An Investigation of the Effect of Virtual Reality on Alleviating Anxiety in Patients With Breast Cancer Undergoing Radiation Therapy: A Randomized Controlled Trial.

PURPOSE: The aim of this study was to evaluate the anxiety-reducing effects of virtual reality (VR) on patients with breast cancer undergoing adjuvant radiation therapy (RT). METHODS AND MATERIALS: This randomized controlled trial was conducted among patients with breast cancer receiving RT at a single institution. Of 196 enrolled and randomized patients, 97 were assigned to a VR explanation group (intervention) and 99 were assigned to the standard-of-care group (control). Anxiety levels were measured using the Amsterdam Preoperative Anxiety and Information Scale (APAIS) as the primary endpoint and the State-Trait Anxiety Inventory (STAI) and Linear Analogue Scale Assessment (LASA) as secondary endpoints. Knowledge of the RT procedure, patient satisfaction, and time spent for counseling were also assessed. RESULTS: Intervention significantly reduced patient anxiety immediately, not only on the primary endpoint, APAIS, but also on the STAI and LASA anxiety scales. Specifically, in the intervention group, there were immediate reductions of 26.0%, 16.1%, and 55.8% for APAIS, STAI, and LASA, respectively, whereas in the control group, the respective reductions were 8.1%, 8.5%, and 13.7%. Among the 3 anxiety scales, long-term anxiety reduction was observed only when anxiety was measured by LASA. Subgroup analyses showed that the effect on anxiety did not differ based on the physician, baseline anxiety level, use of hormone therapy, or health literacy. The intervention also significantly improved knowledge of the RT procedure (81.9/100 vs 76.8/100; P = .006) and patient satisfaction with the explanation manner (6.56 vs 5.72; P < .001) compared with the control group. CONCLUSIONS: Immersive VR applied to the current procedure reduces anxiety during RT planning for patients with breast cancer. Further research is necessary to investigate the long-term effects of VR on anxiety.

Comprehensive clinical evaluation of deep learning-based auto-segmentation for radiotherapy in patients with cervical cancer.

Background and purpose: Deep learning-based models have been actively investigated for various aspects of radiotherapy. However, for cervical cancer, only a few studies dealing with the auto-segmentation of organs-at-risk (OARs) and clinical target volumes (CTVs) exist. This study aimed to train a deep learning-based auto-segmentation model for OAR/CTVs for patients with cervical cancer undergoing radiotherapy and to evaluate the model's feasibility and efficacy with not only geometric indices but also comprehensive clinical evaluation. Materials and methods: A total of 180 abdominopelvic computed tomography images were included (training set, 165; validation set, 15). Geometric indices such as the Dice similarity coefficient (DSC) and the 95% Hausdorff distance (HD) were analyzed. A Turing test was performed and physicians from other institutions were asked to delineate contours with and without using auto-segmented contours to assess inter-physician heterogeneity and contouring time. Results: The correlation between the manual and auto-segmented contours was acceptable for the anorectum, bladder, spinal cord, cauda equina, right and left femoral heads, bowel bag, uterocervix, liver, and left and right kidneys (DSC greater than 0.80). The stomach and duodenum showed DSCs of 0.67 and 0.73, respectively. CTVs showed DSCs between 0.75 and 0.80. Turing test results were favorable for most OARs and CTVs. No auto-segmented contours had large, obvious errors. The median overall satisfaction score of the participating physicians was 7 out of 10. Auto-segmentation reduced heterogeneity and shortened contouring time by 30 min among radiation oncologists from different institutions. Most participants favored the auto-contouring system. Conclusion: The proposed deep learning-based auto-segmentation model may be an efficient tool for patients with cervical cancer undergoing radiotherapy. Although the current model may not completely replace humans, it can serve as a useful and efficient tool in real-world clinics.

Feasibility of partial-orbit irradiation as a treatment strategy for patients with orbital mucosa-associated lymphoid tissue lymphoma.

