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Objectives To develop and evaluate a deep learning–based algorithm (DLA) for automatic detection of bone metastases on CT. Methods This retrospective study included CT scans acquired at a single institution between 2009 and 2019. Positive scans with bone metastases and negative scans without bone metastasis were collected to train the DLA. Another 50 positive and 50 negative scans were collected separately from the training dataset and were divided into validation and test datasets at a 2:3 ratio. The clinical efficacy of the DLA was evaluated in an observer study with board-certified radiologists. Jackknife alternative free-response receiver operating characteristic analysis was used to evaluate observer performance. Results A total of 269 positive scans including 1375 bone metastases and 463 negative scans were collected for the training dataset. The number of lesions identified in the validation and test datasets was 49 and 75, respectively. The DLA achieved a sensitivity of 89.8% (44 of 49) with 0.775 false positives per case for the validation dataset and 82.7% (62 of 75) with 0.617 false positives per case for the test dataset. With the DLA, the overall performance of nine radiologists with reference to the weighted alternative free-response receiver operating characteristic figure of merit improved from 0.746 to 0.899 ( p < .001). Furthermore, the mean interpretation time per case decreased from 168 to 85 s ( p = .004). Conclusion With the aid of the algorithm, the overall performance of radiologists in bone metastases detection improved, and the interpretation time decreased at the same time. Key Points • A deep learning–based algorithm for automatic detection of bone metastases on CT was developed . • In the observer study, overall performance of radiologists in bone metastases detection improved significantly with the aid of the algorithm . • Radiologists’ interpretation time decreased at the same time .

The spread through air spaces (STAS) is recognized as a negative prognostic factor in patients with early-stage lung adenocarcinoma. The present study aimed to develop a machine learning model for the prediction of STAS using peritumoral radiomics features extracted from preoperative CT imaging. A total of 339 patients who underwent lobectomy or limited resection for lung adenocarcinoma were included. The patients were randomly divided (3:2) into training and test cohorts. Two prediction models were created using the training cohort: a conventional model based on the tumor consolidation/tumor (C/T) ratio and a machine learning model based on peritumoral radiomics features. The areas under the curve for the two models in the testing cohort were 0.70 and 0.76, respectively ( P  = 0.045). The cumulative incidence of recurrence (CIR) was significantly higher in the STAS high-risk group when using the radiomics model than that in the low-risk group (44% vs. 4% at 5 years; P  = 0.002) in patients who underwent limited resection in the testing cohort. In contrast, the 5-year CIR was not significantly different among patients who underwent lobectomy (17% vs. 11%; P  = 0.469). In conclusion, the machine learning model for STAS prediction based on peritumoral radiomics features performed better than the C/T ratio model.

Aims/Introduction This systematic review and meta‐analysis aimed to investigate the efficacy and safety of acceptance and commitment therapy (ACT) for people with type 2 diabetes mellitus. Materials and Methods Several electronic databases were examined on 16 January 2021, including PubMed, CENTRAL, PsycINFO, International Clinical Trials Registry Platform and ClinicalTrials.gov. Randomized controlled trials were included to compare ACT with usual treatment for people with type 2 diabetes reported in any language. Primary outcome measures were glycated hemoglobin, self‐care ability assessed by the summary of diabetes self‐care activities and all adverse events. The secondary outcome measure was acceptance assessed by the acceptance and action diabetes questionnaire. Results Of 678 publications initially identified, three trials were included in the meta‐analysis. ACT resulted in a reduction in glycated hemoglobin (mean difference −0.62 points lower in the intervention group; 95% confidence interval −1.07 to −0.16; I2 = 0%; low‐quality evidence). In addition, ACT increased the score of the summary of diabetes self‐care activities (mean difference 8.48 points higher in the intervention group; 95% confidence interval 2.16–14.80; high‐quality evidence). Adverse events were not measured in all trials. ACT increased scores of the acceptance and action diabetes questionnaire (mean difference 5.98 points higher in the intervention group; 95% confidence interval, 1.42–10.54; I2 = 43%; low‐quality evidence). Conclusions ACT might reduce glycated hemoglobin, and increase self‐care ability and acceptance among people with type 2 diabetes. Summary of findings. Efficacy of ACT for type 2 diabetes.

The purpose of this study is to evaluate whether thin-slice high-resolution 2D fat-suppressed proton density-weighted image of the knee joint using denoising approach with deep learning-based reconstruction (dDLR) with MPR is more useful than 3D FS-PD multi planar voxel image. Twelve patients who underwent MRI of the knee at 3T and 13 knees were enrolled. Denoising effect was quantitatively evaluated by comparing the coefficient of variation (CV) before and after dDLR. For the qualitative assessment, two radiologists evaluated image quality, artifacts, anatomical structures, and abnormal findings using a 5-point Likert scale between 2D and 3D. All of them were statistically analyzed. Gwet’s agreement coefficients were also calculated. For the scores of abnormal findings, we calculated the percentages of the cases with agreement with high confidence. The CV after dDLR was significantly lower than the one before dDLR ( p  < 0.05). As for image quality, artifacts and anatomical structure, no significant differences were found except for flow artifact ( p  < 0.05). The agreement was significantly higher in 2D than in 3D in abnormal findings ( p  < 0.05). In abnormal findings, the percentage with high confidence was higher in 2D than in 3D ( p  < 0.05). By applying dDLR to 2D, almost equivalent image quality to 3D could be obtained. Furthermore, abnormal findings could be depicted with greater confidence and consistency, indicating that 2D with dDLR can be a promising imaging method for the knee joint disease evaluation.

