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BMC Medical Education Nov 2023Oral histopathology is a bridge course connecting oral basic medicine and clinical dentistry. However, the application of outcomes-based education via flipped classroom...
INTRODUCTION
Oral histopathology is a bridge course connecting oral basic medicine and clinical dentistry. However, the application of outcomes-based education via flipped classroom (FC) in oral histopathology has not been well explored. This study has assessed the efficacy of outcomes-based education via FC in undergraduate oral histopathology module learning in Nanjing Medical University of China.
MATERIALS AND METHODS
A total of 214 third-year students were enrolled and assigned to the FC group of the batch 2022-23 (n = 110) and the traditional classroom (TC) group of the batch 2021-22 (n = 104) to participate the oral histopathology sessions respectively in the study. The FC group were required to preview the online course materials pre-class, followed by in-class quizz, in-class interactive group discussion, and slides microscopic observation. The outcomes-based formative and summative assessments for FC were designed. The TC group attended traditional laboratory class for the same glass slides microscopic observation. In addition, a questionnaire was performed to investigate the satisfaction of learning. Along with this, the performances of FC group in written theory tests and oral histopathology slide tests were compared with TC group.
RESULTS
Students in the FC group gained significantly final higher scores of the course than those in the TC group (score: 83.79 ± 11 vs. 76.73 ± 10.93, P<0.0001). Data from the student questionnaires indicated a preference for outcomes-based module education via FC. In the questionnaires, most students considered outcomes-based module education via FC to be beneficial to learning motivation, knowledge comprehension, critical thinking and teamwork. FC group had a higher level of satisfaction with oral histopathology teaching than TC group (satisfaction score: 4.599 ± 0.1027 vs. 4.423 ± 0.01366, P<0.01).
CONCLUSION
An outcomes-based module education via FC has a promising effect on undergraduate oral histopathology education.
Topics: Humans; Learning; Students; Thinking; Motivation; Surveys and Questionnaires; Problem-Based Learning; Curriculum
PubMed: 37946163
DOI: 10.1186/s12909-023-04753-9 -
Skin Research and Technology : Official... Jan 2024There are no standards for evaluating skin photoaging. Dermoscopy is a non-invasive detection method that might be useful for evaluating photoaging.
BACKGROUND
There are no standards for evaluating skin photoaging. Dermoscopy is a non-invasive detection method that might be useful for evaluating photoaging.
OBJECTIVE
To assess the correlation between the dermoscopic evaluation of photoaging and clinical and pathological evaluations.
METHODS
The age, clinical evaluation (Fitzpatrick classification, Glogau Photoaging Classification, and Chung's standardized image ruler), histopathology (Masson staining and MMP-1 immunohistochemistry), and dermoscopy (Hu's and Isik's) of 40 donor skin samples were analyzed statistically, and Spearman rank correlation analysis was performed.
RESULTS
There was a robust correlation between the total Hu scores and Isik dermoscopy. The correlation of dermoscopy with histopathology was higher than that of clinical evaluation methods. There is a strong correlation between telangiectases and lentigo. Xerosis, superficial wrinkle, diffuse erythema, telangiectases, and reticular pigmentation were significantly correlated with the three clinical evaluation methods. Superficial wrinkles were correlated with Masson, MMP-1, various clinical indicators, and other dermoscopic items.
CONCLUSION
There is a good correlation between dermoscopy and clinical and histopathological examination. Dermoscopy might help evaluate skin photoaging.
