AIMC Topic: Immunotherapy

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Machine Learning Links T-cell Function and Spatial Localization to Neoadjuvant Immunotherapy and Clinical Outcome in Pancreatic Cancer.

Cancer immunology research
Tumor molecular data sets are becoming increasingly complex, making it nearly impossible for humans alone to effectively analyze them. Here, we demonstrate the power of using machine learning (ML) to analyze a single-cell, spatial, and highly multipl...

Weakly Supervised Deep Learning Predicts Immunotherapy Response in Solid Tumors Based on PD-L1 Expression.

Cancer research communications
UNLABELLED: Programmed death-ligand 1 (PD-L1) IHC is the most commonly used biomarker for immunotherapy response. However, quantification of PD-L1 status in pathology slides is challenging. Neither manual quantification nor a computer-based mimicking...

Predicting Immune Checkpoint Inhibitor-Related Pneumonitis via Computed Tomography and Whole-Lung Analysis Deep Learning.

Current medical imaging
BACKGROUND: Immune checkpoint inhibitor-related pneumonitis (ICI-P) is a fatal adverse event of immunotherapy. However, there is a lack of methods to identify patients who have a high risk of developing ICI-P in immunotherapy.

Insights into a Machine Learning-Based Palmitoylation-Related Gene Model for Predicting the Prognosis and Treatment Response of Breast Cancer Patients.

Technology in cancer research & treatment
BACKGROUND: Breast cancer is a prevalent public health concern affecting numerous women globally and is associated with palmitoylation, a post-translational protein modification. Despite increasing focus on palmitoylation, its specific implications f...

Multiomics Analysis of Disulfidptosis Patterns and Integrated Machine Learning to Predict Immunotherapy Response in Lung Adenocarcinoma.

Current medicinal chemistry
BACKGROUND: Recent studies have unveiled disulfidptosis as a phenomenon intimately associated with cellular damage, heralding new avenues for exploring tumor cell dynamics. We aimed to explore the impact of disulfide cell death on the tumor immune mi...

Artificial Intelligence in Lung Cancer Imaging: From Data to Therapy.

Critical reviews in oncogenesis
Lung cancer remains a global health challenge, leading to substantial morbidity and mortality. While prevention and early detection strategies have improved, the need for precise diagnosis, prognosis, and treatment remains crucial. In this comprehens...

Predicting colorectal cancer tumor mutational burden from histopathological images and clinical information using multi-modal deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: Tumor mutational burden (TMB) is an indicator of the efficacy and prognosis of immune checkpoint therapy in colorectal cancer (CRC). In general, patients with higher TMB values are more likely to benefit from immunotherapy. Though whole-e...

Artificial intelligence and radiomics: fundamentals, applications, and challenges in immunotherapy.

Journal for immunotherapy of cancer
Immunotherapy offers the potential for durable clinical benefit but calls into question the association between tumor size and outcome that currently forms the basis for imaging-guided treatment. Artificial intelligence (AI) and radiomics allow for d...

Artificial Intelligence-based Radiomics in the Era of Immuno-oncology.

The oncologist
The recent, rapid advances in immuno-oncology have revolutionized cancer treatment and spurred further research into tumor biology. Yet, cancer patients respond variably to immunotherapy despite mounting evidence to support its efficacy. Current meth...