Oncology/Hematology

Deep neural networks integrating genomics and histopathological images for predicting stages and survival time-to-event in colon cancer.

MOTIVATION: There exists an unexplained diverse variation within the predefined colon cancer stages using only features from either genomics or histopathological whole slide images as prognostic factors. Unraveling this variation will bring about improved staging and treatment outcomes. Hence, motivated by the advancement of Deep Neural Network (DNN) libraries and complementary factors within some...

Deciphering colorectal cancer radioresistance and immune microrenvironment: unraveling the role of EIF5A through single-cell RNA sequencing and machine learning.

BACKGROUND: Radiotherapy (RT) is a critical component of treatment for locally advanced rectal cancer (LARC), though patient response varies significantly. The variability in treatment outcomes is partly due to the resistance conferred by cancer stem cells (CSCs) and tumor immune microenvironment (TiME). This study investigates the role of EIF5A in radiotherapy response and its impact on the CSCs ...

Serum targeted metabolomics uncovering specific amino acid signature for diagnosis of intrahepatic cholangiocarcinoma.

Intrahepatic cholangiocarcinoma (iCCA) is a hepatobiliary malignancy which accounts for approximately 5-10 % of primary liver cancers and has a high m...

Enhancing metastatic colorectal cancer prediction through advanced feature selection and machine learning techniques.

BACKGROUND AND AIMS: Colorectal cancer (CRC) is the third most prevalent cancer globally, posing a significant challenge due to its high rate of metas...

The emerging role of Artificial Intelligence in proton therapy: A review.

Artificial intelligence (AI) has made a tremendous impact in the space of healthcare, and proton therapy is not an exception. Proton therapy has witne...

SleepGCN: A transition rule learning model based on Graph Convolutional Network for sleep staging.

BACKGROUND AND OBJECTIVE: Automatic sleep staging is essential for assessing and diagnosing sleep disorders, serving millions of people who suffer fro...

High-quality expert annotations enhance artificial intelligence model accuracy for osteosarcoma X-ray diagnosis.

Primary malignant bone tumors, such as osteosarcoma, significantly affect the pediatric and young adult populations, necessitating early diagnosis for...

Prostate cancer treatment recommendation study based on machine learning and SHAP interpreter.

This study utilized data from 140,294 prostate cancer cases from the Surveillance, Epidemiology, and End Results (SEER) database. Here, 10 different m...

ConvNext Mitosis Identification-You Only Look Once (CNMI-YOLO): Domain Adaptive and Robust Mitosis Identification in Digital Pathology.

In digital pathology, accurate mitosis detection in histopathological images is critical for cancer diagnosis and prognosis. However, this remains cha...

Preoperative prediction model of lymph node metastasis in the inguinal and femoral region based on radiomics and artificial intelligence.

OBJECTIVE: To predict preoperative inguinal lymph node metastasis in vulvar cancer patients using a machine learning model based on imaging features a...

Individualized prediction of non-sentinel lymph node metastasis in Chinese breast cancer patients with ≥ 3 positive sentinel lymph nodes based on machine-learning algorithms.

BACKGROUND: Axillary lymph node dissection (ALND) is a standard procedure for early-stage breast cancer (BC) patients with three or more positive sent...

Extracting lung cancer staging descriptors from pathology reports: A generative language model approach.

BACKGROUND: In oncology, electronic health records contain textual key information for the diagnosis, staging, and treatment planning of patients with...

MCMVDRP: a multi-channel multi-view deep learning framework for cancer drug response prediction.

Drug therapy remains the primary approach to treating tumours. Variability among cancer patients, including variations in genomic profiles, often resu...

An unsupervised learning model based on CT radiomics features accurately predicts axillary lymph node metastasis in breast cancer patients: diagnostic study.

BACKGROUND: The accuracy of traditional clinical methods for assessing the metastatic status of axillary lymph nodes (ALNs) is unsatisfactory. In this...

Single-cell and whole-transcriptome sequencing of lymph node metastasis-related gene signature: a large-scale pan-cancer cohort, machine learning and experimental study: Retraction.

Machine learning in paediatric haematological malignancies: a systematic review of prognosis, toxicity and treatment response models.

BACKGROUND: Machine Learning (ML) has demonstrated potential in enhancing care in adult oncology. However, its application in paediatric haematologica...

A Deep Learning Approach for the Identification of the Molecular Subtypes of Pancreatic Ductal Adenocarcinoma Based on Whole Slide Pathology Images.

Delayed diagnosis and treatment resistance result in high pancreatic ductal adenocarcinoma (PDAC) mortality rates. Identifying molecular subtypes can ...

SG-Fusion: A swin-transformer and graph convolution-based multi-modal deep neural network for glioma prognosis.

The integration of morphological attributes extracted from histopathological images and genomic data holds significant importance in advancing tumor d...

Integrated machine learning survival framework to decipher diverse cell death patterns for predicting prognosis in lung adenocarcinoma.

Various forms of programmed cell death (PCD) collectively regulate the occurrence, development and metastasis of tumors. Nevertheless, a comprehensive...

Deciphering Dormant Cells of Lung Adenocarcinoma: Prognostic Insights from O-glycosylation-Related Tumor Dormancy Genes Using Machine Learning.

Lung adenocarcinoma (LUAD) poses significant challenges due to its complex biological characteristics and high recurrence rate. The high recurrence ra...