BackgroundThe abdomen is a very accessible part of the human body, and clinically accessible abdominal masses are particularly common. Some of these lesions arise from abdominal and pelvic organs, such as the liver, gallbladder, gastrointestinal tract, and ovaries, while others manifest in the abdominal wall itself. The abdominal wall anatomy is relatively superficial and hierarchical. The abdominal wall is divided into four major layers: the skin, superficial fascia, deep fascia and enveloped inner muscles, and the peritoneal fascia. A wide variety of masses are derived from the above structural layers, including both true tumors and tumor-like lesions. According to one cohort study, benign masses accounted for 57.8% and malignant masses accounted for 42.2% of these masses (1).Because most abdominal wall lesions are superficial, ultrasound (US) is usually the preferred imaging modality by virtue of its convenience, economy, and high resolution, although the US is operator-dependent and can be problematic for deep and large lesions. The popularization of computed tomography (CT) and magnetic resonance imaging (MRI), have led to their playing an increasingly important role and having great diagnostic value in modern medicine. MRI is widely considered the optimal imaging technique in the evaluation of soft tissue tumors (2). MRI provides high-resolution images of soft tissues, which can be imaged in any orientation. Basic sequences, such as T 1weighted imaging (T 1 WI), T 2 -weighted imaging (T 2 WI), and T 2 WI with fat suppression (T 2 WI/FS), and even diffusion-weighted imaging (DWI) and contrast-enhanced imaging, are generally required. CT has less advantage in the diagnosis of abdominal wall masses, but it can be used

Background: An accurate assessment of lymph node (LN) status in patients with rectal cancer is important for treatment planning and an essential factor for predicting local recurrence and overall survival. In this study, we explored the potential value of histogram parameters of synthetic magnetic resonance imaging (SyMRI) in predicting LN metastasis in rectal cancer and compared their predictive performance with traditional morphological characteristics and chemical shift effect (CSE).Methods: A total of 70 patients with pathologically proven rectal adenocarcinoma who received direct surgical resection were enrolled in this prospective study. Preoperative rectal MRI, including SyMRI, were performed, and morphological characteristics and CSE of LN were assessed. Histogram parameters were extracted on a T1 map, T2 map, and proton density (PD) map, including mean, variance, maximum, minimum, 10th percentile, median, 90th percentile, energy, kurtosis, entropy, and skewness. Receiver operating characteristic (ROC) curves were used to explore their predictive performance for assessing LN status.Results: Significant differences in the energy of the T1, T2, and PD maps were observed between LNnegative and LN-positive groups [all P<0.001; the area under the ROC curve (AUC) was 0.838, 0.858, and 0.823, respectively]. The maximum and kurtosis of the T2 map, maximum, and variance of PD map could also predict LN metastasis with moderate diagnostic power (P=0.032, 0.045, 0.016, and 0.047, respectively).Energy of the T1 map [odds ratio (OR) =1.683, 95% confidence interval (CI): 1.207-2.346, P=0.002] and extramural venous invasion on MRI (mrEMVI) (OR =10.853, 95% CI: 2.339-50.364, P=0.002) were significant predictors of LN metastasis. Moreover, the T1 map energy significantly improved the predictive performance compared to morphological features and CSE (P=0.0002 and 0.0485). Conclusions:The histogram parameters derived from SyMRI of the primary tumor were associated with LN metastasis in rectal cancer and could significantly improve the predictive performance compared with morphological features and CSE.

Background: Multifocal ground glass nodules (GGNs) represent a special radiological pattern indicative of synchronous multiple lung cancers (SMLCs), especially adenocarcinoma. However, the necessity of performing whole-body positron emission tomography/computed tomography (PET-CT) scanning and brain enhanced magnetic resonance imaging (MRI) as a staging workup for multifocal pure GGN (pGGN) patients remains unclear. The purpose of this study was to determine the utility of these two imaging scans for patients with multifocal pGGNs.Methods: This retrospective study was reviewed and approved by the ethics committee of the Cancer Hospital of the Chinese Academy of Medical Sciences. The study cohort was retrospectively selected from patients with multifocal pGGNs who underwent whole-body PET-CT examinations and/or brain enhanced MRIs between January 2010 and February 2019 at our institution. The additional value of the two exams for detecting nodal and distant metastases was evaluated.Results: In total, 73 patients (male-to-female ratio, 20:53; median age, 57 years) with multifocal pGGNs who underwent whole-body PET-CT (55 patients) and/or brain enhanced MRI (25 patients) were enrolled.No clearly metastatic lesions were detected. Among the enrolled patients, 53 (128 pGGNs) underwent complete surgical resection. All pGGNs were adenocarcinomas and/or preneoplasias, and no lymph node metastases were found on final pathology. Whole-body PET-CT and brain enhanced MRI added no definite benefit compared with chest CT alone before surgery.Conclusions: Whole-body PET-CT scans and brain enhanced MRIs are not necessary for patients with multifocal pGGNs.

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.