High Apoptotic Index in Urine Cytology Is Associated with High-Grade Urothelial Carcinoma

BACKGROUND The significance of apoptosis and its association with high-grade urothelial carcinoma (HGUC) in urine cytology has yet to be determined. METHODS A computerized search of the study laboratory information system was performed over a 3-year period for all urine cytology specimens processed using the SurePath liquid-based preparation technique. Only those cases with correlating surgical pathology obtained within 6 months after the urine cytologic samples were included in the current study. Cases from ileal conduit samples were excluded. A semiquantitative numerical scoring system (apoptotic index) was used to assess the amount of pyknosis or karyorrhexis, with 0 indicating none, 1 indicating  30 per 10 high-power fields. Statistical analysis using the Pearson chi-square test was performed. RESULTS A total of 228 cases including 105 benign cases, 79 cases of HGUC, and 44 cases of low-grade urothelial carcinoma (LGUC) diagnosed on follow-up surgical pathology were selected. A score of 0 was observed in 70 benign, 11 HGUC, and 8 LGUC cases; a score of 1 was observed in 31 benign, 21 HGUC, and 23 LGUC cases; a score of 2 was observed in 3 benign, 27 HGUC, and 9 LGUC cases; and a score of 3 was observed in 1 benign, 20 HGUC, and 4 LGUC cases. CONCLUSIONS Excluding ileal conduit urine specimens, the finding of a high apoptotic index (score ≥ 2) with the presence of pyknosis or karyorrhexis in ≥10 per 10 high-power fields in the urine cytology appears to be significantly associated with HGUC (P<.05)

Histologic and Clinical Follow-up of Thyroid Fine Needle Aspirates in Pediatric Patients

BACKGROUND Although fine-needle aspiration (FNA) has an important role in evaluating thyroid nodules in adults, there is little published information regarding its utility in the pediatric population. METHODS A retrospective analysis of thyroid FNAs for patients who were 18 years old or younger at 2 institutions was conducted. Aspirates were retrospectively categorized with the Bethesda System for Reporting Thyroid Cytopathology. These diagnoses were then correlated with either final histopathology or clinical follow-up. RESULTS A total of 186 thyroid FNA samples from 154 patients (122 females and 32 males), who ranged in age from 9 months to 18 years (median, 16 years; mean, 14 years), were identified. FNA was performed to evaluate 1 to 3 nodules for each patient. Aspirates were classified as follows: nondiagnostic (n = 27), benign (n = 114), atypia of undetermined significance (AUS; n = 21), follicular neoplasm (FN; n = 8), suspicious for malignancy (n = 3), and malignant (n = 13). Sixty-one samples had a histologic correlation, 68 were followed clinically for ≥2 years, and 57 either had no follow-up or were followed for <2 years. For statistical purposes, FNA diagnoses of suspicious and malignant were considered positive, and benign lesions were considered negative. The accuracy was 99%, and the sensitivity and specificity were 94% and 100%, respectively. The risk of malignancy, not including papillary microcarcinoma, was 2% for benign aspirates, 21% for AUS, 57% for FN, and 100% for suspicious or malignant aspirates. CONCLUSIONS This analysis demonstrates that FNA is a sensitive and highly specific modality for evaluating thyroid nodules in pediatric patients. Each diagnostic category can facilitate communication and guide appropriate management

Percutaneous Biopsy of the Renal Mass: Fine Needle Aspiration or Core Biopsy?

