Cancer Pathology Reports Research Articles

Optimal vocabulary selection approaches for privacy-preserving deep NLP model training for information extraction and cancer epidemiology.

With the use of artificial intelligence and machine learning techniques for biomedical informatics, security and privacy concerns over the data and subject identities have also become an important issue and essential research topic. Without intentional safeguards, machine learning models may find patterns and features to improve task performance that are associated with private personal information. The privacy vulnerability of deep learning models for information extraction from medical textural contents needs to be quantified since the models are exposed to private health information and personally identifiable information. The objective of the study is to quantify the privacy vulnerability of the deep learning models for natural language processing and explore a proper way of securing patients' information to mitigate confidentiality breaches. The target model is the multitask convolutional neural network for information extraction from cancer pathology reports, where the data for training the model are from multiple state population-based cancer registries. This study proposes the following schemes to collect vocabularies from the cancer pathology reports; (a) words appearing in multiple registries, and (b)words that have higher mutual information. We performed membership inference attacks on the models in high-performance computing environments. The comparison outcomes suggest that the proposed vocabulary selection methods resulted in lower privacy vulnerability while maintaining the same level of clinical task performance.

Establishing Synoptic Cancer Pathology Reporting in Low- and Middle-Income Countries: A Nicaraguan Experience.

PURPOSECancer pathology reports have traditionally been narrative with limited reliable information. In high-income countries, synoptic format reporting has gradually been introduced to improve patient care and cancer registry input. We posited that introduction of synoptic style reporting was possible in Nicaragua and could serve as a model for other low- and middle-income countries.MATERIALS AND METHODSThe Department of Pathology, Hospital Escuela Oscar Danilo Rosales Argüello (HEODRA), León, Nicaragua, decided to introduce and implement synoptic reporting for all cancer cases beginning in 2018. All 10,012 histopathologic case reports issued by the department from January 1, 2018, through June 30, 2020, were reviewed. After excluding benign lesions, recurrent or metastatic tumors, endometrial biopsies or curettage, and primary cytologic specimens, 724 cases met the criteria for synoptic style reporting. The narrative format, previously used for all cases, was intentionally abandoned.RESULTSOf the 10,012 reports reviewed at HEODRA during the study period, synoptic-style reporting was used for all 724 cancer cases that met criteria for inclusion in the study. In addition, all elements were listed in the required order. Narrative format of reporting was not used for any of the reports.CONCLUSIONOur experience in Nicaragua has shown that establishing synoptic-style cancer pathology reporting is achievable in a low- or middle-income country. Just as in high-income countries, a dedicated collaborative step-by-step conversion to synoptic reporting can lead to improvement in cancer patient care and quality of data for population-based registries.

Simplified and more sensitive criteria for identifying individuals with pathogenic CDH1 variants

BackgroundHereditary diffuse gastric cancer (HDGC) is an autosomal-dominant syndrome most often caused by pathogenic variants in CDH1. The International Gastric Cancer Linkage Consortium (IGCLC) recently updated its criteria for genetic...

Automatic Classification of Cancer Pathology Reports: A Systematic Review

Pathology reports primarily consist of unstructured free text and thus the clinical information contained in the reports is not trivial to access or query. Multiple natural language processing (NLP) techniques have been proposed to automate the coding of pathology reports via text classification. In this systematic review, we follow the guidelines proposed by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA; Page et al., 2020: BMJ.) to identify the NLP systems for classifying pathology reports published between the years of 2010 and 2021. Based on our search criteria, a total of 3445 records were retrieved, and 25 articles met the final review criteria. We benchmarked the systems based on methodology, complexity of the prediction task and core types of NLP models: i) Rule-based and Intelligent systems, ii) statistical machine learning, and iii) deep learning. While certain tasks are well addressed by these models, many others have limitations and remain as open challenges, such as, extraction of many cancer characteristics (size, shape, type of cancer, others) from pathology reports. We investigated the final set of papers (25) and addressed their potential as well as their limitations. We hope that this systematic review helps researchers prioritize the development of innovated approaches to tackle the current limitations and help the advancement of cancer research.

