BACKGROUND Desmoid fibroma is a rare soft tissue tumor originating from the aponeurosis, fascia, and muscle, and it is also known as aponeurotic fibroma, invasive fibroma, or ligamentous fibroma. AIM To investigate the clinical and imaging features of desmoid tumors of the extremities. METHODS Thirteen patients with desmoid fibroma of the extremities admitted to our hospital from October 2016 to March 2021 were included. All patients underwent computed tomography (CT), magnetic resonance imaging (MRI), and pathological examination of the lesion. Data on the diameter and distribution of the lesion, the relationship between the lesion morphology and surrounding structures, MRI and CT findings, and pathological features were statistically analyzed. RESULTS The lesion diameter ranged from 1.7 to 8.9 cm, with an average of 5.35 ± 2.39 cm. All lesions were located in the deep muscular space, with the left and right forearm each accounting for 23.08% of cases. Among the 13 patients with desmoid fibroma of the extremities, the lesions were "patchy" in 1 case, irregular in 10, and quasi-round in 2. The boundary between the lesion and surrounding soft tissue was blurred in 10 cases, and the focus infiltrated along the tissue space and invaded the adjacent structures. Furthermore, the edge of the lesion showed "beard-like" infiltration in 2 cases; bone resorption and damage were found in 8, and bending of the bone was present in 2; the boundary of the focus was clear in 1. According to the MRI examination, the lesions were larger than 5 cm (61.54%), round or fusiform in shape (84.62%), had an unclear boundary (76.92%), showed uniform signal (69.23%), inhomogeneous enhancement (84.62%), and "root" or "claw" infiltration (69.23%). Neurovascular tract invasion was present in 30.77% of cases. CT examination showed that the desmoid tumors had slightly a lower density (69.23%), higher enhancement (61.54%), and unclear boundary (84.62%); a CT value < 50 Hu was present in 53.85% of lesions, and the enhancement was uneven in 53.85% of cases. Microscopically, fibroblasts and myofibroblasts were arranged in strands and bundles, without obvious atypia but with occasional karyotyping; cells were surrounded by collagen tissue. There were disparities in the proportion of collagen tissue in different regions, with abundant collagen tissue and few tumor cells in some areas, similar to the structure of aponeuroses or ligaments, and tumor cells invading the surrounding tissues. CONCLUSION Desmoid tumors of the extremities have certain imaging features on CT and MRI. The two imaging techniques can be combined to improve the diagnostic accuracy, achieve a comprehensive diagnosis of the disease in the clinical practice, and reduce the risk of missed diagnosis or misdiagnosis. In addition, their use can ensure timely diagnosis and treatment.

Objectives To construct effective prediction models for neoadjuvant radiotherapy (RT) and targeted therapy based on whole-tumor texture analysis of multisequence MRI for soft tissue sarcoma (STS) patients. Methods Thirty patients with STS of the extremities or trunk from a prospective phase II trial were enrolled for this analysis. All patients underwent pre- and post-neoadjuvant RT MRI examinations from which whole-tumor texture features were extracted, including T1-weighted with fat saturation and contrast enhancement (T1FSGd), T2-weighted with fat saturation (T2FS), and diffusion-weighted imaging (DWI) sequences and their corresponding apparent diffusion coefficient (ADC) maps. According to the postoperative pathological results, the patients were divided into pathological complete response (pCR) and non-pCR (N-pCR) groups. pCR was defined as less than 5% of residual tumor cells by postoperative pathology. Delta features were defined as the percentage change in a texture feature from pre- to post-neoadjuvant RT MRI. After data reduction and feature selection, logistic regression was used to build prediction models. ROC analysis was performed to assess the diagnostic performance. Results Five of 30 patients (16.7%) achieved pCR. The Delta_Model (AUC 0.92) had a better predictive ability than the Pre_Model (AUC 0.78) and Post_Model (AUC 0.76) and was better than AJCC staging (AUC 0.52) and RECIST 1.1 criteria (AUC 0.52). The Combined_Model (pre, post, and delta features) had the best predictive performance (AUC 0.95). Conclusion Whole-tumor texture analysis of multisequence MRI can well predict pCR status after neoadjuvant RT and targeted therapy in STS patients, with better performance than RECIST 1.1 and AJCC staging. Key points • MRI multisequence texture analysis could predict the efficacy of neoadjuvant RT and targeted therapy for STS patients. • Texture features showed incremental value beyond routine clinical factors. • The Combined_Model with features at multiple time points showed the best performance.

