UNASSIGNED: Desmoid tumor (DT) is a rare locally aggressive but non-metastatic mesenchymal soft tissue neoplasm that predominantly occurs in the abdominal wall, abdominal cavity, and extremities. Its occurrence in the mesentery is relatively uncommon.
UNASSIGNED: This article reports two cases of desmoid tumor treated at the Department of Gastrointestinal Surgery, Weifang People\'s Hospital. The first case was a 59-year-old male patient who had previously undergone surgery for esophagogastric junction cancer. Postoperatively, he developed an intra-abdominal mass that rapidly increased in size within three months. The second case was a 60-year-old male patient who incidentally discovered a mass in the left lower abdomen. Both patients underwent surgical treatment, and the postoperative pathological diagnosis was mesenteric desmoid tumor.
UNASSIGNED: The treatment of desmoid tumor remains challenging. Simple surgical resection often yields unsatisfactory outcomes, and the efficacy of adjuvant radiotherapy and chemotherapy is also limited. Further research and clinical practice are necessary to improve diagnostic and therapeutic strategies, aiming to enhance patient survival and quality of life.

Angiosarcoma is a rare aggressive and understudied soft tissue sarcoma with pending evidence-based treatment guidelines due to varying study cohorts and inconsistent outcome measures. Surgery with wide resection is currently considered to be the cornerstone in management. In a population-based cohort identified from Danish National Health Registers between 2000 and 2017, this study aimed to define prognostic factors in patients with newly diagnosed soft tissue angiosarcoma. Kaplan-Meier survival analysis demonstrated 5-year overall survival of 28%. Competing risk analysis demonstrated cumulative incidence of local recurrence of 30% and metastasis of 43%. Multivariable Cox models among 154 included patients demonstrated age above 60 years and metastasis to be independently associated with worse overall survival. Cutaneous tumors, surgery, and negative resection margin were independently associated with improved overall survival. Adjuvant oncological treatment did not improve overall survival, risk of metastasis, or recurrence. Negative margin was not associated with lower risk of recurrence and metastasis. We conclude that, despite demonstrated improved survival after surgery with wide resection, overall survival remains poor.

BACKGROUND: To explore the potential of different quantitative dynamic contrast-enhanced (qDCE)-MRI tracer kinetic (TK) models and qDCE parameters in discriminating benign from malignant soft tissue tumors (STTs).
METHODS: This research included 92 patients (41females, 51 males; age range 16-86 years, mean age 51.24 years) with STTs. The qDCE parameters (Ktrans, Kep, Ve, Vp, F, PS, MTT and E) for regions of interest of STTs were estimated by using the following TK models: Tofts (TOFTS), Extended Tofts (EXTOFTS), adiabatic tissue homogeneity (ATH), conventional compartmental (CC), and distributed parameter (DP). We established a comprehensive model combining the morphologic features, time-signal intensity curve shape, and optimal qDCE parameters. The capacities to identify benign and malignant STTs was evaluated using the area under the curve (AUC), degree of accuracy, and the analysis of the decision curve.
RESULTS: TOFTS-Ktrans, EXTOFTS-Ktrans, EXTOFTS-Vp, CC-Vp and DP-Vp demonstrated good diagnostic performance among the qDCE parameters. Compared with the other TK models, the DP model has a higher AUC and a greater level of accuracy. The comprehensive model (AUC, 0.936, 0.884-0.988) demonstrated superiority in discriminating benign and malignant STTs, outperforming the qDCE models (AUC, 0.899-0.915) and the traditional imaging model (AUC, 0.802, 0.712-0.891) alone.
CONCLUSIONS: Various TK models successfully distinguish benign from malignant STTs. The comprehensive model is a noninvasive approach incorporating morphological imaging aspects and qDCE parameters, and shows significant potential for further development.

