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Pulmonology 2021Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive fibrotic interstitial lung disease of unknown cause, which predominantly manifests in older males. IPF...
INTRODUCTION AND OBJECTIVES
Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive fibrotic interstitial lung disease of unknown cause, which predominantly manifests in older males. IPF diagnosis is a complex, multi-step process and delay in diagnosis cause a negative impact on patient survival. Additionally, a multidisciplinary team of pulmonologists, radiologists and pathologists is necessary for an accurate IPF diagnosis. The present study aims to assess how diagnosis and treatment of IPF are followed in Portugal, as well as the knowledge and implementation of therapeutic guidelines adopted by the Portuguese Society of Pulmonology.
MATERIALS AND METHODS
Seventy-eight practicing pulmonologists were enrolled (May-August 2019) in a survey developed by IPF expert pulmonologists comprised of one round of 31 questions structured in three parts. The first part was related to participant professional profile, the second part assessed participant level of knowledge and practice agreement with national consensus and international guidelines for IPF as well as their access to radiology and pathology for IPF diagnosis, and the third part was a self-evaluation of the guidelines adherence for diagnosis and treatment in their daily practice.
RESULTS
Participants represented a wide spectrum of pulmonologists from 14 districts of Portugal and autonomous regions of Azores and Madeira. The majority were female (65%), with 5-19 years of experience (71%) and working in a public clinical center (83%). Importantly, the majority of pulmonologists follow their IPF patients (n=45) themselves, while 26% referred IPF patients to ILD experts in the same hospital and 22% to another center. Almost all pulmonologists (98%) agreed or absolutely agreed that multidisciplinary discussion is recommended to accurately diagnose IPF. No pulmonologists considered pulmonary biopsy as absolutely required to establish an IPF diagnosis. However, 87% agreed or absolutely agree with considering biopsy in a possible/probable UIP context. If a biopsy is necessary, 96% of pulmonologists absolutely agree or agree with considering criobiopsy as an option for IPF diagnosis. Regarding IPF treatment, 98% absolutely agreed or agreed that antifibrotic therapy should be started once the IPF diagnosis is established. Finally, 76% stated that 6 months is the recommended time for follow-up visit in IPF patients.
CONCLUSIONS
Portuguese pulmonologists understand and agree with national consensus and international guidelines for IPF treatment but their implementation in Portugal is heterogeneous.
Topics: Adult; Awareness; Biopsy; Consensus; Cross-Sectional Studies; Female; Follow-Up Studies; Guideline Adherence; Humans; Idiopathic Pulmonary Fibrosis; Interdisciplinary Communication; Lung; Male; Middle Aged; Pathologists; Portugal; Practice Guidelines as Topic; Pulmonologists; Radiologists
PubMed: 32561352
DOI: 10.1016/j.pulmoe.2020.05.017 -
Cancer Cytopathology Jan 2023Medical errors are a major source of harm to patients. Regulatory bodies mandate and patient safety experts advocate the disclosure of medical errors to patients to...
Medical errors are a major source of harm to patients. Regulatory bodies mandate and patient safety experts advocate the disclosure of medical errors to patients to promote transparency and to create accountability for improving health care processes. Although pathologists regularly report errors-either to pathology or clinical colleagues or via internal safety reporting systems-few pathologists directly disclose those errors to patients. Yet many pathologists are interested in participating in the direct disclosure of medical errors to patients and may even be mandated to do so. When surveyed on why they do not directly disclose errors to patients, pathologists commonly cite a lack of confidence and a lack of training. Another barrier cited is the lack of a preexisting relationship between the pathologist and the patient. With respect to this last barrier, cytopathologists have a distinct advantage over surgical or clinical pathologists, as many cytopathologists regularly interact with and develop a rapport with patients when they are performing fine-needle aspiration (FNA) procedures. To improve the safety culture in pathology, direct error disclosure practices must be developed, supported, and strengthened. It is critical for cytopathologists to be comfortable with disclosing errors to patients. Being comfortable with disclosing an error, however, requires training, practice, and advance reflection. Using a practical, case-based format centered around FNA examples, this article addresses how to disclose a medical error to a patient. It provides a framework, heuristic principles, and structured conversation systems and talking points to guide the inexperienced pathologist to find his or her voice in a challenging disclosure conversation.
Topics: Humans; Male; Female; Truth Disclosure; Communication; Diagnostic Errors; Medical Errors; Pathologists
PubMed: 35904882
DOI: 10.1002/cncy.22627 -
Diagnostic Pathology Mar 2021The role of Artificial intelligence (AI) which is defined as the ability of computers to perform tasks that normally require human intelligence is constantly expanding.... (Review)
Review
Artificial intelligence (AI) in medicine, current applications and future role with special emphasis on its potential and promise in pathology: present and future impact, obstacles including costs and acceptance among pathologists, practical and philosophical considerations. A comprehensive review.
