The influence of Gleason score ≤ 6 histology on the outcome of high-risk localized prostate cancer after modern radiotherapy.

We aimed to retrospectively review outcomes in patients with high-risk prostate cancer and a Gleason score ≤ 6 following modern radiotherapy. We analyzed the outcomes of 1374 patients who had undergone modern radiotherapy, comprising a high-risk low grade [HRLG] group (Gleason score ≤ 6; n = 94) and a high-risk high grade [HRHG] group (Gleason score ≥ 7, n = 1125). We included 955 patients who received brachytherapy with or without external beam radio-therapy (EBRT) and 264 who received modern EBRT (intensity-modulated radiotherapy [IMRT] or stereotactic body radiotherapy [SBRT]). At a median follow-up of 60 (2-177) months, actuarial 5-year biochemical failure-free survival rates were 97.8 and 91.8% (p = 0.017), respectively. The frequency of clinical failure in the HRLG group was less than that in the HRHG group (0% vs 5.4%, p = 0.012). The HRLG group had a better 5-year distant metastasis-free survival than the HRHG group (100% vs 96.0%, p = 0.035). As the HRLG group exhibited no clinical failure and better outcomes than the HRHG group, the HRLG group might potentially be classified as a lower-risk group.

Single-Fraction Palliative High-Dose-Rate Brachytherapy for Symptom Management in a 97-Year-Old Patient With Dementia.

Background: Delivering cancer treatment to elderly patients with dementia is often challenging. We describe performing palliative surface mold brachytherapy (SMBT) in an elderly patient with advanced dementia for pain control using music therapy to assist with agitation. Case Description: The patient was a 97-year-old Japanese woman with advanced dementia. Exudate was observed from her tumor, and she complained of Grade 2 severity pain using Support team assessment schedule (STAS), especially when undergoing would dressings. Given her advanced dementia, she was not considered a candidate for radical surgery or external beam radiotherapy. We instead treated her with high-dose-rate (HDR) SMBT. Due to her advanced dementia associated with agitation, she could not maintain her position. She was able to remain calm while listening to traditional Japanese enka music, which enables our team to complete her radiation without using anesthetics or sedating analgesics. Her localized pain severity decreased ≤21 days and the exudate fluid disappeared ≤63 days after HDR-SMBT. Her tumor was locally controlled until her death from intercurrent disease 1 year after HDR-SMBT. Discussion: Single fraction palliative HDR-SMBT was useful for successful treatment of skin cancer in an elderly patient. Traditional Japanese music helped reduce her agitation to complete HDR-SMBT. For elderly patients with agitation associated with dementia, we should consider using music and music therapy to facilitate radiation therapy.

Synthetic Low-Energy Monochromatic Image Generation in Single-Energy Computed Tomography System Using a Transformer-Based Deep Learning Model.

While dual-energy computed tomography (DECT) technology introduces energy-specific information in clinical practice, single-energy CT (SECT) is predominantly used, limiting the number of people who can benefit from DECT. This study proposed a novel method to generate synthetic low-energy virtual monochromatic images at 50 keV (sVMI50keV) from SECT images using a transformer-based deep learning model, SwinUNETR. Data were obtained from 85 patients who underwent head and neck radiotherapy. Among these, the model was built using data from 70 patients for whom only DECT images were available. The remaining 15 patients, for whom both DECT and SECT images were available, were used to predict from the actual SECT images. We used the SwinUNETR model to generate sVMI50keV. The image quality was evaluated, and the results were compared with those of the convolutional neural network-based model, Unet. The mean absolute errors from the true VMI50keV were 36.5 ± 4.9 and 33.0 ± 4.4 Hounsfield units for Unet and SwinUNETR, respectively. SwinUNETR yielded smaller errors in tissue attenuation values compared with those of Unet. The contrast changes in sVMI50keV generated by SwinUNETR from SECT were closer to those of DECT-derived VMI50keV than the contrast changes in Unet-generated sVMI50keV. This study demonstrated the potential of transformer-based models for generating synthetic low-energy VMIs from SECT images, thereby improving the image quality of head and neck cancer imaging. It provides a practical and feasible solution to obtain low-energy VMIs from SECT data that can benefit a large number of facilities and patients without access to DECT technology.

