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Background: Cancer of the breast has become a global problem for women's health. Though concerns regarding early detection and accurate diagnosis were raised, an effort is required for precision medicine as well as personalized treatment. In the past years, the area of medicinal imaging has seen an unprecedented growth that leads to an advancement of radiomics, which provides countless quantitative biomarkers extracted from modern diagnostic images, including a detailed tumor characterization of breast malignancy. Discussion: In this research, we presented the methodology and implementation of radiomics, together with its future trends and challenges by the basis of published papers. Radiomics could distinguish between malignant from benign tumors, predict prognostic factors, molecular subtypes of breast carcinoma, treatment response to neoadjuvant chemotherapy (NAC), and recurrence survival. The incorporation of quantitative knowledge with clinical, histopathological and genomic information will enable physicians to afford customized care of treatment for patients with breast cancer. Conclusion: Our research was intended to help physicians and radiologists learn fundamental knowledge about radiomics and also to work collaboratively with researchers to explore evidence for further usage in clinical practice.

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The leading cause of deaths among women in the world is Breast Cancer. Neoadjuvant chemotherapy (NAC) offers effective treatment results, thus reducing tumor aggression and allowing treatment monitoring. The Dynamic Contrast Enhanced (DCE) MRI plays a vital role in assessing the treatment response due to NAC. However, quantifying the treatment response in low-grade tumours is visually challenging. Radiomics is an evolving field of medical imaging that reflects the histopathological variations in breast tissues. Integrating radiomics with breast DCE-MRI provides clinically useful measures in evaluating the NAC response. In this work, we have formulated an index called Radiomics based Breast Malignancy Index (RBMI) using texture and Haar wavelets to differentiate the radiological differences of breast tissue due to NAC. The statistically significant radiomic features extracted from 20 DCE-MR images obtained using TCIA database were used in the calculation of RBMI. Results show that, RBMI could statistically differentiate (p=0.007) the treatment response between visit-1 & 2 due to NAC with mean and standard deviation values of 334706.5949 ± 93952.5123 and 296354.9720 ± 77120.6718 respectively. Hence, RBMI seems to be a clinically adjunct measure in evaluating the treatment response of breast cancer due to NAC.

In this work, an attempt has been made to quantify the treatment response due to Neoadjuvant Chemotherapy (NACT) on the publicly available QIN-Breast of TCIA database (N = 25) using Gabor filter derived radiomic features. The Gabor filter bank is constructed using 5 different scales and 7 different orientations. Different radiomic features were extracted from Gabor filtered Dynamic Contrast Enhanced Magnetic Resonance images (DCE-MRI) of patients having 3 different visits (Visit 1: before, Visit 2: after 1st cycle, and Visit 3: the last cycle of NACT). The extracted radiomic features were analyzed statistically and Area Under Receiver Operating Characteristic (AUROC) has been calculated. Results show that the Gabor derived radiomic features could differentiate the pathological differences among all three visits. Energy has shown a significant difference between all the three orientations particularly between Visits 2 & 3. However, Entropy from λ=2 and θ=300 between Visit 2 & 3, Skewness from λ=2 and θ=1200 between Visit 1 & 3 could differentiate the treatment response with high statistical significance of p=0.006 and 0.001 respectively. From the ROC analysis, the better predictors were Short Run Emphasis (SRE), Short Zone Emphasis (SZE), and Energy between Visit 1 & 3 by achieving an AUROC of 76.38%, 75.16%, and 71.10% respectively. Further, the results suggest that the radiomic features are capable of quantitatively compare the breast NACT prognosis that varies across multi-oriented Gabor filters.

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The transformations through technological innovations have influenced the medical field. There are significant developments in medical devices in their usage. The utilization of the devices is automated in a local, remote environment. The medical devices used in the remote cyber environment uses different network protocols. These devices comprise micro, nanofabricated sensors and actuators which have the facility to communicate using network protocols. The devices that have network capability to integrate into cyberspace through physical methods are typical medical cyber physical systems (MCPS). In MCPS, medical device modelling is an important aspect. Several medical devices are available, and here in the current research, emphasis is focused on smart medical pumps in the MCPS environment. This chapter highlights the essential concepts of the smart medical drug delivery device, its architecture, control, actuation, communication, and analysis in the cyber environment.