Oncology/Hematology

Forecasting optimal treatments in relapsed/refractory mature T- and NK-cell lymphomas: A global PETAL Consortium study.

There is no standard of care in relapsed/refractory T-cell/natural killer-cell lymphomas. Patients often cycle through cytotoxic chemotherapy (CC), epigenetic modifiers (EM) or small molecule inhibitors (SMI) empirically. Ideal therapy at each line remains unknown. We conducted a retrospective, multiple intervention, 'target-trial' using the PETAL global cohort. Patients received front-line CC, th...

Utilizing explainable machine learning for progression-free survival prediction in high-grade serous ovarian cancer: insights from a prospective cohort study.

BACKGROUND: High-grade serous ovarian cancer (HGSOC) remains one of the most challenging gynecological malignancies, with over 70% of ovarian cancer patients ultimately experiencing disease progression. The current prognostic tools for progression-free survival (PFS) in HGSOC patients have limitations. This study aims to develop an explainable machine learning (ML) model for predicting PFS in HGSO...

A deep learning-based clinical-radiomics model predicting the treatment response of immune checkpoint inhibitors (ICIs)-based conversion therapy in potentially convertible hepatocelluar carcinoma patients: a tumor marker prognostic study.

BACKGROUND: The majority of patients with hepatocellular carcinoma (HCC) miss the opportunity of radical resection, making immune check-point inhibito...

Development and validation of a machine learning-based risk model for metastatic disease in nmCRPC patients: a tumor marker prognostic study.

BACKGROUND: Nonmetastatic castration-resistant prostate cancer (nmCRPC) is a clinical challenge due to the high progression rate to metastasis and mor...

Identification of Factors Affecting Prostate Cancer Using Machine Learning Methods: A Systematic Review.

BACKGROUND: Prostate cancer is identified as the second cause of malignancy worldwide and the fifth cause of death among men. Considering the upward t...

Predicting Gene Comutation of EGFR and TP53 by Radiomics and Deep Learning in Patients With Lung Adenocarcinomas.

PURPOSE: This study was designed to construct progressive binary classification models based on radiomics and deep learning to predict the presence of...

The Use of Maximum-Intensity Projections and Deep Learning Adds Value to the Fully Automatic Segmentation of Lesions Avid for [F]FDG and [Ga]Ga-PSMA in PET/CT.

This study investigated the added value of using maximum-intensity projection (MIP) images for fully automatic segmentation of lesions using deep lear...

Deep Learning Model of Primary Tumor and Metastatic Cervical Lymph Nodes From CT for Outcome Predictions in Oropharyngeal Cancer.

IMPORTANCE: Primary tumor (PT) and metastatic cervical lymph node (LN) characteristics are highly associated with oropharyngeal squamous cell carcinom...

Machine learning in prediction of epidermal growth factor receptor status in non-small cell lung cancer brain metastases: a systematic review and meta-analysis.

BACKGROUND: Epidermal growth factor receptor (EGFR) mutations are present in 10-60% of all non-small cell lung cancer (NSCLC) patients and are associa...

Identification of relevant features using SEQENS to improve supervised machine learning models predicting AML treatment outcome.

BACKGROUND AND OBJECTIVE: This study has two main objectives. First, to evaluate a feature selection methodology based on SEQENS, an algorithm for ide...

Global burden of non-melanoma skin cancers among older adults: a comprehensive analysis using machine learning approaches.

Non-melanoma skin cancers (NMSCs), including basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), have shown significant global increases in ...

Performance of radiomics analysis in ultrasound imaging for differentiating benign from malignant adnexal masses: A systematic review and meta-analysis.

INTRODUCTION: We present the state of the art of ultrasound-based machine learning (ML) radiomics models in the context of ovarian masses and analyze ...

Organoid-Guided Precision Medicine: From Bench to Bedside.

Organoid technology, as an emerging field within biotechnology, has demonstrated transformative potential in advancing precision medicine. This review...

Ovarian Cancer Detection in Ascites Cytology with Weakly Supervised Model on Nationwide Data Set.

Conventional ascitic fluid cytology for detecting ovarian cancer is limited by its low sensitivity. To address this issue, this multicenter study deve...

Integrative proteomic profiling of tumor and plasma extracellular vesicles identifies a diagnostic biomarker panel for colorectal cancer.

The lack of reliable non-invasive biomarkers for early colorectal cancer (CRC) diagnosis underscores the need for improved diagnostic tools. Extracell...

Deep learning-based classification of coronary arteries and left ventricle using multimodal data for autonomous protocol selection or adjustment in angiography.

Optimal selection of X-ray imaging parameters is crucial in coronary angiography and structural cardiac procedures to ensure optimal image quality and...

Evaluation of deliverable artificial intelligence-based automated volumetric arc radiation therapy planning for whole pelvic radiation in gynecologic cancer.

This study aimed to develop a deep learning (DL)-based deliverable whole pelvic volumetric arc radiation therapy (VMAT) for patients with gynecologic ...

Artificial intelligence entering the pathology arena in oncology: current applications and future perspectives.

BACKGROUND: Artificial intelligence (AI) is rapidly transforming the fields of pathology and oncology, offering novel opportunities for advancing diag...

Clinical Validation of a Noninvasive Multi-Omics Method for Multicancer Early Detection in Retrospective and Prospective Cohorts.

Recent studies highlight the promise of blood-based multicancer early detection (MCED) tests for identifying asymptomatic patients with cancer. Howeve...

A machine learning tool for prediction of vertebral compression fracture following stereotactic body radiation therapy for spinal metastases.

BACKGROUND AND PURPOSE: The most common adverse event following spine stereotactic body radiotherapy (SBRT) is vertebral compression fracture (VCF). T...