Imaging of Colonic and Rectal Cancer

Colorectal cancer is one of the most common cancers worldwide. Thus, its early detection through screening and diagnostic techniques is the key in managing this condition. For this to be possible, it is necessary to know the risk factors and to choose the appropriate screening and diagnostic techniques for each case. Imaging also plays a key role in treatment planning by assessing both local and distance extension of the disease

Noninvasive Evaluation of Microcirculation under Normal and Pathological Conditions Using Contrast-Enhanced Ultrasonography (CEUS)

The present chapter highlights the most important information about microcirculation and its evaluation using contrast-enhanced ultrasonography (CEUS). In the beginning it outlines some general considerations about microcirculation, together with its morphological and physiological particularities under normal and pathological circumstances. The ultrasonographic (US) evaluation of vascularity is based on the Doppler technique and the harmonic technique using contrast agents. Then it presents briefly the Doppler ultrasound (DUS) and discusses its most important current and emerging indications. CEUS is presented extensively, covering the fundamentals of sonographic contrast agents, harmonic imaging and quantification techniques. A special focus is placed not only on the current and emerging indications of CEUS but also on the advantages and limitations of the method. This chapter also incorporates information about experimental CEUS applications and future perspectives. CEUS is the recommended US method for the characterization of microcirculation. The results of the examination are displayed in real-time, under the eyes of the examiner, while the quantitative assessment of the contrast agent kinetics parameters is easy to perform. This method allows a precise definition of the healthy or pathologic state of an organ and the follow-up of treatment response

Combined miRNA and SERS urine liquid biopsy for the point-of-care diagnosis and molecular stratification of bladder cancer

Background: Bladder cancer (BC) has the highest per-patient cost of all cancer types. Hence, we aim to develop a non-invasive, point-of-care tool for the diagnostic and molecular stratification of patients with BC based on combined microRNAs (miRNAs) and surface-enhanced Raman spectroscopy (SERS) profiling of urine. Methods: Next-generation sequencing of the whole miRNome and SERS profiling were performed on urine samples collected from 15 patients with BC and 16 control subjects (CTRLs). A retrospective cohort (BC = 66 and CTRL = 50) and RT-qPCR were used to confirm the selected differently expressed miRNAs. Diagnostic accuracy was assessed using machine learning algorithms (logistic regression, naive Bayes, and random forest), which were trained to discriminate between BC and CTRL, using as input either miRNAs, SERS, or both. The molecular stratification of BC based on miRNA and SERS profiling was performed to discriminate between high-grade and low-grade tumors and between luminal and basal types. Results: Combining SERS data with three differentially expressed miRNAs (miR-34a-5p, miR-205-3p, miR-210-3p) yielded an Area Under the Curve (AUC) of 0.92 +/- 0.06 in discriminating between BC and CTRL, an accuracy which was superior either to miRNAs (AUC = 0.84 +/- 0.03) or SERS data (AUC = 0.84 +/- 0.05) individually. When evaluating the classification accuracy for luminal and basal BC, the combination of miRNAs and SERS profiling averaged an AUC of 0.95 +/- 0.03 across the three machine learning algorithms, again better than miRNA (AUC = 0.89 +/- 0.04) or SERS (AUC = 0.92 +/- 0.05) individually, although SERS alone performed better in terms of classification accuracy. Conclusion: miRNA profiling synergizes with SERS profiling for point-of-care diagnostic and molecular stratification of BC. By combining the two liquid biopsy methods, a clinically relevant tool that can aid BC patients is envisaged

Fourier transform Raman and statistical analysis of thermally altered samples of amber

We report the experimental results that refer to a Fourier transform Raman (FT-Raman) survey of thermally altered Baltic and Romanian amber and the related statistical interpretation of data using principal component analysis (PCA). Although FT-Raman spectra show several small changes in the characteristic features of the investigated amber samples which may be used for discrimination, their visual recognition is relatively difficult, especially when interpreting data from archeological samples, and thus multivariate data analysis may be the solution to more accurately assign the geological origin based on overall characteristic spectral features. The two categories of amber have different behavior in terms of degradation during the experimental alteration, and Romanian amber is more susceptible to physico-chemical transformations by the aggressive environment when compared with Baltic amber. The obtained data were in accordance with the Fourier transform infrared (FT-IR) remarks published previously in a dedicated journal. The Raman technique is an alternative method that requires little to no sample preparation, water does not cause interference, and the spectra can be collected from a small volume (1-50 μm in diameter)

SERS Liquid Biopsy Profiling of Serum for the Diagnosis of Kidney Cancer

Renal cancer (RC) represents 3% of all cancers, with a 2% annual increase in incidence worldwide, opening the discussion about the need for screening. However, no established screening tool currently exists for RC. To tackle this issue, we assessed surface-enhanced Raman scattering (SERS) profiling of serum as a liquid biopsy strategy to detect renal cell carcinoma (RCC), the most prevalent histologic subtype of RC. Thus, serum samples were collected from 23 patients with RCC and 27 controls (CTRL) presenting with a benign urological pathology such as lithiasis or benign prostatic hypertrophy. SERS profiling of deproteinized serum yielded SERS band spectra attributed mainly to purine metabolites, which exhibited higher intensities in the RCC group, and Raman bands of carotenoids, which exhibited lower intensities in the RCC group. Principal component analysis (PCA) of the SERS spectra showed a tendency for the unsupervised clustering of the two groups. Next, three machine learning algorithms (random forest, kNN, naïve Bayes) were implemented as supervised classification algorithms for achieving discrimination between the RCC and CTRL groups, yielding an AUC of 0.78 for random forest, 0.78 for kNN, and 0.76 for naïve Bayes (average AUC 0.77 ± 0.01). The present study highlights the potential of SERS liquid biopsy as a diagnostic and screening strategy for RCC. Further studies involving large cohorts and other urologic malignancies as controls are needed to validate the proposed SERS approach