Oncology/Hematology

Application of deep learning on mammographies to discriminate between low and high-risk DCIS for patient participation in active surveillance trials.

BACKGROUND: Ductal Carcinoma In Situ (DCIS) can progress to invasive breast cancer, but most DCIS lesions never will. Therefore, four clinical trials (COMET, LORIS, LORETTA, AND LORD) test whether active surveillance for women with low-risk Ductal carcinoma In Situ is safe (E. S. Hwang et al., BMJ Open, 9: e026797, 2019, A. Francis et al., Eur J Cancer. 51: 2296-2303, 2015, Chizuko Kanbayashi et a...

Advancing Breast Cancer Diagnosis through Breast Mass Images, Machine Learning, and Regression Models.

Breast cancer results from a disruption of certain cells in breast tissue that undergo uncontrolled growth and cell division. These cells most often accumulate and form a lump called a tumor, which may be benign (non-cancerous) or malignant (cancerous). Malignant tumors can spread quickly throughout the body, forming tumors in other areas, which is called metastasis. Standard screening techniques ...

The Evolving Landscape of Cervical Cancer: Breakthroughs in Screening and Therapy Through Integrating Biotechnology and Artificial Intelligence.

Cervical cancer (CC) continues to be a major worldwide health concern, profoundly impacting the lives of countless females worldwide. In low- and midd...

Identification of KRAS mutation-associated gut microbiota in colorectal cancer and construction of predictive machine learning model.

Gut microbiota has demonstrated an increasingly important role in the onset and development of colorectal cancer (CRC). Nonetheless, the association b...

Sa-TTCA: An SVM-based approach for tumor T-cell antigen classification using features extracted from biological sequencing and natural language processing.

Accurately predicting tumor T-cell antigen (TTCA) sequences is a crucial task in the development of cancer vaccines and immunotherapies. TTCAs derived...

Targeted-BEHRT: Deep Learning for Observational Causal Inference on Longitudinal Electronic Health Records.

Observational causal inference is useful for decision-making in medicine when randomized clinical trials (RCTs) are infeasible or nongeneralizable. Ho...

Scoring PD-L1 Expression in Urothelial Carcinoma: An International Multi-Institutional Study on Comparison of Manual and Artificial Intelligence Measurement Model (AIM-PD-L1) Pathology Assessments.

Assessing programmed death ligand 1 (PD-L1) expression on tumor cells (TCs) using Food and Drug Administration-approved, validated immunoassays can gu...

DBDNMF: A Dual Branch Deep Neural Matrix Factorization method for drug response prediction.

Anti-cancer response of cell lines to drugs is in urgent need for individualized precision medical decision-making in the era of precision medicine. M...

Machine learning-driven prediction of brain metastasis in lung adenocarcinoma using miRNA profile and target gene pathway analysis of an mRNA dataset.

BACKGROUND: Brain metastasis (BM) is common in lung adenocarcinoma (LUAD) and has a poor prognosis, necessitating predictive biomarkers. MicroRNAs (Mi...

Deep learning-based computer-aided diagnosis system for the automatic detection and classification of lateral cervical lymph nodes on original ultrasound images of papillary thyroid carcinoma: a prospective diagnostic study.

PURPOSE: This study aims to develop a deep learning-based computer-aided diagnosis (CAD) system for the automatic detection and classification of late...

Designing a deep hybridized residual and SE model for MRI image-based brain tumor prediction.

Deep learning techniques have become crucial in the detection of brain tumors but classifying numerous images is time-consuming and error-prone, impac...

Boosting predictive models and augmenting patient data with relevant genomic and pathway information.

The recurrence of low-stage lung cancer poses a challenge due to its unpredictable nature and diverse patient responses to treatments. Personalized ca...

Hierarchical decomposed dual-domain deep learning for sparse-view CT reconstruction.

. X-ray computed tomography employing sparse projection views has emerged as a contemporary technique to mitigate radiation dose. However, due to the ...

Outcomes of robot-assisted radical prostatectomy in men after trans-urethral resection of the prostate: a matched-pair analysis.

Prior history of transurethral resection of the prostate (TURP) can complicate Robot-assisted radical prostatectomy (RARP). Very few studies analyse t...

Long-term outcomes of robot-assisted laparoscopic surgery versus conventional laparoscopic surgery for rectal cancer: single-center, retrospective, propensity score analyses.

Although the short-term outcomes of robot-assisted laparoscopic surgery (RALS) for rectal cancer are well known, the long-term oncologic outcomes of R...

Early gastric cancer detection and lesion segmentation based on deep learning and gastroscopic images.

Gastric cancer is a highly prevalent disease that poses a serious threat to public health. In clinical practice, gastroscopy is frequently used by med...

Identification and validation of an immune-derived multiple programmed cell death index for predicting clinical outcomes, molecular subtyping, and drug sensitivity in lung adenocarcinoma.

OBJECTIVES: Comprehensive cross-interaction of multiple programmed cell death (PCD) patterns in the patients with lung adenocarcinoma (LUAD) have not ...

Comparative analysis of the spatial distribution of brain metastases across several primary cancers using machine learning and deep learning models.

OBJECTIVE: Brain metastases (BM) are associated with poor prognosis and increased mortality rates, making them a significant clinical challenge. Study...

Predictive factors of renal function after robot-assisted partial nephrectomy in clinical T1b tumors.

Robot-assisted partial nephrectomy (RAPN) has been shown to be a safe and effective method for treatment of small renal tumors, including clinical T1b...

Real-World Outcomes of Patients with Advanced Epidermal Growth Factor Receptor-Mutated Non-Small Cell Lung Cancer in Canada Using Data Extracted by Large Language Model-Based Artificial Intelligence.

Real-world evidence for patients with advanced -mutated non-small cell lung cancer (NSCLC) in Canada is limited. This study's objective was to use pre...