Analysis of Lymph Nodal Metastases in Malignant Melanoma Using
Analysis of Lymph Nodal Metastases in Malignant Melanoma Using
This article deals with and formalizes 2 notions common to the practice of pathology. The first is that the number of lymph nodes found positive for metastasis relates directly to the total number of lymph nodes examined. The second is that for any patient, there is a chance that the absence of lymph node metastases is a false-negative result. I introduce the Poisson probability density function to deal with the first notion and the Bayes probability rule to deal with the second. To illustrate the insight these 2 models provide, I apply them to data regarding lymph nodal metastases in malignant melanoma. In this preliminary study, the results of these 2 models correlate well with observed survival probabilities in patients with stage N0 melanoma and with observed rates of false-negative results in sentinel lymph node biopsy technology. With further development, the combination of these models should provide a way to estimate the probability of nodal metastasis when, in fact, none have been observed. Thus, these models might provide useful tools for evaluating patients with stage N0 malignant neoplasms.
In malignant melanoma, clinical and pathologic stages are of greatest prognostic importance, and the status of regional lymph nodes is of special interest. To evaluate the regional lymph nodes, sentinel node technology has become widely used, and its success is based on the concept of an orderly progression of tumor cells from the primary site to key regional lymph nodes. Nevertheless, uncertainties remain, and false-negative results occur. In some cases, tumor cells are thought to bypass regional nodes via lymphatic-venous shunts. Uncertainties also might be due to the multistep, multistage molecular processes that comprise metastasis, because at each step and stage, tumor cells might fail to establish a viable focus of metastasis. For these reasons, it seems reasonable to view the observance of metastasis in any lymph node as a probabilistic event, especially if one considers the additional errors of surgical identification and removal of lymph nodes and of pathologic recognition of small foci of metastatic tumor in lymph nodes.
In this article, I introduce and illustrate 2 models that deal with the probabilistic nature of identifying lymph nodal metastases in malignant melanoma: the Poisson probability density function and Bayes rule for conditional probabilities. I have found that both lend insight into observed, or not observed, metastases in regional lymph nodes, so that both may provide useful tools for understanding nodal metastasis of malignant melanoma.
This article deals with and formalizes 2 notions common to the practice of pathology. The first is that the number of lymph nodes found positive for metastasis relates directly to the total number of lymph nodes examined. The second is that for any patient, there is a chance that the absence of lymph node metastases is a false-negative result. I introduce the Poisson probability density function to deal with the first notion and the Bayes probability rule to deal with the second. To illustrate the insight these 2 models provide, I apply them to data regarding lymph nodal metastases in malignant melanoma. In this preliminary study, the results of these 2 models correlate well with observed survival probabilities in patients with stage N0 melanoma and with observed rates of false-negative results in sentinel lymph node biopsy technology. With further development, the combination of these models should provide a way to estimate the probability of nodal metastasis when, in fact, none have been observed. Thus, these models might provide useful tools for evaluating patients with stage N0 malignant neoplasms.
In malignant melanoma, clinical and pathologic stages are of greatest prognostic importance, and the status of regional lymph nodes is of special interest. To evaluate the regional lymph nodes, sentinel node technology has become widely used, and its success is based on the concept of an orderly progression of tumor cells from the primary site to key regional lymph nodes. Nevertheless, uncertainties remain, and false-negative results occur. In some cases, tumor cells are thought to bypass regional nodes via lymphatic-venous shunts. Uncertainties also might be due to the multistep, multistage molecular processes that comprise metastasis, because at each step and stage, tumor cells might fail to establish a viable focus of metastasis. For these reasons, it seems reasonable to view the observance of metastasis in any lymph node as a probabilistic event, especially if one considers the additional errors of surgical identification and removal of lymph nodes and of pathologic recognition of small foci of metastatic tumor in lymph nodes.
In this article, I introduce and illustrate 2 models that deal with the probabilistic nature of identifying lymph nodal metastases in malignant melanoma: the Poisson probability density function and Bayes rule for conditional probabilities. I have found that both lend insight into observed, or not observed, metastases in regional lymph nodes, so that both may provide useful tools for understanding nodal metastasis of malignant melanoma.
