Breast Cancer

Evaluation of deep learning-based deliverable VMAT plan generated by prototype software for automated planning for prostate cancer patients.

This study aims to evaluate the dosimetric accuracy of a deep learning (DL)-based deliverable volumetric arc radiation therapy (VMAT) plan generated using DL-based automated planning assistant system (AIVOT, prototype version) for patients with prostate cancer. The VMAT data (cliDose) of 68 patients with prostate cancer treated with VMAT treatment (70-74 Gy/28-37 fr) at our hospital were used (n =...

ApoA-I and ApoB levels, and ApoB-to-ApoA-I ratio as candidate pre-treatment biomarkers of pathomorphological response to neoadjuvant therapy in gastric and esophago-gastric junction adenocarcinoma.

<b><br>Introduction:</b> Neoadjuvant chemotherapy (NAC) is a part of the current standard of care in a locally advanced gastric adenocarcinoma (GA) and esophagogastric junction adenocarcinoma (EGJA), but only patients with good pathomorphological response (pR) to NAC benefit from prolonged overall survival.</br> <b><br>Aim:</b> The study aims to evaluate A...

[To promote the clinical application of PET/MRI in oncology].

PET/MRI integrates anatomical, functional and metabolic information, and is increasingly used in the field of clinical oncology, including early diagn...

Impact of a reduced iodine load with deep learning reconstruction on abdominal MDCT.

To evaluate the impact of a reduced iodine load using deep learning reconstruction (DLR) on the hepatic parenchyma compared to conventional iterative ...

Comparison of the Diagnostic Accuracy of Mammogram-based Deep Learning and Traditional Breast Cancer Risk Models in Patients Who Underwent Supplemental Screening with MRI.

Background Access to supplemental screening breast MRI is determined using traditional risk models, which are limited by modest predictive accuracy. P...

Histology-Based Prediction of Therapy Response to Neoadjuvant Chemotherapy for Esophageal and Esophagogastric Junction Adenocarcinomas Using Deep Learning.

PURPOSE: Quantifying treatment response to gastroesophageal junction (GEJ) adenocarcinomas is crucial to provide an optimal therapeutic strategy. Rout...

Development of a deep learning-based error detection system without error dose maps in the patient-specific quality assurance of volumetric modulated arc therapy.

To detect errors in patient-specific quality assurance (QA) for volumetric modulated arc therapy (VMAT), we proposed an error detection method based o...

Fast and low-dose medical imaging generation empowered by hybrid deep-learning and iterative reconstruction.

Fast and low-dose reconstructions of medical images are highly desired in clinical routines. We propose a hybrid deep-learning and iterative reconstru...

Spatiotemporal Mammography-based Deep Learning Model for Improved Breast Cancer Risk Prediction.

Breast cancer is one of the most prevalent cancers among women. It is the second leading cause of death in cancer-related deaths. Early detection and ...

Robot-assisted laparoscopic enucleation in the treatment of leiomyosarcoma of urinary bladder: A case report.

Leiomyosarcoma of urinary bladder (LMS-UB) is a highly malignant mesenchymal tumor, accounting for less than 0.5% of all bladder malignancies, with a ...

Radiomics in colorectal cancer patients.

The main therapeutic options for colorectal cancer are surgical resection and adjuvant chemotherapy in non-metastatic disease. However, the evaluation...

[A Case of Refractory Lymphatic Leakage after Robot-Assisted Radical Cystectomy].

A 78-year-old male visited the referring hospital because of asymptomatic gross hematuria. The patient was diagnosed with bladder cancer, clinical sta...

Robot-assisted mastectomy: next major advance in breast cancer surgery.

[A Case of Locally Advanced Sigmoid Colon Cancer Invading the Urinary Bladder Treated with Neoadjuvant Chemotherapy and Robot Assisted Urinary Bladder-Conserving Surgery].

We report a case of 72s male with locally advanced sigmoid colon cancer. Colonoscopy revealed an advanced sigmoid colon cancer(AV 15 cm, type 2, semi-...

COMBINING HI-RESOLUTION SCAN MODE WITH DEEP LEARNING RECONSTRUCTION ALGORITHMS IN CARDIAC CT.

To investigate the impact of combining the high-resolution (Hi-res) scan mode with deep learning image reconstruction (DLIR) algorithm in CT. Two phan...

Prediction of Cancer Treatment Using Advancements in Machine Learning.

Many cancer patients die due to their treatment failing because of their disease's resistance to chemotherapy and other forms of radiation therapy. Re...

New Horizons: Artificial Intelligence for Digital Breast Tomosynthesis.

The use of digital breast tomosynthesis (DBT) in breast cancer screening has become widely accepted, facilitating increased cancer detection and lower...

Research on Segmentation Technology in Lung Cancer Radiotherapy Based on Deep Learning.

BACKGROUND: Lung cancer has the highest mortality rate among cancers. Radiation therapy (RT) is one of the most effective therapies for lung cancer. T...

Review on Computer Aided Breast Cancer Detection and Diagnosis using Machine Learning Methods on Mammogram Image.

Machine Learning (ML) plays an essential part in the research area of medical image processing. The advantages of ML techniques lead to more intellige...

Machine learning analysis of breast ultrasound to classify triple negative and HER2+ breast cancer subtypes.

OBJECTIVES: Early diagnosis of triple-negative (TN) and human epidermal growth factor receptor 2 positive (HER2+) breast cancer is important due to it...