Cardiovascular

Analysis of robot-assisted nipple-sparing mastectomy using the da Vinci SP system.

BACKGROUND: As patients tend to be diagnosed with breast cancer at an early stage, the demand for better cosmetic outcomes has increased. Several studies revealed that robot-assisted nipple-sparing mastectomy (RNSM) shows favorable outcomes. The aim of the study was to reveal the feasibility of RNSM using the da Vinci single-port (SP) system with a minimal incision, hidden in the arm.

A deep learning-based radiomics approach to predict head and neck tumor regression for adaptive radiotherapy.

Early regression-the regression in tumor volume during the initial phase of radiotherapy (approximately 2 weeks after treatment initiation)-is a common occurrence during radiotherapy. This rapid radiation-induced tumor regression may alter target coordinates, necessitating adaptive radiotherapy (ART). We developed a deep learning-based radiomics (DLR) approach to predict early head and neck tumor ...

Prediction of the position of external markers using a recurrent neural network trained with unbiased online recurrent optimization for safe lung cancer radiotherapy.

BACKGROUND AND OBJECTIVE: During lung cancer radiotherapy, the position of infrared reflective objects on the chest can be recorded to estimate the tu...

Deep learning with whole slide images can improve the prognostic risk stratification with stage III colorectal cancer.

BACKGROUND AND OBJECTIVE: Adjuvant chemotherapy is recommended as standard treatment for colorectal cancer (CRC) with stage III according to TNM stage...

Robotic nephroureterectomy in the management of upper tract urothelial cancer: inching toward standard of care?

Upper-tract urothelial carcinoma is a relatively rare malignancy. Current guidelines strongly recommend radical nephroureterectomy with bladder cuff e...

Computed Tomography Texture Features and Risk Factor Analysis of Postoperative Recurrence of Patients with Advanced Gastric Cancer after Radical Treatment under Artificial Intelligence Algorithm.

Computer tomography texture analysis (CTTA) based on the V-Net convolutional neural network (CNN) algorithm was used to analyze the recurrence of adva...

Design of Nuclear Radiation Monitoring System in Floor Exploration Based on Deep Learning.

Nuclear radiation environmental monitoring has become an important issue in floor surveys. From the perspective of regional environmental nuclear radi...

AI-based optimization for US-guided radiation therapy of the prostate.

OBJECTIVES: Fast volumetric ultrasound presents an interesting modality for continuous and real-time intra-fractional target tracking in radiation the...

End-to-end deep learning for interior tomography with low-dose x-ray CT.

There are several x-ray computed tomography (CT) scanning strategies used to reduce radiation dose, such as (1) sparse-view CT, (2) low-dose CT and (3...

Reversing radiation-induced immunosuppression using a new therapeutic modality.

Radiation-induced immune suppression poses significant health challenges for millions of patients undergoing cancer chemotherapy and radiotherapy trea...

MRCON-Net: Multiscale reweighted convolutional coding neural network for low-dose CT imaging.

BACKGROUND AND OBJECTIVE: Low-dose computed tomography (LDCT) has become increasingly important for alleviating X-ray radiation damage. However, reduc...

Target Convergence Analysis of Cancer-Inspired Swarms for Early Disease Diagnosis and Targeted Collective Therapy.

Sensing and perception is generally a challenging aspect of decision-making. In the nanoscale, however, these processes face further complications due...

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...

Deep Learning-Based Model for Identifying Tumors in Endoscopic Images From Patients With Locally Advanced Rectal Cancer Treated With Total Neoadjuvant Therapy.

BACKGROUND: A barrier to the widespread adoption of watch-and-wait management for locally advanced rectal cancer is the inaccuracy and variability of ...

Multi-Task Fusion for Improving Mammography Screening Data Classification.

Machine learning and deep learning methods have become essential for computer-assisted prediction in medicine, with a growing number of applications a...

Prior information-based high-resolution tomography image reconstruction from a single digitally reconstructed radiograph.

Tomography images are essential for clinical diagnosis and trauma surgery, allowing doctors to understand the internal information of patients in more...

A deep learning model (FociRad) for automated detection of γ-H2AX foci and radiation dose estimation.

DNA double-strand breaks (DSBs) are the most lethal form of damage to cells from irradiation. γ-H2AX (phosphorylated form of H2AX histone variant) has...

Deep learning based on hematoxylin-eosin staining outperforms immunohistochemistry in predicting molecular subtypes of gastric adenocarcinoma.

In gastric cancer (GC), there are four molecular subclasses that indicate whether patients respond to chemotherapy or immunotherapy, according to the ...

A retrospective analysis using deep-learning models for prediction of survival outcome and benefit of adjuvant chemotherapy in stage II/III colorectal cancer.

PURPOSE: Most of Stage II/III colorectal cancer (CRC) patients can be cured by surgery alone, and only certain CRC patients benefit from adjuvant chem...

Human-level COVID-19 diagnosis from low-dose CT scans using a two-stage time-distributed capsule network.

Reverse transcription-polymerase chain reaction is currently the gold standard in COVID-19 diagnosis. It can, however, take days to provide the diagno...