scholarly journals Communication among Physicians and Allied Healthcare Associates: Precise Radiology Reports–Minimizing Complications, Maximizing Outcomes

2018 ◽  
Vol 2 (1) ◽  
pp. 1-2
Author(s):  
Nicholas A Kerna
Keyword(s):  
Radiology Reports ◽  
Allied Healthcare

Assessing variability in surgical decision making among attending neurosurgeons at an academic center

2020 ◽  
Vol 132 (6) ◽  
pp. 1970-1976
Author(s):  
Ashwin G. Ramayya ◽  
H. Isaac Chen ◽  
Paul J. Marcotte ◽  
Steven Brem ◽  
Eric L. Zager ◽  
...  
Keyword(s):  
Spine Surgery ◽  
Repeated Measures ◽  
Elective Surgery ◽  
Statistical Tests ◽  
Neurosurgical Procedures ◽  
P Values ◽  
Surgical Interventions ◽  
Radiology Reports ◽  
Operative Notes ◽  
First Time

OBJECTIVEAlthough it is known that intersurgeon variability in offering elective surgery can have major consequences for patient morbidity and healthcare spending, data addressing variability within neurosurgery are scarce. The authors performed a prospective peer review study of randomly selected neurosurgery cases in order to assess the extent of consensus regarding the decision to offer elective surgery among attending neurosurgeons across one large academic institution.METHODSAll consecutive patients who had undergone standard inpatient surgical interventions of 1 of 4 types (craniotomy for tumor [CFT], nonacute redo CFT, first-time spine surgery with/without instrumentation, and nonacute redo spine surgery with/without instrumentation) during the period 2015–2017 were retrospectively enrolled (n = 9156 patient surgeries, n = 80 randomly selected individual cases, n = 20 index cases of each type randomly selected for review). The selected cases were scored by attending neurosurgeons using a need for surgery (NFS) score based on clinical data (patient demographics, preoperative notes, radiology reports, and operative notes; n = 616 independent case reviews). Attending neurosurgeon reviewers were blinded as to performing provider and surgical outcome. Aggregate NFS scores across various categories were measured. The authors employed a repeated-measures mixed ANOVA model with autoregressive variance structure to compute omnibus statistical tests across the various surgery types. Interrater reliability (IRR) was measured using Cohen’s kappa based on binary NFS scores.RESULTSOverall, the authors found that most of the neurosurgical procedures studied were rated as “indicated” by blinded attending neurosurgeons (mean NFS = 88.3, all p values < 0.001) with greater agreement among neurosurgeon raters than expected by chance (IRR = 81.78%, p = 0.016). Redo surgery had lower NFS scores and IRR scores than first-time surgery, both for craniotomy and spine surgery (ANOVA, all p values < 0.01). Spine surgeries with fusion had lower NFS scores than spine surgeries without fusion procedures (p < 0.01).CONCLUSIONSThere was general agreement among neurosurgeons in terms of indication for surgery; however, revision surgery of all types and spine surgery with fusion procedures had the lowest amount of decision consensus. These results should guide efforts aimed at reducing unnecessary variability in surgical practice with the goal of effective allocation of healthcare resources to advance the value paradigm in neurosurgery.

Developing a RadLex-based Named Entity Recognition Tool for Mining Textual Radiology Reports (Preprint)

2020 ◽  
Author(s):  
Shintaro Tsuji ◽  
Andrew Wen ◽  
Naoki Takahashi ◽  
Hongjian Zhang ◽  
Katsuhiko Ogasawara ◽  
...  
Keyword(s):  
Named Entity Recognition ◽  
Noun Phrases ◽  
General Purpose ◽  
Entity Recognition ◽  
Free Text ◽  
Clinical Text ◽  
Named Entity ◽  
Radiology Reports ◽  
Two Measures ◽  
F Measure

