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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. [ABSTRACT FROM AUTHOR]

Objectives: To explore the characteristics of patients and assess the effectiveness of enfortumab vedotin (EV) in those with treatment‐resistant advanced urothelial cancer in a real‐world setting. Patients and Methods: A multicenter observational study was conducted on 103 evaluable patients with advanced urothelial cancer who received EV. Outcomes were assessed by radiographic response, progression‐free survival (PFS), and overall survival (OS), with treatment‐related adverse events (trAEs). Radiographic response was assessed using Response Evaluation Criteria in Solid Tumors version 1.1, while trAEs were studied in line with Common Terminology Criteria for Adverse Events version 5.0. Results: The median follow‐up was 8.9 months (range, 0.1–16.4). The observed objective response rate was 50.5%. The median PFS was 6.0 months (95% CI: 4.7–9.8), and the median OS was 14.5 months (95% CI: 12.4–not reached). Out of the 103 patients, 19 (18.4%) had an Eastern Cooperative Oncology Group performance status of 2 or more, 14 (14.7%) had an non‐urothelial carcinoma histology, and 40 (38.3%) had at least one pre‐existing comorbidity. There were 26 (25.2%) patients who reported 49 trAEs, with 9 (18.3%) being grade 3 or higher. The most common trAEs included rash, occurring in 18.4%. Conclusions: This study describes the characteristics and outcomes of patients with previously treated advanced urothelial cancer receiving EV. The findings demonstrate that EV showed robust anti‐tumor activity and had manageable safety profiles outside the clinical trial setting. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the clinical usefulness of Immunotherapy Response Evaluation Criteria in Solid Tumours (iRECIST) in patients with metastatic urothelial carcinoma (UC) treated with pembrolizumab. The iRECIST is designed to accurately capture the tumour response treated with immunotherapy. Patients and methods: We conducted a multicentre retrospective study evaluating the clinical utility of iRECIST in 91 patients with metastatic UC treated with second‐line pembrolizumab. The objective response (OR) and time to progression (TTP) in accordance with both iRECIST and RECIST version 1.1 were compared with overall survival (OS) and risk of all‐cause mortality, and analysed using log‐rank and multivariable Cox regression models, respectively. Predictive performance of the criteria was studied using Harrell's concordance index (c‐index). The clinical usefulness of each criterion was compared using decision curve analysis. Results: Of 57 patients with progressive disease per RECIST, a considerable number of patients were reclassified to immune stable disease (six, 10.5%), immune partial response (two, 3.5%), and immune complete response (two, 3.5%) per iRECIST. Multivariable Cox regression models showed that both OR (hazard ratio [HR] 0.10, 95% confidence interval [CI] 0.03–0.35; P = 0.001) and TTP (HR 0.59, 95% CI 0.46–0.77; P < 0.001) per iRECIST were significantly associated with all‐cause mortality. Furthermore, iRECIST had a significant, increased predictability of OS compared with RECIST (OR, c‐index: 0.70, increase: 0.04, P = 0.046; TTP, c‐index: 0.88, increase: 0.07, P = 0.039). On decision curve analysis, iRECIST presented better net benefit gains than did RECIST. Conclusions: Compared with RECIST, iRECIST could more accurately predict OS of patients with metastatic UC treated with pembrolizumab. The iRECIST has the potential to be a new standard for tumour response evaluation of these patients. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to develop a deep learning (DL) method for generating virtual noncontrast (VNC) computed tomography (CT) images from contrast‐enhanced (CE) CT images (VNCDL) and to evaluate its performance in dose calculations for head and neck radiotherapy in comparison with VNC images derived from a dual‐energy CT (DECT) scanner (VNCDECT). Methods: This retrospective study included data for 61 patients who underwent head and neck radiotherapy. All planning CT images were obtained with a single‐source DECT scanner (80 and 140 kVp) with rapid kVp switching. The DL‐based method used a pair of virtual monochromatic images (VMIs) at 70 keV with and without contrast materials. VMIs without contrast materials were used as reference true noncontrast (TNC) images. Deformable image registration was used between the TNC and CE images. We used the data of 45 patients, chosen randomly, for training (7922 paired images), and data from the other 16 patients as test data. We generated the VNCDL images with a densely connected convolutional network. As the VNCDECT images, we used VMIs with the iodine signal suppressed, reconstructed from the CE images of the 16 test patients. The CT numbers of the tumor, common carotid artery, internal jugular vein, muscle, fat, bone marrow, cortical bone, and mandible of each VNC image were compared with those of the TNC image. The dose of the reference TNC plan was recalculated using the CE, VNCDL, and VNCDECT images. Difference maps of the dose distributions and dose–volume histograms were evaluated. Results: The mean prediction time for the VNCDL images was 3.4 s per patient, and the mean number of slices was 204. The absolute differences in CT numbers of the VNCDL images were significantly smaller than those of the VNCDECT images for the bone marrow (8.0 ± 6.5 vs 175.1 ± 40.9 HU; P < 0.001) and mandible (20.3 ± 19.3 vs 106.2 ± 80.5 HU; P = 0.002). The DL‐based model provided the dose distribution most similar to that of the TNC plan. With the VNCDECT plans, dose errors >1.0% were observed in bone regions. The dose–volume histogram analysis showed that the VNCDL plans yielded the smallest errors for the primary target, although dose differences were <1.0% for all the approaches. For the maximum dose to the mandible, the mean ± SD errors for the CE, VNCDL, and VNCDECT plans were –0.13% ± 0.23% (range: −0.46% to 0.31%; P = 0.037), –0.01% ± 0.22% (range: −0.40% to 0.36%; P = 1.0), and 0.53% ± 0.47% (range: −0.21% to 1.41%; P < 0.001), respectively. Conclusions: In this study, we developed a method based on DL that can rapidly generate VNC images from CE images without a DECT scanner. Compared with the DECT approach, the DL‐based method improved the prediction accuracy of CT numbers in bone regions. Consequently, there was greater agreement between the VNCDL and TNC plan dose distributions than with the CE and VNCDECT plans, achieved by suppressing the contrast material signals while retaining the CT numbers of bone structures. [ABSTRACT FROM AUTHOR]