OBJECTIVE: For early-stage orbital mucosa-associated lymphoid tissue lymphoma (MALToma), radiotherapy (RT) is known to be the treatment of choice. The classical recommended treatment field is the entire ipsilateral orbit, exposing normal orbital structures such as the lacrimal gland and lens, which are sensitive to moderate doses of radiation, to the full treatment dose. Herein we aimed to evaluate the clinical outcomes and dosimetric values in patients with orbital MALToma who received RT. DESIGN: This study was a retrospective study. PARTICIPANTS: Forty patients with orbital MALToma treated with curative RT. METHODS: The patients were classified into the conjunctival RT (n = 23), partial-orbit RT (n = 10), and whole-orbit RT (n = 7) groups. The treatment outcomes and dosimetric values of the orbital structures were reviewed. RESULTS: We found the 5-year local, contralateral orbit, and overall relapse rates to be 5.0%, 5.9%, and 16.0%, respectively. Local relapse events occurred in 2 patients in the conjunctival RT group. No relapse was observed in the partial-orbit RT group. Whole-orbit RT caused significantly higher rates of dry eyes during treatment. The partial-orbit RT group showed a significantly lower ipsilateral eyeball mean dose and ipsilateral eyelid mean dose than the other groups. CONCLUSION: Partial-orbit RT showed encouraging clinical, toxicity, and dosimetric outcomes in patients with orbital MALToma and has the potential to be a treatment option for such patients.

Acute coronary event (ACE) prediction following breast radiotherapy by features extracted from 3D CT, dose, and cardiac structures.

PURPOSE: Heart toxicity, such as major acute coronary events (ACE), following breast radiation therapy (RT) is of utmost concern. Thus, many studies have been investigating the effect of mean heart dose (MHD) and dose received in heart sub-structures on toxicity. Most studies focused on the dose thresholds in the heart and its sub-structures, while few studies adopted such computational methods as deep neural networks (DNN) and radiomics. This work aims to construct a feature-driven predictive model for ACE after breast RT. METHODS: A recently proposed two-step predictive model that extracts a number of features from a deep auto-segmentation network and processes the selected features for prediction was adopted. This work refined the auto-segmenting network and feature processing algorithms to enhance performance in cardiac toxicity prediction. In the predictive model, the deep convolutional neural network (CNN) extracted features from 3D computed tomography (CT) images and dose distributions in three automatically segmented heart sub-structures, including the left anterior descending artery (LAD), right coronary artery (RCA), and left ventricle (LV). The optimal feature processing workflow for the extracted features was explored to enhance the prediction accuracy. The regions associated with toxicity were visualized using a class activation map (CAM)-based technique. Our proposed model was validated against a conventional DNN (convolutional and fully connected layers) and radiomics with a patient cohort of 84 cases, including 29 and 55 patient cases with and without ACE. Of the entire 84 cases, 12 randomly chosen cases (5 toxicity and 7 non-toxicity cases) were set aside for independent test, and the remaining 72 cases were applied to 4-fold stratified cross-validation. RESULTS: Our predictive model outperformed the conventional DNN by 38% and 10% and radiomics-based predictive models by 9% and 10% in AUC for 4-fold cross-validations and independent test, respectively. The degree of enhancement was greater when incorporating dose information and heart sub-structures into feature extraction. The model whose inputs were CT, dose, and three sub-structures (LV, LAD, and RCA) reached 96% prediction accuracy on average and 0.94 area under the curve (AUC) on average in the cross-validation, and also achieved prediction accuracy of 83% and AUC of 0.83 in the independent test. On 10 correctly predicted cases out of 12 for the independent test, the activation maps implied that for cases of ACE toxicity, the higher intensity was more likely to be observed inside the LV. CONCLUSIONS: The proposed model characterized by modifications in model input with dose distributions and cardiac sub-structures, and serial processing of feature extraction and feature selection techniques can improve the predictive performance in ACE following breast RT.

Biomarkers for Locally Advanced Hepatocellular Carcinoma Patients Treated with Liver-Directed Combined Radiotherapy.