BACKGROUND Named entity recognition (NER) plays an important role in extracting the features of descriptions for mining free-text radiology reports. However, the performance of existing NER tools is limited because the number of entities depends on its dictionary lookup. Especially, the recognition of compound terms is very complicated because there are a variety of patterns. OBJECTIVE The objective of the study is to develop and evaluate a NER tool concerned with compound terms using the RadLex for mining free-text radiology reports. METHODS We leveraged the clinical Text Analysis and Knowledge Extraction System (cTAKES) to develop customized pipelines using both RadLex and SentiWordNet (a general-purpose dictionary, GPD). We manually annotated 400 of radiology reports for compound terms (Cts) in noun phrases and used them as the gold standard for the performance evaluation (precision, recall, and F-measure). Additionally, we also created a compound-term-enhanced dictionary (CtED) by analyzing false negatives (FNs) and false positives (FPs), and applied it for another 100 radiology reports for validation. We also evaluated the stem terms of compound terms, through defining two measures: an occurrence ratio (OR) and a matching ratio (MR). RESULTS The F-measure of the cTAKES+RadLex+GPD was 32.2% (Precision 92.1%, Recall 19.6%) and that of combined the CtED was 67.1% (Precision 98.1%, Recall 51.0%). The OR indicated that stem terms of “effusion”, "node", "tube", and "disease" were used frequently, but it still lacks capturing Cts. The MR showed that 71.9% of stem terms matched with that of ontologies and RadLex improved about 22% of the MR from the cTAKES default dictionary. The OR and MR revealed that the characteristics of stem terms would have the potential to help generate synonymous phrases using ontologies. CONCLUSIONS We developed a RadLex-based customized pipeline for parsing radiology reports and demonstrated that CtED and stem term analysis has the potential to improve dictionary-based NER performance toward expanding vocabularies.

scholarly journals Multi-task weak supervision enables anatomically-resolved abnormality detection in whole-body FDG-PET/CT

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Sabri Eyuboglu ◽  
Geoffrey Angus ◽  
Bhavik N. Patel ◽  
Anuj Pareek ◽  
Guido Davidzon ◽  
...  
Keyword(s):  
Fdg Pet ◽  
Location Estimation ◽  
Supervised Machine Learning ◽  
Whole Body ◽  
Free Text ◽  
Abnormality Detection ◽  
Weak Supervision ◽  
Radiology Reports ◽  
Pet Ct ◽  
Fdg Pet Ct

AbstractComputational decision support systems could provide clinical value in whole-body FDG-PET/CT workflows. However, limited availability of labeled data combined with the large size of PET/CT imaging exams make it challenging to apply existing supervised machine learning systems. Leveraging recent advancements in natural language processing, we describe a weak supervision framework that extracts imperfect, yet highly granular, regional abnormality labels from free-text radiology reports. Our framework automatically labels each region in a custom ontology of anatomical regions, providing a structured profile of the pathologies in each imaging exam. Using these generated labels, we then train an attention-based, multi-task CNN architecture to detect and estimate the location of abnormalities in whole-body scans. We demonstrate empirically that our multi-task representation is critical for strong performance on rare abnormalities with limited training data. The representation also contributes to more accurate mortality prediction from imaging data, suggesting the potential utility of our framework beyond abnormality detection and location estimation.

scholarly journals Templated Text Synthesis for Expert-Guided Multi-Label Extraction from Radiology Reports

2021 ◽  
Vol 3 (2) ◽  
pp. 299-317
Author(s):  
Patrick Schrempf ◽  
Hannah Watson ◽  
Eunsoo Park ◽  
Maciej Pajak ◽  
Hamish MacKinnon ◽  
...  
Keyword(s):  
Domain Knowledge ◽  
Expert Knowledge ◽  
Medical Image Analysis ◽  
Imaging Data ◽  
Data Synthesis ◽  
Domain Specific ◽  
Radiology Reports ◽  
Training Examples ◽  
Analysis Models ◽  
Difficult Cases

Training medical image analysis models traditionally requires large amounts of expertly annotated imaging data which is time-consuming and expensive to obtain. One solution is to automatically extract scan-level labels from radiology reports. Previously, we showed that, by extending BERT with a per-label attention mechanism, we can train a single model to perform automatic extraction of many labels in parallel. However, if we rely on pure data-driven learning, the model sometimes fails to learn critical features or learns the correct answer via simplistic heuristics (e.g., that “likely” indicates positivity), and thus fails to generalise to rarer cases which have not been learned or where the heuristics break down (e.g., “likely represents prominent VR space or lacunar infarct” which indicates uncertainty over two differential diagnoses). In this work, we propose template creation for data synthesis, which enables us to inject expert knowledge about unseen entities from medical ontologies, and to teach the model rules on how to label difficult cases, by producing relevant training examples. Using this technique alongside domain-specific pre-training for our underlying BERT architecture i.e., PubMedBERT, we improve F1 micro from 0.903 to 0.939 and F1 macro from 0.512 to 0.737 on an independent test set for 33 labels in head CT reports for stroke patients. Our methodology offers a practical way to combine domain knowledge with machine learning for text classification tasks.

