-volume histogram and treatment planning parameters. A significant improvement of the D98% coverage for CTV was reached by the applicator-guided IMPT plans (p < 0.01). IMPT also provided a dose reduction in all OARs except for femoral heads due to the lateral beam direction, especially significant reduction of V5Gy, D2cc, D0.1 cc, Dmean, V95% values for the rectum and Dmean, D0.1 cc to bladder, sigmoid, small bowel. Additionally, IMPT plans showed a significant reduction of integral dose to normal tissue with respect to MC-BRT (221.5 cGy.L vs. 653.6 cGy.L, p < 0.01). Applicator-guided IMPT has the potential for improving plan quality in VVI while maintaining the high conformity afforded by the state-of-the-art intracavitary brachytherapy.

Avoiding Dosimetric Risk Factors for Complications in Neoadjuvant Chemoradiotherapy for Lung Cancer: Conventional Radiotherapy Versus Intensity-modulated Radiotherapy.

We compared three-dimensional conformal radiotherapy (3D-CRT) with intensity-modulated radiotherapy (IMRT) for avoiding dosimetric risk factors related to pulmonary complications after neoadjuvant chemoradiotherapy followed by surgery (NACRT-S) for non-small cell lung cancer (NSCLC). We performed simulations in 11 patients with dosimetric risk factors during their treatment with NACRT-S for NSCLC. Radiation treatment plans were generated using 3D-CRT and IMRT to avoid dosimetric risk factors. Regarding dose-volume histogram (DVH) parameters, we calculated the percentage of lung volume that received more than x Gy (Vx) using 1) the total lung volume minus gross tumor volume (DVHg), 2) the lung volume remaining after surgery (DVHr), and 3) the contralateral lung volume (DVHc). We analyzed the dosimetric differences between 3D-CRT and IMRT. V35g and V40g were significantly lower with IMRT than with 3D-CRT (p=0.001 each); the median V35g and V40g were 16.1% and 14.9% with 3D-CRT versus 12.0% and 9.2% with IMRT, respectively. Overall, 0% and 55% of the patients were able to avoid all dosimetric risk factors with 3D-CRT and IMRT, respectively (p=0.006). Even with IMRT, tumor location and length of the planning target volume (PTV) significantly affected the avoidance of all dosimetric risk factors (p=0.015 and 0.022, respectively). IMRT is more useful than 3D-CRT for avoiding dosimetric risk factors in NACRT-S for NSCLC. For further improvements in avoiding these factors, respiratory motion managements to reduce the length of the PTV may be required for patients with middle or lower lobe tumors.

Parameter based 4D dose calculations for proton therapy.

Background and purpose Retrospective log file-based analysis provides the actual dose delivered based on the patient's breathing and the daily beam-delivery dynamics. To predict the motion sensitivity of the treatment plan on a patient-specific basis before treatment start a prospective tool is required. Such a parameter-based tool has been investigated with the aim to be used in clinical routine. Materials and Methods 4D dose calculations (4DDC) were performed for seven cancer patients with small breathing motion treated with scanned pulsed proton beams. Validation of the parameter-based 4DDC (p-4DDC) method was performed with an anthropomorphic phantom and patient data employing measurements and a log file-based 4DDC tool. The dose volume histogram parameters () were investigated for the target and the organs at risk, compared to static and the file-based approach. Results The difference between the measured and the p-4DDC dose was within the deviation of the measurements. The maximum deviation was 0.4Gy. For the planning target volume varied up to 15% compared to the static scenario, while the results from the log file and p-4DDC agreed within 2%. For the liver patients, deviated up to 35% compared to static and 10% comparing the two 4DDC tools, while for the pancreas patients the varied up to 45% and 11%, respectively. Conclusion The results showed that p-4DDC could be used prospectively. The next step will be the clinical implementation of the p-4DDC tool, which can support a decision to either adapt the treatment plan or apply motion mitigation strategies.

Dose-Volume Histogram Parameters and Quality of Life in Patients with Prostate Cancer Treated with Surgery and High-Dose Volumetric-Intensity-Modulated Arc Therapy to the Prostate Bed.