Introduction: In the era of biomarker-driven cancer therapy, robust biomarkers for hepatocellular carcinoma (HCC) have not been well-defined. In this hypothesis-generating study, we investigated biomarkers that can be incorporated to predict treatment outcomes in patients with locally advanced HCC who are administered liver-directed combined radiotherapy (LDCRT). Methods: Ninety-nine patients with HCC who were treated with conventional fractionation LDCRT between July 2016 and October 2018 were enrolled in this prospective single-arm study. Clinical outcomes and possible serum biomarkers, including soluble programmed cell death ligand-1 (sPD-L1), interleukin (IL)-10, IL-6, cell-free DNA (cfDNA), inter-alpha inhibitor H4, and interferon-gamma, were analyzed. The primary endpoint was disease progression, and additional endpoints were local failure-free rate, intrahepatic failure-free rate, and lung metastasis-free rate. Results: The median follow-up period was 18.7 months. The 1-year progression-free rate was 38.2%. Increasing baseline sPD-L1 per pg/mL, previous treatment history, protein induced by vitamin K absence-II >1,629 mAU/mL, and multiple tumors were the adverse factors for progression based on multivariate analysis. Survival tree analysis revealed three prognostic groups for progression, in which patients with multiple lesions and baseline sPD-L1 ≥41.07 pg/mL showed the worst outcomes. For dynamic changes in biomarker levels, sPD-L1 fold change and cfDNA fold-change values were unfavorable factors for progression. Conclusion: Baseline sPD-L1, sPD-L1 fold change, and cfDNA fold-change values showed the highest potential as biomarkers for predicting post-treatment progression after LDCRT in HCC patients. By incorporating clinical factors, these biomarkers may be useful for devising a biomarker-driven treatment paradigm in locally advanced HCC.

Automated coronary artery calcium scoring in patients with breast cancer to assess the risk of heart disease following adjuvant radiation therapy.

AIM: Validation of coronary artery calcium (CAC) scores as prognostic factors of acute coronary events (ACE) development in breast cancer patients are demanded. We investigated prognostic impact of CAC on ACE development with cardiac exposure to radiation. METHODS: We evaluated breast cancer patients with (n = 511) or without (n = 600) adjuvant radiotherapy (RT) between 2005 and 2013. CAC Agatston scores were analyzed using a deep-learning-based algorithm. Individual mean heart dose (MHD) was calculated, and no RT was categorized as 0 Gy. The primary endpoint was the development of ACE following breast surgery. RESULTS: In the RT and no-RT cohorts, 11.2% and 3.7% exhibited CAC >0, respectively. Over a 9.3-year follow-up period, the 10-year ACE rate was 0.7%. In the multivariate analysis, the CAC score was a significant risk factor for ACE (CAC >0 vs CAC = 0, 10-year 6.2% vs 0.2%, P < 0.001). In the subgroup with CAC >0, the 10-year ACE rates were 0%, 3.7%, and 13.7% for patients receiving mean heart doses of 0 Gy, 0-3 Gy, and >3 Gy, respectively (P = 0.133). Although CAC score was not predictive for non-ACE heart disease risk (P > 0.05), the 10-year non-ACE heart disease rates were 1.7%, 5.7%, and 7.1% for patients with CAC = 0 receiving MHD of 0 Gy, 0-3 Gy, and >3 Gy, respectively (P < 0.001). CONCLUSIONS: The CAC score was a significant predictor of ACE in patients with breast cancer. Although further studies are required, CAC score screening on simulation CT in patients undergoing breast RT can help identify those with high risk for ACE on a per-patient basis.

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.

Comparison of clinical outcomes between carbon ion radiotherapy and X-ray radiotherapy for reirradiation in locoregional recurrence of rectal cancer.

