Recent Innovative Machine Learning-Based Techniques for Breast Cancer Diagnosis and Treatment

Abstract

Over the past few decades, the advancement of computational tools has been significantly changing the perspective of research, from almost human-based to almost machine-based, especially in the medical field, including diagnosis processes as well as treatment plans for various diseases and disorders. This editorial will highlight some of the recent breast cancer-related original research articles published in Technology in Cancer Research & Treatment (TCRT) journal, by Sage. The editorial will first discuss innovative machine learning-based techniques for breast cancer diagnosis, followed by techniques for breast cancer treatment. Ge, et al1 developed a noninvasive technique for histological grading of invasive breast cancer based on extracting the radiomics features from ultrasound images, which are obtained noninvasively, as opposed to the commonly used biopsy-based histological grading technique, which is invasive and could result in post-biopsy complications. Their technique that showed to be capable of distinguishing between histological low grade and high grade is based on the presence of a relation between the intensity values in the ultrasound images and the histological grade as discussed in a previous study by Au, et al.2 In their study, Ge, et al1 obtained data from 383 patients at two independent sites, which was used for training and validation. From the noninvasively ultrasound images of the patients, 788 radiomics features were extracted and were then dimensionally reduced to 7 radiomics features. These features were used to train seven machine learning classifiers, from which the logistic regression classifier performed the best and hence they attempted to integrate it with a clinical factor, which is the size of the tumor, resulting in a combined histological grading model for invasive breast cancer. Both models; with and without the clinical factor, showed close performance that is promising, however building them in the future with a larger dataset could reduce any bias resulting from using a relatively small dataset, as well as confirming whether it is necessary to integrate clinical factors in the model or not. Adopting the technique of Ge, et al1 could facilitate the diagnosis and histological grading of invasive breast cancer, which in turns will aid in determining the right treatment plan, that is significantly dependent on the tumor histological grade.

Keywords Biopsy, Breast cancer, Breast radiotherapy, Cancer diagnosis, Cancer growth