BACKGROUND
The role of Artificial intelligence (AI) which is defined as the ability of computers to perform tasks that normally require human intelligence is constantly expanding. Medicine was slow to embrace AI. However, the role of AI in medicine is rapidly expanding and promises to revolutionize patient care in the coming years. In addition, it has the ability to democratize high level medical care and make it accessible to all parts of the world.
MAIN TEXT
Among specialties of medicine, some like radiology were relatively quick to adopt AI whereas others especially pathology (and surgical pathology in particular) are only just beginning to utilize AI. AI promises to play a major role in accurate diagnosis, prognosis and treatment of cancers. In this paper, the general principles of AI are defined first followed by a detailed discussion of its current role in medicine. In the second half of this comprehensive review, the current and future role of AI in surgical pathology is discussed in detail including an account of the practical difficulties involved and the fear of pathologists of being replaced by computer algorithms. A number of recent studies which demonstrate the usefulness of AI in the practice of surgical pathology are highlighted.
CONCLUSION
AI has the potential to transform the practice of surgical pathology by ensuring rapid and accurate results and enabling pathologists to focus on higher level diagnostic and consultative tasks such as integrating molecular, morphologic and clinical information to make accurate diagnosis in difficult cases, determine prognosis objectively and in this way contribute to personalized care.
Topics: Artificial Intelligence; Attitude of Health Personnel; Attitude to Computers; Cost-Benefit Analysis; Health Care Costs; Health Knowledge, Attitudes, Practice; Humans; Image Interpretation, Computer-Assisted; Microscopy; Pathologists; Pathology; Practice Patterns, Physicians'; Predictive Value of Tests; Reproducibility of Results
PubMed: 33731170
DOI: 10.1186/s13000-021-01085-4 -
JAMA Network Open Mar 2023Identifying new prognostic features in colon cancer has the potential to refine histopathologic review and inform patient care. Although prognostic artificial...
IMPORTANCE
Identifying new prognostic features in colon cancer has the potential to refine histopathologic review and inform patient care. Although prognostic artificial intelligence systems have recently demonstrated significant risk stratification for several cancer types, studies have not yet shown that the machine learning-derived features associated with these prognostic artificial intelligence systems are both interpretable and usable by pathologists.
OBJECTIVE
To evaluate whether pathologist scoring of a histopathologic feature previously identified by machine learning is associated with survival among patients with colon cancer.
DESIGN, SETTING, AND PARTICIPANTS
This prognostic study used deidentified, archived colorectal cancer cases from January 2013 to December 2015 from the University of Milano-Bicocca. All available histologic slides from 258 consecutive colon adenocarcinoma cases were reviewed from December 2021 to February 2022 by 2 pathologists, who conducted semiquantitative scoring for tumor adipose feature (TAF), which was previously identified via a prognostic deep learning model developed with an independent colorectal cancer cohort.
MAIN OUTCOMES AND MEASURES
Prognostic value of TAF for overall survival and disease-specific survival as measured by univariable and multivariable regression analyses. Interpathologist agreement in TAF scoring was also evaluated.
RESULTS
A total of 258 colon adenocarcinoma histopathologic cases from 258 patients (138 men [53%]; median age, 67 years [IQR, 65-81 years]) with stage II (n = 119) or stage III (n = 139) cancer were included. Tumor adipose feature was identified in 120 cases (widespread in 63 cases, multifocal in 31, and unifocal in 26). For overall survival analysis after adjustment for tumor stage, TAF was independently prognostic in 2 ways: TAF as a binary feature (presence vs absence: hazard ratio [HR] for presence of TAF, 1.55 [95% CI, 1.07-2.25]; P = .02) and TAF as a semiquantitative categorical feature (HR for widespread TAF, 1.87 [95% CI, 1.23-2.85]; P = .004). Interpathologist agreement for widespread TAF vs lower categories (absent, unifocal, or multifocal) was 90%, corresponding to a κ metric at this threshold of 0.69 (95% CI, 0.58-0.80).
CONCLUSIONS AND RELEVANCE
In this prognostic study, pathologists were able to learn and reproducibly score for TAF, providing significant risk stratification on this independent data set. Although additional work is warranted to understand the biological significance of this feature and to establish broadly reproducible TAF scoring, this work represents the first validation to date of human expert learning from machine learning in pathology. Specifically, this validation demonstrates that a computationally identified histologic feature can represent a human-identifiable, prognostic feature with the potential for integration into pathology practice.
