Other Cancers

Brain metastasis tumor segmentation and detection using deep learning algorithms: A systematic review and meta-analysis.

BACKGROUND: Manual detection of brain metastases is both laborious and inconsistent, driving the need for more efficient solutions. Accordingly, our systematic review and meta-analysis assessed the efficacy of deep learning algorithms in detecting and segmenting brain metastases from various primary origins in MRI images.

Histological classification of canine and feline lymphoma using a modular approach based on deep learning and advanced image processing.

Histopathological examination of tissue samples is essential for identifying tumor malignancy and the diagnosis of different types of tumor. In the case of lymphoma classification, nuclear size of the neoplastic lymphocytes is one of the key features to differentiate the different subtypes. Based on the combination of artificial intelligence and advanced image processing, we provide a workflow for...

Development of a hybrid hydrogel for submucosal injection in endoscopic resection of gastrointestinal neoplasm: From laboratory to clinical trial.

Submucosal injection solution is essential for successful endoscopic resection of the early gastrointestinal tumor. We evaluated a new endoscopic hyd...

An algorithm for drug discovery based on deep learning with an example of developing a drug for the treatment of lung cancer.

In this study, we present an algorithmic framework integrated within the created software platform tailored for the discovery of novel small-molecule ...

A Preliminary Investigation into Search and Matching for Tumor Discrimination in World Health Organization Breast Taxonomy Using Deep Networks.

Breast cancer is one of the most common cancers affecting women worldwide. It includes a group of malignant neoplasms with a variety of biological, cl...

Differentiating adrenal metastases from benign lesions with multiphase CT imaging: Deep learning could play an active role in assisting radiologists.

OBJECTIVES: To develop and externally validate multiphase CT-based deep learning (DL) models for differentiating adrenal metastases from benign lesion...

A multi-task deep learning model for EGFR genotyping prediction and GTV segmentation of brain metastasis.

BACKGROUND: The precise prediction of epidermal growth factor receptor (EGFR) mutation status and gross tumor volume (GTV) segmentation are crucial go...

Decentralized Data-Privacy Preserving Deep-Learning Approaches for Enhancing Inter-Database Generalization in Automatic Sleep Staging.

Automatic sleep staging has been an active field of development. Despite multiple efforts, the area remains a focus of research interest. Indeed, whil...

Deep Learning Radiomics Nomogram Based on Enhanced CT to Predict the Response of Metastatic Lymph Nodes to Neoadjuvant Chemotherapy in Locally Advanced Gastric Cancer.

BACKGROUND: We aimed to construct and validate a deep learning (DL) radiomics nomogram using baseline and restage enhanced computed tomography (CT) im...

Optimization of Rituximab Therapy in Adult Patients With PLA2R1-Associated Membranous Nephropathy With Artificial Intelligence.

INTRODUCTION: Rituximab is a first-line treatment for membranous nephropathy. Nephrotic syndrome limits rituximab exposure due to urinary drug loss. R...

Impact of Visceral Fat Area on Intraoperative Complexity and Surgical Approach Decision for Robot-Assisted Partial Nephrectomy: A Comparative Analysis with BMI.

BACKGROUND Optimizing surgical approaches for robot-assisted partial nephrectomy (RAPN) is vital for better patient outcomes. This retrospective study...

Predicting multiple linear stapler firings in double stapling technique with an MRI-based deep-learning model.

Multiple linear stapler firings is a risk factor for anastomotic leakage (AL) in laparoscopic low anterior resection (LAR) using double stapling techn...

Performance of CT-based deep learning in diagnostic assessment of suspicious lateral lymph nodes in papillary thyroid cancer: a prospective diagnostic study.

BACKGROUND: Preoperative evaluation of the metastasis status of lateral lymph nodes (LNs) in papillary thyroid cancer is challenging. Strategies for u...

Robotic versus open surgery for simultaneous resection of rectal cancer and liver metastases: a randomized controlled trial.

OBJECTIVE: This study aimed to compare the short-term and long-term outcomes between robotic-assisted simultaneous resection and open surgery in patie...

Robot-Assisted Extraperitoneal Radical Prostatectomy for Giant Multilocular Prostatic Cystadenoma: A Case Report and Literature Review.

A 48-year-old man with a medical history of hypertension and no family history of prostate cancer presented with abdominal distension, lower abdominal...

Predictive modeling of dose-volume parameters of carcinoma tongue cases using machine learning models.

The aim of this study is to create a single institution-based machine learning model for a dose prediction generation tool for post-operative carcinom...

Reducing Gadolinium Contrast With Artificial Intelligence.

Gadolinium contrast is an important agent in magnetic resonance imaging (MRI), particularly in neuroimaging where it can help identify blood-brain bar...

Application possibilities of artificial intelligence in facial vascularized composite allotransplantation-a narrative review.

Facial vascularized composite allotransplantation (FVCA) is an emerging field of reconstructive surgery that represents a dogmatic shift in the surgic...

Reducing false positives in deep learning-based brain metastasis detection by using both gradient-echo and spin-echo contrast-enhanced MRI: validation in a multi-center diagnostic cohort.

OBJECTIVES: To develop a deep learning (DL) for detection of brain metastasis (BM) that incorporates both gradient- and turbo spin-echo contrast-enhan...

Screening the phytotoxicity of micro/nanoplastics through non-targeted metallomics with synchrotron radiation X-ray fluorescence and deep learning: Taking micro/nano polyethylene terephthalate as an example.

Microplastics (MPs) and nanoplastics (NPs) are global pollutants with emerging concerns. Methods to predict and screen their toxicity are crucial. Ele...