Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies, with poor survival rates largely due to late-stage diagnosis. Distinguishing PDAC from chronic pancreatitis (CP) remains a major diagnostic challenge, as both conditions exhibit overlapping histopathological features. Traditional diagnostic methods, including histopathology, remain the gold standard but often yield inconclusive results due to the fragmented nature of biopsy samples and the subtlety of distinguishing features. This study explores the integration of artificial intelligence (AI)-driven computer vision techniques to enhance histologic assessments and improve the accuracy of PDAC diagnosis. By leveraging computational pathology, AI models can aid pathologists in identifying critical features, reducing diagnostic uncertainty, and facilitating earlier intervention.
Biliary obstructive disorders are a significant global health challenge, with high morbidity and mortality. Endoscopic retrograde cholangiopancreatography (ERCP) is the standard diagnostic procedure, with over 500,000 performed annually in the US. While ERCP helps distinguish benign from malignant obstructions through morphologic imaging, definitive diagnosis requires tissue sampling. However, conventional pathological assessment of bile duct biopsies has a sensitivity of only 18% to 67%, leading to diagnostic uncertainty. This highlights the need for AI-driven image analysis to enhance histologic assessment and improve clinical outcomes.
Histological remission is emerging as a critical treatment target in IBD, complementing endoscopic remission by providing deeper insights into disease activity and therapeutic efficacy. However, traditional pathologist evaluations are semiquantitative, subjective, and limited in granularity. This study aimed to develop and validate a deep learning framework comprising multiple models for the quantitative assessment of histological features in IBD.
Lymph node (LN) assessment is a critical component in the staging and management of cutaneous melanoma. Traditional histopathological evaluation, supplemented by immunohistochemical staining, is the gold standard for detecting LN metastases. However, the process is labor-intensive, requiring the analysis of multiple tissue levels, which increases both time and cost. With the growing integration of artificial intelligence (AI) into clinical workflows, there is potential to streamline this process, enhancing efficiency and accuracy.
Traditional prognostic systems like the AJCC TNM staging for colorectal cancer (CRC) often fall short in predicting long-term patient outcomes. These systems often rely on limited pathologic features, leading to generalized approaches to treatment despite diverse tumor heterogeneity, such as the complex interaction between the tumor-host microenvironment. There is therefore a pressing need for more accurate, scalable tools to enhance decision-making and predict outcomes for patients with CRC.
We employ advanced AI models to identify the tumor bed and we use AI segmentation algorithms for eosinophils, plasma cells, and lymphocyte predictions, aiming to gain a deeper understanding of the tumor microenvironment (TME).
The interplay between tumor and inflammatory cells is crucial in cancer initiation and progression. The ongoing investigation of the immunoscore across diverse tumor types is underway, empowering our practice with tool to enhance predictive and prognostic markers for patients outcome. Assessing inflammatory infiltrates such as tumor-associated eosinophils can offer valuable prognostic insights. In this study, we present an innovative approach utilizing artificial intelligence (AI) for automated eosinophil detection in colorectal cancer.
The diagnostic challenge in distinguishing pancreatic ductal adenocarcinoma PDAC from chronic pancreatitis lies in the overlapping histopathological features, compounded by the fragmented samples where subtle differences may be difficult to discern. While histopathology is the gold standard for PDAC detection, the subtle differences between the two can be easily overlooked. The integration of computer vision methods, offers promising avenues to enhance the accuracy of PDAC analysis, potentially aiding in improved diagnostics.
Lymph node (LN) assessment is a critical component in the staging and management of cutaneous melanoma. Traditional histopathological evaluation, supplemented by immunohistochemical staining, is the gold standard for detecting LN metastases. However, the process is labor-intensive which increases both time and cost. With the growing integration of artificial intelligence (AI) into clinical workflows, there is potential to streamline this process, enhancing both efficiency and accuracy.
Colorectal carcinoma CRC presents a complex and diverse interplay between tumor and its microenvironment, significantly influencing tumor behavior. Current pathological assessments often fail to fully capture the impact of this multifaceted microenvironment. Traditional prognostic tools, rely on a limited set of predefined features, reducing their effectiveness in predicting patient outcomes. This highlights the unmet need for scalable methods to develop prognostic biomarkers and improve patient care.
