Objective In 2018, colorectal cancer (CRC) was the second most frequent malignancy in Romania after lung cancer. Although CRC is typically encountered in patients >50 years old, CRC's global incidence among younger adults has been increasing. We aimed to compare the disease characteristics of patients with CRC aged ≤50 years with those >50 years old. Methods We retrospectively evaluated data from patients with CRC who underwent standard surgery at “Pius Brinzeu” Emergency County Hospital, Timisoara, Romania. Patients were divided into two groups: Group 1 (patients ≤50 years old) and Group 2 (patients >50 years old). Six parameters were analyzed (sex, residence location, age, tumor localization, microscopic findings, pathological staging). Results Data on age-related CRC were available for 1380 patients treated from January 2012 to December 2018. Group 1 included 120 patients while group 2 included 1260 patients. Significantly more Group 1 patients presented with advanced CRC compared with Group 2 patients (94.2% vs. 87.4%). Furthermore, CRC in younger adults was more likely to be diagnosed at an advanced stage. Conclusions Monitoring the CRC incidence in younger adults is essential to assess whether screening practices require changes and to raise awareness among clinicians of the increasing CRC incidence among younger patients.

Colorectal cancer (CRC) remains a major public health burden worldwide, despite increased knowledge on its pathogenesis and advances in therapy. We aimed to evaluate a new histological grading system based on poorly differentiated clusters (PDCs) counting -the PDCs grade (PDCs-G), and its clinicopathological and prognostic significance, compared to the World Health Organisation (WHO) grading system (WHO grade). We reviewed 71 surgical resection specimens for CRC from the Emergency County Hospital "Pius Brînzeu" Timisoara. The cases were graded using the WHO grade and the PDCs-G, with further analysis of their association with the other recognised prognostic parameters. Using the WHO grade, 9% of the analysed cases were G1, 80% G2, 11% G3, and none of the tumours was graded G4, while in the PDCs-G 16% were G1, 45% G2, and 39% G3. In multivariate analysis PDCs-G was significantly associated with the American Joint Committee on Cancer stage of the disease (AJCC stage) (p = 0.0003), depth of invasion (pT) (p = 0.0084), nodal status (LNM) (p < 0.0001), lymphovascular invasion (LVI) (p < 0.0001), perineural invasion (PNI) (p < 0.0052), and tumour border configuration (p < 0.0001). The novel grading system based on PDCs counting is an additional histological tool in the evaluation of CRC and a promising new prognostic factor for these patients.

Several alternatives to formalin-stored physical specimens have been described in medical literature, but only a few studies have addressed the issue of learning outcomes when these materials were employed. The aim of this study was to conduct a prospective controlled study to assess student performance in learning anatomic pathology when adding three-dimensional (3D) virtual models as adjunct teaching materials in the study of macroscopic lesions. Third-year medical students (n = 501) enrolled at the Victor Babes University of Medicine and Pharmacy in Timisoara, Romania, were recruited to participate. Student performance was assessed through questionnaires. Students performed worse with new method, with poorer results in terms of overall (mean 77.6% ±SD 11.8% vs. 83.6% ±10.5) and individual question scores (percentage of questions with maximum score 34.6% ±25.6 vs. 47.7 ± 24.6). This decreased performance was generalizable, as it was observed across all language divisions and was independent of the teaching assistant involved in the process. In an open-ended feedback evaluation of the new 3D specimens, most students agreed that the new method was better, bringing arguments both for and against these models. Although subjectively the students found the novel teaching materials to be more helpful, their learning performance decreased. A wider implementation as well as exposure to the technique and use of virtual specimens in medical teaching could improve the students' performance outcome by accommodating the needs for novel teaching materials for digital natives. Anat Sci Educ 15: 115-126.

The aim of our study was to assess the prognostic value of the two new grading systems based on the quantification of tumor budding - TB (GBd) and poorly differentiated clusters - PDCs (PDCs-G) in colorectal carcinomas (CRC). We performed a retrospective study on 71 CRC patients who underwent surgery at the Emergency County Hospital, Timișoara. CRC cases were classified based on haematoxylin-eosin slides, using the conventional grading system, GBd and PDCs-G, respectively. We used two-tier and three-tier grading schemes for each system. Subsequently,  we evaluated  associations with other prognostic factors in CRC. Based on the three-tier GBd (GBd-3t)  most cases (34/69, 49.27%) were classified as G3Bd-3t, while based on the conventional grading system, the majority of the cases (55/69, 79.71%) were considered G2. On the other hand, based on the three-tier PDCs-G system (PDCs-G-3t), most cases (31/69, 44.93%) were PDCs-G2-3t. We also noted a more significant association of GBd-3t with other prognostic parameters analyzed, as compared to the conventional grading system. Nodal status, tumor stage, and lymphovascular invasion were strongly correlated with GBd-3t (p=0.0001). Furthermore, we noted that PDCs-G-3t correlated more significantly than the conventional grading system with nodal status (p<0.0001), tumor stage (p=0.0003), lymphovascular invasion (p<0.0001), perineural invasion (p=0.005) and the tumor border configuration (p<0.0001).  High GBd and PDCs-G grades correlate directly with other negative prognostic factors in CRC.Thus, these new parameters/classification methods could be used as additional tools for risk stratification in patients with CRC.

Breast cancer is the most prevalent neoplasia among women, with early and accurate diagnosis critical for effective treatment. In clinical practice, however, the subjective nature of histological grading of infiltrating ductal adenocarcinoma of the breast (DAC-NOS) often leads to inconsistencies among pathologists, posing a significant challenge to achieving optimal patient outcomes. Our study aimed to address this reproducibility problem by leveraging artificial intelligence (AI). We trained a deep-learning model using a convolutional neural network-based algorithm (CNN-bA) on 100 whole slide images (WSIs) of DAC-NOS from the Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) dataset. Our model demonstrated high precision, sensitivity, and F1 score across different grading components in about 17.5 h with 19,000 iterations. However, the agreement between the model’s grading and that of general pathologists varied, showing the highest agreement for the mitotic count score. These findings suggest that AI has the potential to enhance the accuracy and reproducibility of breast cancer grading, warranting further refinement and validation of this approach.

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