Oncology/Hematology

Ensemble learning approach for detecting breast invasive ductal carcinoma from histopathological images.

Invasive ductal carcinoma is a type of breast cancer that is one of the most frequent and aggressive forms of breast malignancy, necessitating accurate and timely diagnosis for effective treatment. Though considered the gold standard, traditional histopathological diagnosis is subject to inter-observer and intra-observer variability, potentially impacting patient outcomes. This study proposed an e...

Foundation model embeddings for quantitative tumor imaging biomarkers.

Foundation models are increasingly used in medical imaging, yet their ability to extract reliable quantitative radiographic phenotypes of cancer across diverse clinical contexts lacks systematic evaluation. Here, we introduce TumorImagingBench, a curated benchmark comprising six public datasets (3,244 scans) with varied oncological endpoints. We evaluate ten medical imaging foundation models, repr...

Optimizing Artificial Intelligence Thresholds for Mammographic Lesion Detection: A Retrospective Study on Diagnostic Performance and Radiologist-Artificial Intelligence Discordance.

Artificial intelligence (AI)-based systems are increasingly being used to assist radiologists in detecting breast cancer on mammograms. However, appl...

Integrating Radiogenomics and Machine Learning in Musculoskeletal Oncology Care.

Musculoskeletal tumors present a diagnostic challenge due to their rarity, histological diversity, and overlapping imaging features. Accurate characte...

Navigating the future of fertility preservation: advanced predictive strategies for treatment outcomes of endometrial atypical hyperplasia and carcinoma.

Due to the decreasing age of onset and the postponement of childbearing, there is a growing number of patients with endometrial carcinoma (EC) and end...

The Integration of Artificial Intelligence Into Precision Medicine for Neuro-Oncology: Ethical, Clinical, and Nursing Implications in Immunotherapy Care.

This paper explores how artificial intelligence (AI) is being woven into precision medicine for neuro-oncology, highlighting its ethical, clinical, an...

Improving cancer detection through computer-aided diagnosis: A comprehensive analysis of nonlinear and texture features in breast thermograms.

Breast cancer is a significant health issue for women, characterized by its high rates of mortality and sickness. However, its early detection is cruc...

Harnessing GPT-4 for automated error detection in pathology reports: Implications for oncology diagnostics.

OBJECTIVE: Accurate pathology reports are crucial for the diagnosis and treatment planning of cancer patients. However, these reports are prone to err...

Current status, hotspots, and trends in cancer prevention, screening, diagnosis, treatment, and rehabilitation: A bibliometric analysis.

OBJECTIVES: Decades of clinical and fundamental research advancements in oncology have led to significant breakthroughs such as early screening, targe...

Development of CSOARG: a single-cell and multi-omics-based machine learning model for ovarian cancer prognosis and drug response prediction.

OBJECTIVE: Ovarian cancer is the most deadly gynaecological malignancy. This study aims to generate a predictive model for prognosis and therapeutic r...

Immunological biomarkers and gene signatures predictive of radiotherapy resistance in non-small cell lung cancer.

INTRODUCTION: A significant challenge in treating non-small cell lung cancer (NSCLC) is its inherent resistance to radiation therapy, leading to poor ...

Deep learning for predicting invasive recurrence of ductal carcinoma in situ: leveraging histopathology images and clinical features.

BACKGROUND: Ductal Carcinoma In Situ (DCIS) can progress to ipsilateral invasive breast cancer (IBC) but over 75% of DCIS lesions do not progress if u...

An integrated analytical approach for biomarker discovery in esophageal cancer: Combining trace element and oxidative stress profiling with machine learning.

BACKGROUND: Early detection of esophageal squamous cell carcinoma (ESCC) significantly improves survival rates, yet reliable biochemical biomarkers fo...

Comparative evaluation of deep learning-based and conventional reconstruction techniques for image quality enhancement in low-dose chest computed tomography.

BACKGROUND: With the increasing use of computed tomography (CT) in clinical practice, greater attention is being paid to the radiation dose. There are...

Transcriptome analysis and artificial intelligence for predicting lymph node metastasis of esophageal squamous cell carcinoma.

BACKGROUND: Lymph node metastasis (LNM) is the most common route of metastasis in esophageal squamous cell carcinoma (ESCC), and the treatment of pati...

Empirical Mode Decomposition and Grassmann Manifold-Based Cervical Cancer Detection.

Cervical cancer is a prevalent malignancy affecting the female reproductive system and is recognized as a prominent factor to female mortality on a gl...

Immunotyping the Tumor Microenvironment Reveals Molecular Heterogeneity for Personalized Immunotherapy in Cancer.

The tumor microenvironment (TME) significantly influences cancer prognosis and therapeutic outcomes, yet its composition remains highly heterogeneous,...

Contrast-Enhanced Ultrasound for Hepatocellular Carcinoma Diagnosis- Expert Panel Narrative Review.

Despite growing clinical use of contrast-enhanced ultrasound (CEUS), inconsistency remains in the modality's role in clinical pathways for hepatocellu...

Toward diffusion MRI in the diagnosis and treatment of pancreatic cancer.

Pancreatic cancer is a highly aggressive malignancy with rising incidence and mortality rates, often diagnosed at advanced stages. Conventional imagin...

Prediction of hematogenous metastasis risk from molecular classification in gastric cancer.

BACKGROUND: Hematogenous metastasis (HM) poses a significant challenge in the treatment and outcome of gastric cancer (GC). This study identified and ...