The feasibility of reduced field-of-view diffusion-weighted imaging in evaluating bladder invasion of uterine cervical cancer

Objectives: Uterine cervical cancer with bladder mucosal invasion is classified as FIGO stage IVA with poor prognosis. MRI can rule out the bladder invasion and skipping cystoscopy may be possible; however, high false-positive rate may be problematic. The purpose of this study is to evaluate the diagnostic performance of reduced field-of-view (FOV) diffusion-weighted imaging (DWI) in evaluating bladder mucosal invasion of cervical cancer. Methods: 3T MRI including T2WI and reduced FOV DWI in 15 women with histologically proven cervical cancer (two stage IIIB, six stage IVA, seven stage IVB) were retrospectively evaluated compared with cystoscopic findings. Results: Cystoscopy revealed mucosal invasion in 13 of 15 cases. The border between the tumor and the bladder wall was unclear on T2WI and clear on reduced FOV DWI in all 15 cases. The diagnosis of mucosal invasion on reduced FOV DWI had a sensitivity of 100%, specificity of 50%, accuracy of 93%, PPV of 93%, and NPV of 100%. Conclusions: Addition of reduced FOV DWI may improve the staging accuracy of MRI for cervical cancer in assessing the bladder mucosal invasion. Advances in knowledge: Reduced FOV DWI may improve the staging accuracy of cervical cancer in assessing bladder mucosal invasion with high NPV and PPV, which may be helpful for avoiding unnecessary cystoscopy.

Clinical and imaging features of carcinosarcoma of the uterus and cervix

Aim The purpose of our study was to analyze the clinical and imaging features of uterine carcinosarcoma (UCS) and cervical carcinosarcoma (CCS), and to explore the diagnostic and staging accuracy of computed tomography (CT) and magnetic resonance imaging (MRI) examinations. Methods 41 patients including 37 with UCS and 4 with CCS from July 2011 to September 2020 were enrolled in the study. Of the 37 UCS cases, 7 had CT images, 27 had MRI images, and 3 had both CT and MRI images. The Clinical data, CT or MRI imaging findings were analyzed. Diagnosis and staging accuracy of CT and MRI images were also analyzed. Results Carcinosarcoma usually occurs in postmenopausal women (40/41), with the typical clinical symptom being vaginal bleeding (33/41). The CA125 degree was significantly different between the two invasion depth groups (p = 0.011). Most uterine carcinosarcomas showed unclear boundaries, uneven density, low or equal signal on T1WI, high or mixed signal on T2WI, uneven high signal on diffusion-weighted image (DWI), and mild enhancement. The diagnostic accuracies of CT and MRI for carcinosarcoma were 0% and 3.33%, respectively. The diagnostic accuracy for malignant tumors on CT and MRI was 50% and 83.33%, respectively. Conclusions Carcinosarcoma lesions presented with huge mass filling in the cavity, and some presented with small polypoid lesions or endometrial thickening. Evaluation of lymph node metastasis is a significant challenge for imaging staging.

An open-source, high-performance tool for automated sleep staging

The clinical and societal measurement of human sleep has increased exponentially in recent years. However, unlike other fields of medical analysis that have become highly automated, basic and clinical sleep research still relies on human visual scoring. Such human-based evaluations are time-consuming, tedious, and can be prone to subjective bias. Here, we describe a novel algorithm trained and validated on +30,000 hr of polysomnographic sleep recordings across heterogeneous populations around the world. This tool offers high sleep-staging accuracy that matches human scoring accuracy and interscorer agreement no matter the population kind. The software is designed to be especially easy to use, computationally low-demanding, open source, and free. Our hope is that this software facilitates the broad adoption of an industry-standard automated sleep staging software package.

513 WHAT FACTORS INFLUENCE THE ACCURACY OF CLINICAL STAGING OF ESOPHAGEAL- AND GASTRIC CANCER IN SWEDEN? A NATIONAL REGISTER STUDY

Cancer treatment is increasingly tailored to the individual patient. Treatment decisions are based on the evaluation of clinical stage which in turn is based on the result of the diagnostic pre-treatment work-up. The aim of this study was to investigate the accuracy of clinical staging of esophageal and gastric cancer in Sweden and what factors influence the quality of the staging procedure. Methods All patients operated for esophageal or gastric cancer, without neoadjuvant treatment, in Sweden from 2006 to 2018 was extracted from the Swedish national registry for esophageal and gastric cancer. Clinical TNM (cTNM) and pathological TNM (pTNM) was compared. The most common preoperative modalities for staging were endoscopy and CT-scan. Data on sex, age, smoking habits, multidisciplinary cancer conferences (yes/no), time of surgery (categorized in 5-year periods), number of resected lymph nodes and region of surgery were extracted from the registry. Uni- and multivariate logistic regression analyses were made comparing patients with correct cTNM to patients with incorrect cTNM. Results A total of 2500 patients met the inclusion criteria. 1173 patients were excluded because of missing data leaving 1327 patients for analyses. cTNM stage and pTNM stage was identical in 38% of patients. In 35% of patients there was a +/−1 stage difference comparing cTNM to pTNM. For esophageal cancer T-stage was on target in 32% and N-stage in 50% of cases. For gastric cancer the corresponding figures were 35% and 48% respectively. Multivariate regression analyzes showed that operation in the later time periods, a higher number of resected lymph nodes and discussion at multidisciplinary cancer conference improved staging accuracy. Conclusion In this study we found that 73% of patients were staged on target or +/− one stage-level. Operation in the later time periods, a higher number of resected lymph nodes and discussion at multidisciplinary cancer conference improved staging accuracy. This data indicate that treatment decisions should be made in a multidisciplinary setting. We believe that the gradual centralization of surgery and treatment decisions in Sweden during this time-period partly explains the improved accuracy over time.

