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Brain Pathology (Zurich, Switzerland) Jul 2023Neuroinflammation has been implicated in frontotemporal lobar degeneration (FTLD) pathophysiology, including in genetic forms with microtubule-associated protein tau...
Neuroinflammation has been implicated in frontotemporal lobar degeneration (FTLD) pathophysiology, including in genetic forms with microtubule-associated protein tau (MAPT) mutations (FTLD-MAPT) or chromosome 9 open reading frame 72 (C9orf72) repeat expansions (FTLD-C9orf72). Iron accumulation as a marker of neuroinflammation has, however, been understudied in genetic FTLD to date. To investigate the occurrence of cortical iron accumulation in FTLD-MAPT and FTLD-C9orf72, iron histopathology was performed on the frontal and temporal cortex of 22 cases (11 FTLD-MAPT and 11 FTLD-C9orf72). We studied patterns of cortical iron accumulation and its colocalization with the corresponding underlying pathologies (tau and TDP-43), brain cells (microglia and astrocytes), and myelination. Further, with ultrahigh field ex vivo MRI on a subset (four FTLD-MAPT and two FTLD-C9orf72), we examined the sensitivity of T2*-weighted MRI for iron in FTLD. Histopathology showed that cortical iron accumulation occurs in both FTLD-MAPT and FTLD-C9orf72 in frontal and temporal cortices, characterized by a diffuse mid-cortical iron-rich band, and by a superficial cortical iron band in some cases. Cortical iron accumulation was associated with the severity of proteinopathy (tau or TDP-43) and neuronal degeneration, in part with clinical severity, and with the presence of activated microglia, reactive astrocytes and myelin loss. Ultra-high field T2*-weighted MRI showed a good correspondence between hypointense changes on MRI and cortical iron observed on histology. We conclude that iron accumulation is a feature of both FTLD-MAPT and FTLD-C9orf72 and is associated with pathological severity. Therefore, in vivo iron imaging using T2*-weighted MRI or quantitative susceptibility mapping may potentially be used as a noninvasive imaging marker to localize pathology in FTLD.
Topics: Humans; C9orf72 Protein; Neuroinflammatory Diseases; Progranulins; Frontotemporal Lobar Degeneration; tau Proteins; Frontotemporal Dementia; DNA-Binding Proteins
PubMed: 36974379
DOI: 10.1111/bpa.13158 -
Nature Medicine May 2024Cancer of unknown primary (CUP) site poses diagnostic challenges due to its elusive nature. Many cases of CUP manifest as pleural and peritoneal serous effusions....
Cancer of unknown primary (CUP) site poses diagnostic challenges due to its elusive nature. Many cases of CUP manifest as pleural and peritoneal serous effusions. Leveraging cytological images from 57,220 cases at four tertiary hospitals, we developed a deep-learning method for tumor origin differentiation using cytological histology (TORCH) that can identify malignancy and predict tumor origin in both hydrothorax and ascites. We examined its performance on three internal (n = 12,799) and two external (n = 14,538) testing sets. In both internal and external testing sets, TORCH achieved area under the receiver operating curve values ranging from 0.953 to 0.991 for cancer diagnosis and 0.953 to 0.979 for tumor origin localization. TORCH accurately predicted primary tumor origins, with a top-1 accuracy of 82.6% and top-3 accuracy of 98.9%. Compared with results derived from pathologists, TORCH showed better prediction efficacy (1.677 versus 1.265, P < 0.001), enhancing junior pathologists' diagnostic scores significantly (1.326 versus 1.101, P < 0.001). Patients with CUP whose initial treatment protocol was concordant with TORCH-predicted origins had better overall survival than those who were administrated discordant treatment (27 versus 17 months, P = 0.006). Our study underscores the potential of TORCH as a valuable ancillary tool in clinical practice, although further validation in randomized trials is warranted.
Topics: Humans; Deep Learning; Neoplasms, Unknown Primary; Female; Male; Aged; Middle Aged; ROC Curve; Adult; Cytodiagnosis; Aged, 80 and over; Ascites; Cytology
PubMed: 38627559
DOI: 10.1038/s41591-024-02915-w -
Clinical and Experimental Dental... Oct 2023Periodic examination of the head and neck includes screening for oral cancer, which is largely performed in dental offices by vigilant oral healthcare providers. The aim...