Topics: Humans; Skin Aging; Matrix Metalloproteinase 1; Dermoscopy; Lentigo; Telangiectasis; Skin Neoplasms
PubMed: 38221782
DOI: 10.1111/srt.13578 -
Current Protocols Sep 2023Advances in genomic technologies have enabled the development of abundant mouse models of human disease, requiring accurate phenotyping to elucidate the consequences of... (Review)
Review
Advances in genomic technologies have enabled the development of abundant mouse models of human disease, requiring accurate phenotyping to elucidate the consequences of genetic manipulation. Anatomic pathology, an important component of the mouse phenotyping pipeline, is ideally performed by human or veterinary pathologists; however, due to insufficient numbers of pathologists qualified to assess these mouse models morphologically, research scientists may perform "do-it-yourself" pathology, resulting in diagnostic error. In the biomedical literature, pathology data is commonly presented as images of tissue sections, stained with either hematoxylin and eosin or antibodies via immunohistochemistry, accompanied by a figure legend. Data presented in such images and figure legends may contain inaccuracies. Furthermore, there is limited guidance for non-pathologist research scientists concerning the elements required in an ideal pathology image and figure legend in a research publication. In this overview, the components of an ideal pathology image and figure legend are outlined and comprise image quality, image composition, and image interpretation. Background knowledge is important for producing accurate pathology images and critically assessing these images in the literature. This foundational knowledge includes understanding relevant human and mouse anatomy and histology and, for cancer researchers, an understanding of human and mouse tumor classification and morphology, mouse stain background lesions, and tissue processing artifacts. Accurate interpretation of immunohistochemistry is also vitally important and is detailed with emphasis on the requirement for tissue controls and the distribution, intensity, and intracellular location of staining. Common pitfalls in immunohistochemistry interpretation are outlined, and a checklist of questions is provided by which any pathology image may be critically examined. Collaboration with pathologist colleagues is encouraged. This overview aims to equip researchers to critically assess the quality and accuracy of pathology images in the literature to improve the reliability and reproducibility of published pathology data. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC.
Topics: Animals; Humans; Mice; Antibodies; Artifacts; Disease Models, Animal; Hyaluronic Acid; Pathologists; Physicians; Reproducibility of Results
PubMed: 37712877
DOI: 10.1002/cpz1.891 -
Cancer Medicine Aug 2023Endoscopic ultrasonography-guided fine-needle aspiration/biopsy (EUS-FNA/B) is considered to be a first-line procedure for the pathological diagnosis of pancreatic...
BACKGROUND AND AIMS
Endoscopic ultrasonography-guided fine-needle aspiration/biopsy (EUS-FNA/B) is considered to be a first-line procedure for the pathological diagnosis of pancreatic cancer owing to its high accuracy and low complication rate. The number of new cases of pancreatic ductal adenocarcinoma (PDAC) is increasing, and its accurate pathological diagnosis poses a challenge for cytopathologists. Our aim was to develop a hyperspectral imaging (HSI)-based convolution neural network (CNN) algorithm to aid in the diagnosis of pancreatic EUS-FNA cytology specimens.
METHODS
HSI images were captured of pancreatic EUS-FNA cytological specimens from benign pancreatic tissues (n = 33) and PDAC (n = 39) prepared using a liquid-based cytology method. A CNN was established to test the diagnostic performance, and Attribution Guided Factorization Visualization (AGF-Visualization) was used to visualize the regions of important classification features identified by the model.
RESULTS
A total of 1913 HSI images were obtained. Our ResNet18-SimSiam model achieved an accuracy of 0.9204, sensitivity of 0.9310 and specificity of 0.9123 (area under the curve of 0.9625) when trained on HSI images for the differentiation of PDAC cytological specimens from benign pancreatic cells. AGF-Visualization confirmed that the diagnoses were based on the features of tumor cell nuclei.
CONCLUSIONS
An HSI-based model was developed to diagnose cytological PDAC specimens obtained using EUS-guided sampling. Under the supervision of experienced cytopathologists, we performed multi-staged consecutive in-depth learning of the model. Its superior diagnostic performance could be of value for cytologists when diagnosing PDAC.
Topics: Humans; Endoscopic Ultrasound-Guided Fine Needle Aspiration; Cytology; Deep Learning; Pancreatic Neoplasms; Carcinoma, Pancreatic Ductal
PubMed: 37455599
DOI: 10.1002/cam4.6335 -
Digestive and Liver Disease : Official... Dec 2023Deep learning (DL) models perform poorly when there are limited gastric signet ring cell carcinoma (SRCC) samples. Few-shot learning (FSL) can address the small sample...
BACKGROUND
Deep learning (DL) models perform poorly when there are limited gastric signet ring cell carcinoma (SRCC) samples. Few-shot learning (FSL) can address the small sample problem.
METHODS
EfficientNetV2-S was first pretrained on ImageNet and then pretrained on esophageal endoscopic images (i.e., base classes: normal vs. early cancer vs. advanced cancer) using transfer learning. Second, images of gastric benign ulcers, adenocarcinoma and SRCC, i.e., novel classes (n = 50 per class), were included. Image features were extracted as vectors using the dual pretrained EfficientNetV2-S. Finally, a k-nearest neighbor classifier was used to identify SRCC. The above proposed 3-way 3-shot FSL framework was conducted in three rounds.