Re-irradiation for isolated neck recurrence in head and neck tumor: impact of rN category.

Unresectable, isolated lymph node recurrence after radiotherapy is rare but a candidate for re-irradiation. However, severe toxicity is anticipated. Therefore, this study aimed to explore the efficacy and toxicity of re-irradiation in isolated lymph node recurrence of head and neck lesions. We analyzed 46 patients who received re-irradiation for lymph node recurrence without local progression. The primary tumor sites included the oral cavity in 17 patients, the hypopharynx in 12, the oropharynx in seven, the larynx in three, the nasopharynx in two, and other sites. During a median follow-up time of 10 months, the median survival time was 10.6 months, and the 1-year overall survival rate was 45.5%. The 1-year local control and progression-free survival rates were 49.8% and 39.3%, respectively. According to univariate analysis, age (≥ 65 years), the interval between treatment (≥ 12 months), rN category (rN1), and gross tumor volume (GTV < 25 cm3) were predisposing factors for better survival. In the multivariate analysis, the rN category and interval were identified as statistically significant predictors. Late toxicity grade ≥ 3 occurred in four patients (8.6%). These were all Grade 5 carotid blowout syndrome, which associated with tumor invasion of the carotid artery and/ or high doses administration for the carotid artery. Small-volume rN1 tumor that recur after a longer interval is a feasible candidate for re-irradiation. However, strict patient selection and meticulous care for the carotid are required.

Artificial intelligence-based image-domain material decomposition in single-energy computed tomography for head and neck cancer.

PURPOSE: While dual-energy computed tomography (DECT) images provide clinically useful information than single-energy CT (SECT), SECT remains the most widely used CT system globally, and only a few institutions can use DECT. This study aimed to establish an artificial intelligence (AI)-based image-domain material decomposition technique using multiple keV-output learning of virtual monochromatic images (VMIs) to create DECT-equivalent images from SECT images. METHODS: This study involved 82 patients with head and neck cancer. Of these, the AI model was built with data from the 67 patients with only DECT scans, while 15 patients with both SECT and DECT scans were used for SECT testing. Our AI model generated VMI50keV and VMI100keV from VMI70keV equivalent to 120-kVp SECT images. We introduced a loss function for material density images (MDIs) in addition to the loss for VMIs. For comparison, we trained the same model with the loss for VMIs only. DECT-equivalent images were generated from SECT images and compared with the true DECT images. RESULTS: The prediction time was 5.4 s per patient. The proposed method with the MDI loss function quantitatively provided more accurate DECT-equivalent images than the model trained with the loss for VMIs only. Using real 120-kVp SECT images, the trained model produced precise DECT images of excellent quality. CONCLUSION: In this study, we developed an AI-based material decomposition approach for head and neck cancer patients by introducing the loss function for MDIs via multiple keV-output learning. Our results suggest the feasibility of AI-based image-domain material decomposition in a conventional SECT system without a DECT scanner.

Effect of systemic inflammatory response on induction chemotherapy followed by chemoradiotherapy for locally advanced pancreatic cancer: an exploratory subgroup analysis on systemic inflammatory response in JCOG1106.

OBJECTIVE: JCOG1106, a randomized phase II trial conducted to compare chemoradiotherapy (S-1 concurrent radiotherapy) with (Arm B) or without (Arm A) induction chemotherapy using gemcitabine in patients with locally advanced pancreatic cancer, showed a more favorable long-term survival in Arm A. This study was aimed at exploring whether some subgroups classified by the systemic inflammatory response might derive greater benefit from either treatment. METHODS: All subjects eligible for JCOG1106 were included in this analysis (n = 51/49 in Arm A/B). This exploratory subgroup analysis was performed by Cox regression analysis to investigate the impact of the systemic inflammatory response, as assessed based on the serum C-reactive protein, serum albumin (albumin), Glasgow Prognostic Score and derived neutrophil-lymphocyte ratio, at the baseline on overall survival. P values <0.1 for the interaction were regarded as denoting significant association. RESULTS: Glasgow prognostic score showed significant treatment interactions for overall survival. Hazard ratios of Arm B to Arm A were 1.35 (95% confidence interval, 0.82-2.23) in the Glasgow Prognostic Score 0 (C-reactive protein ≤10 mg/L and albumin ≥35 g/L) (n = 44/34 in Arm A/B) and 0.59 (95% confidence interval, 0.24-1.50) in the Glasgow Prognostic Score 1/2 (C-reactive protein >10 mg/L and/or albumin <35 g/L) (n = 7/15) (P-interaction = 0.06). C-reactive protein alone and albumin alone also showed significant treatment interactions for overall survival. CONCLUSIONS: Survival benefits of induction chemotherapy in chemoradiotherapy for locally advanced pancreatic cancer were observed in patients with elevated Glasgow Prognostic Score, high C-reactive protein and low albumin. These results suggest that systemic inflammatory response might be considered to apply induction chemotherapy preceding chemoradiotherapy.

