AIMC Topic: Prognosis

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Prognostic features of upstaged pT3a renal tumors with fat invasion after robot-assisted partial nephrectomy: is it time for a new subclassification?

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
INTRODUCTION: The clinical management of pT3a pathologic-upstaged renal cell carcinoma (RCC) patients is actually controversial. Aim of this study was i) to assess the impact of pT3a upstaging on oncologic outcomes after robot-assisted partial nephre...

Development of a Novel Deep Learning-Based Prediction Model for the Prognosis of Operable Cervical Cancer.

Computational and mathematical methods in medicine
BACKGROUND: Cervical cancer ranks as the 4th most common female cancer worldwide. Early stage cervical cancer patients can be treated with operation, but clinical staging system is not a good predictor of patients' survival. We aimed to develop a nov...

Systematic review identifies the design and methodological conduct of studies on machine learning-based prediction models.

Journal of clinical epidemiology
BACKGROUND AND OBJECTIVES: We sought to summarize the study design, modelling strategies, and performance measures reported in studies on clinical prediction models developed using machine learning techniques.

Machine learning for outcome prediction of neurosurgical aneurysm treatment: Current methods and future directions.

Clinical neurology and neurosurgery
INTRODUCTION: Machine learning algorithms have received increased attention in neurosurgical literature for improved accuracy over traditional predictive methods. In this review, the authors sought to assess current applications of machine learning f...

Identification of two robust subclasses of sepsis with both prognostic and therapeutic values based on machine learning analysis.

Frontiers in immunology
BACKGROUND: Sepsis is a heterogeneous syndrome with high morbidity and mortality. Optimal and effective classifications are in urgent need and to be developed.

Sarcopenia identified by computed tomography imaging using a deep learning-based segmentation approach impacts survival in patients with newly diagnosed multiple myeloma.

Cancer
BACKGROUND: Sarcopenia increases with age and is associated with poor survival outcomes in patients with cancer. By using a deep learning-based segmentation approach, clinical computed tomography (CT) images of the abdomen of patients with newly diag...

Survival prediction of stomach cancer using expression data and deep learning models with histopathological images.

Cancer science
Accurately predicting patient survival is essential for cancer treatment decision. However, the prognostic prediction model based on histopathological images of stomach cancer patients is still yet to be developed. We propose a deep learning-based mo...

Deep Scattering Spectrum Germaneness for Fault Detection and Diagnosis for Component-Level Prognostics and Health Management (PHM).

Sensors (Basel, Switzerland)
Most methodologies for fault detection and diagnosis in prognostics and health management (PHM) systems use machine learning (ML) or deep learning (DL), in which either some features are extracted beforehand (in the case of typical ML approaches) or ...

Artificial Intelligence-Enabled Evaluation of Pain Sketches to Predict Outcomes in Headache Surgery.

Plastic and reconstructive surgery
BACKGROUND: Recent evidence has shown that patient drawings of pain can predict poor outcomes in headache surgery. Given that interpretation of pain drawings requires some clinical experience, the authors developed a machine learning framework capabl...

Deep learning-based predictions of clear and eosinophilic phenotypes in clear cell renal cell carcinoma.

Human pathology
We have recently shown that histological phenotypes focusing on clear and eosinophilic cytoplasm in clear cell renal cell carcinoma (ccRCC) correlated with prognosis and the response to angiogenesis inhibition and checkpoint blockade. This study aims...