Objectives:To describe the thickness of mesorectal fat in local Chinese population and its impact on rectal cancer staging.Design: Case series.Setting: Two local regional hospitals in Hong Kong.Patients: Consecutive patients referred for multidisciplinary board meetings from January to October 2012 were selected. Main outcome measures:Reports of cases that had undergone staging magnetic resonance imaging for histologically proven rectal cancer were retrospectively retrieved and reviewed by two radiologists. All magnetic resonance imaging examinations were acquired with 1.5T magnetic resonance imaging. Measurements were made by agreement between the two radiologists. The distance in mm was obtained in the axial plane at levels of 5 cm, 7.5 cm, and 10 cm from the anal verge. Four readings were obtained at each level, namely, anterior, left lateral, posterior, and right lateral positions.Results: A total of 25 patients (16 males, 9 females) with a median age of 69 (range, 38-84) years were included in the study. Mean thickness of the mesorectal fat at 5 cm, 7.5 cm, and 10 cm from the anal verge was 3.1 mm (standard deviation, 3.0 mm), 9.8 mm (5.3 mm), and 11.8 mm (4.2 mm), respectively. The proportions of patients with mean Limitation of radiological T3 subclassification of rectal cancer due to paucity of mesorectal fat in Chinese patients

ImportanceEpithelial ovarian carcinoma is heterogeneous and classified according to the World Health Organization Tumour Classification, which is based on histologic features and molecular alterations. Preoperative prediction of the histologic subtypes could aid in clinical management and disease prognostication.ObjectiveTo assess the value of radiomics based on contrast-enhanced computed tomography (CT) in differentiating histologic subtypes of epithelial ovarian carcinoma in multicenter data sets.Design, Setting, and ParticipantsIn this diagnostic study, 665 patients with histologically confirmed epithelial ovarian carcinoma were retrospectively recruited from 4 centers (Hong Kong, Guangdong Province of China, and Seoul, South Korea) between January 1, 2012, and February 28, 2022. The patients were randomly divided into a training cohort (n = 532) and a testing cohort (n = 133) with a ratio of 8:2. This process was repeated 100 times. Tumor segmentation was manually delineated on each section of contrast-enhanced CT images to encompass the entire tumor. The Mann-Whitney U test and voted least absolute shrinkage and selection operator were performed for feature reduction and selection. Selected features were used to build the logistic regression model for differentiating high-grade serous carcinoma and non–high-grade serous carcinoma.ExposuresContrast-enhanced CT-based radiomics.Main Outcomes and MeasuresIntraobserver and interobserver reproducibility of tumor segmentation were measured by Dice similarity coefficients. The diagnostic efficiency of the model was assessed by receiver operating characteristic curve and area under the curve.ResultsIn this study, 665 female patients (mean [SD] age, 53.6 [10.9] years) with epithelial ovarian carcinoma were enrolled and analyzed. The Dice similarity coefficients of intraobserver and interobserver were all greater than 0.80. Twenty radiomic features were selected for modeling. The areas under the curve of the logistic regression model in differentiating high-grade serous carcinoma and non–high-grade serous carcinoma were 0.837 (95% CI, 0.835-0.838) for the training cohort and 0.836 (95% CI, 0.833-0.840) for the testing cohort.Conclusions and RelevanceIn this diagnostic study, radiomic features extracted from contrast-enhanced CT were useful in the classification of histologic subtypes in epithelial ovarian carcinoma. Intraobserver and interobserver reproducibility of tumor segmentation was excellent. The proposed logistic regression model offered excellent discriminative ability among histologic subtypes.

Incorporation of point-of-care ultrasound in the undergraduate medical curriculum is of great importance to ensure early exposure and safe use of the modality. We aimed to assess the students' learning experiences following implementing an ultrasound module in the medical curriculum at the University of Hong Kong. Medical students in semester 6 (n = 221) were enrolled in the module in 2018. It consisted of 1 hour of didactic lecture, followed by 3 hours of hands-on session. The students had the opportunity to enroll into a four-week Special Study Module to further practice their skills. The students had access to an e-learning platform to assist in their learning. Outcome measures include task-based performance, quizzes, feedback, and round-table discussion to assess the learning experiences. The module was highly rated by over 90% of students (response rate of 96%). Students practiced on peer subject on upper abdominal scanning. Post-training assessment showed an increment of 16% in their understanding of the modality. Students were motivated to enroll into the Special Study Module, where they were trained and became proficient with Focused Assessment with Sonography with Trauma. More than 86% of the students found the e-learning platform easy to use and assisted the training session. Round-table discussion suggested more simulated clinical cases to be added and expansion of future modules. Ultrasound module was successfully implemented into the undergraduate medical curriculum at the University of Hong Kong through new pedagogical approaches. This integration was highly rated by the medical students with improved awareness and better understanding of point-of-care ultrasound.

No abstract

Background Magnetic resonance imaging (MRI) has limited accuracy in detecting pelvic lymph node (PLN) metastasis. This study aimed to examine the use of intravoxel incoherent motion (IVIM) in classifying pelvic lymph node (PLN) involvement in cervical cancer patients. Methods Fifty cervical cancer patients with pre-treatment magnetic resonance imaging (MRI) were examined for PLN involvement by one subspecialist and one non-subspecialist radiologist. PLN status was confirmed by positron emission tomography or histology. The tumours were then segmented by both radiologists. Kruskal-Wallis tests were used to test for differences between diffusion tumour volume (DTV), apparent diffusion coefficient (ADC), pure diffusion coefficient (D), and perfusion fraction (f) in patients with no malignant PLN involvement, those with sub-centimetre and size-significant PLN metastases. These parameters were then considered as classifiers for PLN involvement, and were compared with the accuracies of radiologists. Results Twenty-one patients had PLN involvement of which 10 had sub-centimetre metastatic PLNs. DTV increased (p = 0.013) while ADC (p = 0.015), and f (p = 0.006) decreased as the nodal status progressed from no malignant involvement to sub-centimetre and then size-significant PLN metastases. In determining PLN involvement, a classification model (DTV + f) had similar accuracies (80%) as the non-subspecialist (76%; p = 0.73) and subspecialist (90%; p = 0.31). However, in identifying patients with sub-centimetre PLN metastasis, the model had higher accuracy (90%) than the non-subspecialist (30%; p = 0.01) but had similar accuracy with the subspecialist (90%, p = 1.00). Interobserver variability in tumour delineation did not significantly affect the performance of the classification model. Conclusion IVIM is useful in determining PLN involvement but the added value decreases with reader experience.