Prognostic significance of tumor budding, desmoplastic reaction, and lymphocytic infiltration in patients with gastric adenocarcinoma

BACKGROUND

Recent studies have shown that the tumor microenvironment significantly influences the behavior of solid tumors. In this context, Accumulated data suggests that pathological evaluation of tumor budding (TB), desmoplastic reaction (DR), and tumor-infiltrating lymphocytes (TILs) may be crucial in determining tumor behavior in the gastrointestinal tract. Regarding gastric adenocarcinoma (GAC), although some results suggest that TB and TILs may be effective in determining the course of the disease, the data do not agree. Moreover, very few studies have investigated the relationship between DR and survival. At present, the associations between tumor TB, DR and TILs in GAC patients have not been determined.

AIM

To establish the relationships between TB, DR, and TILs in patients with GAC and to assess their influence on prognosis.

METHODS

Our study group comprised 130 patients diagnosed with GAC. The definition of TB was established based on the International TB Consensus Conference. The DR was categorized into three groups according to the level of tumor stroma maturation. The assessment of TILs was conducted using a semiquantitative approach, employing a cutoff value of 5%. The statistical analysis of the whole group and 100 patients with an intestinal subtype of GAC was performed using SPSS version 27.

RESULTS

A significant correlation between peritumoral budding (PTB) and intratumoral budding (ITB) was noted (r = 0.943). Tumors with high PTBs and ITBs had a greater incidence of immature DRs and low TILs (P < 0.01). PTB and ITB were associated with histological subtype, lymph node metastasis (LNM), and stage (P < 0.01). ITB, PTB, LNM, DR, and stage were significant risk factors associated with poor prognosis. The multivariate Cox regression analysis identified ITB, PTB, and LNM as independent prognostic variables (P < 0.05). In intestinal-type adenocarcinomas, a positive correlation between PTB and ITB was noted (r = 0.972). While univariate analysis revealed that LNM, stage, PTB, ITB, and DR were strong parameters for predicting survival (P < 0.05), only PTB and ITB were found to be independent prognostic factors (P < 0.001).

CONCLUSION

TB may be a potential prognostic marker in GAC. However, further studies are needed to delineate its role in pathology reporting protocols and the predictive effects of DR and TILs.

Tumor budding as a potential prognostic marker in determining the behavior of primary liver cancers

Hepatocellular (HCC) and intrahepatic cholangiocarcinoma (ICC), the most common primary tumors of the liver, are among the most important causes of cancer deaths worldwide. Because patients with primary liver tumors are frequently diagnosed at an advanced stage and have high mortality, many efforts have been made to identify new markers to determine their behavior and treatment, similar to those in other solid organ tumors. Recently, morphological assessment of tumor budding (TB) has been revealed as a promising prognostic finding to predict tumor behavior and survival across several different tumor types. Currently, the TB score in colorectal cancer has been revealed as an important parameter in pathology report protocols to determine the course of the disease. Regarding the liver, despite enormous data showing that many mechanisms involved in TB are associated with tumor behavior in both HCC and ICC, studies focusing on the role of TB in predicting the behavior and prognosis of these tumors have started to be investigated very recently. The purpose of this review is to present data about TB in primary tumors of the liver, pointing out the potential role of this parameter in determining the course of the disease, and emphasize the need to increase the number of further studies focusing on the evaluation of this parameter with an overview of the mechanisms involved in TB.

Artificial intelligence applications in predicting the behavior of gastrointestinal cancers in pathology

Recent research has provided a wealth of data supporting the application of artificial intelligence (AI)-based applications in routine pathology practice. Indeed, it is clear that these methods can significantly support an accurate and rapid diagnosis by eliminating errors, increasing reliability, and improving workflow. In addition, the effectiveness of AI in the pathological evaluation of prognostic parameters associated with behavior, course, and treatment in many types of tumors has also been noted. Regarding gastrointestinal system (GIS) cancers, the contribution of AI methods to pathological diagnosis has been investigated in many studies. On the other hand, studies focusing on AI applications in evaluating parameters to determine tumor behavior are relatively few. For this purpose, the potential of AI models has been studied over a broad spectrum, from tumor subtyping to the identification of new digital biomarkers. The capacity of AI to infer genetic alterations of cancer tissues from digital slides has been demonstrated. Although current data suggest the merit of AI-based approaches in assessing tumor behavior in GIS cancers, a wide range of challenges still need to be solved, from laboratory infrastructure to improving the robustness of algorithms, before incorporating AI applications into real-life GIS pathology practice. This review aims to present data from AI applications in evaluating pathological parameters related to the behavior of GIS cancer with an overview of the opportunities and challenges encountered in implementing AI in pathology.