Oncology/Hematology

Performance evaluation of a deep learning image reconstruction (DLIR) algorithm in "double low" chest CTA in children: a feasibility study.

BACKGROUND: Chest CT angiography (CTA) is a convenient clinical examination for children with an increasing need to reduce both radiation and contrast medium doses. Iterative Reconstruction algorithms are often used to reduce image noise but encounter limitations under low radiation dose and conventional 100 kVp tube voltage may not provide adequate enhancement under low contrast dose.

Application of machine learning to large in-vitro databases to identify cancer cell characteristics: telomerase reverse transcriptase (TERT) expression.

Advances in biotechnology and machine learning have created an enhanced environment for unearthing and exploiting previously unrecognized relationships between genomic and epigenetic data with potential therapeutic implications. We applied advanced algorithms to data from the Cancer Dependency Map to uncover increasingly complex relationships. Specifically, we investigate characteristics of tumor ...

Validated spectral manipulations for determination of an anti-neoplastic drug and its related impurities including its hazardous degradation product.

Innovative and specific double dual wavelength, dual ratio subtraction spectrophotometric methods were carried out along with a successive ratio subtr...

Deep learning for diagnosis and survival prediction in soft tissue sarcoma.

BACKGROUND: Clinical management of soft tissue sarcoma (STS) is particularly challenging. Here, we used digital pathology and deep learning (DL) for d...

Treatment initiation prediction by EHR mapped PPD tensor based convolutional neural networks boosting algorithm.

Electronic health records contain patient's information that can be used for health analytics tasks such as disease detection, disease progression pre...

Alternative splicing associated with cancer stemness in kidney renal clear cell carcinoma.

BACKGROUD: Cancer stemness is associated with metastases in kidney renal clear cell carcinoma (KIRC) and negatively correlates with immune infiltrates...

Semi-Supervised Deep Learning-Based Image Registration Method with Volume Penalty for Real-Time Breast Tumor Bed Localization.

Breast-conserving surgery requires supportive radiotherapy to prevent cancer recurrence. However, the task of localizing the tumor bed to be irradiate...

Cisplatin and paclitaxel co-delivery nanosystem for ovarian cancer chemotherapy.

We have designed and developed an effective drug delivery system using biocompatible polymer of poly (ethylene glycol)-polyaspartic acid (mPEG-PAsp) f...

Cyclic arginine-glycine-aspartic acid-modified red blood cells for drug delivery: Synthesis and evaluation.

Red blood cells (RBCs) are an excellent choice for cell preparation research because of their biocompatibility, high drug loading, and long half-life....

Minimally Invasive Esophagectomy in Semi-Prone Position (Pawar Technique): Technical Aspects and Outcome in 224 Patients.

 There are two patient positions described for minimally invasive esophagectomy (MIE) for esophageal cancer, viz., left lateral and prone positions. ...

Potential and limitations of radiomics in neuro-oncology.

Radiomics seeks to apply classical methods of image processing to obtain quantitative parameters from imaging. Derived features are subsequently fed i...

Future of biomarker evaluation in the realm of artificial intelligence algorithms: application in improved therapeutic stratification of patients with breast and prostate cancer.

Clinical workflows in oncology depend on predictive and prognostic biomarkers. However, the growing number of complex biomarkers contributes to costly...

Deep-learning based classification distinguishes sarcomatoid malignant mesotheliomas from benign spindle cell mesothelial proliferations.

Sarcomatoid mesothelioma is an aggressive malignancy that can be challenging to distinguish from benign spindle cell mesothelial proliferations based ...

Using deep learning to identify bladder cancers with FGFR-activating mutations from histology images.

BACKGROUND: In recent years, the fibroblast growth factor receptor (FGFR) pathway has been proven to be an important therapeutic target in bladder can...

Evaluating machine learning methodologies for identification of cancer driver genes.

Cancer is driven by distinctive sorts of changes and basic variations in genes. Recognizing cancer driver genes is basic for accurate oncological anal...

A different overview of staging PET/CT images in patients with esophageal cancer: the role of textural analysis with machine learning methods.

OBJECTIVE: This study evaluates the ability of several machine learning (ML) algorithms, developed using volumetric and texture data extracted from ba...

Comparison of British Thyroid Association, American College of Radiology TIRADS and Artificial Intelligence TIRADS with histological correlation: diagnostic performance for predicting thyroid malignancy and unnecessary fine needle aspiration rate.

OBJECTIVES: To compare diagnostic performance of British Thyroid Association (BTA), American College of Radiology Thyroid Imaging Reporting and Data S...

A computational pipeline for data augmentation towards the improvement of disease classification and risk stratification models: A case study in two clinical domains.

Virtual population generation is an emerging field in data science with numerous applications in healthcare towards the augmentation of clinical resea...

Oncological, Surgical and Functional Outcomes of Transoral Robotic Cordectomy for Early Glottic Carcinoma.

OBJECTIVE: To investigate oncological, surgical and functional outcomes of transoral robotic surgery cordectomy (TORS-Co).

Deep Learning-based Recalibration of the CUETO and EORTC Prediction Tools for Recurrence and Progression of Non-muscle-invasive Bladder Cancer.

Despite being standard tools for decision-making, the European Organisation for Research and Treatment of Cancer (EORTC), European Association of Urol...