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Lymphocytes, Tumor-Infiltrating

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The Predictive Value of Monocytes in Immune Microenvironment and Prognosis of Glioma Patients Based on Machine Learning.

Frontiers in immunology
Gliomas are primary malignant brain tumors. Monocytes have been proved to actively participate in tumor growth. Weighted gene co-expression network analysis was used to identify meaningful monocyte-related genes for clustering. Neural network and SVM...

PathoNet introduced as a deep neural network backend for evaluation of Ki-67 and tumor-infiltrating lymphocytes in breast cancer.

Scientific reports
The nuclear protein Ki-67 and Tumor infiltrating lymphocytes (TILs) have been introduced as prognostic factors in predicting both tumor progression and probable response to chemotherapy. The value of Ki-67 index and TILs in approach to heterogeneous ...

Deep learning-based predictive biomarker of pathological complete response to neoadjuvant chemotherapy from histological images in breast cancer.

Journal of translational medicine
BACKGROUND: Pathological complete response (pCR) is considered a surrogate endpoint for favorable survival in breast cancer patients treated with neoadjuvant chemotherapy (NAC). Predictive biomarkers of treatment response are crucial for guiding trea...

[Artificial intelligence and medical imaging].

Bulletin du cancer
The use of artificial intelligence methods for image recognition is one of the most developed branches of the AI field and these technologies are now commonly used in our daily lives. In the field of medical imaging, approaches based on artificial in...

Spatial analysis of tumor-infiltrating lymphocytes in histological sections using deep learning techniques predicts survival in colorectal carcinoma.

The journal of pathology. Clinical research
This study aimed to explore the prognostic impact of spatial distribution of tumor-infiltrating lymphocytes (TILs) quantified by deep learning (DL) approaches based on digitalized whole-slide images stained with hematoxylin and eosin in patients with...

Development of a Fully Automated Method to Obtain Reproducible Lymphocyte Counts in Patients With Colorectal Cancer.

Applied immunohistochemistry & molecular morphology : AIMM
Colorectal cancer (CRC) is the third most common cancer worldwide. Although clinical outcome varies among patients diagnosed within the same TNM stage it is the cornerstone in treatment decisions as well as follow-up programmes. Tumor-infiltrating ly...

Deep learning with biopsy whole slide images for pretreatment prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer:A multicenter study.

Breast (Edinburgh, Scotland)
INTRODUCTION: Predicting pathological complete response (pCR) for patients receiving neoadjuvant chemotherapy (NAC) is crucial in establishing individualized treatment. Whole-slide images (WSIs) of tumor tissues reflect the histopathologic informatio...

Microfluidics guided by deep learning for cancer immunotherapy screening.

Proceedings of the National Academy of Sciences of the United States of America
Immunocyte infiltration and cytotoxicity play critical roles in both inflammation and immunotherapy. However, current cancer immunotherapy screening methods overlook the capacity of the T cells to penetrate the tumor stroma, thereby significantly lim...

Deep learning-based scoring of tumour-infiltrating lymphocytes is prognostic in primary melanoma and predictive to PD-1 checkpoint inhibition in melanoma metastases.

EBioMedicine
BACKGROUND: Recent advances in digital pathology have enabled accurate and standardised enumeration of tumour-infiltrating lymphocytes (TILs). Here, we aim to evaluate TILs as a percentage electronic TIL score (eTILs) and investigate its prognostic a...