Oncology/Hematology

Deep learning paradigms in lung cancer diagnosis: A methodological review, open challenges, and future directions.

Lung cancer is the leading cause of global cancer-related deaths, which emphasizes the critical importance of early diagnosis in enhancing patient outcomes. Deep learning has demonstrated significant promise in lung cancer diagnosis, excelling in nodule detection, classification, and prognosis prediction. This methodological review comprehensively explores deep learning models' application in lung...

Detection of metastatic breast carcinoma in sentinel lymph node frozen sections using an artificial intelligence-assisted system.

We developed an automatic method based on a convolutional neural network (CNN) that identifies metastatic lesions in whole slide images (WSI) of intraoperative frozen sections from sentinel lymph nodes in breast cancer. A total of 954 sentinel lymph node frozen sections, encompassing all types of breast cancer, were collected and examined at our institution between January 1, 2021, and September 2...

Deep Learning Radiomics for Survival Prediction in Non-Small-Cell Lung Cancer Patients from CT Images.

This study aims to apply a multi-modal approach of the deep learning method for survival prediction in patients with non-small-cell lung cancer (NSCLC...

A promising AI based super resolution image reconstruction technique for early diagnosis of skin cancer.

Skin cancer can be prevalent in people of any age group who are exposed to ultraviolet (UV) radiation. Among all other types, melanoma is a notable se...

A novel method for screening malignant hematological diseases by constructing an optimal machine learning model based on blood cell parameters.

BACKGROUND: Screening of malignant hematological diseases is of great importance for their diagnosis and subsequent treatment. This study constructed ...

Radiomics integration based on intratumoral and peritumoral computed tomography improves the diagnostic efficiency of invasiveness in patients with pure ground-glass nodules: a machine learning, cross-sectional, bicentric study.

BACKGROUND: Radiomics has shown promise in the diagnosis and prognosis of lung cancer. Here, we investigated the performance of computed tomography-ba...

A privacy-preserved horizontal federated learning for malignant glioma tumour detection using distributed data-silos.

Malignant glioma is the uncontrollable growth of cells in the spinal cord and brain that look similar to the normal glial cells. The most essential pa...

Deformation registration based on reconstruction of brain MRI images with pathologies.

Deformable registration between brain tumor images and brain atlas has been an important tool to facilitate pathological analysis. However, registrati...

An accurate and trustworthy deep learning approach for bladder tumor segmentation with uncertainty estimation.

BACKGROUND AND OBJECTIVE: Although deep learning-based intelligent diagnosis of bladder cancer has achieved excellent performance, the reliability of ...

Real-Time, AI-Guided Photodynamic Laparoscopy Enhances Detection in a Rabbit Model of Peritoneal Cancer Metastasis.

Accurate diagnosis is essential for effective cancer treatment, particularly in peritoneal surface malignancies, where failure to detect metastatic le...

Differential gene expression analysis and machine learning identified structural, TFs, cytokine and glycoproteins, including SOX2, TOP2A, SPP1, COL1A1, and TIMP1 as potential drivers of lung cancer.

BACKGROUND: Lung cancer is a primary global health concern, responsible for a considerable portion of cancer-related fatalities worldwide. Understandi...

Single-Cell Array Enhanced Cell Damage Recognition Using Artificial Intelligence for Anticancer Drug Discovery.

This work developed a cell damage recognition method based on single-cell arrays using an artificial intelligence tool. The method uses micropatterns ...

PhysioEx: a new Python library for explainable sleep staging through deep learning.

Sleep staging is a crucial task in clinical and research contexts for diagnosing and understanding sleep disorders. This work introduces PhysioEx (Phy...

Comparative performance of multiple ensemble learning models for preoperative prediction of tumor deposits in rectal cancer based on MR imaging.

Ensemble learning can effectively mitigate the risk of model overfitting during training. This study aims to evaluate the performance of ensemble lear...

Identifying invasiveness to aid lung adenocarcinoma diagnosis using deep learning and pathomics.

Most classification efforts for primary subtypes of lung adenocarcinoma (LUAD) have not yet been integrated into clinical practice. This study explore...

Machine learning prediction of breast cancer local recurrence localization, and distant metastasis after local recurrences.

Local recurrences (LR) can occur within residual breast tissue, chest wall, skin, or newly formed scar tissue. Artificial intelligence (AI) technologi...

A robust deep learning framework for multiclass skin cancer classification.

Skin cancer represents a significant global health concern, where early and precise diagnosis plays a pivotal role in improving treatment efficacy and...

Interpretable Machine Learning to Predict the Malignancy Risk of Follicular Thyroid Neoplasms in Extremely Unbalanced Data: Retrospective Cohort Study and Literature Review.

BACKGROUND: Diagnosing and managing follicular thyroid neoplasms (FTNs) remains a significant challenge, as the malignancy risk cannot be determined u...

Machine Learning-Enabled Non-Invasive Screening of Tumor-Associated Circulating Transcripts for Early Detection of Colorectal Cancer.

Colorectal cancer (CRC) is a major cause of cancer-related mortality, highlighting the need for accurate and non-invasive diagnostics. This study asse...

Predicting Clinical Anticancer Drug Response of Patients by Using Domain Alignment and Prototypical Learning.

Anticancer drug response prediction is crucial in developing personalized treatment plans for cancer patients. However, High-quality patient anticance...