BACKGROUND In recent years, there have been increasing indications for percutaneous renal biopsy. Fine-needle aspiration (FNA), with or without core needle biopsy (CB), has been used increasingly in the management of renal tumors at the study institution. METHODS A computerized search of laboratory records was conducted to retrieve FNA cases of renal masses as well as the correlating CB and/or nephrectomy specimens. The cases spanned a period of 10 years (2006-2015). The diagnoses were classified into 5 categories: malignant, suspicious for malignancy, neoplastic, atypical, and negative/nondiagnostic. Based on the results of the nephrectomy specimens, the diagnostic rate, sensitivity, and diagnostic accuracy were calculated among 3 groups of specimens: FNA only, CB only, and combined FNA and CB. RESULTS A total of 247 cases of FNA with 123 correlating CB and 101 follow-up nephrectomy specimens were identified. The diagnostic rate, sensitivity, and diagnostic accuracy were 72%, 78%, and 96%, respectively, for FNA; 87%, 92%, and 94%, respectively, for CB; and 92%, 92%, and 94%, respectively, for the combined FNA and CB group. Renal cell carcinoma and its variants were the most common histologic diagnoses (112 of 174 cases; 64%). Significant diagnostic discrepancy was noted in one case: a malignant melanoma that was misdiagnosed as renal cell carcinoma in both the preoperative FNA specimen and in the CB specimen. CONCLUSIONS In the current study, both FNA and CB demonstrated excellent diagnostic accuracy (96% and 94%, respectively). The combination of FNA and CB was found to significantly improve the diagnostic rate when compared with either FNA alone (92% vs 72%; P<.05) or CB alone (92% vs 87%)

The application of the Johns Hopkins Hospital Template on urine cytology

Background To evaluate the utility of the Johns Hopkins Hospital (JHH) template in detection of high-grade urothelial carcinoma (HGUC). Methods A computerized search of our laboratory information system was performed for all urine cytology cases from 2009 to 2011 processed by the SurePath™. We included only cases with correlating surgical pathology within 6 months after the urinary samples were obtained. The original cytologic diagnoses were reclassified according to the JHH template, and these cytolog ic diagnoses were then correlated with the follow-up surgical pathology diagnoses. Results A total of 273 urine samples with histopathologic follow-up were identified. The reclassified cytologic diagnoses included negative for urothelial atypia or malignancy (NUAM) 110; atypical urothelial cells of undetermined significance (AUC-US) 83; atypical urothelial cells, cannot exclude high-grade urothelial carcinoma (AUC-H) 49; HGUC 29; and low-grade urothelial carcinoma (LGUC) 2. More than one-half of patients (58%) who had biopsy-confirmed high-grade urothelial lesions had a preceding cytologic diagnosis of AUC-H or HGUC. AUC-H and HGUC are associated with high-grade urothelial lesions in 80% and 90% of the cases and show statistical significance when compared with AUC-US or NUAM (P < 0.05). Conclusion The JHH template is useful and effective in identifying patients with high-grade urothelial lesions who need to undergo cystoscopy. Diagn. Cytopathol. 2015;43:593–597. © 2015 Wiley Periodicals, Inc

Automated Clinical Coding:What, Why, and Where We Are?

Clinical coding is the task of transforming medical information in a patient's health records into structured codes so that they can be used for statistical analysis. This is a cognitive and time-consuming task that follows a standard process in order to achieve a high level of consistency. Clinical coding could potentially be supported by an automated system to improve the efficiency and accuracy of the process. We introduce the idea of automated clinical coding and summarise its challenges from the perspective of Artificial Intelligence (AI) and Natural Language Processing (NLP), based on the literature, our project experience over the past two and half years (late 2019 - early 2022), and discussions with clinical coding experts in Scotland and the UK. Our research reveals the gaps between the current deep learning-based approach applied to clinical coding and the need for explainability and consistency in real-world practice. Knowledge-based methods that represent and reason the standard, explainable process of a task may need to be incorporated into deep learning-based methods for clinical coding. Automated clinical coding is a promising task for AI, despite the technical and organisational challenges. Coders are needed to be involved in the development process. There is much to achieve to develop and deploy an AI-based automated system to support coding in the next five years and beyond.Comment: accepted for npj Digital Medicin

Automated clinical coding:What, why, and where we are?

