AIMC Topic: Treatment Outcome

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Application of Fourier transform infrared (FTIR) spectroscopy in liquid biopsy to predict the response to the first-line immunotherapy in non-small-cell lung cancer (NSCLC) patients.

Biochemical and biophysical research communications
The direction of anticancer therapies has changed in recent years, including the increasing use of immunotherapy. However, around 50 % of non-small-cell lung cancer (NSCLC) patients do not respond to immunotherapy. Therefore, it is important to find ...

Prediction of tissue and clinical thrombectomy outcome in acute ischaemic stroke using deep learning.

Brain : a journal of neurology
The advent of endovascular thrombectomy has significantly improved outcomes for stroke patients with intracranial large vessel occlusion, yet individual benefits can vary widely. As demand for thrombectomy rises and geographical disparities in stroke...

Artificial Intelligence Can Predict Personalized Immunotherapy Outcomes in Cancer.

Cancer immunology research
The rapid advancement of artificial intelligence (AI) technologies has opened new avenues for advancing personalized immunotherapy in cancer treatment. This review highlights current research progress in applying AI to optimize the use of immunothera...

A supervised machine learning approach for predicting the need for postsurgical intervention in acromegaly.

Neurosurgical focus
OBJECTIVE: Patients with growth hormone (GH)-secreting pituitary adenomas (PAs) experience various symptoms and comorbidities, which can ultimately lead to increased mortality. This study aimed to develop and validate a machine learning (ML) model fo...

A multiregional multimodal machine learning model for predicting outcome of surgery for symptomatic hemorrhagic brainstem cavernous malformations.

Neurosurgical focus
OBJECTIVE: Given that resection of brainstem cavernous malformations (BSCMs) ends hemorrhaging but carries a high risk of neurological deficits, it is necessary to develop and validate a model predicting surgical outcomes. This study aimed to constru...

The implementation of artificial intelligence in serial monitoring of post gamma knife vestibular schwannomas: A pilot study.

Clinical imaging
BACKGROUND: Vestibular schwannomas (VS) are benign tumors that can lead to hearing loss, balance issues, and tinnitus. Gamma Knife Radiosurgery (GKS) is a common treatment for VS, aimed at halting tumor growth and preserving neurological function. Ac...

Machine learning prediction of pathological complete response to neoadjuvant chemotherapy with peritumoral breast tumor ultrasound radiomics: compare with intratumoral radiomics and clinicopathologic predictors.

Breast cancer research and treatment
PURPOSE: Noninvasive, accurate and novel approaches to predict patients who will achieve pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) could assist treatment strategies. The aim of this study was to explore the application...

Vedolizumab in inflammatory bowel disease: Real-world outcomes and their prediction with machine learning-the IG-IBD LIVE study.

Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
BACKGROUND AND AIMS: Real-world studies on vedolizumab in inflammatory bowel disease (IBD) are often limited by small sample size and short follow-up. In this study, we investigated the 2-year effectiveness and safety of vedolizumab in patients with ...

Radiomics-based machine learning in prediction of response to neoadjuvant chemotherapy in osteosarcoma: A systematic review and meta-analysis.

Clinical imaging
BACKGROUND AND AIMS: Osteosarcoma (OS) is the most common primary bone malignancy, and neoadjuvant chemotherapy (NAC) improves survival rates. However, OS heterogeneity results in variable treatment responses, highlighting the need for reliable, non-...