Oncology/Hematology

Predicting benefit from PARP inhibitors using deep learning on H&E-stained ovarian cancer slides.

PURPOSE: Ovarian cancer patients with a Homologous Recombination Deficiency (HRD) often benefit from polyadenosine diphosphate-ribose polymerase (PARP) inhibitor maintenance therapy after response to platinum-based chemotherapy. HR status is currently analyzed via complex molecular tests. Predicting benefit from PARP inhibitors directly on histological whole slide images (WSIs) could be a fast and...

Deep learning-based metabolomics data study of prostate cancer.

As a heterogeneous disease, prostate cancer (PCa) exhibits diverse clinical and biological features, which pose significant challenges for early diagnosis and treatment. Metabolomics offers promising new approaches for early diagnosis, treatment, and prognosis of PCa. However, metabolomics data are characterized by high dimensionality, noise, variability, and small sample sizes, presenting substan...

Detection of Human Bladder Epithelial Cancerous Cells with Atomic Force Microscopy and Machine Learning.

The development of noninvasive methods for bladder cancer identification remains a critical clinical need. Recent studies have shown that atomic force...

Accuracy of machine learning models for pre-diagnosis and diagnosis of pancreatic ductal adenocarcinoma in contrast-CT images: a systematic review and meta-analysis.

PURPOSE: To evaluate the diagnostic ability and methodological quality of ML models in detecting Pancreatic Ductal Adenocarcinoma (PDAC) in Contrast C...

Mucoepidermoid carcinoma: Enhancing diagnostic accuracy and treatment strategy through machine learning models and web-based prognostic tool.

BACKGROUND: Oral cancer, particularly mucoepidermoid carcinoma (MEC), presents diagnostic challenges due to its histological diversity and rarity. Thi...

BCD-TransNet: Automatic breast cancer detection and classification using transfer learning approach.

Breast Cancer (BC) is a predominant form of cancer diagnosed in women and one of the deadliest diseases. The important cause of death owing to the can...

Mechanistic Learning for Predicting Survival Outcomes in Head and Neck Squamous Cell Carcinoma.

We employed a mechanistic learning approach, integrating on-treatment tumor kinetics (TK) modeling with various machine learning (ML) models to addres...

Artificial intelligence and large language models in palliative medicine clinical practice and education.

As we approach 2034, we anticipate significant advancements in digital technologies and their impact across various domains, including palliative and ...

Construction of a random survival forest model based on a machine learning algorithm to predict early recurrence after hepatectomy for adult hepatocellular carcinoma.

BACKGROUND AND AIMS: Hepatocellular carcinoma (HCC) exhibits a propensity for early recurrence following liver resection, resulting in a bleak prognos...

Predicting candidate biomarkers for COVID-19 associated with leukemia in children.

Since the COVID-19 pandemic, a significant number of pediatric leukemia patients have shown to have also contracted COVID-19 several weeks or months p...

A unified deep-learning framework for enhanced patient-specific quality assurance of intensity-modulated radiation therapy plans.

BACKGROUND: Modern radiation therapy techniques, such as intensity-modulated radiation therapy (IMRT) and volumetric-modulated arc therapy (VMAT), use...

Predicting lymph node metastasis in thyroid cancer: systematic review and meta-analysis on the CT/MRI-based radiomics and deep learning models.

BACKGROUND: Thyroid cancer, a common endocrine malignancy, has seen increasing incidence, making lymph node metastasis (LNM) a critical factor for rec...

A feature fusion method based on radiomic features and revised deep features for improving tumor prediction in ultrasound images.

BACKGROUND: Radiomic features and deep features are both vitally helpful for the accurate prediction of tumor information in breast ultrasound. Howeve...

Ensemble learning-based radiomics model for discriminating brain metastasis from glioblastoma.

OBJECTIVE: Differentiating between brain metastasis (BM) and glioblastoma (GBM) preoperatively is challenging due to their similar imaging features on...

Automated treatment planning with deep reinforcement learning for head-and-neck (HN) cancer intensity modulated radiation therapy (IMRT).

To develop a deep reinforcement learning (DRL) agent to self-interact with the treatment planning system to automatically generate intensity modulated...

Longitudinal interpretability of deep learning based breast cancer risk prediction.

Deep-learning-based models have achieved state-of-the-art breast cancer risk (BCR) prediction performance. However, these models are highly complex, a...

Liquid biopsy for Renal Cell Carcinoma: A comprehensive review of techniques, applications, and future prospects.

Liquid biopsy techniques have developed rapidly in recent years and demonstrated success in cancer detection, disease characterization, and ongoing di...

Robust and interpretable deep learning system for prognostic stratification of extranodal natural killer/T-cell lymphoma.

PURPOSE: Extranodal natural killer/T-cell lymphoma (ENKTCL) is an hematologic malignancy with prognostic heterogeneity. We aimed to develop and valida...

Precision in Parsing: Evaluation of an Open-Source Named Entity Recognizer (NER) in Veterinary Oncology.

Integrating Artificial Intelligence (AI) through Natural Language Processing (NLP) can improve veterinary medical oncology clinical record analytics. ...

A prospective study comparing highly qualified Molecular Tumor Boards with AI-powered software as a medical device.

BACKGROUND: The implementation of cancer precision medicine in Japan is deeply intertwined with insurance reimbursement policies and requires case-by-...