Colorectal cancer (CRC) is the second most common cancer in women and the third most common in men, with an increasing incidence. Pathology diagnosis complemented with prognostic and predictive biomarker information is the first step for personalized treatment. The increased diagnostic load in the pathology laboratory, combined with the reported intra- and inter-variability in the assessment of biomarkers, has prompted the quest for reliable machine-based methods to be incorporated into the routine practice. Recently, Artificial Intelligence (AI) has made significant progress in the medical field, showing potential for clinical applications. Herein, we aim to systematically review the current research on AI in CRC image analysis. In histopathology, algorithms based on Deep Learning (DL) have the potential to assist in diagnosis, predict clinically relevant molecular phenotypes and microsatellite instability, identify histological features related to prognosis and correlated to metastasis, and assess the specific components of the tumor microenvironment.

Lung cancer is one of the deadliest cancers worldwide, with a high incidence rate, especially in tobacco smokers. Lung cancer accurate diagnosis is based on distinct histological patterns combined with molecular data for personalized treatment. Precise lung cancer classification from a single H&E slide can be challenging for a pathologist, requiring most of the time additional histochemical and special immunohistochemical stains for the final pathology report. According to WHO, small biopsy and cytology specimens are the available materials for about 70% of lung cancer patients with advanced-stage unresectable disease. Thus, the limited available diagnostic material necessitates its optimal management and processing for the completion of diagnosis and predictive testing according to the published guidelines. During the new era of Digital Pathology, Deep Learning offers the potential for lung cancer interpretation to assist pathologists’ routine practice. Herein, we systematically review the current Artificial Intelligence-based approaches using histological and cytological images of lung cancer. Most of the published literature centered on the distinction between lung adenocarcinoma, lung squamous cell carcinoma, and small cell lung carcinoma, reflecting the realistic pathologist’s routine. Furthermore, several studies developed algorithms for lung adenocarcinoma predominant architectural pattern determination, prognosis prediction, mutational status characterization, and PD-L1 expression status estimation.

Angiogenesis has long been considered to facilitate and sustain cancer growth, making the introduction of anti-angiogenic agents that disrupt the vascular endothelial growth factor/receptor (VEGF/VEGFR) pathway an important milestone at the beginning of the 21st century. Originally research on VEGF signaling focused on its survival and mitogenic effects towards endothelial cells, with moderate so far success of anti-angiogenic therapy. However, VEGF can have multiple effects on additional cell types including immune and tumor cells, by directly influencing and promoting tumor cell survival, proliferation and invasion and contributing to an immunosuppressive microenvironment. In this review, we summarize the effects of the VEGF/VEGFR pathway on non-endothelial cells and the resulting implications of anti-angiogenic agents that include direct inhibition of tumor cell growth and immunostimulatory functions. Finally, we present how previously unappreciated studies on VEGF biology, that have demonstrated immunomodulatory properties and tumor regression by disrupting the VEGF/VEGFR pathway, now provide the scientific basis for new combinational treatments of immunotherapy with anti-angiogenic agents.

Introduction: Use of the prone position in patients with acute lung injury improves their oxygenation. Most of these patients die from multisystem organ failure and not from hypoxia, however. Moreover, there is some evidence that the organ failure is caused by increased cell apoptosis. In the present study we therefore examined whether the position of the patients affects histological changes and apoptosis in the lung and 'end organs', including the brain, heart, diaphragm, liver, kidneys and small intestine. Methods: Ten mechanically ventilated sheep with a tidal volume of 15 ml/kg body weight were studied for 90 minutes. Five sheep were placed in the supine position and five sheep were placed in the prone position during the experiment. Lung changes were analyzed histologically using a semiquantitative scoring system and the extent of apoptosis was investigated with the TUNEL method. Results: In the supine position intra-alaveolar hemorrhage appeared predominantly in the dorsal areas, while the other histopathologic lesions were homogeneously distributed throughout the lungs. In the prone position, all histological changes were homogeneously distributed. A significantly higher score of lung injury was found in the supine position than in the prone position (4.63 ± 0.58 and 2.17 ± 0.19, respectively) (P < 0.0001). The histopathologic changes were accompanied by increased apoptosis (TUNEL method). In the supine position, the apoptotic index in the lung and in most of the 'end organs' was significantly higher compared with the prone position (all P < 0.005). Interestingly, the apoptotic index was higher in dorsal areas compared with ventral areas in both the prone and supine positions (P < 0.003 and P < 0.02, respectively). Conclusion: Our results suggest that the prone position appears to reduce the severity and the extent of lung injury, and is associated with decreased apoptosis in the lung and 'end organs'.

The vast majority of patients with HER2-positive metastatic breast cancer (MBC) treated with trastuzumab eventually develop resistance to this agent. There is an unmet need therefore, for identifying biological markers with possible prognostic/predictive value in such patients. The aim of this study was to investigate the prognostic role of topoisomerase II alpha gene (TOP2A) amplification and protein (TopoIIa) expression in patients treated with trastuzumab-containing regimens.