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Intelligent Automated System for detecting Diagnostically Challenging Breast Cancers

Periodic Reporting for period 2 - SmartMammaCAD (Intelligent Automated System for detecting Diagnostically Challenging Breast Cancers)

Reporting period: 2017-09-01 to 2018-08-31

"To run a chance of surviving breast cancer it is uttermost important to discover malignant tumours at an early stage. Deaths by breast cancer are highly reduced by early treatment. In his fundamental publication “Signs In MR-Mammography” Werner A. Kaiser states: “If we had a diagnostic method that enabled us to detect and remove all breast cancers 5 to 10 mm in size, we could practically eliminate breast cancer deaths”. Methods able to diagnose even very small lesions play an important role in the fight against breast cancer.

Now days, computers have dramatically increased their computation abilities. With the rise of artificial intelligence, high demanding tasks can be achieved in a matter of seconds with the aid of new machine learning algorithms. Also, the field of medical imaging has notably contributed to assist physicians with novel and non-invasive techniques for diagnosis and prognosis, through high resolution images of the internal organs by magnetic resonance imaging (MRI), or nuclear imaging. The main goal and overall objective of this project is to develop computer aided diagnosis (CAD) methods, and image processing techniques to improve diagnostic accuracy and efficiency of cancer-related breast lesions. CAD systems will aid physicians by detecting challenging lesions with high accuracy, ""learning"" to detect them from previously diagnosed cases. Those challenging cases are usually non-mass enhancing lesions, not easy to detect by direct methods, or requiring invasive methods to diagnose them, such as biopsies. Therefore, the use of CAD systems in the clinical practice is not only important for reducing the impact of the most aggressive breast cancers, but also to increase the patient care and comfort.

With the development of this project, several objectives have been achieved such as: analysis of registration algorithms, development of whole breast segmentation algorithms and feature extraction and classification techniques for NME breast lesion detection and classification. The developed analyses and techinques have been shown to increase the performance of CAD systems for NME breast lesion detection and classification, by solving some of the problems involved in detection and classification, including a methodology for false positive control."
In this project, a database of Dynamic-Constrast-enhanced magnetic-resonance-imaging (DCE-MRI) from patients with non-mass enhancing (NME) lesions of breast cancer has been collected and analyzed. Computer Aided Diagnsosis (CAD) systems have been developed to assist radiologist in the diagnosis. To this aim, registration, segmentation, detection and classification techniques have been developed. Several problems in CAD detection of NME with DCE-MRI have been solved, with novel solutions to whole breast segmentation, analysis of registration techinques and accuracy, and novel detection and classification algorithms based on machine learning and signal processing. Solutions to the high false positive rates in CAD systems for breast cancer diagnosis have been proposed, together with CAD system with visual support for radiologist. The results have been presented in specialized conferences, as IWBI and MICCAI and impact journals, as Contrast Media and Molecular Imaging.
For the correct segmentation and isolation of breast tissues from other organs imaged, a novel techinque has been developed that is specially suitable for identifying wall-like structures in medical imaging. This technique has the potential to be used in many other important problems in medical imaging, such as the identification of the hemisphere dividing plane in the brain, which is very relevant for studies on the left-right hemisphere symmetry and it's effects (handedness, gender, language,...) and other problems requiring the identification of wall-like organic structures.

Moreover, blind source separation techinques have been proved to be usefull in the analysis and identification of relevant kinetic features for malignant tissues classification. This prove-of-concept study will be trained and validated on a big dataset of clinical practice cases, with an open source implementation for clinical use, a pilot tool developed in collaboration with radiologists. This tool will be optimized for detecting non-mass enhancing lessions that usually require biopsies for diagnosis, and have been proven to provide a false positive rate control, improving the CAD performance and potentially increasing the radiologist confidence in the CAD system. By augmenting radiologists diagnostic capabilities through CAD systems,