Funding Information: The work is supported by WellCome Trust iTPA Awards (PIII009, PIII032), Health Data Research UK National Phenomics and Text Analytics Implementation Projects, and the United Kingdom Research and Innovation (grant EP/S02431X/1), UKRI Centre for Doctoral Training in Biomedical AI at the University of Edinburgh, School of Informatics. H.D. and J.C. are supported by the Engineering and Physical Sciences Research Council (EP/V050869/1) on “ConCur: Knowledge Base Construction and Curation”. HW was supported by Medical Research Council and Health Data Research UK (MR/S004149/1, MR/S004149/2); British Council (UCL-NMU-SEU international collaboration on Artificial Intelligence in Medicine: tackling challenges of low generalisability and health inequality); National Institute for Health Research (NIHR202639); Advanced Care Research Centre at the University of Edinburgh. We thank constructive comments from Murray Bell and Janice Watson in Terminology Service in Public Health Scotland, and information provided by Allison Reid in the coding department in NHS Lothian, Paul Mitchell, Nicola Symmers, and Barry Hewit in Edinburgh Cancer Informatics, and staff in Epic Systems Corporation. Thanks for the suggestions from Dr. Emma Davidson regarding clinical research. Thanks to the discussions with Dr. Kristiina Rannikmäe regarding the research on clinical coding and with Ruohua Han regarding the social and qualitative aspects of this research. In Fig. , the icon of “Clinical Coders” was from Freepik in Flaticon, https://www.flaticon.com/free-icon/user_747376 ; the icon of “Automated Coding System” was from Free Icon Library, https://icon-library.com/png/272370.html . Funding Information: The work is supported by WellCome Trust iTPA Awards (PIII009, PIII032), Health Data Research UK National Phenomics and Text Analytics Implementation Projects, and the United Kingdom Research and Innovation (grant EP/S02431X/1), UKRI Centre for Doctoral Training in Biomedical AI at the University of Edinburgh, School of Informatics. H.D. and J.C. are supported by the Engineering and Physical Sciences Research Council (EP/V050869/1) on “ConCur: Knowledge Base Construction and Curation”. HW was supported by Medical Research Council and Health Data Research UK (MR/S004149/1, MR/S004149/2); British Council (UCL-NMU-SEU international collaboration on Artificial Intelligence in Medicine: tackling challenges of low generalisability and health inequality); National Institute for Health Research (NIHR202639); Advanced Care Research Centre at the University of Edinburgh. We thank constructive comments from Murray Bell and Janice Watson in Terminology Service in Public Health Scotland, and information provided by Allison Reid in the coding department in NHS Lothian, Paul Mitchell, Nicola Symmers, and Barry Hewit in Edinburgh Cancer Informatics, and staff in Epic Systems Corporation. Thanks for the suggestions from Dr. Emma Davidson regarding clinical research. Thanks to the discussions with Dr. Kristiina Rannikmäe regarding the research on clinical coding and with Ruohua Han regarding the social and qualitative aspects of this research. In Fig. 1 , the icon of “Clinical Coders” was from Freepik in Flaticon, https://www.flaticon.com/free-icon/user_747376 ; the icon of “Automated Coding System” was from Free Icon Library, https://icon-library.com/png/272370.html. Publisher Copyright: © 2022, The Author(s).Clinical coding is the task of transforming medical information in a patient’s health records into structured codes so that they can be used for statistical analysis. This is a cognitive and time-consuming task that follows a standard process in order to achieve a high level of consistency. Clinical coding could potentially be supported by an automated system to improve the efficiency and accuracy of the process. We introduce the idea of automated clinical coding and summarise its challenges from the perspective of Artificial Intelligence (AI) and Natural Language Processing (NLP), based on the literature, our project experience over the past two and half years (late 2019–early 2022), and discussions with clinical coding experts in Scotland and the UK. Our research reveals the gaps between the current deep learning-based approach applied to clinical coding and the need for explainability and consistency in real-world practice. Knowledge-based methods that represent and reason the standard, explainable processof a task may need to be incorporated into deep learning-based methods for clinical coding. Automated clinical coding is a promising task for AI, despite the technical and organisational challenges. Coders are needed to be involved in the development process. There is much to achieve to develop and deploy an AI-based automated system to support coding in the next five years and beyond.Peer reviewe

