Clinical Concept-Based Radiology Reports Classification Pipeline for Lung Carcinoma.
Rising incidence and mortality of cancer have led to an incremental amount of research in the field. To learn from preexisting data, it has become important to capture maximum information related to disease type, stage, treatment, and outcomes. Medical imaging reports are rich in this kind of inform...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 3; pp. 812 - 827 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=164473113&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 164473113 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2023 vid: 36 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 164473113 161876158 164473113 164473113 10.1007/s10278-023-00787-z 164473113 ppf: 812 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Clinical Concept-Based Radiology Reports Classification Pipeline for Lung Carcinoma. aug: au: Mithun, Sneha Jha, Ashish Kumar Sherkhane, Umesh B. Jaiswar, Vinay Purandare, Nilendu C. Dekker, Andre Puts, Sander Bermejo, Inigo Rangarajan, V. Zegers, Catharina M. L. Wee, Leonard affil: Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, 6229 ET, Maastricht, The Netherlands sug: subj: Lung Neoplasms Carcinoma Radiology Service Reports Classification Natural Language Processing Information Retrieval Human Retrospective Design Comparative Studies Machine Learning Deep Learning Neural Networks (Computer) Tomography, X-Ray Computed Positron Emission Tomography Computed Tomography Data Analysis Software Descriptive Statistics Algorithms Automation ab: Rising incidence and mortality of cancer have led to an incremental amount of research in the field. To learn from preexisting data, it has become important to capture maximum information related to disease type, stage, treatment, and outcomes. Medical imaging reports are rich in this kind of information but are only present as free text. The extraction of information from such unstructured text reports is labor-intensive. The use of Natural Language Processing (NLP) tools to extract information from radiology reports can make it less time-consuming as well as more effective. In this study, we have developed and compared different models for the classification of lung carcinoma reports using clinical concepts. This study was approved by the institutional ethics committee as a retrospective study with a waiver of informed consent. A clinical concept-based classification pipeline for lung carcinoma radiology reports was developed using rule-based as well as machine learning models and compared. The machine learning models used were XGBoost and two more deep learning model architectures with bidirectional long short-term neural networks. A corpus consisting of 1700 radiology reports including computed tomography (CT) and positron emission tomography/computed tomography (PET/CT) reports were used for development and testing. Five hundred one radiology reports from MIMIC-III Clinical Database version 1.4 was used for external validation. The pipeline achieved an overall F1 score of 0.94 on the internal set and 0.74 on external validation with the rule-based algorithm using expert input giving the best performance. Among the machine learning models, the Bi-LSTM_dropout model performed better than the ML model using XGBoost and the Bi-LSTM_simple model on internal set, whereas on external validation, the Bi-LSTM_simple model performed relatively better than other 2. This pipeline can be used for clinical concept-based classification of radiology reports related to lung carcinoma from a huge corpus and also for automated annotation of these reports. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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