Desmoid fibromatosis of the breast (also known as desmoid tumor of the breast) is a rare entity infrequently encountered by oncologists and surgeons caring for patients with breast disease. The current body of literature is highly reliant on case series and extrapolations from other sites of desmoid tumor-related disease. Much remains unclear regarding the pathological origins, natural history, and response to treatment of this condition. Traditional treatment strategies have centered on surgical resection, which may result in significantly disfiguring cosmetic and functional outcomes, frequent need for re-operation, and associated morbidity. There are limited data to support the superiority of upfront surgical resection when compared to medical therapy or watchful waiting strategies. Current treatment guidelines for desmoid tumors do not focus on the breast as a site of disease and are purposefully ambiguous due to the paucity of evidence available. We aim to review the literature concerning desmoid fibromatosis of the breast and propose an algorithm for current evidence-based management of this rare disease in the context of our experience with this pathology at a high-volume quaternary referral center.

BACKGROUND: In 2024, the World Health Organization (WHO) is scheduled to publish the WHO Reporting System for Soft Tissue Cytopathology (WHORSSTC). This system establishes categories with well-defined definitions, criteria, and estimated risks of malignancy (ROMs) for soft tissue tumors. The estimates of ROM are based on a relatively small number of published studies. Interobserver reproducibility is not addressed in the reporting system even though reproducibility of a reporting system is highly important.
METHODS: A manual search of one authors personal consultation files and teaching set (L.J.L.) was conducted for all cytologic specimens of soft tissue tumors accessioned between January 1, 1985 and December 31, 2022. Only cases with documented surgical pathology follow-up were included in the study. Slides from each case were evaluated independently by three cytopathologists with each case assigned to one of the WHORSSTC categories. A ROM for each of the WHORSSTC categories was calculated. Interobserver agreement was evaluated by the kappa and weighted kappa statistics.
RESULTS: Risk for malignancy by category were: Category 1: 0%, Category 2: 28%, Category 3: 57%, Category 4: 47%, Category 5: 63%, and Category 6: 88%. Kappa statistics for agreement between raters varied from 0.2183 to 0.3465 and weighted kappa varied from 0.3778 to 0.5217.
CONCLUSIONS: The WHORSSTC showed a progression of malignancy risk from the category \"benign\" (28%) to the category \"malignant\" (88%). Interobserver agreement was only fair.

OBJECTIVE: To systematically evaluate the application value of radiomics and deep learning (DL) in the differential diagnosis of benign and malignant soft tissue tumors (STTs).
METHODS: A systematic review was conducted on studies published up to December 11, 2023, that utilized radiomics and DL methods for the diagnosis of STTs. The methodological quality and risk of bias were evaluated using the Radiomics Quality Score (RQS) 2.0 system and Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool, respectively. A bivariate random-effects model was used to calculate the summarized sensitivity and specificity. To identify factors contributing to heterogeneity, meta-regression and subgroup analyses were performed to assess the following covariates: diagnostic modality, region/volume of interest, imaging examination, study design, and pathology type. The asymmetry of Deeks\' funnel plot was used to assess publication bias.
RESULTS: A total of 21 studies involving 3866 patients were included, with 13 studies using independent test/validation sets included in the quantitative statistical analysis. The average RQS was 21.31, with substantial or near-perfect inter-rater agreement. The combined sensitivity and specificity were 0.84 (95% CI: 0.76-0.89) and 0.88 (95% CI: 0.69-0.96), respectively. Meta-regression and subgroup analyses showed that study design and the region/volume of interest were significant factors affecting study heterogeneity (P < 0.05). No publication bias was observed.
CONCLUSIONS: Radiomics and DL can accurately distinguish between benign and malignant STTs. Future research should concentrate on enhancing the rigor of study designs, conducting multicenter prospective validations, amplifying the interpretability of DL models, and integrating multimodal data to elevate the diagnostic accuracy and clinical utility of soft tissue tumor assessments.

UNASSIGNED: Desmoplastic fibroma presents similar to other soft tissue tumors to such an extent that even a gold standard investigation can miss.
UNASSIGNED: This is to report a mass in a 47-year-old male arising from the chest wall, which was first thought to be a hemangioma but was later diagnosed as a case of desmoplastic fibroblastoma with the help of a biopsy.

