In the data set, which subtype was most commonly missed?

In the data set, which subtype was most commonly missed?

• A. HR+/HER2-
• B. HR-/HER2+
• C. HER2-enriched
• D. Triple-negative

Answer: (A)

Understanding how misclassification happens in datasets often follows how common a category is and how reliably it’s identified. HR+/HER2- cancers are typically the largest group in breast cancer cohorts. If hormone receptor testing isn’t complete, or results are borderline, true HR-positive, HER2-negative cases can be misread (for example, as HR-negative or with ambiguous HER2 status), causing them to be missed. Because there are more of these tumors to begin with, the absolute number of missed cases for this subtype tends to be higher, even if the misclassification rate across subtypes is similar. In contrast, less common subtypes contribute fewer misses in absolute counts, so they’re less likely to be the most commonly missed.

Understanding how misclassification happens in datasets often follows how common a category is and how reliably it’s identified. HR+/HER2- cancers are typically the largest group in breast cancer cohorts. If hormone receptor testing isn’t complete, or results are borderline, true HR-positive, HER2-negative cases can be misread (for example, as HR-negative or with ambiguous HER2 status), causing them to be missed. Because there are more of these tumors to begin with, the absolute number of missed cases for this subtype tends to be higher, even if the misclassification rate across subtypes is similar. In contrast, less common subtypes contribute fewer misses in absolute counts, so they’re less likely to be the most commonly missed.