RESULTS
Dual pretrained FSL performed better than single pretrained FSL, endoscopists and traditional EfficientNetV2-S models. Dual pretrained FSL obtained the highest accuracy (79.4%), sensitivity (68.8%), recall (68.8%), precision (69.3%) and F1-score (0.691), and the senior endoscopist achieved the highest specificity of 93.6% when identifying SRCC. The macro-AUC and F1-score for dual pretraining (0.763 and 0.703, respectively) were higher than those for single pretraining (0.656 and 0.537, respectively), along with endoscopists and traditional EfficientNetV2-S models. The 2-way 3-shot FSL also performed better.
CONCLUSION
The proposed FSL framework showed practical performance in the differentiation of SRCC on endoscopic images, suggesting the potential of FSL in the computer-aided diagnosis for rare diseases.
Topics: Humans; Carcinoma, Signet Ring Cell; Adenocarcinoma; Stomach Neoplasms
PubMed: 37455154
DOI: 10.1016/j.dld.2023.07.005 -
Laboratory Investigation; a Journal of... Nov 2023Digital pathology has transformed the traditional pathology practice of analyzing tissue under a microscope into a computer vision workflow. Whole-slide imaging allows... (Review)
Review
Digital pathology has transformed the traditional pathology practice of analyzing tissue under a microscope into a computer vision workflow. Whole-slide imaging allows pathologists to view and analyze microscopic images on a computer monitor, enabling computational pathology. By leveraging artificial intelligence (AI) and machine learning (ML), computational pathology has emerged as a promising field in recent years. Recently, task-specific AI/ML (eg, convolutional neural networks) has risen to the forefront, achieving above-human performance in many image-processing and computer vision tasks. The performance of task-specific AI/ML models depends on the availability of many annotated training datasets, which presents a rate-limiting factor for AI/ML development in pathology. Task-specific AI/ML models cannot benefit from multimodal data and lack generalization, eg, the AI models often struggle to generalize to new datasets or unseen variations in image acquisition, staining techniques, or tissue types. The 2020s are witnessing the rise of foundation models and generative AI. A foundation model is a large AI model trained using sizable data, which is later adapted (or fine-tuned) to perform different tasks using a modest amount of task-specific annotated data. These AI models provide in-context learning, can self-correct mistakes, and promptly adjust to user feedback. In this review, we provide a brief overview of recent advances in computational pathology enabled by task-specific AI, their challenges and limitations, and then introduce various foundation models. We propose to create a pathology-specific generative AI based on multimodal foundation models and present its potentially transformative role in digital pathology. We describe different use cases, delineating how it could serve as an expert companion of pathologists and help them efficiently and objectively perform routine laboratory tasks, including quantifying image analysis, generating pathology reports, diagnosis, and prognosis. We also outline the potential role that foundation models and generative AI can play in standardizing the pathology laboratory workflow, education, and training.
Topics: Humans; Artificial Intelligence; Image Processing, Computer-Assisted; Machine Learning; Neural Networks, Computer; Pathologists; Pathology
PubMed: 37757969
DOI: 10.1016/j.labinv.2023.100255 -
European Radiology Jan 2024To calculate the pooled diagnostic performances of whole-body [F]FDG PET/MR in M staging of [F]FDG-avid cancer entities. (Meta-Analysis)
Meta-Analysis
OBJECTIVES
To calculate the pooled diagnostic performances of whole-body [F]FDG PET/MR in M staging of [F]FDG-avid cancer entities.
METHODS
A diagnostic meta-analysis was conducted on the [F]FDG PET/MR in M staging, including studies: (1) evaluated [F]FDG PET/MR in detecting distant metastasis; (2) compared[ F]FDG PET/MR with histopathology, follow-up, or asynchronous multimodality imaging as the reference standard; (3) provided data for the whole-body evaluation; (4) provided adequate data to calculate the meta-analytic performances. Pooled performances were calculated with their confidence interval. In addition, forest plots, SROC curves, and likelihood ratio scatterplots were drawn. All analyses were performed using STATA 16.