Deep learning-based detection of patients with bone metastasis from Japanese radiology reports.

PURPOSE: Deep learning (DL) is a state-of-the-art technique for developing artificial intelligence in various domains and it improves the performance of natural language processing (NLP). Therefore, we aimed to develop a DL-based NLP model that classifies the status of bone metastasis (BM) in radiology reports to detect patients with BM. MATERIALS AND METHODS: The DL-based NLP model was developed by training long short-term memory using 1,749 free-text radiology reports written in Japanese. We adopted five-fold cross-validation and used 200 reports for testing the five models. The accuracy, sensitivity, specificity, precision, and area under the receiver operating characteristics curve (AUROC) were used for the model evaluation. RESULTS: The developed model demonstrated classification performance with mean ± standard deviation of 0.912 ± 0.012, 0.924 ± 0.029, 0.901 ± 0.014, 0.898 ± 0.012, and 0.968 ± 0.004 for accuracy, sensitivity, specificity, precision, and AUROC, respectively. CONCLUSION: The proposed DL-based NLP model may help in the early and efficient detection of patients with BM.

Artificial intelligence-aided lytic spinal bone metastasis classification on CT scans.

PURPOSE: Spinal bone metastases directly affect quality of life, and patients with lytic-dominant lesions are at high risk for neurological symptoms and fractures. To detect and classify lytic spinal bone metastasis using routine computed tomography (CT) scans, we developed a deep learning (DL)-based computer-aided detection (CAD) system. METHODS: We retrospectively analyzed 2125 diagnostic and radiotherapeutic CT images of 79 patients. Images annotated as tumor (positive) or not (negative) were randomized into training (1782 images) and test (343 images) datasets. YOLOv5m architecture was used to detect vertebra on whole CT scans. InceptionV3 architecture with the transfer-learning technique was used to classify the presence/absence of lytic lesions on CT images showing the presence of vertebra. The DL models were evaluated via fivefold cross-validation. For vertebra detection, bounding box accuracy was estimated using intersection over union (IoU). We evaluated the area under the curve (AUC) of a receiver operating characteristic curve to classify lesions. Moreover, we determined the accuracy, precision, recall, and F1 score. We used the gradient-weighted class activation mapping (Grad-CAM) technique for visual interpretation. RESULTS: The computation time was 0.44 s per image. The average IoU value of the predicted vertebra was 0.923 ± 0.052 (0.684-1.000) for test datasets. In the binary classification task, the accuracy, precision, recall, F1-score, and AUC value for test datasets were 0.872, 0.948, 0.741, 0.832, and 0.941, respectively. Heat maps constructed using the Grad-CAM technique were consistent with the location of lytic lesions. CONCLUSION: Our artificial intelligence-aided CAD system using two DL models could rapidly identify vertebra bone from whole CT images and detect lytic spinal bone metastasis, although further evaluation of diagnostic accuracy is required with a larger sample size.

HDR-brachytherapy for accelerated partial breast irradiation: Long-term experience from a Japanese institution.

