Lack of Racial Diversity in Surgery and Pathology Textbooks Depicting Diseases of the Breast.

INTRODUCTION: The lack of racial diversity depicted in medical education texts may contribute to an implicit racial bias among clinicians. This bias influences outcomes, as familiarity with the various cutaneous manifestations of disease is essential to making an accurate diagnosis. To better understand the racial disparities in breast surgery, we sought to determine the extent of skin tone representation depicted in images of breast surgery and pathology textbooks. METHODS: Textbooks were screened for color images of conditions with sufficient skin tissue present to assign the Fitzpatrick skin phototype (FSP). Figures were independently assigned an FP score (range: 1-6), and subdivided into "light skin" (FP 1-3) and "dark skin" (FP 4-6). Number of figures in each category and percentage of patients with each skin tone were calculated. RESULTS: 557 figures were included. Among 12 textbooks reviewed, seven textbooks were from the discipline of surgery, while five were pathology-related. Textbook year of publication spanned from 1996 to 2018. Overall, 533 (95.7%) figures depicted patients with light skin color versus 24 (4.3%) with dark skin color. There was no association between FP score and year of textbook publication (P = 0.69). CONCLUSIONS: Patient images in breast textbooks are overwhelmingly of light skin tones, excluding patients with darker skin tones. The dearth of images depicting dark skinned individuals did not improve over time. Inclusion of patients of color in future textbooks may help reduce racial disparities within breast cancer care.

A nomogram for predicting the likelihood of additional nodal metastases in breast cancer patients with a positive sentinel node biopsy.

BACKGROUND: The standard of care for breast cancer patients with sentinel lymph node (SLN) metastases includes complete axillary lymph node dissection (ALND). However, many question the need for complete ALND in every patient with detectable SLN metastases, particularly those perceived to have a low risk of non-SLN metastases. Accurate estimates of the likelihood of additional disease in the axilla could assist greatly in decision-making regarding further treatment. METHODS: Pathological features of the primary tumor and SLN metastases of 702 patients who underwent complete ALND were assessed with multivariable logistic regression to predict the presence of additional disease in the non-SLNs of these patients. A nomogram was created using pathological size, tumor type and nuclear grade, lymphovascular invasion, multifocality, and estrogen-receptor status of the primary tumor; method of detection of SLN metastases; number of positive SLNs; and number of negative SLNs. The model was subsequently applied prospectively to 373 patients. RESULTS: The nomogram for the retrospective population was accurate and discriminating, with an area under the receiver operating characteristic (ROC) curve of 0.76. When applied to the prospective group, the model accurately predicted likelihood of non-SLN disease (ROC, 0.77). CONCLUSIONS: We have developed a user-friendly nomogram that uses information commonly available to the surgeon to easily and accurately calculate the likelihood of having additional, non-SLN metastases for an individual patient.