Oncology/Hematology

Impact of Stain Variation and Color Normalization for Prognostic Predictions in Pathology

In recent years, deep neural networks (DNNs) have demonstrated remarkable performance in pathology applications, potentially even outperforming expert pathologists due to their ability to learn subtle features from large datasets. One complication in preparing digital pathology datasets for DNN tasks is variation in tinctorial qualities. A common way to address this is to perform stain normaliza...

DANCE: Deep Learning-Assisted Analysis of Protein Sequences Using Chaos Enhanced Kaleidoscopic Images

Cancer is a complex disease characterized by uncontrolled cell growth. T cell receptors (TCRs), crucial proteins in the immune system, play a key role in recognizing antigens, including those associated with cancer. Recent advancements in sequencing technologies have facilitated comprehensive profiling of TCR repertoires, uncovering TCRs with potent anti-cancer activity and enabling TCR-based im...

Celcomen: spatial causal disentanglement for single-cell and tissue perturbation modeling

Celcomen leverages a mathematical causality framework to disentangle intra- and inter- cellular gene regulation programs in spatial transcriptomics ...

Towards consistency in pediatric brain tumor measurements: Challenges, solutions, and the role of artificial intelligence-based segmentation.

MR imaging is central to the assessment of tumor burden and changes over time in neuro-oncology. Several response assessment guidelines have been set ...

Explainable AI for computational pathology identifies model limitations and tissue biomarkers

Introduction: Deep learning models hold great promise for digital pathology, but their opaque decision-making processes undermine trust and hinder c...

Establishment of artificial intelligence model for precise histological subtyping of lung adenocarcinoma and its application to quantitative and spatial analysis.

BACKGROUND: The histological subtype of lung adenocarcinoma is a major prognostic factor. We developed a new artificial intelligence model to classify...

MLOmics: Benchmark for Machine Learning on Cancer Multi-Omics Data

Framing the investigation of diverse cancers as a machine learning problem has recently shown significant potential in multi-omics analysis and canc...

NeoaPred: a deep-learning framework for predicting immunogenic neoantigen based on surface and structural features of peptide-human leukocyte antigen complexes.

MOTIVATION: Neoantigens, derived from somatic mutations in cancer cells, can elicit anti-tumor immune responses when presented to autologous T cells b...

Presurgical Upgrade Prediction of DCIS to Invasive Ductal Carcinoma Using Time-dependent Deep Learning Models with DCE MRI.

Purpose To determine whether time-dependent deep learning models can outperform single time point models in predicting preoperative upgrade of ductal ...

Deep Learning Segmentation of Ascites on Abdominal CT Scans for Automatic Volume Quantification.

Purpose To evaluate the performance of an automated deep learning method in detecting ascites and subsequently quantifying its volume in patients with...

Improving Computer-aided Detection for Digital Breast Tomosynthesis by Incorporating Temporal Change.

Purpose To develop a deep learning algorithm that uses temporal information to improve the performance of a previously published framework of cancer l...

Predicting lymph node recurrence in cT1-2N0 tongue squamous cell carcinoma: collaboration between artificial intelligence and pathologists.

Researchers have attempted to identify the factors involved in lymph node recurrence in cT1-2N0 tongue squamous cell carcinoma (SCC). However, studies...

Deep Learning Segmentation of Infiltrative and Enhancing Cellular Tumor at Pre- and Posttreatment Multishell Diffusion MRI of Glioblastoma.

Purpose To develop and validate a deep learning (DL) method to detect and segment enhancing and nonenhancing cellular tumor on pre- and posttreatment ...

Leveraging SEER data through machine learning to predict distant lymph node metastasis and prognosticate outcomes in hepatocellular carcinoma patients.

OBJECTIVES: This study aims to develop and validate machine learning-based diagnostic and prognostic models to predict the risk of distant lymph node ...

From organs to algorithms: Redefining cancer classification in the age of artificial intelligence.

Traditional cancer classification based on organ of origin and histology is increasingly at odds with precision oncology. Tumors in different organs c...

Validation of Non-Small Cell Lung Cancer Clinical Insights Using a Generalized Oncology Natural Language Processing Model.

PURPOSE: Limited studies have used natural language processing (NLP) in the context of non-small cell lung cancer (NSCLC). This study aimed to validat...

Multi-task deep latent spaces for cancer survival and drug sensitivity prediction.

MOTIVATION: Cancer is a very heterogeneous disease that can be difficult to treat without addressing the specific mechanisms driving tumour progressio...

Tailored chemotherapy: Innovative deep-learning model customizing chemotherapy for high-grade serous ovarian carcinoma.

Identification of novel M2 macrophage-related molecule ATP6V1E1 and its biological role in hepatocellular carcinoma based on machine learning algorithms.

Hepatocellular carcinoma (HCC) remains the most prevalent form of primary liver cancer, characterized by late detection and suboptimal response to cur...

Automatic segmentation of esophageal cancer, metastatic lymph nodes and their adjacent structures in CTA images based on the UperNet Swin network.

OBJECTIVE: To create a deep-learning automatic segmentation model for esophageal cancer (EC), metastatic lymph nodes (MLNs) and their adjacent structu...