Class imbalance in out-of-distribution datasets: Improving the robustness of the TextCNN for the classification of rare cancer types

In the last decade, the widespread adoption of electronic health record documentation has created huge opportunities for information mining. Natural language processing (NLP) techniques using machine and deep learning are becoming increasingly widespread for information extraction tasks from unstructured clinical notes. Disparities in performance when deploying machine learning models in the real world have recently received considerable attention. In the clinical NLP domain, the robustness of convolutional neural networks (CNNs) for classifying cancer pathology reports under natural distribution shifts remains understudied. In this research, we aim to quantify and improve the performance of the CNN for text classification on out-of-distribution (OOD) datasets resulting from the natural evolution of clinical text in pathology reports. We identified class imbalance due to different prevalence of cancer types as one of the sources of performance drop and analyzed the impact of previous methods for addressing class imbalance when deploying models in real-world domains. Our results show that our novel class-specialized ensemble technique outperforms other methods for the classification of rare cancer types in terms of macro F1 scores. We also found that traditional ensemble methods perform better in top classes, leading to higher micro F1 scores. Based on our findings, we formulate a series of recommendations for other ML practitioners on how to build robust models with extremely imbalanced datasets in biomedical NLP applications.

A Text Mining Approach in the Classification of Free-Text Cancer Pathology Reports from the South African National Health Laboratory Services

A cancer pathology report is a valuable medical document that provides information for clinical management of the patient and evaluation of health care. However, there are variations in the quality of reporting in free-text style formats, ranging from comprehensive to incomplete reporting. Moreover, the increasing incidence of cancer has generated a high throughput of pathology reports. Hence, manual extraction and classification of information from these reports can be intrinsically complex and resource-intensive. This study aimed to (i) evaluate the quality of over 80,000 breast, colorectal, and prostate cancer free-text pathology reports and (ii) assess the effectiveness of random forest (RF) and variants of support vector machine (SVM) in the classification of reports into benign and malignant classes. The study approach comprises data preprocessing, visualisation, feature selections, text classification, and evaluation of performance metrics. The performance of the classifiers was evaluated across various feature sizes, which were jointly selected by four filter feature selection methods. The feature selection methods identified established clinical terms, which are synonymous with each of the three cancers. Uni-gram tokenisation using the classifiers showed that the predictive power of RF model was consistent across various feature sizes, with overall F-scores of 95.2%, 94.0%, and 95.3% for breast, colorectal, and prostate cancer classification, respectively. The radial SVM achieved better classification performance compared with its linear variant for most of the feature sizes. The classifiers also achieved high precision, recall, and accuracy. This study supports a nationally agreed standard in pathology reporting and the use of text mining for encoding, classifying, and production of high-quality information abstractions for cancer prognosis and research.

Improving Natural Language Information Extraction from Cancer Pathology Reports using Transfer Learning and Zero-Shot String Similarity

Improving Natural Language Information Extraction from Cancer Pathology Reports using Transfer Learning and Zero-Shot String Similarity

Supervised line attention for tumor attribute classification from pathology reports: Higher performance with less data

ObjectiveWe aim to build an accurate machine learning-based system for classifying tumor attributes from cancer pathology reports in the presence of a small amount of annotated data, motivated by the expensive and time-consuming nature of pathology report annotation. An enriched labeling scheme that includes the location of relevant information along with the final label is used along with a corresponding hierarchical method for classifying reports that leverages these enriched annotations. Materials and methodsOur data consists of 250 colon cancer and 250 kidney cancer pathology reports from 2002 to 2019 at the University of California, San Francisco. For each report, we classify attributes such as procedure performed, tumor grade, and tumor site. For each attribute and document, an annotator trained by an oncologist labeled both the value of that attribute as well as the specific lines in the document that indicated the value. We develop a model that uses these enriched annotations that first predicts the relevant lines of the document, then predicts the final value given the predicted lines. We compare our model to multiple state-of-the-art methods for classifying tumor attributes from pathology reports. ResultsOur results show that across colon and kidney cancers and varying training set sizes, our hierarchical method consistently outperforms state-of-the-art methods. Furthermore, performance comparable to these methods can be achieved with approximately half the amount of labeled data. ConclusionDocument annotations that are enriched with location information are shown to greatly increase the sample efficiency of machine learning methods for classifying attributes of pathology reports.

Improving natural language information extraction from cancer pathology reports using transfer learning and zero-shot string similarity.

