Radiomics for the Discrimination of Infiltrative vs In Situ Breast Cancer

Breast cancer is one of the leading causes of cancer-associated
death among the female population worldwide...


Introduction
Breast cancer is one of the leading causes of cancer-associated death among the female population worldwide [1]. In Italy, breast cancer affected about 52,000 new cases out of a total of 178,000 cases of all female cancers in 2018 [2]. Magnetic Resonance Imaging (MRI) is becoming more and more important in the clinical workflow of patients affected by breast carcinoma, because it enables the visual differentiation of normal tissues from pathological lesions owing to the increment of vascularity and capillary permeability of the latter [3][4][5][6]. Breast tumor can be classified into two broad types: in situ and invasive. The former is further subdivided into ductal and lobular, based on growth patterns and cytological characteristics. Ductal carcinoma in situ (DCIS) is more common than lobular carcinoma in situ (LCIS), accounting for 30-50% of all mammography-detected breast cancers [7,8], and consists in neoplastic cells within the ductal epithelium of the breast. It normally does not infiltrate through the basal membrane. The most common malignant lesion is invasive ductal carcinoma (IDC) and accounts for approximately 70% of all malignant cases [9,10]. In recent years, there has been a decrease in number of deaths associated with breast cancer, due to earlier diagnosis, as well as to the introduction of advanced surgical techniques [11].
The identification of diagnostic and prognostic markers that enable the implementation of more targeted drug therapies remains a priority in the era of precision medicine [12]. Over the last years, the scientific community has been showing an increasing interest for the potentiality of quantitative imaging for clinical purposes, encouraged by the significant advancements within the medical image analysis field. This exponential interest led to the development of Radiomics, a new field of research that aims at the conversion of all the information contained in digital medical images into quantifiable features, and the subsequent mining of this data. These computational features, normally related to tumor size, shape, intensity, and texture, may be associated with clinical outcomes, genetic alterations and other characteristics of the lesion, defining what is called tumor Radiomics signature [13]. In this way, Radiomics seems able to offer imaging biomarkers useful to diagnosis and to predict the response to therapy and the risk of recurrence [14]. In this paper, a small review of the applications of Radiomics to breast cancer is given, particularly targeted at the non-invasive distinction between in-situ and infiltrative tumors, and the preliminary results of a limited case study are reported.

Radiomics for Infiltrative vs In-Situ Distinction
Only recently (mainly after 2015), Radiomics approaches were applied to breast cancer [15], with the majority of studies being published in 2017 [12]. Among these studies, Radiomics was mainly investigated with MRI and focused on the ability of predicting malignancy, response to neoadjuvant chemotherapy, prognostic factors, molecular subtypes and risk of recurrence [12,14,16]. Some recent studies addressed the distinction between in situ and invasive breast cancer. For DCIS, upstaging to IDC at surgical excision occurs in roughly 25% of cases [17]. Failure to diagnose invasive cancer prior to surgery may have numerous implications. Normally, DCIS does not have metastatic potential. Thus, evaluation of regional or distant lymph nodes is usually not performed. Secondly, treatments are different between these two groups, so patients with IDC may need to undergo additional surgical procedures. This leads to the need to find different approaches to avoid unnecessary treatments in patients with non-invasive tumors, and many efforts should be made to achieve a diagnostic test for differentiation of in situ from invasive breast cancer. Although a few studies examine a pharmacological intervention as solution [18,19], others would prefer the Watch & Wait approach instead of immediate surgery, which obviously avoids aggressive intervention [20].  [24,25]. The principle underlying the use of ADC to discriminate between in situ and invasive cancer, is that the latter spreads throughout the breast tissue by degrading tissue structure by means of proteolytic activity. Thus, tissue changes and chronic inflammatory reaction to proteolysis lead to a relative or absolute reduction of extracellular water content.
What is then expected, is a reduction of ADC of invasive compared with non-invasive cancer. In order to prove the hypothesis, Bickel

A Case Study
In this section we report the preliminary results of a Radiomics investigation focused on the distinction between DCIS and IDC. The purpose was to determine the capability of machine learning to build statistics models for diagnosis, classification, and prediction

Conclusion
The non-invasive, reliable, pre-operative distinction between infiltrative and in-situ breast cancer represents an important challenge in the biomedical field. The contribution reported in our preliminary monocentric work aims to provide an automated clinical diagnosis tool and shows a final balanced accuracy score of 0.76. Its main limitation consists in the small sample size and the obvious imbalance of diagnoses towards infiltrating breast tumors.
In order to make the system able to generalize, and therefore to increase its quality, it is necessary to increase the size of the dataset, experimenting methods to make the dataset less unbalanced. In perspective, this result is expected to be achieved by involving different hospitals, thus creating a multicenter study.