Prostate bed radiotherapy (RT) is a major affecter of patients' long-term quality of life (QoL). To ensure the best possible outcome of these patients, dose constraints are key for optimal RT planning and delivery. However, establishing refined dose constraints requires access to patient-level data. Therefore, we aimed to provide such data on the relationship between OAR and gastrointestinal (GI) as well as genitourinary (GU) QoL outcomes of a homogenous patient cohort who received dose-intensified post-operative RT to the prostate bed. Furthermore, we aimed to conduct an exploratory analysis of the resulting data. Patients who were treated with prostate bed RT between 2010 and 2020 were inquired about their QoL based on the Expanded Prostate Cancer Index Composite (EPIC). Those (n = 99) who received volumetric arc therapy (VMAT) of at least 70 Gy to the prostate bed were included. Dose-volume histogram (DVH) parameters were gathered and correlated with the EPIC scores. The median age at the time of prostate bed RT was 68.9 years, and patients were inquired about their QoL in the median 2.3 years after RT. The median pre-RT prostate-specific antigen (PSA) serum level was 0.35 ng/mL. The median duration between surgery and RT was 1.5 years. The median prescribed dose to the prostate bed was 72 Gy. A total of 61.6% received prostate bed RT only. For the bladder, the highest level of statistical correlation (p < 0.01) was seen for V10-20Gy, Dmean and Dmedian with urinary QoL. For bladder wall, the highest level of statistically significant correlation (p < 0.01) was seen for V5-25Gy, Dmean and Dmedian with urinary QoL. Penile bulb V70Gy was statistically significantly correlated with sexual QoL (p < 0.05). A larger rectal volume was significantly correlated with improved bowel QoL (p < 0.05). Sigmoid and urethral DVH parameters as well as the surgical approach were not statistically significantly correlated with QoL. Specific dose constraints for bladder volumes receiving low doses seem desirable for the further optimization of prostate bed RT. This may be particularly relevant in the context of the aspiration of establishing focal RT of prostate cancer and its local recurrences. Our comprehensive dataset may aid future researchers in achieving these goals.

Detection of suboptimal IMRT treatment plan using machine learning on radiomics features of dose distribution for lung cancers

Quality assurance in radiotherapy is an important process so that the use of radiation provides maximum benefits. Currently, the implementation of machine learning (ML) in the quality assurance of treatment planning is growing. In this study, 34 optimal intensity-modulated radiation therapy (IMRT) treatment plans and 10 suboptimal IMRT treatment plans obtained from Siloam MRCCC Semanggi Hospital were used to train an ML model of the type autoencoder for anomaly detection developed using PyTorch. There were four stages in this study, namely the preparation stage, development stage, validation stage, and evaluation stage. At the development stage, the raw data was prepared so that it is ready to be used for training. At the development stage, the model was developed and a hyperparameters optimization was performed. The accuracy of the model was analyzed at the validation stage. Finally, at the evaluation stage, the model performance was evaluated along with a Mann-Whitney U test on dose-volume histogram (DVH) parameters, radiomics features, and DVH metrics (conformity index and homogeneity index) to show the difference between treatment plans. The model used 161 radiomics features with an epoch of 1250 iterations, 150-50-17 hidden layers configuration, and a learning rate of 0.2 as the most optimal configuration. The results showed an accuracy of 36% with 7% of radiomics features, 50% of DVH parameters, and the homogeneity index being different significantly. After refinement, that is removing data with conformity index below one, the accuracy became 50% with 12% of radiomics features, 45% of DVH parameters, and both DVH metrics being different significantly. If the radiomics features used are those that were significantly different, the accuracy increased to 93%. From these results, it can be concluded that most radiomics features are noise that need to be removed so the model can detect suboptimal treatment plans. In addition, the planning target volume segment along with the firstorder radiomics features group is the main differentiator between the optimal and suboptimal treatment plans.

Clinical impact of radiation-induced myocardial damage detected by cardiac magnetic resonance imaging and dose-volume histogram parameters of the left ventricle as prognostic factors of cardiac events after chemoradiotherapy for esophageal cancer.

This prospective study aimed to evaluate whether radiation (RT)-induced myocardial damage by cardiac magnetic resonance (CMR) imaging could be a predictor of cardiac events after chemoradiotherapy (CRT) for esophageal cancer and determine the dose-volume histogram (DVH) parameters of the left ventricle (LV) in predicting cardiac events. CMR imaging was performed before and 6months after CRT in patients receiving definitive CRT. RT-induced myocardial damage was defined as abnormal CMR findings indicating myocardial fibrosis corresponding to an isodose line of ≥30Gy. The cutoff values of the LV DVH parameters were calculated using the receiver operating characteristic curve based on the presence of RT-induced myocardial damage. The prognostic factors related to cardiac events of Grade 3 or higher were examined. Twenty-three patients were enrolled in the study. RT-induced myocardial damage by late gadolinium enhancement and/or an increase of 100ms or higher in native T1 post-CRT was detected in 10 of the 23 patients. LV V45 was the best predictive factor for RT-induced myocardial damage with a cutoff value of 2.1% and an area under the curve of 0.75. The median follow-up period was 82.1months. The 5- and 7-year cumulative incidences of cardiac events of Grade 3 or higher were 14.7 and 22.4%, respectively. RT-induced myocardial damage and LV V45 were significant risk factors (P = 0.015 and P = 0.013, respectively). RT-induced myocardial damage is a significant predictor of cardiac events. LV V45 is associated with RT-induced myocardial damage and subsequent cardiac events.

