Breast Cancer

Novel Imaging Biomarkers to Assess Oncologic Treatment-Related Changes.

Cancer therapeutics cause various treatment-related changes that may impact patient follow-up and disease monitoring. Although atypical responses such as pseudoprogression may be misinterpreted as treatment nonresponse, other changes, such as hyperprogressive disease seen with immunotherapy, must be recognized early for timely management. Radiation necrosis in the brain is a known response to radi...

[Deep Learning-based Risk Prediction Model for Postoperative Healthcare-associated Infections].

Objective To develop a risk prediction model combining pre/intraoperative risk factors and intraoperative vital signs for postoperative healthcare-associated infection(HAI)based on deep learning. Methods We carried out a retrospective study based on two randomized controlled trials(NCT02715076,ChiCTR-IPR-17011099).The patients who underwent elective radical resection of advanced digestive system t...

Prediction of seroma after total mastectomy using an artificial neural network algorithm.

Seroma is a common complication after mastectomy. To the best of our knowledge, no prediction models have been developed for this. Henceforth, medical...

Risk of Developing Breast Reconstruction Complications: A Machine-Learning Nomogram for Individualized Risk Estimation with and without Postmastectomy Radiation Therapy.

BACKGROUND: The purpose of this study was to create a nomogram using machine learning models predicting risk of breast reconstruction complications wi...

De novo Prediction of Cell-Drug Sensitivities Using Deep Learning-based Graph Regularized Matrix Factorization.

Application of artificial intelligence (AI) in precision oncology typically involves predicting whether the cancer cells of a patient (previously unse...

Detection and Weak Segmentation of Masses in Gray-Scale Breast Mammogram Images Using Deep Learning.

PURPOSE: In this paper, we propose deep-learning methodology with which to enhance the mass differentiation performance of convolutional neural networ...

HER2-ResNet: A HER2 classification method based on deep residual network.

BACKGROUND: HER2 gene expression is one of the main reference indicators for breast cancer detection and treatment, and it is also an important target...

Low-dose CT noise reduction based on local total variation and improved wavelet residual CNN.

BACKGROUND: Low-dose computed tomography (LDCT) is an effective method for reducing radiation exposure. However, reducing radiation dose leads to cons...

Deep learning-based classification of breast cancer cells using transmembrane receptor dynamics.

MOTIVATION: Motions of transmembrane receptors on cancer cell surfaces can reveal biophysical features of the cancer cells, thus providing a method fo...

Detection of subtype-specific breast cancer surface protein biomarkers via a novel transcriptomics approach.

BACKGROUND: Cell-surface proteins have been widely used as diagnostic and prognostic markers in cancer research and as targets for the development of ...

Development and Validation of a Deep Learning CT Signature to Predict Survival and Chemotherapy Benefit in Gastric Cancer: A Multicenter, Retrospective Study.

OBJECTIVE: We aimed to develop a deep learning-based signature to predict prognosis and benefit from adjuvant chemotherapy using preoperative computed...

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HER2 is an important prognostic and predictive biomarker in breast cancer. Its detection makes it possible to define which patients will benefit from ...

Integration and interplay of machine learning and bioinformatics approach to identify genetic interaction related to ovarian cancer chemoresistance.

Although chemotherapy is the first-line treatment for ovarian cancer (OCa) patients, chemoresistance (CR) decreases their progression-free survival. T...

Robotics, imaging, and artificial intelligence in the catheterisation laboratory.

The catheterisation laboratory today combines diagnosis and therapeutics, through various imaging modalities and a prolific list of interventional too...

Automatic contour segmentation of cervical cancer using artificial intelligence.

In cervical cancer treatment, radiation therapy is selected based on the degree of tumor progression, and radiation oncologists are required to deline...

Prediction of Neutropenic Events in Chemotherapy Patients: A Machine Learning Approach.

PURPOSE: Severe and febrile neutropenia present serious hazards to patients with cancer undergoing chemotherapy. We seek to develop a machine learning...

Highly accurate diagnosis of papillary thyroid carcinomas based on personalized pathways coupled with machine learning.

Thyroid nodules are neoplasms commonly found among adults, with papillary thyroid carcinoma (PTC) being the most prevalent malignancy. However, curren...

Artificial Intelligence in Diagnostic Imaging and Radiation Therapy.

Artificial intelligence (AI) is a rapidly growing profession that shows continued promise for diagnostic imaging in various disciplines, including der...

Artificial intelligence technologies empowering identification of novel diagnostic molecular markers in gastric cancer.

In recent clinical practice the molecular diagnostics have been significantly empowered and upgraded by the use of Artificial Intelligence and its ass...

Survey and comparative assessments of computational multi-omics integrative methods with multiple regulatory networks identifying distinct tumor compositions across pan-cancer data sets.

The significance of pan-cancer categories has recently been recognized as widespread in cancer research. Pan-cancer categorizes a cancer based on its ...