Breast Cancer

[Rectal Gastrointestinal Stromal Tumor(GIST)Excised by Two Teams Following Neoadjuvant Chemotherapy-A Case Report].

A 67-year-old man presented with bloody stools. Colonoscopy showed a small submucosal tumor in the lower rectum. As the tumor was small, follow-up was chosen. Although he was instructed to undergo reexamination 1 year later, he did not comply. Four years later, he was reexamined by the previous doctor for disorders of defecation. He was admitted to our hospital for examination and treatment, as th...

[Stage Ⅳ Breast Cancer with Difficulty in Initiating Chemotherapy-A Case Report].

We report a case of breast cancer(T4b[skin], N1, M1[lung], ER-, PR-, HER2 3+)in a 63-year-old woman with liver dysfunction of unknown cause(T-Bil 3.6mg/dL, ALP 3,483 U/L, AST 214 U/L, ALT 320 U/L, g / -GTP 1,943 U/L). Further- more, serum CA19-9(4,670 U/mL)and HbA1c(8.8%)levels were both elevated. First, she underwent chemotherapy with trastuzumab and capecitabine. Subsequently, liver dysfunction ...

A convolutional neural network approach for IMRT dose distribution prediction in prostate cancer patients.

The purpose of the study was to compare a 3D convolutional neural network (CNN) with the conventional machine learning method for predicting intensity...

Prior to Initiation of Chemotherapy, Can We Predict Breast Tumor Response? Deep Learning Convolutional Neural Networks Approach Using a Breast MRI Tumor Dataset.

We hypothesize that convolutional neural networks (CNN) can be used to predict neoadjuvant chemotherapy (NAC) response using a breast MRI tumor datase...

Full-Dose PET Image Estimation from Low-Dose PET Image Using Deep Learning: a Pilot Study.

Positron emission tomography (PET) imaging is an effective tool used in determining disease stage and lesion malignancy; however, radiation exposure t...

Using Artificial Intelligence to Improve the Quality and Safety of Radiation Therapy.

Within artificial intelligence, machine learning (ML) efforts in radiation oncology have augmented the transition from generalized to personalized tre...

The Role of Generative Adversarial Networks in Radiation Reduction and Artifact Correction in Medical Imaging.

Adversarial networks were developed to complete powerful image-processing tasks on the basis of example images provided to train the networks. These n...

Automated Detection of Measurements and Their Descriptors in Radiology Reports Using a Hybrid Natural Language Processing Algorithm.

Radiological measurements are reported in free text reports, and it is challenging to extract such measures for treatment planning such as lesion summ...

Lens Identification to Prevent Radiation-Induced Cataracts Using Convolutional Neural Networks.

Exposure of the lenses to direct ionizing radiation during computed tomography (CT) examinations predisposes patients to cataract formation and should...

Deep Learning Based Dosimetry Evaluation at Organs-at-Risk in Esophageal Radiation Treatment Planning.

Rapid esophageal radiation treatment planning is often obstructed by manually adjusting optimization parameters. The adjustment process is commonly gu...

Restoration of Full Data from Sparse Data in Low-Dose Chest Digital Tomosynthesis Using Deep Convolutional Neural Networks.

Chest digital tomosynthesis (CDT) provides more limited image information required for diagnosis when compared to computed tomography. Moreover, the r...

Automatic Nasopharyngeal Carcinoma Segmentation Using Fully Convolutional Networks with Auxiliary Paths on Dual-Modality PET-CT Images.

Nasopharyngeal carcinoma (NPC) is prevalent in certain areas, such as South China, Southeast Asia, and the Middle East. Radiation therapy is the most ...

A novel machine learning-derived decision tree including uPA/PAI-1 for breast cancer care.

Background uPA and PAI-1 are breast cancer biomarkers that evaluate the benefit of chemotherapy (CT) for HER2-negative, estrogen receptor-positive, lo...

Use of Crowd Innovation to Develop an Artificial Intelligence-Based Solution for Radiation Therapy Targeting.

IMPORTANCE: Radiation therapy (RT) is a critical cancer treatment, but the existing radiation oncologist work force does not meet growing global deman...

A parsimonious 3-gene signature predicts clinical outcomes in an acute myeloid leukemia multicohort study.

Acute myeloid leukemia (AML) is a genetically heterogeneous hematological malignancy with variable responses to chemotherapy. Although recurring cytog...

Automatic Labeling of Special Diagnostic Mammography Views from Images and DICOM Headers.

Applying state-of-the-art machine learning techniques to medical images requires a thorough selection and normalization of input data. One of such ste...

Predicting Breast Cancer Molecular Subtype with MRI Dataset Utilizing Convolutional Neural Network Algorithm.

To develop a convolutional neural network (CNN) algorithm that can predict the molecular subtype of a breast cancer based on MRI features. An IRB-appr...

Machine learning approaches to decipher hormone and HER2 receptor status phenotypes in breast cancer.

Breast cancer prognosis and administration of therapies are aided by knowledge of hormonal and HER2 receptor status. Breast cancer lacking estrogen re...

Deep Learning Approach for Assessment of Bladder Cancer Treatment Response.

We compared the performance of different Deep learning-convolutional neural network (DL-CNN) models for bladder cancer treatment response assessment b...

MR-based treatment planning in radiation therapy using a deep learning approach.

PURPOSE: To develop and evaluate the feasibility of deep learning approaches for MR-based treatment planning (deepMTP) in brain tumor radiation therap...