Oncology/Hematology

Deep learning for drug response prediction in cancer.

Predicting the sensitivity of tumors to specific anti-cancer treatments is a challenge of paramount importance for precision medicine. Machine learning(ML) algorithms can be trained on high-throughput screening data to develop models that are able to predict the response of cancer cell lines and patients to novel drugs or drug combinations. Deep learning (DL) refers to a distinct class of ML algor...

canSAR: update to the cancer translational research and drug discovery knowledgebase.

canSAR (http://cansar.icr.ac.uk) is the largest, public, freely available, integrative translational research and drug discovery knowledgebase for oncology. canSAR integrates vast multidisciplinary data from across genomic, protein, pharmacological, drug and chemical data with structural biology, protein networks and more. It also provides unique data, curation and annotation and crucially, AI-inf...

Artificial intelligence in cancer diagnostics and therapy: current perspectives.

Artificial intelligence (AI) has found its way into every sphere of human life including the field of medicine. Detection of cancer might be AI's most...

Deep learning with transfer learning in pathology. Case study: classification of basal cell carcinoma.

Establishing basal cell carcinoma (BCC) subtype is sometimes challenging for pathologists. Deep-learning (DL) algorithms are an emerging approach in i...

Simultaneous laparoscopic nephroureterectomy and robot-assisted anterior pelvic exenteration with intracorporeal ileal conduit urinary diversion: step-by-step video-illustrated technique.

INTRODUCTION:: One of the most remarkable characteristics of urothelial carcinomas is multifocality. However, occurrence of synchronous bladder cancer...

Machine Learning Approaches on High Throughput NGS Data to Unveil Mechanisms of Function in Biology and Disease.

In this review, the fundamental basis of machine learning (ML) and data mining (DM) are summarized together with the techniques for distilling knowled...

Robot-Assisted Thyroidectomy Versus Open Thyroidectomy in the Treatment of Well Differentiated Thyroid Carcinoma.

BACKGROUND: The incidence of thyroid cancer has increased worldwide during the last decade, becoming the most common endocrine malignancy and accounti...

Therapeutic and Clinical Outcomes of Robot-assisted Partial Nephrectomy Cryoablation for T1 Renal Cell Carcinoma.

BACKGROUND: In cT1 renal cell carcinoma (RCC), very few studies have compared oncological outcomes and renal function preservation rates in nephron-sp...

Deep learning based analysis of sentiment dynamics in online cancer community forums: An experience.

Online health communities (OHC) provide various opportunities for patients with chronic or life-threatening illnesses, especially for cancer patients ...

A Survey on Machine Learning and Deep Learning-based Computer-Aided Methods for Detection of Polyps in CT Colonography.

BACKGROUND: Colon cancer generally begins as a neoplastic growth of tissue, called polyps, originating from the inner lining of the colon wall. Most c...

Detecting breast cancer using artificial intelligence: Convolutional neural network.

BACKGROUND: One of the most broadly founded approaches to envisage cancer treatment relies upon a pathologist's efficiency to visually inspect the app...

Study of the Usefulness of Bone Scan Index Calculated From 99m-Technetium- Hydroxymethylene Diphosphonate (Tc-HMDP) Bone Scintigraphy for Bone Metastases from Prostate Cancer Using Deep Learning Algorithms.

BACKGROUND: BSI calculated from bone scintigraphy using technetium-methylene diphosphonate (Tc-MDP) is used as a quantitative indicator of metastatic ...

Differentiation of rare brain tumors through unsupervised machine learning: Clinical significance of in-depth methylation and copy number profiling illustrated through an unusual case of IDH wildtype glioblastoma.

Methylation profiling has become a mainstay in brain tumor diagnostics since the introduction of the first publicly available classification tool by t...

Synergistic Drug Combination Prediction by Integrating Multiomics Data in Deep Learning Models.

Intrinsic and acquired drug resistance is a major challenge in cancer therapy. Synergistic drug combinations could help to overcome drug resistance. H...

CT Dosimetry: What Has Been Achieved and What Remains to Be Done.

Radiation dose in computed tomography (CT) has become a hot topic due to an upward trend in the number of CT procedures worldwide and the relatively h...

Development of Machine Learning Models for Predicting Postoperative Delayed Remission in Patients With Cushing's Disease.

CONTEXT: Postoperative hypercortisolemia mandates further therapy in patients with Cushing's disease (CD). Delayed remission (DR) is defined as not ac...

Ultrasound image analysis using deep neural networks for discriminating between benign and malignant ovarian tumors: comparison with expert subjective assessment.

OBJECTIVES: To develop and test the performance of computerized ultrasound image analysis using deep neural networks (DNNs) in discriminating between ...

Chemotherapy Knowledge Base Management in the Era of Precision Oncology.

Cancer medicine has grown increasingly complex in recent years with the advent of precision oncology and wide utilization of multidrug regimens. Repre...

Using machine learning method to identify as a novel marker to predict biochemical recurrence in prostate cancer.

This study aims to identify novel marker to predict biochemical recurrence (BCR) in prostate cancer patients after radical prostatectomy with negativ...

Design of 1-year mortality forecast at hospital admission: A machine learning approach.

Palliative care is referred to a set of programs for patients that suffer life-limiting illnesses. These programs aim to maximize the quality of life ...