ObjectiveWe develop natural language processing (NLP) methods capable of accurately classifying tumor attributes from pathology reports given minimal labeled examples. Our hierarchical cancer to cancer transfer (HCTC) and zero-shot string similarity (ZSS) methods are designed to exploit shared information between cancers and auxiliary class features, respectively, to boost performance using enriched annotations which give both location-based information and document level labels for each pathology report.Materials and MethodsOur data consists of 250 pathology reports each for kidney, colon, and lung cancer from 2002 to 2019 from a single institution (UCSF). For each report, we classified 5 attributes: procedure, tumor location, histology, grade, and presence of lymphovascular invasion. We develop novel NLP techniques involving transfer learning and string similarity trained on enriched annotations. We compare HCTC and ZSS methods to the state-of-the-art including conventional machine learning methods as well as deep learning methods.ResultsFor our HCTC method, we see an improvement of up to 0.1 micro-F1 score and 0.04 macro-F1 averaged across cancer and applicable attributes. For our ZSS method, we see an improvement of up to 0.26 micro-F1 and 0.23 macro-F1 averaged across cancer and applicable attributes. These comparisons are made after adjusting training data sizes to correct for the 20% increase in annotation time for enriched annotations compared to ordinary annotations.ConclusionsMethods based on transfer learning across cancers and augmenting information methods with string similarity priors can significantly reduce the amount of labeled data needed for accurate information extraction from pathology reports.

Dataset for Pathology Reporting of Colorectal Cancer: Recommendations From the International Collaboration on Cancer Reporting (ICCR).

Objective:The aim of this study to describe a new international dataset for pathology reporting of colorectal cancer surgical specimens, produced under the auspices of the International Collaboration on Cancer Reporting (ICCR).Background:Quality of pathology reporting and mutual understanding between colorectal surgeon, pathologist and oncologist are vital to patient management. Some pathology parameters are prone to variable interpretation, resulting in differing positions adopted by existing national datasets.Methods:The ICCR, a global alliance of major pathology institutions with links to international cancer organizations, has developed and ratified a rigorous and efficient process for the development of evidence-based, structured datasets for pathology reporting of common cancers. Here we describe the production of a dataset for colorectal cancer resection specimens by a multidisciplinary panel of internationally recognized experts.Results:The agreed dataset comprises eighteen core (essential) and seven non-core (recommended) elements identified from a review of current evidence. Areas of contention are addressed, some highly relevant to surgical practice, with the aim of standardizing multidisciplinary discussion. The summation of all core elements is considered to be the minimum reporting standard for individual cases. Commentary is provided, explaining each element's clinical relevance, definitions to be applied where appropriate for the agreed list of value options and the rationale for considering the element as core or non-core.Conclusions:This first internationally agreed dataset for colorectal cancer pathology reporting promotes standardization of pathology reporting and enhanced clinicopathological communication. Widespread adoption will facilitate international comparisons, multinational clinical trials and help to improve the management of colorectal cancer globally.

Quality Assessment of Gallbladder Cancer Pathology Reports: A Dutch Nationwide Study.

Simple SummaryAppropriate reporting of pathological findings is required for optimal patient care and to perform high-quality research. The aim of our study was to assess the completeness of pathology reports of gallbladder cancer (GBC) in a large nation-wide patient cohort from the Netherlands. Results showed that reports were often incomplete; information on essential items that can predict prognosis were often missing. Whereas certain items were often missing in the report, they could be retrospectively detected in a large proportion of patients during pathology review. Our findings showed that significant improvements could be made in the reporting of gallbladder cancer in the Netherlands. To this end, the added value of standardized (synoptic) reporting should be explored, of which the beneficial effects have already been demonstrated in other tumor types.Adequate reporting of pathological findings is essential for optimal patient management and to perform high-quality research. The aim of this study was to assess the completeness of pathology reports of gallbladder cancer (GBC) at the nationwide level to assess guideline adherence and make recommendations for improvement. A retrospective population-based cohort of GBC patients diagnosed in the Netherlands from 2000 to 2019 was collected using data from the Dutch Cancer Registry and the nationwide network and registry of histology. Pathology reports were scored on the presence and content of essential and optional items according to the Dutch consensus-based guideline on biliary tract cancer. By histopathological review of cases, we compared findings with the conclusion of the corresponding pathology report. All pathology reports (n = 849) had a narrative, nonstructured format. Overall completeness was low. Information on key prognostic factors, such as tumor side (hepatic vs. serosal), status of cystic duct and liver surgical margins and venous and perineural invasion, was frequently lacking (80%, 23%, 59%, 74% and 74% missing, respectively). Whereas certain items were often missing from the report, they could be retrospectively detected in a substantial proportion of cases during pathology review (n = 738). In conclusion, significant improvements could be made in the reporting of GBC in the Netherlands. Synoptic reporting could greatly enhance the completeness of reports, as already demonstrated for tumor types.

