Oncology/Hematology

Machine learning for predicting neoadjuvant chemotherapy effectiveness using ultrasound radiomics features and routine clinical data of patients with breast cancer.

BACKGROUND: This study explores the clinical value of a machine learning (ML) model based on ultrasound radiomics features of primary foci, combined with clinicopathologic factors to predict the pathological complete response (pCR) of neoadjuvant chemotherapy (NAC) for patients with breast cancer (BC).

AlgaeSperm: Microalgae-Based Soft Magnetic Microrobots for Targeted Tumor Treatment.

Magnetic microrobots are significant platforms for targeted drug delivery, among which sperm-inspired types have attracted much attention due to their flexible undulation. However, mass production of sperm-like soft magnetic microrobots with high-speed propulsion is still challenging due to the need of more reasonable structure design and facile fabrication. Herein, a novel strategy is proposed fo...

Larger sample sizes are needed when developing a clinical prediction model using machine learning in oncology: methodological systematic review.

BACKGROUND AND OBJECTIVES: Having a sufficient sample size is crucial when developing a clinical prediction model. We reviewed details of sample size ...

Application of deep learning in automated localization and interpretation of coronary artery calcification in oncological PET/CT scans.

Coronary artery calcification (CAC) is a key marker of coronary artery disease (CAD) but is often underreported in cancer patients undergoing non-gate...

Guidance on selecting and evaluating AI auto-segmentation systems in clinical radiotherapy: insights from a six-vendor analysis.

Artificial Intelligence (AI) based auto-segmentation has demonstrated numerous benefits to clinical radiotherapy workflows. However, the rapidly chang...

Personalized treatment strategies for breast adenoid cystic carcinoma: A machine learning approach.

BACKGROUND: Breast adenoid cystic carcinoma (BACC) is a rare subtype of breast cancer that accounts for less than 0.1 % of all cases. This study was d...

Exploring the influence of age on the causes of death in advanced nasopharyngeal carcinoma patients undergoing chemoradiotherapy using machine learning methods.

The present study analyzed the impact of age on the causes of death (CODs) in patients with nasopharyngeal carcinoma (NPC) undergoing chemoradiotherap...

Blood cancer prediction model based on deep learning technique.

Blood cancer is among the critical health concerns among people around the world and normally emanates from genetic and environmental issues. Early de...

Opioid growth factor receptor overexpression exerts anti-hepatocellular carcinoma effects by activating P16 and P21 to inhibit proliferation and migration of HepG2 cells.

INTRODUCTION: Hepatocellular carcinoma (HCC) is the sixth most common type of cancer and the second leading cause of cancer death worldwide [19]. Opio...

Construction of an anaplastic thyroid cancer stratification signature to guide immune therapy selection and validation of the pivotal gene HLF through experiments.

INTRODUCTION: While most thyroid cancer patients have a favorable prognosis, anaplastic thyroid carcinoma (ATC) remains a particularly aggressive form...

Early detection of canine hemangiosarcoma via cfDNA fragmentation and copy number alterations in liquid biopsies using machine learning.

Hemangiosarcoma is a highly malignant tumor commonly affecting canines, originating from endothelial cells that line blood vessels, underscoring the i...

Current state and promise of user-centered design to harness explainable AI in clinical decision-support systems for patients with CNS tumors.

In neuro-oncology, MR imaging is crucial for obtaining detailed brain images to identify neoplasms, plan treatment, guide surgical intervention, and m...

Artificial intelligence in gastrointestinal cancers: Diagnostic, prognostic, and surgical strategies.

GI (Gastrointestinal) malignancies are one of the most common and lethal cancers globally. The dawn of precision medicine and developing technologies ...

Integrated multi-omics analysis identifies a machine learning-derived signature for predicting prognosis and therapeutic vulnerability in clear cell renal cell carcinoma.

AIMS: Clear cell renal cell carcinoma (ccRCC) shows considerable variation within and between tumors, presents varying treatment responses among patie...

Annotation-free deep learning algorithm trained on hematoxylin & eosin images predicts epithelial-to-mesenchymal transition phenotype and endocrine response in estrogen receptor-positive breast cancer.

Recent evidence indicates that endocrine resistance in estrogen receptor-positive (ER+) breast cancer is closely correlated with phenotypic characteri...

Leveraging explainable AI and large-scale datasets for comprehensive classification of renal histologic types.

Recently, as the number of cancer patients has increased, much research is being conducted for efficient treatment, including the use of artificial in...

A benchmark of deep learning approaches to predict lung cancer risk using national lung screening trial cohort.

Deep learning (DL) methods have demonstrated remarkable effectiveness in assisting with lung cancer risk prediction tasks using computed tomography (C...

Integrated RNA sequencing analysis and machine learning identifies a metabolism-related prognostic signature in clear cell renal cell carcinoma.

The connection between metabolic reprogramming and tumor progression has been demonstrated in an increasing number of researches. However, further res...

The role of mitophagy-related genes in prognosis and immunotherapy of cutaneous melanoma: a comprehensive analysis based on single-cell RNA sequencing and machine learning.

Mitophagy, the selective degradation of mitochondria by autophagy, plays a crucial role in cancer progression and therapy response. This study aims to...

Semi-Supervised Learning Allows for Improved Segmentation With Reduced Annotations of Brain Metastases Using Multicenter MRI Data.

BACKGROUND: Deep learning-based segmentation of brain metastases relies on large amounts of fully annotated data by domain experts. Semi-supervised le...