scholarly journals Magnetic resonance imaging template to standardize reporting of anal fistulas

2021 ◽  
Author(s):  
I. Sudoł-Szopińska ◽  
G. A. Santoro ◽  
M. Kołodziejczak ◽  
A. Wiaczek ◽  
U. Grossi
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Treatment Strategies ◽  
Cross Sectional Study ◽  
Resonance Imaging ◽  
Anal Fistulas ◽  
Cross Sectional ◽  
Radiology Reports ◽  
Colorectal Surgeons ◽  
Magnetic Resonance Imaging Mri

AbstractAnal fistula (AF) is a common referral to colorectal surgeons. Management remains challenging and sometimes controversial. Magnetic resonance imaging (MRI) is commonly performed in initial workup for AF. However, reports often lack key information for guiding treatment strategies. It has been shown that with structured radiology reports, there is less missing information. We present a structured MRI template report including 8 key descriptors of anal fistulas, whose effectiveness and acceptability are being assessed in a cross-sectional study (NCT04541238).

Hip surgery and radiology reporting for children with cerebral palsy prior to initiation of a hip surveillance program

2021 ◽  
pp. 1-7
Author(s):  
Kyra Kane ◽  
Marshall Siemens ◽  
Shane Wunder ◽  
Jacqueline Kraushaar ◽  
J. Alexandra Mortimer ◽  
...  
Keyword(s):  
Cerebral Palsy ◽  
Retrospective Chart Review ◽  
Surveillance Program ◽  
Radiology Reporting ◽  
Reporting Practices ◽  
Hip Displacement ◽  
Primary Surgery ◽  
Radiology Reports ◽  
Children With Cerebral Palsy ◽  
Surveillance Programs

PURPOSE: Hip displacement impacts quality of life for many children with cerebral palsy (CP). While early detection can help avoid dislocation and late-stage surgery, formalized surveillance programs are not ubiquitous. This study aimed to examine: 1) surgical practices around pediatric hip displacement for children with CP in a region without formalized hip surveillance; and 2) utility of MP compared to traditional radiology reporting for quantifying displacement. METHODS: A retrospective chart review examined hip displacement surgeries performed on children with CP between 2007–2016. Surgeries were classified as preventative, reconstructive, or salvage. Pre- and post-operative migration percentage (MP) was calculated for available radiographs using a mobile application and compared using Wilcoxon Signed Ranks test. MPs were also compared with descriptions in the corresponding radiology reports using directed and conventional content analyses. RESULTS: Data from 67 children (115 surgical hips) was included. Primary surgery types included preventative (63.5% hips), reconstructive (36.5%), or salvage (0%). For the 92 hips with both radiology reports and radiographs available, reports contained a range of descriptors that inconsistently reflected the retrospectively-calculated MPs. CONCLUSION: Current radiology reporting practices do not appear to effectively describe hip displacement for children with CP. Therefore, standardized reporting of MP is recommended.

Referral patterns for catheter-directed thrombolysis for iliofemoral deep venous thrombosis

2021 ◽  
pp. 026835552097728
Author(s):  
Kirtan D Patel ◽  
Alison YY Tang ◽  
Ashik DJ Zala ◽  
Rakesh Patel ◽  
Kishan R Parmar ◽  
...  
Keyword(s):  
Lower Limb ◽  
Management Option ◽  
Venous Anatomy ◽  
Nice Guidelines ◽  
Patient Demographics ◽  
Radiology Reports ◽  
Catheter Directed Thrombolysis ◽  
Nice Guidance ◽  
Icd 10 ◽  
Venous Thromboses