Association of the COVID-19 pandemic with patterns of cancer services.

1514 Background: The COVID-19 pandemic led to delays in medical care in the United States. We examined changes in patterns of cancer diagnosis and surgical treatment in 2020 using real-time electronic pathology report data from population-based SEER cancer registries in Georgia and Louisiana. Methods: Bi-weekly numbers, distributions, and patterns of pathology reports were compared between January 1st and December 31st in 2020 and the same period in 2019 by age group and cancer site. Results: During 2020, there were 29,905 fewer pathology reports than in 2019, representing a 10.2% decline. Absolute declines were greatest among adults aged ≥50 years (N=23,065); percentage declines were greatest among children and young adults ≤18 years (38.3%). By cancer site, percentage declines were greatest for lung cancer (17.4%), followed by colorectal (12.0%), breast (9.0%) and prostate (5.8%) cancers. Biweekly reports were statistically significantly lower in 2020 than in 2019 from late March through the end of December in most biweekly periods. The nadir was the month of April 2020 – the number of reports was at least 40% lower than in April 2019. The number of reports in 2020 compared with 2019 also declined sharply in early November (26.8%) and late December (32.0%). Numbers of reports in 2020 never consistently exceeded those in 2019 after the first decline. Patterns were similar by cancer site, with variation in magnitude and duration of declines. Conclusions: Significant declines in cancer pathology reports from population-based registries during 2020 suggest substantial delays in screening, evaluation of signs and symptoms, diagnosis, and treatment services for cancers with effective screening tests as well as in cancer sites and age groups without effective screening tests as an indirect result of the COVID-19 pandemic. Ongoing evaluation will be critical for informing public health efforts to minimize any lasting adverse effects of the pandemic on cancer screening, diagnosis, treatment, and survival.[Table: see text]

Deep active learning for classifying cancer pathology reports

BackgroundAutomated text classification has many important applications in the clinical setting; however, obtaining labelled data for training machine learning and deep learning models is often difficult and expensive. Active learning techniques may mitigate this challenge by reducing the amount of labelled data required to effectively train a model. In this study, we analyze the effectiveness of 11 active learning algorithms on classifying subsite and histology from cancer pathology reports using a Convolutional Neural Network as the text classification model.ResultsWe compare the performance of each active learning strategy using two differently sized datasets and two different classification tasks. Our results show that on all tasks and dataset sizes, all active learning strategies except diversity-sampling strategies outperformed random sampling, i.e., no active learning. On our large dataset (15K initial labelled samples, adding 15K additional labelled samples each iteration of active learning), there was no clear winner between the different active learning strategies. On our small dataset (1K initial labelled samples, adding 1K additional labelled samples each iteration of active learning), marginal and ratio uncertainty sampling performed better than all other active learning techniques. We found that compared to random sampling, active learning strongly helps performance on rare classes by focusing on underrepresented classes.ConclusionsActive learning can save annotation cost by helping human annotators efficiently and intelligently select which samples to label. Our results show that a dataset constructed using effective active learning techniques requires less than half the amount of labelled data to achieve the same performance as a dataset constructed using random sampling.

Abstract PR-12: Towards verifying results from biomedical deep learning models using the UMLS: Cases of primary tumor site classification and cancer Named Entity Recognition