Purpose: We investigated the long-term oncological outcome of high-dose-rate (HDR) multicatheter interstitial brachytherapy (MIB) for adjuvant accelerated partial breast irradiation (APBI) after breast conserving surgery in Japanese patients. Material and methods: Between June 2002 and October 2011, 86 breast cancer patients were treated at National Hospital Organization Osaka National Hospital (trial number of the local institutional review board, 0329). Median age was 48 years (range, 26-73 years). Eighty patients had invasive and 6 patients non-invasive ductal carcinoma. Tumor stage distribution was pT0 in 2, pTis in 6, pT1 in 55, pT2 in 22, and pT3 in one patient, respectively. Twenty-seven patients had close/positive resection margins. Total physical HDR dose was 36-42 Gy in 6-7 fractions. Results: At a median follow-up of 119 months (range, 13-189 months), the 10-year local control (LC) and overall survival rate was 93% and 88%, respectively. Concerning the 2009 Groupe Européen de Curiethérapie-European Society for Therapeutic Radiology and Oncology risk stratification scheme, the 10-year LC rate was 100%, 100%, and 91% for patients considered as low-risk, intermediate-risk, and high-risk, respectively. According to the 2018 American Brachytherapy Society risk stratification scheme, the 10-year LC rate was 100% and 90% for patients 'acceptable' and 'unacceptable' for APBI, respectively. Wound complications were observed in 7 patients (8%). Risk factors for wound complications were the omission of prophylactic antibiotics during MIB, open cavity implantation, and V100 ≥ 190 cc. No grade ≥ 3 late complications (CTCVE version 4.0) were observed. Conclusions: Adjuvant APBI using MIB is associated with favorable long-term oncological outcomes in Japanese patients for low-risk, intermediate-risk, and acceptable groups of patients.

Effective optimization strategy for large optimization volume object, remaining volume at risk (RVR):α-value selection and usage from generalized equivalent uniform dose (gEUD) curve deviation perspective.

Objective.A large optimization volume for intensity-modulated radiation therapy (IMRT), such as the remaining volume at risk (RVR), is traditionally unsuitable for dose-volume constraint control and requires planner-specific empirical considerations owing to the patient-specific shape. To enable less empirical optimization, the generalized equivalent uniform dose (gEUD) optimization is effective; however, the utilization of parametera-values remains elusive. Our study clarifies thea-value characteristics for optimization and to enable effectivea-value use.Approach.The gEUD can be obtained as a function of itsa-value, which is the weighted generalized mean; its curve has a continuous, differentiable, and sigmoid shape, deforming in its optimization state with retained curve characteristics. Using differential geometry, the gEUD curve changes in optimization is considered a geodesic deviation intervened by the forces between deforming and retaining the curve. The curvature and gradient of the curve are radically related to optimization. The vertex point (a=ak) was set and thea-value roles were classified into the following three parts of the curve with respect to thea-value: (i) high gradient and middle curvature, (ii) middle gradient and high curvature, and (iii) low gradient and low curvature. Then, a strategy for multiplea-values was then identified using RVR optimization.Main results.Eleven head and neck patients who underwent static seven-field IMRT were used to verify thea-value characteristics and curvature effect for optimization. The lowera-value (i) (a= 1-3) optimization was effective for the whole dose-volume range; in contrast, the effect of highera-value (iii) (a= 12-20) optimization addressed strongly the high-dose range of the dose volume. The middlea-value (ii) (arounda=ak) showed intermediate but effective high-to-low dose reduction. Thesea-value characteristics were observed as superimpositions in the optimization. Thus, multiple gEUD-based optimization was significantly superior to the exponential constraints normally applied to the RVR that surrounds the PTV, normal tissue objective (NTO), resulting in up to 25.9% and 8.1% improvement in dose-volume indices D2% and V10Gy, respectively.Significance.This study revealed an appropriatea-value for gEUD optimization, leading to favorable dose-volume optimization for the RVR region using fixed multiplea-value conditions, despite the very large and patient-specific shape of the region.

Patient-specific three-dimensional dose distribution prediction via deep learning for prostate cancer therapy: Improvement with the structure loss.

