A group of biomedical researchers developed a diagnostic process that applies the same shortwave rays used in airport security scanners to distinguish cancerous skin lesions from normal skin. The technology uses millimeter-wave radiation, which bounces off different materials with quantifiable reflectivity signatures. The researchers found that cancerous tumors reflect more calibrated energy than normal skin; therefore, the technology identifies diseased tissue by detecting reflectivity hotspots. A preliminary evaluation of the performance of the diagnostic tool showed that 92% of all skin tissue samples with a positive test result were cancerous, and that 99% of all skin tissue samples with a negative test result were normal. Based on these preliminary data, which of the following is the most accurate interpretation of the results?
A)92% is the false-positive rate, and 99% is the false-negative rate
B)92% is the positive likelihood ratio of the test, and 99% is the negative likelihood ratio of the test
C)92% is the positive predictive value of the test, and 99% is the negative predictive value of the test
D)92% is the sensitivity of the test, and 99% is the specificity of the test

Question

A group of biomedical researchers developed a diagnostic process that applies the same shortwave rays used in airport security scanners to distinguish cancerous skin lesions from normal skin.  The technology uses millimeter-wave radiation, which bounces off different materials with quantifiable reflectivity signatures.  The researchers found that cancerous tumors reflect more calibrated energy than normal skin; therefore, the technology identifies diseased tissue by detecting reflectivity hotspots.  A preliminary evaluation of the performance of the diagnostic tool showed that 92% of all skin tissue samples with a positive test result were cancerous, and that 99% of all skin tissue samples with a negative test result were normal.  Based on these preliminary data, which of the following is the most accurate interpretation of the results?
A)92% is the false-positive rate, and 99% is the false-negative rate
B)92% is the positive likelihood ratio of the test, and 99% is the negative likelihood ratio of the test
C)92% is the positive predictive value of the test, and 99% is the negative predictive value of the test
D)92% is the sensitivity of the test, and 99% is the specificity of the test

Quizplus · Accepted Answer

Explanation: 11ec2b69_c105_407d_8f63_9f3a5abe2d2d_MD0005_00 The percentage of all tissue samples with a positive test result that are cancerous is known as the positive predictive value (PPV) of a test. It is equal to the number of tissue samples that are cancerous and that test positive (ie, true positive [TP]) divided by the number of all tissue samples with a positive test result (ie, TP + false positive [FP]): PPV = TP / (TP + FP) In this case, the diagnostic tool showed that 92% of all tissue samples with a positive test result were cancerous; therefore, PPV = 92%. The percentage of all tissue samples with a negative test result that are normal is known as the negative predictive value (NPV) of a test. It is equal to the number of tissue samples that are normal and that test negative (ie, true negative [TN]) divided by the number of all tissue samples with a negative test result (ie, TN + false negative [FN]): NPV = TN / (TN + FN) The diagnostic tool showed that 99% of all tissue samples with a negative test result were normal; therefore, NPV = 99%. (Choices A and D) Sensitivity describes the proportion of cancerous tissue samples for which the test result is positive. It is calculated as TP / (TP + FN); its complement (1 − sensitivity) is the false-negative rate. Specificity describes the proportion of normal tissue samples for which the test result is negative. It is calculated as TN / (TN + FP); its complement (1 − specificity) is the false-positive rate. (Choice B) Negative likelihood ratio (LR) describes the probability of a negative test result occurring in a cancerous tissue sample compared to a normal tissue sample; it is calculated as (1 − sensitivity) / specificity. Positive LR describes the probability of a positive test result occurring in a cancerous tissue sample compared to a normal tissue sample; it is calculated as sensitivity / (1 − specificity). Educational objective: Positive predictive value is the probability that an individual has a disease given a positive test result. Negative predictive value is the probability that an individual does not have a disease given a negative test result.

A Group of Biomedical Researchers Developed a Diagnostic Process That