Study on stress performance and free brickwork height limit of traditional chinese cavity wall

Tradicionalni kineski zid sa šupljinom često je izložen in-plane i out-of-plane oštećenjima tijekom prirodnih nepogoda poput oluja i zemljotresa. Međutim, otpor potresu i vjetru zida sa šupljinom, zatvorene konstrukcije, rijetko se proučava. Umjesto toga, sprječavanje najgorega i napori za stvaranje sigurnosti koncentrirani su na konstrukcijsku analizu i štete od potresa glavne konstrukcije. Usmjerivši se na tehnike zidanja kod 2 uobičajena tipa zida sa šupljinom i 1 vrste punog zida, u ovom se radu konstruira specijalni uređaj za opterećenje i koristi za ispitivanje in-plane i out-of-plane naprezanja zida sa šupljinom i punog zida pod horizontalnim opterećenjem. Rezultati pokazuju da su sva out-of-plane oštećenja rezultat nedovoljne izdržljivosti na savijanje; zid sa šupljinom ima daleko nižu out-of-plane nosivost nego puni zid. Uz to, postoje znatna ograničenja visine kod zidanja zida sa šupljinom zbog potresa i snažnih vjetrova, s obzirom na shematski dijagram interne sile konzolnog nosača i na osnovu izmjerene savojno-vlačne i smične čvrstoće. Ustanovljeno je da out-of-plane ponašanje određuje granice zidanja opekom. Autori predlažu da se na svakom katu postave vezne konstrukcije ako zid sa šupljinom treba biti povezan s glavnom konstrukcijom.Traditional Chinese cavity wall often suffers in-plane and out-of-plane damages in natural disasters like gales and earthquakes. However, the seismic and wind resistance of the cavity wall, an enclosure structure, are seldom studied in the engineering field. Instead, the disaster prevention and relief efforts are concentrated on the structural analysis and seismic damage of the main structure. Focusing on the bricklaying methods for 2 common types of cavity walls and 1 kind of solid wall, this paper designs a special loading device and uses it to examine the in-plane and out-of-plane stress performance of cavity wall and solid wall under the horizontal load. The results show that all out-of-plane damages have resulted from the flexural-bending failure of the bend; the cavity wall has far lower out-of-plane bearing capacity than the solid wall. Moreover, the free brickwork height limits of the cavity wall under the action of earthquake and wind load are deducted respectively, in reference to the schematic diagram of the internal force of the cantilever beam and on the basis of the measured flexural-tensile strength and shear strength. It is found that the out-of-plane performance controls the brickwork limits. The authors suggest that connecting structures should be installed on each floor if the cavity wall is to be connected with the main structure

3D Shape Perception from Monocular Vision, Touch, and Shape Priors

Perceiving accurate 3D object shape is important for robots to interact with the physical world. Current research along this direction has been primarily relying on visual observations. Vision, however useful, has inherent limitations due to occlusions and the 2D-3D ambiguities, especially for perception with a monocular camera. In contrast, touch gets precise local shape information, though its efficiency for reconstructing the entire shape could be low. In this paper, we propose a novel paradigm that efficiently perceives accurate 3D object shape by incorporating visual and tactile observations, as well as prior knowledge of common object shapes learned from large-scale shape repositories. We use vision first, applying neural networks with learned shape priors to predict an object's 3D shape from a single-view color image. We then use tactile sensing to refine the shape; the robot actively touches the object regions where the visual prediction has high uncertainty. Our method efficiently builds the 3D shape of common objects from a color image and a small number of tactile explorations (around 10). Our setup is easy to apply and has potentials to help robots better perform grasping or manipulation tasks on real-world objects.Comment: IROS 2018. The first two authors contributed equally to this wor