Carbon ion radiotherapy (CIRT) has garnered interest for the treatment of locoregional rectal cancer recurrence. No study has compared CIRT and X-ray radiotherapy (XRT) for reirradiation (reRT) in such cases. We analyzed and compared the clinical outcomes such as local control, overall survival, and late toxicity rate between CIRT and XRT, for treating locoregional rectal cancer recurrence. Patients with rectal cancer who received reRT to the pelvis by CIRT or XRT from March 2005 to July 2019 were included. The CIRT treatment schedule was 70.4 Gy (relative biological effectiveness) in 16 fractions. For the XRT group, the median reRT dose was 50 Gy (range 25-62.5 Gy) with a median of 25 fractions (range 3-33). Thirty-five and 31 patients received CIRT and XRT, respectively. Tumour and treatment characteristics such as recurrence location and chemotherapy treatment differed between the two groups. CIRT showed better control of local recurrence (adjusted hazard ratio [HR] 0.17; p = 0.002), better overall survival (HR 0.30; p = 0.004), and lower severe late toxicity rate (HR 0.15; p = 0.015) than XRT. CIRT was effective for treating locoregional rectal cancer recurrence, with high rates of local control and survival, and a low late severe toxicity rate.

Evaluation of deep learning-based autosegmentation in breast cancer radiotherapy.

PURPOSE: To study the performance of a proposed deep learning-based autocontouring system in delineating organs at risk (OARs) in breast radiotherapy with a group of experts. METHODS: Eleven experts from two institutions delineated nine OARs in 10 cases of adjuvant radiotherapy after breast-conserving surgery. Autocontours were then provided to the experts for correction. Overall, 110 manual contours, 110 corrected autocontours, and 10 autocontours of each type of OAR were analyzed. The Dice similarity coefficient (DSC) and Hausdorff distance (HD) were used to compare the degree of agreement between the best manual contour (chosen by an independent expert committee) and each autocontour, corrected autocontour, and manual contour. Higher DSCs and lower HDs indicated a better geometric overlap. The amount of time reduction using the autocontouring system was examined. User satisfaction was evaluated using a survey. RESULTS: Manual contours, corrected autocontours, and autocontours had a similar accuracy in the average DSC value (0.88 vs. 0.90 vs. 0.90). The accuracy of autocontours ranked the second place, based on DSCs, and the first place, based on HDs among the manual contours. Interphysician variations among the experts were reduced in corrected autocontours, compared to variations in manual contours (DSC: 0.89-0.90 vs. 0.87-0.90; HD: 4.3-5.8 mm vs. 5.3-7.6 mm). Among the manual delineations, the breast contours had the largest variations, which improved most significantly with the autocontouring system. The total mean times for nine OARs were 37 min for manual contours and 6 min for corrected autocontours. The results of the survey revealed good user satisfaction. CONCLUSIONS: The autocontouring system had a similar performance in OARs as that of the experts' manual contouring. This system can be valuable in improving the quality of breast radiotherapy and reducing interphysician variability in clinical practice.

Clinical feasibility of deep learning-based auto-segmentation of target volumes and organs-at-risk in breast cancer patients after breast-conserving surgery.

BACKGROUND: In breast cancer patients receiving radiotherapy (RT), accurate target delineation and reduction of radiation doses to the nearby normal organs is important. However, manual clinical target volume (CTV) and organs-at-risk (OARs) segmentation for treatment planning increases physicians' workload and inter-physician variability considerably. In this study, we evaluated the potential benefits of deep learning-based auto-segmented contours by comparing them to manually delineated contours for breast cancer patients. METHODS: CTVs for bilateral breasts, regional lymph nodes, and OARs (including the heart, lungs, esophagus, spinal cord, and thyroid) were manually delineated on planning computed tomography scans of 111 breast cancer patients who received breast-conserving surgery. Subsequently, a two-stage convolutional neural network algorithm was used. Quantitative metrics, including the Dice similarity coefficient (DSC) and 95% Hausdorff distance, and qualitative scoring by two panels from 10 institutions were used for analysis. Inter-observer variability and delineation time were assessed; furthermore, dose-volume histograms and dosimetric parameters were also analyzed using another set of patient data. RESULTS: The correlation between the auto-segmented and manual contours was acceptable for OARs, with a mean DSC higher than 0.80 for all OARs. In addition, the CTVs showed favorable results, with mean DSCs higher than 0.70 for all breast and regional lymph node CTVs. Furthermore, qualitative subjective scoring showed that the results were acceptable for all CTVs and OARs, with a median score of at least 8 (possible range: 0-10) for (1) the differences between manual and auto-segmented contours and (2) the extent to which auto-segmentation would assist physicians in clinical practice. The differences in dosimetric parameters between the auto-segmented and manual contours were minimal. CONCLUSIONS: The feasibility of deep learning-based auto-segmentation in breast RT planning was demonstrated. Although deep learning-based auto-segmentation cannot be a substitute for radiation oncologists, it is a useful tool with excellent potential in assisting radiation oncologists in the future. Trial registration Retrospectively registered.