RESULTS
From 52 eligible studies, 2289 patients and 2072 metastases were entered in the meta-analysis. The whole-body pooled sensitivities were 0.95 (95%CI: 0.91-0.97) and 0.97 (95%CI: 0.91-0.99) at the patient and lesion levels, respectively. The pooled specificities were 0.99 (95%CI: 0.97-1.00) and 0.97 (95%CI: 0.90-0.99), respectively. Additionally, subgroup analyses were performed. The calculated pooled sensitivities for lung, gastrointestinal, breast, and gynecological cancers were 0.90, 0.93, 1.00, and 0.97, respectively. The pooled specificities were 1.00, 0.98, 0.97, and 1.00, respectively. Furthermore, the pooled sensitivities for non-small cell lung, colorectal, and cervical cancers were 0.92, 0.96, and 0.86, respectively. The pooled specificities were 1.00, 0.95, and 1.00, respectively.
CONCLUSION
[F]FDG PET/MR was a highly accurate modality in M staging in the reported [F]FDG-avid malignancies. The results showed high sensitivity and specificity in each reviewed malignancy type. Thus, our findings may help clinicians and patients to be confident about the performance of [F]FDG PET/MR in the clinic.
CLINICAL RELEVANCE STATEMENT
Although [F]FDG PET/MR is not a routine imaging technique in current guidelines, mostly due to its availability and logistic issues, our findings might add to the limited evidence regarding its performance, showing a sensitivity of 0.95 and specificity of 0.97.
KEY POINTS
• The whole-body [F]FDG PET/MR showed high accuracy in detecting distant metastases at both patient and lesion levels. • The pooled sensitivities were 95% and 97% and pooled specificities were 99% and 97% at patient and lesion levels, respectively. • The results suggested that F-FDG PET/MR was a strong modality in the exclusion and confirmation of distant metastases.
Topics: Humans; Fluorodeoxyglucose F18; Radiopharmaceuticals; Sensitivity and Specificity; Multimodal Imaging; Neoplasm Staging; Neoplasms; Positron-Emission Tomography; Positron Emission Tomography Computed Tomography
PubMed: 37535156
DOI: 10.1007/s00330-023-10009-3 -
Oral Surgery, Oral Medicine, Oral... Sep 2023We assessed the efficacy of anti-desmoglein 1 (anti-DSG1) and anti-DSG3 levels by enzyme-linked immunosorbent assay (ELISA) as a preliminary diagnostic test in the... (Observational Study)
Observational Study
Efficacy of anti-desmoglein 1 and anti-desmoglein 3 levels by enzyme-linked immunosorbent assay compared to biopsy of chronic oral ulcerative diseases with positive Nikolsky's sign to diagnose oral pemphigus vulgaris with or without skin involvement: a retrospective institutional observational...
OBJECTIVE
We assessed the efficacy of anti-desmoglein 1 (anti-DSG1) and anti-DSG3 levels by enzyme-linked immunosorbent assay (ELISA) as a preliminary diagnostic test in the diagnosis of oral pemphigus vulgaris (OPV) with or without skin involvement compared to biopsy.
STUDY DESIGN
We retrospectively analyzed data collected from 23 patients (mean age 45.13 years) who had presented with chronic oral ulcerations, desquamative gingivitis, and a positive Nikolsky's sign. We performed ELISA, histopathologic examination, and direct immunofluorescence (DIF) and then calculated the sensitivity and specificity of the results of ELISA, histopathology, DIF, and the presence of a positive Nikolsky's sign in diagnosis.
RESULTS
The ELISA results showed that 18 patients had elevated anti-DSG3 levels, of whom 8 also had elevated anti-DSG1 levels. The histopathology results indicated that 18 patients had OPV, of whom 4 had oral lichen planus, and 1 had sub-epithelial blistering disease confirmed to be mucous membrane pemphigoid MMP by DIF. ELISA, histopathology, and DIF had a 100% sensitivity and specificity, and the presence of a positive Nikolsky's sign had a sensitivity and specificity of 100% and 78.26%, respectively.
CONCLUSIONS
Measurement of anti-DSG1 and anti-DSG3 levels by ELISA warrants consideration as a first-line diagnostic test for early detection of OPV with or without skin involvement over biopsy.
Topics: Humans; Middle Aged; Pemphigus; Retrospective Studies; Pilot Projects; Enzyme-Linked Immunosorbent Assay; Oral Ulcer; Stomatitis; Chronic Disease; Cellulitis; Biopsy; Autoantibodies
PubMed: 37507320
DOI: 10.1016/j.oooo.2023.05.016 -
European Journal of Cancer (Oxford,... Sep 2023Intra-tumour heterogeneity (ITH) causes diagnostic challenges and increases the risk for disease recurrence. Quantification of ITH is challenging and has not been...
