Oncology/Hematology

Unsupervised risk factor identification across cancer types and data modalities via explainable artificial intelligence.

Risk stratification is an important tool in clinical decision-making, yet current approaches often fail to translate sophisticated survival analysis into actionable clinical criteria. We present a novel method for training any neural network architecture on any data modality to identify prognostically distinct patient groups by directly optimizing for survival heterogeneity across patient clusters...

Multimodal deep learning framework for recurrence risk stratification in soft tissue sarcoma: a multicenter study.

Accurate prediction of recurrence risk is essential to devise effective and personalized treatment strategies for patients with soft tissue sarcoma (STS). This study aimed to develop and validate a multimodal deep learning framework that integrates clinical features, preoperative MR images, and hematoxylin and eosin-stained whole slide images (WSIs) to predict recurrence in patients with STS. A to...

Comparative oncology of male and female breast cancer: diagnostic paradigms and machine learning approaches in treatment.

Breast cancer is associated mostly with women; however, breast cancer also appears in men, which dictates the need to know about gender-specific diffe...

Artificial Intelligence in Breast Cancer Lymph Node Evaluation: Is Neoadjuvant Chemotherapy the Achilles' Heel?

CONTEXT.—: Artificial intelligence (AI) has demonstrated high accuracy in detecting lymph node (LN) metastases in treatment-naïve invasive breast canc...

Single-cell and bulk omics uncover fibroblast heterogeneity and HSPH1 as a key driver in Barrett's esophagus to esophageal adenocarcinoma progression.

BACKGROUND: Esophageal adenocarcinoma (EAC) is a highly aggressive malignancy with poor prognosis, often evolving from Barrett's esophagus (BE). Under...

Precision theranostics in oncology: integrating antibody-drug conjugates, radioimmunotherapy, and immuno‑PET for adaptive cancer care.

The century-old vision of a "magic bullet" in oncology is being realized through the paradigm of precision theranostics, which formally integrates tar...

Dynamic machine learning model integrating resting energy expenditure for predicting postoperative complications after gastrectomy for gastric cancer.

PURPOSE: To investigate whether perioperative resting energy expenditure (REE) dynamics improve prediction of postoperative complications after gastre...

STAID: A Self-Refining Deep Learning Framework for Spatial Cell-Type Deconvolution with Biologically Informed Modeling.

Spatial transcriptomics provides high-throughput measurement of gene expression while retaining spatial context; however, inferring accurate cell-type...

Can ChatGPT Take My Call? evaluating AI in gynecologic oncology telephone triage.

BACKGROUND: As access to artificial intelligence (AI) expands, patients and clinicians increasingly rely on these platforms for medical information an...

Artificial intelligence model predicts malignant transformation of oral leukoplakia and optimizes interventions.

PURPOSE: Oral leukoplakia (OL), the most common oral potentially malignant disorder (OPMD), poses a significant risk for transformation to oral squamo...

Single-cell profiling identifies a pro-tumoral VCAN positive macrophage subset and defines a prognostic signature in glioblastoma.

Glioblastoma (GBM), the most aggressive primary brain tumor, develops within a tumor microenvironment (TME) dominated by tumor-associated macrophages ...

Machine Learning-driven Prediction of Cervical Cancer Cell Viability After Treatment With Thymoquinone, Curcumin, and 5-Fluorouracil.

Cervical cancer remains prevalent among women globally, driven by uncontrolled cell proliferation and evasion of apoptosis. Phytochemicals like Thymoq...

Integrating machine learning and single-cell sequencing to reveal the role of kinase-related genes in subtype classification and prognostic significance of lung squamous cell carcinoma.

BACKGROUND: Lung squamous cell carcinoma (LUSC) exhibits poor prognosis and a highly complex tumor immune microenvironment (TIME), creating an urgent ...

Combining single cell and bulk transcriptomics with Mendelian randomization identifies gut microbiota related prognostic genes and mechanisms in thyroid cancer.

BACKGROUND: Imbalance in gut microbiota (GM) may play a role in the development of thyroid cancer (TC), but the specific mechanisms remain unclear. Th...

Feasibility of retrieval-augmented generation for large language models with Japanese input in radiotherapy.

Large language models (LLMs) have recently gained attention for their potential. However, concerns remain regarding their reliability due to limitatio...

Explainable artificial intelligence with pyramid vision transformer model for multi-class malignant cell classification on cytology slides.

Digital pathology involves the digitisation of histology slides, which is vital in modern healthcare, especially for cancer detection and diagnosis. S...

A human-AI collaborative workflow for improved gross tumor volume delineation in esophageal cancer radiotherapy.

BACKGROUND: Esophageal cancer tumors exhibit complex and variable distribution. Due to differences in clinical experience, junior oncologists often sh...

Exploring the prognostic role of senescence-related genes in gastric cancer through multi-omics integration and machine learning.

Cellular senescence plays a context-dependent role in gastric cancer (GC), functioning both through tumor-suppressive arrest and the tumor-promoting s...

Deep learning based on CD3 histological slides for prediction of colon cancer outcome: analysis of three international stage III colon cancer cohorts.

Prognostic stratification in stage III colon cancer remains poor, despite treatment advances. Tumor-infiltrating lymphocytes, particularly CD3+ T cell...

Development of Models for Predicting the 7-Month Risk of Venous Thromboembolism and Clinically Relevant Bleeding in Ambulatory Patients with Cancer: Analysis from the AVERT Trial.

BACKGROUND: Patients with cancer are at elevated risk of venous thromboembolism (VTE). While primary thromboprophylaxis reduces VTE incidence, it also...