PURPOSE: Deep learning (DL)-based dose distribution prediction can potentially reduce the cost of inverse planning process. We developed and introduced a structure-focused loss (Lstruct) for 3D dose prediction to improve prediction accuracy. This study investigated the influence of Lstruct on DL-based dose prediction for patients with prostate cancer. The proposed Lstruct, which is similar in concept to dose-volume histogram (DVH)-based optimization in clinical practice, has the potential to provide more interpretable and accurate DL-based optimization. METHODS: This study involved 104 patients who underwent prostate radiotherapy. We used 3D U-Net-based architecture to predict dose distributions from computed tomography and contours of the planning target volume and organs-at-risk. We trained two models using different loss functions: L2 loss and Lstruct. Predicted doses were compared in terms of dose-volume parameters and the Dice similarity coefficient of isodose volume. RESULTS: DVH analysis showed that the Lstruct model had smaller errors from the ground truth than the L2 model. The Lstruct model achieved more consistent dose distributions than the L2 model, with errors close to zero. The isodose Dice score of the Lstruct model was greater than that of the L2 model by >20% of the prescribed dose. CONCLUSIONS: We developed Lstruct using labels of inputted contours for DL-based dose prediction for prostate radiotherapy. Lstruct can be generalized to any DL architecture, thereby enhancing the dose prediction accuracy.

Reirradiation for local recurrence of oral, pharyngeal, and laryngeal cancers: a multi-institutional study.

This study aimed to examine the efficacy and toxicity of reirradiation in patients with locally recurrent oral, pharyngeal, and laryngeal cancers. We conducted a retrospective, multi-institutional analysis of 129 patients with previously irradiated cancer. The most frequent primary sites were the nasopharynx (43.4%), oral cavity (24.8%), and oropharynx (18.6%). With a median follow-up duration of 10.6 months, the median overall survival was 14.4 months and the 2-year overall survival rate was 40.6%. For each primary site, the 2-year overall survival rates were 32.1%, 34.6%, 30%, 60.8%, and 5.7% for the hypopharynx, oral cavity, larynx, nasopharynx, and oropharynx, respectively. Prognostic factors for overall survival were primary site (nasopharynx versus other sites) and gross tumor volume (GTV) (≤ 25 cm3 versus > 25 cm3). The 2-year local control rate was 41.2%. Twenty-four patients (18.6%) presented with grade ≥ 3 toxicities, including nine with hemorrhages that led to grade 5 toxicities in seven patients. All nine tumors that caused hemorrhage showed tumor encasement of the carotid ≥ 180 degrees and eight of nine tumors had larger GTV > 25 cm3. Reirradiation is a feasible treatment option for small local recurrence of oral, pharyngeal, and laryngeal cancers, with the requirement of a strict eligibility assessment for large tumors with carotid encasement.

Clinical target volume segmentation based on gross tumor volume using deep learning for head and neck cancer treatment.

Accurate clinical target volume (CTV) delineation is important for head and neck intensity-modulated radiation therapy. However, delineation is time-consuming and susceptible to interobserver variability (IOV). Based on a manual contouring process commonly used in clinical practice, we developed a deep learning (DL)-based method to delineate a low-risk CTV with computed tomography (CT) and gross tumor volume (GTV) input and compared it with a CT-only input. A total of 310 patients with oropharynx cancer were randomly divided into the training set (250) and test set (60). The low-risk CTV and primary GTV contours were used to generate label data for the input and ground truth. A 3D U-Net with a two-channel input of CT and GTV (U-NetGTV) was proposed and its performance was compared with a U-Net with only CT input (U-NetCT). The Dice similarity coefficient (DSC) and average Hausdorff distance (AHD) were evaluated. The time required to predict the CTV was 0.86 s per patient. U-NetGTV showed a significantly higher mean DSC value than U-NetCT (0.80 ± 0.03 and 0.76 ± 0.05) and a significantly lower mean AHD value (3.0 ± 0.5 mm vs 3.5 ± 0.7 mm). Compared to the existing DL method with only CT input, the proposed GTV-based segmentation using DL showed a more precise low-risk CTV segmentation for head and neck cancer. Our findings suggest that the proposed method could reduce the contouring time of a low-risk CTV, allowing the standardization of target delineations for head and neck cancer.

The Dosimetric Analysis of Duodenal and Intestinal Toxicity After a Curative Dose Re-irradiation Using the Intensity-Modulated Radiotherapy for Abdominopelvic Lymph Node Lesions.

