Abstract:
Breast cancer continues to be the most occurring cancer in women around the globe. With advancements in medical procedures, breast cancers are now virtually treatable in their early stage. Early detection, in turn, is vastly improved with community-wide organized sustainable programs. However, such schemes demand resources, and sizable amount of fund is being wasted for biopsy of benign lesions. Ultrasound imaging, because of its low ionization, effectiveness in diagnosing lesions of younger women and lesser resource intensiveness makes it a viable option for mass population programs. It has been shown to be effective in prevention of significant number of unnecessary biopsies. Thus, ultrasound imaging modality remains a prominent tool for diagnosis of breast cancer. Rigorous work is being done to improve overall imaging modality. In light of breast cancer, quantization of acoustic and morphometric features has proven to give good Receiver Operating Characteristic (ROC) area performance. However, ambiguity may still be there at concluding a lesion to be benign. The natural trend for such scenarios is the follow-up checkups recommended by experts. With thousands of follow-up checkups occurring each year, the need for a systematic study is imminent. We develop an effective algorithm that systematically processes over-time-data with the use of thin plate spline smoothing. This is followed by systematic categorization of different sets of physiological changes in light of benign and malignant lesions. Finally a versatile community wide scheme is outlined.