Role of Interleukin-7 in the Development of and Recovery from Radiation-Induced Lymphopenia: A Post-hoc Analysis of a Prospective Cohort.

PURPOSE: Radiation-induced lymphopenia is associated with worse outcomes in solid tumors. We assessed the impact of interleukin-7 (IL-7), a key cytokine in lymphocyte homeostasis, on radiation-induced lymphopenia. MATERIALS AND METHODS: A post-hoc analysis was performed in a prospective cohort of 98 patients with hepatocellular carcinoma who were treated with radiotherapy in 2016-2018. Blood IL-7 levels were assayed before and at the end of radiotherapy. Acute severe lymphopenia (ASL) was defined as a total lymphocyte count of < 200/µL during radiotherapy. Cox and logistic regression analyses were performed to identify predictors of survival and ASL development, respectively. RESULTS: Patients with ASL (n=41) had significantly poorer overall survival than those without (12.0 months vs. 25.3 months, p=0.001). Patients with lymphocyte recovery showed significantly longer overall survival than those without (21.8 months vs. 10.3 months, p=0.042). ASL was an independent predictor of poor survival (hazard ratio, 2.07; p=0.015). Patients with ASL had significantly lower pre-radiotherapy IL-7 levels (2.07 pg/mL vs. 3.01 pg/mL, p=0.010). A high pre-radiotherapy IL-7 level was an independent predictor of a reduced risk of ASL development (hazard ratio, 0.40; p=0.004). IL-7 levels reflected a feedback response to ASL, with a higher ΔIL-7 in patients with ASL and a lower ΔIL-7 in those without ASL (0.48 pg/mL vs. -0.66 pg/mL, p < 0.001). Post-radiotherapy IL-7 levels were significantly positively correlated with the total lymphocyte counts at 2 months. CONCLUSION: IL-7 is associated with the development of and recovery from ASL, which may impact survival. To overcome radiation-induced lymphopenia, a novel strategy using IL-7 may be considered.

Risk of Cardiac Disease in Patients With Breast Cancer: Impact of Patient-Specific Factors and Individual Heart Dose From Three-Dimensional Radiation Therapy Planning.

PURPOSE: This retrospective cohort study aimed to determine whether adjuvant radiation therapy increases the risk of cardiac toxicity in Asian women with breast cancer, with a focus on patient-specific factors. METHODS AND MATERIALS: We evaluated women who underwent primary breast surgery for breast cancer with (n = 520) or without (n = 774) adjuvant radiation therapy between January 2005 and May 2013. Patients who underwent breast surgery without radiation therapy were categorized as patients who received 0 Gy to the heart. The primary endpoint was the occurrence of a breast cancer treatment-related heart disease (BCT-HD), defined as a diagnosis of angina pectoris, unstable angina, myocardial infarction, ischemic heart disease, heart failure, or atrial fibrillation. RESULTS: In total, 1294 patients were included. The overall 5- and 10-year BCT-HD rates were 2.4% and 5.7%, respectively. The risk of an BCT-HD significantly increased per 1-Gy increase in the mean heart dose (adjusted hazard ratio: 1.23). Additionally, histories of hypertension (hazard ratio: 1.92), and diabetes (hazard ratio: 2.51) were found to be adverse risk factors, whereas regular physical exercise (hazard ratio: 0.17) was a protective factor. Subgroup analysis according to risk groups showed that the effect of increasing mean heart dose (per Gy) was similar between women without or with minimal risk factors (hazard ratio: 1.23) and women with multiple risk factors (hazard ratio: 1.27). CONCLUSIONS: The results indicate a radiation dose-effect relationship for cardiac disease in breast cancer patients, highlighting that there remains a considerable risk of cardiac toxicity even with 3-dimensional radiation therapy planning. Thus, measures to minimize the heart dose in breast cancer patients undergoing adjuvant radiation therapy, even in those without any risk factor for cardiac disease, should be routinely implemented.