Objectives Post thrombotic syndrome (PTS) is a serious complication of deep venous thromboses (DVTs). PTS occurs more frequently and severely following iliofemoral DVT compared to distal DVTs. Catheter directed thrombolysis (CDT) of iliofemoral DVTs may reduce PTS incidence and severity. We aimed to determine the rate of iliofemoral DVT within our institution, their subsequent management, and compliance with NICE guidelines. Methods Retrospective review of all DVTs diagnosed over a 3-year period was conducted. Cases of iliofemoral DVT were identified using ICD-10 codes from patient notes, and radiology reports of Duplex scans. Further details were retrieved, such as patient demographics and referrals to vascular services. NICE guidance was applied to determine if patients would have been suitable for CDT. A survey was sent to clinicians within medicine to identify awareness of CDT and local guidelines for iliofemoral DVT management. Results 225 patients with lower limb DVTs were identified. Of these, 96 were radiographically confirmed as iliofemoral DVTs. The median age was 77. 67.7% of iliofemoral DVTs affected the left leg. Right leg DVTs made up 30.2% and 2.1% were bilateral DVTs. Of the 96 iliofemoral DVTs, 21 were deemed eligible for CDT. Only 3 patients (14.3%) were referred to vascular services, and 3 received thrombolysis. From our survey, 95.5% of respondents suggested anticoagulation alone as management for iliofemoral DVT. Only one respondent recommended referral to vascular services. There was a knowledge deficiency regarding venous anatomy, including superficial versus deep veins. Conclusions CDT and other mechanochemical procedures have been shown to improve outcomes of patients post-iliofemoral DVT, however a lack of awareness regarding CDT as a management option results in under-referral to vascular services. We suggest closer relations between vascular services and their “tributary” DVT clinics, development of guidelines and robust care pathways in the management of iliofemoral DVT.

scholarly journals Musculoskeletal Soft-Tissue Sarcoma: Quality Assessment of Initial MRI Reports Shows Frequent Deviation from ESSR Guidelines

Diagnostics ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 695
Author(s):  
Sebastian Weiss ◽  
Alexander Korthaus ◽  
Nora Baumann ◽  
Jin Yamamura ◽  
Alexander S. Spiro ◽  
...  
Keyword(s):  
Soft Tissue ◽  
Soft Tissue Sarcomas ◽  
Quality Criteria ◽  
Final Diagnosis ◽  
Tissue Mass ◽  
Musculoskeletal Radiology ◽  
Radiology Reports ◽  
Patients At Risk ◽  
Magnetic Resonance Imaging Mri ◽  
The Masses

Soft-tissue sarcomas (STS) are a rare subtype of soft-tissue mass and are frequently misinterpreted as benign lesions. Magnetic resonance imaging (MRI) is the primary recommended type of diagnostics. To assess the quality of primary radiology reports, we investigated whether recommended MRI report elements were included in compliance with European Society of Musculoskeletal Radiology (ESSR) guidelines. A total of 1107 patients were evaluated retrospectively, and 126 radiological reports on patients with malignant STS were assessed for ESSR quality criteria. One or more required sequences or planes were missing in 67% of the reports. In all 126 cases, the report recognized the mass as anomalous (100%). Sixty-eight percent of the reports mentioned signs of malignancy. The majority of reports (n = 109, 87%) articulated a suspected diagnosis, 32 of which showed a mismatch with the final diagnosis (25%). Thirty-two percent of the reports had a misinterpretation of the masses as benign. Benign misinterpretations were more common in masses smaller than 5 cm (65% vs. 27%). Thirty percent of the reports suggested tissue biopsy and 6% recommended referral to a sarcoma center. MRI reports showed frequent deviations from ESSR guidelines, and protocol guidelines were not routinely met. Deviations from standard protocol and reporting guidelines could put patients at risk for inadequate therapy.

scholarly journals De-identifying Spanish medical texts - named entity recognition applied to radiology reports

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Irene Pérez-Díez ◽  
Raúl Pérez-Moraga ◽  
Adolfo López-Cerdán ◽  
Jose-Maria Salinas-Serrano ◽  
María de la Iglesia-Vayá
Keyword(s):  
Electronic Health Records ◽  
English Language ◽  
Personal Information ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Medical Texts ◽  
Health Records ◽  
Named Entity ◽  
Radiology Reports ◽  
Electronic Health

Abstract Background Medical texts such as radiology reports or electronic health records are a powerful source of data for researchers. Anonymization methods must be developed to de-identify documents containing personal information from both patients and medical staff. Although currently there are several anonymization strategies for the English language, they are also language-dependent. Here, we introduce a named entity recognition strategy for Spanish medical texts, translatable to other languages. Results We tested 4 neural networks on our radiology reports dataset, achieving a recall of 97.18% of the identifying entities. Alongside, we developed a randomization algorithm to substitute the detected entities with new ones from the same category, making it virtually impossible to differentiate real data from synthetic data. The three best architectures were tested with the MEDDOCAN challenge dataset of electronic health records as an external test, achieving a recall of 69.18%. Conclusions The strategy proposed, combining named entity recognition tasks with randomization of entities, is suitable for Spanish radiology reports. It does not require a big training corpus, thus it could be easily extended to other languages and medical texts, such as electronic health records.

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