The classical Kinney method for predicting the boiling points of acyclic alkanes is taken as the starting point for the development of a much more accurate group contribution method developed using multiparametric linear regression. The procedure involves calculating a revised "boiling point number" (Y R ) from a count of structural features, including the length of the longest carbon chain, the nature and location of substituents, and the overall shape of the molecule. For a combined data set of 198 acyclic alkanes having from 6 to 30 carbon atoms, the correlation of predicted and literature boiling points has an R 2 of 0.999 and an average absolute deviation of 1.45 K. Thus, the method reported here is comparable in accuracy to, but much easier to apply than, more elaborate molecular connectivity, nonlinear regression, and neural network methods that were developed for narrower ranges of molecular weights.

When each modality is optimally used, a stepwise imaging approach that begins with graded compression US and proceeds to CT or MRI in select cases is currently best practice.