PhiΦBreast New Diagnostic Techniques for Breast Cancer Detection

Abstract

Background: Today, breast cancer is one of the most aggressive cancers in women and new cases continue to increase worldwide. In order to reduce mortality, we need to detect this life-threatening disease at an earlier stage. For two years, we have conducted a study for the identification and characterization of suspicious breast lesions using a new diagnostic technique applied to ultrasonography and mammography called “PhiΦBreast.” Methods: We have conducted a study for the identification and characterization of suspicious breast lesions using a new diagnostic technique applied to mammography and ultrasonography. Ultrasound systems with high frequency probes was used, 10-15 MHz, and Digital Breast Tomosynthesis (DBT). Identification and characterization of category C4-C5 lesions of the breast with high Predictive Positive PPV value, with a new innovative method called “PhiΦBreast” using the Golden Ratio (Phi, or Φ 1.618...) Fibonacci sequence and a Predictive Algorithm, applied to the ultrasonography and mammography with subsequent deepening with cytological examination using Fine Needle Aspiration Cytology (FNAC), according to evaluation criteria of the Breast Imaging Report Data System (BI-RADS) and the American College of Radiology (ACR). Usefulness of this research and the use of this new diagnostic tecnique is to detect the breast cancer in early stage. In addition to develop a classification model of the histological type identified in the section areas and the percentage of probability in relation between the Golden Ratio (Φ) and Fibonacci sequence. Results: With the use of Golden Ratio and Fibonacci sequence, applied to ultrasonography and mammography, we have experimented and developed a diagnostic map with characteristics of high probability of identifying suspicious lesions at an early stage. We examined 987 women, 55 lesions detected with PhiΦBreast pattern were classified according to BI-RADS descriptors for US-imaging, including morphologic features that had a high predictive value for the malignancy (p <0.001) This innovative diagnostic technique has shown a sensitivity of 95%, a specificity of 97%, a positive predictive value (PPV) of 97%, and negative predictive value (NPV) of 96%. Furthermore, with a Predictive Algorithm associated with malignant cytology after FNAC, we have classified different types of potentially life-threatening cancers for patients. Conclusion: PhiΦBreast could be an important new model diagnostic technique to be applied mammography and ultrasound for detection of malignant lesions of category C4-C5. In diagnostic imaging is fundamental to recognize predictively the characteristics of a potentially aggressive tumor. This new model could represent the cornerstone as an important contribution for early diagnosis of breast cancer.