OBJECTIVE
Periodic examination of the head and neck includes screening for oral cancer, which is largely performed in dental offices by vigilant oral healthcare providers. The aim of this study was to assess practice patterns among Virginia dentists in performing head and neck exams and the referral rates of biopsies after completion of head and neck exams. We hypothesized that not all dentists perform head and neck exams and there is a difference between dentists who refer patients for a biopsy and those that perform biopsies.
METHODS
General dentists and dental specialists who are members of the Virginia Dental Association were invited to participate in a cross-sectional survey study through REDCap to self-report their head and neck exam protocols.
RESULTS
A total of 224 providers completed the survey. The majority of respondents were general dentists with more than 20 years in practice, who practice in a private setting, and see more than 10 patients in a day. All respondents stated they perform intraoral examinations, but 10 respondents stated they do not perform extraoral examinations. Nearly a third of respondents reported doing their own biopsies.
CONCLUSIONS
Although only 8.5% of oral healthcare providers in Virginia responded to our survey, respondents are following the 2017 ADA good practice statement by providing their patients with head and neck exams to screen for oral cancer. Additional education pertaining to extraoral anatomy, malignant transformation of oral potentially malignant disorders, and pathology procedures may be helpful to clinicians.
Topics: Humans; Cross-Sectional Studies; Mouth Neoplasms; Mouth Diseases; Referral and Consultation; Dentists
PubMed: 37759423
DOI: 10.1002/cre2.772 -
International Journal of Gynecological... Oct 2023Visual inspection with acetic acid is limited by subjectivity and a lack of skilled human resource. A decision support system based on artificial intelligence could...
INTRODUCTION
Visual inspection with acetic acid is limited by subjectivity and a lack of skilled human resource. A decision support system based on artificial intelligence could address these limitations. We conducted a diagnostic study to assess the diagnostic performance using visual inspection with acetic acid under magnification of healthcare workers, experts, and an artificial intelligence algorithm.
METHODS
A total of 22 healthcare workers, 9 gynecologists/experts in visual inspection with acetic acid, and the algorithm assessed a set of 83 images from existing datasets with expert consensus as the reference. Their diagnostic performance was determined by analyzing sensitivity, specificity, and area under the curve, and intra- and inter-observer agreement was measured using Fleiss kappa values.
RESULTS
Sensitivity, specificity, and area under the curve were, respectively, 80.4%, 80.5%, and 0.80 (95% CI 0.70 to 0.90) for the healthcare workers, 81.6%, 93.5%, and 0.93 (95% CI 0.87 to 1.00) for the experts, and 80.0%, 83.3%, and 0.84 (95% CI 0.75 to 0.93) for the algorithm. Kappa values for the healthcare workers, experts, and algorithm were 0.45, 0.68, and 0.63, respectively.
CONCLUSION
This study enabled simultaneous assessment and demonstrated that expert consensus can be an alternative to histopathology to establish a reference standard for further training of healthcare workers and the artificial intelligence algorithm to improve diagnostic accuracy.
Topics: Female; Humans; Uterine Cervical Neoplasms; Artificial Intelligence; Early Detection of Cancer; Sensitivity and Specificity; Physical Examination; Acetic Acid
PubMed: 37666527
DOI: 10.1136/ijgc-2023-004397 -
WMJ : Official Publication of the State... May 2024Blastomycosis is a fungal infection caused by Blastomyces dermatitidis that is hyperendemic in Wisconsin. It commonly presents as a pulmonary infection and frequently...
INTRODUCTION
Blastomycosis is a fungal infection caused by Blastomyces dermatitidis that is hyperendemic in Wisconsin. It commonly presents as a pulmonary infection and frequently disseminates to the skin. Studies evaluating the presentation and diagnosis of blastomycosis with skin as a presenting sign have not been thoroughly evaluated, and understanding the most accurate way to diagnose this infection is important for earlier therapeutic intervention.
METHODS
This is a retrospective chart review study of a single institution. Subjects were identified through a search of ICD-9 () and ICD-10 () codes for blastomycosis in the clinical record and pathology database. Patients were included if diagnosed with cutaneous blastomycosis infection or involvement of the skin from systemic infection from January 1, 2009, to June 1, 2021.
