AIMC Topic: Neoplasm Staging
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Machine learning-based prediction model for brain metastasis in patients with extensive-stage small cell lung cancer.
Scientific reports
Nov 20, 2024
Brain metastases (BMs) in extensive-stage small cell lung cancer (ES-SCLC) are often associated with poor survival rates and quality of life, making the timely identification of high-risk patients for BMs in ES-SCLC crucial. Patients diagnosed with E...
Automatic TNM staging of colorectal cancer radiology reports using pre-trained language models.
Computer methods and programs in biomedicine
Nov 16, 2024
BACKGROUND AND OBJECTIVE: Colorectal cancer is one of the major causes of cancer death worldwide. Essential for prognosis and treatment planning, TNM staging offers critical insights into the advancement of colorectal cancer. However, manual TNM stag...
Response prediction for neoadjuvant treatment in locally advanced rectal cancer patients-improvement in decision-making: A systematic review.
European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
Nov 15, 2024
BACKGROUND: Predicting pathological complete response (pCR) from pre or post-treatment features could be significant in improving the process of making clinical decisions and providing a more personalized treatment approach for better treatment outco...
Improved prognostication of overall survival after radiotherapy in lung cancer patients by an interpretable machine learning model integrating lung and tumor radiomics and clinical parameters.
La Radiologia medica
Nov 14, 2024
BACKGROUND: Accurate prognostication of overall survival (OS) for non-small cell lung cancer (NSCLC) patients receiving definitive radiotherapy (RT) is crucial for developing personalized treatment strategies. This study aims to construct an interpre...
The established of a machine learning model for predicting the efficacy of adjuvant interferon alpha1b in patients with advanced melanoma.
Frontiers in immunology
Nov 12, 2024
BACKGROUND: Interferon-alpha1b (IFN-α1b) has shown remarkable therapeutic potential as adjuvant therapy for melanoma. This study aimed to develop five machine learning models to evaluate the efficacy of postoperative IFN-α1b in patients with advanced...
Impact of different nephrectomy types on M0 renal cell carcinoma outcomes in a propensity score matching and deep learning study.
Scientific reports
Nov 11, 2024
There are few analyses comparing complete nephrectomy with resection of the renal parenchyma only (CN) or radical nephrectomy that includes simultaneous resection of the parenchyma, affected perirenal fascia, perirenal fat, and ureter (RN) relative t...
Enhanced NSCLC subtyping and staging through attention-augmented multi-task deep learning: A novel diagnostic tool.
International journal of medical informatics
Nov 5, 2024
OBJECTIVES: The objective of this study is to develop a novel multi-task learning approach with attention encoders for classifying histologic subtypes and clinical stages of non-small cell lung cancer (NSCLC), with superior performance compared to cu...
Exploring patient stratification in head and neck squamous cell carcinoma using machine learning techniques: Preliminary results.
Current problems in cancer
Nov 2, 2024
BACKGROUND: Head and Neck Squamous Cell Carcinoma (HNSCC) presents a significant challenge in oncology due to its inherent heterogeneity. Traditional staging systems, such as TNM (Tumor, Node, Metastasis), provide limited information regarding patien...
Combining metabolomics and machine learning to discover biomarkers for early-stage breast cancer diagnosis.
PloS one
Oct 21, 2024
There is an urgent need for better biomarkers for the detection of early-stage breast cancer. Utilizing untargeted metabolomics and lipidomics in conjunction with advanced data mining approaches for metabolism-centric biomarker discovery and validati...
Machine learning and explainable artificial intelligence to predict pathologic stage in men with localized prostate cancer.
The Prostate
Oct 13, 2024
BACKGROUND: Though several nomograms exist, machine learning (ML) approaches might improve prediction of pathologic stage in patients with prostate cancer. To develop ML models to predict pathologic stage that outperform existing nomograms that use r...
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