Impact of radiation dose on complications among women with breast cancer who underwent breast reconstruction and post-mastectomy radiotherapy: A multi-institutional validation study.

PURPOSE: Emerging data suggest that higher radiation doses in post-mastectomy radiotherapy may be associated with an increased risk of reconstruction complications. This study aimed to validate previous findings regarding the impact of radiation dose on complications among women with breast cancer using a multi-center dataset. METHODS: Fifteen institutions participated, and women with breast cancer who received radiotherapy after either autologous or prosthetic breast reconstruction were included. The primary endpoint was major post-radiation therapy complications requiring re-operation for explantation, flap failure, or bleeding control. RESULTS: In total, 314 patients were included. Radiotherapy was performed using both conventional fractionation and hypofractionation in various schedules. The range of the radiation therapy dose in Equivalent Dose in 2 Gy fractions (EQD2; α/ß = 3.5) varied from 43.4 to 71.0 Gy (median dose: 48.6 Gy). Boost radiation therapy was administered to 49 patients. Major post-radiation therapy complications were observed in 24 (7.6%) patients. In multivariate analysis, an increasing EQD2 per Gy (odds ratio [OR]: 1.58, 95% confidence interval [CI]: 1.26-1.98; p < 0.001), current smoking status (OR: 25.48, 95% CI: 1.56-415.65; p = 0.023), and prosthetic breast reconstruction (OR: 9.28, 95% CI: 1.84-46.70; p = 0.007) were independently associated with an increased risk of major complications. CONCLUSION: A dose-response relationship between radiation dose and the risk of complications was validated in this multi-center dataset. In this context, we hypothesize that the use of hypofractionated radiotherapy (40 Gy in 15 fractions) may improve breast reconstruction outcomes. Our multi-center prospective observational study (NCT03523078) is underway to further validate this hypothesis.

Magnetic resonance imaging-based validation of the 2018 FIGO staging system in patients treated with definitive radiotherapy for locally advanced cervix cancer.

OBJECTIVE: To validate the revised 2018 International Federation of Gynecologic and Obstetrics (FIGO) staging system in patients who underwent diagnostic magnetic resonance imaging (MRI) and radiotherapy (RT) for locally advanced cervix cancer. METHODS: We analyzed 677 patients who were diagnosed with pelvic MRI and treated with definitive (chemo-)RT for locally advanced cervix cancer (stage IB2/IIA2-IVA or N+) between 1992 and 2018. Patients were classified according to 2009 and 2018 FIGO staging, and survival outcomes were compared. We developed a nomogram to improve prediction of progression-free survival (PFS). RESULTS: Pelvic and paraaortic lymph nodes were positive in 331 (48.9%) and 78 (11.5%) patients, respectively. At a median follow-up of 77.9 months, the 5-year PFS was 83.5%, 65.2%, 71.0%, 60.6%, 37.6% and 38.9% for IB, IIA, IIB, IIIA, IIIB and IVA according to FIGO 2009 and 88.9%, 60.0%, 73.8%, 66.7%, 36.3%, 68.9%, 43.6%, and 38.9% for IB, IIA, IIB, IIIA, IIIB, IIIC1, IIIC2, and IVA according to FIGO 2018, respectively. Survival of stage IIIC cervix cancer depended on the local extent of the tumor: the 5-year PFS of T1, T2, and T3 stages were 80.3%, 73.9%, and 45.5% for IIIC1 and 100%, 44.9%, and 23.4% for IIIC2. Histology, tumor size, node metastasis, FIGO 2009, and treatment modality were independent prognostic factors in the Cox regression analysis, and the nomogram incorporating these factors outperformed FIGO 2009 and FIGO 2018 (AUC 0.718 vs. 0.616 vs. 0.594). CONCLUSIONS: FIGO 2018 revision was associated with heterogenous outcomes among stage III cervix cancer patients. Our nomogram can assist the FIGO system in predicting PFS after definitive RT.