INTRODUCTION: This study aimed to examine the influence of dosimetric factors on gastrointestinal toxicity after radical re-irradiation for lymph node recurrence in the abdominopelvic region using a composite plan. METHODS: Between January 2008 and March 2017, 33 patients underwent radical re-irradiation for lymph node recurrence in the abdominopelvic region with a complete overlap with previous radiation therapy (RT) with the median prescription dose of the second RT of 71.7 Gy10. Re-irradiation planning protocol for target volume and organs at risk (OARs) (duodenum, small and large intestines) was decided as follows: more than equal to 97% of the prescription dose was administered to the D95 (percentage of the minimum dose that covered 95% of the target volume) of planning target volume (PTV); minimal dose to the maximally irradiated doses delivered to 1cc [D1 cc] and 5cc [D5 cc] of OARs was set below 70 Gy3 and 50 Gy3, respectively; and D1 cc and D5 cc in the cumulative plans to OARs were 120 Gy3 and 100 Gy3. Kaplan-Meier analyses were performed to evaluate overall survival (OS) and univariate log-rank and multivariate Cox proportional hazards model analyses were performed to explore predictive factors. Using dose summation of the first and re-irradiation plans, we conducted a dosimetric analysis for grade ≥ 3 toxicities of the duodenum and intestine. RESULTS: With a median follow-up of 18 months, the two-year OS rate was 45.5%. The number of RT fields (localized or multiple) was a significant predisposing factor for OS rate with a hazard ratio of 0.23 (95% confidence interval 0.07-0.73). The two-year OS of the patients with a localized RT field was 63.6% and 9.1% for multiple RT fields (p= 0.00007). Four patients experienced grade ≥3 gastrointestinal toxicity related to re-irradiation (4/33=12.1%). We could not find any predisposing dosimetric value in the comparisons with and without toxicity. CONCLUSIONS: The dose constraints presented in this study are relatively low rates of toxicity, which may be useful when planning re-irradiation. Especially, for the patients who could be treated with localized RT field, radical re-irradiation with a high curative dose is a good option. No dosimetric predisposing factor was found for radical re-irradiation of abdominopelvic lesions in the composite plan.

Role of Brachytherapy Boost in Clinically Localized Intermediate and High-Risk Prostate Cancer: Lack of Benefit in Patients with Very High-Risk Factors T3b-4 and/or Gleason 9-10.

This study examined the role of brachytherapy boost (BT-boost) and external beam radiotherapy (EBRT) in intermediate- to high-risk prostate cancer, especially in patients with very high-risk factors (VHR: T3b-4 or Gleason score 9-10) as patients with double very high-risk factors (VHR-2: T3b-4 and Gleason score 9-10) previously showed worst prognosis in localized prostate cancer. We retrospectively reviewed multi-institutional data of 1961 patients that were administered radiotherapy (1091 BT-boost and 872 EBRT: 593 conventional-dose RT (Conv RT: equivalent to doses of 2 Gy per fraction = EQD2 ≤ 72 Gy) and 216 dose-escalating RT (DeRT = EQD2 ≥ 74 Gy). We found that BT-boost improved PSA control and provided an equivalent overall survival rate in the intermediate- and high-risk groups, except for patients within the VHR factor group. In the VHR-1 group (single VHR), BT-boost showed a superior biochemical control rate to the Conv RT group but not to the DeRT group. In the VHR-2 group, BT-boost did not improve outcomes of either Conv RT or DeRT groups. In conclusion, BT-boost showed no benefit to modern DeRT in the patients with VHR; therefore, they are not good candidates for BT-boost to improve outcome and may be amenable to clinical trials using multimodal intensified systemic treatments.

Pseudo low-energy monochromatic imaging of head and neck cancers: Deep learning image reconstruction with dual-energy CT.

