AIMC Topic: Neoplasm Staging

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Detection, localization, and staging of breast cancer lymph node metastasis in digital pathology whole slide images using selective neighborhood attention-based deep learning.

Scientific reports
Accurate detection, localization, and staging of breast cancer lymph node metastases are critical for guiding treatment decisions and predicting patient outcomes. This study presents a selective neighborhood attention-based deep learning framework th...

Multicenter study of CT-based deep learning for predicting preoperative T staging and TNM staging in clear cell renal cell carcinoma.

BMC cancer
BACKGROUND: Accurate preoperative T and TNM staging of clear cell renal cell carcinoma (ccRCC) is crucial for diagnosis and treatment, but these assessments often depend on subjective radiologist judgment, leading to interobserver variability. This s...

Automated Esophageal Cancer Staging From Free-Text Radiology Reports: Large Language Model Evaluation Study.

JMIR medical informatics
BACKGROUND: Accurate staging of esophageal cancer is crucial for determining prognosis and guiding treatment strategies, but manual interpretation of radiology reports by clinicians is prone to variability and limited accuracy, resulting in reduced s...

AI-driven 3D CT imaging prediction model for improving preoperative detection of visceral pleural invasion in early-stage lung cancer.

PloS one
Visceral pleural invasion (VPI) is a critical prognostic factor in early-stage non-small-cell lung cancer (NSCLC), significantly affecting patient outcomes. Conventional computed tomography (CT) often fails to diagnose VPI accurately. This retrospect...

Pathology image-based predictive model for individual survival time of early-stage lung adenocarcinoma patients.

Scientific reports
The tumor microenvironment (TME) is associated with tumor prognosis, immunotherapy response, and prognosis in patients. Here, we hypothesized that the entire TME in pathology image is associated with the survival time prediction. To address this hypo...

Computational pathology approach for assessment of prognosis and immunotherapy response in pan-gastrointestinal cancer.

Journal of translational medicine
BACKGROUND: Current cancer staging methods cannot accurately predict survival outcomes and therapeutic benefits in cancer patients. Digital pathomics, a rapidly evolving field, holds significant potential to revolutionize disease evaluation.

Machine Learning-Based Pathomics Signature for Perineural Invasion in Colorectal Cancer.

Medical science monitor : international medical journal of experimental and clinical research
BACKGROUND Perineural invasion (PNI) is strongly associated with poor clinical outcomes in colorectal cancer (CRC). However, no machine learning diagnostic model based on pathomics has been established for PNI detection in CRC. To address this issue,...

Assessing the risk of recurrence in early-stage breast cancer through H&E stained whole slide images.

Scientific reports
Accurate prediction of the likelihood of recurrence is important in the selection of postoperative treatment for patients with early-stage breast cancer. In this study, we investigated whether deep learning algorithms can predict patients' risk of re...

Machine learning-based prediction of N2 lymph node metastasis in non-small cell lung cancer.

BMC pulmonary medicine
BACKGROUND: Lung cancer is a leading cause of cancer-related mortality worldwide. Accurate staging of mediastinal lymph nodes is a crucial step in determining appropriate treatment approaches. Current noninvasive diagnostic methods do not provide suf...

Historical evolution and current research status of lymph node staging in gastric cancer: a review.

World journal of surgical oncology
Lymph node metastasis (LNM) is an independent prognostic factor for patients with gastric cancer (GC), and an accurate lymph node (LN) staging system is crucial for guiding adjuvant therapy and assessing patient prognosis. The most commonly used stag...