A Retrospective Dosimetric Analysis of the New ESTRO-ACROP Target Volume Delineation Guidelines for Postmastectomy Volumetric Modulated Arc Therapy After Implant-Based Immediate Breast Reconstruction.

Purpose: The European Society of Radiation & Oncology and Advisory Committee on Radiation Oncology Practice (ESTRO-ACROP) presented new guidelines for clinical target volume (CTV) delineation in post-mastectomy radiation therapy (PMRT) after implant-based immediate breast reconstruction (IBR-i). This study evaluated the dosimetric characteristics, dosimetric accuracy, and delivery accuracy of these guidelines in volumetric modulated arc therapy (VMAT). Methods and Materials: This retrospective study included 15 patients with left breast cancer who underwent mastectomy with tissue expander placement followed by PMRT. An experienced radiation oncologist delineated the CTV twice on the same image datasets based on the ESTRO-ACROP (EA-TVD) and conventional target volume delineation (C-TVD) guidelines. All VMAT plans, which used a double partial arc, were generated using six MV photons. Clinically relevant dose-volume parameters for organs at risk were compared. Dosimetric accuracy of the treatment plans and delivery accuracy were assessed. Results: Target volume of EA-TVD was significantly smaller than that of C-TVD. Although no statistically significant difference was noted in the target coverage between the two VMAT plans, EA-TVD VMAT significantly reduced the mean heart dose (3.99 ± 1.02 vs. 5.84 ± 1.78 Gy, p = 0.000), the maximum left anterior descending coronary artery (LAD) dose (9.43 ± 3.04 vs. 13.97 ± 6.04 Gy, p = 0.026), and the mean LAD dose (4.52 ± 1.31 vs. 6.35 ± 2.79 Gy, p = 0.028) compared with C-TVD VMAT. No significant difference was observed with respect to the total monitor units, plan complexity, and delivery quality assurance. Conclusions: This is the first study to show significant dose reduction for the normal heart and LAD tissue offered by the EA-TVD, while maintaining dosimetric and delivery accuracy, in PMRT after IBR-i in VMAT for left-sided breast cancer patients.

Clinical evaluation of atlas- and deep learning-based automatic segmentation of multiple organs and clinical target volumes for breast cancer.

Manual segmentation is the gold standard method for radiation therapy planning; however, it is time-consuming and prone to inter- and intra-observer variation, giving rise to interests in auto-segmentation methods. We evaluated the feasibility of deep learning-based auto-segmentation (DLBAS) in comparison to commercially available atlas-based segmentation solutions (ABAS) for breast cancer radiation therapy. This study used contrast-enhanced planning computed tomography scans from 62 patients with breast cancer who underwent breast-conservation surgery. Contours of target volumes (CTVs), organs, and heart substructures were generated using two commercial ABAS solutions and DLBAS using fully convolutional DenseNet. The accuracy of the segmentation was assessed using 14 test patients using the Dice Similarity Coefficient and Hausdorff Distance referencing the expert contours. A sensitivity analysis was performed using non-contrast planning CT from 14 additional patients. Compared to ABAS, the proposed DLBAS model yielded more consistent results and the highest average Dice Similarity Coefficient values and lowest Hausdorff Distances, especially for CTVs and the substructures of the heart. ABAS showed limited performance in soft-tissue-based regions, such as the esophagus, cardiac arteries, and smaller CTVs. The results of sensitivity analysis between contrast and non-contrast CT test sets showed little difference in the performance of DLBAS and conversely, a large discrepancy for ABAS. The proposed DLBAS algorithm was more consistent and robust in its performance than ABAS across the majority of structures when examining both CTVs and normal organs. DLBAS has great potential to aid a key process in the radiation therapy workflow, helping optimise and reduce the clinical workload.

Treatment outcome of anaplastic ependymoma under the age of 3 treated by intensity-modulated radiotherapy.

