Conclusions

In order to assess the potential for decision support in mammography, it is first necessary to identify the different decisions which are being made in the interpretation o f mammograms. At one level the radiologist’s decision is to do with the management of the patient: “Should she be recalled for a biopsy or further investigation or can she be safely returned to normal screening?” The same question can be cast in different terms: “Is the most likely diagnosis one of cancer or of benign disease?”. Authors writing about mammography sometimes assume that the radiologist’s task is simply to evaluate the information in the mammogram, sometimes it is assumed that the radiol­ ogist has access to additional information about the patient. It is, therefore, possible to identify three different, but related, decisions which radiologists are called upon to make, or to which radiologists contribute: the classification of an abnormality on an image, a preliminary diagnosis based on all available information and the selection of a course of action. The radiological knowledge required to make these decisions is the same. An interpretation of an image could result in a single set of observations which would serve as the basis of arguments to be used in support o f a classification of intra­ ductal microcalcifications, a diagnosis of cancer and a recommendation to biopsy. The framework proposed in Chapters Four and Five for decision support would allow a knowledge base and a set of image-processing measures to be developed which could be used with different decision specifications to support each o f these three decisions.

In assessing the potential for a decision support tool based on image processing and symbolic reasoning, the following criteria must be considered:

• the difficulty of the decision • the importance of the decision

• the role to be played by image processing • the role to be played by symbolic reasoning

In the analysis of breast masses, for example, the cases of stellate lesions and circumscribed lesions are rather different. Stellate lesions are often difficult to detect but, if found, the risk of misclassification is small, since there are only a few possible benign interpretations. Where the possibility of confusion does exist, in the case of radial scar (a benign lesion similar in radiological appearance to a stellate lesion), biopsy is recommended [Feig 1992], that is to say the radiologist does not even attempt to decide if the lesion is benign or malignant, and so support for the decision is not required.

The number of possible diagnoses of circumscribed lesions is somewhat greater. It might be argued that the decision space is defined not by the number of different diagnoses, but by the number of possibilities for management, which remain limited to surveillance, further investigations and biopsy. However the choice of management strategy is determined by the diagnosis. The decision here is important, since circumscribed masses are so common and have such a low specificity for cancer that to biopsy all of them would be impractical [Feig 1992], and so radiologists are obliged to try and identify those lesions which can be managed by surveillance.

A number of authors have discussed the detection of breast masses by means of image processing [Lai 1986, Yin 1994, Chan 1995a, G upta1995, Zheng 1995], but less attention has been paid to the characterisation of masses. Giger [Gigerl994] and Huo [Huol995] used measures of spiculation to distinguish benign and malignant masses, on the assumption that all stellate lesions should be treated as malignant and that circumscribed lesions need be treated as malignant only if judged to be so by a radio­ logist. Claridge and Richter [Claridgel994] measured edge blur, Stewart et al. [Stewart 1994] measured density, form, contour and size to assess circumscribed lesions.

The knowledge base used to drive symbolic reasoning about breast masses would be rather limited since there are relatively few properties to be considered. Tabar and Dean list two which are of primary importance and two of secondary importance, Feig lists nine. One group have developed a knowledge base for the characterisation of circumscribed lesions, to be used in association with the image-processing measures described in the previous paragraph [Stewart1994].

In the case of calcifications, the decision is clearly difficult. There are a number of competing explanations for the cause of calcifications and, in some cases, reasonably complex descriptions of their characteristic properties. The radiologist’s interpretation is important since it is possible to establish that some calcifications are benign on the basis of their radiological appearance, and in addition to this there is an identifiable category for which surveillance is a better option than biopsy.

A number of authors have used image-processing techniques as an aid to the classification of microcalcifications; and this work is summarised in Section 6.5. A considerable amount of information about the differential diagnosis of calcifications is contained in the articles reviewed earlier in this section, suggesting that a worthwhile knowledge base could be constructed describing the various types of microcalcification and their diagnostic significance.

On the basis of the above assessment, I decided to develop a knowledge base that would support the following decisions: feature classification, selection of next action and assessment of tissue density. I concentrated on the problem o f the classifi­ cation of calcifications since the radiological literature suggested that there was a significant body of knowledge on this problem and the literature on the application of image processing to this area suggested that there were a number of automated techniques which could be used. These two factors suggest that this was a promising area for the combination of image processing and symbolic reasoning.