BACKGROUND
Intra-tumour heterogeneity (ITH) causes diagnostic challenges and increases the risk for disease recurrence. Quantification of ITH is challenging and has not been demonstrated in large studies. It has previously been shown that deep learning can enable spatially resolved prediction of molecular phenotypes from digital histopathology whole slide images (WSIs). Here we propose a novel method (Deep-ITH) to predict and measure ITH, and we evaluate its prognostic performance in breast cancer.
METHODS
Deep convolutional neural networks were used to spatially predict gene-expression (PAM50 set) from WSIs. For each predicted transcript, 12 measures of heterogeneity were extracted in the training data set (N = 931). A prognostic score to dichotomise patients into Deep-ITH low- and high-risk groups was established using an elastic-net regularised Cox proportional hazards model (recurrence-free survival). Prognostic performance was evaluated in two independent data sets: SöS-BC-1 (N = 1358) and SCAN-B-Lund (N = 1262).
RESULTS
We observed an increase in risk of recurrence in the high-risk group with hazard ratio (HR) 2.11 (95%CI:1.22-3.60; p = 0.007) using nested cross-validation. Subgroup analyses confirmed the prognostic performance in oestrogen receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative, grade 3, and large tumour subgroups. The prognostic value was confirmed in the independent SöS-BC-1 cohort (HR=1.84; 95%CI:1.03-3.3; p = 3.99 ×10). In the other external cohort, significant HR was observed in the subgroup of histological grade 2 patients, as well as in the subgroup of patients with small tumours (<20 mm).
CONCLUSION
We developed a novel method for an automated, scalable, and cost-efficient measure of ITH from WSIs that provides independent prognostic value for breast cancer.
SIGNIFICANCE
Transcriptional ITH predicted by deep learning models enables prediction of patient survival from routine histopathology WSIs in breast cancer.
Topics: Humans; Female; Prognosis; Biomarkers, Tumor; Deep Learning; Neoplasm Recurrence, Local; Breast Neoplasms
PubMed: 37494846
DOI: 10.1016/j.ejca.2023.112953 -
BMC Cancer Nov 2023The pathological diagnosis and prognosis prediction of hepatocellular carcinoma (HCC) is challenging due to the lack of specific biomarkers. This study aimed to validate...
PURPOSE
The pathological diagnosis and prognosis prediction of hepatocellular carcinoma (HCC) is challenging due to the lack of specific biomarkers. This study aimed to validate the diagnostic and prognostic efficiency of Kidney-type glutaminase (GLS1) for HCC in prospective cohorts with a large sample size.
METHODS
A total of 1140 HCC patients were enrolled in our prospective clinical trials. Control cases included 114 nontumour tissues. The registered clinical trial (ChiCTR-DDT-14,005,102, chictr.org.cn) was referred to for the exact protocol. GLS1 immunohistochemistry was performed on the whole tumour section. The diagnostic and prognostic performances of GLS1 was evaluated by the receiver operating characteristic curve and Cox regression model.
RESULTS
The sensitivity, specificity, positive predictive value, negative predictive value, Youden index, and area under the curve of GLS1 for the diagnosis of HCC were 0.746, 0.842, 0.979, 0.249, 0.588, and 0.814, respectively, which could be increased to 0.846, 0.886, 0.987,0.366, 0.732, and 0.921 when combined with glypican 3 (GPC3) and alpha-fetoprotein (AFP), indicating better diagnostic performance. Further, we developed a nomogram with GPC3 and GLS1 for identifying HCC which showed good discrimination and calibration. GLS1 expression was also related with age, T stage, TNM stage, Edmondson-Steiner grade, microvascular invasion, Ki67, VEGFR2, GPC3, and AFP expression in HCC. GLS1 expression was negatively correlated with disease-free survival (P < 0.001) probability of patients with HCC.
CONCLUSIONS
It was validated that GLS1 was a sensitive and specific biomarker for pathological diagnosis of HCC and had prognostic value, thus having practical value for clinical application.
Topics: Humans; Carcinoma, Hepatocellular; alpha-Fetoproteins; Prospective Studies; Liver Neoplasms; Glutaminase; Biomarkers, Tumor; Prognosis; Kidney; Glypicans
PubMed: 37946141
DOI: 10.1186/s12885-023-11601-y