PURPOSE: Intensity-modulated radiotherapy (IMRT) allows for more precise treatment, reducing unwanted radiation to nearby structures. We investigated the safety and feasibility of IMRT for anaplastic ependymoma patients below 3 years of age. MATERIALS AND METHODS: A total of 9 anaplastic ependymoma patients below 3 years of age, who received IMRT between October 2011 and December 2017 were retrospectively reviewed. The median equivalent dose in 2 Gy fractions was 52.0 Gy (range, 48.0 to 60.0 Gy). Treatment outcomes and neurologic morbidities were reviewed in detail. RESULTS: The median patient age was 20.9 months (range, 12.1 to 31.2 months). All patients underwent surgery. The rates of 5-year overall survival, freedom from local recurrence, and progression-free survival were 40.6%, 53.3%, and 26.7%, respectively. Of the 9 patients, 5 experienced recurrences (3 had local recurrence, 1 had both local recurrence and cerebrospinal fluid [CSF] seeding, and 1 had CSF seeding alone). Five patients died because of disease progression. Assessment of neurologic morbidity revealed motor dysfunction in 3 patients, all of whom presented with hydrocephalus at initial diagnosis because of the location of the tumor and already had neurologic deficits before radiotherapy (RT). CONCLUSION: Neurologic morbidity is not caused by RT alone but may result from mass effects of the tumor and surgical sequelae. Administration of IMRT to anaplastic ependymoma patients below 3 years of age yielded encouraging local control and tolerable morbidities. High-precision modern RT such as IMRT can be considered for very young patients with anaplastic ependymoma.

Feasibility of single vocal cord irradiation as a treatment strategy for T1a glottic cancer.

BACKGROUND: Generally, radiotherapy for patients with early glottic cancer includes treatment of the whole larynx. This study was conducted to evaluate the treatment outcomes and toxicity in patients who received single vocal cord irradiation (SVCI) for T1a classification glottic cancer. METHODS: A total of 34 patients diagnosed with clinical T1aN0M0 classification squamous cell carcinoma of the glottis who received radiotherapy to the single vocal cord were included for analysis. RESULTS: Median follow-up period was 41.3 months (range, 6.4-124.5 months). The 3-year and 5-year local control (LC) rates were both 96.8%. Grade 3 radiation dermatitis was observed as severe acute toxicity in two (6%) patients. No patients experienced any severe late toxicity events during follow-up. CONCLUSIONS: SVCI showed good LC, low acute and late toxicities, and reasonable voice recovery. SVCI may be considered a feasible treatment strategy for patients with T1a glottic cancer.

Treatment outcomes based on radiation therapy fields for bifocal germinoma: Synchronous or disseminated disease?

Intracranial germinoma sometimes present as bifocal germinoma, and whether bifocal germinoma should be treated as a synchronous or disseminated disease remains unclear. This study aimed to determine the optimal treatment modality for bifocal germinoma. Patients with bifocal germinoma who received radiotherapy (RT) from March 1990 to August 2017 were included for analysis. A total of 21 patients were included. The median follow-up period was 76.2 months (range, 6.2-305.4 months). There were 17 patients who received cranio-spinal irradiation (CSI) with local RT; 3, whole ventricular RT (WVRT) with local RT; and 1, local RT only. Three recurrences occurred (1 patient each among those who underwent CSI, WVRT, and local RT). Recurrence in the patient who received CSI and who received WVRT occurred in the right thalamus and right frontal convexity, respectively. Meanwhile, the patient who received local RT showed not only a recurred lesion in the hypothalamus, but also cerebrospinal fluid seeding. For this patient, salvage CSI was performed and complete response was achieved after treatment. However, after 9 years and 6 months, he was diagnosed with glioblastoma and expired. As for toxicity, although 17 patients showed decrease in complete blood count levels during treatment, all patients recovered soon after treatment completion. Our findings suggest that bifocal germinoma may be considered as a disseminated disease when considering the patterns of failure according to RT fields. In addition, patients who received CSI showed low acute toxicity rates. However, further studies are necessary to confirm these findings.