Synoptic reporting increases quality of upper gastrointestinal cancer pathology reports

Baranov, Nikolaj; Nagtegaal, Irıs D.; Grieken, Nicole C.T. van; Verhoeven, Rob H.A.; Voorham, Quirinus J.M.; Rosman, Camiel; Post, Rachel S. van der

doi:10.1007/s00428-019-02586-w

Cited by 24 publications

(21 citation statements)

References 12 publications

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“…The usage of synoptic reporting reduced time for documentation of examination results compared to traditional narrative reporting, which is in line with previous studies [32]. Potential reasons include the reduced effort for text formatting [32,33], correction of dictation errors (casus, numerus, and genus of word endings) [33], use of defaults (normal finding), automated calculations (prostate volume and tumor volumes in biopsies), proofreading for completeness, and consistency of the report [11,13,14]. The comparison of time savings between diagnostic departments showed greater time savings for radiology compared to pathology.…”

Section: Discussionsupporting

confidence: 72%

“…This way of documentation is suitable for different purposes like medical history, radiological, and histological examinations, and it has already been shown that such reports are more accurate, clearer, and coherent than narrative reports [10]. The broadest application of synoptic reporting is currently in oncologic pathology [11][12][13][14][15][16], radiology [17,18], and operative reports in surgery [19,20]. An essential prerequisite for the successful expansion of this reporting method is the development of international standards based on the best available evidence.…”

Section: Introductionmentioning

confidence: 99%

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Toward a Platform for Structured Data Acquisition in Oncology: A Pilot Study on Prostate Cancer Screening

et al. 2021

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Introduction: Physicians spend an ever-rising amount of time to collect relevant information from highly variable medical reports and integrate them into the patient’s health condition. Objectives: We compared synoptic reporting based on data elements to narrative reporting in order to evaluate its capabilities to collect and integrate clinical information. Methods: We developed a novel system to align medical reporting to data integration requirements and tested it in prostate cancer screening. We compared expenditure of time, data quality, and user satisfaction for data acquisition, integration, and evaluation. Results: In a total of 26 sessions, 2 urologists, 2 radiologists, and 2 pathologists conducted the diagnostic work-up for prostate cancer screening with both narrative reporting and the novel system. The novel system led to a significantly reduced time for collection and integration of patient information (91%, p < 0.001), reporting in radiology (44%, p < 0.001) and pathology (33%, p = 0.154). The system usage showed a high positive effect on evaluated data quality parameters completeness, format, understandability, as well as user satisfaction. Conclusion: This study provides evidence that synoptic reporting based on data elements is effectively reducing time for collection and integration of patient information. Further research is needed to assess the system’s impact for different patient journeys.

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Section: Discussionsupporting

confidence: 72%

Section: Introductionmentioning

confidence: 99%

Toward a Platform for Structured Data Acquisition in Oncology: A Pilot Study on Prostate Cancer Screening

et al. 2021

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“…Currently, SR has been implemented in more than 31 different protocols, all of which have been formally approved by the Dutch Society of Pathology. Although the benefits of SR for Dutch patients has been investigated in a few types of cancer (e.g., colorectal [35,42], gallbladder [43]), this has not been done in prostate cancer.…”

Section: Synoptic Reportingmentioning

confidence: 99%

Using Explainable Machine Learning to Explore the Impact of Synoptic Reporting on Prostate Cancer

et al. 2022

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Machine learning (ML) models have proven to be an attractive alternative to traditional statistical methods in oncology. However, they are often regarded as black boxes, hindering their adoption for answering real-life clinical questions. In this paper, we show a practical application of explainable machine learning (XML). Specifically, we explored the effect that synoptic reporting (SR; i.e., reports where data elements are presented as discrete data items) in Pathology has on the survival of a population of 14,878 Dutch prostate cancer patients. We compared the performance of a Cox Proportional Hazards model (CPH) against that of an eXtreme Gradient Boosting model (XGB) in predicting patient ranked survival. We found that the XGB model (c-index = 0.67) performed significantly better than the CPH (c-index = 0.58). Moreover, we used Shapley Additive Explanations (SHAP) values to generate a quantitative mathematical representation of how features—including usage of SR—contributed to the models’ output. The XGB model in combination with SHAP visualizations revealed interesting interaction effects between SR and the rest of the most important features. These results hint that SR has a moderate positive impact on predicted patient survival. Moreover, adding an explainability layer to predictive ML models can open their black box, making them more accessible and easier to understand by the user. This can make XML-based techniques appealing alternatives to the classical methods used in oncological research and in health care in general.

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“…A complete and accurate pathology report is crucial to diagnostic workup, therapeutic management, and posttherapeutic follow-up of every cancer patient. Baranov et al 83 analyzed more than 1000 narrative and synoptic (structured) esophageal and gastric cancer pathology reports for completeness. Completeness significantly increased from 56% to 97% in the structured reports compared with unstructured narrative reports.…”

Section: Noncore Elementsmentioning

confidence: 99%

“…Completeness significantly increased from 56% to 97% in the structured reports compared with unstructured narrative reports. 83 The ICCR data sets contain all the parameters needed to guide management and prognostication for various cancers. In addition, synoptic reporting may increase awareness of quality indicators, 16 thereby improving the quality of pathologic specimen evaluation.…”

Section: Noncore Elementsmentioning

confidence: 99%

Data Set for Reporting Carcinoma of the Stomach in Gastrectomy

Shi

Badgwell

Grabsch

et al. 2021

Archives of Pathology &Amp; Laboratory Medicine

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Context.— A standardized detailed surgical pathology report is the cornerstone of gastric cancer management. Objective.— To guide management and prognostication for patients with gastric carcinomas globally, the International Collaboration on Cancer Reporting aimed to produce an evidence-based international pathology reporting data set with a panel of globally recognized expert pathologists and clinicians. Design.— Based on published guidelines/data sets for gastric carcinomas, a working draft was developed by the chair of the expert panel of pathologists and clinicians. The draft was then circulated to the panel and discussed in a series of teleconferences and email communications until consensus was achieved. The draft data set was uploaded on the International Collaboration on Cancer Reporting Web site for public comment. The data set was reviewed in consideration of the feedback, and a final version was approved by the panel. Results.— This data set was developed for gastrectomy specimens for primary gastric carcinomas, including neuroendocrine carcinomas and mixed neuroendocrine-nonneuroendocrine neoplasms. Well-differentiated neuroendocrine tumors, nonepithelial malignancies, and secondary tumors were excluded from this data set. The final data set contains 15 core (required) elements and 8 noncore (recommended) elements. A commentary is provided for each element. Conclusions.— The International Collaboration on Cancer Reporting has published freely available, evidence-based data sets for gastric cancer reporting. Standardized reporting has been shown to improve patient care and facilitates data exchange and analysis for quality assurance, cancer epidemiology, and clinical and basic research.

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Synoptic reporting increases quality of upper gastrointestinal cancer pathology reports

Cited by 24 publications

References 12 publications

Toward a Platform for Structured Data Acquisition in Oncology: A Pilot Study on Prostate Cancer Screening

Toward a Platform for Structured Data Acquisition in Oncology: A Pilot Study on Prostate Cancer Screening

Using Explainable Machine Learning to Explore the Impact of Synoptic Reporting on Prostate Cancer

Data Set for Reporting Carcinoma of the Stomach in Gastrectomy

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