The aim of this work is to generate synthetic computed tomography (sCT) images from multi-sequence magnetic resonance (MR) images using an adversarial network and to assess the feasibility of sCT-based treatment planning for brain radiotherapy. Datasets for 15 patients with glioblastoma were selected and 580 pairs of CT and MR images were used. T1-weighted, T2-weighted and fluid-attenuated inversion recovery MR sequences were combined to create a three-channel image as input data. A conditional generative adversarial network (cGAN) was trained using image patches. The image quality was evaluated using voxel-wise mean absolute errors (MAEs) of the CT number. For the dosimetric evaluation, 3D conformal radiotherapy (3D-CRT) and volumetric modulated arc therapy (VMAT) plans were generated using the original CT set and recalculated using the sCT images. The isocenter dose and dose–volume parameters were compared for 3D-CRT and VMAT plans, respectively. The equivalent path length was also compared. The mean MAEs for the whole body, soft tissue and bone region were 108.1 ± 24.0, 38.9 ± 10.7 and 366.2 ± 62.0 hounsfield unit, respectively. The dosimetric evaluation revealed no significant difference in the isocenter dose for 3D-CRT plans. The differences in the dose received by 2% of the volume (D 2%), D 50% and D 98% relative to the prescribed dose were <1.0%. The overall equivalent path length was shorter than that for real CT by 0.6 ± 1.9 mm. A treatment planning study using generated sCT detected only small, clinically negligible differences. These findings demonstrated the feasibility of generating sCT images for MR-only radiotherapy from multi-sequence MR images using cGAN. [ABSTRACT FROM AUTHOR]

Background/aim: This study evaluated the calculation accuracy of the stopping power ratio (SPR) using dual-energy computed tomography with fast kilovoltage switching (FKSCT) for particle therapy., Materials and Methods: A tissue characterization phantom with various reference materials was scanned to obtain single-energy computed tomography (SECT) images and generate virtual monochromatic images at 77 keV (VMI 77keV ) and 140 keV (VMI 140keV ), water density (WD) images, and effective Z (Z eff ) images. For SECT, VMI 77keV and VMI 140keV lookup tables were generated to convert the measured Hounsfield value into the theoretical SPR for a normal phantom size. Subsequently, the reference materials were scanned in small and large phantoms. The SPR was calculated using the lookup tables of SECT (SPR SECT ) images, VMI 77keV (SPR 77keV ), and VMI 140keV (SPR 140keV ), and it was derived from the WD and Z eff (SPR WD )., Results: In the normal-sized phantom, the overall mean difference between SPR WD and theoretical SPR was -0.3%, and remained below 2% for most reference materials. For the large phantom, the overall mean absolute difference for SPR 140keV (3.0%, p=0.006) and SPR WD (3.2%, p=0.002) for the reference materials was significantly lower than that for SPR SECT (5.9%). For the small phantom, a significant reduction in the mean difference in the SPR calculation was observed in SPR 77keV (1.0%, p=0.001) and SPR 140keV (1.1%, p=0.013) compared with SPR SECT (2.2%)., Conclusion: VMI 140keV generated using FKSCT significantly improves the estimation accuracy of SPR compared with SECT. Thus, FKSCT may be used to improve the dose calculation accuracy for treatment planning of particle therapy., (Copyright © 2022 International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.)