RESULTS
Twenty patients with a diagnosis of cutaneous involvement of blastomycosis were identified; 65% (n = 13) were male. Median age of diagnosis was 55.5 years. Fifty-five percent of patients were White, 35% were Black or African American. In addition to residence in an endemic area, 50% (n = 10) had exposure risk factors. Fifty percent of patients (n = 10) initially presented with a skin concerns; 65% (n = 13) had extracutaneous involvement. Diagnosis was made by histopathology alone in 55% (n = 11), culture plus histopathology in 35% (n = 7), and culture alone in 5% (n = 1) of cases.
CONCLUSIONS
Our study highlighted similarities to those previously performed. Half of the patients (n = 10) who had cutaneous involvement of blastomycosis did not demonstrate clinically significant pulmonary involvement. Histopathology and culture remain critical in diagnosing cutaneous blastomycosis.
Topics: Humans; Wisconsin; Blastomycosis; Male; Female; Retrospective Studies; Middle Aged; Adult; Aged; Risk Factors; Blastomyces
PubMed: 38718236
DOI: No ID Found -
Journal of Pathology Informatics Dec 2024Numerous machine learning (ML) models have been developed for breast cancer using various types of data. Successful external validation (EV) of ML models is important... (Review)
Review
Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer: A systematic review.
Numerous machine learning (ML) models have been developed for breast cancer using various types of data. Successful external validation (EV) of ML models is important evidence of their generalizability. The aim of this systematic review was to assess the performance of externally validated ML models based on histopathology images for diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer. A systematic search of MEDLINE, EMBASE, CINAHL, IEEE, MICCAI, and SPIE conferences was performed for studies published between January 2010 and February 2022. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was employed, and the results were narratively described. Of the 2011 non-duplicated citations, 8 journal articles and 2 conference proceedings met inclusion criteria. Three studies externally validated ML models for diagnosis, 4 for classification, 2 for prognosis, and 1 for both classification and prognosis. Most studies used Convolutional Neural Networks and one used logistic regression algorithms. For diagnostic/classification models, the most common performance metrics reported in the EV were accuracy and area under the curve, which were greater than 87% and 90%, respectively, using pathologists' annotations/diagnoses as ground truth. The hazard ratios in the EV of prognostic ML models were between 1.7 (95% CI, 1.2-2.6) and 1.8 (95% CI, 1.3-2.7) to predict distant disease-free survival; 1.91 (95% CI, 1.11-3.29) for recurrence, and between 0.09 (95% CI, 0.01-0.70) and 0.65 (95% CI, 0.43-0.98) for overall survival, using clinical data as ground truth. Despite EV being an important step before the clinical application of a ML model, it hasn't been performed routinely. The large variability in the training/validation datasets, methods, performance metrics, and reported information limited the comparison of the models and the analysis of their results. Increasing the availability of validation datasets and implementing standardized methods and reporting protocols may facilitate future analyses.
PubMed: 38089005
DOI: 10.1016/j.jpi.2023.100348 -
Computational Intelligence and... 2023Histopathological images are very effective for investigating the status of various biological structures and diagnosing diseases like cancer. In addition, digital...
Histopathological images are very effective for investigating the status of various biological structures and diagnosing diseases like cancer. In addition, digital histopathology increases diagnosis precision and provides better image quality and more detail for the pathologist with multiple viewing options and team annotations. As a result of the benefits above, faster treatment is available, increasing therapy success rates and patient recovery and survival chances. However, the present manual examination of these images is tedious and time-consuming for pathologists. Therefore, reliable automated techniques are needed to effectively classify normal and malignant cancer images. This paper applied a deep learning approach, namely, EfficientNet and its variants from B0 to B7. We used different image resolutions for each model, from 224 × 224 pixels to 600 × 600 pixels. We also applied transfer learning and parameter tuning techniques to improve the results and overcome the overfitting problem. We collected the dataset from the Lung and Colon Cancer Histopathological Image LC25000 image dataset. The dataset acquisition consists of 25,000 histopathology images of five classes (lung adenocarcinoma, lung squamous cell carcinoma, benign lung tissue, colon adenocarcinoma, and colon benign tissue). Then, we performed preprocessing on the dataset to remove the noisy images and bring them into a standard format. The model's performance was evaluated in terms of classification accuracy and loss. We have achieved good accuracy results for all variants; however, the results of EfficientNetB2 stand excellent, with an accuracy of 97% for 260 × 260 pixels resolution images.