Dosimetric Predictors of Toxicity after Prostate Stereotactic Body Radiotherapy: A Single-Institutional Experience of 145 Patients

The indications for stereotactic body radiotherapy (SBRT) for prostate cancer have increased. However, the relationships between adverse events and risk factors remain unclear. This study aimed to clarify associations between adverse events and dose index for prostate SBRT. Participants comprised 145 patients irradiated with 32-36 Gy in 4 fractions. Radiotherapy-related risk factors such as dose-volume histogram parameters and patient-related risk factors such as T stage and Gleason score were evaluated in a competing risk analysis. Median follow-up duration was 42.9 months. A total of 9.7% had acute Grade ≥ 2 GU toxicities and 4.8% had acute Grade ≥ 2 GI toxicities. A total of 11.1% had late Grade ≥ 2 GU toxicities and 7.6% had late Grade ≥ 2 GI toxicities. Two (1.4%) patients suffered from late Grade 3 GU toxicities. Similarly, two (1.4%) patients suffered from late Grade 3 GI toxicities. Acute GU and GI events correlated with prostate volume and dose to the hottest 10 cc volume (D10cc)/volumes receiving a minimum of 30 Gy (V30 Gy) of rectum, respectively. Late GI toxicity, frequency, and rectal hemorrhage correlated with rectal D0.1 cc/D1 cc, maximum dose to the bladder, and rectal D0.1 cc, respectively. Toxicities after prostate SBRT using 32-36 Gy/4 fractions were acceptable. Our analysis showed that acute toxicities correlated with volume receiving a medium dose level, and late toxicities correlated with highest point dose of organs at risk.

Extension of matRad with a modified microdosimetric kinetic model for carbon ion treatment planning: Comparison with Monte Carlo calculation.

Treatment planning is essential for in silico particle therapy studies. matRad is an open-source research treatment planning system (TPS) based on the local effect model, which is a type of relative biological effectiveness (RBE) model. This study aims to implement a microdosimetric kinetic model (MKM) in matRad and develop an automation algorithm for Monte Carlo (MC) dose recalculation using the TOPAS code. In addition, we provide the developed MKM extension as open-source tool for users. Carbon beam data were generated using TOPAS MC pencil beam irradiation. We parameterized the TOPAS MC beam data with a double-Gaussian fit and modeled the integral depth doses and lateral spot profiles in the range of 100-430 MeV/u. To implement the MKM, the specific energy data table for Z=1-6 and integrated depth-specific energy data were acquired based on the Kiefer-Chatterjee track structure and TOPAS MC simulation, respectively. Generic data were integrated into matRad, and treatment planning was performed based on these data. The optimized plan parameters were automatically converted into MC simulation input. Finally, the matRad TPS and TOPAS MC simulations were compared using the RBE-weighted dose calculation results. A comparison was made for three geometries: homogeneous water phantom, inhomogeneous phantom, and patient. The RBE-weighted dose (DRBE ) distribution agreed with TOPAS MC within 1.8% for all target sizes for the homogeneous phantom. For the inhomogeneous phantom, the relative difference in the range of 80% of the prescription dose in the distal fall-off region (R80) between the matRad TPS and TOPAS MC was 0.6% (1.1mm). DRBE between the TPS and the MC was within 4.0%. In the patient case, the difference in the dose-volume histogram parameters for the target volume between the TPS and the MC was less than 2.7%. The relative difference in R80 was 0.7% (1.2mm). The MKM was successfully implemented in matRad TPS, and the RBE-weighted dose was comparable to that of TOPAS MC. The MKM-implemented matRad was released as an open-source tool. Further investigations with MC simulations can be conducted using this tool, providing a good option for carbon ion research.

Pulmonary fibrosis prevalence after adjuvant radiotherapy of Iranian patients with breast cancer: A single-center cross-sectional study.

This study aims to investigate the incidence rate of pulmonary fibrosis as a late radiotherapy complication and identify the associated dosimetric and demographic factors using radiological findings between Iranian patients with breast cancer. Breast cancer patients treated at the education hospital of Shohada-e Tajrish Hospital, Tehran, Iran, from 2017 to 2021 were considered. Patients have included for whom a secondary chest CT scan was available at least six months after radiotherapy. Dose-volume histogram (DVH) parameters of three-dimensional conformal radiotherapy (3D-CRT) treatment plans were exported. Demographic features and data on underlying lung diseases, diabetes, and smoking history were extracted. A total of 250 patients were included in the study with a mean age of 46.1 ± 7.5 yrs and a mean body mass index (BMI) of 24.5 ± 4.2 kg/m2. Pulmonary fibrosis was detected for sixty-two cases. A significant relationship was obtained between the ipsilateral lung DVH parameters of patients with pulmonary fibrosis (P value < 0.05). The V5Gy, V10Gy, V13Gy, V20Gy, V30Gy, MLD, and DMax for individuals with pulmonary fibrosis were significantly higher than those without this injury. Pulmonary fibrosis was distinguished for 25% of the breast cancer cases at least six months after adjuvant radiotherapy. A significant relationship between the DVH parameters, underlying lung disease, diabetes, radiotherapy fields (i.e., Breast + LN + SC or Breast/Chest-wall only), age, and BMI with the frequency of the ipsilateral pulmonary fibrosis was obtained. V13Gy and V30Gy of the ipsilateral lung may be the most predictor of pulmonary fibrosis incidence.