PURPOSE: Low-energy virtual monochromatic images (VMIs) derived from dual-energy computed tomography (DECT) systems improve lesion conspicuity of head and neck cancer over single-energy CT (SECT). However, DECT systems are installed in a limited number of facilities; thus, only a few facilities benefit from VMIs. In this work, we present a deep learning (DL) architecture suitable for generating pseudo low-energy VMIs of head and neck cancers for facilities that employ SECT imaging. METHODS: We retrospectively analyzed 115 patients with head and neck cancers who underwent contrast enhanced DECT. VMIs at 70 and 50 keV were used as the input and ground truth (GT), respectively. We divided them into two datasets: for DL (104 patients) and for inference with SECT (11 patients). We compared four DL architectures: U-Net, DenseNet-based, and two ResNet-based models. Pseudo VMIs at 50 keV (pVMI50keV) were compared with the GT in terms of the mean absolute error (MAE) of Hounsfield unit (HU) values, peak signal-to-noise ratio (PSNR), and structural similarity (SSIM). The HU values for tumors, vessels, parotid glands, muscle, fat, and bone were evaluated. pVMI50keV were generated from actual SECT images and the HU values were evaluated. RESULTS: U-Net produced the lowest MAE (13.32 ± 2.20 HU) and highest PSNR (47.03 ± 2.33 dB) and SSIM (0.9965 ± 0.0009), with statistically significant differences (P < 0.001). The HU evaluation showed good agreement between the GT and U-Net. U-Net produced the smallest absolute HU difference for the tumor, at < 5.0 HU. CONCLUSION: Quantitative comparisons of physical parameters demonstrated that the proposed U-Net could generate high accuracy pVMI50keV in a shorter time compared with the established DL architectures. Although further evaluation on diagnostic accuracy is required, our method can help obtain low-energy VMI from SECT images without DECT systems.

Comparison of toxicities between ultrahypofractionated radiotherapy versus brachytherapy with or without external beam radiotherapy for clinically localized prostate cancer.

To compare gastrointestinal (GI) and genitourinary (GU) toxicities in patients with localized prostate cancer treated with ultrahypofractionated radiotherapy (UHF) or brachytherapy [BT; low dose rate, LDR or high dose rate (HDR) with or without external beam radiotherapy (EBRT)]. We compared 253 UHF and 1664 BT ± EBRT groups. The main outcomes were the incidence and severity of acute and late GU and GI toxicities. The secondary endpoint was biochemical control rate. Cumulative late actuarial GU toxicity did not differ for grade ≥ 2 (8.6% at 5-years in UHF and 13.3% in BT ± EBRT, hazard ratio [HR], 0.7066; 95% CI, 0.4093-1.22, p = 0.2127). Actuarial grade ≥ 2 late GI toxicity was higher in UHF (5.8% at 5-years, HR: 3.619; 95% CI, 1.774-7.383, p < 0.001) than in BT ± EBRT (1.1%). In detailed subgroup analyses, the high-dose UHF group (H-UHF) using BED ≥ 226 Gy1.5, showed higher GI toxicity profiles than the other subgroups (HDR + EBRT, LDR + EBRT, and LDR monotherapy, and L-UHF BED < 226 Gy1.5) with equivalent GU toxicity to other modalities. With a median follow-up period of 32 months and 75 months, the actuarial biochemical control rates were equivalent between the UHF and BT ± EBRT groups. UHF showed equivalent efficacy, higher GI and equivalent GU accumulated toxicity to BT ± EBRT, and the toxicity of UHF was largely dependent on the UHF schedule.

Reirradiation for recurrent head and neck carcinoma using high-dose-rate brachytherapy: A multi-institutional study.

AIM: This study presents multi-institutional individual data of reirradiation (ReRT) for head and neck cancer using brachytherapy (ReRT-BT) collected by national surveillance in Japan. METHODS AND MATERIALS: We distributed an e-mail-based questionnaire to 153 institutions equipped with high-dose-rate (HDR) brachytherapy facilities and received responses from 76 institutions (49.7%). Of these 76 institutions, only four (5.2%) performed ReRT-BT for head and neck cancers, and three provided individual patient's data. RESULTS: Six ReRT-BT cases of patients with recurrent head and neck cancer, treated with HDR brachytherapy in seven ReRT sessions, were identified from three institutions. Three patients (two cases of lips and one case of gingiva) who underwent curative-intent treatment achieved complete response at the treated area. Three patients who received palliative treatment (one case of tongue and two cases of maxillary sinus) had sustained tumor growth at the treated site, but with improvement in symptoms. No grade ≥3 toxicity was found after HDR ReRT-BT. CONCLUSIONS: ReRT-BT for head and neck cancer using HDR brachytherapy is a safe and useful approach to treat recurrent cancer after initial radiotherapy with curative and palliative intent. However, the scarce availability of ReRT-BT is a barrier to the wider utility of this effective procedure.