INTRODUCTION: This study develops a method that discriminates between normal and cancerous tissue sections (i.e., populations of cells) using a statistical model applied to high-dimensional quantitative measurements made on a sample of cells. MATERIALS AND METHODS: We use a cumulative log-odds model to create a score for a tissue section using the information from the cells within that tissue section. Then, a threshold is determined using receiver operating characteristic (ROC) curve analysis. The method was tested using data from cervical adenocarcinomas, adenocarcinoma in situ, and normal columnar tissue. RESULTS: Using 120 potential features, we analyzed the data for staining-independent features. Twenty-two features were statistically significant. We then calculated the log-odds and created a score, followed by ROC curve analysis. The operating point which maximizes the sum of the specificity and sensitivity achieved a sensitivity of 100% with a specificity of 85%. CONCLUSION: The cumulative log-odds performs well in classifying tissue sections using high-dimensional data measured at the cellular level, like that of quantitative pathology. This methodology potentially has applications in pathology, radiology, and optical technologies.
INTRODUCTION: This study develops a method that discriminates between normal and cancerous tissue sections (i.e., populations of cells) using a statistical model applied to high-dimensional quantitative measurements made on a sample of cells. MATERIALS AND METHODS: We use a cumulative log-odds model to create a score for a tissue section using the information from the cells within that tissue section. Then, a threshold is determined using receiver operating characteristic (ROC) curve analysis. The method was tested using data from cervical adenocarcinomas, adenocarcinoma in situ, and normal columnar tissue. RESULTS: Using 120 potential features, we analyzed the data for staining-independent features. Twenty-two features were statistically significant. We then calculated the log-odds and created a score, followed by ROC curve analysis. The operating point which maximizes the sum of the specificity and sensitivity achieved a sensitivity of 100% with a specificity of 85%. CONCLUSION: The cumulative log-odds performs well in classifying tissue sections using high-dimensional data measured at the cellular level, like that of quantitative pathology. This methodology potentially has applications in pathology, radiology, and optical technologies.
Authors: Timon P H Buys; Scott B Cantor; Martial Guillaud; Karen Adler-Storthz; Dennis D Cox; Clement Okolo; Oyedunni Arulogon; Oladimeji Oladepo; Karen Basen-Engquist; Eileen Shinn; José-Miguel Yamal; J Robert Beck; Michael E Scheurer; Dirk van Niekerk; Anais Malpica; Jasenka Matisic; Gregg Staerkel; Edward Neely Atkinson; Luc Bidaut; Pierre Lane; J Lou Benedet; Dianne Miller; Tom Ehlen; Roderick Price; Isaac F Adewole; Calum MacAulay; Michele Follen Journal: Gend Med Date: 2011-09-22
Authors: Jose-Miguel Yamal; Martial Guillaud; E Neely Atkinson; Michele Follen; Calum MacAulay; Scott B Cantor; Dennis D Cox Journal: Stat Anal Data Min Date: 2015-04-08 Impact factor: 1.051