Abstract Background Advances in Machine learning (ML) for biomedical research have led to ground-breaking results that are evident in several healthcare settings. In the field of NLP, there are applications for text mining, named entity recognition, classification of pathology and radiology reports, and text generation. Despite these advancements, the main barrier towards the use of AI systems in a clinical setting is their lack of explainability and interpretability. Objective From the various avenues available through which to create transparent and explainable ML models, we investigated how a stable, accurate, and trusted biomedical standard, the Unified Medical Language System (UMLS) can be applied to retrospectively justify and explain the results of ML models. Methods We developed a novel architecture that places a UMLS-based system after the ML model, and this then acts as a verifier to confirm the accuracy, or lack thereof, of the results from an ML model, and goes on further to explain the results from the model. This architecture is intended to be model-agnostic, so we evaluated its effectiveness using two NLP tasks: classification and Named Entity Recognition (NER). For classification, the UMLS-based verifier was applied to the results from classifying the topographies in 1964 unstructured and anonymized breast cancer pathology reports by a Multi-Task Convolutional Neural Network (MT-CNN). For NER, the UMLS-based verifier was applied to the results of the HunFlair model on unstructured and anonymized breast, colon, and small intestine cancer pathology reports. Results For the classification evaluation, we found that an entity's National Cancer Institute term (NCIt) code can be used to obtain a topographical range for individual entities in a pathology report. We found, further, that, whilst there are entities whose topographical range contributes positively towards a report's overall topography classification, there are also entities that contribute negatively, and that the number of these negative-contributing entities is inversely proportional to the confidence value from the ML model. For the NER evaluation, we found that the UMLS-based verifier is able to both confirm accurate model annotations, and group together the different kinds of inaccuracies found. Additionally, the grouping of an incorrectly tagged entity was found to be correlated with lower confidence values from the model. Conclusion The architecture we propose retrospectively verifies and explains the results of ML models, thus providing a level of interpretability to a model’s outputs. Our use of an industry standard healthcare knowledge repository, the UMLS, is an important contribution towards trusting the results of AI systems in healthcare. Citation Format: Joan Byamugisha, Waheeda Saib, Theodore Gaelejwe, Asad Jeewa, Maletsabisa Molapo. Towards verifying results from biomedical deep learning models using the UMLS: Cases of primary tumor site classification and cancer Named Entity Recognition [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PR-12.

Limitations of Transformers on Clinical Text Classification.

Bidirectional Encoder Representations from Transformers (BERT) and BERT-based approaches are the current state-of-the-art in many natural language processing (NLP) tasks; however, their application to document classification on long clinical texts is limited. In this work, we introduce four methods to scale BERT, which by default can only handle input sequences up to approximately 400 words long, to perform document classification on clinical texts several thousand words long. We compare these methods against two much simpler architectures - a word-level convolutional neural network and a hierarchical self-attention network - and show that BERT often cannot beat these simpler baselines when classifying MIMIC-III discharge summaries and SEER cancer pathology reports. In our analysis, we show that two key components of BERT - pretraining and WordPiece tokenization - may actually be inhibiting BERT's performance on clinical text classification tasks where the input document is several thousand words long and where correctly identifying labels may depend more on identifying a few key words or phrases rather than understanding the contextual meaning of sequences of text.

A review of breast cancer pathology reports in Nigeria

BackgroundDiagnosis and treatment of cancer rely heavily on imaging, histopathology and molecular information. Incomplete or missing tumour information can hinder the delivery of high-quality care in oncology practice, especially in resource-limited countries. To evaluate the completeness of histopathology reporting in a real-world setting and identify areas for future cancer care delivery research efforts, we retrospectively analysed reports from patients diagnosed with breast cancer who received care at a high-volume oncology department at a hospital in Lagos, Nigeria.MethodsDemographic, institutional and histopathology characteristics were retrospectively obtained from 1,001 patient records from 2007 to 2016. Completeness was defined as reporting five tumour features (tumour histology, tumour grade, laterality, oestrogen receptor (ER) or progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2)) for biopsy specimens and seven tumour features (tumour size, tumour histology, tumour grade, laterality, ER/PR, HER2 and lymph node involvement) for surgical specimens.ResultsThe mean age of patients was 48.6 ± 11.7 years with a predominantly female population (99.3%). A majority of pathologic reports were produced after 2011, and two-thirds of the reports originated from centres or labs within Lagos, Nigeria (67.7%). Most reports documented primary site (98.0%) and specimen type (85.0%) while other characteristics were less often recorded. This led to substantial variation in reporting between biopsy (13.4%) and surgical (6.1%) specimens for an overall low pathology report completeness <10%.ConclusionThe majority of patient records analysed lacked complete documentation of breast cancer histopathological characteristics commonly used in oncology practice. Our study highlights a need to identify and address the contributing factors for incomplete histopathological reporting in Nigeria and will guide future clinical programmatic developments.