Adoption of Computed Tomography Images under Iterative Reconstruction Algorithm in Diagnosis of Gastric Cancer

Objective. This work aimed to study the application of iterative reconstruction algorithm-based computed tomography (CT) imaging in the diagnosis of gastric cancer (GC). Methods. 40 cases of GC patients diagnosed by gastroscopy biopsy and pathology in hospital were retrospectively analyzed. Scanning images of the upper abdomen were obtained after plain scanning and double-phase enhanced scanning. Then, the image was reconstructed by the iterative reconstruction algorithm, and the CT value under the algorithm was analyzed statistically. Results. It was revealed that the detection rate of both spiral CT and iterative reconstruction algorithm-based CT was 100%. After the iterative reconstruction algorithm, the image quality, image information, and image mean square error (MSE) were notably improved. The degree of tumor invasion (T) staging accuracy was 82.6%, lymph node metastasis (N) staging accuracy was 73.2%, and tumor node metastasis (TNM) staging accuracy was 79.1%. The accuracy of the iterative reconstruction algorithm-based CT was 90% for T staging, 83% for N staging, and 85.5% for TNM staging. Conclusion. Iterative reconstruction algorithm can effectively improve the spatial resolution of CT images in GC diagnosis, with high accuracy. It can provide reliable and objective imaging data for the diagnosis of GC clinically, which was worthy of further application in clinical practice.

Assessment of tumor depth in oral tongue squamous cell carcinoma with multiparametric MRI: correlation with pathology

Objectives To compare the correlation of depth of invasion (DOI) measured on multiple magnetic resonance imaging (MRI) sequences and pathological DOI, in order to determine the optimal MRI sequence for measurement. Methods A total of 122 oral tongue squamous cell carcinoma (OTSCC) patients were retrospectively analyzed, who had received preoperative MRI and surgical resection. DOIs measured on fat-suppressed T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), dynamic enhanced-T1 high-resolution insotropic volume examination (e-THRIVE), and contrast-enhanced fat-suppressed T1WI (CE-T1WI) were respectively compared to those measured in pathologic specimens. The cutoff value of the best correlated MRI sequence was determined, and the T staging accuracy of MRI-derived DOI was evaluated. Results DOI derived from e-THRIVE showed the best correlation (r = 0.936, p < 0.001) with pathological DOI. The area under the curve values of MRI-derived DOI distinguishing T1 stage from T2 stage and distinguishing T2 stage from T3 stage were 0.969 and 0.974, respectively. The T staging criteria of MRI-derived DOI were 6.2 mm and 11.4 mm, with a staging accuracy of 86.9% compared to pathological DOI criteria of 5 mm and 10 mm. Conclusion E-THRIVE was the optimal MR sequence to measure the MR-derived DOI, and DOI derived from e-THRIVE could serve as a potential cut-off value as a clinical T staging indicator of OTSCC. Key Points • Multiparametric MRI helps radiologists to assess the neoplasm invasion in patients with oral tongue squamous cell carcinoma. • Retrospective study indicated that measurement was most accurate on enhanced-T1 high-resolution insotropic volume examination dynamic contrast enhancement images. • T staging of oral tongue squamous cell carcinoma was accurate according to the dynamic contrast enhancement MRI-derived depth of invasion.

A universal, open-source, high-performance tool for automated sleep staging

The creation of a completely automated sleep-scoring system that is highly accurate, flexible, well validated, free and simple to use by anyone has yet to be accomplished. In part, this is due to the difficulty of use of existing algorithms, algorithms having been trained on too small samples, and paywall demotivation. Here we describe a novel algorithm trained and validated on +27,000 hours of polysomnographic sleep recordings across heterogeneous populations around the world. This tool offers high sleep-staging accuracy matching or exceeding human accuracy and interscorer agreement no matter the population kind. The software is easy to use, computationally low-demanding, open source, and free. Such software has the potential to facilitate broad adoption of automated sleep staging with the hope of becoming an industry standard.