Oncology/Hematology

Gadoxetic acid-enhanced MRI for identifying cholangiocyte phenotype hepatocellular carcinoma by interpretable machine learning: individual application of SHAP.

PURPOSE: Cholangiocyte phenotype hepatocellular carcinoma (HCC) is highly invasive. This study aims to develop and validate an optimal machine learning model to predict cholangiocyte phenotype HCC based on T1 mapping gadoxetic acid-enhanced MRI and to implement individual applications via the Shapley Additive explanation (SHAP).

Deep learning for malignant lymph node segmentation and detection: a review.

Radiation therapy remains a cornerstone in the treatment of cancer, with the delineation of Organs at Risk (OARs), tumors, and malignant lymph nodes playing a critical role in the planning process. However, the manual segmentation of these anatomical structures is both time-consuming and costly, with inter-observer and intra-observer variability often leading to delineation errors. In recent years...

Predictive modeling of response to repetitive transcranial magnetic stimulation in treatment-resistant depression.

Identifying predictors of treatment response to repetitive transcranial magnetic stimulation (rTMS) remain elusive in treatment-resistant depression (...

Machine Learning to Predict Early Death Despite Pancreaticoduodenectomy.

INTRODUCTION: About 25% of patients undergoing pancreaticoduodenectomy (PD) for right-sided pancreatic ductal adenocarcinoma (PDAC) die within 1 y of ...

Peritoneal cytology predicting distant metastasis in uterine carcinosarcoma: machine learning model development and validation.

OBJECTIVE: This study develops and validates a machine learning model using peritoneal cytology to predict distant metastasis in uterine carcinosarcom...

Diagnostic Adjuncts and Biopsy Techniques for Oral Potentially Malignant Disorders and Oral Cavity Squamous Cell Carcinoma.

Diagnostic adjuncts for oral potentially malignant disorders such as leukoplakia or erythroplakia can aid the clinician in triaging abnormal lesions a...

Circulating miRNAs in genitourinary cancer: pioneering advances in early detection and diagnosis.

MicroRNAs (miRNAs) are noncoding, single-stranded RNAs molecules modulating gene expression thanks to their ability to bind mRNAs. Indeed, they regula...

A pioneering artificial intelligence tool to predict treatment outcomes in ovarian cancer via diagnostic laparoscopy.

Ovarian cancer is associated with high rates of patient mortality and morbidity. Laparoscopic assessment of tumor localization can be used for treatme...

A novel cancer-associated fibroblast-related gene signature for predicting diffuse large B cell lymphoma prognosis using weighted gene co-expression network analysis and machine learning.

ObjectiveOur objective was to investigate a novel cancer-associated fibroblast-related gene signature for predicting clinical outcomes in patients wit...

Predictive Model of Objective Response to Nivolumab Monotherapy for Advanced Renal Cell Carcinoma by Machine Learning Using Genetic and Clinical Data: The SNiP-RCC Study.

PURPOSE: Anti-PD-1 antibodies are widely used for cancer treatment, including in advanced renal cell carcinoma (RCC). However, the therapeutic respons...

Can ChatGPT provide parent education for oral immunotherapy?

BACKGROUND: Oral Immunotherapy (OIT) has exhibited great potential in the treatment of food allergy. However, there is no global consensus on best pra...

Breast Pathology Through the Digital Lens.

Digital pathology (DP) has significantly transformed breast pathology at Mount Sinai Hospital by enhancing diagnostic accuracy, collaboration, and edu...

Artificial Intelligence in bone Metastases: A systematic review in guideline adherence of 92 studies.

BACKGROUND: The last decade has witnessed a surge in artificial intelligence (AI). With bone metastases becoming more prevalent, there is an increasin...

A novel nomogram based on machine learning predicting overall survival for hepatocellular carcinoma patients with dynamic α‑fetoprotein level changes after local resection.

The principal aim of the present study was to develop and validate a nomogram predicting overall survival (OS) in patients with α-fetoprotein (AFP)-ne...

UBTD2 protein molecules emerges as a key prognostic protein marker in glioma: Insights from integrated omics and machine learning analysis of GRM7, NCAPG, CEP55, and other biomarkers.

Glioma is a malignant brain tumor with poor prognosis, and there is an urgent need to find effective biomarkers for early diagnosis and treatment. The...

Role of artificial intelligence in advancing immunology.

Artificial intelligence (AI) has revolutionized various biomedical fields, particularly immunology, by enhancing vaccine development, immunotherapies,...

Variational mode directed deep learning framework for breast lesion classification using ultrasound imaging.

Breast cancer is the most prevalent cancer and the second cause of cancer related death among women in the United States. Accurate and early detection...

Leveraging TME features and multi-omics data with an advanced deep learning framework for improved Cancer survival prediction.

Glioma, a malignant intracranial tumor with high invasiveness and heterogeneity, significantly impacts patient survival. This study integrates multi-o...

Personalized therapeutic strategies and prognosis for advanced laryngeal squamous cell carcinoma: Insights from machine learning models.

PURPOSE: Despite the development of diverse treatment options, there has been an increase in mortality rates for laryngeal squamous cell carcinoma (LS...

Development and validation of an interpretable machine learning model for diagnosing pathologic complete response in breast cancer.

BACKGROUND: Pathologic complete response (pCR) following neoadjuvant chemotherapy (NACT) is a critical prognostic marker for patients with breast canc...