Other Cancers

Evolutionary Strategies AI Addresses Multiple Technical Challenges in Deep Learning Deployment: Proof-of-Principle Demonstration for Neuroblastoma Brain Metastasis Detection.

Two significant obstacles hinder the advancement of Radiology AI. The first is the challenge of overfitting, where small training data sets can result in unreliable outcomes. The second challenge is the need for more generalizability, the lack of which creates difficulties in implementing the technology across various institutions and practices. A recent innovation, deep neuroevolution (DNE), has ...

Performance Evaluation of a Novel Artificial Intelligence-Assisted Digital Microscopy System for the Routine Analysis of Bone Marrow Aspirates.

Bone marrow aspiration (BMA) smear analysis is essential for diagnosis, treatment, and monitoring of a variety of benign and neoplastic hematological conditions. Currently, this analysis is performed by manual microscopy. We conducted a multicenter study to validate a computational microscopy approach with an artificial intelligence-driven decision support system. A total of 795 BMA specimens (615...

Robust brain tumor classification by fusion of deep learning and channel-wise attention mode approach.

Diagnosing brain tumors is a complex and time-consuming process that relies heavily on radiologists' expertise and interpretive skills. However, the a...

Diagnostic accuracy of CT-based radiomics and deep learning for predicting lymph node metastasis in esophageal cancer.

BACKGROUND: Esophageal cancer remains a global challenge due to late diagnoses and limited treatments. Lymph node metastasis (LNM) is crucial for prog...

Multimodal data integration using machine learning to predict the risk of clear cell renal cancer metastasis: a retrospective multicentre study.

PURPOSE: To develop and validate a predictive combined model for metastasis in patients with clear cell renal cell carcinoma (ccRCC) by integrating mu...

Deep learning-based pathological prediction of lymph node metastasis for patient with renal cell carcinoma from primary whole slide images.

BACKGROUND: Metastasis renal cell carcinoma (RCC) patients have extremely high mortality rate. A predictive model for RCC micrometastasis based on pat...

Ultrasensitive plasma-based monitoring of tumor burden using machine-learning-guided signal enrichment.

In solid tumor oncology, circulating tumor DNA (ctDNA) is poised to transform care through accurate assessment of minimal residual disease (MRD) and t...

A machine learning radiomics based on enhanced computed tomography to predict neoadjuvant immunotherapy for resectable esophageal squamous cell carcinoma.

BACKGROUND: Patients with resectable esophageal squamous cell carcinoma (ESCC) receiving neoadjuvant immunotherapy (NIT) display variable treatment re...

Deep Learning for Grading Endometrial Cancer.

Endometrial cancer is the fourth most common cancer in women in the United States, with a lifetime risk of approximately 2.8%. Precise histologic eval...

Deep Learning Classification and Quantification of Pejorative and Nonpejorative Architectures in Resected Hepatocellular Carcinoma from Digital Histopathologic Images.

Liver resection is one of the best treatments for small hepatocellular carcinoma (HCC), but post-resection recurrence is frequent. Biotherapies have e...

Using machine learning to develop preoperative model for lymph node metastasis in patients with bladder urothelial carcinoma.

BACKGROUND: Lymph node metastasis (LNM) is associated with worse prognosis in bladder urothelial carcinoma (BUC) patients. This study aimed to develop...

AI Detection of Glottic Neoplasm Using Voice Signals, Demographics, and Structured Medical Records.

OBJECTIVE: This study investigated whether artificial intelligence (AI) models combining voice signals, demographics, and structured medical records c...

Histological Subtype Classification of Non-Small Cell Lung Cancer with Radiomics and 3D Convolutional Neural Networks.

Non-small cell lung carcinoma (NSCLC) is the most common type of pulmonary cancer, one of the deadliest malignant tumors worldwide. Given the increase...

Artificial Intelligence Imaging Diagnosis Using Super-Resolution and Three-Dimensional Shape for Lymph Node Metastasis of Low Rectal Cancer: A Pilot Study From a Single Center.

BACKGROUND: Although accurate preoperative diagnosis of lymph node metastasis is essential for optimizing treatment strategies for low rectal cancer, ...

Intraoperative near infrared functional imaging of rectal cancer using artificial intelligence methods - now and near future state of the art.

Colorectal cancer remains a major cause of cancer death and morbidity worldwide. Surgery is a major treatment modality for primary and, increasingly, ...

Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unannotated pathology slides.

Cancer diagnosis and management depend upon the extraction of complex information from microscopy images by pathologists, which requires time-consumin...

Parallel CNN-Deep Learning Clinical-Imaging Signature for Assessing Pathologic Grade and Prognosis of Soft Tissue Sarcoma Patients.

BACKGROUND: Traditional biopsies pose risks and may not accurately reflect soft tissue sarcoma (STS) heterogeneity. MRI provides a noninvasive, compre...

Identification of novel biomarkers to distinguish clear cell and non-clear cell renal cell carcinoma using bioinformatics and machine learning.

Renal cell carcinoma (RCC), accounting for 90% of all kidney cancer, is categorized into clear cell RCC (ccRCC) and non-clear cell RCC (non-ccRCC) for...

Deep learning models for predicting the survival of patients with hepatocellular carcinoma based on a surveillance, epidemiology, and end results (SEER) database analysis.

Hepatocellular carcinoma (HCC) is a common malignancy with poor survival and requires long-term follow-up. Hence, we collected information on patients...

A Kernelized Classification Approach for Cancer Recognition Using Markovian Analysis of DNA Structure Patterns as Feature Mining.

Nucleotide-based molecules called DNA and RNA are essential for several biological processes that affect both normal and cancerous cells. They contain...