Angioleiomyomas are uncommon, noncancerous, smooth muscle tumors that primarily arise from blood vessels. Previous studies have yielded limited data due to the lack of interdisciplinary approaches or restricted patient pools. This study aims to provide a comprehensive analysis of angioleiomyomas, including the demographic, clinical, radiological, and histopathological features, with a large number of patients. Conducted as a retrospective investigation at a single center from January 2005 to June 2023, this study involved 142 patients. Relevant information was extracted from electronic medical records, covering clinical, radiological, histological, and demographic details. Angioleiomyomas mostly occurred at age 59 (1-87), predominately affect females (53%) and commonly arise in subcutaneous tissue (85%) and the lower limbs (76%). MRI findings revealed characteristic signals, with a high prevalence of the solid histologic type (65%), often displaying a reticular sign. Smooth muscle Actin was universally present (n = 95/95), while Desmin and Caldesmon showed positive expression in 83% (n = 71/85) and 98% (n = 92/94) of cases, respectively. This study presents an updated and comprehensive analysis of angioleiomyomas. Typically appearing as well-defined nodules in the extremities, these tumors can be effectively diagnosed using MRI, though histopathological analysis is generally essential for confirmation. Treatment primarily involves straightforward excision, with notable low complication and recurrence rates.

Soft tissue tumors (STTs) pose diagnostic and therapeutic challenges due to their rarity, complexity, and morphological overlap. Accurate differentiation between benign and malignant STTs is important to set treatment directions, however, this task can be difficult. The integration of machine learning and artificial intelligence (AI) models can potentially be helpful in classifying these tumors. The aim of this study was to investigate AI and machine learning tools in the classification of STT into benign and malignant categories. This study consisted of three components: (1) Evaluation of whole-slide images (WSIs) to classify STT into benign and malignant entities. Five specialized soft tissue pathologists from different medical centers independently reviewed 100 WSIs, representing 100 different cases, with limited clinical information and no additional workup. The results showed an overall concordance rate of 70.4% compared to the reference diagnosis. (2) Identification of cell-specific parameters that can distinguish benign and malignant STT. Using an image analysis software (QuPath) and a cohort of 95 cases, several cell-specific parameters were found to be statistically significant, most notably cell count, nucleus/cell area ratio, nucleus hematoxylin density mean, and cell max caliper. (3) Evaluation of machine learning library (Scikit-learn) in differentiating benign and malignant STTs. A total of 195 STT cases (156 cases in the training group and 39 cases in the validation group) achieved approximately 70% sensitivity and specificity, and an AUC of 0.68. Our limited study suggests that the use of WSI and AI in soft tissue pathology has the potential to enhance diagnostic accuracy and identify parameters that can differentiate between benign and malignant STTs. We envision the integration of AI as a supportive tool to augment the pathologists\' diagnostic capabilities.

OBJECTIVE: To determine if gadolinium-based contrast agents increase the sensitivity, specificity or reader confidence of malignant potential in musculoskeletal soft tissue tumors.
METHODS: Pre- and post-contrast MRI studies from 87 patients were read by three independent radiologists of different experience. Readers noted malignant potential and confidence in their diagnosis based on pre-contrast and post-contrast MRI studies. Statistical models assessed for agreement between MRI reader diagnosis and pathologic results as well as analyzing effects of contrast on reader confidence. Inter- and intra-observer variabilities of malignant potential were also calculated.
RESULTS: 87 patients (48 benign and 39 malignant; mean [± SD] age 51 ± 17.9 and 57.1 ± 17.1, respectively) were evaluated. For all readers, pre-contrast and post-contrast sensitivities were 68.1 % and 70.6 % while pre-contrast and post-contrast specificities were 84.6 % and 83.8 %, respectively without significant change (p=0.88). There was not a significant association with the use of contrast and prediction of malignant potential with or without the resident reader (p=0.65 and p=0.82). Use of contrast was significantly associated with higher levels of reader confidence (p=0.02) for all readers. Inter- and intra-observer variabilities were in good agreement (W = 0.77 and 0.70).
CONCLUSIONS: The addition of a post-contrast sequence increased reader confidence in their diagnosis without a corresponding significant increase in accurate prediction of malignant potential.