![]() ![]() The heterogeneous nature of breast cancer makes its initial characterization a critical step in treatment planning and decision making. This may enable optimized prognostic models, opportunities to improve access to consistent grading, and approaches to better understand the links between histologic features and clinical outcomes in breast cancer.īreast cancer is the most common cancer in women and one of the leading causes of cancer death worldwide 1. By providing scores for each component feature, the deep-learning based approach also provides the potential to identify the grading components contributing most to prognostic value. Further, prognostic performance using deep learning-based grading is on par with that of pathologists performing review of matched slides. The individual component models perform at or above published benchmarks for algorithm-based grading approaches, achieving high concordance rates with pathologist grading. To complement this typical approach to evaluation, we further evaluate the deep learning models via prognostic analyses. We first evaluate model performance using pathologist-based reference standards for each component. In this study, we develop deep learning models to perform histologic scoring of all three components using digitized hematoxylin and eosin-stained slides containing invasive breast carcinoma. Taken together, these features form the basis of the Nottingham Grading System which is used to inform breast cancer characterization and prognosis. Determined to be regional node metastasis from breast primary.Histologic grading of breast cancer involves review and scoring of three well-established morphologic features: mitotic count, nuclear pleomorphism, and tubule formation. * *Example:* No breast tumor identified, but 2/3 axillary nodes were positive. Some of the terminology may include differentiation terms without some of the morphologic features used in Nottingham (e.g., well differentiated (G1), moderately differentiated (G2), or poorly/undifferentiated (G3)). Grade would be coded using G1, G2, or G3, even if the grading is not strictly Nottingham, which is difficult to perform in nodal tissue. **Note 8:** Grade from nodal tissue may be used **ONLY** when there was **never** any evidence of primary tumor (T0). * Do not calculate the score unless all three components are available A combined score of 3–5 points is designated as grade 1 a combined score of 6–7 points is grade 2 a combined score of 8–9 points is grade 3. The grade for a tumor is determined by assessing morphologic features (tubule formation, nuclear pleomorphism, and mitotic count), assigning a value from 1 (favorable) to 3 (unfavorable) for each feature, and totaling the scores for all three categories. The Nottingham combined histologic grade (Nottingham modification of the SBR grading system) is recommended. ![]() **Note 7:** All invasive breast carcinomas should be assigned a histologic grade. SBR is also referred to as: Bloom-Richardson, Nottingham, Nottingham modification of Bloom-Richardson score, Nottingham modification, Nottingham-Tenovus grade, or Nottingham score. **Note 6:** Scarff-Bloom-Richardson (SBR) score is used for grade. * In situ cancers: codes L, M, H take priority over A-D * Invasive cancers: codes 1-3 take priority over A-D. ![]() **Note 4:** If there are multiple tumors with different grades abstracted as one primary, code the highest grade. **Note 3:** Assign the highest grade from the primary tumor. Code Grade Pathological as C (nuclear Grade 2), per the Coding Guidelines for Generic Grade Categories Code Grade Clinical 2 (G2) since Nottingham is the preferred grading system Lumpectomy, invasive ductal carcinoma, nuclear grade 3 * Example: Breast biopsy, invasive ductal carcinoma, Nottingham grade 2. Assign Grade Pathological using the applicable generic grade codes (A-D). If the clinical grade given uses the preferred grading system and the pathological grade does not use the preferred grading system, do not record the Grade Clinical in the Grade Pathological field. **Note 2:** There is a preferred grading system for this schema. Notes **Note 1:** Grade Pathological must not be blank. ![]()
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