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Abstract Objectives We aimed to test the hypothesis that in retroperitoneal dedifferentiated liposarcoma (DDLS) the presence of the dedifferentiated (DD) component at the resection margin is associated with adverse outcome. Methods We retrospectively searched the archive for primary resections of retroperitoneal DDLS performed at our institution between 1990 and 2017. Slides were rereviewed for diagnosis, Fédération Nationale des Centres de Lutte Contre le Cancer grade, myogenic differentiation, and the presence of the well-differentiated (WD) or DD component at the resection margin. The medical records were reviewed for patient age, sex, tumor size, tumor focality, adjuvant/neoadjuvant therapy, local recurrence, distant metastases, local recurrence-free survival (LRFS), overall survival (OS), and follow-up duration. Results The presence of the DD component at the resection margin was associated with worse LRFS compared with cases without the DD component at the margin (P = .002). However, OS was not significantly affected (P = .11). Conclusions LRFS is significantly shorter in cases with the DD component at the margin compared with cases without DD tumor at the margin, while there is no association with OS. We recommend reporting the presence or absence of DD tumor at the margin in retroperitoneal DDLS, as it adds meaningful prognostic information.

Background/Objectives: Giant Retroperitoneal Liposarcomas (giant RPLs) represent a rare malignant disease of adulthood that does not yet have a univocal definition in the scientific literature. The symptoms may be late, depending on the position and the size reached. The weight may exceed 20 kg, and the diameter 25 cm. The main treatment is the surgical approach. This systematic review aims to collect data from the present literature and to answer some questions on the nature of this pathology. Methods: We performed a search on the PubMed, Cochrane, and Scopus databases using specific search strings. Non-English written articles and abstracts were excluded. Results: Dimensional, histological, and pathological data of giant RPLs were extracted and recorded in an electronic database, and charts were used to synthesize the results. We selected 126 manuscripts, all case reports and case series, and obtained data for 157 giant RPLs. The major axis varied from 15 to 80 cm, and the weight ranged between 2.5 and 98 kg. Sex distribution was homogenous. Age was reported 146 times, and almost 1/3 of the study population was under 50 years old. The most frequent hystotype reported was well-differentiated liposarcomas, while the rarest was pleomorphic liposarcomas. In 139 cases, the symptoms were reported and generally included a mass effect on surrounding organs. The exclusive surgical operation was the most frequent treatment option, and it included both the resection of the tumor and other organs involved. Chemo- and radiotherapy were also performed, in a few cases. In 36 reports, distant metastasis was suspected, but only 6 were effectively positive. Conclusions: Giant RPLs are a clinical entity that shares some common features with normal-size liposarcomas but are different in dimensions, age distribution, histologic prevalence, rate of incidental diagnosis, organ sparing, and R0 resection. More studies are needed to completely characterize these tumors.

Soft-tissue sarcomas (STS) are rare tumors that account for 1% of all adult malignancies, with over 100 different histologic subtypes occurring predominately in the trunk, extremity, and retroperitoneum. This low incidence is further complicated by their variable presentation, behavior, and long-term outcomes, which emphasize the importance of centralized care in specialized centers with a multidisciplinary team approach. In the last decade, there has been an effort to improve the quality of care for patients with STS based on anatomic site and histology, and multiple ongo-ing clinical trials are focusing on tailoring therapy to histologic subtype. This report summarizes the latest evidence guiding the histiotype-specific management of ex-tremity/truncal and retroperitoneal STS with regard to surgery, radiation, and chem-otherapy.

ABSTRACTIgG4 related disease was recognized as a unified disease entity only 15 years ago. Awareness of IgG4 related disease has increased worldwide since then, and specialists are now familiar with most of its clinical manifestations. Involvement of the pancreato-biliary tract, retroperitoneum/aorta, head and neck, and salivary glands are the most frequently observed disease phenotypes, differing in epidemiological features, serological findings, and prognostic outcomes. In view of this multifaceted presentation, IgG4 related disease represents a great mimicker of many neoplastic, inflammatory, and infectious conditions. Histopathology remains key to diagnosis because reliable biomarkers are lacking. Recently released classification criteria will be invaluable in improving early recognition of the disease. IgG4 related disease is highly treatable and responds promptly to glucocorticoids, but it can lead to end stage organ failure and even death if unrecognized. Prolonged courses of corticosteroids are often needed to maintain remission because the disease relapses in most patients. Rapid advancement in our understanding of the pathophysiology of IgG4 related disease is leading to the identification of novel therapeutic targets and possible personalized approaches to treatment.

