Oncology/Hematology

Real-Time Artificial Intelligence-Based Optical Diagnosis of Neoplastic Polyps during Colonoscopy.

BACKGROUND: Artificial intelligence using computer-aided diagnosis (CADx) in real time with images acquired during colonoscopy may help colonoscopists distinguish between neoplastic polyps requiring removal and nonneoplastic polyps not requiring removal. In this study, we tested whether CADx analyzed images helped in this decision-making process. METHODS: We performed a multicenter clinical study ...

Deep learning of chest X-rays can predict mechanical ventilation outcome in ICU-admitted COVID-19 patients.

The COVID-19 pandemic repeatedly overwhelms healthcare systems capacity and forced the development and implementation of triage guidelines in ICU for scarce resources (e.g. mechanical ventilation). These guidelines were often based on known risk factors for COVID-19. It is proposed that image data, specifically bedside computed X-ray (CXR), provide additional predictive information on mortality fo...

Deep Learning Prediction of Ovarian Malignancy at US Compared with O-RADS and Expert Assessment.

Background Deep learning (DL) algorithms could improve the classification of ovarian tumors assessed with multimodal US. Purpose To develop DL algorit...

3D Kinect Camera Scheme with Time-Series Deep-Learning Algorithms for Classification and Prediction of Lung Tumor Motility.

This paper proposes a time-series deep-learning 3D Kinect camera scheme to classify the respiratory phases with a lung tumor and predict the lung tumo...

Development and evaluation of machine learning models based on X-ray radiomics for the classification and differentiation of malignant and benign bone tumors.

OBJECTIVES: To develop and validate machine learning models to distinguish between benign and malignant bone lesions and compare the performance to ra...

Deep learning-based algorithm improved radiologists' performance in bone metastases detection on CT.

OBJECTIVES: To develop and evaluate a deep learning-based algorithm (DLA) for automatic detection of bone metastases on CT.

Assessment of deep learning assistance for the pathological diagnosis of gastric cancer.

Previous studies on deep learning (DL) applications in pathology have focused on pathologist-versus-algorithm comparisons. However, DL will not replac...

Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review.

BACKGROUND: Describe and evaluate the methodological conduct of prognostic prediction models developed using machine learning methods in oncology.

Pseudoprogression prediction in high grade primary CNS tumors by use of radiomics.

Our aim is to define the capabilities of radiomics and machine learning in predicting pseudoprogression development from pre-treatment MR images in a ...

Development and validation of a combined nomogram model based on deep learning contrast-enhanced ultrasound and clinical factors to predict preoperative aggressiveness in pancreatic neuroendocrine neoplasms.

OBJECTIVES: This study aimed to develop and validate a combined nomogram model based on deep learning (DL) contrast-enhanced ultrasound (CEUS) and cli...

Robot-Assisted Right Colectomy with Sequential Wedge Resection of Segments 4 and 5 of The Liver and Cholecystectomy for Colon Cancer with Metastasis to The Liver.

The liver is the most common place for colon adenocarcinoma metastasis because of portal circulation. The surgical intervention for patients with colo...

A content-based image retrieval system for the diagnosis of lymphoma using blood micrographs: An incorporation of deep learning with a traditional learning approach.

Lymphomas, or cancers of the lymphatic system, account for around half of all blood cancers diagnosed each year. Lymphoma is a condition that is diffi...

Effectiveness of Artificial Intelligence for Personalized Medicine in Neoplasms: A Systematic Review.

PURPOSE: Artificial intelligence (AI) techniques are used in precision medicine to explore novel genotypes and phenotypes data. The main aims of preci...

Non-Invasive Measurement Using Deep Learning Algorithm Based on Multi-Source Features Fusion to Predict PD-L1 Expression and Survival in NSCLC.

BACKGROUND: Programmed death-ligand 1 (PD-L1) assessment of lung cancer in immunohistochemical assays was only approved diagnostic biomarker for immun...

Single utility port approach in robot-assisted sleeve segmentectomy for bronchial carcinoid tumor.

Bronchial carcinoid tumors are low-grade malignant and lung-sparing surgery is preferred for the removal of these tumors. We describe a surgical techn...

Testing the reproducibility and robustness of the cancer biology literature by robot.

Scientific results should not just be 'repeatable' (replicable in the same laboratory under identical conditions), but also 'reproducible' (replicable...

Clinicians' Attitudes and Behaviors Towards Communicating Electronically with Patients: A Grounded Practical Theory Approach.

Secure messaging (SM), asynchronous communication between patients and clinicians, is an increasingly popular tool among patients to contact clinician...

Robot-assisted radical prostatectomy in the treatment of patients with clinically high-risk localized and locally advanced prostate cancer: single surgeons functional and oncologic outcomes.

BACKGROUND: Optimal treatment approaches for high-risk localized and locally advanced prostate cancer remain controversial and there are currently no ...

Bayesian networks elucidate complex genomic landscapes in cancer.

Bayesian networks (BNs) are disciplined, explainable Artificial Intelligence models that can describe structured joint probability spaces. In the cont...

Automated prediction of the neoadjuvant chemotherapy response in osteosarcoma with deep learning and an MRI-based radiomics nomogram.

OBJECTIVES: To implement a pipeline to automatically segment the ROI and to use a nomogram integrating the MRI-based radiomics score and clinical vari...