This study aimed to evaluate the value of the deep learning image reconstruction (DLIR) algorithm (GE Healthcare’s TrueFidelity™) in improving the image quality of low-dose computed tomography (LDCT) of the chest. First, we retrospectively extracted raw data of chest LDCT from 50 patients and reconstructed them by using model-based adaptive statistical iterative reconstruction-Veo at 50% (ASIR-V 50%) and DLIR at medium and high strengths (DLIR-M and DLIR-H). Three sets of images were obtained. Next, two radiographers measured the mean CT value/image signal and standard deviation (SD) in Hounsfield units at the region of interest (ROI) and calculated the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Two radiologists subjectively evaluated the image quality using a 5-point Likert scale. The differences between the groups of data were analyzed through a repeated measures ANOVA or the Friedman test. Last, our result show that the three reconstructions did not differ significantly in signal (p > 0.05) but had significant differences in noise, SNR, and CNR (p < 0.001). The subjective scores significantly differed among the three reconstruction modalities in soft tissue (p < 0.001) but not in lung tissue (p > 0.05). DLIR-H had the best noise reduction ability and improved SNR and CNR without distorting the image texture, followed by DLIR-M and ASIR-V 50%. In summary, DLIR can provide a higher image quality at the same dose, enhancing the physicians’ diagnostic confidence and improving the diagnostic efficacy of LDCT for lung cancer screening.

ObjectiveTo determine the efficacy of contrast-enhanced MRI in differentiating glioma (GL) from the metastatic tumor of the brain (MTB) and its association with patients’ neurological function.MethodsA retrospective analysis was conducted on 49 cases of pathologically confirmed GL and 42 cases of MTB admitted between April 2019 and January 2022. All patients were examined by a set of MRI sequences that included T1WI, T2WI, FLAIR, and DWI. The values of fractional anisotropy (FA), apparent diffusion coefficient (ADC), and operation coefficient (Ktrans) were calculated by taking the tumor parenchyma area, cystic area, and peritumor edema area as the regions of interest (ROIs). And according to the Mini-mental state examination (MMSE) results, the contrast-enhanced MRI with patients’ neurological dysfunction was observed.ResultsThe clinical symptoms and MRI findings of MTB and GL were basically the same, mainly showing neurological symptoms. The tumor parenchyma area and cystic area were mainly located in the tumor periphery and tumor central area, respectively, while the peritumor edema area was widely distributed, showing an irregular patchy edema zone. Contrast-enhanced scans suggested an obvious enhancement in the tumor parenchymal area, presenting with nodular and annular enhancement, but no enhancement in the tumor cystic and peritumor edema areas. There was no difference between GL and MTB in FA values of tumor cystic area and peritumor edema area (P &gt; 0.05), but the FA value of the parenchyma area of GL was higher (P &lt; 0.05). Besides, GL and MTB showed no difference in ADC and Ktrans values (P &gt; 0.05), while the former presented lower ADC values and higher Ktrans values of the peritumor edema area than the latter (P &lt; 0.05). In patients with GL and MTB, the FA and Ktrans values of all ROIs in those with neurological dysfunction were higher compared with those without neurological dysfunction, while the ADC values were lower (P &lt; 0.05).ConclusionContrast-enhanced MRI of peritumor edema area can effectively distinguish GL from MTB, and improve the accuracy of early clinical screening, thus providing more reliable life security for patients.

This paper proposes a song accompaniment generation method that combines audio analysis and symbolic music generation so that human music theory can be used to build a reinforcement learning model, training an agent to create music. The key to this algorithm is to extract music theory concepts from audio and a reward model that works well in reinforcement learning. However, some music theory rules are complex and challenging to describe. It is difficult to achieve competitive results only by hardcoding the reward. Therefore, to build an effective reward model, a neural network is used to evaluate the perceptual part of composition quality, and program discrimination is used to model easy-to-describe music theory, and the two work together. Experiments show that the proposed algorithm can generate accompaniment arrangements close to human composers, is compatible with various musical styles, and outperforms the baseline algorithm in multiple evaluation metrics.