Other Cancers

Evaluation of the prostate cancer and its metastases in the [ 68 Ga]Ga-PSMA PET/CT images: deep learning method vs. conventional PET/CT processing.

PURPOSE: This study demonstrates the feasibility and benefits of using a deep learning-based approac...

Exploiting histopathological imaging for early detection of lung and colon cancer via ensemble deep learning model.

Cancer seems to have a vast number of deaths due to its heterogeneity, aggressiveness, and significa...

Low-pass whole genome sequencing of circulating tumor cells to evaluate chromosomal instability in triple-negative breast cancer.

Chromosomal Instability (CIN) is a common and evolving feature in breast cancer. Large-scale Transit...

Cost-Sensitive Weighted Contrastive Learning Based on Graph Convolutional Networks for Imbalanced Alzheimer's Disease Staging.

Identifying the progression stages of Alzheimer's disease (AD) can be considered as an imbalanced mu...

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 ...

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

Intrahepatic cholangiocarcinoma (iCCA) is a hepatobiliary malignancy which accounts for approximatel...

Artificial Intelligence-Enhanced Risk Stratification of Cancer Therapeutics-Related Cardiac Dysfunction Using Electrocardiographic Images.

BACKGROUND: Risk stratification strategies for cancer therapeutics-related cardiac dysfunction (CTRC...

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 s...

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 di...

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 ...

Magnetic Resonance-Guided Cancer Therapy Radiomics and Machine Learning Models for Response Prediction.

Magnetic resonance imaging (MRI) is known for its accurate soft tissue delineation of tumors and nor...

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 can...

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 ...

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...

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.

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 axil...

A commentary on 'Single-cell and bulk RNA-sequence identified fibroblasts signature and CD8 + T cell - fibroblast subtype predicting prognosis and immune therapeutic response of bladder cancer, based on machine-learning'.

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. Ho...

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...

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 m...

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 characteristic...