The interest in implementing digital pathology (DP) workflows to obtain whole slide image (WSI) files for diagnostic purposes has increased in the last few years. The increasing performance of technical components and the Food and Drug Administration (FDA) approval of systems for primary diagnosis led to increased interest in applying DP workflows. However, despite this revolutionary transition, real world data suggest that a fully digital approach to the histological workflow has been implemented in only a minority of pathology laboratories. The objective of this study is to facilitate the implementation of DP workflows in pathology laboratories, helping those involved in this process of transformation to identify: (a) the scope and the boundaries of the DP transformation; (b) how to introduce automation to reduce errors; (c) how to introduce appropriate quality control to guarantee the safety of the process and (d) the hardware and software needed to implement DP systems inside the pathology laboratory. The European Society of Digital and Integrative Pathology (ESDIP) provided consensus-based recommendations developed through discussion among members of the Scientific Committee. The recommendations are thus based on the expertise of the panel members and on the agreement obtained after virtual meetings. Prior to publication, the recommendations were reviewed by members of the ESDIP Board. The recommendations comprehensively cover every step of the implementation of the digital workflow in the anatomic pathology department, emphasizing the importance of interoperability, automation and tracking of the entire process before the introduction of a scanning facility. Compared to the available national and international guidelines, the present document represents a practical, handy reference for the correct implementation of the digital workflow in Europe.

Colorectal cancer (CRC) remains one of the most important global health problems, being in the top 3 neoplasms in terms of the number of cases worldwide. Although CRC develops predominantly from the adenoma–adenocarcinoma sequence through APC gene mutations, in recent years, studies have demonstrated the role of chronic inflammation in this neoplasia pathogenesis. Cytokines are important components of chronic inflammation, being some of the host regulators in response to inflammation. The pro-inflammatory cytokines IL-1β, IL-6, and TNF-α are involved in tumor cell proliferation, angiogenesis, and metastasis and seem to strengthen each other’s mode of action, these being stimulated by the same mediators. In our study, we collected data on 68 patients with CRC and 20 healthy patients from the Gastroenterology Department of Craiova County Emergency Clinical Hospital, who were assessed between January 2022 and February 2023. The main purpose of this study was to investigate the correlation between increased plasma levels of the cytokines and the extent of the tumor, lymph nodes, and metastasis—(TNM stage), as well as the patients’ prognoses. We also compared the plasma levels of cytokines and acute inflammatory markers, namely, the erythrocyte sedimentation rate (ESR), c-reactive protein (CRP), and fibrinogen, along with the tumor markers, carcinoembryonic antigen (CEA) and carbohydrate antigen 19.9 (CA 19.9), in CRC patients. We showed that all the pro-inflammatory cytokines studied had higher levels in patients with CRC in comparison with the control group. We also showed that the acute inflammatory markers of erythrocyte sedimentation rate, C-reactive protein, and fibrinogen, and the tumor markers of CEA and CA 19.9 can be useful in diagnosis and prognosis in patients with CRC. Considering the association between pro-inflammatory cytokines and CRC, the development of new targeted therapies against IL-1β, IL-6, and TNF-α can improve patient care and the CRC survival rate.

(1) Background: Coronavirus disease 2019 (COVID-19) is an infectious disease caused by SARS-CoV-2. Reverse transcription polymerase chain reaction (RT-PCR) remains the current gold standard for detecting SARS-CoV-2 infections in nasopharyngeal swabs. In Romania, the first reported patient to have contracted COVID-19 was officially declared on 26 February 2020. (2) Methods: This study proposes a federated learning approach with pre-trained deep learning models for COVID-19 detection. Three clients were locally deployed with their own dataset. The goal of the clients was to collaborate in order to obtain a global model without sharing samples from the dataset. The algorithm we developed was connected to our internal picture archiving and communication system and, after running backwards, it encountered chest CT changes suggestive for COVID-19 in a patient investigated in our medical imaging department on the 28 January 2020. (4) Conclusions: Based on our results, we recommend using an automated AI-assisted software in order to detect COVID-19 based on the lung imaging changes as an adjuvant diagnostic method to the current gold standard (RT-PCR) in order to greatly enhance the management of these patients and also limit the spread of the disease, not only to the general population but also to healthcare professionals.

Establishing basal cell carcinoma (BCC) subtype is sometimes challenging for pathologists. Deep-learning (DL) algorithms are an emerging approach in image classification due to their performance, accompanied by a new concept – transfer learning, which implies replacing the final layers of a trained network and retraining it for a new task, while keeping the weights from the imported layers. A DL convolution-based software, capable of classifying 10 subtypes of BCC, was designed. Transfer learning from three general-purpose image classification networks (AlexNet, GoogLeNet, and ResNet-18) was used. Three pathologists independently labeled 2249 patches. Ninety percent of data was used for training and 10% for testing on 100 independent training sequences. Each of the resulted networks independently labeled the whole dataset. Mean and standard deviation (SD) accuracy (ACC) [%]/sensitivity (SN) [%]/specificity (SP) [%]/area under the curve (AUC) for all the networks was 82.53±2.63/72.52±3.63/97.94±0.3/0.99. The software was validated on another 50-image dataset, and its results are comparable with the result of three pathologists in terms of agreement. All networks had similar classification accuracies, which demonstrated that they reached a maximum classification rate on the dataset. The software shows promising results, and with further development can be successfully used on histological images, assisting pathologists’ diagnosis and teaching.

Background: Colorectal cancer affects a large number of patients worldwide, with numerous factors being involved in its etiopathogenesis and chronic inflammation playing an essential role in tumor development. In this study, we analyzed and compared several markers of inflammation that are relatively easy to obtain for a rapid and accurate diagnosis and prognosis. Methods: This study included 219 patients diagnosed with colorectal cancer, analyzing the inflammation scores derived from their blood cells and inflammatory circulating proteins. These inflammatory markers are neutrophil-to-lymphocyte ratio—NLR; platelet-to-lymphocyte ratio—PLR; lymphocyte-to-monocyte ratio—LMR; systemic immune inflammation index—SII; systemic inflammatory response index—SIRI; aggregate index of systemic inflammation—AISI; derived neutrophil-to-lymphocyte ratio—dNLR; C-reactive protein-to-albumin ratio—CAR; and fibrinogen-to-albumin ratio—FAR. In the analysis of patients with colorectal cancer, we have also introduced two new recently developed inflammatory markers: the cumulative inflammatory index (IIC) and the ratio between the mean corpuscular volume and lymphocytes (MCVL). This study aimed to correlate the inflammatory markers’ levels with the colorectal cancer diagnostic stage, the tumor and clinical characteristics of the colorectal cancer patients, and 36 months’ survival time and to evaluate the diagnostic and prognostic capacity and accuracy of these inflammatory markers in this type of cancer. Results: We showed that the levels of the analyzed inflammation markers correlate with the TNM stage, the tumor pathological differentiation grade, the age and gender of the patients, and overall survival, with their increased levels being associated with a lower survival rate. Conclusions: The analyzed markers, which are easy to perform right from the patient’s admission, can be helpful both in diagnosis and, mostly, in prognosis, sustaining the role of inflammation in cancer. By comparing them, we showed which one can be useful for increased sensitivity and specificity in the diagnosis and prognosis of colorectal cancer patients.