AIMC Topic: Neoplasm Staging

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Artificial intelligence-based machine learning models for preoperative diagnosis and staging of ovarian tumors.

Journal of the Egyptian National Cancer Institute
BACKGROUND: Ovarian cancer remains the most lethal gynecological malignancy, necessitating precise diagnostic strategies to improve patient outcomes. This study aims to develop and evaluate machine learning models that utilize patient history, imagin...

Deep learning-based artificial intelligence models predict survival in patients with oral cavity squamous cell carcinoma.

Scientific reports
Traditional survival predictions for oral squamous cell carcinoma (OSCC) rely on TNM staging, which lacks individualized prognostic value. Clinical factors such as performance status, age, sex, and lifestyle affect outcomes but are underrepresented i...

Novel insights into predicting the presence of micropapillary and solid components in stage IA lung adenocarcinoma using machine learning models of modifiable risk factors.

Annals of medicine
BACKGROUND: Lung adenocarcinoma (LUAC) patients with micropapillary (MP) and/or solid (S) generally demonstrate a poorer survival prognosis. In the diagnosis and treatment of stage IA LUAC, precisely establishing personalized treatment strategies for...

Worse survival despite indolent features for triple-negative invasive lobular carcinoma: a Swedish nationwide registry-based study.

Breast cancer research and treatment
PURPOSE: To evaluate differences in clinical outcomes, treatments received, recurrence, and sociodemographic characteristics in patients with triple-negative breast cancer (TNBC) classified as invasive lobular carcinoma (TNBC-ILC) or invasive carcino...

Development and validation of a machine learning-based prognostic model for gastric cancer: a multicenter retrospective study.

Langenbeck's archives of surgery
BACKGROUND: Machine learning has emerged as a promising tool for survival prediction in various diseases; however, its application and external validation in real-world gastric cancer populations remain limited.

DCS-NET: a multi-task model for uterine ROI detection and automatic staging of early endometrial cancer in MRI.

Scientific reports
Endometrial cancer (EC) is the most common gynecologic malignancy, with a steadily increasing incidence worldwide. Abnormal vaginal bleeding, a hallmark symptom, enables early diagnosis, which is critical for improving clinical outcomes. Pelvic magne...

Biologically explainable multi-omics feature demonstrates greater learning potential by identifying tissue of origin, stages, and subtypes for pan-cancer classification.

Scientific reports
Cancer is a complex disease characterized by uncontrolled cell growth, which can invade surrounding tissues and spread to distant organs. Most of the conventional methods of diagnosis fails to identify the primary organ when cancer spreads to other o...

Multimodal AI and tumour microenvironment integration predicts metastasis in cutaneous melanoma.

Nature communications
Accurate prognostication is essential to guide clinical management in localised cutaneous melanoma (CM), the form of skin cancer with the highest mortality. While the tumour microenvironment (TME) plays a key role in disease progression, current stag...

Machine learning prediction of overall survival in patients with cT1b renal cell carcinoma after surgical resection using the SEER database.

Scientific reports
Accurate survival prediction is essential for guiding follow-up strategies in patients with cT1b renal cell carcinoma (RCC). Traditional AJCC TNM staging systems provide limited prognostic accuracy. Data from the SEER database were used, which includ...

Causal predictive modeling of survival of lung and bronchus cancer patients diagnosed during 2010-2011 in Texas.

PloS one
BACKGROUND: Lung and Bronchus cancer is the most fatal type of cancer in the United States. According to the American Cancer Society, there were more than 127,000 deaths from lung cancer in 2023. Lung cancer care cost 23.8 billion dollars in 2020. In...