PurposeTo explore the value of CT-enhanced quantitative features combined with machine learning for differential diagnosis of renal chromophobe cell carcinoma (chRCC) and renal oncocytoma (RO).MethodsSixty-one cases of renal tumors (chRCC = 44; RO = 17) that were pathologically confirmed at our hospital between 2008 and 2018 were retrospectively analyzed. All patients had undergone preoperative enhanced CT scans including the corticomedullary (CMP), nephrographic (NP), and excretory phases (EP) of contrast enhancement. Volumes of interest (VOIs), including lesions on the images, were manually delineated using the RadCloud platform. A LASSO regression algorithm was used to screen the image features extracted from all VOIs. Five machine learning classifications were trained to distinguish chRCC from RO by using a fivefold cross-validation strategy. The performance of the classifier was mainly evaluated by areas under the receiver operating characteristic (ROC) curve and accuracy.ResultsIn total, 1029 features were extracted from CMP, NP, and EP. The LASSO regression algorithm was used to screen out the four, four, and six best features, respectively, and eight features were selected when CMP and NP were combined. All five classifiers had good diagnostic performance, with area under the curve (AUC) values greater than 0.850, and support vector machine (SVM) classifier showed a diagnostic accuracy of 0.945 (AUC 0.964 ± 0.054; sensitivity 0.999; specificity 0.800), showing the best performance.ConclusionsAccurate preoperative differential diagnosis of chRCC and RO can be facilitated by a combination of CT-enhanced quantitative features and machine learning.

PurposeFor patients affected by autosomal-dominant polycystic kidney disease (ADPKD), successful differentiation of cysts is useful for automatic classification of patient phenotypes, clinical decision-making, and disease progression. The objective was to develop and evaluate a fully automated semantic segmentation method to differentiate and analyze renal cysts in patients with ADPKD.MethodsAn automated deep learning approach using a convolutional neural network was trained, validated, and tested on a set of 60 MR T2-weighted images. A three-fold cross-validation approach was used to train three models on distinct training and validation sets (n = 40). An ensemble model was then built and tested on the hold out cases (n = 20), with each of the cases compared to manual segmentations performed by two readers. Segmentation agreement between readers and the automated method was assessed.ResultsThe automated approach was found to perform at the level of interobserver variability. The automated approach had a Dice coefficient (mean ± standard deviation) of 0.86 ± 0.10 vs Reader-1 and 0.84 ± 0.11 vs. Reader-2. Interobserver Dice was 0.86 ± 0.08. In terms of total cyst volume (TCV), the automated approach had a percent difference of 3.9 ± 19.1% vs Reader-1 and 8.0 ± 24.1% vs Reader-2, whereas interobserver variability was − 2.0 ± 16.4%.ConclusionThis study developed and validated a fully automated approach for performing semantic segmentation of kidney cysts in MR images of patients affected by ADPKD. This approach will be useful for exploring additional imaging biomarkers of ADPKD and automatically classifying phenotypes.

Abstract Introduction/Objective Rosia-Dorfman Disease (RDD) is a rare benign non-Langerhans cell histiocytic disorder of unknown etiology with heterogeneous clinical features. RDD was first described by Destombes in 1965 under the term “adenitis with lipid excess.” However, it is named after Rosai and Dorfman, who characterized the further histopathological features of the disease in 1969. The characteristic presentation of RDD is lymphadenopathy, and extra nodal sites include bone, upper respiratory tract, central nervous system, retroperitoneum, and skin. The management of RDD depends on the site of involvement and the presence or absence of the symptoms. Multifocal and refractory diseases require systemic treatment. Surgical resection can be considered in symptomatic and resect- able diseases. Methods/Case Report A 16-year-old Asian female with no significant past medical history presented with a painful verrucous lesion on the left 5th finger for one year with bleeding and purulent discharge. Excision was performed, and histologically, there was extensive infiltration of large histocytes in the dermis with occasional lymphocytes and plasma cells. The histiocytes were engulfing the intact inflammatory cells, exhibiting the emperipolesis phenomenon. Immunohistochemically, S100 and CD68 were positive in the large histiocytes, and leukocyte common antigen (LCA) was positive in the background lymphocytes. Based on histology and immunohistochemistry, RDD diagnosis was rendered. Results (if a Case Study enter NA) N/A Conclusion RDD is a rare condition that poses challenges in diagnosis and management. The key diagnostic factors are histological features and immunohistochemistry to exclude other pathological disorders. Treatment should be tailored according to the needs of each patient and as required to optimize the outcome. We recommend an appropriate diagnosis of this entity in a timely manner to prevent any further long-term complications.