Topics: Male; Humans; Aged; Colonic Neoplasms; Pathologists; Artificial Intelligence; Adenocarcinoma; Machine Learning; Risk Assessment
PubMed: 36917112
DOI: 10.1001/jamanetworkopen.2022.54891 -
Gastroenterology Apr 2021
Topics: Colitis, Ulcerative; Colonoscopy; Humans; Pathologists
PubMed: 33508287
DOI: 10.1053/j.gastro.2021.01.213 -
Modern Pathology : An Official Journal... May 2023The assessment of the expression of programmed cell death ligand-1 (PD-L1) using immunohistochemistry (IHC) has been controversial since its introduction. The methods of...
Multi-Institutional Study of Pathologist Reading of the Programmed Cell Death Ligand-1 Combined Positive Score Immunohistochemistry Assay for Gastric or Gastroesophageal Junction Cancer.
The assessment of the expression of programmed cell death ligand-1 (PD-L1) using immunohistochemistry (IHC) has been controversial since its introduction. The methods of assessment and the range of assays and platforms contribute to confusion. Perhaps the most challenging aspect of PD-L1 IHC is the combined positive score (CPS) method of interpretation of IHC results. Although the CPS method is prescribed for more indications than any other PD-L1 scoring system, its reproducibility has never been rigorously assessed. In this study, we collected a series of 108 gastric or gastroesophageal junction cancer cases, stained them using the Food and Drug Administration-approved 22C3 assay, scanned them, and then circulated them to 14 pathologists at 13 institutions for the assessment of interpretative concordance for the CPS system. We found that higher cut points (10 or 20) performed better than a CPS of <1 or >1. We used the Observers Needed to Evaluate Subjective Tests algorithm to assess how the CPS system might perform in the real-world setting and found that the cut points of <1 or >1 showed an overall percent agreement of only 30% among the pathologist raters, with a plateau occurring at 8 raters. The raters performed better at higher cut points. However, the best cut point of <20 versus that of >20 was still disappointing, with a plateau at an overall percent agreement of 70% (at 7 raters). Although there is no ground truth for CPS, we compared the score with quantitative messenger RNA measurement and showed no relationship between the score (at any cut point) and messenger RNA amount. In summary, we showed that CPS shows high subjective variability among pathologist readers and is likely to perform poorly in the real-world setting. This system may be the root cause of the poor specificity and relatively low predictive value of IHC companion diagnostic tests for PD-1 axis therapies that use the CPS system.
Topics: Humans; Apoptosis; B7-H1 Antigen; Biomarkers, Tumor; Esophageal Neoplasms; Esophagogastric Junction; Immunohistochemistry; Ligands; Pathologists; Reproducibility of Results; Stomach Neoplasms
PubMed: 36889057
DOI: 10.1016/j.modpat.2023.100128 -
Pathologica Jun 2023The digital revolution in pathology represents an invaluable resource fto optimise costs, reduce the risk of error and improve patient care, even though it is still... (Review)
Review
OBJECTIVE
The digital revolution in pathology represents an invaluable resource fto optimise costs, reduce the risk of error and improve patient care, even though it is still adopted in a minority of laboratories. Barriers include concerns about initial costs, lack of confidence in using whole slide images for primary diagnosis, and lack of guidance on transition. To address these challenges and develop a programme to facilitate the introduction of digital pathology (DP) in Italian pathology departments, a panel discussion was set up to identify the key points to be considered.
METHODS
On 21 July 2022, an initial conference call was held on Zoom to identify the main issues to be discussed during the face-to-face meeting. The final summit was divided into four different sessions: (I) the definition of DP, (II) practical applications of DP, (III) the use of AI in DP, (IV) DP and education.
RESULTS
Essential requirements for the implementation of DP are a fully tracked and automated workflow, selection of the appropriate scanner based on the specific needs of each department, and a strong commitment combined with coordinated teamwork (pathologists, technicians, biologists, IT service and industries). This could reduce human error, leading to the application of AI tools for diagnosis, prognosis and prediction. Open challenges are the lack of specific regulations for virtual slide storage and the optimal storage solution for large volumes of slides.
CONCLUSION
Teamwork is key to DP transition, including close collaboration with industry. This will ease the transition and help bridge the gap that currently exists between many labs and full digitisation. The ultimate goal is to improve patient care.
Topics: Humans; Health Personnel; Pathologists
PubMed: 37387439
DOI: 10.32074/1591-951X-868 -
Journal of Deaf Studies and Deaf... Sep 2022Increasing cultural and linguistic diversity among children and families brings new challenges for early intervention professionals. The purpose of this study was to...