Topics: Humans; Adenocarcinoma; Algorithms; Colonic Neoplasms; Lung Neoplasms; Lung
PubMed: 37876944
DOI: 10.1155/2023/7282944 -
Brain Sciences Mar 2024Agency is central to remote actions, and it may enhance skills learning due to a partial overlap between brain structures and networks, the promotion of confidence... (Review)
Review
Agency is central to remote actions, and it may enhance skills learning due to a partial overlap between brain structures and networks, the promotion of confidence towards a telemanipulator, and the feeling of congruence of the motor choice to the motor plan. We systematically reviewed studies aiming to verify the role of agency in improving learning. Fifteen studies were selected from MEDLINE and Scopus. When a mismatch is introduced between observed and performed actions, the decrease in agency and learning is proportional to the intensity of the mismatch, which is due to greater interference with the motor programming. Thanks to multisensory integration, agency and learning benefit both from sensory and performance feedback and from the timing of feedback based on control at the goal level or the perceptual-motor level. This work constitutes a bedrock for professional teleoperation settings (e.g., robotic surgery), with particular reference to the role of agency in performing complex tasks with remote control.
PubMed: 38672002
DOI: 10.3390/brainsci14040350 -
Canadian Journal of Veterinary Research... Oct 2023A 6-month-old, intact female, French bulldog was presented to the Emergency Department for evaluation of vomiting and diarrhea over the preceding week which had not...
A 6-month-old, intact female, French bulldog was presented to the Emergency Department for evaluation of vomiting and diarrhea over the preceding week which had not responded to supportive medical therapy. Imaging studies identified an incarcerated para-esophageal hernia with peritoneal effusion and gas consistent with gastrointestinal perforation. Following stabilization, the dog underwent an exploratory laparotomy which confirmed an incarcerated hiatal hernia and gastric perforation. A gastrectomy was performed to repair the defect, and to prevent recurrence both a herniorrhaphy and esophagopexy were performed. Post-operative care required treatment for septic shock including vasopressor and hydrocortisone infusions and plasma transfusions for colloidal support. The patient was successfully discharged 4 days after surgery. The histopathology results identified spiral bacteria consistent with spp. which was subsequently treated with oral antibiotics and a proton pump inhibitor. The dog has had no further gastrointestinal signs in the 90 days since surgery. Gastric perforation and peritonitis can occur secondary to an incarcerated esophageal hiatal hernia, and if treated promptly can result in a successful outcome. This case demonstrates a novel etiology of gastric perforation which may be associated with brachycephalic breeds.
Topics: Humans; Dogs; Female; Animals; Hernia, Hiatal; Laparoscopy; Dog Diseases
PubMed: 37790265
DOI: No ID Found -
F1000Research 2023Introduction Orbital lipoma is an extremely rare tumor, representing less than 1% of all orbital tumors. We review the literature and describe the presentation, the...
Introduction Orbital lipoma is an extremely rare tumor, representing less than 1% of all orbital tumors. We review the literature and describe the presentation, the differential diagnosis and the management of this tumor. Case report We report the case of a 63-year-old patient who was referred for a diplopia with recent hemi-cranial headache. Physical examination showed no exophthalmos nor decrease in visual acuity. The patient complained of diplopia on elevation and oculomotricity examination showed limited elevation of the right eye. The Hess Lancaster test was in favor of a limited course of the right inferior rectus muscle. Magnetic resonance imaging revealed a fusiform tissue process in the right inferior rectus muscle with a fatty signal. A complete excision of the tumor was performed by a trasncunjonctival approach. Cytopathological examination was consistent with a pleomorphic lipoma. The postoperative period was uneventful. The definitive histopathologic diagnosis was a lipoma. The postoperative Magnetic resonance imaging showed the complete disappearance of the lesion. With 3 years of follow up, there is no sign of recurrence or ocular motility trouble. Lipomas are rare tumors in the orbit. The clinic is variable depending on the size and the site. The clinical diagnosis is difficult to make. Only histology allows the final diagnosis.
Topics: Humans; Middle Aged; Orbital Neoplasms; Magnetic Resonance Imaging; Lipoma; Male
PubMed: 38726301
DOI: 10.12688/f1000research.130056.2