AIMC Topic: Prognosis

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Exploring prognostic indicators in the pathological images of hepatocellular carcinoma based on deep learning.

Gut
OBJECTIVE: Tumour pathology contains rich information, including tissue structure and cell morphology, that reflects disease progression and patient survival. However, phenotypic information is subtle and complex, making the discovery of prognostic i...

Prognostic machine learning models for COVID-19 to facilitate decision making.

International journal of clinical practice
An increasing number of COVID-19 cases worldwide has overwhelmed the healthcare system. Physicians are struggling to allocate resources and to focus their attention on high-risk patients, partly because early identification of high-risk individuals i...

Predicting clinically significant motor function improvement after contemporary task-oriented interventions using machine learning approaches.

Journal of neuroengineering and rehabilitation
BACKGROUND: Accurate prediction of motor recovery after stroke is critical for treatment decisions and planning. Machine learning has been proposed to be a promising technique for outcome prediction because of its high accuracy and ability to process...

Characterizing CDK12-Mutated Prostate Cancers.

Clinical cancer research : an official journal of the American Association for Cancer Research
PURPOSE: Cyclin-dependent kinase 12 (CDK12) aberrations have been reported as a biomarker of response to immunotherapy for metastatic castration-resistant prostate cancer (mCRPC). Herein, we characterize CDK12-mutated mCRPC, presenting clinical, geno...

An immune-related gene signature for determining Ewing sarcoma prognosis based on machine learning.

Journal of cancer research and clinical oncology
PURPOSE: Ewing sarcoma (ES) is one of the most common malignant bone tumors in children and adolescents. The immune microenvironment plays an important role in the development of ES. Here, we developed an optimal signature for determining ES patient ...

Intensity harmonization techniques influence radiomics features and radiomics-based predictions in sarcoma patients.

Scientific reports
Intensity harmonization techniques (IHT) are mandatory to homogenize multicentric MRIs before any quantitative analysis because signal intensities (SI) do not have standardized units. Radiomics combine quantification of tumors' radiological phenotype...

Transcription factor expression as a predictor of colon cancer prognosis: a machine learning practice.

BMC medical genomics
BACKGROUND: Colon cancer is one of the leading causes of cancer deaths in the USA and around the world. Molecular level characters, such as gene expression levels and mutations, may provide profound information for precision treatment apart from path...

Application of machine learning to the prediction of postoperative sepsis after appendectomy.

Surgery
BACKGROUND: We applied various machine learning algorithms to a large national dataset to model the risk of postoperative sepsis after appendectomy to evaluate utility of such methods and identify factors associated with postoperative sepsis in these...