PurposeTotal kidney volume (TKV) is the most important imaging biomarker for quantifying the severity of autosomal-dominant polycystic kidney disease (ADPKD). 3D ultrasound (US) can accurately measure kidney volume compared to 2D US; however, manual segmentation is tedious and requires expert annotators. We investigated a deep learning-based approach for automated segmentation of TKV from 3D US in ADPKD patients.MethodWe used axially acquired 3D US-kidney images in 22 ADPKD patients where each patient and each kidney were scanned three times, resulting in 132 scans that were manually segmented. We trained a convolutional neural network to segment the whole kidney and measure TKV. All patients were subsequently imaged with MRI for measurement comparison.ResultsOur method automatically segmented polycystic kidneys in 3D US images obtaining an average Dice coefficient of 0.80 on the test dataset. The kidney volume measurement compared with linear regression coefficient and bias from human tracing were R2 = 0.81, and − 4.42%, and between AI and reference standard were R2 = 0.93, and − 4.12%, respectively. MRI and US measured kidney volumes had R2 = 0.84 and a bias of 7.47%.ConclusionThis is the first study applying deep learning to 3D US in ADPKD. Our method shows promising performance for auto-segmentation of kidneys using 3D US to measure TKV, close to human tracing and MRI measurement. This imaging and analysis method may be useful in a number of settings, including pediatric imaging, clinical studies, and longitudinal tracking of patient disease progression.

BackgroundTo develop and compare machine learning models based on triphasic contrast-enhanced CT (CECT) for distinguishing between benign and malignant renal tumors.Materials and MethodsIn total, 427 patients were enrolled from two medical centers: Center 1 (serving as the training set) and Center 2 (serving as the external validation set). First, 1781 radiomic features were individually extracted from corticomedullary phase (CP), nephrographic phase (NP), and excretory phase (EP) CECT images, after which 10 features were selected by the minimum redundancy maximum relevance method. Second, random forest (RF) models were constructed from single-phase features (CP, NP, and EP) as well as from the combination of features from all three phases (TP). Third, the RF models were assessed in the training and external validation sets. Finally, the internal prediction mechanisms of the models were explained by the SHapley Additive exPlanations (SHAP) approach.ResultsA total of 266 patients with renal tumors from Center 1 and 161 patients from Center 2 were included. In the training set, the AUCs of the RF models constructed from the CP, NP, EP, and TP features were 0.886, 0.912, 0.930, and 0.944, respectively. In the external validation set, the models achieved AUCs of 0.860, 0.821, 0.921, and 0.908, respectively. The “original_shape_Flatness” feature played the most important role in the prediction outcome for the RF model based on EP features according to the SHAP method.ConclusionsThe four RF models efficiently differentiated benign from malignant solid renal tumors, with the EP feature-based RF model displaying the best performance.

PurposeTo investigate the epidemiology and distribution of disease characteristics of urolithiasis by data mining structured radiology reports.MethodsThe content of structured radiology reports of 2028 urolithiasis CTs was extracted from the department’s structured reporting (SR) platform. The investigated cohort represented the full spectrum of a tertiary care center, including mostly symptomatic outpatients as well as inpatients. The prevalences of urolithiasis in general and of nephro- and ureterolithasis were calculated. The distributions of age, sex, calculus size, density and location, and the number of ureteral and renal calculi were calculated. For ureterolithiasis, the impact of calculus characteristics on the degree of possible obstructive uropathy was calculated.ResultsThe prevalence of urolithiasis in the investigated cohort was 72%. Of those patients, 25% had nephrolithiasis, 40% ureterolithiasis, and 35% combined nephro- and ureterolithiasis. The sex distribution was 2.3:1 (M:F). The median patient age was 50 years (IQR 36–62). The median number of calculi per patient was 1. The median size of calculi was 4 mm, and the median density was 734 HU. Of the patients who suffered from ureterolithiasis, 81% showed obstructive uropathy, with 2nd-degree uropathy being the most common. Calculus characteristics showed no impact on the degree of obstructive uropathy.ConclusionSR-based data mining is a simple method by which to obtain epidemiologic data and distributions of disease characteristics, for the investigated cohort of urolithiasis patients. The added information can be useful for multiple purposes, such as clinical quality assurance, radiation protection, and scientific or economic investigations. To benefit from these, the consistent use of SR is mandatory. However, in clinical routine SR usage can be elaborate and requires radiologists to adapt.