Oncology/Hematology

Development of a MVI associated HCC prognostic model through single cell transcriptomic analysis and 101 machine learning algorithms.

Hepatocellular carcinoma (HCC) is an exceedingly aggressive form of cancer that often carries a poor prognosis, especially when it is complicated by the presence of microvascular invasion (MVI). Identifying patients at high risk of MVI is crucial for personalized treatment strategies. Utilizing the single-cell RNA-sequencing dataset (GSE242889) of HCC, we identified malignant cell subtypes associa...

Continuous nursing symptom management in cancer chemotherapy patients using deep learning.

To assess the efficacy of a deep learning platform for managing symptoms in chemotherapy patients, aiming to enhance their quality of life. A non-randomized controlled trial was conducted from September 2022 to March 2024, involving 144 chemotherapy patients divided into intervention (n = 72) and control (n = 72) groups. The intervention group received the deep learning platform, whereas the contr...

Prediction of STAS in lung adenocarcinoma with nodules ≤ 2 cm using machine learning: a multicenter retrospective study.

BACKGROUND AND OBJECTIVE: Spread through air spaces (STAS) is an important factor in determining the aggressiveness and recurrence risk of lung cancer...

Advanced pathological subtype classification of thyroid cancer using efficientNetB0.

BACKGROUND: Thyroid cancer is a prevalent malignancy requiring accurate subtype identification for effective treatment planning and prognosis evaluati...

Hallmarks of artificial intelligence contributions to precision oncology.

The integration of artificial intelligence (AI) into oncology promises to revolutionize cancer care. In this Review, we discuss ten AI hallmarks in pr...

A deep-learning model for quantifying circulating tumour DNA from the density distribution of DNA-fragment lengths.

The quantification of circulating tumour DNA (ctDNA) in blood enables non-invasive surveillance of cancer progression. Here we show that a deep-learni...

A Preoperative CT-based Multiparameter Deep Learning and Radiomic Model with Extracellular Volume Parameter Images Can Predict the Tumor Budding Grade in Rectal Cancer Patients.

RATIONALE AND OBJECTIVES: To investigate a computed tomography (CT)-based multiparameter deep learning-radiomic model (DLRM) for predicting the preope...

Interstitial-guided automatic clinical tumor volume segmentation network for cervical cancer brachytherapy.

Automatic clinical tumor volume (CTV) delineation is pivotal to improving outcomes for interstitial brachytherapy cervical cancer. However, the promin...

Accurate phenotyping of luminal A breast cancer in magnetic resonance imaging: A new 3D CNN approach.

Breast cancer (BC) remains a predominant and deadly cancer in women worldwide. By 2040, projections indicate that more than 3 million new cases of bre...

A multi-stage fusion deep learning framework merging local patterns with attention-driven contextual dependencies for cancer detection.

Cancer is a severe threat to public health. Early diagnosis of disease is critical, but the lack of experts in this field, the personal assessment pro...

Leveraging swin transformer with ensemble of deep learning model for cervical cancer screening using colposcopy images.

Cervical cancer (CC) is the leading cancer, which mainly affects women worldwide. It generally occurs from abnormal cell evolution in the cervix and a...

Automatic detecting multiple bone metastases in breast cancer using deep learning based on low-resolution bone scan images.

Whole-body bone scan (WBS) is usually used as the effective diagnostic method for early-stage and comprehensive bone metastases of breast cancer. WBS ...

Preoperative multiclass classification of thymic mass lesions based on radiomics and machine learning.

BACKGROUND: Apart from rare cases such as lymphomas, germ cell tumors, neuroendocrine neoplasms, and thymic hyperplasia, thymic mass lesions (TMLs) ar...

Use of large language models as clinical decision support tools for management pancreatic adenocarcinoma using National Comprehensive Cancer Network guidelines.

BACKGROUND: Large language models may form the basis of clinical decision support tools to improve rates of guideline concordant care for pancreatic d...

TARSL: Triple-Attention Cross-Network Representation Learning to Predict Synthetic Lethality for Anti-Cancer Drug Discovery.

Cancer is a multifaceted disease that results from co-mutations of multi biological molecules. A promising strategy for cancer therapy involves in exp...

Multi-Omics Deep-Learning Prediction of Homologous Recombination Deficiency-Like Phenotype Improved Risk Stratification and Guided Therapeutic Decisions in Gynecological Cancers.

Homologous recombination deficiency (HRD) is a well-recognized important biomarker in determining the clinical benefits of platinum-based chemotherapy...

Enhancing Drug Repositioning Through Local Interactive Learning With Bilinear Attention Networks.

Drug repositioning has emerged as a promising strategy for identifying new therapeutic applications for existing drugs. In this study, we present DRGB...

Deep Augmented Metric Learning Network for Prostate Cancer Classification in Ultrasound Images.

Prostate cancer screening often relies on cost-intensive MRIs and invasive needle biopsies. Transrectal ultrasound imaging, as a more affordable and n...

CDAF-Net: A Contextual Contrast Detail Attention Feature Fusion Network for Low-Dose CT Denoising.

Low-dose computed tomography (LDCT) is a specialized CT scan with a lower radiation dose than normal-dose CT. However, the reduced radiation dose can ...

Syn-Net: A Synchronous Frequency-Perception Fusion Network for Breast Tumor Segmentation in Ultrasound Images.

Accurate breast tumor segmentation in ultrasound images is a crucial step in medical diagnosis and locating the tumor region. However, segmentation fa...