Transformation of the National Breast Cancer Guideline Into Data-Driven Clinical Decision Trees

Abstract

Purpose: The essence of guideline recommendations often is intertwined in large texts. This impedes clinical implementation and evaluation and delays timely modular revisions needed to deal with an ever-growing amount of knowledge and application of personalized medicine. The aim of this project was to model guideline recommendations as data-driven clinical decision trees (CDTs) that are clinically interpretable and suitable for implementation in decision support systems.

Methods: All recommendations of the Dutch national breast cancer guideline for nonmetastatic breast cancer were translated into CDTs. CDTs were constructed by nodes, branches, and leaves that represent data items (patient and tumor characteristics [eg, T stage]), data item values (eg, T2 or less), and recommendations (eg, chemotherapy), respectively. For all data items, source of origin was identified (eg, pathology), and where applicable, data item values were defined on the basis of existing classification and coding systems (eg, TNM, Breast Imaging Reporting and Data System, Systematized Nomenclature of Medicine). All unique routes through all CDTs were counted to measure the degree of data-based personalization of recommendations.

Results: In total, 60 CDTs were necessary to cover the whole guideline and were driven by 114 data items. Data items originated from pathology (49%), radiology (27%), clinical (12%), and multidisciplinary team (12%) reports. Of all data items, 101 (89%) could be classified by existing classification and coding systems. All 60 CDTs could be integrated in an interactive decision support app that contained 376 unique patient subpopulations.

Conclusion: By defining data items unambiguously and unequivocally and coding them to an international coding system, it was possible to present a complex guideline as systematically constructed modular data-driven CDTs that are clinically interpretable and accessible in a decision support app.

FIG 1.

Conceptual and simplified reflection of the breast cancer care pathway and related clinical decision trees (CDTs). See the Methods section for a detailed description. A, age; CT, computed tomography; D, tumor diameter; DCIS, ductal carcinoma in situ; G, tumor grade; M, tumor morphology; MDT, multidisciplinary team; MRI, magnetic resonance imaging; R, residual tumor; T, tumor stage; US, ultrasound.

FIG 2.

Example of a clinical decision tree (CDT). (A) The top rectangle reflects the trunk of the CDT postoperative treatment. The rhombuses reflect the nodes and represent the data items. The branches define the cutoff values, which lead to additional nodes (rhombuses) or guideline recommendations (bottom rectangles; a delineated recommendation [rectangle with a curly bottom] means referral to another CDT, such as locoregional treatment after breast-conserving surgery[BCS]). (B) Note the double-delineated rhombus margin status, which can be unfolded to define the value of margin status. In contrast to other countries, the Dutch national breast cancer guideline does not recommend re-excision for focally positive margins after BCS in invasive tumor and recommends whole-breast irradiation, including boost.

FIG 3.

Screenshot that shows a part of the clinical decision tree (CDT) for indication genomic testing in Oncoguide (translated into English). The green path through the CDT highlights the data provided in the data panel on the left side projected onto the CDT, which in this case leads to the recommendation that genomic testing is indicated. The full tree (in Dutch) is also accessible in an interactive format through https://oncoguide.nl/#!/projects/7/guideline/17/tree/153/10494. ER, estrogen receptor; HER2, human epidermal growth factor receptor 2; neg, negative; pos, positive.

FIG A1.

A unique patient route within a clinical decision tree that is based on the Dutch breast cancer guideline 2012. ALND, axillary lymph node dissection; BCS, breast-conserving surgery; LCIS, lobular carcinoma in situ; mi, micrometastasis; MDT, multidisciplinary team; paget, Paget’s disease; sn, sentinel node; SNP, sentinel node procedure.