Oncology/Hematology

Summary Report of the SNMMI AI Task Force Radiomics Challenge 2024.

In medical imaging, challenges are competitions that aim to provide a fair comparison of different methodologic solutions to a common problem. Challenges typically focus on addressing real-world problems, such as segmentation, detection, and prediction tasks, using various types of medical images and associated data. Here, we describe the organization and results of such a challenge to compare mac...

Multi-omics integration of TPX2 in prognostic prediction in resectable hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a malignant tumor with high incidence and mortality rates globally, significantly affecting patient prognosis and quality of life. Currently, predicting postoperative recurrence risk and survival in HCC patients remains challenging, as precise and effective quantitative indicators are lacking, limiting clinicians' ability to make individualized treatment decisions...

CT-based deep learning model for improved disease-free survival prediction in clinical stage I lung cancer: a real-world multicenter study.

OBJECTIVES: To develop a deep learning (DL) model for predicting disease-free survival (DFS) in clinical stage I lung cancer patients who underwent su...

AI-Supported Documentation and Clinical Monitoring in Palliative Care: A Real-World Observational Study.

BackgroundPatients in palliative care often experience prolonged hospital stays, requiring detailed documentation, complex symptom management, and mul...

Artificial intelligence for radiotherapy dose prediction: A comprehensive review.

Patient outcomes are significantly impacted by the effectiveness and quality of radiation treatment planning. Deep learning, a branch of artificial in...

Analytical Comparison of Commonly Used Laboratory-Developed Tests for the Assessment of Ki-67 in Breast Carcinoma With an FDA-Approved Benchmark.

Ki-67 immunohistochemistry (IHC), a commonly used assay for breast cancer risk prognostication, has significant inter-laboratory heterogeneity. This s...

Multiscale-fusion models with genomic, topological and pathomic features to predict response to radiation therapy for non-small cell lung cancer patients.

Artificial intelligence models with biomarkers to predict treatment responses to radiation would be necessary to maximise the treatment outcomes of in...

Analysis and characterization of extrachromosomal circular DNA in prostate cancer: Potential biomarker discovery from urine, plasma, and tumor samples.

Extrachromosomal circular DNA (eccDNA) may contribute to genomic rearrangements and tumor heterogeneity, playing a role in cancer development and prog...

Pretreatment Multi-sequence Contrast-Enhanced MRI to Predict Response to Immunotherapy in Unresectable Hepatocellular Carcinoma Using Transformer: A Multicenter Study.

Targeted combined immunotherapy (TCI) has shown certain antitumor effects in patients with unresectable hepatocellular carcinoma(uHCC), but only a su...

Integrated machine learning based on cuproptosis and RNA methylation regulators to explore the molecular model of prostate cancer and provide novel insights to immunotherapy.

As a highly prevalent tumor in males, prostate cancer (PCa) needs newly developed biomarkers to guide prognosis and treatment. However, few researche...

Integrative multi-omics profiling deciphers tumor microenvironment heterogeneity and immunotherapy vulnerabilities in lung neuroendocrine carcinomas.

INTRODUCTION: Lung neuroendocrine carcinomas (Lu-NECs) are rare, highly aggressive lung tumors with poor prognosis and limited therapeutic options. Un...

Time-Gated Raman Spectroscopy Combined with Deep Learning for Rapid, Label-Free Histopathological Discrimination of Gastric Cancer.

Gastric cancer is one of the most common malignant tumors of the digestive system, with a high mortality rate due to late-stage diagnosis. Current cli...

Multivariate and Machine Learning-Derived Virtual Staining and Biochemical Quantification of Cancer Cells through Raman Hyperspectral Imaging.

Advances in virtual staining and spatial omics have revolutionized our ability to explore cellular architecture and molecular composition with unprece...

Next-Generation Sequencing-Based Testing Among Patients With Advanced or Metastatic Nonsquamous Non-Small Cell Lung Cancer in the United States: Predictive Modeling Using Machine Learning Methods.

BACKGROUND: Next-generation sequencing (NGS) has become a cornerstone of treatment for lung cancer and is recommended in current treatment guidelines ...

DeepHeme, a high-performance, generalizable deep ensemble for bone marrow morphometry and hematologic diagnosis.

Cytomorphological analysis of the bone marrow aspirate (BMA) is pivotal for the diagnostic workup of a broad range of hematological disorders. However...

Challenges and Opportunities for Global Cervical Cancer Elimination: How Can We Build a Model for Other Cancers?

Cervical cancer remains a leading cause of cancer-related death among women globally, despite the availability of effective prevention through human p...

Extremity Soft Tissue Sarcoma Reconstruction Nomograms: A Clinicoradiomic, Machine Learning-Powered Predictor of Postoperative Outcomes.

PURPOSE: The choice of wound closure modality after limb-sparing extremity soft-tissue sarcoma (eSTS) resection is fraught with uncertainty. Leveragin...

Improving lung cancer diagnosis and survival prediction with deep learning and CT imaging.

Lung cancer is a major cause of cancer-related deaths, and early diagnosis and treatment are crucial for improving patients' survival outcomes. In thi...

Monitoring of Early Skin Reactions by Optical Coherence Tomography and Dermatoscopy in Patients Receiving Radiation Therapy for Head and Neck Cancer.

PURPOSE: Patients with head and neck cancer undergoing radiation therapy (RT) may experience pronounced acute skin reactions. We tested whether optica...

Label-free diagnosis of lung cancer by Fourier transform infrared microspectroscopy coupled with domain adversarial learning.

Lung cancer is one of the most prevalent malignancies, characterized by high morbidity and mortality rates. Current diagnostic approaches primarily re...