Cohort selection for construction of a clinical natural language processing corpus

In Electronic Health Record (EHR) systems, key patient information is often captured in the form of unstructured clinical notes. The information from these notes can be extracted using Clinical Natural Language Processing (NLP). Training corpus is a key factor in development of efficient clinical NLP models. Clinical NLP corpus construction is complex and multifaceted. There are several challenges in corpus construction, but one challenge often not researched well is cohort selection aspect. In this study, we present methods employed and challenges encountered in cohort selection for construction of a clinical NLP corpus. In specific we present our methods in selection of cancer pathology reports to construct a corpus for automatic deidentification. 2100 pathology reports were extracted from 1833 (518 male and 1313 female) cancer patients using Health Level-7 (HL7) message standard. In terms of the age group distribution, the age group 60–70 years was highest with 872 patients. Our findings suggest deciphering the segment information from HL7 messages that are collected from different hospitals is a challenging task. The quality of HL7 messages also varied significantly with inconsistent tags making it difficult to identify reports that meet criteria set a priori. One of key lessons learned is linking the HL7 reports data with additional EMR data such as admissions, would help in identifying high quality reports and resolve duplicates. Also, our findings suggest that, in general the EHR data quality is poor with varying clinical coding and metadata standards between different hospitals. It is vital to identify and address these challenges for development of a high-quality corpus.

The Association of Cachexia with Head and Neck Cancer Burden and Pathologic Features

Background and Hypothesis: Head and neck cancer (HNC) is frequently associated with cachexia, characterized by involuntary weight loss, sarcopenia, and malnutrition. In HNC patients, dysphagia and anorexia from obstructive aerodigestive tumors propagates cachexia even further. However, the impact of pathologic features and burden of HNC on cachexia has yet to be investigated. We therefore hypothesize that larger, more aggressive tumors impose greater cachexia severity in HNC patients. &#x0D; &#x0D; Methods: A single-institution, retrospective study of adult patients undergoing surgical resection of head and neck carcinoma from 2014-2017 was performed. Patients without 30-day preoperative abdominal CT imaging for skeletal muscle index (SMI, cm2/m2) measurements were excluded. Patient demographics, comorbidities, nutrition data, and cancer pathology reports were collected. Cachexia was defined as unintentional weight loss &gt;5% over 6 months or &gt;2% with BMI &lt;20 kg/m2. Statistical analyses were performed using 2-sided one-way Welch’s ANOVA or Pearson’s c2 tests. Significance was determined at p &lt;0.05. &#x0D; &#x0D; Results: The cohort included 125 predominantly white (92.0%), male (75.2%) HNC patients age 59.9 ± 11.5 years. Sixty-seven (53.6%) patients had cachexia, twenty (16.0%) of whom were severe (weight loss ≥15%). Patients with severe cachexia had larger tumors (5.5 ± 2.1 cm, p=0.021) than patients with mild-to-moderate cachexia (weight loss 5-14.9%; 4.9 ± 2.1 cm) or no cachexia (4.1 ± 1.9 cm). Worsening cachexia severity was also associated with lower SMI (p=0.004), BMI (p=0.002), and serum albumin (p=0.011). There was no statistically significant difference between cachexia groups comparing patient age, comorbidities, tumor grade, depth of invasion, nodal metastases, cancer stage, perineural invasion, lymphovascular invasion, or extranodal extension. &#x0D; &#x0D; Conclusion and Potential Impact: Tumor burden of HNC patients, but not adverse pathologic features, is associated with greater cachexia severity. Identifying pro-cachectic markers produced by larger tumors could provide a molecular target for anti-cachexia therapies and improve cancer patient outcomes.

Improving the impact of clinical documentation through patient-driven co-design: experiences with cancer pathology reports

ObjectiveWith the unprecedented rise of patient access to clinical documentation through electronic health records, there is a need for health systems to understand best practices for redesigning clinical documentation to...

Expert Second Opinions Frequently Change Histologic Diagnoses: An Argument in Favor of Standardizing Thyroid Cancer Pathology Reports

Expert Second Opinions Frequently Change Histologic Diagnoses: An Argument in Favor of Standardizing Thyroid Cancer Pathology Reports