AIMC Topic: Lymphocytes, Tumor-Infiltrating

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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...

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...

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...

[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...

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...

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 ...

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...

Novel deep learning-based survival prediction for oral cancer by analyzing tumor-infiltrating lymphocyte profiles through CIBERSORT.

Oncoimmunology
The tumor microenvironment (TME) within mucosal neoplastic tissue in oral cancer (ORCA) is greatly influenced by tumor-infiltrating lymphocytes (TILs). Here, a clustering method was performed using CIBERSORT profiles of ORCA data that were filtered f...