Oncology/Hematology

Medical imaging and multimodal artificial intelligence models for streamlining and enhancing cancer care: opportunities and challenges.

INTRODUCTION: Artificial intelligence (AI) has the potential to transform oncologic care. There have been significant developments in AI applications in medical imaging and increasing interest in multimodal models. These are likely to enable improved oncologic care through more precise diagnosis, increasingly in a more personalized and less invasive manner. In this review, we provide an overview o...

The automatic diagnosis artificial intelligence system for preoperative magnetic resonance imaging of uterine sarcoma.

OBJECTIVE: Magnetic resonance imaging (MRI) is efficient for the diagnosis of preoperative uterine sarcoma; however, misdiagnoses may occur. In this study, we developed a new artificial intelligence (AI) system to overcome the limitations of requiring specialists to manually process datasets and a large amount of computer resources.

A Fully Automated Artificial Intelligence System to Assist Pathologists' Diagnosis to Predict Histologically High-grade Urothelial Carcinoma from Digitized Urine Cytology Slides Using Deep Learning.

BACKGROUND: Urine cytology, although a useful screening method for urothelial carcinoma, lacks sensitivity. As an emerging technology, artificial inte...

Artificial intelligence-aided diagnostic imaging: A state-of-the-art technique in precancerous screening.

BACKGROUND AND AIM: Chromoendoscopy with the use of indigo carmine (IC) dye is a crucial endoscopic technique to identify gastrointestinal neoplasms. ...

Enhancing Bone Cancer Diagnosis Through Image Extraction and Machine Learning: A State-of-the-Art Approach.

Bone cancer is a severe condition often leading to patient mortality. Diagnosis relies on X-rays, MRIs, or CT scans, which require time-consuming man...

Robot-Assisted versus Laparoscopic Surgery for Pelvic Lymph Node Dissection in Patients with Gynecologic Malignancies.

OBJECTIVES: The objective of this study was to compare the surgical outcomes for pelvic lymph node dissection (PLND) performed through conventional la...

Single-Cell Radiation Response Scoring with the Deep Learning Algorithm CeCILE 2.0.

External stressors, such as ionizing radiation, have massive effects on life, survival, and the ability of mammalian cells to divide. Different types ...

Smart Sensor Control and Monitoring of an Automated Cell Expansion Process.

Immune therapy for cancer patients is a new and promising area that in the future may complement traditional chemotherapy. The cell expansion phase is...

ARCHERY: a prospective observational study of artificial intelligence-based radiotherapy treatment planning for cervical, head and neck and prostate cancer - study protocol.

INTRODUCTION: Fifty per cent of patients with cancer require radiotherapy during their disease course, however, only 10%-40% of patients in low-income...

Clinical evaluation of deep learning and atlas-based auto-segmentation for organs at risk delineation.

Manual delineation of organs at risk and clinical target volumes is essential in radiotherapy planning. Atlas-based auto-segmentation (ABAS) algorithm...

CEMRI-Based Quantification of Intratumoral Heterogeneity for Predicting Aggressive Characteristics of Hepatocellular Carcinoma Using Habitat Analysis: Comparison and Combination of Deep Learning.

RATIONALE AND OBJECTIVES: To explore both an intratumoral heterogeneity (ITH) model based on habitat analysis and a deep learning (DL) model based on ...

Advancements in Robotic-Assisted Spine Surgery.

Applications and workflows around spinal robotics have evolved since these systems were first introduced in 2004. Initially approved for lumbar pedicl...

Susceptibility of periodontal pathogens to a novel target-specific drug delivery system containing self-nanoemulsifying curcumin: An in vitro study.

BACKGROUND: Long-term use of many classic chemotherapeutic agents as adjuncts in the management of periodontitis has adverse complications, leading to...

Application of artificial intelligence in endoscopic gastrointestinal tumors.

With an increasing number of patients with gastrointestinal cancer, effective and accurate early diagnostic clinical tools are required provide better...

Deep Learning-Guided Dosimetry for Mitigating Local Failure of Patients With Non-Small Cell Lung Cancer Receiving Stereotactic Body Radiation Therapy.

PURPOSE: Non-small cell lung cancer (NSCLC) stereotactic body radiation therapy with 50 Gy/5 fractions is sometimes considered controversial, as the n...

Deep learning radiomics of multimodal ultrasound for classifying metastatic cervical lymphadenopathy into primary cancer sites: a feasibility study.

PURPOSE: To investigate the feasibility of deep learning radiomics (DLR) based on multimodal ultrasound to differentiate the primary cancer sites of m...

Diagnostic accuracy of radiomics-based machine learning for neoadjuvant chemotherapy response and survival prediction in gastric cancer patients: A systematic review and meta-analysis.

BACKGROUND: In recent years, researchers have explored the use of radiomics to predict neoadjuvant chemotherapy outcomes in gastric cancer (GC). Yet, ...

Deep learning-based whole-body characterization of prostate cancer lesions on [Ga]Ga-PSMA-11 PET/CT in patients with post-prostatectomy recurrence.

PURPOSE: The automatic segmentation and detection of prostate cancer (PC) lesions throughout the body are extremely challenging due to the lesions' co...

The effect of deep learning-based lesion segmentation on failure load calculations of metastatic femurs using finite element analysis.

Bone ranks as the third most frequent tissue affected by cancer metastases, following the lung and liver. Bone metastases are often painful and may re...

Deep learning-enabled breast cancer endocrine response determination from H&E staining based on ESR1 signaling activity.

Estrogen receptor (ER) positivity by immunohistochemistry has long been a main selection criterium for breast cancer patients to be treated with endoc...