Background Little is known about whether central airway morphological changes beyond traction bronchiectasis develop and affect clinical outcomes in patients with idiopathic pulmonary fibrosis (IPF). This study aimed to compare central airway structure comprehensively between patients with IPF, subjects with interstitial lung abnormality (ILA), and those without ILA (control) using computed tomography (CT). We further examined the prognostic impact of IPF-specific CT airway parameters in patients with IPF. Methods This retrospective study included male patients with IPF, and male health checkup subjects divided into those with ILA and control based on lung cancer screening CT. Using an artificial intelligence-based segmentation technique, the extent of fibrotic regions in the lung was quantified. After airway tree segmentation, CT parameters for central airway morphology, including the lumen area of the extrapulmonary airways (LA extra ), wall and lumen area of the segmental/subsegmental intrapulmonary airways (WA intra and LA intra ), tracheal distortion (tortuosity and curvature) and bifurcation angle of the main carina, were calculated. Results There were 106 patients with IPF, 53 subjects with ILA, and 1295 controls. Multivariable models adjusted for age, height and smoking history revealed that LA intra and WA intra were larger in both ILA and IPF, and that tracheal tortuosity and curvature were higher in IPF, but not in ILA, than in the control, whereas the bifurcation angle did not differ between the 3 groups. According to multivariable Cox proportional hazards models including only patients with IPF, increased WA intra was significantly associated with greater mortality (standardized hazard ratio [95% confidence interval] = 1.58 [1.17, 2.14]), independent of the volume of fibrotic regions, normal-appearing regions, or the whole airway tree in the lung. Conclusion Increased lumen area and wall thickening of the central airways may be involved in the pathogenesis of ILA and IPF, and wall thickening may affect the prognosis of patients with IPF.

Anxiety can make it difficult for patients to manage their illness. Therefore, it is important to reduce their anxiety if possible. However, few studies have examined the efficacy of drugs in the treatment of anxiety in patients with cancer. Our case had failed to respond to benzodiazepines, and it was difficult to use a selective serotonin reuptake inhibitor (SSRI) as the next drug. This case report describes the effective use of quetiapine to treat anxiety. We report this rare case along with a literature review. Few studies have assessed the treatment of anxiety in patients with rare cancers. In our case, quetiapine effectively alleviated anxiety associated with cystic adenoid carcinoma. However, in clinical practice, it is possible that anxiety is treated without differentiating the effects of cancer status, e.g. life prognosis, treatment progress. In our patient, benzodiazepines had no effect on anxiety. Thus, different drugs may be required to treat anxiety associated with cancer. The present study demonstrated that quetiapine is a useful modality for the palliative care of patients with rare cancer and intractable anxiety. Quetiapine may be an effective alternative to benzodiazepines (BZ) and SSRIs for treating anxiety in patients with cancer. However, further investigation is needed to clarify the efficacy of treatments for anxiety associated with rare cancers.

Background Dedifferentiated liposarcoma (DDLPS) is a rare tumor and generally shows poor prognosis with the lung frequent metastatic site. 18F-fluorodeoxyglucose-positron emission tomography/computed tomography (FDG-PET/CT) is used for staging or metastatic evaluation of this disease. We report a case of bilateral lung metastases of DDLPS showing uncommon imaging on FDG-PET/CT with completely different FDG uptake, which made preoperative diagnosis difficult. Case presentation The patient was a male in his 60 s and bilateral lung nodules were noted after proton beam therapy for retroperitoneal DDLPS. FDG-PET/CT showed high FDG uptake in the left S3 15-mm nodule but no uptake in the right S8 10-mm nodule. Thoracoscopic wedge resection for the left nodule was performed, and the pathology revealed metastasis of dedifferentiated liposarcoma. After resection of the left nodule, the right S8 nodule enlarged without FDG uptake. Thoracoscopic right S8 segmentectomy was performed, and metastasis of dedifferentiated liposarcoma was diagnosed. The 2 tumors showed completely different appearances on FDG-PET/CT with similar histopathological findings. Conclusions We encountered a case of multiple pulmonary metastases of DDLPS which did not follow the same imaging appearance on FDG-PET/CT. Appropriate timing of surgical resection for pathological diagnosis should be determined carefully.

Abstract Background We hypothesized that the high-dose opioid requirement in patients carrying the rs4680-GG variant in the COMT gene encoding catechol-O-methyltransferase would be greater for patients taking morphine than for those taking oxycodone, thus providing a much-needed biomarker to inform opioid selection for cancer pain. Methods A randomized, multicenter, open-label trial was conducted at a Japanese hospital’s palliative care service. Patients with cancer pain treated with regular doses of nonsteroidal anti-inflammatory drugs or acetaminophen were enrolled and randomized (1:1) into morphine (group M) and oxycodone (group O) groups. The minimum standard dose of immediate-release (IR) oral opioids was repeatedly administered by palliative care physicians to achieve pain-reduction goals (Pain reduction ≥ 33% from baseline and up to ≤ 3 on a numerical rating scale). The primary endpoint was the proportion of subjects requiring high-dose opioids on day 0 with the GG genotype. Results Of 140 participants who developed cancer-related pain among 378 subjects registered and pre-screened for the genotype, 139 were evaluated in the current study. Among patients carrying a COMT rs4680-GG genotype, 48.3% required high-dose opioids in group M, compared with the 20.0% in group O (95% CI, 3.7%-50.8%; P = .029). Of those with the non-GG genotype, 41.5% treated with morphine and 23.1% with oxycodone required high-dose opioids (95% CI, 3.3%-38.3%; P = 0.098). Conclusion Using the COMT rs4680 genotype alone is not recommended for selecting between morphine and oxycodone for pain relief. This report evaluates the potential for the COMT rs4680 genotype to serve as a biomarker for opioid choice.