Deep learning for lung cancer diagnosis, prognosis and prediction using histological and cytological images: a systematic review
Simple Summary Lung cancer is one of the most common and deadly malignancies
worldwide. Microscopic examination of histological and cytological lung specimens can be a …
worldwide. Microscopic examination of histological and cytological lung specimens can be a …
Evaluating cancer-related biomarkers based on pathological images: a systematic review
X Xie, X Wang, Y Liang, J Yang, Y Wu, L Li… - Frontiers in …, 2021 - frontiersin.org
Many diseases are accompanied by changes in certain biochemical indicators called
biomarkers in cells or tissues. A variety of biomarkers, including proteins, nucleic acids …
biomarkers in cells or tissues. A variety of biomarkers, including proteins, nucleic acids …
The impact of site-specific digital histology signatures on deep learning model accuracy and bias
Abstract The Cancer Genome Atlas (TCGA) is one of the largest biorepositories of digital
histology. Deep learning (DL) models have been trained on TCGA to predict numerous …
histology. Deep learning (DL) models have been trained on TCGA to predict numerous …
[HTML][HTML] Prediction of HER2-positive breast cancer recurrence and metastasis risk from histopathological images and clinical information via multimodal deep learning
J Yang, J Ju, L Guo, B Ji, S Shi, Z Yang, S Gao… - Computational and …, 2022 - Elsevier
HER2-positive breast cancer is a highly heterogeneous tumor, and about 30% of patients
still suffer from recurrence and metastasis after trastuzumab targeted therapy. Predicting …
still suffer from recurrence and metastasis after trastuzumab targeted therapy. Predicting …
Uncertainty-informed deep learning models enable high-confidence predictions for digital histopathology
JM Dolezal, A Srisuwananukorn, D Karpeyev… - Nature …, 2022 - nature.com
A model's ability to express its own predictive uncertainty is an essential attribute for
maintaining clinical user confidence as computational biomarkers are deployed into real …
maintaining clinical user confidence as computational biomarkers are deployed into real …
An enhanced histopathology analysis: An ai-based system for multiclass grading of oral squamous cell carcinoma and segmenting of epithelial and stromal tissue
Simple Summary An established dataset of histopathology images obtained by biopsy and
reviewed by two pathologists is used to create a two-stage oral squamous cell carcinoma …
reviewed by two pathologists is used to create a two-stage oral squamous cell carcinoma …
Predicting colorectal cancer tumor mutational burden from histopathological images and clinical information using multi-modal deep learning
K Huang, B Lin, J Liu, Y Liu, J Li, G Tian, J Yang - Bioinformatics, 2022 - academic.oup.com
Motivation Tumor mutational burden (TMB) is an indicator of the efficacy and prognosis of
immune checkpoint therapy in colorectal cancer (CRC). In general, patients with higher TMB …
immune checkpoint therapy in colorectal cancer (CRC). In general, patients with higher TMB …
ICSDA: a multi-modal deep learning model to predict breast cancer recurrence and metastasis risk by integrating pathological, clinical and gene expression data
Y Yao, Y Lv, L Tong, Y Liang, S Xi, B Ji… - Briefings in …, 2022 - academic.oup.com
Breast cancer patients often have recurrence and metastasis after surgery. Predicting the
risk of recurrence and metastasis for a breast cancer patient is essential for the development …
risk of recurrence and metastasis for a breast cancer patient is essential for the development …
Deep-learning algorithm and concomitant biomarker identification for NSCLC prediction using multi-omics data integration
MK Park, JM Lim, J Jeong, Y Jang, JW Lee, JC Lee… - Biomolecules, 2022 - mdpi.com
Early diagnosis of lung cancer to increase the survival rate, which is currently at a low range
of mid-30%, remains a critical need. Despite this, multi-omics data have rarely been applied …
of mid-30%, remains a critical need. Despite this, multi-omics data have rarely been applied …
DeepRePath: identifying the prognostic features of early-stage lung adenocarcinoma using multi-scale pathology images and deep convolutional neural networks
Simple Summary Pathology images are vital for understanding solid cancers. In this study,
we created DeepRePath using multi-scale pathology images with two-channel deep …
we created DeepRePath using multi-scale pathology images with two-channel deep …