Increasing cultural and linguistic diversity among children and families brings new challenges for early intervention professionals. The purpose of this study was to identify the specific roles and needs of speech-language pathologists (SLPs) who practice in early intervention settings with culturally and linguistically diverse families of d/Deaf multilingual learners (DMLs). Thirteen SLPs completed an online survey about their practices and needs. Interviews were conducted with five parents of DMLs. Results showed that SLPs have lower self-satisfaction with families of DMLs compared to mainstream families. Parents were highly satisfied with the support they received. Both groups of participants reported a need for specific tools or adaptations, especially if there was no shared language. Thematic analysis identified three themes: communication and partnership, professional resources for responding to diversity, and diversity of parental profiles. This article provides an insight into the perspectives of both professionals and culturally and linguistically diverse parents, and identifies specific aspects of early intervention services with parents of DMLs: developing partnership in the context of cultural and/or linguistic differences, discussing topics related to multilingualism, and providing highly adaptable family-centered services.
Topics: Child; Communication Disorders; Cultural Diversity; Humans; Multilingualism; Parents; Pathologists; Persons With Hearing Impairments; Speech; Speech-Language Pathology
PubMed: 35989645
DOI: 10.1093/deafed/enac024 -
Laboratory Investigation; a Journal of... Sep 2019The external validity of the scientific literature has recently come into question, popularly referred to as the "reproducibility crisis." It is now generally...
The external validity of the scientific literature has recently come into question, popularly referred to as the "reproducibility crisis." It is now generally acknowledged that too many false positive or non-reproducible results are being published throughout the biomedical and social science literature due to misaligned incentives and poor methodology. Pathology is likely no exception to this problem, and may be especially prone to false positives due to common observational methodologies used in our research. Spurious findings in pathology contribute inefficiency to the scientific literature and detrimentally influence patient care. In particular, false positives in pathology affect patients through biomarker development, prognostic classification, and cancer overdiagnosis. We discuss possible sources of non-reproducible pathology studies and describe practical ways our field can improve research habits, especially among trainees.
Topics: False Positive Reactions; Humans; Medical Overuse; Pathologists; Pathology; Reproducibility of Results
PubMed: 31019290
DOI: 10.1038/s41374-019-0257-2 -
Journal of the Formosan Medical... Dec 2022The accuracy of histopathology diagnosis largely depends on the pathologist's experience. It usually takes over 10 years to cultivate a senior pathologist, and small...
BACKGROUND
The accuracy of histopathology diagnosis largely depends on the pathologist's experience. It usually takes over 10 years to cultivate a senior pathologist, and small numbers of them lead to a high workload for those available. Meanwhile, inconsistent diagnostic results may arise among different pathologists, especially in complex cases, because diagnosis based on morphology is subjective. Computerized analysis based on deep learning has shown potential benefits as a diagnostic strategy.
METHODS
This research aims to automatically determine the location of gastric cancer (GC) in the images of GC slices through artificial intelligence. We use image data from a regional teaching hospital in Taiwan for training. We collect images of patients diagnosed with GC from January 1, 2019 to December 31, 2020. In this study, scanned images are used to dissect 13,600 images from 50 different patients with GC sections whose GC sections are stained with hematoxylin and eosin (H&E stained) through a whole slide scanner, the scanned images from 50 different GC slice patients are divided into 80% training combinations, 2200 images of 40 patients are trained. The remaining 20%, totaling 10 people, are validated from a test set of 550 images.
RESULTS
The validation results show that 91% of the correct rates are interpreted as GC images through deep learning. The sensitivity, specificity, PPV, and NPV were 84.9%, 94%, 87.7%, and 92.5%, respectively. After creating a 3D model through the grayscale value, the position of the GC is completely marked by the 3D model. The purpose of this research is to use artificial intelligence (AI) to determine the location of the GC in the image of GC slice.
CONCLUSION
In patients undergoing pancreatectomy for pancreatic cancer, intraoperative infusion of lidocaine did not improve overall or disease-free survival. Reduced formation of circulating NETs was absent in pancreatic tumour tissue.
CONCLUSION
For AI to assist pathologists in daily practice, to help a pathologist making a definite diagnosis is not the prime purpose at present time. The benefits could come from cancer screening and double-check quality control in a heavy workload which could distract the attention of pathologist during the time constraint examination process. We propose a two-steps method to identify cancerous areas in endoscopic gastric biopsy slices via deep learning. Then a 3D model is used to further mark all the positions of GC in the picture, and the model overcomes the problem that deep learning can't catch all GC.
Topics: Humans; Deep Learning; Artificial Intelligence; Stomach Neoplasms; Pathologists; Biopsy
PubMed: 35667953
DOI: 10.1016/j.jfma.2022.05.004