AIMC Topic: Neoplasm Staging

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Machine Learning Prediction of Early Recurrence in Gastric Cancer: A Nationwide Real-World Study.

Annals of surgical oncology
BACKGROUND: Patients with gastric cancer (GC) who experience early recurrence (ER) within 2 years postoperatively have poor prognoses. This study aimed to analyze and predict ER after curative surgery for patients with GC using machine learning (ML) ...

Multimodal Deep Learning Fusing Clinical and Radiomics Scores for Prediction of Early-Stage Lung Adenocarcinoma Lymph Node Metastasis.

Academic radiology
RATIONALE AND OBJECTIVES: To develop and validate a multimodal deep learning (DL) model based on computed tomography (CT) images and clinical knowledge to predict lymph node metastasis (LNM) in early lung adenocarcinoma.

Value of the combination of intraepithelial tumor-infiltrating lymphocyte density and the heterogeneity of density as a prognostic marker in stage III colorectal cancers.

Histology and histopathology
Tumor-infiltrating lymphocyte (TIL) density is both a prognostic and a predictive factor in colorectal cancer (CRC). Whether the heterogeneity of TIL density across the tumor plays an important role in the clinical outcome of CRC is not well known. A...

Machine learning in personalized laryngeal cancer management: insights into clinical characteristics, therapeutic options, and survival predictions.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
PURPOSE: Over the last 40 years, there has been an unusual trend where, even though there are more varied treatments, survival rates have not improved much. Our study used survival analysis and machine learning (ML) to investigate this odd situation ...

Automated Pathologic TN Classification Prediction and Rationale Generation From Lung Cancer Surgical Pathology Reports Using a Large Language Model Fine-Tuned With Chain-of-Thought: Algorithm Development and Validation Study.

JMIR medical informatics
BACKGROUND: Traditional rule-based natural language processing approaches in electronic health record systems are effective but are often time-consuming and prone to errors when handling unstructured data. This is primarily due to the substantial man...

Combined inflammation-related biomarkers and clinicopathological features for the prognosis of stage II/III colorectal cancer by machine learning.

BMC cancer
BACKGROUND: Inflammation-related biomarkers, such as systemic inflammation score (SIS) and neutrophil-lymphocyte ratio (NLR), are associated with colorectal cancer prognosis. However, the combined role of SIS, NLR, and clinicopathological factors in ...

Uncertainty-aware automatic TNM staging classification for [F] Fluorodeoxyglucose PET-CT reports for lung cancer utilising transformer-based language models and multi-task learning.

BMC medical informatics and decision making
BACKGROUND: [F] Fluorodeoxyglucose (FDG) PET-CT is a clinical imaging modality widely used in diagnosing and staging lung cancer. The clinical findings of PET-CT studies are contained within free text reports, which can currently only be categorised ...

Artificial intelligence-enhanced magnetic resonance imaging-based pre-operative staging in patients with endometrial cancer.

International journal of gynecological cancer : official journal of the International Gynecological Cancer Society
OBJECTIVE: Evaluation of prognostic factors is crucial in patients with endometrial cancer for optimal treatment planning and prognosis assessment. This study proposes a deep learning pipeline for tumor and uterus segmentation from magnetic resonance...

Novel models based on machine learning to predict the prognosis of metaplastic breast cancer.

Breast (Edinburgh, Scotland)
BACKGROUND: Metaplastic breast cancer (MBC) is a rare and highly aggressive histological subtype of breast cancer. There remains a significant lack of precise predictive models available for use in clinical practice.

Machine learning-based prognostic prediction and surgical guidance for intrahepatic cholangiocarcinoma.

Bioscience trends
The prognosis following radical surgery for intrahepatic cholangiocarcinoma (ICC) is poor, and optimal follow-up strategies remain unclear, with ongoing debates regarding anatomic resection (AR) versus non-anatomic resection (NAR). This study include...