Oncology/Hematology

Development and initial validation of a deep learning algorithm to quantify histological features in colorectal carcinoma including tumour budding/poorly differentiated clusters.

AIMS: To develop and validate a deep learning algorithm to quantify a broad spectrum of histological features in colorectal carcinoma.

Challenges and opportunities for artificial intelligence in oncological imaging.

Imaging plays a key role in oncology, including the diagnosis and detection of cancer, determining clinical management, assessing treatment response, and complications of treatment or disease. The current use of clinical oncology is predominantly qualitative in nature with some relatively crude size-based measurements of tumours for assessment of disease progression or treatment response; however,...

A primer on applying AI synergistically with domain expertise to oncology.

BACKGROUND: The concurrent growth of large-scale oncology data alongside the computational methods with which to analyze and model it has created a pr...

Liver tumor segmentation using 2.5D UV-Net with multi-scale convolution.

Liver tumor segmentation networks are generally based on U-shaped encoder-decoder network with 2D or 3D structure. However, 2D networks lose the inter...

Cancer Classification with a Cost-Sensitive Naive Bayes Stacking Ensemble.

Ensemble learning combines multiple learners to perform combinatorial learning, which has advantages of good flexibility and higher generalization per...

Cytoreductive radical prostatectomy after chemohormonal therapy in patients with primary metastatic prostate cancer.

OBJECTIVE: Cytoreductive radical prostatectomy (cRP) has been proposed as local treatment option in metastatic hormone-sensitive prostate cancer (mHSP...

Machine-Learning-Based Evaluation of Intratumoral Heterogeneity and Tumor-Stroma Interface for Clinical Guidance.

Assessment of intratumoral heterogeneity and tumor-host interaction within the tumor microenvironment is becoming increasingly important for innovativ...

Artificial Intelligence-Assisted Amphiregulin and Epiregulin IHC Predicts Panitumumab Benefit in Wild-Type Metastatic Colorectal Cancer.

PURPOSE: High tumor mRNA levels of the EGFR ligands amphiregulin (AREG) and epiregulin (EREG) are associated with anti-EGFR agent response in metastat...

Mask-Guided Convolutional Neural Network for Breast Tumor Prognostic Outcome Prediction on 3D DCE-MR Images.

In this proof-of-concept work, we have developed a 3D-CNN architecture that is guided by the tumor mask for classifying several patient-outcomes in br...

Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey.

Artificial Intelligence is providing astonishing results, with medicine being one of its favourite playgrounds. Machine Learning and, in particular, D...

Using Graph Representation Learning to Predict Salivary Cortisol Levels in Pancreatic Cancer Patients.

Cortisol is a glucocorticoid hormone that is critical to immune system functioning. Studies show that prolonged exposure to high levels of cortisol ca...

Deep Learning Improves Speed and Accuracy of Prostate Gland Segmentations on Magnetic Resonance Imaging for Targeted Biopsy.

PURPOSE: Targeted biopsy improves prostate cancer diagnosis. Accurate prostate segmentation on magnetic resonance imaging (MRI) is critical for accura...

Tumor classification in automated breast ultrasound (ABUS) based on a modified extracting feature network.

People can get consistent Automated Breast Ultrasound (ABUS) images due to the imaging mechanism of scanning. Therefore, it has unique advantages in b...

Magnetically Actuated Drug Delivery Helical Microrobot with Magnetic Nanoparticle Retrieval Ability.

Therapeutic drug delivery microrobots capable of accurate targeting using an electromagnetic actuation (EMA) system are being developed. However, thes...

Role of Machine Learning and Artificial Intelligence in Interventional Oncology.

PURPOSE OF REVIEW: The purpose of this review is to highlight the current role of machine learning and artificial intelligence and in the field of int...

Distinguishing Rectal Cancer from Colon Cancer Based on the Support Vector Machine Method and RNA-sequencing Data.

Colorectal cancer (CRC) is the third most commonly diagnosed cancer worldwide. Several studies have indicated that rectal cancer is significantly diff...

Detection of flat colorectal neoplasia by artificial intelligence: A systematic review.

OBJECTIVES: This study review focuses on a deep learning method for the detection of colorectal lesions in colonoscopy and AI support for detecting co...

Exploring photoacoustic spectroscopy-based machine learning together with metabolomics to assess breast tumor progression in a xenograft model ex vivo.

In the current study, a breast tumor xenograft was established in athymic nude mice by subcutaneous injection of the MCF-7 cell line and assessed the ...

Prospective assessment of breast cancer risk from multimodal multiview ultrasound images via clinically applicable deep learning.

The clinical application of breast ultrasound for the assessment of cancer risk and of deep learning for the classification of breast-ultrasound image...

Radiomics analysis on CT images for prediction of radiation-induced kidney damage by machine learning models.

INTRODUCTION: We aimed to assess the power of radiomic features based on computed tomography to predict risk of chronic kidney disease in patients und...