Publications by authors named "J M Bokhorst"
Background: Tumor Budding (TB) and Immunoscore are independent prognostic markers in colon cancer (CC). Given their respective representation of tumor aggressiveness and immune response, we examined their combination in association with patient disease-free survival (DFS) in pTNM stage I-III CC.
Methods: In a series of pTNM stage I-III CCs (n = 654), the Immunoscore was computed and TB detected automatically using a deep learning network.
Article Synopsis
- This study explores the significance of tumor budding (TB) in colorectal cancer, particularly focusing on intratumoral budding (ITB) in resection specimens and its feasibility in biopsy samples.* -
- The research found that high-grade TB, whether intratumoral or peritumoral, is linked with worse outcomes such as advanced cancer stages and lower overall survival rates.* -
- Results indicated that ITB is a strong predictor of overall survival and can help in improving risk assessment and predicting responses to neoadjuvant therapy in cancer patients, highlighting the need for TB evaluation in biopsies.*
In recent years, it has become clear that artificial intelligence (AI) models can achieve high accuracy in specific pathology-related tasks. An example is our deep-learning model, designed to automatically detect serous tubal intraepithelial carcinoma (STIC), the precursor lesion to high-grade serous ovarian carcinoma, found in the fallopian tube. However, the standalone performance of a model is insufficient to determine its value in the diagnostic setting.
View Article and Find Full Text PDFColorectal cancer (CRC) raises considerable clinical challenges, including a high mortality rate once the tumor spreads to distant sites. At this advanced stage, more accurate prediction of prognosis and treatment outcome is urgently needed. The role of cancer immunity in metastatic CRC (mCRC) is poorly understood.
View Article and Find Full Text PDFPurpose: This study aims to introduce an innovative multi-step pipeline for automatic tumor-stroma ratio (TSR) quantification as a potential prognostic marker for pancreatic cancer, addressing the limitations of existing staging systems and the lack of commonly used prognostic biomarkers.
Methods: The proposed approach involves a deep-learning-based method for the automatic segmentation of tumor epithelial cells, tumor bulk, and stroma from whole-slide images (WSIs). Models were trained using five-fold cross-validation and evaluated on an independent external test set.